From a2a68b80d39ebe64d526ee1172e0be2a311273a6 Mon Sep 17 00:00:00 2001 From: Kai-Hsun Chen Date: Thu, 21 Nov 2024 11:35:53 -0800 Subject: [PATCH 01/54] [core][autoscaler] Make the head group name consistent with the head Pod's `ray.io/group` label. (#48840) ## Why are these changes needed? The value of the `ray.io/group` label in the head Pod is `headgroup`, whereas `KUBERAY_TYPE_HEAD` is `head-group`. image ## Related issue number ## Checks - [ ] I've signed off every commit(by using the -s flag, i.e., `git commit -s`) in this PR. - [ ] I've run `scripts/format.sh` to lint the changes in this PR. - [ ] I've included any doc changes needed for https://docs.ray.io/en/master/. - [ ] I've added any new APIs to the API Reference. For example, if I added a method in Tune, I've added it in `doc/source/tune/api/` under the corresponding `.rst` file. - [ ] I've made sure the tests are passing. Note that there might be a few flaky tests, see the recent failures at https://flakey-tests.ray.io/ - Testing Strategy - [ ] Unit tests - [ ] Release tests - [ ] This PR is not tested :( Signed-off-by: kaihsun --- .../kubernetes/configs/ray-cluster.gpu.yaml | 2 +- .../_private/kuberay/autoscaling_config.py | 2 +- .../autoscaler/_private/kuberay/node_provider.py | 14 ++++++-------- .../autoscaler/kuberay/ray-cluster.complete.yaml | 2 +- .../cloud_providers/kuberay/cloud_provider.py | 12 ++++-------- .../ray/autoscaler/v2/tests/test_node_provider.py | 9 +++------ .../ray/tests/kuberay/test_autoscaling_config.py | 4 ++-- .../tests/kuberay/test_kuberay_node_provider.py | 6 +++--- .../ray_v1alpha1_rayservice_template.yaml | 2 +- 9 files changed, 22 insertions(+), 31 deletions(-) diff --git a/doc/source/cluster/kubernetes/configs/ray-cluster.gpu.yaml b/doc/source/cluster/kubernetes/configs/ray-cluster.gpu.yaml index 5a2d01839e9b..b42a7cf10a06 100644 --- a/doc/source/cluster/kubernetes/configs/ray-cluster.gpu.yaml +++ b/doc/source/cluster/kubernetes/configs/ray-cluster.gpu.yaml @@ -12,7 +12,7 @@ spec: ######################headGroupSpec################################# # head group template and specs, (perhaps 'group' is not needed in the name) headGroupSpec: - # logical group name, for this called head-group, also can be functional + # logical group name, for this called headgroup, also can be functional # pod type head or worker # rayNodeType: head # Not needed since it is under the headgroup # the following params are used to complete the ray start: ray start --head --block ... diff --git a/python/ray/autoscaler/_private/kuberay/autoscaling_config.py b/python/ray/autoscaler/_private/kuberay/autoscaling_config.py index d74bb253560a..96aec03744b2 100644 --- a/python/ray/autoscaler/_private/kuberay/autoscaling_config.py +++ b/python/ray/autoscaler/_private/kuberay/autoscaling_config.py @@ -30,7 +30,7 @@ # Logical group name for the KubeRay head group. # Used as the name of the "head node type" by the autoscaler. -_HEAD_GROUP_NAME = "head-group" +_HEAD_GROUP_NAME = "headgroup" class AutoscalingConfigProducer: diff --git a/python/ray/autoscaler/_private/kuberay/node_provider.py b/python/ray/autoscaler/_private/kuberay/node_provider.py index 060e4794867d..5378347ba78a 100644 --- a/python/ray/autoscaler/_private/kuberay/node_provider.py +++ b/python/ray/autoscaler/_private/kuberay/node_provider.py @@ -38,8 +38,6 @@ # Kind label value indicating the pod is the worker. KUBERAY_KIND_WORKER = "worker" -# Group name (node type) to use for the head. -KUBERAY_TYPE_HEAD = "head-group" # KubeRay CRD version KUBERAY_CRD_VER = os.getenv("KUBERAY_CRD_VER", "v1alpha1") @@ -104,12 +102,12 @@ def kind_and_type(pod: Dict[str, Any]) -> Tuple[NodeKind, NodeType]: from a Ray pod's labels. """ labels = pod["metadata"]["labels"] - if labels[KUBERAY_LABEL_KEY_KIND] == KUBERAY_KIND_HEAD: - kind = NODE_KIND_HEAD - type = KUBERAY_TYPE_HEAD - else: - kind = NODE_KIND_WORKER - type = labels[KUBERAY_LABEL_KEY_TYPE] + kind = ( + NODE_KIND_HEAD + if labels[KUBERAY_LABEL_KEY_KIND] == KUBERAY_KIND_HEAD + else NODE_KIND_WORKER + ) + type = labels[KUBERAY_LABEL_KEY_TYPE] return kind, type diff --git a/python/ray/autoscaler/kuberay/ray-cluster.complete.yaml b/python/ray/autoscaler/kuberay/ray-cluster.complete.yaml index f9e1a6cef375..f51ac9d4c242 100644 --- a/python/ray/autoscaler/kuberay/ray-cluster.complete.yaml +++ b/python/ray/autoscaler/kuberay/ray-cluster.complete.yaml @@ -18,7 +18,7 @@ spec: serviceType: ClusterIP # the pod replicas in this group typed head (assuming there could be more than 1 in the future) replicas: 1 - # logical group name, for this called head-group, also can be functional + # logical group name, for this called headgroup, also can be functional # pod type head or worker # rayNodeType: head # Not needed since it is under the headgroup # the following params are used to complete the ray start: ray start --head --block --port=6379 ... diff --git a/python/ray/autoscaler/v2/instance_manager/cloud_providers/kuberay/cloud_provider.py b/python/ray/autoscaler/v2/instance_manager/cloud_providers/kuberay/cloud_provider.py index c685be58cf60..19e236cb4d19 100644 --- a/python/ray/autoscaler/v2/instance_manager/cloud_providers/kuberay/cloud_provider.py +++ b/python/ray/autoscaler/v2/instance_manager/cloud_providers/kuberay/cloud_provider.py @@ -55,13 +55,12 @@ def __init__( """ Args: cluster_name: The name of the RayCluster resource. - namespace: The namespace of the RayCluster resource. + provider_config: The namespace of the RayCluster. k8s_api_client: The client to the Kubernetes API server. This could be used to mock the Kubernetes API server for testing. """ self._cluster_name = cluster_name self._namespace = provider_config["namespace"] - self._head_node_type = provider_config["head_node_type"] self._k8s_api_client = k8s_api_client or KubernetesHttpApiClient( namespace=self._namespace @@ -478,26 +477,23 @@ def _fetch_instances(self) -> Dict[CloudInstanceId, CloudInstance]: # Ignore pods marked for termination. continue pod_name = pod["metadata"]["name"] - cloud_instance = self._cloud_instance_from_pod(pod, self._head_node_type) + cloud_instance = self._cloud_instance_from_pod(pod) if cloud_instance: cloud_instances[pod_name] = cloud_instance return cloud_instances @staticmethod - def _cloud_instance_from_pod( - pod: Dict[str, Any], head_node_type: NodeType - ) -> Optional[CloudInstance]: + def _cloud_instance_from_pod(pod: Dict[str, Any]) -> Optional[CloudInstance]: """ Convert a pod to a Ray CloudInstance. Args: pod: The pod resource dict. - head_node_type: The node type of the head node. """ labels = pod["metadata"]["labels"] if labels[KUBERAY_LABEL_KEY_KIND] == KUBERAY_KIND_HEAD: kind = NodeKind.HEAD - type = head_node_type + type = labels[KUBERAY_LABEL_KEY_TYPE] elif labels[KUBERAY_LABEL_KEY_KIND] == KUBERAY_KIND_WORKER: kind = NodeKind.WORKER type = labels[KUBERAY_LABEL_KEY_TYPE] diff --git a/python/ray/autoscaler/v2/tests/test_node_provider.py b/python/ray/autoscaler/v2/tests/test_node_provider.py index 5141891c0a36..02d84e376b8d 100644 --- a/python/ray/autoscaler/v2/tests/test_node_provider.py +++ b/python/ray/autoscaler/v2/tests/test_node_provider.py @@ -19,10 +19,7 @@ AUTOSCALER_MAX_LAUNCH_BATCH, ) from ray.autoscaler._private.fake_multi_node.node_provider import FakeMultiNodeProvider -from ray.autoscaler._private.kuberay.node_provider import ( - KUBERAY_TYPE_HEAD, - IKubernetesHttpApiClient, -) +from ray.autoscaler._private.kuberay.node_provider import IKubernetesHttpApiClient from ray.autoscaler.v2.instance_manager.cloud_providers.kuberay.cloud_provider import ( KubeRayProvider, ) @@ -372,7 +369,7 @@ def setUp(self): cluster_name="test", provider_config={ "namespace": "default", - "head_node_type": KUBERAY_TYPE_HEAD, + "head_node_type": "headgroup", }, k8s_api_client=self.mock_client, ) @@ -389,7 +386,7 @@ def test_get_nodes(self): "raycluster-autoscaler-head-8zsc8": CloudInstance( cloud_instance_id="raycluster-autoscaler-head-8zsc8", node_kind=NodeKind.HEAD, - node_type="head-group", + node_type="headgroup", is_running=True, ), # up-to-date status because the Ray container is in running status "raycluster-autoscaler-worker-small-group-dkz2r": CloudInstance( diff --git a/python/ray/tests/kuberay/test_autoscaling_config.py b/python/ray/tests/kuberay/test_autoscaling_config.py index 10fea2fb3b4e..6ae9ab7fc4fe 100644 --- a/python/ray/tests/kuberay/test_autoscaling_config.py +++ b/python/ray/tests/kuberay/test_autoscaling_config.py @@ -69,7 +69,7 @@ def _get_basic_autoscaling_config() -> dict: "type": "kuberay", }, "available_node_types": { - "head-group": { + "headgroup": { "max_workers": 0, "min_workers": 0, "node_config": {}, @@ -125,7 +125,7 @@ def _get_basic_autoscaling_config() -> dict: "cluster_synced_files": [], "file_mounts": {}, "file_mounts_sync_continuously": False, - "head_node_type": "head-group", + "head_node_type": "headgroup", "head_setup_commands": [], "head_start_ray_commands": [], "idle_timeout_minutes": 1.0, diff --git a/python/ray/tests/kuberay/test_kuberay_node_provider.py b/python/ray/tests/kuberay/test_kuberay_node_provider.py index 4d2f94c1d086..3d5ea52009cb 100644 --- a/python/ray/tests/kuberay/test_kuberay_node_provider.py +++ b/python/ray/tests/kuberay/test_kuberay_node_provider.py @@ -126,7 +126,7 @@ def test_create_node_cap_at_max( { "raycluster-autoscaler-head-8zsc8": NodeData( kind="head", - type="head-group", + type="headgroup", replica_index=None, ip="10.4.2.6", status="up-to-date", @@ -149,7 +149,7 @@ def test_create_node_cap_at_max( { "raycluster-autoscaler-head-8zsc8": NodeData( kind="head", - type="head-group", + type="headgroup", replica_index=None, ip="10.4.2.6", status="up-to-date", @@ -217,7 +217,7 @@ def mock_get(node_provider, path): { "raycluster-autoscaler-head-8zsc8": NodeData( kind="head", - type="head-group", + type="headgroup", replica_index=None, ip="10.4.2.6", status="up-to-date", diff --git a/release/k8s_tests/ray_v1alpha1_rayservice_template.yaml b/release/k8s_tests/ray_v1alpha1_rayservice_template.yaml index c47e9572ceb1..7bed4fc1066c 100644 --- a/release/k8s_tests/ray_v1alpha1_rayservice_template.yaml +++ b/release/k8s_tests/ray_v1alpha1_rayservice_template.yaml @@ -126,7 +126,7 @@ spec: serviceType: ClusterIP # the pod replicas in this group typed head (assuming there could be more than 1 in the future) replicas: 1 - # logical group name, for this called head-group, also can be functional + # logical group name, for this called headgroup, also can be functional # pod type head or worker # rayNodeType: head # Not needed since it is under the headgroup # the following params are used to complete the ray start: ray start --head --block --redis-port=6379 ... From bc8ae37e5f33491d368ebd09d02cee38a9d802bd Mon Sep 17 00:00:00 2001 From: Saihajpreet Singh Date: Thu, 21 Nov 2024 20:09:15 +0000 Subject: [PATCH 02/54] docs: fine tune llama with trainium (#48768) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Introduce a new Ray Train example for AWS Trainium. ![CleanShot 2024-11-16 at 12 48 57@2x](https://github.com/user-attachments/assets/8b7d12d8-846f-497f-ba25-fd8a613f9007) Marked it as a community example as it is something we are collaborating with AWS Neuron team. ![CleanShot 2024-11-16 at 12 48 37@2x](https://github.com/user-attachments/assets/589d8ff3-fcb6-4b90-865d-006bcb4815a3) Docs screenshots Screenshot 2024-11-20 at 11 19 39 AM Screenshot 2024-11-20 at 11 19 47 AM Screenshot 2024-11-20 at 11 19 54 AM --------- Signed-off-by: Saihajpreet Singh Co-authored-by: Chris Zhang --- doc/source/custom_directives.py | 1 + doc/source/train/examples.yml | 12 +- .../train/examples/aws-trainium/llama3.rst | 103 ++++++++++++++++++ 3 files changed, 115 insertions(+), 1 deletion(-) create mode 100644 doc/source/train/examples/aws-trainium/llama3.rst diff --git a/doc/source/custom_directives.py b/doc/source/custom_directives.py index 6e81d401c833..2683160332d7 100644 --- a/doc/source/custom_directives.py +++ b/doc/source/custom_directives.py @@ -481,6 +481,7 @@ def key(cls: type) -> str: class Framework(ExampleEnum): """Framework type for example metadata.""" + AWSNEURON = "AWS Neuron" PYTORCH = "PyTorch" LIGHTNING = "Lightning" TRANSFORMERS = "Transformers" diff --git a/doc/source/train/examples.yml b/doc/source/train/examples.yml index 8b4f1c7cf8f2..0e7f6725e100 100644 --- a/doc/source/train/examples.yml +++ b/doc/source/train/examples.yml @@ -119,7 +119,17 @@ examples: contributor: community link: examples/intel_gaudi/llama_pretrain - - title: Fine-tune a Llama-2 text generation models with DeepSpeed and Hugging Face Accelerate + - title: Fine-tune Llama3.1 with AWS Trainium + frameworks: + - pytorch + - aws neuron + skill_level: advanced + use_cases: + - natural language processing + - large language models + contributor: community + link: examples/aws-trainium/llama3 + - title: Fine-tune a Llama-2 text generation model with DeepSpeed and Hugging Face Accelerate frameworks: - accelerate - deepspeed diff --git a/doc/source/train/examples/aws-trainium/llama3.rst b/doc/source/train/examples/aws-trainium/llama3.rst new file mode 100644 index 000000000000..ee7b89faf39e --- /dev/null +++ b/doc/source/train/examples/aws-trainium/llama3.rst @@ -0,0 +1,103 @@ +:orphan: + +Distributed fine-tuning of Llama 3.1 8B on AWS Trainium with Ray and PyTorch Lightning +====================================================================================== + + +This example demonstrates how to fine-tune the `Llama 3.1 8B `__ model on `AWS +Trainium `__ instances using Ray Train, PyTorch Lightning, and AWS Neuron SDK. + +AWS Trainium is the machine learning (ML) chip that AWS built for deep +learning (DL) training of 100B+ parameter models. `AWS Neuron +SDK `__ helps +developers train models on Trainium accelerators. + +Prepare the environment +----------------------- + +See `Setup EKS cluster and tools `__ for setting up an Amazon EKS cluster leveraging AWS Trainium instances. + +Create a Docker image +--------------------- +When the EKS cluster is ready, create an Amazon ECR repository for building and uploading the Docker image containing artifacts for fine-tuning a Llama3.1 8B model: + +1. Clone the repo. + +:: + + git clone https://github.com/aws-neuron/aws-neuron-eks-samples.git + +2. Go to the ``llama3.1_8B_finetune_ray_ptl_neuron`` directory. + +:: + + cd aws-neuron-eks-samples/llama3.1_8B_finetune_ray_ptl_neuron + +3. Trigger the script. + +:: + + chmod +x 0-kuberay-trn1-llama3-finetune-build-image.sh + ./0-kuberay-trn1-llama3-finetune-build-image.sh + +4. Enter the zone your cluster is running in, for example: us-east-2. + +5. Verify in the AWS console that the Amazon ECR service has the newly + created ``kuberay_trn1_llama3.1_pytorch2`` repository. + +6. Update the ECR image ARN in the manifest file used for creating the Ray cluster. + +Replace the and placeholders with actual values in the ``1-llama3-finetune-trn1-create-raycluster.yaml`` file using commands below to reflect the ECR image ARN created above: + +:: + + export AWS_ACCOUNT_ID= # for ex: 111222333444 + export REGION= # for ex: us-east-2 + sed -i "s//$AWS_ACCOUNT_ID/g" 1-llama3-finetune-trn1-create-raycluster.yaml + sed -i "s//$REGION/g" 1-llama3-finetune-trn1-create-raycluster.yaml + +Configuring Ray Cluster +----------------------- + +The ``llama3.1_8B_finetune_ray_ptl_neuron`` directory in the AWS Neuron samples repository simplifies the +Ray configuration. KubeRay provides a manifest that you can apply +to the cluster to set up the head and worker pods. + +Run the following command to set up the Ray cluster: + +:: + + kubectl apply -f 1-llama3-finetune-trn1-create-raycluster.yaml + + +Accessing Ray Dashboard +----------------------- +Port forward from the cluster to see the state of the Ray dashboard and +then view it on `http://localhost:8265 `__. +Run it in the background with the following command: + +:: + + kubectl port-forward service/kuberay-trn1-head-svc 8265:8265 & + +Launching Ray Jobs +------------------ + +The Ray cluster now ready to handle workloads. Initiate the data preparation and fine-tuning Ray jobs: + +1. Launch the Ray job for downloading the dolly-15k dataset and the Llama3.1 8B model artifacts: + +:: + + kubectl apply -f 2-llama3-finetune-trn1-rayjob-create-data.yaml + +2. When the job has executed successfully, run the following fine-tuning job: + +:: + + kubectl apply -f 3-llama3-finetune-trn1-rayjob-submit-finetuning-job.yaml + +3. Monitor the jobs via the Ray Dashboard + + +For detailed information on each of the steps above, see the `AWS documentation link `__. \ No newline at end of file From 8a0f8109161382a94ee5d733bc392a521d9f4cba Mon Sep 17 00:00:00 2001 From: zhilong <121425509+Bye-legumes@users.noreply.github.com> Date: Thu, 21 Nov 2024 16:41:55 -0500 Subject: [PATCH 03/54] [Data] Fix pandas memory calculation. (#46939) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ## Why are these changes needed? close https://github.com/ray-project/ray/issues/46785 Current the memory usage for pandas is not accurate when it's object, so we just implement to calculated it in recursion in case of nested. ## Related issue number closes https://github.com/ray-project/ray/issues/46785, closes https://github.com/ray-project/ray/issues/48506 ## Checks - [√] I've signed off every commit(by using the -s flag, i.e., `git commit -s`) in this PR. - [√] I've run `scripts/format.sh` to lint the changes in this PR. - [ ] I've included any doc changes needed for https://docs.ray.io/en/master/. - [ ] I've added any new APIs to the API Reference. For example, if I added a method in Tune, I've added it in `doc/source/tune/api/` under the corresponding `.rst` file. - [ ] I've made sure the tests are passing. Note that there might be a few flaky tests, see the recent failures at https://flakey-tests.ray.io/ - Testing Strategy - [√] Unit tests - [ ] Release tests - [ ] This PR is not tested :( --------- Signed-off-by: zhilong Signed-off-by: Richard Liaw Co-authored-by: Richard Liaw --- python/ray/data/_internal/pandas_block.py | 70 ++++++- python/ray/data/tests/test_pandas_block.py | 229 ++++++++++++++++++++- 2 files changed, 296 insertions(+), 3 deletions(-) diff --git a/python/ray/data/_internal/pandas_block.py b/python/ray/data/_internal/pandas_block.py index 119469b46c1b..ff1686c1b355 100644 --- a/python/ray/data/_internal/pandas_block.py +++ b/python/ray/data/_internal/pandas_block.py @@ -1,5 +1,6 @@ import collections import heapq +import sys from typing import ( TYPE_CHECKING, Any, @@ -294,7 +295,74 @@ def num_rows(self) -> int: return self._table.shape[0] def size_bytes(self) -> int: - return int(self._table.memory_usage(index=True, deep=True).sum()) + from pandas.api.types import is_object_dtype + + from ray.data.extensions import TensorArrayElement, TensorDtype + + pd = lazy_import_pandas() + + def get_deep_size(obj): + """Calculates the memory size of objects, + including nested objects using an iterative approach.""" + seen = set() + total_size = 0 + objects = collections.deque([obj]) + while objects: + current = objects.pop() + + # Skip interning-eligible immutable objects + if isinstance(current, (str, bytes, int, float)): + size = sys.getsizeof(current) + total_size += size + continue + + # Check if the object has been seen before + if id(current) in seen: + continue + seen.add(id(current)) + + try: + size = sys.getsizeof(current) + except TypeError: + size = 0 + total_size += size + + # Handle specific cases + if isinstance(current, np.ndarray): + total_size += current.nbytes - size # Avoid double counting + elif isinstance(current, pd.DataFrame): + total_size += ( + current.memory_usage(index=True, deep=True).sum() - size + ) + elif isinstance(current, (list, tuple, set)): + objects.extend(current) + elif isinstance(current, dict): + objects.extend(current.keys()) + objects.extend(current.values()) + elif isinstance(current, TensorArrayElement): + objects.extend(current.to_numpy()) + return total_size + + # Get initial memory usage including deep introspection + memory_usage = self._table.memory_usage(index=True, deep=True) + + # TensorDtype for ray.air.util.tensor_extensions.pandas.TensorDtype + object_need_check = (TensorDtype,) + # Handle object columns separately + for column in self._table.columns: + # Check pandas object dtype and the extenstion dtype + if is_object_dtype(self._table[column].dtype) or isinstance( + self._table[column].dtype, object_need_check + ): + column_memory = 0 + for element in self._table[column]: + column_memory += get_deep_size(element) + memory_usage[column] = column_memory + + # Sum up total memory usage + total_memory_usage = memory_usage.sum() + + return int(total_memory_usage) def _zip(self, acc: BlockAccessor) -> "pandas.DataFrame": r = self.to_pandas().copy(deep=False) diff --git a/python/ray/data/tests/test_pandas_block.py b/python/ray/data/tests/test_pandas_block.py index 4585d0e2a133..5e83abbf04d7 100644 --- a/python/ray/data/tests/test_pandas_block.py +++ b/python/ray/data/tests/test_pandas_block.py @@ -1,4 +1,10 @@ +import pickle +import random +import sys + +import numpy as np import pandas as pd +import pyarrow as pa import pytest import ray @@ -48,7 +54,226 @@ def fn2(batch): assert isinstance(block, pd.DataFrame) -if __name__ == "__main__": - import sys +class TestSizeBytes: + def test_small(ray_start_regular_shared): + animals = ["Flamingo", "Centipede"] + block = pd.DataFrame({"animals": animals}) + + block_accessor = PandasBlockAccessor.for_block(block) + bytes_size = block_accessor.size_bytes() + + # check that memory usage is within 10% of the size_bytes + # For strings, Pandas seems to be fairly accurate, so let's use that. + memory_usage = block.memory_usage(index=True, deep=True).sum() + assert bytes_size == pytest.approx(memory_usage, rel=0.1), ( + bytes_size, + memory_usage, + ) + + def test_large_str(ray_start_regular_shared): + animals = [ + random.choice(["alligator", "crocodile", "centipede", "flamingo"]) + for i in range(100_000) + ] + block = pd.DataFrame({"animals": animals}) + block["animals"] = block["animals"].astype("string") + + block_accessor = PandasBlockAccessor.for_block(block) + bytes_size = block_accessor.size_bytes() + + memory_usage = block.memory_usage(index=True, deep=True).sum() + assert bytes_size == pytest.approx(memory_usage, rel=0.1), ( + bytes_size, + memory_usage, + ) + + def test_large_str_object(ray_start_regular_shared): + """Note - this test breaks if you refactor/move the list of animals.""" + num = 100_000 + animals = [ + random.choice(["alligator", "crocodile", "centipede", "flamingo"]) + for i in range(num) + ] + block = pd.DataFrame({"animals": animals}) + + block_accessor = PandasBlockAccessor.for_block(block) + bytes_size = block_accessor.size_bytes() + + memory_usage = sum([sys.getsizeof(animal) for animal in animals]) + + assert bytes_size == pytest.approx(memory_usage, rel=0.1), ( + bytes_size, + memory_usage, + ) + + def test_large_floats(ray_start_regular_shared): + animals = [random.random() for i in range(100_000)] + block = pd.DataFrame({"animals": animals}) + + block_accessor = PandasBlockAccessor.for_block(block) + bytes_size = block_accessor.size_bytes() + + memory_usage = pickle.dumps(block).__sizeof__() + # check that memory usage is within 10% of the size_bytes + assert bytes_size == pytest.approx(memory_usage, rel=0.1), ( + bytes_size, + memory_usage, + ) + + def test_bytes_object(ray_start_regular_shared): + def generate_data(batch): + for _ in range(8): + yield {"data": [[b"\x00" * 128 * 1024 * 128]]} + + ds = ( + ray.data.range(1, override_num_blocks=1) + .map_batches(generate_data, batch_size=1) + .map_batches(lambda batch: batch, batch_format="pandas") + ) + + true_value = 128 * 1024 * 128 * 8 + for bundle in ds.iter_internal_ref_bundles(): + size = bundle.size_bytes() + # assert that true_value is within 10% of bundle.size_bytes() + assert size == pytest.approx(true_value, rel=0.1), ( + size, + true_value, + ) + + def test_nested_numpy(ray_start_regular_shared): + size = 1024 + rows = 1_000 + data = [ + np.random.randint(size=size, low=0, high=100, dtype=np.int8) + for _ in range(rows) + ] + df = pd.DataFrame({"data": data}) + + block_accessor = PandasBlockAccessor.for_block(df) + block_size = block_accessor.size_bytes() + true_value = rows * size + assert block_size == pytest.approx(true_value, rel=0.1), ( + block_size, + true_value, + ) + + def test_nested_objects(ray_start_regular_shared): + size = 10 + rows = 10_000 + lists = [[random.randint(0, 100) for _ in range(size)] for _ in range(rows)] + data = {"lists": lists} + block = pd.DataFrame(data) + + block_accessor = PandasBlockAccessor.for_block(block) + bytes_size = block_accessor.size_bytes() + # List overhead + 10 integers per list + true_size = rows * ( + sys.getsizeof([random.randint(0, 100) for _ in range(size)]) + size * 28 + ) + + assert bytes_size == pytest.approx(true_size, rel=0.1), ( + bytes_size, + true_size, + ) + + def test_mixed_types(ray_start_regular_shared): + rows = 10_000 + + data = { + "integers": [random.randint(0, 100) for _ in range(rows)], + "floats": [random.random() for _ in range(rows)], + "strings": [ + random.choice(["apple", "banana", "cherry"]) for _ in range(rows) + ], + "object": [b"\x00" * 128 for _ in range(rows)], + } + block = pd.DataFrame(data) + block_accessor = PandasBlockAccessor.for_block(block) + bytes_size = block_accessor.size_bytes() + + # Manually calculate the size + int_size = rows * 8 + float_size = rows * 8 + str_size = sum(sys.getsizeof(string) for string in data["strings"]) + object_size = rows * sys.getsizeof(b"\x00" * 128) + + true_size = int_size + float_size + str_size + object_size + assert bytes_size == pytest.approx(true_size, rel=0.1), (bytes_size, true_size) + + def test_nested_lists_strings(ray_start_regular_shared): + rows = 5_000 + nested_lists = ["a"] * 3 + ["bb"] * 4 + ["ccc"] * 3 + data = { + "nested_lists": [nested_lists for _ in range(rows)], + } + block = pd.DataFrame(data) + block_accessor = PandasBlockAccessor.for_block(block) + bytes_size = block_accessor.size_bytes() + + # Manually calculate the size + list_overhead = sys.getsizeof(block["nested_lists"].iloc[0]) + sum( + [sys.getsizeof(x) for x in nested_lists] + ) + true_size = rows * list_overhead + assert bytes_size == pytest.approx(true_size, rel=0.1), (bytes_size, true_size) + + @pytest.mark.parametrize("size", [10, 1024]) + def test_multi_level_nesting(ray_start_regular_shared, size): + rows = 1_000 + data = { + "complex": [ + {"list": [np.random.rand(size)], "value": {"key": "val"}} + for _ in range(rows) + ], + } + block = pd.DataFrame(data) + block_accessor = PandasBlockAccessor.for_block(block) + bytes_size = block_accessor.size_bytes() + + numpy_size = np.random.rand(size).nbytes + + values = ["list", "value", "key", "val"] + str_size = sum([sys.getsizeof(v) for v in values]) + + list_ref_overhead = sys.getsizeof([np.random.rand(size)]) + + dict_overhead1 = sys.getsizeof({"key": "val"}) + + dict_overhead3 = sys.getsizeof( + {"list": [np.random.rand(size)], "value": {"key": "val"}} + ) + + true_size = ( + numpy_size + str_size + list_ref_overhead + dict_overhead1 + dict_overhead3 + ) * rows + assert bytes_size == pytest.approx(true_size, rel=0.15), ( + bytes_size, + true_size, + ) + + def test_boolean(ray_start_regular_shared): + data = [random.choice([True, False, None]) for _ in range(100_000)] + block = pd.DataFrame({"flags": pd.Series(data, dtype="boolean")}) + block_accessor = PandasBlockAccessor.for_block(block) + bytes_size = block_accessor.size_bytes() + + # No object case + true_size = block.memory_usage(index=True, deep=True).sum() + assert bytes_size == pytest.approx(true_size, rel=0.1), (bytes_size, true_size) + + def test_arrow(ray_start_regular_shared): + data = [ + random.choice(["alligator", "crocodile", "flamingo"]) for _ in range(50_000) + ] + arrow_dtype = pd.ArrowDtype(pa.string()) + block = pd.DataFrame({"animals": pd.Series(data, dtype=arrow_dtype)}) + block_accessor = PandasBlockAccessor.for_block(block) + bytes_size = block_accessor.size_bytes() + + true_size = block.memory_usage(index=True, deep=True).sum() + assert bytes_size == pytest.approx(true_size, rel=0.1), (bytes_size, true_size) + + +if __name__ == "__main__": sys.exit(pytest.main(["-v", __file__])) From 9482343f845a484ed541846fe5c26e4082aef837 Mon Sep 17 00:00:00 2001 From: Rui Qiao <161574667+ruisearch42@users.noreply.github.com> Date: Thu, 21 Nov 2024 13:54:34 -0800 Subject: [PATCH 04/54] [core] Fix test_output::test_disable_driver_logs_breakpoint (#48820) Signed-off-by: Rui Qiao --- python/ray/tests/test_output.py | 17 +++++++++-------- 1 file changed, 9 insertions(+), 8 deletions(-) diff --git a/python/ray/tests/test_output.py b/python/ray/tests/test_output.py index 4b84b8285534..505a02e84226 100644 --- a/python/ray/tests/test_output.py +++ b/python/ray/tests/test_output.py @@ -575,26 +575,27 @@ def test_disable_driver_logs_breakpoint(): @ray.remote def f(): while True: - start_time = time.time() - while time.time() - start_time < 1: + start_time = time.monotonic() + while time.monotonic() - start_time < 1: time.sleep(0.1) + print(f"slept {time.monotonic() - start_time} seconds") print("hello there") sys.stdout.flush() def kill(): - start_time = time.time() - while time.time() - start_time < 5: + start_time = time.monotonic() + while time.monotonic() - start_time < 5: time.sleep(0.1) sys.stdout.flush() - start_time = time.time() - while time.time() - start_time < 1: + start_time = time.monotonic() + while time.monotonic() - start_time < 1: time.sleep(0.1) os._exit(0) t = threading.Thread(target=kill) t.start() x = f.remote() -time.sleep(2) # Enough time to print one hello. +time.sleep(3) # Enough time to print one hello. breakpoint() # This should disable worker logs. """ @@ -602,7 +603,7 @@ def kill(): out_str = proc.stdout.read().decode("ascii") num_hello = out_str.count("hello") assert num_hello >= 1, out_str - assert num_hello < 3, out_str + assert num_hello <= 3, out_str assert "Temporarily disabling Ray worker logs" in out_str, out_str # TODO(ekl) nice to test resuming logs too, but it's quite complicated From 8e6d1102746e8635cbc6edcc28dc4d95031c2808 Mon Sep 17 00:00:00 2001 From: Balaji Veeramani Date: Thu, 21 Nov 2024 15:58:41 -0600 Subject: [PATCH 05/54] [Data] Move release test config to separate file (#48852) Storing all of the Data release test configs in a separate file makes it easier to maintain the Data release tests. Signed-off-by: Balaji Veeramani --- doc/source/templates/README.md | 2 +- release/ray_release/config.py | 5 + release/ray_release/scripts/build_pipeline.py | 7 +- .../ray_release/scripts/get_test_summary.py | 7 +- release/ray_release/scripts/ray_bisect.py | 7 +- .../ray_release/scripts/run_release_test.py | 3 +- release/ray_release/tests/test_config.py | 1 + release/release_data_tests.yaml | 910 ++++++++++++++++++ release/release_tests.yaml | 910 ------------------ 9 files changed, 934 insertions(+), 918 deletions(-) create mode 100644 release/release_data_tests.yaml diff --git a/doc/source/templates/README.md b/doc/source/templates/README.md index 912d3174c75f..306b31bc3dc8 100644 --- a/doc/source/templates/README.md +++ b/doc/source/templates/README.md @@ -32,7 +32,7 @@ To add a template: Your template does not need to be a Jupyter notebook. It can also be presented as a Python script with `README` instructions of how to run. -2. Add a release test for the template in `release/release_tests.yaml` (for both AWS and GCE). +2. Add a release test for the template in `release/release_tests.yaml` (for both AWS and GCE). For Data tests, use `release/release_data_tests.yaml` instead. See the section on workspace templates for an example. Note that the cluster env and compute config are a little different for release tests. Use the files in the diff --git a/release/ray_release/config.py b/release/ray_release/config.py index 93b69e56601c..884292545f1d 100644 --- a/release/ray_release/config.py +++ b/release/ray_release/config.py @@ -38,6 +38,11 @@ RELEASE_TEST_SCHEMA_FILE = bazel_runfile("release/ray_release/schema.json") +RELEASE_TEST_CONFIG_FILES = [ + "release/release_tests.yaml", + "release/release_data_tests.yaml", +] + def read_and_validate_release_test_collection( config_files: List[str], diff --git a/release/ray_release/scripts/build_pipeline.py b/release/ray_release/scripts/build_pipeline.py index 735ddb2e8cdf..29e448d8f4fd 100644 --- a/release/ray_release/scripts/build_pipeline.py +++ b/release/ray_release/scripts/build_pipeline.py @@ -14,7 +14,10 @@ build_anyscale_base_byod_images, build_anyscale_custom_byod_image, ) -from ray_release.config import read_and_validate_release_test_collection +from ray_release.config import ( + read_and_validate_release_test_collection, + RELEASE_TEST_CONFIG_FILES, +) from ray_release.configs.global_config import init_global_config from ray_release.exception import ReleaseTestCLIError, ReleaseTestConfigError from ray_release.logger import logger @@ -92,7 +95,7 @@ def main( try: test_collection = read_and_validate_release_test_collection( - test_collection_file or ["release/release_tests.yaml"] + test_collection_file or RELEASE_TEST_CONFIG_FILES ) except ReleaseTestConfigError as e: raise ReleaseTestConfigError( diff --git a/release/ray_release/scripts/get_test_summary.py b/release/ray_release/scripts/get_test_summary.py index b4793a860d06..ccb5066713b2 100644 --- a/release/ray_release/scripts/get_test_summary.py +++ b/release/ray_release/scripts/get_test_summary.py @@ -5,7 +5,10 @@ import click from ray_release.buildkite.concurrency import get_test_resources -from ray_release.config import read_and_validate_release_test_collection +from ray_release.config import ( + read_and_validate_release_test_collection, + RELEASE_TEST_CONFIG_FILES, +) @click.command() @@ -26,7 +29,7 @@ def main(test_collection_file: Optional[str] = None, output: Optional[str] = Non output = output or os.path.join(os.path.dirname(__file__), "test_summary.csv") tests = read_and_validate_release_test_collection( - test_collection_file or ["release/release_tests.yaml"] + test_collection_file or RELEASE_TEST_CONFIG_FILES ) with open(output, "w") as f: diff --git a/release/ray_release/scripts/ray_bisect.py b/release/ray_release/scripts/ray_bisect.py index 46d3c521bb93..15ebd4c9d396 100644 --- a/release/ray_release/scripts/ray_bisect.py +++ b/release/ray_release/scripts/ray_bisect.py @@ -14,7 +14,10 @@ build_anyscale_base_byod_images, build_anyscale_custom_byod_image, ) -from ray_release.config import read_and_validate_release_test_collection +from ray_release.config import ( + read_and_validate_release_test_collection, + RELEASE_TEST_CONFIG_FILES, +) from ray_release.configs.global_config import init_global_config from ray_release.test import Test from ray_release.test_automation.release_state_machine import ReleaseTestStateMachine @@ -241,7 +244,7 @@ def _obtain_test_result( def _get_test(test_name: str, test_collection_file: Tuple[str]) -> Test: test_collection = read_and_validate_release_test_collection( - test_collection_file or ["release/release_tests.yaml"], + test_collection_file or RELEASE_TEST_CONFIG_FILES, ) return [test for test in test_collection if test["name"] == test_name][0] diff --git a/release/ray_release/scripts/run_release_test.py b/release/ray_release/scripts/run_release_test.py index 13830139554a..e03912b9f681 100644 --- a/release/ray_release/scripts/run_release_test.py +++ b/release/ray_release/scripts/run_release_test.py @@ -9,6 +9,7 @@ as_smoke_test, find_test, read_and_validate_release_test_collection, + RELEASE_TEST_CONFIG_FILES, ) from ray_release.configs.global_config import init_global_config from ray_release.env import DEFAULT_ENVIRONMENT, load_environment, populate_os_env @@ -114,7 +115,7 @@ def main( ) init_global_config(global_config_file) test_collection = read_and_validate_release_test_collection( - test_collection_file or ["release/release_tests.yaml"], + test_collection_file or RELEASE_TEST_CONFIG_FILES, test_definition_root, ) test = find_test(test_collection, test_name) diff --git a/release/ray_release/tests/test_config.py b/release/ray_release/tests/test_config.py index f922d6512d99..9b1f5adbb929 100644 --- a/release/ray_release/tests/test_config.py +++ b/release/ray_release/tests/test_config.py @@ -14,6 +14,7 @@ _TEST_COLLECTION_FILES = [ "release/release_tests.yaml", + "release/release_data_tests.yaml", "release/ray_release/tests/test_collection_data.yaml", ] diff --git a/release/release_data_tests.yaml b/release/release_data_tests.yaml new file mode 100644 index 000000000000..9b3c9d3bd7ac --- /dev/null +++ b/release/release_data_tests.yaml @@ -0,0 +1,910 @@ +############### +# Dataset tests +############### + +- name: parquet_metadata_resolution + group: data-tests + working_dir: nightly_tests/dataset + + frequency: nightly + team: data + + cluster: + byod: + type: gpu + cluster_compute: single_node_benchmark_compute.yaml + + run: + # Expect the test to finish around 40 seconds. + timeout: 100 + script: python parquet_metadata_resolution.py --num-files 915 --cloud aws + + variations: + - __suffix__: aws + - __suffix__: gce + env: gce + frequency: manual + cluster: + cluster_compute: single_node_benchmark_compute_gce.yaml + run: + script: python parquet_metadata_resolution.py --num-files 915 --cloud gcp + +- name: stable_diffusion_benchmark + group: data-tests + working_dir: nightly_tests/dataset + + frequency: nightly + team: data + + cluster: + byod: + type: gpu + post_build_script: byod_stable_diffusion.sh + cluster_compute: stable_diffusion_benchmark_compute.yaml + + run: + timeout: 1800 + script: python stable_diffusion_benchmark.py + + variations: + - __suffix__: aws + - __suffix__: gce + env: gce + frequency: manual + cluster: + cluster_compute: stable_diffusion_benchmark_compute_gce.yaml + +- name: streaming_data_ingest_benchmark_1tb + group: data-tests + working_dir: nightly_tests/dataset + + frequency: nightly + team: data + + cluster: + byod: + type: gpu + cluster_compute: data_ingest_benchmark_compute.yaml + + run: + timeout: 300 + script: python data_ingest_benchmark.py --dataset-size-gb=1000 --num-workers=20 --streaming + wait_for_nodes: + num_nodes: 20 + + variations: + - __suffix__: aws + - __suffix__: gce + env: gce + frequency: manual + cluster: + cluster_compute: data_ingest_benchmark_compute_gce.yaml + +- name: streaming_data_ingest_benchmark_100gb_gpu + group: data-tests + working_dir: nightly_tests/dataset + + frequency: nightly + team: data + + cluster: + byod: + type: gpu + cluster_compute: data_ingest_benchmark_compute_gpu.yaml + + run: + timeout: 300 + script: python data_ingest_benchmark.py --dataset-size-gb=100 --num-workers=4 --streaming --use-gpu + wait_for_nodes: + num_nodes: 3 + + variations: + - __suffix__: aws + - __suffix__: gce + env: gce + frequency: manual + cluster: + cluster_compute: data_ingest_benchmark_compute_gpu_gce.yaml + +# This test case will early stop the data ingestion iteration on the GPU actors. +# This is a common usage in PyTorch Lightning +# (https://lightning.ai/docs/pytorch/stable/common/trainer.html#limit-train-batches). +# There was a bug in Ray Data that caused GPU memoy leak (see #3.919). +# We add this test case to cover this scenario. +- name: streaming_data_ingest_benchmark_100gb_gpu_early_stop + group: data-tests + working_dir: nightly_tests/dataset + + frequency: nightly + team: data + + cluster: + byod: + type: gpu + cluster_compute: data_ingest_benchmark_compute_gpu.yaml + + run: + timeout: 300 + script: python data_ingest_benchmark.py --dataset-size-gb=100 --num-workers=4 --streaming --use-gpu --early-stop + wait_for_nodes: + num_nodes: 3 + + variations: + - __suffix__: aws + - __suffix__: gce + env: gce + frequency: manual + cluster: + cluster_compute: data_ingest_benchmark_compute_gpu_gce.yaml + +- name: aggregate_benchmark + group: data-tests + working_dir: nightly_tests/dataset + + frequency: nightly + team: data + + cluster: + byod: + type: gpu + cluster_compute: single_node_benchmark_compute.yaml + + run: + timeout: 1800 + script: python aggregate_benchmark.py + + variations: + - __suffix__: aws + - __suffix__: gce + env: gce + frequency: manual + cluster: + cluster_compute: single_node_benchmark_compute_gce.yaml + +- name: read_parquet_benchmark_single_node + group: data-tests + working_dir: nightly_tests/dataset + + frequency: nightly + team: data + + cluster: + byod: + type: gpu + post_build_script: byod_install_mosaicml.sh + cluster_compute: single_node_benchmark_compute.yaml + + run: + # Expect the benchmark to finish in 400 seconds. + timeout: 400 + script: python read_parquet_benchmark.py + + variations: + - __suffix__: aws + - __suffix__: gce + env: gce + frequency: manual + cluster: + cluster_compute: single_node_benchmark_compute_gce.yaml + +- name: read_images_benchmark_single_node + group: data-tests + working_dir: nightly_tests/dataset + + frequency: nightly + team: data + + cluster: + byod: + type: gpu + post_build_script: byod_install_mosaicml.sh + cluster_compute: single_node_benchmark_compute.yaml + + run: + timeout: 1800 + script: python read_images_benchmark.py --single-node + + variations: + - __suffix__: aws + - __suffix__: gce + env: gce + frequency: manual + cluster: + cluster_compute: single_node_benchmark_compute_gce.yaml + +# TODO: Re-enable this test once we fix https://github.com/ray-project/ray/issues/40686. +# - name: read_images_benchmark_multi_node +# group: data-tests +# working_dir: nightly_tests/dataset + +# frequency: nightly-3x +# team: data + +# cluster: +# byod: +# type: gpu +# cluster_compute: multi_node_read_images_benchmark_compute.yaml + +# run: +# timeout: 28800 +# script: python read_images_benchmark.py --multi-node + +# variations: +# - __suffix__: aws +# - __suffix__: gce +# env: gce +# frequency: manual +# cluster: +# cluster_compute: multi_node_read_images_benchmark_compute_gce.yaml + +- name: read_images_comparison_microbenchmark_single_node + group: data-tests + working_dir: nightly_tests/dataset + + frequency: nightly + team: data + cluster: + byod: + type: gpu + post_build_script: byod_install_mosaicml.sh + cluster_compute: single_worker_node_0_head_node_benchmark_compute.yaml + + run: + timeout: 1800 + script: bash run_image_loader_microbenchmark.sh + + variations: + - __suffix__: aws + - __suffix__: gce + env: gce + frequency: manual + cluster: + cluster_compute: single_node_benchmark_compute_gce.yaml + +- name: read_images_train_4_gpu + group: data-tests + working_dir: nightly_tests/dataset + + frequency: nightly + team: data + cluster: + byod: + type: gpu + post_build_script: byod_install_mosaicml.sh + cluster_compute: multi_node_train_4_workers.yaml + + run: + timeout: 18000 + script: python multi_node_train_benchmark.py --num-workers 4 --file-type image --use-gpu --num-epochs 2 + + variations: + - __suffix__: aws + - __suffix__: gce + env: gce + frequency: manual + cluster: + cluster_compute: ../../air_tests/air_benchmarks/compute_gpu_2x2_gce.yaml + +- name: read_images_train_4_gpu_worker_chaos + group: data-tests + working_dir: nightly_tests + + frequency: nightly + team: data + cluster: + byod: + type: gpu + post_build_script: byod_install_mosaicml.sh + cluster_compute: dataset/multi_node_train_4_workers.yaml + + run: + timeout: 18000 + prepare: python setup_chaos.py --kill-workers --kill-interval 100 --max-to-kill 3 --task-names "ReadImage->Map(wnid_to_index)->Map(crop_and_flip_image)" + script: python dataset/multi_node_train_benchmark.py --num-workers 4 --file-type image --use-gpu --num-epochs 1 + + variations: + - __suffix__: aws + - __suffix__: gce + env: gce + frequency: manual + cluster: + cluster_compute: ../air_tests/air_benchmarks/compute_gpu_2x2_gce.yaml + +- name: read_images_train_4_gpu_node_chaos + group: data-tests + working_dir: nightly_tests + + frequency: nightly + team: data + cluster: + byod: + type: gpu + post_build_script: byod_install_mosaicml.sh + cluster_compute: dataset/multi_node_train_4_workers.yaml + + run: + timeout: 18000 + prepare: python setup_chaos.py --kill-interval 200 --max-to-kill 1 --task-names "_RayTrainWorker__execute.get_next" + script: python dataset/multi_node_train_benchmark.py --num-workers 4 --file-type image --use-gpu --num-epochs 1 + + variations: + - __suffix__: aws + - __suffix__: gce + env: gce + frequency: manual + cluster: + cluster_compute: ../air_tests/air_benchmarks/compute_gpu_2x2_gce.yaml + +- name: read_images_train_16_gpu + group: data-tests + working_dir: nightly_tests/dataset + + frequency: nightly + team: data + cluster: + byod: + type: gpu + post_build_script: byod_install_mosaicml.sh + cluster_compute: multi_node_train_16_workers.yaml + + run: + timeout: 18000 + script: python multi_node_train_benchmark.py --num-workers 16 --file-type image --use-gpu --num-epochs 2 + + variations: + - __suffix__: aws + - __suffix__: gce + env: gce + frequency: manual + cluster: + cluster_compute: ../../air_tests/air_benchmarks/compute_gpu_4x4_gce.yaml + +- name: read_images_train_16_gpu_preserve_order + group: data-tests + working_dir: nightly_tests/dataset + + frequency: nightly + team: data + cluster: + byod: + type: gpu + post_build_script: byod_install_mosaicml.sh + cluster_compute: multi_node_train_16_workers.yaml + + run: + timeout: 18000 + script: python multi_node_train_benchmark.py --num-workers 16 --file-type image --preserve-order --use-gpu --num-epochs 2 + + variations: + - __suffix__: aws + - __suffix__: gce + env: gce + frequency: manual + cluster: + cluster_compute: ../../air_tests/air_benchmarks/compute_gpu_4x4_gce.yaml + +- name: read_parquet_train_4_gpu + group: data-tests + working_dir: nightly_tests/dataset + + frequency: nightly + team: data + cluster: + byod: + type: gpu + post_build_script: byod_install_mosaicml.sh + cluster_compute: multi_node_train_4_workers.yaml + + run: + timeout: 3600 + script: python multi_node_train_benchmark.py --num-workers 4 --file-type parquet --target-worker-gb 50 --use-gpu + + variations: + - __suffix__: aws + - __suffix__: gce + env: gce + frequency: manual + cluster: + cluster_compute: ../../air_tests/air_benchmarks/compute_gpu_2x2_gce.yaml + +- name: read_parquet_train_16_gpu + group: data-tests + working_dir: nightly_tests/dataset + + frequency: nightly + team: data + cluster: + byod: + type: gpu + post_build_script: byod_install_mosaicml.sh + cluster_compute: multi_node_train_16_workers.yaml + + run: + timeout: 3600 + script: python multi_node_train_benchmark.py --num-workers 16 --file-type parquet --target-worker-gb 50 --use-gpu + + variations: + - __suffix__: aws + - __suffix__: gce + env: gce + frequency: manual + cluster: + cluster_compute: ../../air_tests/air_benchmarks/compute_gpu_4x4_gce.yaml + +- name: read_images_train_1_gpu_5_cpu + group: data-tests + working_dir: nightly_tests/dataset + + frequency: nightly + team: data + cluster: + byod: + type: gpu + post_build_script: byod_install_mosaicml.sh + cluster_compute: multi_node_train_1g5c.yaml + + run: + timeout: 2400 + script: python multi_node_train_benchmark.py --num-workers 1 --file-type image --use-gpu --num-epochs 2 --skip-train-model --prefetch-batches 16 --batch-size -1 --disable-locality-with-output + + variations: + - __suffix__: aws + - __suffix__: gce + env: gce + frequency: manual + cluster: + cluster_compute: compute_gpu_1g5c_gce.yaml + +- name: read_tfrecords_benchmark_single_node + group: data-tests + working_dir: nightly_tests/dataset + + frequency: nightly + team: data + + cluster: + byod: + type: gpu + post_build_script: byod_install_mosaicml.sh + cluster_compute: single_node_benchmark_compute.yaml + + run: + # Expect the benchmark to finish around 30 minutes. + timeout: 2700 + script: python read_tfrecords_benchmark.py + + variations: + - __suffix__: aws + - __suffix__: gce + env: gce + frequency: manual + cluster: + cluster_compute: single_node_benchmark_compute_gce.yaml + +- name: map_batches_benchmark_single_node + group: data-tests + working_dir: nightly_tests/dataset + + frequency: nightly + team: data + + cluster: + byod: + type: gpu + cluster_compute: single_node_benchmark_compute.yaml + + run: + # Expect the benchmark to finish around 30 minutes. + timeout: 2400 + script: python map_batches_benchmark.py + + variations: + - __suffix__: aws + - __suffix__: gce + env: gce + frequency: manual + cluster: + cluster_compute: single_node_benchmark_compute_gce.yaml + +- name: iter_tensor_batches_benchmark_single_node + group: data-tests + working_dir: nightly_tests/dataset + + frequency: nightly + team: data + + cluster: + byod: + type: gpu + cluster_compute: single_node_benchmark_compute.yaml + + run: + # Expect the benchmark to finish around 30 minutes. + timeout: 2400 + script: python iter_tensor_batches_benchmark.py + + variations: + - __suffix__: aws + - __suffix__: gce + env: gce + frequency: manual + cluster: + cluster_compute: single_node_benchmark_compute_gce.yaml + +- name: iter_tensor_batches_benchmark_multi_node + group: data-tests + working_dir: nightly_tests/dataset + + frequency: nightly + team: data + + cluster: + byod: + type: gpu + cluster_compute: multi_node_benchmark_compute.yaml + + run: + # Expect the benchmark to finish within 90 minutes. + timeout: 5400 + script: python iter_tensor_batches_benchmark.py --data-size-gb=10 + + variations: + - __suffix__: aws + - __suffix__: gce + env: gce + frequency: manual + cluster: + cluster_compute: multi_node_benchmark_compute_gce.yaml + +- name: iter_batches_benchmark_single_node + group: data-tests + working_dir: nightly_tests/dataset + + frequency: nightly + team: data + + cluster: + byod: + type: gpu + cluster_compute: single_node_benchmark_compute.yaml + + run: + # Expect the benchmark to finish around 12 minutes. + timeout: 1080 + script: python iter_batches_benchmark.py + + variations: + - __suffix__: aws + - __suffix__: gce + env: gce + frequency: manual + cluster: + cluster_compute: single_node_benchmark_compute_gce.yaml + +- name: dataset_shuffle_random_shuffle_1tb + group: data-tests + working_dir: nightly_tests + + frequency: nightly + team: data + + cluster: + byod: + runtime_env: + - RAY_worker_killing_policy=retriable_lifo + pip: + - ray[default] + cluster_compute: shuffle/datasets_large_scale_compute_small_instances.yaml + + run: + timeout: 7200 + script: python dataset/sort.py --num-partitions=1000 --partition-size=1e9 --shuffle + wait_for_nodes: + num_nodes: 20 + + variations: + - __suffix__: aws + - __suffix__: gce + env: gce + frequency: manual + cluster: + cluster_compute: shuffle/datasets_large_scale_compute_small_instances_gce.yaml + +- name: dataset_shuffle_sort_1tb + group: data-tests + working_dir: nightly_tests + + frequency: nightly + team: data + stable: False + + cluster: + byod: + runtime_env: + - RAY_worker_killing_policy=retriable_lifo + pip: + - ray[default] + cluster_compute: shuffle/datasets_large_scale_compute_small_instances.yaml + + run: + timeout: 7200 + script: python dataset/sort.py --num-partitions=1000 --partition-size=1e9 + wait_for_nodes: + num_nodes: 20 + + variations: + - __suffix__: aws + - __suffix__: gce + env: gce + frequency: manual + cluster: + cluster_compute: shuffle/datasets_large_scale_compute_small_instances_gce.yaml + +- name: dataset_shuffle_push_based_random_shuffle_1tb + group: data-tests + working_dir: nightly_tests + + stable: false + + frequency: nightly + team: data + + cluster: + byod: + runtime_env: + - RAY_worker_killing_policy=retriable_lifo + pip: + - ray[default] + cluster_compute: shuffle/datasets_large_scale_compute_small_instances.yaml + + run: + timeout: 7200 + script: RAY_DATA_PUSH_BASED_SHUFFLE=1 python dataset/sort.py --num-partitions=1000 --partition-size=1e9 --shuffle + wait_for_nodes: + num_nodes: 20 + + variations: + - __suffix__: aws + - __suffix__: gce + env: gce + frequency: manual + cluster: + cluster_compute: shuffle/datasets_large_scale_compute_small_instances_gce.yaml + +- name: dataset_shuffle_push_based_sort_1tb + group: data-tests + working_dir: nightly_tests + + frequency: nightly + team: data + stable: False + + cluster: + byod: + runtime_env: + - RAY_worker_killing_policy=retriable_lifo + pip: + - ray[default] + cluster_compute: shuffle/datasets_large_scale_compute_small_instances.yaml + + run: + timeout: 7200 + script: RAY_DATA_PUSH_BASED_SHUFFLE=1 python dataset/sort.py --num-partitions=1000 --partition-size=1e9 + wait_for_nodes: + num_nodes: 20 + + variations: + - __suffix__: aws + - __suffix__: gce + env: gce + frequency: manual + cluster: + cluster_compute: shuffle/datasets_large_scale_compute_small_instances_gce.yaml + +- name: dataset_shuffle_push_based_random_shuffle_100tb + group: data-tests + working_dir: nightly_tests + stable: false + + frequency: weekly + team: data + cluster: + byod: + runtime_env: + - RAY_object_spilling_config={"type":"filesystem","params":{"directory_path":["/tmp/data0","/tmp/data1"]}} + post_build_script: byod_dataset_shuffle.sh + cluster_compute: shuffle/100tb_shuffle_compute.yaml + + run: + timeout: 28800 + script: RAY_DATA_PUSH_BASED_SHUFFLE=1 python dataset/sort.py --num-partitions=100000 --partition-size=1e9 --shuffle + wait_for_nodes: + num_nodes: 100 + + variations: + - __suffix__: aws + - __suffix__: gce + env: gce + frequency: manual + cluster: + cluster_compute: shuffle/100tb_shuffle_compute_gce.yaml + run: + timeout: 28800 + script: RAY_DATA_PUSH_BASED_SHUFFLE=1 python dataset/sort.py --num-partitions=40000 --partition-size=1e9 --shuffle + wait_for_nodes: + num_nodes: 100 + +############################ +# Batch Inference Benchmarks +############################ + +# 10 GB image classification raw images with 1 GPU. +# 1 g4dn.4xlarge +- name: torch_batch_inference_1_gpu_10gb_raw + group: data-tests + working_dir: nightly_tests/dataset + + frequency: nightly + team: data + cluster: + byod: + type: gpu + cluster_compute: compute_gpu_1_cpu_16_aws.yaml + + run: + timeout: 500 + script: python gpu_batch_inference.py --data-directory=10G-image-data-synthetic-raw --data-format raw + + alert: default + + variations: + - __suffix__: aws + - __suffix__: gce + env: gce + frequency: manual + cluster: + cluster_compute: compute_gpu_1_cpu_16_gce.yaml + +# 10 GB image classification parquet with 1 GPU. +# 1 g4dn.4xlarge +- name: torch_batch_inference_1_gpu_10gb_parquet + group: data-tests + working_dir: nightly_tests/dataset + + frequency: nightly + team: data + cluster: + byod: + type: gpu + cluster_compute: compute_gpu_1_cpu_16_aws.yaml + + run: + timeout: 500 + script: python gpu_batch_inference.py --data-directory=10G-image-data-synthetic-raw-parquet --data-format parquet + + alert: default + + variations: + - __suffix__: aws + - __suffix__: gce + env: gce + frequency: manual + cluster: + cluster_compute: compute_gpu_1_cpu_16_gce.yaml + + +# 300 GB image classification raw images with 16 GPUs +# 4 g4dn.12xlarge +- name: torch_batch_inference_16_gpu_300gb_raw + group: data-tests + working_dir: nightly_tests/dataset + + frequency: nightly + team: data + cluster: + byod: + type: gpu + cluster_compute: compute_gpu_4x4_aws.yaml + + run: + timeout: 1000 + script: python gpu_batch_inference.py --data-directory 300G-image-data-synthetic-raw --data-format raw + + wait_for_nodes: + num_nodes: 4 + + alert: default + + variations: + - __suffix__: aws + - __suffix__: gce + env: gce + frequency: manual + cluster: + cluster_compute: compute_gpu_4x4_gce.yaml + + +- name: chaos_torch_batch_inference_16_gpu_300gb_raw + group: data-tests + working_dir: nightly_tests + stable: false + + frequency: nightly + team: data + cluster: + byod: + type: gpu + cluster_compute: dataset/compute_gpu_4x4_aws.yaml + + run: + timeout: 1000 + prepare: python setup_chaos.py --max-to-kill 2 --kill-delay 30 + script: python dataset/gpu_batch_inference.py --data-directory 300G-image-data-synthetic-raw --data-format raw + + wait_for_nodes: + num_nodes: 4 + + alert: default + + variations: + - __suffix__: aws + - __suffix__: gce + env: gce + frequency: manual + cluster: + cluster_compute: dataset/compute_gpu_4x4_gce.yaml + + +# 300 GB image classification parquet data with 16 GPUs +# 4 g4dn.12xlarge +- name: torch_batch_inference_16_gpu_300gb_parquet + group: data-tests + working_dir: nightly_tests/dataset + + frequency: nightly + team: data + + cluster: + byod: + type: gpu + cluster_compute: compute_gpu_4x4_aws.yaml + + run: + timeout: 1000 + script: python gpu_batch_inference.py --data-directory 300G-image-data-synthetic-raw-parquet --data-format parquet + + wait_for_nodes: + num_nodes: 4 + + alert: default + + variations: + - __suffix__: aws + - __suffix__: gce + env: gce + frequency: manual + cluster: + cluster_compute: compute_gpu_4x4_gce.yaml + +# 10 TB image classification parquet data with heterogenous cluster +# 10 g4dn.12xlarge, 10 m5.16xlarge +- name: torch_batch_inference_hetero_10tb_parquet + group: data-tests + working_dir: nightly_tests/dataset + + frequency: weekly + team: data + + cluster: + byod: + type: gpu + cluster_compute: compute_hetero_10x10_aws.yaml + + run: + timeout: 2000 + script: python gpu_batch_inference.py --data-directory 10T-image-data-synthetic-raw-parquet --data-format parquet + + wait_for_nodes: + num_nodes: 20 + + alert: default diff --git a/release/release_tests.yaml b/release/release_tests.yaml index d8aad5b4bf72..4ffa492e6cde 100644 --- a/release/release_tests.yaml +++ b/release/release_tests.yaml @@ -106,181 +106,6 @@ alert: default -############################ -# Batch Inference Benchmarks -############################ - -# 10 GB image classification raw images with 1 GPU. -# 1 g4dn.4xlarge -- name: torch_batch_inference_1_gpu_10gb_raw - group: data-tests - working_dir: nightly_tests/dataset - - frequency: nightly - team: data - cluster: - byod: - type: gpu - cluster_compute: compute_gpu_1_cpu_16_aws.yaml - - run: - timeout: 500 - script: python gpu_batch_inference.py --data-directory=10G-image-data-synthetic-raw --data-format raw - - alert: default - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: compute_gpu_1_cpu_16_gce.yaml - -# 10 GB image classification parquet with 1 GPU. -# 1 g4dn.4xlarge -- name: torch_batch_inference_1_gpu_10gb_parquet - group: data-tests - working_dir: nightly_tests/dataset - - frequency: nightly - team: data - cluster: - byod: - type: gpu - cluster_compute: compute_gpu_1_cpu_16_aws.yaml - - run: - timeout: 500 - script: python gpu_batch_inference.py --data-directory=10G-image-data-synthetic-raw-parquet --data-format parquet - - alert: default - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: compute_gpu_1_cpu_16_gce.yaml - - -# 300 GB image classification raw images with 16 GPUs -# 4 g4dn.12xlarge -- name: torch_batch_inference_16_gpu_300gb_raw - group: data-tests - working_dir: nightly_tests/dataset - - frequency: nightly - team: data - cluster: - byod: - type: gpu - cluster_compute: compute_gpu_4x4_aws.yaml - - run: - timeout: 1000 - script: python gpu_batch_inference.py --data-directory 300G-image-data-synthetic-raw --data-format raw - - wait_for_nodes: - num_nodes: 4 - - alert: default - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: compute_gpu_4x4_gce.yaml - - -- name: chaos_torch_batch_inference_16_gpu_300gb_raw - group: data-tests - working_dir: nightly_tests - stable: false - - frequency: nightly - team: data - cluster: - byod: - type: gpu - cluster_compute: dataset/compute_gpu_4x4_aws.yaml - - run: - timeout: 1000 - prepare: python setup_chaos.py --max-to-kill 2 --kill-delay 30 - script: python dataset/gpu_batch_inference.py --data-directory 300G-image-data-synthetic-raw --data-format raw - - wait_for_nodes: - num_nodes: 4 - - alert: default - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: dataset/compute_gpu_4x4_gce.yaml - - -# 300 GB image classification parquet data with 16 GPUs -# 4 g4dn.12xlarge -- name: torch_batch_inference_16_gpu_300gb_parquet - group: data-tests - working_dir: nightly_tests/dataset - - frequency: nightly - team: data - - cluster: - byod: - type: gpu - cluster_compute: compute_gpu_4x4_aws.yaml - - run: - timeout: 1000 - script: python gpu_batch_inference.py --data-directory 300G-image-data-synthetic-raw-parquet --data-format parquet - - wait_for_nodes: - num_nodes: 4 - - alert: default - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: compute_gpu_4x4_gce.yaml - -# 10 TB image classification parquet data with heterogenous cluster -# 10 g4dn.12xlarge, 10 m5.16xlarge -- name: torch_batch_inference_hetero_10tb_parquet - group: data-tests - working_dir: nightly_tests/dataset - - frequency: weekly - team: data - - cluster: - byod: - type: gpu - cluster_compute: compute_hetero_10x10_aws.yaml - - run: - timeout: 2000 - script: python gpu_batch_inference.py --data-directory 10T-image-data-synthetic-raw-parquet --data-format parquet - - wait_for_nodes: - num_nodes: 20 - - alert: default - ######################### # AIR release tests @@ -3876,741 +3701,6 @@ # stable: false -############### -# Dataset tests -############### - -- name: parquet_metadata_resolution - group: data-tests - working_dir: nightly_tests/dataset - - frequency: nightly - team: data - - cluster: - byod: - type: gpu - cluster_compute: single_node_benchmark_compute.yaml - - run: - # Expect the test to finish around 40 seconds. - timeout: 100 - script: python parquet_metadata_resolution.py --num-files 915 --cloud aws - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: single_node_benchmark_compute_gce.yaml - run: - script: python parquet_metadata_resolution.py --num-files 915 --cloud gcp - -- name: stable_diffusion_benchmark - group: data-tests - working_dir: nightly_tests/dataset - - frequency: nightly - team: data - - cluster: - byod: - type: gpu - post_build_script: byod_stable_diffusion.sh - cluster_compute: stable_diffusion_benchmark_compute.yaml - - run: - timeout: 1800 - script: python stable_diffusion_benchmark.py - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: stable_diffusion_benchmark_compute_gce.yaml - -- name: streaming_data_ingest_benchmark_1tb - group: data-tests - working_dir: nightly_tests/dataset - - frequency: nightly - team: data - - cluster: - byod: - type: gpu - cluster_compute: data_ingest_benchmark_compute.yaml - - run: - timeout: 300 - script: python data_ingest_benchmark.py --dataset-size-gb=1000 --num-workers=20 --streaming - wait_for_nodes: - num_nodes: 20 - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: data_ingest_benchmark_compute_gce.yaml - -- name: streaming_data_ingest_benchmark_100gb_gpu - group: data-tests - working_dir: nightly_tests/dataset - - frequency: nightly - team: data - - cluster: - byod: - type: gpu - cluster_compute: data_ingest_benchmark_compute_gpu.yaml - - run: - timeout: 300 - script: python data_ingest_benchmark.py --dataset-size-gb=100 --num-workers=4 --streaming --use-gpu - wait_for_nodes: - num_nodes: 3 - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: data_ingest_benchmark_compute_gpu_gce.yaml - -# This test case will early stop the data ingestion iteration on the GPU actors. -# This is a common usage in PyTorch Lightning -# (https://lightning.ai/docs/pytorch/stable/common/trainer.html#limit-train-batches). -# There was a bug in Ray Data that caused GPU memoy leak (see #3.919). -# We add this test case to cover this scenario. -- name: streaming_data_ingest_benchmark_100gb_gpu_early_stop - group: data-tests - working_dir: nightly_tests/dataset - - frequency: nightly - team: data - - cluster: - byod: - type: gpu - cluster_compute: data_ingest_benchmark_compute_gpu.yaml - - run: - timeout: 300 - script: python data_ingest_benchmark.py --dataset-size-gb=100 --num-workers=4 --streaming --use-gpu --early-stop - wait_for_nodes: - num_nodes: 3 - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: data_ingest_benchmark_compute_gpu_gce.yaml - -- name: aggregate_benchmark - group: data-tests - working_dir: nightly_tests/dataset - - frequency: nightly - team: data - - cluster: - byod: - type: gpu - cluster_compute: single_node_benchmark_compute.yaml - - run: - timeout: 1800 - script: python aggregate_benchmark.py - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: single_node_benchmark_compute_gce.yaml - -- name: read_parquet_benchmark_single_node - group: data-tests - working_dir: nightly_tests/dataset - - frequency: nightly - team: data - - cluster: - byod: - type: gpu - post_build_script: byod_install_mosaicml.sh - cluster_compute: single_node_benchmark_compute.yaml - - run: - # Expect the benchmark to finish in 400 seconds. - timeout: 400 - script: python read_parquet_benchmark.py - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: single_node_benchmark_compute_gce.yaml - -- name: read_images_benchmark_single_node - group: data-tests - working_dir: nightly_tests/dataset - - frequency: nightly - team: data - - cluster: - byod: - type: gpu - post_build_script: byod_install_mosaicml.sh - cluster_compute: single_node_benchmark_compute.yaml - - run: - timeout: 1800 - script: python read_images_benchmark.py --single-node - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: single_node_benchmark_compute_gce.yaml - -# TODO: Re-enable this test once we fix https://github.com/ray-project/ray/issues/40686. -# - name: read_images_benchmark_multi_node -# group: data-tests -# working_dir: nightly_tests/dataset - -# frequency: nightly-3x -# team: data - -# cluster: -# byod: -# type: gpu -# cluster_compute: multi_node_read_images_benchmark_compute.yaml - -# run: -# timeout: 28800 -# script: python read_images_benchmark.py --multi-node - -# variations: -# - __suffix__: aws -# - __suffix__: gce -# env: gce -# frequency: manual -# cluster: -# cluster_compute: multi_node_read_images_benchmark_compute_gce.yaml - -- name: read_images_comparison_microbenchmark_single_node - group: data-tests - working_dir: nightly_tests/dataset - - frequency: nightly - team: data - cluster: - byod: - type: gpu - post_build_script: byod_install_mosaicml.sh - cluster_compute: single_worker_node_0_head_node_benchmark_compute.yaml - - run: - timeout: 1800 - script: bash run_image_loader_microbenchmark.sh - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: single_node_benchmark_compute_gce.yaml - -- name: read_images_train_4_gpu - group: data-tests - working_dir: nightly_tests/dataset - - frequency: nightly - team: data - cluster: - byod: - type: gpu - post_build_script: byod_install_mosaicml.sh - cluster_compute: multi_node_train_4_workers.yaml - - run: - timeout: 18000 - script: python multi_node_train_benchmark.py --num-workers 4 --file-type image --use-gpu --num-epochs 2 - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: ../../air_tests/air_benchmarks/compute_gpu_2x2_gce.yaml - -- name: read_images_train_4_gpu_worker_chaos - group: data-tests - working_dir: nightly_tests - - frequency: nightly - team: data - cluster: - byod: - type: gpu - post_build_script: byod_install_mosaicml.sh - cluster_compute: dataset/multi_node_train_4_workers.yaml - - run: - timeout: 18000 - prepare: python setup_chaos.py --kill-workers --kill-interval 100 --max-to-kill 3 --task-names "ReadImage->Map(wnid_to_index)->Map(crop_and_flip_image)" - script: python dataset/multi_node_train_benchmark.py --num-workers 4 --file-type image --use-gpu --num-epochs 1 - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: ../air_tests/air_benchmarks/compute_gpu_2x2_gce.yaml - -- name: read_images_train_4_gpu_node_chaos - group: data-tests - working_dir: nightly_tests - - frequency: nightly - team: data - cluster: - byod: - type: gpu - post_build_script: byod_install_mosaicml.sh - cluster_compute: dataset/multi_node_train_4_workers.yaml - - run: - timeout: 18000 - prepare: python setup_chaos.py --kill-interval 200 --max-to-kill 1 --task-names "_RayTrainWorker__execute.get_next" - script: python dataset/multi_node_train_benchmark.py --num-workers 4 --file-type image --use-gpu --num-epochs 1 - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: ../air_tests/air_benchmarks/compute_gpu_2x2_gce.yaml - -- name: read_images_train_16_gpu - group: data-tests - working_dir: nightly_tests/dataset - - frequency: nightly - team: data - cluster: - byod: - type: gpu - post_build_script: byod_install_mosaicml.sh - cluster_compute: multi_node_train_16_workers.yaml - - run: - timeout: 18000 - script: python multi_node_train_benchmark.py --num-workers 16 --file-type image --use-gpu --num-epochs 2 - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: ../../air_tests/air_benchmarks/compute_gpu_4x4_gce.yaml - -- name: read_images_train_16_gpu_preserve_order - group: data-tests - working_dir: nightly_tests/dataset - - frequency: nightly - team: data - cluster: - byod: - type: gpu - post_build_script: byod_install_mosaicml.sh - cluster_compute: multi_node_train_16_workers.yaml - - run: - timeout: 18000 - script: python multi_node_train_benchmark.py --num-workers 16 --file-type image --preserve-order --use-gpu --num-epochs 2 - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: ../../air_tests/air_benchmarks/compute_gpu_4x4_gce.yaml - -- name: read_parquet_train_4_gpu - group: data-tests - working_dir: nightly_tests/dataset - - frequency: nightly - team: data - cluster: - byod: - type: gpu - post_build_script: byod_install_mosaicml.sh - cluster_compute: multi_node_train_4_workers.yaml - - run: - timeout: 3600 - script: python multi_node_train_benchmark.py --num-workers 4 --file-type parquet --target-worker-gb 50 --use-gpu - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: ../../air_tests/air_benchmarks/compute_gpu_2x2_gce.yaml - -- name: read_parquet_train_16_gpu - group: data-tests - working_dir: nightly_tests/dataset - - frequency: nightly - team: data - cluster: - byod: - type: gpu - post_build_script: byod_install_mosaicml.sh - cluster_compute: multi_node_train_16_workers.yaml - - run: - timeout: 3600 - script: python multi_node_train_benchmark.py --num-workers 16 --file-type parquet --target-worker-gb 50 --use-gpu - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: ../../air_tests/air_benchmarks/compute_gpu_4x4_gce.yaml - -- name: read_images_train_1_gpu_5_cpu - group: data-tests - working_dir: nightly_tests/dataset - - frequency: nightly - team: data - cluster: - byod: - type: gpu - post_build_script: byod_install_mosaicml.sh - cluster_compute: multi_node_train_1g5c.yaml - - run: - timeout: 2400 - script: python multi_node_train_benchmark.py --num-workers 1 --file-type image --use-gpu --num-epochs 2 --skip-train-model --prefetch-batches 16 --batch-size -1 --disable-locality-with-output - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: compute_gpu_1g5c_gce.yaml - -- name: read_tfrecords_benchmark_single_node - group: data-tests - working_dir: nightly_tests/dataset - - frequency: nightly - team: data - - cluster: - byod: - type: gpu - post_build_script: byod_install_mosaicml.sh - cluster_compute: single_node_benchmark_compute.yaml - - run: - # Expect the benchmark to finish around 30 minutes. - timeout: 2700 - script: python read_tfrecords_benchmark.py - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: single_node_benchmark_compute_gce.yaml - -- name: map_batches_benchmark_single_node - group: data-tests - working_dir: nightly_tests/dataset - - frequency: nightly - team: data - - cluster: - byod: - type: gpu - cluster_compute: single_node_benchmark_compute.yaml - - run: - # Expect the benchmark to finish around 30 minutes. - timeout: 2400 - script: python map_batches_benchmark.py - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: single_node_benchmark_compute_gce.yaml - -- name: iter_tensor_batches_benchmark_single_node - group: data-tests - working_dir: nightly_tests/dataset - - frequency: nightly - team: data - - cluster: - byod: - type: gpu - cluster_compute: single_node_benchmark_compute.yaml - - run: - # Expect the benchmark to finish around 30 minutes. - timeout: 2400 - script: python iter_tensor_batches_benchmark.py - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: single_node_benchmark_compute_gce.yaml - -- name: iter_tensor_batches_benchmark_multi_node - group: data-tests - working_dir: nightly_tests/dataset - - frequency: nightly - team: data - - cluster: - byod: - type: gpu - cluster_compute: multi_node_benchmark_compute.yaml - - run: - # Expect the benchmark to finish within 90 minutes. - timeout: 5400 - script: python iter_tensor_batches_benchmark.py --data-size-gb=10 - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: multi_node_benchmark_compute_gce.yaml - -- name: iter_batches_benchmark_single_node - group: data-tests - working_dir: nightly_tests/dataset - - frequency: nightly - team: data - - cluster: - byod: - type: gpu - cluster_compute: single_node_benchmark_compute.yaml - - run: - # Expect the benchmark to finish around 12 minutes. - timeout: 1080 - script: python iter_batches_benchmark.py - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: single_node_benchmark_compute_gce.yaml - -- name: dataset_shuffle_random_shuffle_1tb - group: data-tests - working_dir: nightly_tests - - frequency: nightly - team: data - - cluster: - byod: - runtime_env: - - RAY_worker_killing_policy=retriable_lifo - pip: - - ray[default] - cluster_compute: shuffle/datasets_large_scale_compute_small_instances.yaml - - run: - timeout: 7200 - script: python dataset/sort.py --num-partitions=1000 --partition-size=1e9 --shuffle - wait_for_nodes: - num_nodes: 20 - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: shuffle/datasets_large_scale_compute_small_instances_gce.yaml - -- name: dataset_shuffle_sort_1tb - group: data-tests - working_dir: nightly_tests - - frequency: nightly - team: data - stable: False - - cluster: - byod: - runtime_env: - - RAY_worker_killing_policy=retriable_lifo - pip: - - ray[default] - cluster_compute: shuffle/datasets_large_scale_compute_small_instances.yaml - - run: - timeout: 7200 - script: python dataset/sort.py --num-partitions=1000 --partition-size=1e9 - wait_for_nodes: - num_nodes: 20 - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: shuffle/datasets_large_scale_compute_small_instances_gce.yaml - -- name: dataset_shuffle_push_based_random_shuffle_1tb - group: data-tests - working_dir: nightly_tests - - stable: false - - frequency: nightly - team: data - - cluster: - byod: - runtime_env: - - RAY_worker_killing_policy=retriable_lifo - pip: - - ray[default] - cluster_compute: shuffle/datasets_large_scale_compute_small_instances.yaml - - run: - timeout: 7200 - script: RAY_DATA_PUSH_BASED_SHUFFLE=1 python dataset/sort.py --num-partitions=1000 --partition-size=1e9 --shuffle - wait_for_nodes: - num_nodes: 20 - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: shuffle/datasets_large_scale_compute_small_instances_gce.yaml - -- name: dataset_shuffle_push_based_sort_1tb - group: data-tests - working_dir: nightly_tests - - frequency: nightly - team: data - stable: False - - cluster: - byod: - runtime_env: - - RAY_worker_killing_policy=retriable_lifo - pip: - - ray[default] - cluster_compute: shuffle/datasets_large_scale_compute_small_instances.yaml - - run: - timeout: 7200 - script: RAY_DATA_PUSH_BASED_SHUFFLE=1 python dataset/sort.py --num-partitions=1000 --partition-size=1e9 - wait_for_nodes: - num_nodes: 20 - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: shuffle/datasets_large_scale_compute_small_instances_gce.yaml - -- name: dataset_shuffle_push_based_random_shuffle_100tb - group: data-tests - working_dir: nightly_tests - stable: false - - frequency: weekly - team: data - cluster: - byod: - runtime_env: - - RAY_object_spilling_config={"type":"filesystem","params":{"directory_path":["/tmp/data0","/tmp/data1"]}} - post_build_script: byod_dataset_shuffle.sh - cluster_compute: shuffle/100tb_shuffle_compute.yaml - - run: - timeout: 28800 - script: RAY_DATA_PUSH_BASED_SHUFFLE=1 python dataset/sort.py --num-partitions=100000 --partition-size=1e9 --shuffle - wait_for_nodes: - num_nodes: 100 - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: shuffle/100tb_shuffle_compute_gce.yaml - run: - timeout: 28800 - script: RAY_DATA_PUSH_BASED_SHUFFLE=1 python dataset/sort.py --num-partitions=40000 --partition-size=1e9 --shuffle - wait_for_nodes: - num_nodes: 100 ################## # Core Chaos tests From 7d081ece584262d4f85438f667fe331a19332aff Mon Sep 17 00:00:00 2001 From: Rui Qiao <161574667+ruisearch42@users.noreply.github.com> Date: Thu, 21 Nov 2024 16:57:59 -0800 Subject: [PATCH 06/54] [core] Fix typos (#48849) Fix typos Signed-off-by: Rui Qiao --- python/ray/_private/state.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/python/ray/_private/state.py b/python/ray/_private/state.py index bebca03c0044..b8af96491b08 100644 --- a/python/ray/_private/state.py +++ b/python/ray/_private/state.py @@ -525,7 +525,7 @@ def chrome_tracing_object_transfer_dump(self, filename=None): """Return a list of transfer events that can viewed as a timeline. To view this information as a timeline, simply dump it as a json file - by passing in "filename" or using using json.dump, and then load go to + by passing in "filename" or using json.dump, and then load go to chrome://tracing in the Chrome web browser and load the dumped file. Make sure to enable "Flow events" in the "View Options" menu. @@ -748,7 +748,7 @@ def _live_node_ids(self): return set(self.total_resources_per_node().keys()) def available_resources_per_node(self): - """Returns a dictionary mapping node id to avaiable resources.""" + """Returns a dictionary mapping node id to available resources.""" self._check_connected() available_resources_by_id = {} @@ -952,7 +952,7 @@ def timeline(filename=None): variable prior to starting Ray, and set RAY_task_events_report_interval_ms=0 To view this information as a timeline, simply dump it as a json file by - passing in "filename" or using using json.dump, and then load go to + passing in "filename" or using json.dump, and then load go to chrome://tracing in the Chrome web browser and load the dumped file. Args: @@ -970,7 +970,7 @@ def object_transfer_timeline(filename=None): """Return a list of transfer events that can viewed as a timeline. To view this information as a timeline, simply dump it as a json file by - passing in "filename" or using using json.dump, and then load go to + passing in "filename" or using json.dump, and then load go to chrome://tracing in the Chrome web browser and load the dumped file. Make sure to enable "Flow events" in the "View Options" menu. From d4c2c8d796c09a2e9f1f1dfaf1fbc90b762c6971 Mon Sep 17 00:00:00 2001 From: akyang-anyscale Date: Thu, 21 Nov 2024 17:16:31 -0800 Subject: [PATCH 07/54] [Serve] Propagate replica constructor error to deployment status message and print num retries left (#48531) ## Why are these changes needed? This change will surface the replica constructor error as soon as the replica constructor fails for whatever reason. The exception will be populated in the deployment status so that it's viewable from the ray dashboard. Additionally, the number of replica constructor retries left will also be updated in the error message. This will help users more quickly debug a deployment that is failing to start. ## Related issue number Closes https://github.com/ray-project/ray/issues/35604 Signed-off-by: akyang-anyscale --- python/ray/serve/_private/deployment_state.py | 44 ++++++++++++------- python/ray/serve/tests/test_cli.py | 28 ++++++++++++ .../test_config_files/deployment_fail_2.yaml | 3 ++ .../serve/tests/test_config_files/fail_2.py | 13 ++++++ .../serve/tests/unit/test_deployment_state.py | 10 ++--- 5 files changed, 76 insertions(+), 22 deletions(-) create mode 100644 python/ray/serve/tests/test_config_files/deployment_fail_2.yaml create mode 100644 python/ray/serve/tests/test_config_files/fail_2.py diff --git a/python/ray/serve/_private/deployment_state.py b/python/ray/serve/_private/deployment_state.py index 562fd62f62c6..09fa27876397 100644 --- a/python/ray/serve/_private/deployment_state.py +++ b/python/ray/serve/_private/deployment_state.py @@ -1384,6 +1384,13 @@ def deployment_name(self) -> str: def app_name(self) -> str: return self._id.app_name + @property + def _failed_to_start_threshold(self) -> int: + return min( + MAX_DEPLOYMENT_CONSTRUCTOR_RETRY_COUNT, + self._target_state.target_num_replicas * 3, + ) + def get_alive_replica_actor_ids(self) -> Set[str]: return {replica.actor_id for replica in self._replicas.get()} @@ -1845,11 +1852,10 @@ def scale_deployment_replicas( if to_add > 0: # Exponential backoff - failed_to_start_threshold = min( - MAX_DEPLOYMENT_CONSTRUCTOR_RETRY_COUNT, - self._target_state.target_num_replicas * 3, - ) - if self._replica_constructor_retry_counter >= failed_to_start_threshold: + if ( + self._replica_constructor_retry_counter + >= self._failed_to_start_threshold + ): # Wait 1, 2, 4, ... seconds before consecutive retries, with random # offset added to avoid synchronization if ( @@ -1909,17 +1915,13 @@ def check_curr_status(self) -> Tuple[bool, bool]: ) failed_to_start_count = self._replica_constructor_retry_counter - failed_to_start_threshold = min( - MAX_DEPLOYMENT_CONSTRUCTOR_RETRY_COUNT, - self._target_state.target_num_replicas * 3, - ) # Got to make a call to complete current deploy() goal after # start failure threshold reached, while we might still have # pending replicas in current goal. if ( - failed_to_start_count >= failed_to_start_threshold - and failed_to_start_threshold != 0 + failed_to_start_count >= self._failed_to_start_threshold + and self._failed_to_start_threshold != 0 ): if running_at_target_version_replica_cnt > 0: # At least one RUNNING replica at target state, partial @@ -2043,17 +2045,27 @@ def record_replica_startup_failure(self, error_msg: str): self._replica_constructor_retry_counter += 1 self._replica_constructor_error_msg = error_msg + retrying_msg = "Retrying" + if self._failed_to_start_threshold != 0: + remaining_retries = ( + self._failed_to_start_threshold + - self._replica_constructor_retry_counter + ) + retrying_msg += f" {remaining_retries} more time(s)" + + message = ( + f"A replica failed to start with exception. {retrying_msg}. Error:\n" + f"{error_msg}" + ) + self._curr_status_info = self._curr_status_info.update_message(message) + def update_replica_startup_backoff_time(self): """Updates the replica startup backoff time.""" # If replicas have failed enough times, execute exponential backoff # Wait 1, 2, 4, ... seconds before consecutive retries (or use a custom # backoff factor by setting EXPONENTIAL_BACKOFF_FACTOR) - failed_to_start_threshold = min( - MAX_DEPLOYMENT_CONSTRUCTOR_RETRY_COUNT, - self._target_state.target_num_replicas * 3, - ) - if self._replica_constructor_retry_counter > failed_to_start_threshold: + if self._replica_constructor_retry_counter > self._failed_to_start_threshold: self._backoff_time_s = min( EXPONENTIAL_BACKOFF_FACTOR * self._backoff_time_s, MAX_BACKOFF_TIME_S ) diff --git a/python/ray/serve/tests/test_cli.py b/python/ray/serve/tests/test_cli.py index 943024cfbe38..2b2c0ff279dd 100644 --- a/python/ray/serve/tests/test_cli.py +++ b/python/ray/serve/tests/test_cli.py @@ -606,6 +606,34 @@ def check_for_failed_deployment(): wait_for_condition(check_for_failed_deployment) +@pytest.mark.skipif(sys.platform == "win32", reason="File path incorrect on Windows.") +def test_status_constructor_retry_error(ray_start_stop): + """Deploys Serve deployment that errors out in constructor, checks that the + retry message is surfaced. + """ + + config_file_name = os.path.join( + os.path.dirname(__file__), "test_config_files", "deployment_fail_2.yaml" + ) + + subprocess.check_output(["serve", "deploy", config_file_name]) + + def check_for_failed_deployment(): + cli_output = subprocess.check_output( + ["serve", "status", "-a", "http://localhost:52365/"] + ) + status = yaml.safe_load(cli_output)["applications"][SERVE_DEFAULT_APP_NAME] + assert status["status"] == "DEPLOYING" + + deployment_status = status["deployments"]["A"] + assert deployment_status["status"] == "UPDATING" + assert deployment_status["status_trigger"] == "CONFIG_UPDATE_STARTED" + assert "ZeroDivisionError" in deployment_status["message"] + return True + + wait_for_condition(check_for_failed_deployment) + + @pytest.mark.skipif(sys.platform == "win32", reason="File path incorrect on Windows.") def test_status_package_unavailable_in_controller(ray_start_stop): """Test that exceptions raised from packages that are installed on deployment actors diff --git a/python/ray/serve/tests/test_config_files/deployment_fail_2.yaml b/python/ray/serve/tests/test_config_files/deployment_fail_2.yaml new file mode 100644 index 000000000000..38da015b0bb3 --- /dev/null +++ b/python/ray/serve/tests/test_config_files/deployment_fail_2.yaml @@ -0,0 +1,3 @@ +applications: + - name: default + import_path: ray.serve.tests.test_config_files.fail_2.node diff --git a/python/ray/serve/tests/test_config_files/fail_2.py b/python/ray/serve/tests/test_config_files/fail_2.py new file mode 100644 index 000000000000..2e95aa93d98f --- /dev/null +++ b/python/ray/serve/tests/test_config_files/fail_2.py @@ -0,0 +1,13 @@ +import time + +from ray import serve + + +@serve.deployment +class A: + def __init__(self): + time.sleep(5) + 1 / 0 + + +node = A.bind() diff --git a/python/ray/serve/tests/unit/test_deployment_state.py b/python/ray/serve/tests/unit/test_deployment_state.py index dfeb9fc7524c..42facaf40282 100644 --- a/python/ray/serve/tests/unit/test_deployment_state.py +++ b/python/ray/serve/tests/unit/test_deployment_state.py @@ -2492,9 +2492,7 @@ def create_deployment_state( check_counts(ds1, total=3, by_state=[(ReplicaState.STOPPING, 3, None)]) assert ds1._replica_constructor_retry_counter == 3 - # An error message should show up after - # 3 * num_replicas startup failures. - assert "" == ds1.curr_status_info.message + assert "Retrying 6 more time(s)" in ds1.curr_status_info.message # Set all of ds1's replicas to stopped. for replica in ds1._replicas.get(): @@ -2512,7 +2510,7 @@ def create_deployment_state( assert ds1.curr_status_info.status == DeploymentStatus.UPDATING check_counts(ds1, total=3, by_state=[(ReplicaState.STOPPING, 3, None)]) assert ds1._replica_constructor_retry_counter == 6 - assert "" == ds1.curr_status_info.message + assert "Retrying 3 more time(s)" in ds1.curr_status_info.message # Set all of ds1's replicas to stopped. for replica in ds1._replicas.get(): @@ -2527,7 +2525,7 @@ def create_deployment_state( assert ds1.curr_status_info.status == DeploymentStatus.UPDATING check_counts(ds1, total=3, by_state=[(ReplicaState.STOPPING, 3, None)]) assert ds1._replica_constructor_retry_counter == 9 - assert "" == ds1.curr_status_info.message + assert "Retrying 0 more time(s)" in ds1.curr_status_info.message # Set all of ds1's replicas to stopped. for replica in ds1._replicas.get(): @@ -2540,7 +2538,7 @@ def create_deployment_state( assert ds1.curr_status_info.status == DeploymentStatus.UNHEALTHY check_counts(ds1, total=0) assert ds1._replica_constructor_retry_counter == 9 - assert "Replica scheduling failed" in ds1.curr_status_info.message + assert "The deployment failed to start" in ds1.curr_status_info.message def test_deploy_with_transient_constructor_failure(mock_deployment_state_manager): From 335bd668924e9e5c67b17444b4848f9c3983fe69 Mon Sep 17 00:00:00 2001 From: Artur Niederfahrenhorst Date: Fri, 22 Nov 2024 02:28:25 +0100 Subject: [PATCH 08/54] [Data] Re-implement APIs like select_columns with PyArrow batch format (#48140) ## Related issue number Closes #48090 Prerequisite: https://github.com/ray-project/ray/issues/48575 --------- Signed-off-by: Artur Niederfahrenhorst Co-authored-by: Balaji Veeramani Co-authored-by: Alexey Kudinkin --- python/ray/data/block.py | 6 ++ python/ray/data/dataset.py | 100 ++++++++++++++++++++---- python/ray/data/iterator.py | 3 +- python/ray/data/tests/test_map.py | 97 +++++++++++++++++++++-- python/ray/data/tests/test_mongo.py | 53 ++++++------- python/ray/data/tests/test_object_gc.py | 3 +- 6 files changed, 208 insertions(+), 54 deletions(-) diff --git a/python/ray/data/block.py b/python/ray/data/block.py index 56a3c6dbc40d..fcab3feb67eb 100644 --- a/python/ray/data/block.py +++ b/python/ray/data/block.py @@ -72,6 +72,12 @@ class BlockType(Enum): # returned from batch UDFs. DataBatch = Union["pyarrow.Table", "pandas.DataFrame", Dict[str, np.ndarray]] +# User-facing data column type. This is the data type for data that is supplied to and +# returned from column UDFs. +DataBatchColumn = Union[ + "pyarrow.ChunkedArray", "pyarrow.Array", "pandas.Series", np.ndarray +] + # A class type that implements __call__. CallableClass = type diff --git a/python/ray/data/dataset.py b/python/ray/data/dataset.py index d576b8eb2ea7..779a5bd3295e 100644 --- a/python/ray/data/dataset.py +++ b/python/ray/data/dataset.py @@ -87,6 +87,7 @@ Block, BlockAccessor, DataBatch, + DataBatchColumn, T, U, UserDefinedFunction, @@ -529,7 +530,8 @@ def __call__(self, batch: Dict[str, np.ndarray]) -> Dict[str, np.ndarray]: compute: This argument is deprecated. Use ``concurrency`` argument. batch_format: If ``"default"`` or ``"numpy"``, batches are ``Dict[str, numpy.ndarray]``. If ``"pandas"``, batches are - ``pandas.DataFrame``. + ``pandas.DataFrame``. If ``"pyarrow"``, batches are + ``pyarrow.Table``. zero_copy_batch: Whether ``fn`` should be provided zero-copy, read-only batches. If this is ``True`` and no copy is required for the ``batch_format`` conversion, the batch is a zero-copy, read-only @@ -700,16 +702,21 @@ def _map_batches_without_batch_size_validation( def add_column( self, col: str, - fn: Callable[["pandas.DataFrame"], "pandas.Series"], + fn: Callable[ + [DataBatch], + DataBatchColumn, + ], *, + batch_format: Optional[str] = "pandas", compute: Optional[str] = None, concurrency: Optional[Union[int, Tuple[int, int]]] = None, **ray_remote_args, ) -> "Dataset": """Add the given column to the dataset. - A function generating the new column values given the batch in pandas - format must be specified. + A function generating the new column values given the batch in pyarrow or pandas + format must be specified. This function must operate on batches of + `batch_format`. Examples: @@ -729,11 +736,6 @@ def add_column( id int64 new_id int64 - Overwrite the existing values with zeros. - - >>> ds.add_column("id", lambda df: 0).take(3) - [{'id': 0}, {'id': 0}, {'id': 0}] - Time complexity: O(dataset size / parallelism) Args: @@ -741,6 +743,11 @@ def add_column( column is overwritten. fn: Map function generating the column values given a batch of records in pandas format. + batch_format: If ``"default"`` or ``"numpy"``, batches are + ``Dict[str, numpy.ndarray]``. If ``"pandas"``, batches are + ``pandas.DataFrame``. If ``"pyarrow"``, batches are + ``pyarrow.Table``. If ``"numpy"``, batches are + ``Dict[str, numpy.ndarray]``. compute: This argument is deprecated. Use ``concurrency`` argument. concurrency: The number of Ray workers to use concurrently. For a fixed-sized worker pool of size ``n``, specify ``concurrency=n``. For @@ -749,17 +756,72 @@ def add_column( ray_remote_args: Additional resource requirements to request from ray (e.g., num_gpus=1 to request GPUs for the map tasks). """ + # Check that batch_format + accepted_batch_formats = ["pandas", "pyarrow", "numpy"] + if batch_format not in accepted_batch_formats: + raise ValueError( + f"batch_format argument must be on of {accepted_batch_formats}, " + f"got: {batch_format}" + ) - def add_column(batch: "pandas.DataFrame") -> "pandas.DataFrame": - batch.loc[:, col] = fn(batch) - return batch + def add_column(batch: DataBatch) -> DataBatch: + column = fn(batch) + if batch_format == "pandas": + import pandas as pd + + assert isinstance(column, pd.Series), ( + f"For pandas batch format, the function must return a pandas " + f"Series, got: {type(column)}" + ) + if col in batch: + raise ValueError( + f"Trying to add an existing column with name" f" {col}" + ) + batch.loc[:, col] = column + return batch + elif batch_format == "pyarrow": + import pyarrow as pa + + assert isinstance(column, (pa.Array, pa.ChunkedArray)), ( + f"For pyarrow batch format, the function must return a pyarrow " + f"Array, got: {type(column)}" + ) + # Historically, this method was written for pandas batch format. + # To resolve https://github.com/ray-project/ray/issues/48090, + # we also allow pyarrow batch format which is preferred but would be + # a breaking change to enforce. + + # For pyarrow, the index of the column will be -1 if it is missing in + # which case we'll want to append it + column_idx = batch.schema.get_field_index(col) + if column_idx == -1: + # Append the column to the table + return batch.append_column(col, column) + else: + raise ValueError( + f"Trying to add an existing column with name {col}" + ) + + else: + # batch format is assumed to be numpy since we checked at the + # beginning of the add_column function + assert isinstance(column, np.ndarray), ( + f"For numpy batch format, the function must return a " + f"numpy.ndarray, got: {type(column)}" + ) + if col in batch: + raise ValueError( + f"Trying to add an existing column with name" f" {col}" + ) + batch[col] = column + return batch if not callable(fn): raise ValueError("`fn` must be callable, got {}".format(fn)) return self.map_batches( add_column, - batch_format="pandas", # TODO(ekl) we should make this configurable. + batch_format=batch_format, compute=compute, concurrency=concurrency, zero_copy_batch=False, @@ -801,7 +863,7 @@ def drop_columns( Args: cols: Names of the columns to drop. If any name does not exist, - an exception is raised. + an exception is raised. Column names must be unique. compute: This argument is deprecated. Use ``concurrency`` argument. concurrency: The number of Ray workers to use concurrently. For a fixed-sized worker pool of size ``n``, specify ``concurrency=n``. For an autoscaling @@ -810,12 +872,15 @@ def drop_columns( ray (e.g., num_gpus=1 to request GPUs for the map tasks). """ # noqa: E501 + if len(cols) != len(set(cols)): + raise ValueError(f"drop_columns expects unique column names, got: {cols}") + def drop_columns(batch): - return batch.drop(columns=cols) + return batch.drop(cols) return self.map_batches( drop_columns, - batch_format="pandas", + batch_format="pyarrow", zero_copy_batch=True, compute=compute, concurrency=concurrency, @@ -4316,7 +4381,8 @@ def to_tf( If your model accepts additional metadata aside from features and label, specify a single additional column or a list of additional columns. A common use case is to include sample weights in the data samples and train a ``tf.keras.Model`` with ``tf.keras.Model.fit``. - >>> ds = ds.add_column("sample weights", lambda df: 1) + >>> import pandas as pd + >>> ds = ds.add_column("sample weights", lambda df: pd.Series([1] * len(df))) >>> ds.to_tf(feature_columns="features", label_columns="target", additional_columns="sample weights") <_OptionsDataset element_spec=(TensorSpec(shape=(None, 4), dtype=tf.float64, name='features'), TensorSpec(shape=(None,), dtype=tf.int64, name='target'), TensorSpec(shape=(None,), dtype=tf.int64, name='sample weights'))> diff --git a/python/ray/data/iterator.py b/python/ray/data/iterator.py index 58e9a1b7355e..2f19111af80f 100644 --- a/python/ray/data/iterator.py +++ b/python/ray/data/iterator.py @@ -734,7 +734,8 @@ def to_tf( If your model accepts additional metadata aside from features and label, specify a single additional column or a list of additional columns. A common use case is to include sample weights in the data samples and train a ``tf.keras.Model`` with ``tf.keras.Model.fit``. - >>> ds = ds.add_column("sample weights", lambda df: 1) + >>> import pandas as pd + >>> ds = ds.add_column("sample weights", lambda df: pd.Series([1] * len(df))) >>> it = ds.iterator() >>> it.to_tf(feature_columns="sepal length (cm)", label_columns="target", additional_columns="sample weights") <_OptionsDataset element_spec=(TensorSpec(shape=(None,), dtype=tf.float64, name='sepal length (cm)'), TensorSpec(shape=(None,), dtype=tf.int64, name='target'), TensorSpec(shape=(None,), dtype=tf.int64, name='sample weights'))> diff --git a/python/ray/data/tests/test_map.py b/python/ray/data/tests/test_map.py index 9b1a4f8d4575..d4e7e2c374de 100644 --- a/python/ray/data/tests/test_map.py +++ b/python/ray/data/tests/test_map.py @@ -9,6 +9,7 @@ import numpy as np import pandas as pd import pyarrow as pa +import pyarrow.compute as pc import pyarrow.parquet as pq import pytest @@ -330,18 +331,99 @@ def map_generator(item: dict) -> Iterator[int]: def test_add_column(ray_start_regular_shared): - ds = ray.data.range(5).add_column("foo", lambda x: 1) + """Tests the add column API.""" + + # Test with pyarrow batch format + ds = ray.data.range(5).add_column( + "foo", lambda x: pa.array([1] * x.num_rows), batch_format="pyarrow" + ) + assert ds.take(1) == [{"id": 0, "foo": 1}] + + # Test with chunked array batch format + ds = ray.data.range(5).add_column( + "foo", lambda x: pa.chunked_array([[1] * x.num_rows]), batch_format="pyarrow" + ) + assert ds.take(1) == [{"id": 0, "foo": 1}] + + ds = ray.data.range(5).add_column( + "foo", lambda x: pc.add(x["id"], 1), batch_format="pyarrow" + ) + assert ds.take(1) == [{"id": 0, "foo": 1}] + + # Adding a column that is already there should result in an error + with pytest.raises( + ray.exceptions.UserCodeException, + match="Trying to add an existing column with name id", + ): + ds = ray.data.range(5).add_column( + "id", lambda x: pc.add(x["id"], 1), batch_format="pyarrow" + ) + assert ds.take(2) == [{"id": 1}, {"id": 2}] + + # Adding a column in the wrong format should result in an error + with pytest.raises( + ray.exceptions.UserCodeException, match="For pyarrow batch " "format" + ): + ds = ray.data.range(5).add_column("id", lambda x: [1], batch_format="pyarrow") + assert ds.take(2) == [{"id": 1}, {"id": 2}] + + # Test with numpy batch format + ds = ray.data.range(5).add_column( + "foo", lambda x: np.array([1] * len(list(x.keys())[0])), batch_format="numpy" + ) + assert ds.take(1) == [{"id": 0, "foo": 1}] + + ds = ray.data.range(5).add_column( + "foo", lambda x: np.add(x["id"], 1), batch_format="numpy" + ) + assert ds.take(1) == [{"id": 0, "foo": 1}] + + # Adding a column that is already there should result in an error + with pytest.raises( + ray.exceptions.UserCodeException, + match="Trying to add an existing column with name id", + ): + ds = ray.data.range(5).add_column( + "id", lambda x: np.add(x["id"], 1), batch_format="numpy" + ) + assert ds.take(2) == [{"id": 1}, {"id": 2}] + + # Adding a column in the wrong format should result in an error + with pytest.raises( + ray.exceptions.UserCodeException, match="For numpy batch " "format" + ): + ds = ray.data.range(5).add_column("id", lambda x: [1], batch_format="numpy") + assert ds.take(2) == [{"id": 1}, {"id": 2}] + + # Test with pandas batch format + ds = ray.data.range(5).add_column("foo", lambda x: pd.Series([1] * x.shape[0])) assert ds.take(1) == [{"id": 0, "foo": 1}] ds = ray.data.range(5).add_column("foo", lambda x: x["id"] + 1) assert ds.take(1) == [{"id": 0, "foo": 1}] - ds = ray.data.range(5).add_column("id", lambda x: x["id"] + 1) - assert ds.take(2) == [{"id": 1}, {"id": 2}] + # Adding a column that is already there should result in an error + with pytest.raises( + ray.exceptions.UserCodeException, + match="Trying to add an existing column with name id", + ): + ds = ray.data.range(5).add_column("id", lambda x: x["id"] + 1) + assert ds.take(2) == [{"id": 1}, {"id": 2}] + + # Adding a column in the wrong format should result in an error + with pytest.raises( + ray.exceptions.UserCodeException, match="For pandas batch " "format" + ): + ds = ray.data.range(5).add_column("id", lambda x: [1], batch_format="pandas") + assert ds.take(2) == [{"id": 1}, {"id": 2}] with pytest.raises(ValueError): ds = ray.data.range(5).add_column("id", 0) + # Test that an invalid batch_format raises an error + with pytest.raises(ValueError): + ray.data.range(5).add_column("foo", lambda x: x["id"] + 1, batch_format="foo") + @pytest.mark.parametrize("names", (["foo", "bar"], {"spam": "foo", "ham": "bar"})) def test_rename_columns(ray_start_regular_shared, names): @@ -362,14 +444,15 @@ def test_drop_columns(ray_start_regular_shared, tmp_path): assert ds.drop_columns(["col2"]).take(1) == [{"col1": 1, "col3": 3}] assert ds.drop_columns(["col1", "col3"]).take(1) == [{"col2": 2}] assert ds.drop_columns([]).take(1) == [{"col1": 1, "col2": 2, "col3": 3}] - assert ds.drop_columns(["col1", "col2", "col3"]).take(1) == [{}] - assert ds.drop_columns(["col1", "col1", "col2", "col1"]).take(1) == [ - {"col3": 3} - ] + assert ds.drop_columns(["col1", "col2", "col3"]).take(1) == [] + assert ds.drop_columns(["col1", "col2"]).take(1) == [{"col3": 3}] # Test dropping non-existent column with pytest.raises((UserCodeException, KeyError)): ds.drop_columns(["dummy_col", "col1", "col2"]).materialize() + with pytest.raises(ValueError, match="drop_columns expects unique column names"): + ds1.drop_columns(["col1", "col2", "col2"]) + def test_select_columns(ray_start_regular_shared): # Test pandas and arrow diff --git a/python/ray/data/tests/test_mongo.py b/python/ray/data/tests/test_mongo.py index 97828aae6bea..eb03aab39f80 100644 --- a/python/ray/data/tests/test_mongo.py +++ b/python/ray/data/tests/test_mongo.py @@ -93,13 +93,13 @@ def test_read_write_mongo(ray_start_regular_shared, start_mongo): override_num_blocks=2, ) assert ds._block_num_rows() == [3, 2] - assert str(ds) == ( - "Dataset(\n" - " num_rows=5,\n" - " schema={_id: fixed_size_binary[12], float_field: double, " - "int_field: int32}\n" - ")" - ) + assert ds.count() == 5 + assert ds.schema().names == ["_id", "float_field", "int_field"] + # We are not testing the datatype of _id here, because it varies per platform + assert ds.schema().types[1:] == [ + pa.float64(), + pa.int32(), + ] assert df.equals(ds.drop_columns(["_id"]).to_pandas()) # Read a subset of the collection. @@ -111,13 +111,8 @@ def test_read_write_mongo(ray_start_regular_shared, start_mongo): override_num_blocks=2, ) assert ds._block_num_rows() == [2, 1] - assert str(ds) == ( - "Dataset(\n" - " num_rows=3,\n" - " schema={_id: fixed_size_binary[12], float_field: double, " - "int_field: int32}\n" - ")" - ) + assert ds.count() == 3 + assert ds.schema().names == ["_id", "float_field", "int_field"] df[df["int_field"] < 3].equals(ds.drop_columns(["_id"]).to_pandas()) # Read with auto-tuned parallelism. @@ -126,13 +121,14 @@ def test_read_write_mongo(ray_start_regular_shared, start_mongo): database=foo_db, collection=foo_collection, ) - assert str(ds) == ( - "Dataset(\n" - " num_rows=5,\n" - " schema={_id: fixed_size_binary[12], float_field: double, " - "int_field: int32}\n" - ")" - ) + + assert ds.count() == 5 + assert ds.schema().names == ["_id", "float_field", "int_field"] + # We are not testing the datatype of _id here, because it varies per platform + assert ds.schema().types[1:] == [ + pa.float64(), + pa.int32(), + ] assert df.equals(ds.drop_columns(["_id"]).to_pandas()) # Read with a parallelism larger than number of rows. @@ -142,13 +138,14 @@ def test_read_write_mongo(ray_start_regular_shared, start_mongo): collection=foo_collection, override_num_blocks=1000, ) - assert str(ds) == ( - "Dataset(\n" - " num_rows=5,\n" - " schema={_id: fixed_size_binary[12], float_field: double, " - "int_field: int32}\n" - ")" - ) + + assert ds.count() == 5 + assert ds.schema().names == ["_id", "float_field", "int_field"] + # We are not testing the datatype of _id here, because it varies per platform + assert ds.schema().types[1:] == [ + pa.float64(), + pa.int32(), + ] assert df.equals(ds.drop_columns(["_id"]).to_pandas()) # Add a column and then write back to MongoDB. diff --git a/python/ray/data/tests/test_object_gc.py b/python/ray/data/tests/test_object_gc.py index b56c4542618d..2b1947e0498d 100644 --- a/python/ray/data/tests/test_object_gc.py +++ b/python/ray/data/tests/test_object_gc.py @@ -1,6 +1,7 @@ import sys import threading +import pandas as pd import pytest import ray @@ -107,7 +108,7 @@ def test_tf_iteration(shutdown_only): # The size of dataset is 500*(80*80*4)*8B, about 100MB. ds = ray.data.range_tensor( 500, shape=(80, 80, 4), override_num_blocks=100 - ).add_column("label", lambda x: 1) + ).add_column("label", lambda df: pd.Series([1] * len(df))) # to_tf check_to_tf_no_spill(ctx, ds.map(lambda x: x)) From 8d358857a100c859dd3cf9f0236ba3c37a95594c Mon Sep 17 00:00:00 2001 From: dentiny Date: Thu, 21 Nov 2024 19:53:14 -0800 Subject: [PATCH 09/54] [core] C++ update on gcs health check (#48843) As titled. Signed-off-by: hjiang --- .../gcs_server/gcs_health_check_manager.cc | 50 ++++++++++++------- .../gcs/gcs_server/gcs_health_check_manager.h | 28 ++++++++--- src/ray/util/thread_checker.cc | 2 +- src/ray/util/thread_checker.h | 4 +- 4 files changed, 55 insertions(+), 29 deletions(-) diff --git a/src/ray/gcs/gcs_server/gcs_health_check_manager.cc b/src/ray/gcs/gcs_server/gcs_health_check_manager.cc index 2cefb37f6b7c..efa279daf56a 100644 --- a/src/ray/gcs/gcs_server/gcs_health_check_manager.cc +++ b/src/ray/gcs/gcs_server/gcs_health_check_manager.cc @@ -14,15 +14,17 @@ #include "ray/gcs/gcs_server/gcs_health_check_manager.h" +#include + #include "ray/stats/metric.h" + DEFINE_stats(health_check_rpc_latency_ms, "Latency of rpc request for health check.", (), ({1, 10, 100, 1000, 10000}, ), ray::stats::HISTOGRAM); -namespace ray { -namespace gcs { +namespace ray::gcs { GcsHealthCheckManager::GcsHealthCheckManager( instrumented_io_context &io_service, @@ -38,17 +40,18 @@ GcsHealthCheckManager::GcsHealthCheckManager( period_ms_(period_ms), failure_threshold_(failure_threshold) { RAY_CHECK(on_node_death_callback != nullptr); - RAY_CHECK(initial_delay_ms >= 0); - RAY_CHECK(timeout_ms >= 0); - RAY_CHECK(period_ms >= 0); - RAY_CHECK(failure_threshold >= 0); + RAY_CHECK_GE(initial_delay_ms, 0); + RAY_CHECK_GE(timeout_ms, 0); + RAY_CHECK_GE(period_ms, 0); + RAY_CHECK_GE(failure_threshold, 0); } -GcsHealthCheckManager::~GcsHealthCheckManager() {} +GcsHealthCheckManager::~GcsHealthCheckManager() = default; void GcsHealthCheckManager::RemoveNode(const NodeID &node_id) { io_service_.dispatch( [this, node_id]() { + thread_checker_.IsOnSameThread(); auto iter = health_check_contexts_.find(node_id); if (iter == health_check_contexts_.end()) { return; @@ -61,6 +64,7 @@ void GcsHealthCheckManager::RemoveNode(const NodeID &node_id) { void GcsHealthCheckManager::FailNode(const NodeID &node_id) { RAY_LOG(WARNING).WithField(node_id) << "Node is dead because the health check failed."; + thread_checker_.IsOnSameThread(); auto iter = health_check_contexts_.find(node_id); if (iter != health_check_contexts_.end()) { on_node_death_callback_(node_id); @@ -69,7 +73,9 @@ void GcsHealthCheckManager::FailNode(const NodeID &node_id) { } std::vector GcsHealthCheckManager::GetAllNodes() const { + thread_checker_.IsOnSameThread(); std::vector nodes; + nodes.reserve(health_check_contexts_.size()); for (const auto &[node_id, _] : health_check_contexts_) { nodes.emplace_back(node_id); } @@ -84,24 +90,29 @@ void GcsHealthCheckManager::HealthCheckContext::StartHealthCheck() { new (&context_) grpc::ClientContext(); response_.Clear(); - auto deadline = - std::chrono::system_clock::now() + std::chrono::milliseconds(manager_->timeout_ms_); + const auto now = std::chrono::system_clock::now(); + auto deadline = now + std::chrono::milliseconds(manager_->timeout_ms_); context_.set_deadline(deadline); stub_->async()->Check( - &context_, &request_, &response_, [this, now = absl::Now()](::grpc::Status status) { + &context_, + &request_, + &response_, + [this, start = absl::FromChrono(now)](::grpc::Status status) { // This callback is done in gRPC's thread pool. STATS_health_check_rpc_latency_ms.Record( - absl::ToInt64Milliseconds(absl::Now() - now)); + absl::ToInt64Milliseconds(absl::Now() - start)); manager_->io_service_.post( [this, status]() { if (stopped_) { delete this; return; } - RAY_LOG(DEBUG) << "Health check status: " << int(response_.status()); + RAY_LOG(DEBUG) << "Health check status: " + << HealthCheckResponse_ServingStatus_Name( + response_.status()); if (status.ok() && response_.status() == HealthCheckResponse::SERVING) { - // Health check passed + // Health check passed. health_check_remaining_ = manager_->failure_threshold_; } else { --health_check_remaining_; @@ -118,6 +129,9 @@ void GcsHealthCheckManager::HealthCheckContext::StartHealthCheck() { delete this; } else { // Do another health check. + // + // TODO(hjiang): Able to reduce a few health check based on know resource + // usage communication between GCS and raylet. timer_.expires_from_now( boost::posix_time::milliseconds(manager_->period_ms_)); timer_.async_wait([this](auto) { StartHealthCheck(); }); @@ -132,13 +146,13 @@ void GcsHealthCheckManager::HealthCheckContext::Stop() { stopped_ = true; } void GcsHealthCheckManager::AddNode(const NodeID &node_id, std::shared_ptr channel) { io_service_.dispatch( - [this, channel, node_id]() { - RAY_CHECK(health_check_contexts_.count(node_id) == 0); + [this, channel = std::move(channel), node_id]() { + thread_checker_.IsOnSameThread(); auto context = new HealthCheckContext(this, channel, node_id); - health_check_contexts_.emplace(std::make_pair(node_id, context)); + auto [_, is_new] = health_check_contexts_.emplace(node_id, context); + RAY_CHECK(is_new); }, "GcsHealthCheckManager::AddNode"); } -} // namespace gcs -} // namespace ray +} // namespace ray::gcs diff --git a/src/ray/gcs/gcs_server/gcs_health_check_manager.h b/src/ray/gcs/gcs_server/gcs_health_check_manager.h index d877a217d803..a6e36d82972a 100644 --- a/src/ray/gcs/gcs_server/gcs_health_check_manager.h +++ b/src/ray/gcs/gcs_server/gcs_health_check_manager.h @@ -16,16 +16,19 @@ #include +#include +#include +#include +#include + #include "absl/container/flat_hash_map.h" #include "ray/common/asio/instrumented_io_context.h" #include "ray/common/id.h" #include "ray/common/ray_config.h" +#include "ray/util/thread_checker.h" #include "src/proto/grpc/health/v1/health.grpc.pb.h" -class GcsHealthCheckManagerTest; - -namespace ray { -namespace gcs { +namespace ray::gcs { /// GcsHealthCheckManager is used to track the healthiness of the nodes in the ray /// cluster. The health check is done in pull based way, which means this module will send @@ -35,6 +38,9 @@ namespace gcs { /// node will be removed from GcsHealthCheckManager. The node can be added into this class /// later. Although the same node id is not supposed to be reused in ray cluster, this is /// not enforced in this class. +/// +/// All IO operations happens on the same thread, which is managed by the pass-ed in +/// [io_service]. /// TODO (iycheng): Move the GcsHealthCheckManager to ray/common. class GcsHealthCheckManager { public: @@ -58,24 +64,27 @@ class GcsHealthCheckManager { ~GcsHealthCheckManager(); /// Start to track the healthiness of a node. + /// Safe to call from non-io-context threads. /// /// \param node_id The id of the node. /// \param channel The gRPC channel to the node. void AddNode(const NodeID &node_id, std::shared_ptr channel); /// Stop tracking the healthiness of a node. + /// Safe to call from non-io-context threads. /// /// \param node_id The id of the node to stop tracking. void RemoveNode(const NodeID &node_id); - /// Return all the nodes monitored. + /// Return all the nodes monitored and alive. + /// Notice: have to invoke from io-context thread. /// /// \return A list of node id which are being monitored by this class. std::vector GetAllNodes() const; private: /// Fail a node when health check failed. It'll stop the health checking and - /// call on_node_death_callback. + /// call `on_node_death_callback_`. /// /// \param node_id The id of the node. void FailNode(const NodeID &node_id); @@ -133,8 +142,12 @@ class GcsHealthCheckManager { std::function on_node_death_callback_; /// The context of the health check for each nodes. + /// Only living nodes are bookkept, while failed one will be removed. absl::flat_hash_map health_check_contexts_; + /// Checker to make sure there's no concurrent access for node addition and removal. + const ThreadChecker thread_checker_; + /// The delay for the first health check request. const int64_t initial_delay_ms_; /// Timeout for each health check request. @@ -145,5 +158,4 @@ class GcsHealthCheckManager { const int64_t failure_threshold_; }; -} // namespace gcs -} // namespace ray +} // namespace ray::gcs diff --git a/src/ray/util/thread_checker.cc b/src/ray/util/thread_checker.cc index 73a0072c7575..0f33dfd4c712 100644 --- a/src/ray/util/thread_checker.cc +++ b/src/ray/util/thread_checker.cc @@ -16,7 +16,7 @@ namespace ray { -bool ThreadChecker::IsOnSameThread() { +bool ThreadChecker::IsOnSameThread() const { const auto cur_id = std::this_thread::get_id(); std::thread::id uninitialized_id; return thread_id_.compare_exchange_strong(uninitialized_id, cur_id) || diff --git a/src/ray/util/thread_checker.h b/src/ray/util/thread_checker.h index 622624859b75..2e3dcf1ed3df 100644 --- a/src/ray/util/thread_checker.h +++ b/src/ray/util/thread_checker.h @@ -34,10 +34,10 @@ class ThreadChecker { public: // Return true at initialization, or current invocation happens on the same thread as // initialization. - bool IsOnSameThread(); + bool IsOnSameThread() const; private: - std::atomic thread_id_{}; + mutable std::atomic thread_id_{}; }; } // namespace ray From be0bbd0f37d9d7666b9bed504971f6a349097ab9 Mon Sep 17 00:00:00 2001 From: Ruiyang Wang <56065503+rynewang@users.noreply.github.com> Date: Thu, 21 Nov 2024 23:13:23 -0800 Subject: [PATCH 10/54] [core] Erases PopWorkerRequest on registration timeout. (#48858) This fixes a bug from #47694. When a PopWorkerRequest is time out, we should erase it from bookkeeping and invoke the callback with a `WorkerPendingRegistration` status. However only the latter is done and we forgot to erase, causing segfaults on a second call. Signed-off-by: Ruiyang Wang --- src/ray/raylet/worker_pool.cc | 3 ++- src/ray/raylet/worker_pool_test.cc | 13 +++++++++++++ 2 files changed, 15 insertions(+), 1 deletion(-) diff --git a/src/ray/raylet/worker_pool.cc b/src/ray/raylet/worker_pool.cc index 943ea89b24b5..eda893e8efbe 100644 --- a/src/ray/raylet/worker_pool.cc +++ b/src/ray/raylet/worker_pool.cc @@ -593,7 +593,8 @@ void WorkerPool::MonitorPopWorkerRequestForRegistration( auto &requests = state.pending_registration_requests; auto it = std::find(requests.begin(), requests.end(), pop_worker_request); if (it != requests.end()) { - // Fail the task... + // Pop and fail the task... + requests.erase(it); PopWorkerStatus status = PopWorkerStatus::WorkerPendingRegistration; PopWorkerCallbackAsync(pop_worker_request->callback, nullptr, status); } diff --git a/src/ray/raylet/worker_pool_test.cc b/src/ray/raylet/worker_pool_test.cc index d945384b7277..0da18903fdfe 100644 --- a/src/ray/raylet/worker_pool_test.cc +++ b/src/ray/raylet/worker_pool_test.cc @@ -1928,6 +1928,19 @@ TEST_F(WorkerPoolDriverRegisteredTest, PopWorkerStatus) { worker_pool_->ClearProcesses(); } +TEST_F(WorkerPoolDriverRegisteredTest, WorkerPendingRegistrationErasesRequest) { + std::shared_ptr popped_worker; + PopWorkerStatus status; + auto task_spec = ExampleTaskSpec(); + // Create a task without push worker. It should time out (WorkerPendingRegistration). + popped_worker = worker_pool_->PopWorkerSync(task_spec, false, &status); + ASSERT_EQ(popped_worker, nullptr); + ASSERT_EQ(status, PopWorkerStatus::WorkerPendingRegistration); + // The request should be erased. + ASSERT_EQ(worker_pool_->NumPendingRegistrationRequests(), 0); + worker_pool_->ClearProcesses(); +} + TEST_F(WorkerPoolDriverRegisteredTest, TestIOWorkerFailureAndSpawn) { std::unordered_set> spill_worker_set; auto spill_worker_callback = From f1316e8af8c8930cd062f78b1d46f7933f744bf8 Mon Sep 17 00:00:00 2001 From: Sven Mika Date: Fri, 22 Nov 2024 08:14:04 +0100 Subject: [PATCH 11/54] [RLlib] APPO (new API stack) enhancements vol 04: Smaller cleanups and preparations. (#48844) --- rllib/algorithms/algorithm.py | 22 +++++++++++--- rllib/algorithms/algorithm_config.py | 21 ++++++++++--- rllib/algorithms/appo/appo.py | 3 +- rllib/algorithms/impala/impala.py | 34 +++++++++++---------- rllib/core/learner/learner.py | 6 ++-- rllib/core/learner/learner_group.py | 8 ++--- rllib/core/learner/torch/torch_learner.py | 37 ++++++++++++----------- rllib/utils/metrics/__init__.py | 3 ++ rllib/utils/metrics/metrics_logger.py | 4 +-- rllib/utils/metrics/stats.py | 24 ++++++--------- 10 files changed, 96 insertions(+), 66 deletions(-) diff --git a/rllib/algorithms/algorithm.py b/rllib/algorithms/algorithm.py index bf01aff4be89..7ed33f749713 100644 --- a/rllib/algorithms/algorithm.py +++ b/rllib/algorithms/algorithm.py @@ -129,6 +129,7 @@ NUM_ENV_STEPS_TRAINED_LIFETIME, NUM_EPISODES, NUM_EPISODES_LIFETIME, + NUM_TRAINING_STEP_CALLS_PER_ITERATION, RESTORE_WORKERS_TIMER, RESTORE_EVAL_WORKERS_TIMER, SYNCH_ENV_CONNECTOR_STATES_TIMER, @@ -3215,7 +3216,17 @@ def _run_one_training_iteration(self) -> Tuple[ResultDict, "TrainIterCtx"]: "one single result dict per training iteration." ) - # Only here, reduce the results into a single result dict. + # TODO (sven): Resolve this metric through log_time's future + # ability to compute throughput. + self.metrics.log_value( + NUM_TRAINING_STEP_CALLS_PER_ITERATION, + 1, + reduce="sum", + clear_on_reduce=True, + ) + + # Only here (at the end of the iteration), reduce the results into a single + # result dict. return self.metrics.reduce(), train_iter_ctx def _run_one_evaluation( @@ -3527,13 +3538,16 @@ def _compile_iteration_results_new_api_stack(self, *, train_results, eval_result # Fault tolerance stats. results[FAULT_TOLERANCE_STATS] = { "num_healthy_workers": self.env_runner_group.num_healthy_remote_workers(), - "num_in_flight_async_reqs": ( - self.env_runner_group.num_in_flight_async_reqs() - ), "num_remote_worker_restarts": ( self.env_runner_group.num_remote_worker_restarts() ), } + results["env_runner_group"] = { + "actor_manager_num_outstanding_async_reqs": ( + self.env_runner_group.num_in_flight_async_reqs() + ), + } + # Resolve all `Stats` leafs by peeking (get their reduced values). return tree.map_structure( lambda s: s.peek() if isinstance(s, Stats) else s, diff --git a/rllib/algorithms/algorithm_config.py b/rllib/algorithms/algorithm_config.py index 542240a00dac..051a0307367a 100644 --- a/rllib/algorithms/algorithm_config.py +++ b/rllib/algorithms/algorithm_config.py @@ -357,6 +357,11 @@ def __init__(self, algo_class: Optional[type] = None): self.num_gpus_per_learner = 0 self.num_cpus_per_learner = 1 self.local_gpu_idx = 0 + # TODO (sven): This probably works even without any restriction + # (allowing for any arbitrary number of requests in-flight). Test with + # 3 first, then with unlimited, and if both show the same behavior on + # an async algo, remove this restriction entirely. + self.max_requests_in_flight_per_learner = 3 # `self.training()` self.gamma = 0.99 @@ -1798,15 +1803,16 @@ def env_runners( synchronously in turn with their update step (e.g., PPO or DQN). Not relevant for any algos that sample asynchronously, such as APPO or IMPALA. - max_requests_in_flight_per_env_runner: Max number of inflight requests - to each EnvRunner worker. See the FaultTolerantActorManager class for - more details. + max_requests_in_flight_per_env_runner: Max number of in-flight requests + to each EnvRunner (actor)). See the + `ray.rllib.utils.actor_manager.FaultTolerantActorManager` class for more + details. Tuning these values is important when running experiments with large sample batches, where there is the risk that the object store may fill up, causing spilling of objects to disk. This can cause any asynchronous requests to become very slow, making your experiment run slowly as well. You can inspect the object store during your experiment - via a call to Ray memory on your head node, and by using the Ray + via a call to `ray memory` on your head node, and by using the Ray dashboard. If you're seeing that the object store is filling up, turn down the number of remote requests in flight or enable compression. sample_collector: For the old API stack only. The SampleCollector class to @@ -2110,6 +2116,7 @@ def learners( num_cpus_per_learner: Optional[Union[float, int]] = NotProvided, num_gpus_per_learner: Optional[Union[float, int]] = NotProvided, local_gpu_idx: Optional[int] = NotProvided, + max_requests_in_flight_per_learner: Optional[int] = NotProvided, ): """Sets LearnerGroup and Learner worker related configurations. @@ -2135,6 +2142,10 @@ def learners( an index into the available CUDA devices. For example if `os.environ["CUDA_VISIBLE_DEVICES"] = "1"` and `local_gpu_idx=0`, RLlib uses the GPU with ID=1 on the node. + max_requests_in_flight_per_learner: Max number of in-flight requests + to each Learner (actor)). See the + `ray.rllib.utils.actor_manager.FaultTolerantActorManager` class for more + details. Returns: This updated AlgorithmConfig object. @@ -2147,6 +2158,8 @@ def learners( self.num_gpus_per_learner = num_gpus_per_learner if local_gpu_idx is not NotProvided: self.local_gpu_idx = local_gpu_idx + if max_requests_in_flight_per_learner is not NotProvided: + self.max_requests_in_flight_per_learner = max_requests_in_flight_per_learner return self diff --git a/rllib/algorithms/appo/appo.py b/rllib/algorithms/appo/appo.py index b27e96b02d16..5302f7540248 100644 --- a/rllib/algorithms/appo/appo.py +++ b/rllib/algorithms/appo/appo.py @@ -118,7 +118,6 @@ def __init__(self, algo_class=None): # Override some of IMPALAConfig's default values with APPO-specific values. self.num_env_runners = 2 - self.min_time_s_per_iteration = 10 self.target_network_update_freq = 2 self.broadcast_interval = 1 self.grad_clip = 40.0 @@ -233,6 +232,8 @@ def training( if use_critic != DEPRECATED_VALUE: deprecation_warning( old="use_critic", + help="`use_critic` no longer supported! APPO always uses a value " + "function (critic).", error=True, ) diff --git a/rllib/algorithms/impala/impala.py b/rllib/algorithms/impala/impala.py index 69f140fda775..862c2cf84388 100644 --- a/rllib/algorithms/impala/impala.py +++ b/rllib/algorithms/impala/impala.py @@ -80,12 +80,16 @@ class IMPALAConfig(AlgorithmConfig): .. testcode:: from ray.rllib.algorithms.impala import IMPALAConfig - config = IMPALAConfig() - config = config.training(lr=0.0003, train_batch_size_per_learner=512) - config = config.learners(num_learners=1) - config = config.env_runners(num_env_runners=1) + + config = ( + IMPALAConfig() + .environment("CartPole-v1") + .env_runners(num_env_runners=1) + .training(lr=0.0003, train_batch_size_per_learner=512) + .learners(num_learners=1) + ) # Build a Algorithm object from the config and run 1 training iteration. - algo = config.build(env="CartPole-v1") + algo = config.build() algo.train() del algo @@ -94,16 +98,14 @@ class IMPALAConfig(AlgorithmConfig): from ray.rllib.algorithms.impala import IMPALAConfig from ray import air from ray import tune - config = IMPALAConfig() - # Update the config object. - config = config.training( - lr=tune.grid_search([0.0001, 0.0002]), grad_clip=20.0 + config = ( + IMPALAConfig() + .environment("CartPole-v1") + .env_runners(num_env_runners=1) + .training(lr=tune.grid_search([0.0001, 0.0002]), grad_clip=20.0) + .learners(num_learners=1) ) - config = config.learners(num_learners=1) - config = config.env_runners(num_env_runners=1) - # Set the config object's env. - config = config.environment(env="CartPole-v1") # Run with tune. tune.Tuner( "IMPALA", @@ -146,8 +148,6 @@ def __init__(self, algo_class=None): self.broadcast_interval = 1 self.num_aggregation_workers = 0 self.num_gpu_loader_threads = 8 - # IMPALA takes care of its own EnvRunner (weights, connector, metrics) synching. - self._dont_auto_sync_env_runner_states = True self.grad_clip = 40.0 # Note: Only when using enable_rl_module_and_learner=True can the clipping mode @@ -168,6 +168,9 @@ def __init__(self, algo_class=None): # __sphinx_doc_end__ # fmt: on + # IMPALA takes care of its own EnvRunner (weights, connector, metrics) synching. + self._dont_auto_sync_env_runner_states = True + self.lr_schedule = None # @OldAPIStack self.entropy_coeff_schedule = None # @OldAPIStack self.num_multi_gpu_tower_stacks = 1 # @OldAPIstack @@ -181,7 +184,6 @@ def __init__(self, algo_class=None): self.epsilon = 0.1 # @OldAPIstack self._separate_vf_optimizer = False # @OldAPIstack self._lr_vf = 0.0005 # @OldAPIstack - self.train_batch_size = 500 # @OldAPIstack self.num_gpus = 1 # @OldAPIstack self._tf_policy_handles_more_than_one_loss = True # @OldAPIstack diff --git a/rllib/core/learner/learner.py b/rllib/core/learner/learner.py index 2b58743a52df..5c3aa575ea9e 100644 --- a/rllib/core/learner/learner.py +++ b/rllib/core/learner/learner.py @@ -1134,6 +1134,9 @@ def _finalize_fn(batch: Dict[str, numpy.ndarray]) -> Dict[str, Any]: fwd_out, loss_per_module, tensor_metrics = self._update( batch.policy_batches ) + # Convert logged tensor metrics (logged during tensor-mode of MetricsLogger) + # to actual (numpy) values. + self.metrics.tensors_to_numpy(tensor_metrics) self._set_slicing_by_batch_id(batch, value=False) # If `num_iters` is reached break and return. @@ -1143,9 +1146,6 @@ def _finalize_fn(batch: Dict[str, numpy.ndarray]) -> Dict[str, Any]: logger.info( f"===> [Learner {id(self)}] number of iterations run in this epoch: {i}" ) - # Convert logged tensor metrics (logged during tensor-mode of MetricsLogger) - # to actual (numpy) values. - self.metrics.tensors_to_numpy(tensor_metrics) # Log all individual RLModules' loss terms and its registered optimizers' # current learning rates. diff --git a/rllib/core/learner/learner_group.py b/rllib/core/learner/learner_group.py index 31994fa5dcce..a80de4cd2e76 100644 --- a/rllib/core/learner/learner_group.py +++ b/rllib/core/learner/learner_group.py @@ -171,11 +171,9 @@ def __init__( self._worker_manager = FaultTolerantActorManager( self._workers, - # TODO (sven): This probably works even without any restriction - # (allowing for any arbitrary number of requests in-flight). Test with - # 3 first, then with unlimited, and if both show the same behavior on - # an async algo, remove this restriction entirely. - max_remote_requests_in_flight_per_actor=3, + max_remote_requests_in_flight_per_actor=( + self.config.max_requests_in_flight_per_learner + ), ) # Counters for the tags for asynchronous update requests that are # in-flight. Used for keeping trakc of and grouping together the results of diff --git a/rllib/core/learner/torch/torch_learner.py b/rllib/core/learner/torch/torch_learner.py index c8cdff3b8ca3..e04a8b491c9a 100644 --- a/rllib/core/learner/torch/torch_learner.py +++ b/rllib/core/learner/torch/torch_learner.py @@ -147,23 +147,7 @@ def _uncompiled_update( # Activate tensor-mode on our MetricsLogger. self.metrics.activate_tensor_mode() - # Log off-policy'ness of this update. - off_policyness = { - (mid, DIFF_NUM_GRAD_UPDATES_VS_SAMPLER_POLICY): ( - (self._weights_seq_no - module_batch[WEIGHTS_SEQ_NO]).float() - ) - for mid, module_batch in batch.items() - if WEIGHTS_SEQ_NO in module_batch - } - for key in off_policyness.keys(): - mid = key[0] - if Columns.LOSS_MASK not in batch[mid]: - off_policyness[key] = torch.mean(off_policyness[key]) - else: - mask = batch[mid][Columns.LOSS_MASK] - num_valid = torch.sum(mask) - off_policyness[key] = torch.sum(off_policyness[key][mask]) / num_valid - self.metrics.log_dict(off_policyness, window=1) + self._compute_off_policyness(batch) fwd_out = self.module.forward_train(batch) loss_per_module = self.compute_losses(fwd_out=fwd_out, batch=batch) @@ -630,6 +614,25 @@ def _map_module_to_device(self, module: MultiRLModule) -> None: if isinstance(module[key], torch.nn.Module): module[key].to(self._device) + def _compute_off_policyness(self, batch): + # Log off-policy'ness of this batch wrt the current weights. + off_policyness = { + (mid, DIFF_NUM_GRAD_UPDATES_VS_SAMPLER_POLICY): ( + (self._weights_seq_no - module_batch[WEIGHTS_SEQ_NO]).float() + ) + for mid, module_batch in batch.items() + if WEIGHTS_SEQ_NO in module_batch + } + for key in off_policyness.keys(): + mid = key[0] + if Columns.LOSS_MASK not in batch[mid]: + off_policyness[key] = torch.mean(off_policyness[key]) + else: + mask = batch[mid][Columns.LOSS_MASK] + num_valid = torch.sum(mask) + off_policyness[key] = torch.sum(off_policyness[key][mask]) / num_valid + self.metrics.log_dict(off_policyness, window=1) + @override(Learner) def _get_tensor_variable( self, value, dtype=None, trainable=False diff --git a/rllib/utils/metrics/__init__.py b/rllib/utils/metrics/__init__.py index d9a67933998c..5e7e3ad071bd 100644 --- a/rllib/utils/metrics/__init__.py +++ b/rllib/utils/metrics/__init__.py @@ -11,6 +11,9 @@ NUM_TRAINABLE_PARAMETERS = "num_trainable_parameters" NUM_NON_TRAINABLE_PARAMETERS = "num_non_trainable_parameters" +# Number of times `training_step()` was called in one iteration. +NUM_TRAINING_STEP_CALLS_PER_ITERATION = "num_training_step_calls_per_iteration" + # Counters for sampling, sampling (on eval workers) and # training steps (env- and agent steps). MEAN_NUM_EPISODE_LISTS_RECEIVED = "mean_num_episode_lists_received" diff --git a/rllib/utils/metrics/metrics_logger.py b/rllib/utils/metrics/metrics_logger.py index f1f6f4cc12e8..276d6891b14a 100644 --- a/rllib/utils/metrics/metrics_logger.py +++ b/rllib/utils/metrics/metrics_logger.py @@ -937,10 +937,10 @@ def deactivate_tensor_mode(self): assert self.tensor_mode self._tensor_mode = False # Return all logged tensors (logged during the tensor-mode phase). - ret = {key: self._get_key(key).peek() for key in self._tensor_keys} + logged_tensors = {key: self._get_key(key).peek() for key in self._tensor_keys} # Clear out logged tensor keys. self._tensor_keys.clear() - return ret + return logged_tensors def tensors_to_numpy(self, tensor_metrics): """Converts all previously logged and returned tensors back to numpy values.""" diff --git a/rllib/utils/metrics/stats.py b/rllib/utils/metrics/stats.py index 1929cec2b063..b13b7edb3b48 100644 --- a/rllib/utils/metrics/stats.py +++ b/rllib/utils/metrics/stats.py @@ -216,7 +216,8 @@ def __init__( # Code to execute when exiting a with-context. self._on_exit = on_exit - # On each `.reduce()` call, we store the result of this call in + # On each `.reduce()` call, we store the result of this call in hist[0] and the + # previous `reduce()` result in hist[1]. self._hist = (0, 0) def push(self, value) -> None: @@ -274,6 +275,7 @@ def peek(self, *, previous: bool = False) -> Any: The result of reducing the internal values list (or the previously computed reduced result, if `previous` is True). """ + # Return previously reduced value. if previous: return self._hist[1] return self._reduced_values()[0] @@ -355,10 +357,8 @@ def merge_in_parallel(self, *others: "Stats") -> None: # - Thereby always reducing across the different Stats objects' at the # current index. # - The resulting reduced value (across Stats at current index) is then - # repeated AND - # added to the new merged-values list n times (where n is the number of - # Stats, across - # which we merge). + # repeated AND added to the new merged-values list n times (where n is + # the number of Stats, across which we merge). # - The merged-values list is reversed. # Here: # index -1: [3, 6] -> [4.5, 4.5] @@ -381,13 +381,11 @@ def merge_in_parallel(self, *others: "Stats") -> None: stats.merge_in_parallel(stats1, stats2) # Same here: Fill new merged-values list: # - Start with index -1, moving to the start. - # - Thereby always reducing across the different Stats objects' at the + # - Thereby always reduce across the different Stats objects' at the # current index. # - The resulting reduced value (across Stats at current index) is then - # repeated AND - # added to the new merged-values list n times (where n is the number of - # Stats, across - # which we merge). + # repeated AND added to the new merged-values list n times (where n is the + # number of Stats, across which we merge). # - The merged-values list is reversed. # Here: # index -1: [3, 6] -> [6, 6] @@ -420,7 +418,7 @@ def merge_in_parallel(self, *others: "Stats") -> None: # Parallel-merge two (reduce=sum) stats with no window. # Note that when reduce="sum", we do NOT reduce across the indices of the - # parallel + # parallel values. stats = Stats(reduce="sum") stats1 = Stats(reduce="sum") stats1.push(1) @@ -435,7 +433,6 @@ def merge_in_parallel(self, *others: "Stats") -> None: # index -2: [0, 5] -> [3, 6, 0, 5] # index -3: [2, 4] -> [3, 6, 0, 5, 2, 4] # index -4: [1] -> [3, 6, 0, 5, 2, 4, 1] - # STOP after merged list contains >= 4 items (window size) # reverse: [1, 4, 2, 5, 0, 6, 3] stats.merge_in_parallel(stats1, stats2) check(stats.values, [1, 4, 2, 5, 0, 6, 3]) @@ -443,7 +440,7 @@ def merge_in_parallel(self, *others: "Stats") -> None: # Parallel-merge two "concat" (reduce=None) stats with no window. # Note that when reduce=None, we do NOT reduce across the indices of the - # parallel + # parallel values. stats = Stats(reduce=None, window=float("inf"), clear_on_reduce=True) stats1 = Stats(reduce=None, window=float("inf"), clear_on_reduce=True) stats1.push(1) @@ -586,7 +583,6 @@ def from_state(state: Dict[str, Any]) -> "Stats": def similar_to( other: "Stats", init_value: Optional[Any] = None, - prev_values: Optional[Tuple[Any, Any]] = None, ) -> "Stats": """Returns a new Stats object that's similar to `other`. From 64454cc04672b11a692d7b71c93f1c38e70e9391 Mon Sep 17 00:00:00 2001 From: Lonnie Liu <95255098+aslonnie@users.noreply.github.com> Date: Fri, 22 Nov 2024 02:35:40 -0800 Subject: [PATCH 12/54] Revert "[Data] Fix pandas memory calculation." (#48866) Reverts ray-project/ray#46939 for https://github.com/ray-project/ray/issues/48865 https://github.com/ray-project/ray/issues/48864 https://github.com/ray-project/ray/issues/48863 https://github.com/ray-project/ray/issues/48862 --- python/ray/data/_internal/pandas_block.py | 70 +------ python/ray/data/tests/test_pandas_block.py | 229 +-------------------- 2 files changed, 3 insertions(+), 296 deletions(-) diff --git a/python/ray/data/_internal/pandas_block.py b/python/ray/data/_internal/pandas_block.py index ff1686c1b355..119469b46c1b 100644 --- a/python/ray/data/_internal/pandas_block.py +++ b/python/ray/data/_internal/pandas_block.py @@ -1,6 +1,5 @@ import collections import heapq -import sys from typing import ( TYPE_CHECKING, Any, @@ -295,74 +294,7 @@ def num_rows(self) -> int: return self._table.shape[0] def size_bytes(self) -> int: - from pandas.api.types import is_object_dtype - - from ray.data.extensions import TensorArrayElement, TensorDtype - - pd = lazy_import_pandas() - - def get_deep_size(obj): - """Calculates the memory size of objects, - including nested objects using an iterative approach.""" - seen = set() - total_size = 0 - objects = collections.deque([obj]) - while objects: - current = objects.pop() - - # Skip interning-eligible immutable objects - if isinstance(current, (str, bytes, int, float)): - size = sys.getsizeof(current) - total_size += size - continue - - # Check if the object has been seen before - if id(current) in seen: - continue - seen.add(id(current)) - - try: - size = sys.getsizeof(current) - except TypeError: - size = 0 - total_size += size - - # Handle specific cases - if isinstance(current, np.ndarray): - total_size += current.nbytes - size # Avoid double counting - elif isinstance(current, pd.DataFrame): - total_size += ( - current.memory_usage(index=True, deep=True).sum() - size - ) - elif isinstance(current, (list, tuple, set)): - objects.extend(current) - elif isinstance(current, dict): - objects.extend(current.keys()) - objects.extend(current.values()) - elif isinstance(current, TensorArrayElement): - objects.extend(current.to_numpy()) - return total_size - - # Get initial memory usage including deep introspection - memory_usage = self._table.memory_usage(index=True, deep=True) - - # TensorDtype for ray.air.util.tensor_extensions.pandas.TensorDtype - object_need_check = (TensorDtype,) - # Handle object columns separately - for column in self._table.columns: - # Check pandas object dtype and the extenstion dtype - if is_object_dtype(self._table[column].dtype) or isinstance( - self._table[column].dtype, object_need_check - ): - column_memory = 0 - for element in self._table[column]: - column_memory += get_deep_size(element) - memory_usage[column] = column_memory - - # Sum up total memory usage - total_memory_usage = memory_usage.sum() - - return int(total_memory_usage) + return int(self._table.memory_usage(index=True, deep=True).sum()) def _zip(self, acc: BlockAccessor) -> "pandas.DataFrame": r = self.to_pandas().copy(deep=False) diff --git a/python/ray/data/tests/test_pandas_block.py b/python/ray/data/tests/test_pandas_block.py index 5e83abbf04d7..4585d0e2a133 100644 --- a/python/ray/data/tests/test_pandas_block.py +++ b/python/ray/data/tests/test_pandas_block.py @@ -1,10 +1,4 @@ -import pickle -import random -import sys - -import numpy as np import pandas as pd -import pyarrow as pa import pytest import ray @@ -54,226 +48,7 @@ def fn2(batch): assert isinstance(block, pd.DataFrame) -class TestSizeBytes: - def test_small(ray_start_regular_shared): - animals = ["Flamingo", "Centipede"] - block = pd.DataFrame({"animals": animals}) - - block_accessor = PandasBlockAccessor.for_block(block) - bytes_size = block_accessor.size_bytes() - - # check that memory usage is within 10% of the size_bytes - # For strings, Pandas seems to be fairly accurate, so let's use that. - memory_usage = block.memory_usage(index=True, deep=True).sum() - assert bytes_size == pytest.approx(memory_usage, rel=0.1), ( - bytes_size, - memory_usage, - ) - - def test_large_str(ray_start_regular_shared): - animals = [ - random.choice(["alligator", "crocodile", "centipede", "flamingo"]) - for i in range(100_000) - ] - block = pd.DataFrame({"animals": animals}) - block["animals"] = block["animals"].astype("string") - - block_accessor = PandasBlockAccessor.for_block(block) - bytes_size = block_accessor.size_bytes() - - memory_usage = block.memory_usage(index=True, deep=True).sum() - assert bytes_size == pytest.approx(memory_usage, rel=0.1), ( - bytes_size, - memory_usage, - ) - - def test_large_str_object(ray_start_regular_shared): - """Note - this test breaks if you refactor/move the list of animals.""" - num = 100_000 - animals = [ - random.choice(["alligator", "crocodile", "centipede", "flamingo"]) - for i in range(num) - ] - block = pd.DataFrame({"animals": animals}) - - block_accessor = PandasBlockAccessor.for_block(block) - bytes_size = block_accessor.size_bytes() - - memory_usage = sum([sys.getsizeof(animal) for animal in animals]) - - assert bytes_size == pytest.approx(memory_usage, rel=0.1), ( - bytes_size, - memory_usage, - ) - - def test_large_floats(ray_start_regular_shared): - animals = [random.random() for i in range(100_000)] - block = pd.DataFrame({"animals": animals}) - - block_accessor = PandasBlockAccessor.for_block(block) - bytes_size = block_accessor.size_bytes() - - memory_usage = pickle.dumps(block).__sizeof__() - # check that memory usage is within 10% of the size_bytes - assert bytes_size == pytest.approx(memory_usage, rel=0.1), ( - bytes_size, - memory_usage, - ) - - def test_bytes_object(ray_start_regular_shared): - def generate_data(batch): - for _ in range(8): - yield {"data": [[b"\x00" * 128 * 1024 * 128]]} - - ds = ( - ray.data.range(1, override_num_blocks=1) - .map_batches(generate_data, batch_size=1) - .map_batches(lambda batch: batch, batch_format="pandas") - ) - - true_value = 128 * 1024 * 128 * 8 - for bundle in ds.iter_internal_ref_bundles(): - size = bundle.size_bytes() - # assert that true_value is within 10% of bundle.size_bytes() - assert size == pytest.approx(true_value, rel=0.1), ( - size, - true_value, - ) - - def test_nested_numpy(ray_start_regular_shared): - size = 1024 - rows = 1_000 - data = [ - np.random.randint(size=size, low=0, high=100, dtype=np.int8) - for _ in range(rows) - ] - df = pd.DataFrame({"data": data}) - - block_accessor = PandasBlockAccessor.for_block(df) - block_size = block_accessor.size_bytes() - true_value = rows * size - assert block_size == pytest.approx(true_value, rel=0.1), ( - block_size, - true_value, - ) - - def test_nested_objects(ray_start_regular_shared): - size = 10 - rows = 10_000 - lists = [[random.randint(0, 100) for _ in range(size)] for _ in range(rows)] - data = {"lists": lists} - block = pd.DataFrame(data) - - block_accessor = PandasBlockAccessor.for_block(block) - bytes_size = block_accessor.size_bytes() - - # List overhead + 10 integers per list - true_size = rows * ( - sys.getsizeof([random.randint(0, 100) for _ in range(size)]) + size * 28 - ) - - assert bytes_size == pytest.approx(true_size, rel=0.1), ( - bytes_size, - true_size, - ) - - def test_mixed_types(ray_start_regular_shared): - rows = 10_000 - - data = { - "integers": [random.randint(0, 100) for _ in range(rows)], - "floats": [random.random() for _ in range(rows)], - "strings": [ - random.choice(["apple", "banana", "cherry"]) for _ in range(rows) - ], - "object": [b"\x00" * 128 for _ in range(rows)], - } - block = pd.DataFrame(data) - block_accessor = PandasBlockAccessor.for_block(block) - bytes_size = block_accessor.size_bytes() - - # Manually calculate the size - int_size = rows * 8 - float_size = rows * 8 - str_size = sum(sys.getsizeof(string) for string in data["strings"]) - object_size = rows * sys.getsizeof(b"\x00" * 128) - - true_size = int_size + float_size + str_size + object_size - assert bytes_size == pytest.approx(true_size, rel=0.1), (bytes_size, true_size) - - def test_nested_lists_strings(ray_start_regular_shared): - rows = 5_000 - nested_lists = ["a"] * 3 + ["bb"] * 4 + ["ccc"] * 3 - data = { - "nested_lists": [nested_lists for _ in range(rows)], - } - block = pd.DataFrame(data) - block_accessor = PandasBlockAccessor.for_block(block) - bytes_size = block_accessor.size_bytes() - - # Manually calculate the size - list_overhead = sys.getsizeof(block["nested_lists"].iloc[0]) + sum( - [sys.getsizeof(x) for x in nested_lists] - ) - true_size = rows * list_overhead - assert bytes_size == pytest.approx(true_size, rel=0.1), (bytes_size, true_size) - - @pytest.mark.parametrize("size", [10, 1024]) - def test_multi_level_nesting(ray_start_regular_shared, size): - rows = 1_000 - data = { - "complex": [ - {"list": [np.random.rand(size)], "value": {"key": "val"}} - for _ in range(rows) - ], - } - block = pd.DataFrame(data) - block_accessor = PandasBlockAccessor.for_block(block) - bytes_size = block_accessor.size_bytes() - - numpy_size = np.random.rand(size).nbytes - - values = ["list", "value", "key", "val"] - str_size = sum([sys.getsizeof(v) for v in values]) - - list_ref_overhead = sys.getsizeof([np.random.rand(size)]) - - dict_overhead1 = sys.getsizeof({"key": "val"}) - - dict_overhead3 = sys.getsizeof( - {"list": [np.random.rand(size)], "value": {"key": "val"}} - ) - - true_size = ( - numpy_size + str_size + list_ref_overhead + dict_overhead1 + dict_overhead3 - ) * rows - assert bytes_size == pytest.approx(true_size, rel=0.15), ( - bytes_size, - true_size, - ) - - def test_boolean(ray_start_regular_shared): - data = [random.choice([True, False, None]) for _ in range(100_000)] - block = pd.DataFrame({"flags": pd.Series(data, dtype="boolean")}) - block_accessor = PandasBlockAccessor.for_block(block) - bytes_size = block_accessor.size_bytes() - - # No object case - true_size = block.memory_usage(index=True, deep=True).sum() - assert bytes_size == pytest.approx(true_size, rel=0.1), (bytes_size, true_size) - - def test_arrow(ray_start_regular_shared): - data = [ - random.choice(["alligator", "crocodile", "flamingo"]) for _ in range(50_000) - ] - arrow_dtype = pd.ArrowDtype(pa.string()) - block = pd.DataFrame({"animals": pd.Series(data, dtype=arrow_dtype)}) - block_accessor = PandasBlockAccessor.for_block(block) - bytes_size = block_accessor.size_bytes() - - true_size = block.memory_usage(index=True, deep=True).sum() - assert bytes_size == pytest.approx(true_size, rel=0.1), (bytes_size, true_size) - - if __name__ == "__main__": + import sys + sys.exit(pytest.main(["-v", __file__])) From b9cd63a5947accfc86e9cf5711ca204120fd9228 Mon Sep 17 00:00:00 2001 From: Dongjun Na Date: Sat, 23 Nov 2024 02:41:50 +0900 Subject: [PATCH 13/54] [Feat][Dashboard] Add global UTC timezone button in navbar with local storage (#48510) Signed-off-by: Dongjun Na --- python/ray/dashboard/client/src/App.tsx | 50 +- .../client/src/common/formatUtils.ts | 14 +- .../dashboard/client/src/common/timezone.ts | 794 ++++++++++++++++++ .../client/src/components/SearchComponent.tsx | 174 +++- .../client/src/pages/layout/MainNavLayout.tsx | 15 +- .../pages/metrics/Metrics.component.test.tsx | 4 + .../client/src/pages/metrics/Metrics.tsx | 6 +- .../client/src/pages/metrics/utils.ts | 24 + .../overview/OverviewPage.component.test.tsx | 2 + .../overview/cards/ClusterUtilizationCard.tsx | 5 +- .../pages/overview/cards/NodeCountCard.tsx | 5 +- ...eploymentMetricsSection.component.test.tsx | 4 + .../serve/ServeDeploymentMetricsSection.tsx | 11 +- .../ServeMetricsSection.component.test.tsx | 4 + .../src/pages/serve/ServeMetricsSection.tsx | 11 +- .../dashboard/client/src/util/test-utils.tsx | 2 + python/ray/dashboard/http_server_head.py | 13 + python/ray/dashboard/timezone_utils.py | 56 ++ 18 files changed, 1173 insertions(+), 21 deletions(-) create mode 100644 python/ray/dashboard/client/src/common/timezone.ts create mode 100644 python/ray/dashboard/timezone_utils.py diff --git a/python/ray/dashboard/client/src/App.tsx b/python/ray/dashboard/client/src/App.tsx index bc30e06ccc0c..84b4a2c88e7b 100644 --- a/python/ray/dashboard/client/src/App.tsx +++ b/python/ray/dashboard/client/src/App.tsx @@ -23,7 +23,12 @@ import { StateApiLogViewerPage, } from "./pages/log/Logs"; import { Metrics } from "./pages/metrics"; -import { DashboardUids, getMetricsInfo } from "./pages/metrics/utils"; +import { + DashboardUids, + getMetricsInfo, + getTimeZoneInfo, + TimezoneInfo, +} from "./pages/metrics/utils"; import Nodes, { ClusterMainPageLayout } from "./pages/node"; import { ClusterDetailInfoPage } from "./pages/node/ClusterDetailInfoPage"; import { ClusterLayout } from "./pages/node/ClusterLayout"; @@ -91,6 +96,14 @@ export type GlobalContextType = { * The name of the current selected datasource. */ dashboardDatasource: string | undefined; + /** + * The timezone set on the ray cluster. + */ + serverTimeZone: TimezoneInfo | null | undefined; + /** + * The globally selected current time zone. + */ + currentTimeZone: string | undefined; }; export const GlobalContext = React.createContext({ nodeMap: {}, @@ -102,10 +115,15 @@ export const GlobalContext = React.createContext({ prometheusHealth: undefined, sessionName: undefined, dashboardDatasource: undefined, + serverTimeZone: undefined, + currentTimeZone: undefined, }); const App = () => { - const [context, setContext] = useState({ + const [currentTimeZone, setCurrentTimeZone] = useState(); + const [context, setContext] = useState< + Omit + >({ nodeMap: {}, nodeMapByIp: {}, namespaceMap: {}, @@ -115,6 +133,7 @@ const App = () => { prometheusHealth: undefined, sessionName: undefined, dashboardDatasource: undefined, + serverTimeZone: undefined, }); useEffect(() => { getNodeList().then((res) => { @@ -158,11 +177,36 @@ const App = () => { doEffect(); }, []); + useEffect(() => { + const updateTimezone = async () => { + // Sets the intial timezone to localStorage value if it exists + const storedTimeZone = localStorage.getItem("timezone"); + if (storedTimeZone) { + setCurrentTimeZone(storedTimeZone); + } + + // Fetch the server time zone. + const tzInfo = await getTimeZoneInfo(); + + const timeZone = + storedTimeZone || + tzInfo?.value || + Intl.DateTimeFormat().resolvedOptions().timeZone; + + setCurrentTimeZone(timeZone); + setContext((existingContext) => ({ + ...existingContext, + serverTimeZone: tzInfo, + })); + }; + updateTimezone(); + }, []); + return ( - + diff --git a/python/ray/dashboard/client/src/common/formatUtils.ts b/python/ray/dashboard/client/src/common/formatUtils.ts index 03610fd82553..4b0c9a24a7d1 100644 --- a/python/ray/dashboard/client/src/common/formatUtils.ts +++ b/python/ray/dashboard/client/src/common/formatUtils.ts @@ -1,4 +1,9 @@ import dayjs from "dayjs"; +import timezone from "dayjs/plugin/timezone"; +import utc from "dayjs/plugin/utc"; + +dayjs.extend(utc); +dayjs.extend(timezone); export const formatByteAmount = ( amount: number, @@ -60,5 +65,10 @@ export const formatValue = (rawFloat: number) => { } }; -export const formatDateFromTimeMs = (time: number) => - dayjs(time).format("YYYY/MM/DD HH:mm:ss"); +export const formatTimeZone = (UTC: string) => { + dayjs.tz.setDefault(UTC); +}; + +export const formatDateFromTimeMs = (time: number) => { + return dayjs.utc(time).tz().format("YYYY/MM/DD HH:mm:ss"); +}; diff --git a/python/ray/dashboard/client/src/common/timezone.ts b/python/ray/dashboard/client/src/common/timezone.ts new file mode 100644 index 000000000000..d02b785550fe --- /dev/null +++ b/python/ray/dashboard/client/src/common/timezone.ts @@ -0,0 +1,794 @@ +export const timezones = [ + { + utc: "GMT-12:00", + value: "Etc/GMT+12", + group: "Pacific", + country: "International Date Line West", + }, + { + utc: "GMT-11:00", + value: "Pacific/Pago_Pago", + group: "Pacific", + country: "American Samoa", + }, + { + utc: "GMT-11:00", + value: "Pacific/Midway", + group: "Pacific", + country: "Midway Island", + }, + { + utc: "GMT-10:00", + value: "Pacific/Honolulu", + group: "Pacific", + country: "Hawaii", + }, + { + utc: "GMT-09:00", + value: "America/Anchorage", + group: "America", + country: "Alaska", + }, + { + utc: "GMT-08:00", + value: "America/Los_Angeles", + group: "America", + country: "Pacific Time (US & Canada)", + }, + { + utc: "GMT-08:00", + value: "America/Tijuana", + group: "America", + country: "Tijuana", + }, + { + utc: "GMT-07:00", + value: "America/Phoenix", + group: "America", + country: "Arizona", + }, + { + utc: "GMT-07:00", + value: "America/Mazatlan", + group: "America", + country: "Mazatlan", + }, + { + utc: "GMT-07:00", + value: "America/Denver", + group: "America", + country: "Mountain Time (US & Canada)", + }, + { + utc: "GMT-06:00", + value: "America/Guatemala", + group: "America", + country: "Central America", + }, + { + utc: "GMT-06:00", + value: "America/Chicago", + group: "America", + country: "Central Time (US & Canada)", + }, + { + utc: "GMT-06:00", + value: "America/Chihuahua", + group: "America", + country: "Chihuahua", + }, + { + utc: "GMT-06:00", + value: "America/Guadalajara", + group: "America", + country: "Guadalajara", + }, + { + utc: "GMT-06:00", + value: "America/Mexico_City", + group: "America", + country: "Mexico City", + }, + { + utc: "GMT-06:00", + value: "America/Monterrey", + group: "America", + country: "Monterrey", + }, + { + utc: "GMT-06:00", + value: "America/Regina", + group: "America", + country: "Saskatchewan", + }, + { + utc: "GMT-05:00", + value: "America/Bogota", + group: "America", + country: "Bogota", + }, + { + utc: "GMT-05:00", + value: "America/New_York", + group: "America", + country: "Eastern Time (US & Canada)", + }, + { + utc: "GMT-05:00", + value: "America/Indiana/Indianapolis", + group: "America", + country: "Indiana (East)", + }, + { + utc: "GMT-05:00", + value: "America/Lima", + group: "America", + country: "Lima", + }, + { + utc: "GMT-05:00", + value: "America/Guayaquil", + group: "America", + country: "Quito", + }, + { + utc: "GMT-04:00", + value: "America/Halifax", + group: "America", + country: "Atlantic Time (Canada)", + }, + { + utc: "GMT-04:00", + value: "America/Caracas", + group: "America", + country: "Caracas", + }, + { + utc: "GMT-04:00", + value: "America/Guyana", + group: "America", + country: "Georgetown", + }, + { + utc: "GMT-04:00", + value: "America/La_Paz", + group: "America", + country: "La Paz", + }, + { + utc: "GMT-04:00", + value: "America/Puerto_Rico", + group: "America", + country: "Puerto Rico", + }, + { + utc: "GMT-04:00", + value: "America/Santiago", + group: "America", + country: "Santiago", + }, + { + utc: "GMT-03:30", + value: "America/St_Johns", + group: "America", + country: "Newfoundland", + }, + { + utc: "GMT-03:00", + value: "America/Sao_Paulo", + group: "America", + country: "Brasilia", + }, + { + utc: "GMT-03:00", + value: "America/Argentina/Buenos_Aires", + group: "America", + country: "Buenos Aires", + }, + { + utc: "GMT-03:00", + value: "America/Montevideo", + group: "America", + country: "Montevideo", + }, + { + utc: "GMT-02:00", + value: "America/Godthab", + group: "America", + country: "Greenland", + }, + { + utc: "GMT-02:00", + value: "Etc/GMT+2", + group: "Atlantic", + country: "Mid-Atlantic", + }, + { + utc: "GMT-01:00", + value: "Atlantic/Azores", + group: "Atlantic", + country: "Azores", + }, + { + utc: "GMT-01:00", + value: "Atlantic/Cape_Verde", + group: "Atlantic", + country: "Cape Verde Is.", + }, + { + utc: "GMT+00:00", + value: "Europe/London", + group: "Europe", + country: "Edinburgh", + }, + { + utc: "GMT+00:00", + value: "Europe/Lisbon", + group: "Europe", + country: "Lisbon", + }, + { + utc: "GMT+00:00", + value: "Europe/London", + group: "Europe", + country: "London", + }, + { + utc: "GMT+00:00", + value: "Africa/Monrovia", + group: "Africa", + country: "Monrovia", + }, + { + utc: "GMT+01:00", + value: "Europe/Amsterdam", + group: "Europe", + country: "Amsterdam", + }, + { + utc: "GMT+01:00", + value: "Europe/Belgrade", + group: "Europe", + country: "Belgrade", + }, + { + utc: "GMT+01:00", + value: "Europe/Berlin", + group: "Europe", + country: "Berlin", + }, + { + utc: "GMT+01:00", + value: "Europe/Brussels", + group: "Europe", + country: "Brussels", + }, + { + utc: "GMT+01:00", + value: "Europe/Budapest", + group: "Europe", + country: "Budapest", + }, + { + utc: "GMT+01:00", + value: "Europe/Copenhagen", + group: "Europe", + country: "Copenhagen", + }, + { + utc: "GMT+01:00", + value: "Europe/Madrid", + group: "Europe", + country: "Madrid", + }, + { + utc: "GMT+01:00", + value: "Europe/Paris", + group: "Europe", + country: "Paris", + }, + { + utc: "GMT+01:00", + value: "Europe/Prague", + group: "Europe", + country: "Prague", + }, + { + utc: "GMT+01:00", + value: "Europe/Rome", + group: "Europe", + country: "Rome", + }, + { + utc: "GMT+01:00", + value: "Europe/Sarajevo", + group: "Europe", + country: "Sarajevo", + }, + { + utc: "GMT+01:00", + value: "Europe/Stockholm", + group: "Europe", + country: "Stockholm", + }, + { + utc: "GMT+01:00", + value: "Europe/Vienna", + group: "Europe", + country: "Vienna", + }, + { + utc: "GMT+01:00", + value: "Europe/Warsaw", + group: "Europe", + country: "Warsaw", + }, + { + utc: "GMT+01:00", + value: "Africa/Lagos", + group: "Africa", + country: "West Central Africa", + }, + { + utc: "GMT+02:00", + value: "Asia/Amman", + group: "Asia", + country: "Amman", + }, + { + utc: "GMT+02:00", + value: "Europe/Athens", + group: "Europe", + country: "Athens", + }, + { + utc: "GMT+02:00", + value: "Asia/Beirut", + group: "Asia", + country: "Beirut", + }, + { + utc: "GMT+02:00", + value: "Europe/Bucharest", + group: "Europe", + country: "Bucharest", + }, + { + utc: "GMT+02:00", + value: "Africa/Cairo", + group: "Africa", + country: "Cairo", + }, + { + utc: "GMT+02:00", + value: "Africa/Harare", + group: "Africa", + country: "Harare", + }, + { + utc: "GMT+02:00", + value: "Europe/Helsinki", + group: "Europe", + country: "Helsinki", + }, + { + utc: "GMT+02:00", + value: "Europe/Istanbul", + group: "Europe", + country: "Istanbul", + }, + { + utc: "GMT+02:00", + value: "Asia/Jerusalem", + group: "Asia", + country: "Jerusalem", + }, + { + utc: "GMT+02:00", + value: "Europe/Kiev", + group: "Europe", + country: "Kyiv", + }, + { + utc: "GMT+02:00", + value: "Europe/Minsk", + group: "Europe", + country: "Minsk", + }, + { + utc: "GMT+02:00", + value: "Europe/Riga", + group: "Europe", + country: "Riga", + }, + { + utc: "GMT+02:00", + value: "Europe/Sofia", + group: "Europe", + country: "Sofia", + }, + { + utc: "GMT+02:00", + value: "Europe/Tallinn", + group: "Europe", + country: "Tallinn", + }, + { + utc: "GMT+02:00", + value: "Europe/Vilnius", + group: "Europe", + country: "Vilnius", + }, + { + utc: "GMT+03:00", + value: "Asia/Baghdad", + group: "Asia", + country: "Baghdad", + }, + { + utc: "GMT+03:00", + value: "Asia/Kuwait", + group: "Asia", + country: "Kuwait", + }, + { + utc: "GMT+03:00", + value: "Europe/Moscow", + group: "Europe", + country: "Moscow", + }, + { + utc: "GMT+03:00", + value: "Africa/Nairobi", + group: "Africa", + country: "Nairobi", + }, + { + utc: "GMT+03:00", + value: "Asia/Riyadh", + group: "Asia", + country: "Riyadh", + }, + { + utc: "GMT+03:30", + value: "Asia/Tehran", + group: "Asia", + country: "Tehran", + }, + { + utc: "GMT+04:00", + value: "Asia/Dubai", + group: "Asia", + country: "Abu Dhabi", + }, + { + utc: "GMT+04:00", + value: "Asia/Baku", + group: "Asia", + country: "Baku", + }, + { + utc: "GMT+04:00", + value: "Asia/Muscat", + group: "Asia", + country: "Muscat", + }, + { + utc: "GMT+04:00", + value: "Asia/Tbilisi", + group: "Asia", + country: "Tbilisi", + }, + { + utc: "GMT+04:00", + value: "Asia/Yerevan", + group: "Asia", + country: "Yerevan", + }, + { + utc: "GMT+04:30", + value: "Asia/Kabul", + group: "Asia", + country: "Kabul", + }, + { + utc: "GMT+05:00", + value: "Asia/Karachi", + group: "Asia", + country: "Islamabad", + }, + { + utc: "GMT+05:00", + value: "Asia/Tashkent", + group: "Asia", + country: "Tashkent", + }, + { + utc: "GMT+05:30", + value: "Asia/Kolkata", + group: "Asia", + country: "Chennai", + }, + { + utc: "GMT+05:30", + value: "Asia/Kolkata", + group: "Asia", + country: "Kolkata", + }, + { + utc: "GMT+05:30", + value: "Asia/Kolkata", + group: "Asia", + country: "Mumbai", + }, + { + utc: "GMT+05:30", + value: "Asia/Kolkata", + group: "Asia", + country: "New Delhi", + }, + { + utc: "GMT+05:45", + value: "Asia/Kathmandu", + group: "Asia", + country: "Kathmandu", + }, + { + utc: "GMT+06:00", + value: "Asia/Almaty", + group: "Asia", + country: "Almaty", + }, + { + utc: "GMT+06:00", + value: "Asia/Dhaka", + group: "Asia", + country: "Dhaka", + }, + { + utc: "GMT+06:00", + value: "Asia/Yekaterinburg", + group: "Asia", + country: "Yekaterinburg", + }, + { + utc: "GMT+06:30", + value: "Asia/Yangon", + group: "Asia", + country: "Yangon (Rangoon)", + }, + { + utc: "GMT+07:00", + value: "Asia/Bangkok", + group: "Asia", + country: "Bangkok", + }, + { + utc: "GMT+07:00", + value: "Asia/Hanoi", + group: "Asia", + country: "Hanoi", + }, + { + utc: "GMT+07:00", + value: "Asia/Jakarta", + group: "Asia", + country: "Jakarta", + }, + { + utc: "GMT+07:00", + value: "Asia/Novosibirsk", + group: "Asia", + country: "Novosibirsk", + }, + { + utc: "GMT+08:00", + value: "Asia/Shanghai", + group: "Asia", + country: "Beijing", + }, + { + utc: "GMT+08:00", + value: "Asia/Chongqing", + group: "Asia", + country: "Chongqing", + }, + { + utc: "GMT+08:00", + value: "Asia/Hong_Kong", + group: "Asia", + country: "Hong Kong", + }, + { + utc: "GMT+08:00", + value: "Asia/Krasnoyarsk", + group: "Asia", + country: "Krasnoyarsk", + }, + { + utc: "GMT+08:00", + value: "Asia/Kuala_Lumpur", + group: "Asia", + country: "Kuala Lumpur", + }, + { + utc: "GMT+08:00", + value: "Australia/Perth", + group: "Australia", + country: "Perth", + }, + { + utc: "GMT+08:00", + value: "Asia/Singapore", + group: "Asia", + country: "Singapore", + }, + { + utc: "GMT+08:00", + value: "Asia/Taipei", + group: "Asia", + country: "Taipei", + }, + { + utc: "GMT+08:00", + value: "Asia/Ulaanbaatar", + group: "Asia", + country: "Ulaan Bataar", + }, + { + utc: "GMT+08:00", + value: "Asia/Urumqi", + group: "Asia", + country: "Urumqi", + }, + { + utc: "GMT+09:00", + value: "Asia/Irkutsk", + group: "Asia", + country: "Irkutsk", + }, + { + utc: "GMT+09:00", + value: "Asia/Tokyo", + group: "Asia", + country: "Osaka", + }, + { + utc: "GMT+09:00", + value: "Asia/Tokyo", + group: "Asia", + country: "Sapporo", + }, + { + utc: "GMT+09:00", + value: "Asia/Seoul", + group: "Asia", + country: "Seoul", + }, + { + utc: "GMT+09:00", + value: "Asia/Tokyo", + group: "Asia", + country: "Tokyo", + }, + { + utc: "GMT+09:30", + value: "Australia/Adelaide", + group: "Australia", + country: "Adelaide", + }, + { + utc: "GMT+09:30", + value: "Australia/Darwin", + group: "Australia", + country: "Darwin", + }, + { + utc: "GMT+10:00", + value: "Australia/Brisbane", + group: "Australia", + country: "Brisbane", + }, + { + utc: "GMT+10:00", + value: "Australia/Sydney", + group: "Australia", + country: "Canberra", + }, + { + utc: "GMT+10:00", + value: "Pacific/Guam", + group: "Pacific", + country: "Guam", + }, + { + utc: "GMT+10:00", + value: "Australia/Hobart", + group: "Australia", + country: "Hobart", + }, + { + utc: "GMT+10:00", + value: "Australia/Melbourne", + group: "Australia", + country: "Melbourne", + }, + { + utc: "GMT+10:00", + value: "Pacific/Port_Moresby", + group: "Pacific", + country: "Port Moresby", + }, + { + utc: "GMT+10:00", + value: "Australia/Sydney", + group: "Australia", + country: "Sydney", + }, + { + utc: "GMT+11:00", + value: "Asia/Magadan", + group: "Asia", + country: "Magadan", + }, + { + utc: "GMT+11:00", + value: "Pacific/Noumea", + group: "Pacific", + country: "New Caledonia", + }, + { + utc: "GMT+11:00", + value: "Pacific/Guadalcanal", + group: "Pacific", + country: "Solomon Is.", + }, + { + utc: "GMT+12:00", + value: "Pacific/Auckland", + group: "Pacific", + country: "Auckland", + }, + { + utc: "GMT+12:00", + value: "Pacific/Fiji", + group: "Pacific", + country: "Fiji", + }, + { + utc: "GMT+12:00", + value: "Asia/Kamchatka", + group: "Asia", + country: "Kamchatka", + }, + { + utc: "GMT+12:00", + value: "Pacific/Majuro", + group: "Pacific", + country: "Marshall Is.", + }, + { + utc: "GMT+12:00", + value: "Pacific/Auckland", + group: "Pacific", + country: "Wellington", + }, + { + utc: "GMT+13:00", + value: "Pacific/Tongatapu", + group: "Pacific", + country: "Nuku'alofa", + }, + { + utc: "GMT+13:00", + value: "Pacific/Apia", + group: "Pacific", + country: "Samoa", + }, + { + utc: "GMT+13:00", + value: "Pacific/Fakaofo", + group: "Pacific", + country: "Tokelau Is.", + }, +]; diff --git a/python/ray/dashboard/client/src/components/SearchComponent.tsx b/python/ray/dashboard/client/src/components/SearchComponent.tsx index 8cfc426c6818..495c3d9fc0a2 100644 --- a/python/ray/dashboard/client/src/components/SearchComponent.tsx +++ b/python/ray/dashboard/client/src/components/SearchComponent.tsx @@ -1,6 +1,18 @@ import { SearchOutlined } from "@mui/icons-material"; -import { InputAdornment, MenuItem, TextField } from "@mui/material"; -import React from "react"; +import { + Autocomplete, + Box, + Divider, + InputAdornment, + MenuItem, + TextField, + Typography, +} from "@mui/material"; + +import React, { useEffect, useState } from "react"; +import { formatTimeZone } from "../common/formatUtils"; +import { timezones } from "../common/timezone"; +import { TimezoneInfo } from "../pages/metrics/utils"; export const SearchInput = ({ label, @@ -77,3 +89,161 @@ export const SearchSelect = ({ ); }; + +export const SearchTimezone = ({ + serverTimeZone, + currentTimeZone, +}: { + serverTimeZone?: TimezoneInfo | null; + currentTimeZone?: string; +}) => { + const [timezone, setTimezone] = useState(""); + + useEffect(() => { + if (currentTimeZone !== undefined) { + formatTimeZone(currentTimeZone); + setTimezone(currentTimeZone); + } + }, [currentTimeZone]); + + const handleTimezoneChange = (value: string) => { + localStorage.setItem("timezone", value); + window.location.reload(); + }; + + const options = timezones + .map((x) => x) // Create a copy + .sort((a, b) => a.group.localeCompare(b.group)); + options.unshift({ + value: "Etc/UTC", + utc: "GMT+00:00", + group: "System", + country: "Coordinated Universal Time", + }); + + const browserTimezone = Intl.DateTimeFormat().resolvedOptions().timeZone; + + const browserOffset = (() => { + const offset = new Date().getTimezoneOffset(); + const sign = offset < 0 ? "+" : "-"; + const hours = Math.abs(Math.floor(offset / 60)) + .toString() + .padStart(2, "0"); + const minutes = Math.abs(offset % 60) + .toString() + .padStart(2, "0"); + return `GMT${sign}${hours}:${minutes}`; + })(); + + if (browserOffset) { + options.unshift({ + value: browserTimezone, + utc: browserOffset, + group: "System", + country: "Browser Time", + }); + } + + const serverUtc = + serverTimeZone?.value && + timezones.find((t) => t.value === serverTimeZone.value)?.utc; + if (serverUtc) { + options.unshift({ + value: serverTimeZone.value, + utc: serverUtc, + group: "System", + country: "Dashboard Server Timezone", + }); + } + + const curUtc = timezones.find((t) => t.value === timezone)?.utc; + return ( + { + if (newValue) { + handleTimezoneChange(newValue.value); + } + }} + options={options} + getOptionLabel={(option) => option.value} + groupBy={(option) => option.group} + filterOptions={(options, { inputValue }) => + options.filter( + (item) => + item.value.includes(inputValue) || + item.utc.includes(inputValue) || + item.country.toLowerCase().includes(inputValue.toLowerCase()) || + item.group.toLowerCase().includes(inputValue.toLowerCase()), + ) + } + renderOption={(props, option) => ( + + + {option.country} + + + {option.value} + + + + {option.utc} + + + )} + renderInput={(params) => ( + + )} + renderGroup={(params) => ( +
  • + + {params.group} + + + {params.children} + + +
    • + )} + slotProps={{ + paper: { + style: { + width: "400px", + }, + }, + popper: { + placement: "bottom-end", + style: { + width: "fit-content", + }, + }, + }} + /> + ); +}; diff --git a/python/ray/dashboard/client/src/pages/layout/MainNavLayout.tsx b/python/ray/dashboard/client/src/pages/layout/MainNavLayout.tsx index 256c830cd29f..ea261a454b75 100644 --- a/python/ray/dashboard/client/src/pages/layout/MainNavLayout.tsx +++ b/python/ray/dashboard/client/src/pages/layout/MainNavLayout.tsx @@ -3,6 +3,7 @@ import React, { useContext } from "react"; import { RiBookMarkLine, RiFeedbackLine } from "react-icons/ri/"; import { Outlet, Link as RouterLink } from "react-router-dom"; import { GlobalContext } from "../../App"; +import { SearchTimezone } from "../../components/SearchComponent"; import Logo from "../../logo.svg"; import { MainNavContext, useMainNavState } from "./mainNavContext"; @@ -106,7 +107,8 @@ const NAV_ITEMS = [ const MainNavBar = () => { const { mainNavPageHierarchy } = useContext(MainNavContext); const rootRouteId = mainNavPageHierarchy[0]?.id; - const { metricsContextLoaded, grafanaHost } = useContext(GlobalContext); + const { metricsContextLoaded, grafanaHost, serverTimeZone, currentTimeZone } = + useContext(GlobalContext); let navItems = NAV_ITEMS; if (!metricsContextLoaded || grafanaHost === "DISABLED") { @@ -179,6 +181,17 @@ const MainNavBar = () => {     + + + + + ); }; diff --git a/python/ray/dashboard/client/src/pages/metrics/Metrics.component.test.tsx b/python/ray/dashboard/client/src/pages/metrics/Metrics.component.test.tsx index c2cf646b743d..a7e3e06f0cea 100644 --- a/python/ray/dashboard/client/src/pages/metrics/Metrics.component.test.tsx +++ b/python/ray/dashboard/client/src/pages/metrics/Metrics.component.test.tsx @@ -22,6 +22,8 @@ const Wrapper = ({ children }: PropsWithChildren<{}>) => { nodeMapByIp: {}, namespaceMap: {}, dashboardDatasource: "Prometheus", + serverTimeZone: undefined, + currentTimeZone: undefined, }} > {children} @@ -47,6 +49,8 @@ const MetricsDisabledWrapper = ({ children }: PropsWithChildren<{}>) => { nodeMapByIp: {}, namespaceMap: {}, dashboardDatasource: "Prometheus", + serverTimeZone: undefined, + currentTimeZone: undefined, }} > {children} diff --git a/python/ray/dashboard/client/src/pages/metrics/Metrics.tsx b/python/ray/dashboard/client/src/pages/metrics/Metrics.tsx index fd542e534cd1..99d459a4fe54 100644 --- a/python/ray/dashboard/client/src/pages/metrics/Metrics.tsx +++ b/python/ray/dashboard/client/src/pages/metrics/Metrics.tsx @@ -571,8 +571,8 @@ const MetricsSection = ({ dashboardUid, dashboardDatasource, }: MetricsSectionProps) => { - const { grafanaHost, sessionName } = useContext(GlobalContext); - + const { grafanaHost, sessionName, currentTimeZone } = + useContext(GlobalContext); return ( { const path = `/d-solo/${dashboardUid}?${pathParams}` + - `&${refreshParams}${timeRangeParams}&var-SessionName=${sessionName}&var-datasource=${dashboardDatasource}`; + `&${refreshParams}&timezone=${currentTimeZone}${timeRangeParams}&var-SessionName=${sessionName}&var-datasource=${dashboardDatasource}`; return ( { return await get(GRAFANA_HEALTHCHECK_URL); }; @@ -68,3 +74,21 @@ export const getMetricsInfo = async () => { return info; }; + +export type TimezoneInfo = { + offset: string; + value: string; +}; + +export const getTimeZoneInfo = async () => { + try { + const resp = await get(TIMEZONE_URL); + if (resp.data) { + return { + offset: resp.data.offset, + value: resp.data.value, + }; + } + } catch (e) {} + return null; +}; diff --git a/python/ray/dashboard/client/src/pages/overview/OverviewPage.component.test.tsx b/python/ray/dashboard/client/src/pages/overview/OverviewPage.component.test.tsx index a92ebb6f4701..9b0d432b9f92 100644 --- a/python/ray/dashboard/client/src/pages/overview/OverviewPage.component.test.tsx +++ b/python/ray/dashboard/client/src/pages/overview/OverviewPage.component.test.tsx @@ -84,6 +84,8 @@ const Wrapper = nodeMapByIp: {}, namespaceMap: {}, dashboardDatasource: "Prometheus", + serverTimeZone: undefined, + currentTimeZone: undefined, }} > {children} diff --git a/python/ray/dashboard/client/src/pages/overview/cards/ClusterUtilizationCard.tsx b/python/ray/dashboard/client/src/pages/overview/cards/ClusterUtilizationCard.tsx index ab76f014f22f..a1c04c8047ab 100644 --- a/python/ray/dashboard/client/src/pages/overview/cards/ClusterUtilizationCard.tsx +++ b/python/ray/dashboard/client/src/pages/overview/cards/ClusterUtilizationCard.tsx @@ -20,11 +20,12 @@ export const ClusterUtilizationCard = ({ sessionName, dashboardUids, dashboardDatasource, + currentTimeZone, } = useContext(GlobalContext); const grafanaDefaultDashboardUid = dashboardUids?.default ?? "rayDefaultDashboard"; const path = `/d-solo/${grafanaDefaultDashboardUid}/default-dashboard?orgId=1&theme=light&panelId=41&var-datasource=${dashboardDatasource}`; - const timeRangeParams = "&from=now-30m&to=now"; + const timeRangeParams = "&from=now-1h&to=now"; if (!metricsContextLoaded || grafanaHost === "DISABLED") { return null; @@ -51,7 +52,7 @@ export const ClusterUtilizationCard = ({ component="iframe" title="Cluster Utilization" sx={{ flex: 1 }} - src={`${grafanaHost}${path}&refresh${timeRangeParams}&var-SessionName=${sessionName}`} + src={`${grafanaHost}${path}&refresh&timezone=${currentTimeZone}${timeRangeParams}&var-SessionName=${sessionName}`} frameBorder="0" /> { sessionName, dashboardUids, dashboardDatasource, + currentTimeZone, } = useContext(GlobalContext); const grafanaDefaultDashboardUid = dashboardUids?.default ?? "rayDefaultDashboard"; const path = `/d-solo/${grafanaDefaultDashboardUid}/default-dashboard?orgId=1&theme=light&panelId=24&var-datasource=${dashboardDatasource}`; - const timeRangeParams = "&from=now-30m&to=now"; + const timeRangeParams = "&from=now-1h&to=now"; if (!metricsContextLoaded || grafanaHost === "DISABLED") { return null; @@ -45,7 +46,7 @@ export const NodeCountCard = ({ className, sx }: NodeCountCardProps) => { component="iframe" title="Node Count" sx={{ flex: 1 }} - src={`${grafanaHost}${path}&refresh${timeRangeParams}&var-SessionName=${sessionName}`} + src={`${grafanaHost}${path}&refresh&timezone=${currentTimeZone}${timeRangeParams}&var-SessionName=${sessionName}`} frameBorder="0" /> )} diff --git a/python/ray/dashboard/client/src/pages/serve/ServeDeploymentMetricsSection.component.test.tsx b/python/ray/dashboard/client/src/pages/serve/ServeDeploymentMetricsSection.component.test.tsx index d0629ed7832c..a1f552514043 100644 --- a/python/ray/dashboard/client/src/pages/serve/ServeDeploymentMetricsSection.component.test.tsx +++ b/python/ray/dashboard/client/src/pages/serve/ServeDeploymentMetricsSection.component.test.tsx @@ -22,6 +22,8 @@ const Wrapper = ({ children }: PropsWithChildren<{}>) => { nodeMapByIp: {}, namespaceMap: {}, dashboardDatasource: "Prometheus", + serverTimeZone: undefined, + currentTimeZone: undefined, }} > {children} @@ -47,6 +49,8 @@ const MetricsDisabledWrapper = ({ children }: PropsWithChildren<{}>) => { nodeMapByIp: {}, namespaceMap: {}, dashboardDatasource: "Prometheus", + serverTimeZone: undefined, + currentTimeZone: undefined, }} > {children} diff --git a/python/ray/dashboard/client/src/pages/serve/ServeDeploymentMetricsSection.tsx b/python/ray/dashboard/client/src/pages/serve/ServeDeploymentMetricsSection.tsx index 33f62879a1d4..921f804e7fb6 100644 --- a/python/ray/dashboard/client/src/pages/serve/ServeDeploymentMetricsSection.tsx +++ b/python/ray/dashboard/client/src/pages/serve/ServeDeploymentMetricsSection.tsx @@ -51,8 +51,13 @@ export const ServeReplicaMetricsSection = ({ className, sx, }: ServeDeploymentMetricsSectionProps) => { - const { grafanaHost, prometheusHealth, dashboardUids, dashboardDatasource } = - useContext(GlobalContext); + const { + grafanaHost, + prometheusHealth, + dashboardUids, + dashboardDatasource, + currentTimeZone, + } = useContext(GlobalContext); const grafanaServeDashboardUid = dashboardUids?.serveDeployment ?? "rayServeDashboard"; @@ -179,7 +184,7 @@ export const ServeReplicaMetricsSection = ({ {METRICS_CONFIG.map(({ title, pathParams }) => { const path = `/d-solo/${grafanaServeDashboardUid}?${pathParams}` + - `${refreshParams}${timeRangeParams}&var-Deployment=${encodeURIComponent( + `${refreshParams}&timezone=${currentTimeZone}${timeRangeParams}&var-Deployment=${encodeURIComponent( deploymentName, )}&var-Replica=${encodeURIComponent( replicaId, diff --git a/python/ray/dashboard/client/src/pages/serve/ServeMetricsSection.component.test.tsx b/python/ray/dashboard/client/src/pages/serve/ServeMetricsSection.component.test.tsx index 6f5826087a63..c276d0fde417 100644 --- a/python/ray/dashboard/client/src/pages/serve/ServeMetricsSection.component.test.tsx +++ b/python/ray/dashboard/client/src/pages/serve/ServeMetricsSection.component.test.tsx @@ -26,6 +26,8 @@ const Wrapper = ({ children }: PropsWithChildren<{}>) => { nodeMapByIp: {}, namespaceMap: {}, dashboardDatasource: "Prometheus", + serverTimeZone: undefined, + currentTimeZone: undefined, }} > {children} @@ -51,6 +53,8 @@ const MetricsDisabledWrapper = ({ children }: PropsWithChildren<{}>) => { nodeMapByIp: {}, namespaceMap: {}, dashboardDatasource: "Prometheus", + serverTimeZone: undefined, + currentTimeZone: undefined, }} > {children} diff --git a/python/ray/dashboard/client/src/pages/serve/ServeMetricsSection.tsx b/python/ray/dashboard/client/src/pages/serve/ServeMetricsSection.tsx index efb3be529314..b03cfbcc56f8 100644 --- a/python/ray/dashboard/client/src/pages/serve/ServeMetricsSection.tsx +++ b/python/ray/dashboard/client/src/pages/serve/ServeMetricsSection.tsx @@ -77,8 +77,13 @@ export const ServeMetricsSection = ({ metricsConfig, sx, }: ServeMetricsSectionProps) => { - const { grafanaHost, prometheusHealth, dashboardUids, dashboardDatasource } = - useContext(GlobalContext); + const { + grafanaHost, + prometheusHealth, + dashboardUids, + dashboardDatasource, + currentTimeZone, + } = useContext(GlobalContext); const grafanaServeDashboardUid = dashboardUids?.serve ?? "rayServeDashboard"; const [refreshOption, setRefreshOption] = useState( RefreshOptions.FIVE_SECONDS, @@ -196,7 +201,7 @@ export const ServeMetricsSection = ({ {metricsConfig.map(({ title, pathParams }) => { const path = `/d-solo/${grafanaServeDashboardUid}?${pathParams}` + - `${refreshParams}${timeRangeParams}&var-datasource=${dashboardDatasource}`; + `${refreshParams}&timezone=${currentTimeZone}${timeRangeParams}&var-datasource=${dashboardDatasource}`; return ( ) => { prometheusHealth: true, sessionName: "session-name", dashboardDatasource: "Prometheus", + serverTimeZone: undefined, + currentTimeZone: undefined, }; return ( diff --git a/python/ray/dashboard/http_server_head.py b/python/ray/dashboard/http_server_head.py index e1c427b1b288..078d4a97dd38 100644 --- a/python/ray/dashboard/http_server_head.py +++ b/python/ray/dashboard/http_server_head.py @@ -12,6 +12,7 @@ import ray import ray.dashboard.optional_utils as dashboard_optional_utils +import ray.dashboard.timezone_utils as timezone_utils import ray.dashboard.utils as dashboard_utils from ray._private.usage.usage_lib import TagKey, record_extra_usage_tag from ray._private.utils import get_or_create_event_loop @@ -139,6 +140,18 @@ async def get_favicon(self, req) -> aiohttp.web.FileResponse: ) ) + @routes.get("/timezone") + async def get_timezone(self, req) -> aiohttp.web.Response: + try: + current_timezone = timezone_utils.get_current_timezone_info() + return aiohttp.web.json_response(current_timezone) + + except Exception as e: + logger.error(f"Error getting timezone: {e}") + return aiohttp.web.Response( + status=500, text="Internal Server Error:" + str(e) + ) + def get_address(self): assert self.http_host and self.http_port return self.http_host, self.http_port diff --git a/python/ray/dashboard/timezone_utils.py b/python/ray/dashboard/timezone_utils.py new file mode 100644 index 000000000000..6a0d68b9c1a9 --- /dev/null +++ b/python/ray/dashboard/timezone_utils.py @@ -0,0 +1,56 @@ +import logging +from datetime import datetime + +logger = logging.getLogger(__name__) + +timezones = [ + {"offset": "-12:00", "value": "Etc/+12"}, + {"offset": "-11:00", "value": "Pacific/Pago_Pago"}, + {"offset": "-10:00", "value": "Pacific/Honolulu"}, + {"offset": "-09:00", "value": "America/Anchorage"}, + {"offset": "-08:00", "value": "America/Los_Angeles"}, + {"offset": "-07:00", "value": "America/Phoenix"}, + {"offset": "-06:00", "value": "America/Guatemala"}, + {"offset": "-05:00", "value": "America/Bogota"}, + {"offset": "-04:00", "value": "America/Halifax"}, + {"offset": "-03:30", "value": "America/St_Johns"}, + {"offset": "-03:00", "value": "America/Sao_Paulo"}, + {"offset": "-02:00", "value": "America/Godthab"}, + {"offset": "-01:00", "value": "Atlantic/Azores"}, + {"offset": "+00:00", "value": "Europe/London"}, + {"offset": "+01:00", "value": "Europe/Amsterdam"}, + {"offset": "+02:00", "value": "Asia/Amman"}, + {"offset": "+03:00", "value": "Asia/Baghdad"}, + {"offset": "+03:30", "value": "Asia/Tehran"}, + {"offset": "+04:00", "value": "Asia/Dubai"}, + {"offset": "+04:30", "value": "Asia/Kabul"}, + {"offset": "+05:00", "value": "Asia/Karachi"}, + {"offset": "+05:30", "value": "Asia/Kolkata"}, + {"offset": "+05:45", "value": "Asia/Kathmandu"}, + {"offset": "+06:00", "value": "Asia/Almaty"}, + {"offset": "+06:30", "value": "Asia/Yangon"}, + {"offset": "+07:00", "value": "Asia/Bangkok"}, + {"offset": "+08:00", "value": "Asia/Shanghai"}, + {"offset": "+09:00", "value": "Asia/Irkutsk"}, + {"offset": "+09:30", "value": "Australia/Adelaide"}, + {"offset": "+10:00", "value": "Australia/Brisbane"}, + {"offset": "+11:00", "value": "Asia/Magadan"}, + {"offset": "+12:00", "value": "Pacific/Auckland"}, + {"offset": "+13:00", "value": "Pacific/Tongatapu"}, +] + + +def get_current_timezone_info(): + current_tz = datetime.now().astimezone().tzinfo + offset = current_tz.utcoffset(None) + hours, remainder = divmod(offset.total_seconds(), 3600) + minutes = remainder // 60 + sign = "+" if hours >= 0 else "-" + current_offset = f"{sign}{abs(int(hours)):02d}:{abs(int(minutes)):02d}" + + current_timezone = next( + (tz for tz in timezones if tz["offset"] == current_offset), + {"offset": None, "value": None}, + ) + + return current_timezone From 61dc075770b4a183e0cd9436f24ac1a1ce3fab9c Mon Sep 17 00:00:00 2001 From: Balaji Veeramani Date: Fri, 22 Nov 2024 11:51:17 -0600 Subject: [PATCH 14/54] [Data] Add new Parquet counting release test (#48853) Issues with this current counting Parquet release test: * It doesn't test autoscaling * It doesn't test multi-node * It uses a list of paths as input, which is extremely less common than a directory as input --------- Signed-off-by: Balaji Veeramani --- .../multi_node_autoscaling_compute.yaml | 18 +++++ .../dataset/parquet_metadata_resolution.py | 36 ---------- .../dataset/read_and_consume_benchmark.py | 69 +++++++++++++++++++ release/release_data_tests.yaml | 19 ++--- 4 files changed, 91 insertions(+), 51 deletions(-) create mode 100644 release/nightly_tests/dataset/multi_node_autoscaling_compute.yaml delete mode 100644 release/nightly_tests/dataset/parquet_metadata_resolution.py create mode 100644 release/nightly_tests/dataset/read_and_consume_benchmark.py diff --git a/release/nightly_tests/dataset/multi_node_autoscaling_compute.yaml b/release/nightly_tests/dataset/multi_node_autoscaling_compute.yaml new file mode 100644 index 000000000000..7b3612d3b4b8 --- /dev/null +++ b/release/nightly_tests/dataset/multi_node_autoscaling_compute.yaml @@ -0,0 +1,18 @@ +# This config matches the default config for Anyscale workspaces with autoscaling. +cloud_id: {{env["ANYSCALE_CLOUD_ID"]}} +region: us-west-2 + +max_workers: 0 + +head_node_type: + name: head_node + instance_type: m5.2xlarge + resources: + cpu: 0 + +worker_node_types: + - name: worker_node + instance_type: m5.2xlarge + min_workers: 0 + max_workers: 10 + use_spot: false diff --git a/release/nightly_tests/dataset/parquet_metadata_resolution.py b/release/nightly_tests/dataset/parquet_metadata_resolution.py deleted file mode 100644 index b9f634f3f17f..000000000000 --- a/release/nightly_tests/dataset/parquet_metadata_resolution.py +++ /dev/null @@ -1,36 +0,0 @@ -import argparse -import os - -from benchmark import Benchmark - -parser = argparse.ArgumentParser(description="Parquet Metadata Read") -parser.add_argument("--num-files", type=int, default=30) -parser.add_argument("--cloud", type=str, choices=["aws", "gcp"]) - - -if __name__ == "__main__": - args = parser.parse_args() - import ray - - print("Connecting to Ray cluster...") - ray.init(address="auto") - - num = args.num_files - - assert args.cloud in {"aws", "gcp"}, args.cloud - if args.cloud == "aws": - prefix = "s3://shuffling-data-loader-benchmarks/data/r10_000_000_000-f1000" - if args.cloud == "gcp": - # NOTE(@bveeramani): I made a mistake while transferring the files from S3 to - # GCS, so there's an extra "r10_000_000_000-f1000" in the URI. Don't worry about - # it. The files are the same. - prefix = "gs://shuffling-data-loader-benchmarks/data/r10_000_000_000-f1000/r10_000_000_000-f1000" # noqa: E501 - files = [f"{prefix}/input_data_{i}.parquet.snappy" for i in range(args.num_files)] - - def _trigger_parquet_metadata_load(): - # This should only read Parquet metadata. - ray.data.read_parquet(files).count() - - benchmark = Benchmark("parquet_metadata_resolution") - benchmark.run_fn("read_metadata", _trigger_parquet_metadata_load) - benchmark.write_result(os.environ["TEST_OUTPUT_JSON"]) diff --git a/release/nightly_tests/dataset/read_and_consume_benchmark.py b/release/nightly_tests/dataset/read_and_consume_benchmark.py new file mode 100644 index 000000000000..f833d27035ce --- /dev/null +++ b/release/nightly_tests/dataset/read_and_consume_benchmark.py @@ -0,0 +1,69 @@ +import ray + +from benchmark import Benchmark + +import argparse +from typing import Callable + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser() + parser.add_argument("path", type=str) + parser.add_argument( + "--format", + choices=["image", "parquet"], + required=True, + ) + + consume_group = parser.add_mutually_exclusive_group() + consume_group.add_argument("--count", action="store_true") + consume_group.add_argument("--iterate", action="store_true") + + return parser.parse_args() + + +def main(args): + benchmark = Benchmark("read-and-consume") + read_fn = get_read_fn(args) + consume_fn = get_consume_fn(args) + + def benchmark_fn(): + ds = read_fn(args.path) + consume_fn(ds) + + benchmark.run_fn(str(vars(args)), benchmark_fn) + benchmark.write_result() + + +def get_read_fn(args: argparse.Namespace) -> Callable[[str], ray.data.Dataset]: + if args.format == "image": + read_fn = ray.data.read_images + elif args.format == "parquet": + read_fn = ray.data.read_parquet + else: + assert False, f"Invalid data format argument: {args}" + + return read_fn + + +def get_consume_fn(args: argparse.Namespace) -> Callable[[ray.data.Dataset], None]: + if args.count: + + def consume_fn(ds): + ds.count() + + elif args.iterate: + + def consume_fn(ds): + for _ in ds.iter_internal_ref_bundles(): + pass + + else: + assert False, f"Invalid consume arguments: {args}" + + return consume_fn + + +if __name__ == "__main__": + args = parse_args() + main(args) diff --git a/release/release_data_tests.yaml b/release/release_data_tests.yaml index 9b3c9d3bd7ac..49fa85ccc38d 100644 --- a/release/release_data_tests.yaml +++ b/release/release_data_tests.yaml @@ -2,7 +2,7 @@ # Dataset tests ############### -- name: parquet_metadata_resolution +- name: count_parquet group: data-tests working_dir: nightly_tests/dataset @@ -12,22 +12,11 @@ cluster: byod: type: gpu - cluster_compute: single_node_benchmark_compute.yaml + cluster_compute: multi_node_autoscaling_compute.yaml run: - # Expect the test to finish around 40 seconds. - timeout: 100 - script: python parquet_metadata_resolution.py --num-files 915 --cloud aws - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: single_node_benchmark_compute_gce.yaml - run: - script: python parquet_metadata_resolution.py --num-files 915 --cloud gcp + timeout: 600 + script: python read_and_consume_benchmark.py s3://ray-benchmark-data/parquet/10TiB --format parquet --count - name: stable_diffusion_benchmark group: data-tests From 8508cda556b5fc02ca73af83181a26c82c7b830a Mon Sep 17 00:00:00 2001 From: Kai-Hsun Chen Date: Fri, 22 Nov 2024 10:25:51 -0800 Subject: [PATCH 15/54] [metrics][prometheus] Support `ray metrics shutdown-prometheus` (#48799) Signed-off-by: kaihsun --- doc/source/cluster/metrics.md | 12 +++++++++++- .../modules/metrics/install_and_start_prometheus.py | 2 ++ .../modules/tests/test_metrics_integration.py | 13 +++++++++++++ python/ray/scripts/scripts.py | 10 ++++++++++ 4 files changed, 36 insertions(+), 1 deletion(-) diff --git a/doc/source/cluster/metrics.md b/doc/source/cluster/metrics.md index ad9e42cd77dd..361a5d1e3c74 100644 --- a/doc/source/cluster/metrics.md +++ b/doc/source/cluster/metrics.md @@ -67,7 +67,17 @@ ray_dashboard_api_requests_count_requests_total You can then see the number of requests to the Ray Dashboard API over time. -To stop Prometheus, run `kill ` where `` is the PID of the Prometheus process that was printed out when you ran the command. To find the PID, you can also run `ps aux | grep prometheus`. +To stop Prometheus, run the following commands: + +```sh +# case 1: Ray > 2.40 +ray metrics shutdown-prometheus + +# case 2: Otherwise +# Run `ps aux | grep prometheus` to find the PID of the Prometheus process. Then, kill the process. +kill +``` + ### [Optional] Manual: Running Prometheus locally diff --git a/python/ray/dashboard/modules/metrics/install_and_start_prometheus.py b/python/ray/dashboard/modules/metrics/install_and_start_prometheus.py index cf7cb31c3607..ea0ff2459f65 100644 --- a/python/ray/dashboard/modules/metrics/install_and_start_prometheus.py +++ b/python/ray/dashboard/modules/metrics/install_and_start_prometheus.py @@ -93,6 +93,7 @@ def start_prometheus(prometheus_dir): f"{prometheus_dir}/prometheus", "--config.file", str(config_file), + "--web.enable-lifecycle", ] try: process = subprocess.Popen(prometheus_cmd) @@ -107,6 +108,7 @@ def print_shutdown_message(process_id): message = ( f"Prometheus is running with PID {process_id}.\n" "To stop Prometheus, use the command: " + "`ray metrics shutdown-prometheus`, " f"'kill {process_id}', or if you need to force stop, " f"use 'kill -9 {process_id}'." ) diff --git a/python/ray/dashboard/modules/tests/test_metrics_integration.py b/python/ray/dashboard/modules/tests/test_metrics_integration.py index 7974b4a43cf9..0b125ca2dd5b 100644 --- a/python/ray/dashboard/modules/tests/test_metrics_integration.py +++ b/python/ray/dashboard/modules/tests/test_metrics_integration.py @@ -1,11 +1,14 @@ import subprocess import sys +import time import pytest +from click.testing import CliRunner from ray.dashboard.consts import PROMETHEUS_CONFIG_INPUT_PATH from ray.dashboard.modules.metrics import install_and_start_prometheus from ray.dashboard.modules.metrics.templates import PROMETHEUS_YML_TEMPLATE +from ray.scripts.scripts import metrics_group @pytest.mark.parametrize( @@ -40,6 +43,16 @@ def test_e2e(capsys): subprocess.run(["kill", str(pid)]) +def test_shutdown_prometheus(): + install_and_start_prometheus.main() + runner = CliRunner() + # Sleep for a few seconds to make sure Prometheus is running + # before we try to shut it down. + time.sleep(5) + result = runner.invoke(metrics_group, ["shutdown-prometheus"]) + assert result.exit_code == 0 + + def test_prometheus_config_content(): # Test to make sure the content in the hardcoded file # (python/ray/dashboard/modules/metrics/export/prometheus/prometheus.yml) will diff --git a/python/ray/scripts/scripts.py b/python/ray/scripts/scripts.py index a69c1369db72..1f26a483a7aa 100644 --- a/python/ray/scripts/scripts.py +++ b/python/ray/scripts/scripts.py @@ -15,6 +15,7 @@ from typing import Optional, Set, List, Tuple from ray.dashboard.modules.metrics import install_and_start_prometheus from ray.util.check_open_ports import check_open_ports +import requests import click import psutil @@ -2583,6 +2584,15 @@ def launch_prometheus(): install_and_start_prometheus.main() +@metrics_group.command(name="shutdown-prometheus") +def shutdown_prometheus(): + try: + requests.post("http://localhost:9090/-/quit") + except requests.exceptions.RequestException as e: + print(f"An error occurred: {e}") + sys.exit(1) + + def add_command_alias(command, name, hidden): new_command = copy.deepcopy(command) new_command.hidden = hidden From 365266d760ed3e292062aaf1d57e05dab4b64180 Mon Sep 17 00:00:00 2001 From: Sven Mika Date: Fri, 22 Nov 2024 19:35:16 +0100 Subject: [PATCH 16/54] [RLlib] Fix broken APPO release test (config error). (#48869) Fix broken APPO release test (config error). Signed-off-by: sven1977 --- rllib/tuned_examples/appo/pong_appo.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/rllib/tuned_examples/appo/pong_appo.py b/rllib/tuned_examples/appo/pong_appo.py index d79dbaa13fc7..ca36ca60fb7c 100644 --- a/rllib/tuned_examples/appo/pong_appo.py +++ b/rllib/tuned_examples/appo/pong_appo.py @@ -65,7 +65,7 @@ def _env_creator(cfg): entropy_coeff=[[0, 0.05], [3000000, 0.0]], # <- crucial parameter to finetune # Only update connector states and model weights every n training_step calls. broadcast_interval=5, - learner_queue_size=1, + circular_buffer_num_batches=1, ) .rl_module( model_config=DefaultModelConfig( From d18c9541746889b4f2854cc6c4e06c33b846abdd Mon Sep 17 00:00:00 2001 From: Rui Qiao <161574667+ruisearch42@users.noreply.github.com> Date: Fri, 22 Nov 2024 12:06:28 -0800 Subject: [PATCH 17/54] =?UTF-8?q?[core]=20Use=20m6i.large=20for=20distribu?= =?UTF-8?q?ted/many=5Fnodes=5Ftests/compute=5Fconfig.=E2=80=A6=20(#48855)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Signed-off-by: Rui Qiao --- .../benchmarks/distributed/many_nodes_tests/compute_config.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/release/benchmarks/distributed/many_nodes_tests/compute_config.yaml b/release/benchmarks/distributed/many_nodes_tests/compute_config.yaml index 7f1e59497146..2a8de6119ea3 100644 --- a/release/benchmarks/distributed/many_nodes_tests/compute_config.yaml +++ b/release/benchmarks/distributed/many_nodes_tests/compute_config.yaml @@ -23,7 +23,7 @@ head_node_type: worker_node_types: - name: worker_node - instance_type: m5.large + instance_type: m6i.large min_workers: 500 max_workers: 2000 use_spot: false From 56b6e1d46b55e687e78e9b526397e3d278bccee7 Mon Sep 17 00:00:00 2001 From: Cuong Nguyen <128072568+can-anyscale@users.noreply.github.com> Date: Fri, 22 Nov 2024 12:19:57 -0800 Subject: [PATCH 18/54] [data][dashboard] fix confusing dataset operator name (#48805) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit In https://github.com/ray-project/ray/issues/48482, a user reported that the name used in ray data dashboard is confusing, in case the name of the operator ends with a number. The name used in ray data dashboard is actually just a key identifier of the dataset and not its name. I add another field to encode its name and use it for data dashboard. Closes https://github.com/ray-project/ray/issues/48482 Test: - CI - More beautiful than ever Screenshot 2024-11-21 at 12 17 21 PM Signed-off-by: can --- .../src/components/DataOverviewTable.tsx | 2 +- .../data/DataOverview.component.test.tsx | 7 +++--- python/ray/dashboard/client/src/type/data.ts | 1 + .../modules/data/tests/test_data_head.py | 24 ++++++++++++++----- .../_internal/execution/streaming_executor.py | 1 + python/ray/data/tests/test_stats.py | 1 + 6 files changed, 26 insertions(+), 10 deletions(-) diff --git a/python/ray/dashboard/client/src/components/DataOverviewTable.tsx b/python/ray/dashboard/client/src/components/DataOverviewTable.tsx index c52bdee0c28b..c3538ba9ccd4 100644 --- a/python/ray/dashboard/client/src/components/DataOverviewTable.tsx +++ b/python/ray/dashboard/client/src/components/DataOverviewTable.tsx @@ -193,7 +193,7 @@ const DataRow = ({ {isDatasetRow && datasetMetrics.dataset} - {isOperatorRow && operatorMetrics.operator} + {isOperatorRow && operatorMetrics.name} { operators: [ { operator: "test_ds1_op1", + name: "test_ds1_op", state: "RUNNING", progress: 99, total: 101, @@ -104,11 +105,11 @@ describe("DataOverview", () => { expect(screen.getByText("70/80")).toBeVisible(); // Operator dropdown - expect(screen.queryByText("test_ds1_op1")).toBeNull(); + expect(screen.queryByText("test_ds1_op")).toBeNull(); await user.click(screen.getByTitle("Expand Dataset test_ds1")); - expect(screen.getByText("test_ds1_op1")).toBeVisible(); + expect(screen.getByText("test_ds1_op")).toBeVisible(); await user.click(screen.getByTitle("Collapse Dataset test_ds1")); - expect(screen.queryByText("test_ds1_op1")).toBeNull(); + expect(screen.queryByText("test_ds1_op")).toBeNull(); // Second Dataset expect(screen.getByText("test_ds2")).toBeVisible(); diff --git a/python/ray/dashboard/client/src/type/data.ts b/python/ray/dashboard/client/src/type/data.ts index dbbfc5889238..64d91a006568 100644 --- a/python/ray/dashboard/client/src/type/data.ts +++ b/python/ray/dashboard/client/src/type/data.ts @@ -12,6 +12,7 @@ export type DatasetMetrics = DataMetrics & { export type OperatorMetrics = DataMetrics & { operator: string; + name: string; }; export type DataMetrics = { diff --git a/python/ray/dashboard/modules/data/tests/test_data_head.py b/python/ray/dashboard/modules/data/tests/test_data_head.py index 650079360a8b..c94a50878c95 100644 --- a/python/ray/dashboard/modules/data/tests/test_data_head.py +++ b/python/ray/dashboard/modules/data/tests/test_data_head.py @@ -32,6 +32,7 @@ ] + DATA_SCHEMA OPERATOR_SCHEMA = [ + "name", "operator", ] + DATA_SCHEMA @@ -64,12 +65,23 @@ def test_get_datasets(): operators = dataset["operators"] assert len(operators) == 2 op0 = operators[0] + op1 = operators[1] assert sorted(op0.keys()) == sorted(OPERATOR_SCHEMA) - assert op0["operator"] == "Input0" - assert op0["progress"] == 20 - assert op0["total"] == 20 - assert op0["state"] == "FINISHED" - assert operators[1]["operator"] == "ReadRange->MapBatches()1" + assert sorted(op1.keys()) == sorted(OPERATOR_SCHEMA) + assert { + "operator": "Input0", + "name": "Input", + "state": "FINISHED", + "progress": 20, + "total": 20, + }.items() <= op0.items() + assert { + "operator": "ReadRange->MapBatches()1", + "name": "ReadRange->MapBatches()", + "state": "FINISHED", + "progress": 20, + "total": 20, + }.items() <= op1.items() ds.map_batches(lambda x: x).materialize() data = requests.get(DATA_HEAD_URLS["GET"].format(job_id=job_id)).json() @@ -83,4 +95,4 @@ def test_get_datasets(): if __name__ == "__main__": - sys.exit(pytest.main(["-v", __file__])) + sys.exit(pytest.main(["-vv", __file__])) diff --git a/python/ray/data/_internal/execution/streaming_executor.py b/python/ray/data/_internal/execution/streaming_executor.py index ca48d7766c35..a4276e2bafe6 100644 --- a/python/ray/data/_internal/execution/streaming_executor.py +++ b/python/ray/data/_internal/execution/streaming_executor.py @@ -399,6 +399,7 @@ def _get_state_dict(self, state): "end_time": time.time() if state != "RUNNING" else None, "operators": { f"{op.name}{i}": { + "name": op.name, "progress": op_state.num_completed_tasks, "total": op.num_outputs_total(), "state": state, diff --git a/python/ray/data/tests/test_stats.py b/python/ray/data/tests/test_stats.py index cb0d31f22774..a41e060fb3f5 100644 --- a/python/ray/data/tests/test_stats.py +++ b/python/ray/data/tests/test_stats.py @@ -1648,6 +1648,7 @@ def test_stats_actor_datasets(ray_start_cluster): assert "Input0" in operators assert "ReadRange->MapBatches()1" in operators for value in operators.values(): + assert value["name"] in ["Input", "ReadRange->MapBatches()"] assert value["progress"] == 20 assert value["total"] == 20 assert value["state"] == "FINISHED" From 62533084e74684649abd43dcfd1ed7e26b985cf2 Mon Sep 17 00:00:00 2001 From: Balaji Veeramani Date: Fri, 22 Nov 2024 16:18:12 -0600 Subject: [PATCH 19/54] [Data] Remove unhelpful all-to-all release tests (#48872) Push-based shuffle hasn't been maintained for a very long time. The release tests often fail, and we don't do anything about it. Aggregate benchmark release test operators on <1 GiB of data, so it's not very useful. --------- Signed-off-by: Balaji Veeramani --- .../dataset/aggregate_benchmark.py | 141 ------------------ release/release_data_tests.yaml | 117 --------------- 2 files changed, 258 deletions(-) delete mode 100644 release/nightly_tests/dataset/aggregate_benchmark.py diff --git a/release/nightly_tests/dataset/aggregate_benchmark.py b/release/nightly_tests/dataset/aggregate_benchmark.py deleted file mode 100644 index 8085ed0ca6d9..000000000000 --- a/release/nightly_tests/dataset/aggregate_benchmark.py +++ /dev/null @@ -1,141 +0,0 @@ -from typing import Tuple - -import ray -from ray.data._internal.aggregate import ( - _AggregateOnKeyBase, - Max, - Mean, - Min, - Sum, -) -from ray.data.block import Block -from ray.data.dataset import Dataset -import pyarrow.compute as pac - -from benchmark import Benchmark - - -def run_h2oai(benchmark: Benchmark): - """This benchmark is originally from https://github.com/h2oai/db-benchmark - - Here we run all group-by queries from the benchmark on Ray Datasets. - The input files are pre-generated and stored in AWS S3 beforehand. - """ - - # Test input file schema={ - # id1: string, id2: string, id3: string, id4: int64, id5: int64, id6: int64, - # v1: int64, v2: int64, v3: double - # }) - test_input = [ - ("s3://air-example-data/h2oai_benchmark/G1_1e7_1e2_0_0.csv", "h2oai-500M") - ] - for path, test_name in test_input: - input_ds = ray.data.read_csv(path) - # Number of blocks (parallelism) should be set as number of available CPUs - # to get best performance. - num_blocks = int(ray.cluster_resources().get("CPU", 1)) - input_ds = input_ds.repartition(num_blocks).materialize() - - q_list = [ - (h2oai_q1, "q1"), - (h2oai_q3, "q3"), - (h2oai_q4, "q4"), - (h2oai_q5, "q5"), - (h2oai_q7, "q7"), - (h2oai_q8, "q8"), - ] - - for q, name in q_list: - benchmark.run_materialize_ds(f"{test_name}-{name}", q, ds=input_ds) - - -def h2oai_q1(ds: Dataset) -> Dataset: - return ds.groupby("id1").sum("v1") - - -def h2oai_q2(ds: Dataset) -> Dataset: - # TODO(chengsu): Run this after dataset supports multiple group-by keys. - # return ds.groupby(["id1", "id2"]).sum("v1") - raise NotImplementedError - - -def h2oai_q3(ds: Dataset) -> Dataset: - return ds.groupby("id3").aggregate(Sum("v1"), Mean("v3")) - - -def h2oai_q4(ds: Dataset) -> Dataset: - return ds.groupby("id4").aggregate(Mean("v1"), Mean("v2"), Mean("v3")) - - -def h2oai_q5(ds: Dataset) -> Dataset: - return ds.groupby("id6").aggregate(Sum("v1"), Sum("v2"), Sum("v3")) - - -def h2oai_q6(ds: Dataset) -> Dataset: - # TODO(chengsu): Run this after dataset supports multiple group-by keys. - # return ds.groupby(["id4", "id5"]).aggregate(Median("v3"), Std("v3")) - raise NotImplementedError - - -def h2oai_q7(ds: Dataset) -> Dataset: - ds = ds.groupby("id3").aggregate(Max("v1"), Min("v2")) - ds = ds.map_batches( - lambda df: df.assign(result=df["max(v1)"] - df["min(v2)"]), - batch_format="pandas", - ) - return ds - - -def h2oai_q8(ds: Dataset) -> Dataset: - def accumulate_block(agg: Tuple[float, float], block: Block) -> Tuple[float, float]: - column = block["v3"] - top_k_indices = pac.top_k_unstable(column, k=2) - top_k_result = pac.take(column, top_k_indices).to_pylist() - top_k_result.extend([float("-inf")] * (2 - len(top_k_result))) - top_k_result = (top_k_result[0], top_k_result[1]) - return merge(agg, top_k_result) - - def merge( - agg1: Tuple[float, float], - agg2: Tuple[float, float], - ) -> Tuple[float, float]: - if agg1[0] >= agg2[0]: - value1 = agg1[0] - value2 = max(agg1[1], agg2[0]) - else: - value1 = agg2[0] - value2 = max(agg1[0], agg2[1]) - return (value1, value2) - - class Top2(_AggregateOnKeyBase): - def __init__(self, on): - self._set_key_fn(on) - super().__init__( - init=lambda _: (float("-inf"), float("-inf")), - merge=merge, - accumulate_block=accumulate_block, - name=(f"top2({str(on)})"), - ) - - return ds.groupby("id6").aggregate(Top2("v3")) - - -def h2oai_q9(ds: Dataset) -> Dataset: - # TODO(chengsu): Run this after dataset supports multiple group-by keys. - # return ds.groupby(["id2", "id4"]).aggregate(pow(corr("v1", "v2"), 2)) - raise NotImplementedError - - -def h2oai_q10(ds: Dataset) -> Dataset: - # TODO(chengsu): Run this after dataset supports multiple group-by keys. - # return ds.groupby(["id1", "id2", "id3", "id4", "id5", "id6"]) - # .aggregate(Count(), Sum("v3")) - raise NotImplementedError - - -if __name__ == "__main__": - benchmark = Benchmark("aggregate") - - run_h2oai(benchmark) - - benchmark.write_result() diff --git a/release/release_data_tests.yaml b/release/release_data_tests.yaml index 49fa85ccc38d..e8e71b7dadbf 100644 --- a/release/release_data_tests.yaml +++ b/release/release_data_tests.yaml @@ -126,30 +126,6 @@ cluster: cluster_compute: data_ingest_benchmark_compute_gpu_gce.yaml -- name: aggregate_benchmark - group: data-tests - working_dir: nightly_tests/dataset - - frequency: nightly - team: data - - cluster: - byod: - type: gpu - cluster_compute: single_node_benchmark_compute.yaml - - run: - timeout: 1800 - script: python aggregate_benchmark.py - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: single_node_benchmark_compute_gce.yaml - - name: read_parquet_benchmark_single_node group: data-tests working_dir: nightly_tests/dataset @@ -629,99 +605,6 @@ cluster: cluster_compute: shuffle/datasets_large_scale_compute_small_instances_gce.yaml -- name: dataset_shuffle_push_based_random_shuffle_1tb - group: data-tests - working_dir: nightly_tests - - stable: false - - frequency: nightly - team: data - - cluster: - byod: - runtime_env: - - RAY_worker_killing_policy=retriable_lifo - pip: - - ray[default] - cluster_compute: shuffle/datasets_large_scale_compute_small_instances.yaml - - run: - timeout: 7200 - script: RAY_DATA_PUSH_BASED_SHUFFLE=1 python dataset/sort.py --num-partitions=1000 --partition-size=1e9 --shuffle - wait_for_nodes: - num_nodes: 20 - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: shuffle/datasets_large_scale_compute_small_instances_gce.yaml - -- name: dataset_shuffle_push_based_sort_1tb - group: data-tests - working_dir: nightly_tests - - frequency: nightly - team: data - stable: False - - cluster: - byod: - runtime_env: - - RAY_worker_killing_policy=retriable_lifo - pip: - - ray[default] - cluster_compute: shuffle/datasets_large_scale_compute_small_instances.yaml - - run: - timeout: 7200 - script: RAY_DATA_PUSH_BASED_SHUFFLE=1 python dataset/sort.py --num-partitions=1000 --partition-size=1e9 - wait_for_nodes: - num_nodes: 20 - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: shuffle/datasets_large_scale_compute_small_instances_gce.yaml - -- name: dataset_shuffle_push_based_random_shuffle_100tb - group: data-tests - working_dir: nightly_tests - stable: false - - frequency: weekly - team: data - cluster: - byod: - runtime_env: - - RAY_object_spilling_config={"type":"filesystem","params":{"directory_path":["/tmp/data0","/tmp/data1"]}} - post_build_script: byod_dataset_shuffle.sh - cluster_compute: shuffle/100tb_shuffle_compute.yaml - - run: - timeout: 28800 - script: RAY_DATA_PUSH_BASED_SHUFFLE=1 python dataset/sort.py --num-partitions=100000 --partition-size=1e9 --shuffle - wait_for_nodes: - num_nodes: 100 - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: shuffle/100tb_shuffle_compute_gce.yaml - run: - timeout: 28800 - script: RAY_DATA_PUSH_BASED_SHUFFLE=1 python dataset/sort.py --num-partitions=40000 --partition-size=1e9 --shuffle - wait_for_nodes: - num_nodes: 100 ############################ # Batch Inference Benchmarks From 27e349ee9f5a2bedb39670373fdc62dd39a215ea Mon Sep 17 00:00:00 2001 From: Cindy Zhang Date: Fri, 22 Nov 2024 14:42:50 -0800 Subject: [PATCH 20/54] [serve] debug gcs failure (#48873) ## Why are these changes needed? - add debug statements - shutdown serve before shutting down ray (deflake some test failures I've seen) - remove `--forked` so test output is captured when test fails --------- Signed-off-by: Cindy Zhang --- python/ray/serve/tests/test_gcs_failure.py | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/python/ray/serve/tests/test_gcs_failure.py b/python/ray/serve/tests/test_gcs_failure.py index cb582a3df51c..3dabb58a99fc 100644 --- a/python/ray/serve/tests/test_gcs_failure.py +++ b/python/ray/serve/tests/test_gcs_failure.py @@ -30,9 +30,14 @@ def serve_ha(external_redis, monkeypatch): # noqa: F811 ) serve.start() yield (address_info, _get_global_client()) - ray.shutdown() + + # When GCS is down, right now some core worker members are not cleared + # properly in ray.shutdown. + ray.worker._global_node.start_gcs_server() + # Clear cache and global serve client serve.shutdown() + ray.shutdown() @pytest.mark.skipif( @@ -127,6 +132,7 @@ def router_populated_with_replicas( else: replicas = get_replicas_func() + print(f"Replica set in router: {replicas}") assert len(replicas) >= threshold # Return early if we don't need to check cache @@ -299,7 +305,4 @@ def test_proxy_router_updated_replicas_then_gcs_failure(serve_ha): if __name__ == "__main__": - # When GCS is down, right now some core worker members are not cleared - # properly in ray.shutdown. Given that this is not hi-pri issue, - # using --forked for isolation. - sys.exit(pytest.main(["-v", "-s", "--forked", __file__])) + sys.exit(pytest.main(["-v", "-s", __file__])) From 5fdfddc1b1756c061bc0219041e79d740dd73189 Mon Sep 17 00:00:00 2001 From: Lonnie Liu <95255098+aslonnie@users.noreply.github.com> Date: Fri, 22 Nov 2024 15:41:21 -0800 Subject: [PATCH 21/54] [test] use async with for async timeout (#48881) or on newest version of async-timeout it will panic and fail Signed-off-by: Lonnie Liu --- python/ray/dashboard/modules/tests/test_utils.py | 2 +- python/ray/tests/test_gcs_utils.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/python/ray/dashboard/modules/tests/test_utils.py b/python/ray/dashboard/modules/tests/test_utils.py index fc4fa85dfaed..38b24da02504 100644 --- a/python/ray/dashboard/modules/tests/test_utils.py +++ b/python/ray/dashboard/modules/tests/test_utils.py @@ -6,7 +6,7 @@ async def http_get(http_session, url, timeout_seconds=60): - with async_timeout.timeout(timeout_seconds): + async with async_timeout.timeout(timeout_seconds): async with http_session.get(url) as response: return await response.json() diff --git a/python/ray/tests/test_gcs_utils.py b/python/ray/tests/test_gcs_utils.py index c25beac6e598..50862f386346 100644 --- a/python/ray/tests/test_gcs_utils.py +++ b/python/ray/tests/test_gcs_utils.py @@ -267,7 +267,7 @@ async def test_gcs_aio_client_is_async(ray_start_regular): gcs_client = gcs_utils.GcsAioClient(address=gcs_address, nums_reconnect_retry=0) await gcs_client.internal_kv_put(b"A", b"B", False, b"NS", timeout=2) - with async_timeout.timeout(3): + async with async_timeout.timeout(3): none, result = await asyncio.gather( asyncio.sleep(2), gcs_client.internal_kv_get(b"A", b"NS", timeout=2) ) From a8e1709f19d0026c757daec6decb05432a80aa46 Mon Sep 17 00:00:00 2001 From: Balaji Veeramani Date: Fri, 22 Nov 2024 18:10:46 -0600 Subject: [PATCH 22/54] [Data] Add new Parquet and image reading release tests (#48870) The existing reading release tests for Parquet and images aren't representative of real-world workloads: the datasets are too small and they're run on single-node clusters. --------- Signed-off-by: Balaji Veeramani --- ...ti_node_read_images_benchmark_compute.yaml | 15 -- ...ode_read_images_benchmark_compute_gce.yaml | 17 -- .../dataset/read_images_benchmark.py | 148 ------------------ .../dataset/read_parquet_benchmark.py | 120 -------------- release/release_data_tests.yaml | 112 +++++-------- 5 files changed, 36 insertions(+), 376 deletions(-) delete mode 100644 release/nightly_tests/dataset/multi_node_read_images_benchmark_compute.yaml delete mode 100644 release/nightly_tests/dataset/multi_node_read_images_benchmark_compute_gce.yaml delete mode 100644 release/nightly_tests/dataset/read_images_benchmark.py delete mode 100644 release/nightly_tests/dataset/read_parquet_benchmark.py diff --git a/release/nightly_tests/dataset/multi_node_read_images_benchmark_compute.yaml b/release/nightly_tests/dataset/multi_node_read_images_benchmark_compute.yaml deleted file mode 100644 index 9655daad50cd..000000000000 --- a/release/nightly_tests/dataset/multi_node_read_images_benchmark_compute.yaml +++ /dev/null @@ -1,15 +0,0 @@ -cloud_id: {{env["ANYSCALE_CLOUD_ID"]}} -region: us-west-2 - -max_workers: 19 - -head_node_type: - name: head_node - instance_type: m5.16xlarge - -worker_node_types: - - name: worker_node - instance_type: m5.4xlarge - max_workers: 19 - min_workers: 19 - use_spot: false diff --git a/release/nightly_tests/dataset/multi_node_read_images_benchmark_compute_gce.yaml b/release/nightly_tests/dataset/multi_node_read_images_benchmark_compute_gce.yaml deleted file mode 100644 index bca10d5c5447..000000000000 --- a/release/nightly_tests/dataset/multi_node_read_images_benchmark_compute_gce.yaml +++ /dev/null @@ -1,17 +0,0 @@ -cloud_id: {{env["ANYSCALE_CLOUD_ID"]}} -region: us-west1 -allowed_azs: - - us-west1-c - -max_workers: 19 - -head_node_type: - name: head_node - instance_type: n2-standard-64 # aws m5.16xlarge - -worker_node_types: - - name: worker_node - instance_type: n2-standard-16 # aws m5.4xlarge - max_workers: 19 - min_workers: 19 - use_spot: false diff --git a/release/nightly_tests/dataset/read_images_benchmark.py b/release/nightly_tests/dataset/read_images_benchmark.py deleted file mode 100644 index 2a5f68db6e2f..000000000000 --- a/release/nightly_tests/dataset/read_images_benchmark.py +++ /dev/null @@ -1,148 +0,0 @@ -import argparse -import os -import random -import shutil -import tempfile -from typing import List, Tuple - -from PIL import Image - -import ray - -from benchmark import Benchmark - - -def parse_args(): - parser = argparse.ArgumentParser() - group = parser.add_mutually_exclusive_group() - group.add_argument( - "--single-node", - action="store_true", - help="Run single-node read_images benchmark.", - ) - group.add_argument( - "--multi-node", - action="store_true", - help="Run multi-node read_images benchmark.", - ) - return parser.parse_args() - - -def main(args): - ray.init() - - benchmark = Benchmark("read-images") - if args.single_node: - run_images_benchmark_single_node(benchmark) - elif args.multi_node: - run_images_benchmark_multi_node(benchmark) - - benchmark.write_result() - - -def generate_images( - num_images: int, sizes: List[Tuple[int, int]], modes: List[str], formats: List[str] -) -> str: - - dimensions = [] - for mode in modes: - if mode in ["1", "L", "P"]: - dimension = 1 - elif mode in ["RGB", "YCbCr", "LAB", "HSV"]: - dimension = 3 - elif mode in ["RGBA", "CMYK", "I", "F"]: - dimension = 4 - else: - raise ValueError(f"Found unknown image mode: {mode}.") - dimensions.append(dimension) - - images_dir = tempfile.mkdtemp() - - for image_idx in range(num_images): - size = random.choice(sizes) - file_format = random.choice(formats) - mode_idx = random.randrange(len(modes)) - mode = modes[mode_idx] - dimension = dimensions[mode_idx] - - width, height = size - file_name = f"{images_dir}/{image_idx}.{file_format}" - pixels_per_dimension = [] - for _ in range(dimension): - pixels = os.urandom(width * height) - pixels_per_dimension.append(pixels) - - image = Image.new(mode, size) - if len(pixels_per_dimension) == 1: - image.putdata(pixels_per_dimension[0]) - else: - image.putdata(list(zip(*pixels_per_dimension))) - image.save(file_name) - - return images_dir - - -def run_images_benchmark_single_node(benchmark: Benchmark): - # Set global random seed. - random.seed(42) - - test_input = [ - generate_images(100, [(256, 256)], ["RGB"], ["jpg"]), - generate_images(100, [(2048, 2048)], ["RGB"], ["jpg"]), - generate_images( - 1000, [(64, 64), (256, 256)], ["RGB", "L"], ["jpg", "jpeg", "png"] - ), - ] - - benchmark.run_materialize_ds( - "images-100-256-rbg-jpg", ray.data.read_images, test_input[0] - ) - benchmark.run_materialize_ds( - "images-100-2048-rbg-jpg", ray.data.read_images, test_input[1] - ) - benchmark.run_materialize_ds( - "images-100-2048-to-256-rbg-jpg", - ray.data.read_images, - test_input[1], - size=(256, 256), - ) - benchmark.run_materialize_ds( - "images-1000-mix", - ray.data.read_images, - test_input[2], - size=(256, 256), - mode="RGB", - ) - - for root in test_input: - shutil.rmtree(root) - - # TODO(chengsu): run benchmark on 20G and 100G imagenet data in multi-nodes - # cluster. - benchmark.run_materialize_ds( - "images-imagenet-1g", - ray.data.read_images, - "s3://air-example-data-2/1G-image-data-synthetic-raw", - ) - - -def run_images_benchmark_multi_node(benchmark: Benchmark): - hundred_thousand_image_paths = [ - f"s3://air-example-data-2/100k-images-data-synthetic-raw/dog_{i}/dog_0.jpg" - for i in range(100_000) - ] - hundred_million_image_paths = [] - for _ in range(100_000_000 // 100_000): - hundred_million_image_paths.extend(hundred_thousand_image_paths) - - def fn(): - ds = ray.data.read_images(hundred_million_image_paths) - for _ in ds.iter_batches(batch_size=None, batch_format="pyarrow"): - pass - - benchmark.run_fn("images-100M", fn) - - -if __name__ == "__main__": - args = parse_args() - main(args) diff --git a/release/nightly_tests/dataset/read_parquet_benchmark.py b/release/nightly_tests/dataset/read_parquet_benchmark.py deleted file mode 100644 index 96ceff9ff55c..000000000000 --- a/release/nightly_tests/dataset/read_parquet_benchmark.py +++ /dev/null @@ -1,120 +0,0 @@ -import ray -from ray.data.dataset import Dataset - -from benchmark import Benchmark -from parquet_data_generator import generate_data - -import shutil -import tempfile -from typing import Optional - - -def read_parquet( - root: str, - override_num_blocks: Optional[int] = None, - use_threads: bool = False, - filter=None, - columns=None, -) -> Dataset: - return ray.data.read_parquet( - paths=root, - override_num_blocks=override_num_blocks, - use_threads=use_threads, - filter=filter, - columns=columns, - ) - - -def run_read_parquet_benchmark(benchmark: Benchmark): - # Test with different override_num_blocks (multi-processing for single node) - # and threading. - for override_num_blocks in [1, 2, 4]: - for use_threads in [True, False]: - test_name = f"read-parquet-downsampled-nyc-taxi-2009-{override_num_blocks}-{use_threads}" # noqa: E501 - benchmark.run_materialize_ds( - test_name, - read_parquet, - root="s3://anonymous@air-example-data/ursa-labs-taxi-data/downsampled_2009_full_year_data.parquet", # noqa: E501 - override_num_blocks=override_num_blocks, - use_threads=use_threads, - ) - - # TODO: Test below is currently excluded, due to failure around - # pickling the Dataset involving the filter expression. - # The error is present on Python < 3.8, and involves the pickle/pickle5 - # libraries. `pickle` is included as a default library from Python 3.8+, - # whereas Python versions before this must import the backported `pickle5` library - # to maintain the same functionality. - - # Test with projection and filter pushdowns. - # Since we have projection and filter pushdown, we can run the read on the full - # size of one year data fast enough on a single node. - # test_name = "read-parquet-nyc-taxi-2018-pushdown" - # filter_expr = (pa.dataset.field("passenger_count") <= 10) & ( - # pa.dataset.field("passenger_count") > 0 - # ) - # benchmark.run( - # test_name, - # read_parquet, - # root="s3://anonymous@air-example-data/ursa-labs-taxi-data/by_year/2018", - # columns=["passenger_count", "trip_distance"], - # filter=filter_expr, - # ) - - # Test with different number files to handle: from a few to many. - data_dirs = [] - # Each test set has same total number of rows, which are distributed - # to different number of files. - total_rows = 1024 * 1024 * 8 - for num_files in [8, 128, 1024]: - for compression in ["snappy", "gzip"]: - data_dirs.append(tempfile.mkdtemp()) - generate_data( - num_rows=total_rows, - num_files=num_files, - num_row_groups_per_file=16, - compression=compression, - data_dir=data_dirs[-1], - ) - test_name = f"read-parquet-random-data-{num_files}-{compression}" - benchmark.run_materialize_ds( - test_name, - read_parquet, - root=data_dirs[-1], - override_num_blocks=1, # We are testing one task to handle N files - ) - for dir in data_dirs: - shutil.rmtree(dir) - - # Test reading many small files. - num_files = 1000 - num_row_groups_per_file = 2 - total_rows = num_files * num_row_groups_per_file - compression = "gzip" - - many_files_dir = "s3://air-example-data-2/read-many-parquet-files/" - # If needed, use the following utility to generate files on S3. - # Otherwise, the benchmark will read pre-generated files in the above bucket. - # generate_data( - # num_rows=total_rows, - # num_files=num_files, - # num_row_groups_per_file=num_row_groups_per_file, - # compression=compression, - # data_dir=many_files_dir, - # ) - test_name = f"read-many-parquet-files-s3-{num_files}-{compression}" - benchmark.run_materialize_ds( - test_name, - read_parquet, - root=many_files_dir, - ) - - -if __name__ == "__main__": - ray.init() - - benchmark = Benchmark("read-parquet") - - run_read_parquet_benchmark(benchmark) - - benchmark.write_result() diff --git a/release/release_data_tests.yaml b/release/release_data_tests.yaml index e8e71b7dadbf..752aaacb4fb0 100644 --- a/release/release_data_tests.yaml +++ b/release/release_data_tests.yaml @@ -1,3 +1,39 @@ +############### +# Reading tests +############### + +- name: read_parquet + group: data-tests + working_dir: nightly_tests/dataset + + frequency: nightly + team: data + + cluster: + byod: + type: gpu + cluster_compute: multi_node_autoscaling_compute.yaml + + run: + timeout: 3600 + script: python read_and_consume_benchmark.py s3://ray-benchmark-data/parquet/10TiB --format parquet --iterate + +- name: read_images + group: data-tests + working_dir: nightly_tests/dataset + + frequency: nightly + team: data + + cluster: + byod: + type: gpu + cluster_compute: multi_node_autoscaling_compute.yaml + + run: + timeout: 3600 + script: python read_and_consume_benchmark.py s3://air-example-data-2/300G-image-data-synthetic-raw --format image --iterate + ############### # Dataset tests ############### @@ -126,82 +162,6 @@ cluster: cluster_compute: data_ingest_benchmark_compute_gpu_gce.yaml -- name: read_parquet_benchmark_single_node - group: data-tests - working_dir: nightly_tests/dataset - - frequency: nightly - team: data - - cluster: - byod: - type: gpu - post_build_script: byod_install_mosaicml.sh - cluster_compute: single_node_benchmark_compute.yaml - - run: - # Expect the benchmark to finish in 400 seconds. - timeout: 400 - script: python read_parquet_benchmark.py - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: single_node_benchmark_compute_gce.yaml - -- name: read_images_benchmark_single_node - group: data-tests - working_dir: nightly_tests/dataset - - frequency: nightly - team: data - - cluster: - byod: - type: gpu - post_build_script: byod_install_mosaicml.sh - cluster_compute: single_node_benchmark_compute.yaml - - run: - timeout: 1800 - script: python read_images_benchmark.py --single-node - - variations: - - __suffix__: aws - - __suffix__: gce - env: gce - frequency: manual - cluster: - cluster_compute: single_node_benchmark_compute_gce.yaml - -# TODO: Re-enable this test once we fix https://github.com/ray-project/ray/issues/40686. -# - name: read_images_benchmark_multi_node -# group: data-tests -# working_dir: nightly_tests/dataset - -# frequency: nightly-3x -# team: data - -# cluster: -# byod: -# type: gpu -# cluster_compute: multi_node_read_images_benchmark_compute.yaml - -# run: -# timeout: 28800 -# script: python read_images_benchmark.py --multi-node - -# variations: -# - __suffix__: aws -# - __suffix__: gce -# env: gce -# frequency: manual -# cluster: -# cluster_compute: multi_node_read_images_benchmark_compute_gce.yaml - - name: read_images_comparison_microbenchmark_single_node group: data-tests working_dir: nightly_tests/dataset From 4b4f3c669bc71027cbae99d5b12ec750b70d96d4 Mon Sep 17 00:00:00 2001 From: Hao Chen Date: Sat, 23 Nov 2024 09:24:55 +0800 Subject: [PATCH 23/54] [data] fix a bug that may cause async map tasks to hang (#48861) ## Why are these changes needed? Fix a bug that may cause async map tasks to hang. See code comments for details. This issue can be reproduced with an existing test `test_map_batches_async_generator` on slow machines. --------- Signed-off-by: Hao Chen --- .../data/_internal/planner/plan_udf_map_op.py | 32 +++++++++++-------- 1 file changed, 19 insertions(+), 13 deletions(-) diff --git a/python/ray/data/_internal/planner/plan_udf_map_op.py b/python/ray/data/_internal/planner/plan_udf_map_op.py index 9cd81c0d5f3b..605efe7c95cc 100644 --- a/python/ray/data/_internal/planner/plan_udf_map_op.py +++ b/python/ray/data/_internal/planner/plan_udf_map_op.py @@ -352,6 +352,8 @@ def transform_fn( # generators, and in the main event loop, yield them from # the queue as they become available. output_batch_queue = queue.Queue() + # Sentinel object to signal the end of the async generator. + sentinel = object() async def process_batch(batch: DataBatch): try: @@ -366,29 +368,33 @@ async def process_batch(batch: DataBatch): ) # Put the exception into the queue to signal an error async def process_all_batches(): - loop = ray.data._map_actor_context.udf_map_asyncio_loop - tasks = [loop.create_task(process_batch(x)) for x in input_iterable] + try: + loop = ray.data._map_actor_context.udf_map_asyncio_loop + tasks = [loop.create_task(process_batch(x)) for x in input_iterable] - ctx = ray.data.DataContext.get_current() - if ctx.execution_options.preserve_order: - for task in tasks: - await task() - else: - for task in asyncio.as_completed(tasks): - await task + ctx = ray.data.DataContext.get_current() + if ctx.execution_options.preserve_order: + for task in tasks: + await task() + else: + for task in asyncio.as_completed(tasks): + await task + finally: + output_batch_queue.put(sentinel) # Use the existing event loop to create and run Tasks to process each batch loop = ray.data._map_actor_context.udf_map_asyncio_loop - future = asyncio.run_coroutine_threadsafe(process_all_batches(), loop) + asyncio.run_coroutine_threadsafe(process_all_batches(), loop) # Yield results as they become available. - # After all futures are completed, drain the queue to - # yield any remaining results. - while not future.done() or not output_batch_queue.empty(): + while True: # Here, `out_batch` is a one-row output batch # from the async generator, corresponding to a # single row from the input batch. out_batch = output_batch_queue.get() + if out_batch is sentinel: + # Break out of the loop when the sentinel is received. + break if isinstance(out_batch, Exception): raise out_batch _validate_batch_output(out_batch) From 354dea6e25929c611b95f569162a036117c60eab Mon Sep 17 00:00:00 2001 From: Mengjin Yan Date: Fri, 22 Nov 2024 20:46:04 -0800 Subject: [PATCH 24/54] [Core] Fix the Flaky Test GcsClientReconnectionTest (#48879) Signed-off-by: Mengjin Yan --- .../test/gcs_client_reconnection_test.cc | 55 +++++++++++++------ 1 file changed, 38 insertions(+), 17 deletions(-) diff --git a/src/ray/gcs/gcs_client/test/gcs_client_reconnection_test.cc b/src/ray/gcs/gcs_client/test/gcs_client_reconnection_test.cc index 99a92034aed2..2e003ec39977 100644 --- a/src/ray/gcs/gcs_client/test/gcs_client_reconnection_test.cc +++ b/src/ray/gcs/gcs_client/test/gcs_client_reconnection_test.cc @@ -14,6 +14,7 @@ #include #include +#include #include "absl/strings/substitute.h" #include "gtest/gtest.h" @@ -198,7 +199,7 @@ TEST_F(GcsClientReconnectionTest, ReconnectionBasic) { ASSERT_TRUE(status.ok()) << status.ToString(); p1.set_value(*p); })); - ASSERT_EQ(f1.wait_for(1s), std::future_status::timeout); + ASSERT_EQ(std::future_status::timeout, f1.wait_for(1s)); // Make sure io context is not blocked std::promise p2; @@ -210,7 +211,7 @@ TEST_F(GcsClientReconnectionTest, ReconnectionBasic) { StartGCS(); // Make sure the request is executed - ASSERT_EQ(f1.get(), "B"); + ASSERT_EQ("B", f1.get()); } TEST_F(GcsClientReconnectionTest, ReconnectionBackoff) { @@ -241,8 +242,14 @@ TEST_F(GcsClientReconnectionTest, ReconnectionBackoff) { ShutdownGCS(); + std::promise p2; + auto f2 = p2.get_future(); RAY_UNUSED(client->InternalKV().AsyncInternalKVPut( - "", "A", "B", false, gcs::GetGcsTimeoutMs(), [](auto, auto) {})); + "", "A", "B", false, gcs::GetGcsTimeoutMs(), [&p2](auto status, auto) { + ASSERT_TRUE(status.ok()) << status.ToString(); + p2.set_value(); + })); + ASSERT_EQ(std::future_status::timeout, f2.wait_for(1s)); ASSERT_TRUE(WaitUntil( [channel]() { @@ -305,27 +312,41 @@ TEST_F(GcsClientReconnectionTest, QueueingAndBlocking) { ShutdownGCS(); // Send one request which should fail - RAY_UNUSED(client->InternalKV().AsyncInternalKVPut( - "", "A", "B", false, gcs::GetGcsTimeoutMs(), [](auto status, auto) {})); - - // Make sure it's not blocking std::promise p2; - client_io_service_->post([&p2]() { p2.set_value(); }, ""); auto f2 = p2.get_future(); - ASSERT_EQ(std::future_status::ready, f2.wait_for(1s)); - - // Send the second one and it should block the thread RAY_UNUSED(client->InternalKV().AsyncInternalKVPut( - "", "A", "B", false, gcs::GetGcsTimeoutMs(), [](auto status, auto) {})); - std::this_thread::sleep_for(1s); + "", "A", "B", false, gcs::GetGcsTimeoutMs(), [&p2](auto status, auto) { + ASSERT_TRUE(status.ok()) << status.ToString(); + p2.set_value(); + })); + ASSERT_EQ(std::future_status::timeout, f2.wait_for(1s)); + + // Make sure it's not blocking std::promise p3; client_io_service_->post([&p3]() { p3.set_value(); }, ""); auto f3 = p3.get_future(); - ASSERT_EQ(std::future_status::timeout, f3.wait_for(1s)); + ASSERT_EQ(std::future_status::ready, f3.wait_for(1s)); + + // Send the second one and it should block the thread + std::promise p4; + auto f4 = p4.get_future(); + RAY_UNUSED(client->InternalKV().AsyncInternalKVPut( + "", "A", "B", false, gcs::GetGcsTimeoutMs(), [&p4](auto status, auto) { + ASSERT_TRUE(status.ok()) << status.ToString(); + p4.set_value(); + })); + ASSERT_EQ(std::future_status::timeout, f4.wait_for(1s)); + + std::promise p5; + client_io_service_->post([&p5]() { p5.set_value(); }, ""); + auto f5 = p5.get_future(); + ASSERT_EQ(std::future_status::timeout, f5.wait_for(1s)); // Resume GCS server and it should unblock StartGCS(); - ASSERT_EQ(std::future_status::ready, f3.wait_for(5s)); + ASSERT_EQ(std::future_status::ready, f5.wait_for(5s)); + ASSERT_EQ(std::future_status::ready, f2.wait_for(1s)); + ASSERT_EQ(std::future_status::ready, f4.wait_for(1s)); } TEST_F(GcsClientReconnectionTest, Timeout) { @@ -335,7 +356,7 @@ TEST_F(GcsClientReconnectionTest, Timeout) { "gcs_rpc_server_reconnect_timeout_s": 60, "gcs_storage": "redis", "gcs_grpc_max_request_queued_max_bytes": 10, - "gcs_server_request_timeout_seconds": 3 + "gcs_server_request_timeout_seconds": 10 } )"); StartGCS(); @@ -346,11 +367,11 @@ TEST_F(GcsClientReconnectionTest, Timeout) { ASSERT_TRUE(added); ShutdownGCS(); - std::vector values; ASSERT_TRUE( client->InternalKV().Keys("", "A", gcs::GetGcsTimeoutMs(), values).IsTimedOut()); ASSERT_TRUE(values.empty()); + StartGCS(); ASSERT_TRUE(client->InternalKV().Keys("", "A", gcs::GetGcsTimeoutMs(), values).ok()); ASSERT_EQ(std::vector{"A"}, values); From 56413d29f7c1ff58e221a29429932326e965298e Mon Sep 17 00:00:00 2001 From: Alexey Kudinkin Date: Fri, 22 Nov 2024 21:15:40 -0800 Subject: [PATCH 25/54] Fixing pyarrow dep constraints in `setup.py` (#48886) ## Why are these changes needed? This is a follow-up to a recent change upgrading minimal supported PyArrow version from 6.0.1 to 9.0.0 ## Related issue number ## Checks - [ ] I've signed off every commit(by using the -s flag, i.e., `git commit -s`) in this PR. - [ ] I've run `scripts/format.sh` to lint the changes in this PR. - [ ] I've included any doc changes needed for https://docs.ray.io/en/master/. - [ ] I've added any new APIs to the API Reference. For example, if I added a method in Tune, I've added it in `doc/source/tune/api/` under the corresponding `.rst` file. - [ ] I've made sure the tests are passing. Note that there might be a few flaky tests, see the recent failures at https://flakey-tests.ray.io/ - Testing Strategy - [ ] Unit tests - [ ] Release tests - [ ] This PR is not tested :( Signed-off-by: Alexey Kudinkin --- python/setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/setup.py b/python/setup.py index 2e6958d021da..16017fa5447a 100644 --- a/python/setup.py +++ b/python/setup.py @@ -228,7 +228,7 @@ def get_packages(self): pandas_dep = "pandas >= 1.3" numpy_dep = "numpy >= 1.20" pyarrow_deps = [ - "pyarrow >= 6.0.1", + "pyarrow >= 9.0.0", "pyarrow <18; sys_platform == 'darwin' and platform_machine == 'x86_64'", ] setup_spec.extras = { From 37aa0c66110fc235762c29612b90f1c73869e6cf Mon Sep 17 00:00:00 2001 From: Lonnie Liu <95255098+aslonnie@users.noreply.github.com> Date: Fri, 22 Nov 2024 21:47:03 -0800 Subject: [PATCH 26/54] [vm launcher] pin gcp image to a working version in release tests (#48890) the latest version of the image changes the default user from ubuntu to jupyter. Signed-off-by: Lonnie Liu --- python/ray/autoscaler/gcp/example-full.yaml | 4 +-- .../gcp/example-minimal-pinned.yaml | 36 +++++++++++++++++++ release/release_tests.yaml | 2 +- 3 files changed, 39 insertions(+), 3 deletions(-) create mode 100644 python/ray/autoscaler/gcp/example-minimal-pinned.yaml diff --git a/python/ray/autoscaler/gcp/example-full.yaml b/python/ray/autoscaler/gcp/example-full.yaml index f5b30613aed9..2fa4c1211752 100644 --- a/python/ray/autoscaler/gcp/example-full.yaml +++ b/python/ray/autoscaler/gcp/example-full.yaml @@ -70,7 +70,7 @@ available_node_types: initializeParams: diskSizeGb: 50 # See https://cloud.google.com/compute/docs/images for more images - sourceImage: projects/deeplearning-platform-release/global/images/family/common-cpu + sourceImage: projects/deeplearning-platform-release/global/images/common-cpu-v20240922 # Additional options can be found in in the compute docs at # https://cloud.google.com/compute/docs/reference/rest/v1/instances/insert @@ -105,7 +105,7 @@ available_node_types: initializeParams: diskSizeGb: 50 # See https://cloud.google.com/compute/docs/images for more images - sourceImage: projects/deeplearning-platform-release/global/images/family/common-cpu + sourceImage: projects/deeplearning-platform-release/global/images/common-cpu-v20240922 # Run workers on preemtible instance by default. # Comment this out to use on-demand. scheduling: diff --git a/python/ray/autoscaler/gcp/example-minimal-pinned.yaml b/python/ray/autoscaler/gcp/example-minimal-pinned.yaml new file mode 100644 index 000000000000..ce42e6705854 --- /dev/null +++ b/python/ray/autoscaler/gcp/example-minimal-pinned.yaml @@ -0,0 +1,36 @@ +auth: + ssh_user: ubuntu +cluster_name: minimal +provider: + availability_zone: us-west1-a + project_id: null # TODO: set your GCP project ID here + region: us-west1 + type: gcp + +# Needs to pin the VM images for stability.. +available_node_types: + ray_head_default: + resources: {"CPU": 2} + node_config: + machineType: n1-standard-2 + disks: + - boot: true + autoDelete: true + type: PERSISTENT + initializeParams: + diskSizeGb: 50 + sourceImage: projects/deeplearning-platform-release/global/images/common-cpu-v20240922 + ray_worker_small: + min_workers: 0 + resources: {"CPU": 2} + node_config: + machineType: n1-standard-2 + disks: + - boot: true + autoDelete: true + type: PERSISTENT + initializeParams: + diskSizeGb: 50 + sourceImage: projects/deeplearning-platform-release/global/images/common-cpu-v20240922 + scheduling: + - preemptible: true diff --git a/release/release_tests.yaml b/release/release_tests.yaml index 4ffa492e6cde..1c150aedf601 100644 --- a/release/release_tests.yaml +++ b/release/release_tests.yaml @@ -4131,7 +4131,7 @@ run: timeout: 1200 - script: python launch_and_verify_cluster.py gcp/example-minimal.yaml + script: python launch_and_verify_cluster.py gcp/example-minimal-pinned.yaml - name: gcp_cluster_launcher_full group: cluster-launcher-test From 5cd8967f1c0c16d3ae5fedb8449d0d25dd4f9f3e Mon Sep 17 00:00:00 2001 From: Lonnie Liu <95255098+aslonnie@users.noreply.github.com> Date: Sat, 23 Nov 2024 06:33:16 -0800 Subject: [PATCH 27/54] [data] add back gen image function (#48895) which was imported from another file that has been deleted. Signed-off-by: Lonnie Liu --- .../dataset/read_tfrecords_benchmark.py | 39 ++++++++++++++++++- 1 file changed, 38 insertions(+), 1 deletion(-) diff --git a/release/nightly_tests/dataset/read_tfrecords_benchmark.py b/release/nightly_tests/dataset/read_tfrecords_benchmark.py index 48d1bb229195..34fecb02fd41 100644 --- a/release/nightly_tests/dataset/read_tfrecords_benchmark.py +++ b/release/nightly_tests/dataset/read_tfrecords_benchmark.py @@ -1,3 +1,4 @@ +import os import random import shutil import tempfile @@ -7,11 +8,47 @@ from ray.data.dataset import Dataset from benchmark import Benchmark -from read_images_benchmark import generate_images +from PIL import Image import pyarrow as pa import numpy as np +def generate_images( + num_images: int, sizes: List[Tuple[int, int]], modes: List[str], formats: List[str] +) -> str: + dimensions = [] + for mode in modes: + if mode in ["1", "L", "P"]: + dimension = 1 + elif mode in ["RGB", "YCbCr", "LAB", "HSV"]: + dimension = 3 + elif mode in ["RGBA", "CMYK", "I", "F"]: + dimension = 4 + else: + raise ValueError(f"Found unknown image mode: {mode}.") + dimensions.append(dimension) + images_dir = tempfile.mkdtemp() + for image_idx in range(num_images): + size = random.choice(sizes) + file_format = random.choice(formats) + mode_idx = random.randrange(len(modes)) + mode = modes[mode_idx] + dimension = dimensions[mode_idx] + width, height = size + file_name = f"{images_dir}/{image_idx}.{file_format}" + pixels_per_dimension = [] + for _ in range(dimension): + pixels = os.urandom(width * height) + pixels_per_dimension.append(pixels) + image = Image.new(mode, size) + if len(pixels_per_dimension) == 1: + image.putdata(pixels_per_dimension[0]) + else: + image.putdata(list(zip(*pixels_per_dimension))) + image.save(file_name) + return images_dir + + def read_tfrecords(path: str) -> Dataset: return ray.data.read_tfrecords(paths=path).materialize() From 4345c6c1fdcd2b79edf9563ce5e590cda89dcaa6 Mon Sep 17 00:00:00 2001 From: Lonnie Liu <95255098+aslonnie@users.noreply.github.com> Date: Sat, 23 Nov 2024 14:28:19 -0800 Subject: [PATCH 28/54] [doc] document the right dask version (#48899) 2024.6.0 is used since ray 2.34 Signed-off-by: Lonnie Liu --- doc/source/ray-more-libs/dask-on-ray.rst | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/doc/source/ray-more-libs/dask-on-ray.rst b/doc/source/ray-more-libs/dask-on-ray.rst index 3e130bfcaa35..1e6ae2f39129 100644 --- a/doc/source/ray-more-libs/dask-on-ray.rst +++ b/doc/source/ray-more-libs/dask-on-ray.rst @@ -31,7 +31,10 @@ workload. Using the Dask-on-Ray scheduler, the entire Dask ecosystem can be exec * - Ray Version - Dask Version - * - ``2.8.0`` or above + * - ``2.34.0`` or above + - | ``2022.10.1 (Python version < 3.12)`` + | ``2024.6.0 (Python version >= 3.12)`` + * - ``2.8.0`` to ``2.33.x`` - ``2022.10.1`` * - ``2.5.0`` to ``2.7.x`` - | ``2022.2.0 (Python version < 3.8)`` From 4a295714b670c1c8d8eccb03722d129c6fcd58a8 Mon Sep 17 00:00:00 2001 From: ryanaoleary <113500783+ryanaoleary@users.noreply.github.com> Date: Sun, 24 Nov 2024 00:52:50 +0000 Subject: [PATCH 29/54] [Core][Autoscaler] Configure idleTimeoutSeconds per node type (#48813) ## Why are these changes needed? Adds `idle_timeout_s` as a field to `node_type_configs`, enabling the v2 autoscaler to configure idle termination per worker type. This PR depends on a change in KubeRay to the RayCluster CRD, since we want to support passing `idleTimeoutSeconds` to individual worker groups such that they can specify a custom idle duration: https://github.com/ray-project/kuberay/pull/2558 ## Related issue number Closes #36888 ## Checks - [x] I've signed off every commit(by using the -s flag, i.e., `git commit -s`) in this PR. - [ ] I've run `scripts/format.sh` to lint the changes in this PR. - [ ] I've included any doc changes needed for https://docs.ray.io/en/master/. - [ ] I've added any new APIs to the API Reference. For example, if I added a method in Tune, I've added it in `doc/source/tune/api/` under the corresponding `.rst` file. - [x] I've made sure the tests are passing. Note that there might be a few flaky tests, see the recent failures at https://flakey-tests.ray.io/ - Testing Strategy - [x] Unit tests - [ ] Release tests - [ ] This PR is not tested :( --------- Signed-off-by: ryanaoleary Signed-off-by: ryanaoleary <113500783+ryanaoleary@users.noreply.github.com> Co-authored-by: Kai-Hsun Chen Co-authored-by: Ricky Xu --- .../_private/kuberay/autoscaling_config.py | 8 +- python/ray/autoscaler/ray-schema.json | 1 + .../autoscaler/v2/instance_manager/config.py | 3 + python/ray/autoscaler/v2/scheduler.py | 6 +- .../ray/autoscaler/v2/tests/test_scheduler.py | 76 +++++++++++++++++++ 5 files changed, 92 insertions(+), 2 deletions(-) diff --git a/python/ray/autoscaler/_private/kuberay/autoscaling_config.py b/python/ray/autoscaler/_private/kuberay/autoscaling_config.py index 96aec03744b2..0bf61b311128 100644 --- a/python/ray/autoscaler/_private/kuberay/autoscaling_config.py +++ b/python/ray/autoscaler/_private/kuberay/autoscaling_config.py @@ -219,7 +219,7 @@ def _node_type_from_group_spec( resources = _get_ray_resources_from_group_spec(group_spec, is_head) - return { + node_type = { "min_workers": min_workers, "max_workers": max_workers, # `node_config` is a legacy field required for compatibility. @@ -228,6 +228,12 @@ def _node_type_from_group_spec( "resources": resources, } + idle_timeout_s = group_spec.get(IDLE_SECONDS_KEY) + if idle_timeout_s is not None: + node_type["idle_timeout_s"] = float(idle_timeout_s) + + return node_type + def _get_ray_resources_from_group_spec( group_spec: Dict[str, Any], is_head: bool diff --git a/python/ray/autoscaler/ray-schema.json b/python/ray/autoscaler/ray-schema.json index ad5da68ea2a0..2e07dadac912 100644 --- a/python/ray/autoscaler/ray-schema.json +++ b/python/ray/autoscaler/ray-schema.json @@ -345,6 +345,7 @@ }, "min_workers": {"type": "integer"}, "max_workers": {"type": "integer"}, + "idle_timeout_s": {"type": "number", "nullable": true}, "resources": { "type": "object", "patternProperties": { diff --git a/python/ray/autoscaler/v2/instance_manager/config.py b/python/ray/autoscaler/v2/instance_manager/config.py index c9597eef6c73..a7e582eacdbc 100644 --- a/python/ray/autoscaler/v2/instance_manager/config.py +++ b/python/ray/autoscaler/v2/instance_manager/config.py @@ -128,6 +128,8 @@ class NodeTypeConfig: min_worker_nodes: int # The maximal number of worker nodes can be launched for this node type. max_worker_nodes: int + # Idle timeout seconds for worker nodes of this node type. + idle_timeout_s: Optional[float] = None # The total resources on the node. resources: Dict[str, float] = field(default_factory=dict) # The labels on the node. @@ -346,6 +348,7 @@ def get_node_type_configs(self) -> Dict[NodeType, NodeTypeConfig]: name=node_type, min_worker_nodes=node_config.get("min_workers", 0), max_worker_nodes=max_workers_nodes, + idle_timeout_s=node_config.get("idle_timeout_s", None), resources=node_config.get("resources", {}), labels=node_config.get("labels", {}), launch_config_hash=launch_config_hash, diff --git a/python/ray/autoscaler/v2/scheduler.py b/python/ray/autoscaler/v2/scheduler.py index 3732a6282632..2d5a70065066 100644 --- a/python/ray/autoscaler/v2/scheduler.py +++ b/python/ray/autoscaler/v2/scheduler.py @@ -1584,6 +1584,11 @@ def _enforce_idle_termination( continue idle_timeout_s = ctx.get_idle_timeout_s() + # Override the scheduler idle_timeout_s if set for this node_type. + node_type = node.node_type + if node_type in node_type_configs: + if node_type_configs[node_type].idle_timeout_s is not None: + idle_timeout_s = node_type_configs[node_type].idle_timeout_s if idle_timeout_s is None: # No idle timeout is set, skip the idle termination. continue @@ -1606,7 +1611,6 @@ def _enforce_idle_termination( # Honor the min_worker_nodes setting for the node type. min_count = 0 - node_type = node.node_type if node_type in node_type_configs: min_count = node_type_configs[node_type].min_worker_nodes if ( diff --git a/python/ray/autoscaler/v2/tests/test_scheduler.py b/python/ray/autoscaler/v2/tests/test_scheduler.py index e6d6cb71978d..3a188bdaf2ce 100644 --- a/python/ray/autoscaler/v2/tests/test_scheduler.py +++ b/python/ray/autoscaler/v2/tests/test_scheduler.py @@ -1434,6 +1434,82 @@ def test_idle_termination_with_min_worker(min_workers): assert len(to_terminate) == 0 +@pytest.mark.parametrize("node_type_idle_timeout_s", [1, 2, 10]) +def test_idle_termination_with_node_type_idle_timeout(node_type_idle_timeout_s): + """ + Test that idle nodes are terminated when idle_timeout_s is set for node type. + """ + scheduler = ResourceDemandScheduler(event_logger) + + node_type_configs = { + "type_cpu_with_idle_timeout": NodeTypeConfig( + name="type_cpu", + resources={"CPU": 1}, + min_worker_nodes=0, + max_worker_nodes=5, + idle_timeout_s=node_type_idle_timeout_s, + launch_config_hash="hash1", + ), + } + + idle_time_s = 5 + constraints = [] + + request = sched_request( + node_type_configs=node_type_configs, + instances=[ + make_autoscaler_instance( + im_instance=Instance( + instance_type="type_cpu_with_idle_timeout", + status=Instance.RAY_RUNNING, + launch_config_hash="hash1", + instance_id="i-1", + node_id="r-1", + ), + ray_node=NodeState( + node_id=b"r-1", + ray_node_type_name="type_cpu_with_idle_timeout", + available_resources={"CPU": 0}, + total_resources={"CPU": 1}, + idle_duration_ms=0, # Non idle + status=NodeStatus.RUNNING, + ), + cloud_instance_id="c-1", + ), + make_autoscaler_instance( + im_instance=Instance( + instance_id="i-2", + instance_type="type_cpu_with_idle_timeout", + status=Instance.RAY_RUNNING, + launch_config_hash="hash1", + node_id="r-2", + ), + ray_node=NodeState( + ray_node_type_name="type_cpu_with_idle_timeout", + node_id=b"r-2", + available_resources={"CPU": 1}, + total_resources={"CPU": 1}, + idle_duration_ms=idle_time_s * 1000, + status=NodeStatus.IDLE, + ), + cloud_instance_id="c-2", + ), + ], + # Set autoscaler idle_timeout_s to a value greater than + # node_type_idle_timeout_s and idle_time_s. + idle_timeout_s=idle_time_s * 1000, + cluster_resource_constraints=constraints, + ) + + reply = scheduler.schedule(request) + _, to_terminate = _launch_and_terminate(reply) + if node_type_idle_timeout_s <= idle_time_s: + assert len(to_terminate) == 1 + assert to_terminate == [("i-2", "r-2", TerminationRequest.Cause.IDLE)] + else: + assert len(to_terminate) == 0 + + def test_gang_scheduling(): """ Test that gang scheduling works. From c2f93341a6c310892c624e815aa000f305b37186 Mon Sep 17 00:00:00 2001 From: Lonnie Liu <95255098+aslonnie@users.noreply.github.com> Date: Sat, 23 Nov 2024 21:34:55 -0800 Subject: [PATCH 30/54] [air] dreambooth pandas type fix (#48904) lambda for `add_column` must return a `panda.Series` now. Signed-off-by: Lonnie Liu --- .../05_dreambooth_finetuning/dreambooth/dataset.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/doc/source/templates/05_dreambooth_finetuning/dreambooth/dataset.py b/doc/source/templates/05_dreambooth_finetuning/dreambooth/dataset.py index 6c7e1e52aa48..bba46f34208b 100644 --- a/doc/source/templates/05_dreambooth_finetuning/dreambooth/dataset.py +++ b/doc/source/templates/05_dreambooth_finetuning/dreambooth/dataset.py @@ -88,7 +88,9 @@ def transform_image( transform_image, fn_kwargs={"output_column_name": "instance_image"} ) .drop_columns(["image"]) - .add_column("instance_prompt_ids", lambda df: [instance_prompt_ids] * len(df)) + .add_column( + "instance_prompt_ids", lambda df: pd.Series([instance_prompt_ids] * len(df)) + ) ) # END: Apply preprocessing steps as Ray Dataset operations @@ -97,7 +99,9 @@ def transform_image( transform_image, fn_kwargs={"output_column_name": "class_image"} ) .drop_columns(["image"]) - .add_column("class_prompt_ids", lambda df: [class_prompt_ids] * len(df)) + .add_column( + "class_prompt_ids", lambda df: pd.Series([class_prompt_ids] * len(df)) + ) ) # --- Ray Data From 02ac0cdc7adf5e611134840c73fa47dd7866140d Mon Sep 17 00:00:00 2001 From: dentiny Date: Sat, 23 Nov 2024 21:35:15 -0800 Subject: [PATCH 31/54] [core] Fix gcs unit tests dependency (#48892) Our usage for gtest and dependency is incorrect, in short, we shouldn't declare `@com_google_googletest//:gtest_main` as dependency and **meanwhile** have main function defined in the test. One weird behavior I noticed is, `SetUp` overriden function will be invoked twice somehow. Signed-off-by: hjiang Co-authored-by: Lonnie Liu <95255098+aslonnie@users.noreply.github.com> --- BUILD.bazel | 6 +++--- .../gcs/gcs_server/test/gcs_actor_manager_test.cc | 5 ----- .../gcs_server/test/gcs_actor_scheduler_test.cc | 5 ----- .../test/gcs_autoscaler_state_manager_test.cc | 5 ----- .../test/gcs_health_check_manager_test.cc | 14 -------------- .../gcs/gcs_server/test/gcs_job_manager_test.cc | 4 ---- .../gcs/gcs_server/test/gcs_node_manager_test.cc | 5 ----- .../test/gcs_placement_group_manager_test.cc | 5 ----- .../test/gcs_placement_group_scheduler_test.cc | 5 ----- .../gcs_server/test/gcs_resource_manager_test.cc | 5 ----- .../gcs/gcs_server/test/gcs_worker_manager_test.cc | 5 ----- .../test/in_memory_gcs_table_storage_test.cc | 5 ----- .../gcs/gcs_server/test/usage_stats_client_test.cc | 5 ----- 13 files changed, 3 insertions(+), 71 deletions(-) diff --git a/BUILD.bazel b/BUILD.bazel index 03c002fc1256..dd31e976d6c2 100644 --- a/BUILD.bazel +++ b/BUILD.bazel @@ -1627,7 +1627,7 @@ ray_cc_test( deps = [ ":gcs_server_lib", ":gcs_test_util_lib", - "@com_google_googletest//:gtest_main", + "@com_google_googletest//:gtest", ], ) @@ -1649,7 +1649,7 @@ ray_cc_test( deps = [ ":gcs_server_lib", ":gcs_test_util_lib", - "@com_google_googletest//:gtest_main", + "@com_google_googletest//:gtest", ], ) @@ -1883,7 +1883,7 @@ ray_cc_test( ":gcs_table_storage_test_lib", ":gcs_test_util_lib", ":store_client_test_lib", - "@com_google_googletest//:gtest_main", + "@com_google_googletest//:gtest", ], ) diff --git a/src/ray/gcs/gcs_server/test/gcs_actor_manager_test.cc b/src/ray/gcs/gcs_server/test/gcs_actor_manager_test.cc index c3b8c9f2a421..9bb274af97bd 100644 --- a/src/ray/gcs/gcs_server/test/gcs_actor_manager_test.cc +++ b/src/ray/gcs/gcs_server/test/gcs_actor_manager_test.cc @@ -1424,8 +1424,3 @@ TEST_F(GcsActorManagerTest, TestDestroyActorWhenActorIsCreating) { } // namespace gcs } // namespace ray - -int main(int argc, char **argv) { - ::testing::InitGoogleTest(&argc, argv); - return RUN_ALL_TESTS(); -} diff --git a/src/ray/gcs/gcs_server/test/gcs_actor_scheduler_test.cc b/src/ray/gcs/gcs_server/test/gcs_actor_scheduler_test.cc index c14497db7eaa..48a0c0becbe9 100644 --- a/src/ray/gcs/gcs_server/test/gcs_actor_scheduler_test.cc +++ b/src/ray/gcs/gcs_server/test/gcs_actor_scheduler_test.cc @@ -1191,8 +1191,3 @@ TEST_F(GcsActorSchedulerTest, TestReleaseUnusedActorWorkersByGcs) { } // namespace gcs } // namespace ray - -int main(int argc, char **argv) { - ::testing::InitGoogleTest(&argc, argv); - return RUN_ALL_TESTS(); -} diff --git a/src/ray/gcs/gcs_server/test/gcs_autoscaler_state_manager_test.cc b/src/ray/gcs/gcs_server/test/gcs_autoscaler_state_manager_test.cc index b0b31b182d77..856261b032e4 100644 --- a/src/ray/gcs/gcs_server/test/gcs_autoscaler_state_manager_test.cc +++ b/src/ray/gcs/gcs_server/test/gcs_autoscaler_state_manager_test.cc @@ -847,8 +847,3 @@ TEST_F(GcsAutoscalerStateManagerTest, TestGcsKvManagerInternalConfig) { } // namespace gcs } // namespace ray - -int main(int argc, char **argv) { - ::testing::InitGoogleTest(&argc, argv); - return RUN_ALL_TESTS(); -} diff --git a/src/ray/gcs/gcs_server/test/gcs_health_check_manager_test.cc b/src/ray/gcs/gcs_server/test/gcs_health_check_manager_test.cc index 1f0722a6d6d7..35fc308f28a9 100644 --- a/src/ray/gcs/gcs_server/test/gcs_health_check_manager_test.cc +++ b/src/ray/gcs/gcs_server/test/gcs_health_check_manager_test.cc @@ -271,17 +271,3 @@ TEST_F(GcsHealthCheckManagerTest, StressTest) { io_service.stop(); t->join(); } - -int main(int argc, char **argv) { - InitShutdownRAII ray_log_shutdown_raii(ray::RayLog::StartRayLog, - ray::RayLog::ShutDownRayLog, - argv[0], - ray::RayLogLevel::INFO, - /*log_dir=*/""); - - ray::RayLog::InstallFailureSignalHandler(argv[0]); - ray::RayLog::InstallTerminateHandler(); - - ::testing::InitGoogleTest(&argc, argv); - return RUN_ALL_TESTS(); -} diff --git a/src/ray/gcs/gcs_server/test/gcs_job_manager_test.cc b/src/ray/gcs/gcs_server/test/gcs_job_manager_test.cc index 107af6752a5d..b18658dffc95 100644 --- a/src/ray/gcs/gcs_server/test/gcs_job_manager_test.cc +++ b/src/ray/gcs/gcs_server/test/gcs_job_manager_test.cc @@ -720,8 +720,4 @@ TEST_F(GcsJobManagerTest, TestNodeFailure) { EXPECT_TRUE(WaitForCondition(condition, 2000)); } -int main(int argc, char **argv) { - ::testing::InitGoogleTest(&argc, argv); - return RUN_ALL_TESTS(); -} } // namespace ray diff --git a/src/ray/gcs/gcs_server/test/gcs_node_manager_test.cc b/src/ray/gcs/gcs_server/test/gcs_node_manager_test.cc index a8a0157e0d54..fcc273aa4f9f 100644 --- a/src/ray/gcs/gcs_server/test/gcs_node_manager_test.cc +++ b/src/ray/gcs/gcs_server/test/gcs_node_manager_test.cc @@ -97,8 +97,3 @@ TEST_F(GcsNodeManagerTest, TestListener) { } } // namespace ray - -int main(int argc, char **argv) { - ::testing::InitGoogleTest(&argc, argv); - return RUN_ALL_TESTS(); -} diff --git a/src/ray/gcs/gcs_server/test/gcs_placement_group_manager_test.cc b/src/ray/gcs/gcs_server/test/gcs_placement_group_manager_test.cc index 268096815cbe..d407332d8021 100644 --- a/src/ray/gcs/gcs_server/test/gcs_placement_group_manager_test.cc +++ b/src/ray/gcs/gcs_server/test/gcs_placement_group_manager_test.cc @@ -1011,8 +1011,3 @@ TEST_F(GcsPlacementGroupManagerTest, TestCheckCreatorJobIsDeadWhenGcsRestart) { } // namespace gcs } // namespace ray - -int main(int argc, char **argv) { - ::testing::InitGoogleTest(&argc, argv); - return RUN_ALL_TESTS(); -} diff --git a/src/ray/gcs/gcs_server/test/gcs_placement_group_scheduler_test.cc b/src/ray/gcs/gcs_server/test/gcs_placement_group_scheduler_test.cc index 5d3f11ed39b0..8bbcaedefc63 100644 --- a/src/ray/gcs/gcs_server/test/gcs_placement_group_scheduler_test.cc +++ b/src/ray/gcs/gcs_server/test/gcs_placement_group_scheduler_test.cc @@ -1476,8 +1476,3 @@ TEST_F(GcsPlacementGroupSchedulerTest, TestBundlesRemovedWhenNodeDead) { } } // namespace ray - -int main(int argc, char **argv) { - ::testing::InitGoogleTest(&argc, argv); - return RUN_ALL_TESTS(); -} diff --git a/src/ray/gcs/gcs_server/test/gcs_resource_manager_test.cc b/src/ray/gcs/gcs_server/test/gcs_resource_manager_test.cc index 31fdc58530d8..cc2d3dec33a8 100644 --- a/src/ray/gcs/gcs_server/test/gcs_resource_manager_test.cc +++ b/src/ray/gcs/gcs_server/test/gcs_resource_manager_test.cc @@ -255,8 +255,3 @@ TEST_F(GcsResourceManagerTest, TestGetDrainingNodes) { } } // namespace ray - -int main(int argc, char **argv) { - ::testing::InitGoogleTest(&argc, argv); - return RUN_ALL_TESTS(); -} diff --git a/src/ray/gcs/gcs_server/test/gcs_worker_manager_test.cc b/src/ray/gcs/gcs_server/test/gcs_worker_manager_test.cc index 3dd5c1e720e2..37d6a67b7b0d 100644 --- a/src/ray/gcs/gcs_server/test/gcs_worker_manager_test.cc +++ b/src/ray/gcs/gcs_server/test/gcs_worker_manager_test.cc @@ -297,8 +297,3 @@ TEST_F(GcsWorkerManagerTest, TestUpdateWorkerNumPausedThreads) { ASSERT_EQ(reply.worker_table_data(0).num_paused_threads(), num_paused_threads_delta); } } - -int main(int argc, char **argv) { - ::testing::InitGoogleTest(&argc, argv); - return RUN_ALL_TESTS(); -} diff --git a/src/ray/gcs/gcs_server/test/in_memory_gcs_table_storage_test.cc b/src/ray/gcs/gcs_server/test/in_memory_gcs_table_storage_test.cc index 0a63277536c8..dba6ddce5922 100644 --- a/src/ray/gcs/gcs_server/test/in_memory_gcs_table_storage_test.cc +++ b/src/ray/gcs/gcs_server/test/in_memory_gcs_table_storage_test.cc @@ -35,8 +35,3 @@ TEST_F(InMemoryGcsTableStorageTest, TestGcsTableWithJobIdApi) { } } // namespace ray - -int main(int argc, char **argv) { - ::testing::InitGoogleTest(&argc, argv); - return RUN_ALL_TESTS(); -} diff --git a/src/ray/gcs/gcs_server/test/usage_stats_client_test.cc b/src/ray/gcs/gcs_server/test/usage_stats_client_test.cc index 21e660b1f1cd..9448f0000b9f 100644 --- a/src/ray/gcs/gcs_server/test/usage_stats_client_test.cc +++ b/src/ray/gcs/gcs_server/test/usage_stats_client_test.cc @@ -45,8 +45,3 @@ TEST_F(UsageStatsClientTest, TestRecordExtraUsageTag) { ASSERT_EQ(value.value(), "value2"); }); } - -int main(int argc, char **argv) { - ::testing::InitGoogleTest(&argc, argv); - return RUN_ALL_TESTS(); -} From 559dd6735c602cb9977b09a2b04fd5a9852597ec Mon Sep 17 00:00:00 2001 From: Kai-Hsun Chen Date: Sun, 24 Nov 2024 14:25:39 -0800 Subject: [PATCH 32/54] [core][autoscaler] Health check logs are not visible in the autoscaler container's stdout (#48905) ## Why are these changes needed? * The Autoscaler container doesn't display information like `print("The Ray head is ready. Starting the autoscaler.")` in STDOUT/STDERR for some reason. To display logs to STDOUT/STDERR, we need to explicitly specify `flush` in `print()` or use the logging module. I don't know why the flush isn't triggered. The default end of `print` is `\n`, which should trigger a line-buffered flush. * Change `logging.warn` to `logging.warning` because `logging.warn` is deprecated. See [this doc](https://docs.python.org/3/library/logging.html#logging.Logger.warning) for more details. image ## Related issue number ## Checks - [ ] I've signed off every commit(by using the -s flag, i.e., `git commit -s`) in this PR. - [ ] I've run `scripts/format.sh` to lint the changes in this PR. - [ ] I've included any doc changes needed for https://docs.ray.io/en/master/. - [ ] I've added any new APIs to the API Reference. For example, if I added a method in Tune, I've added it in `doc/source/tune/api/` under the corresponding `.rst` file. - [ ] I've made sure the tests are passing. Note that there might be a few flaky tests, see the recent failures at https://flakey-tests.ray.io/ - Testing Strategy - [ ] Unit tests - [ ] Release tests - [ ] This PR is not tested :( --------- Signed-off-by: kaihsun --- python/ray/autoscaler/_private/kuberay/run_autoscaler.py | 8 ++++---- .../autoscaler/v2/instance_manager/instance_manager.py | 6 +++--- python/ray/autoscaler/v2/monitor.py | 3 ++- 3 files changed, 9 insertions(+), 8 deletions(-) diff --git a/python/ray/autoscaler/_private/kuberay/run_autoscaler.py b/python/ray/autoscaler/_private/kuberay/run_autoscaler.py index 452bfef66c3e..efafac6d8f37 100644 --- a/python/ray/autoscaler/_private/kuberay/run_autoscaler.py +++ b/python/ray/autoscaler/_private/kuberay/run_autoscaler.py @@ -44,12 +44,12 @@ def run_kuberay_autoscaler(cluster_name: str, cluster_namespace: str): "--skip-version-check", ] ) - # Logging is not ready yet. Print to stdout for now. - print("The Ray head is ready. Starting the autoscaler.") + logger.info("The Ray head is ready. Starting the autoscaler.") break except subprocess.CalledProcessError: - print("The Ray head is not yet ready.") - print(f"Will check again in {BACKOFF_S} seconds.") + logger.warning( + f"The Ray head is not ready. Will check again in {BACKOFF_S} seconds." + ) time.sleep(BACKOFF_S) # The Ray head container sets up the log directory. Thus, we set up logging diff --git a/python/ray/autoscaler/v2/instance_manager/instance_manager.py b/python/ray/autoscaler/v2/instance_manager/instance_manager.py index aa926ba92747..6a1f6e207408 100644 --- a/python/ray/autoscaler/v2/instance_manager/instance_manager.py +++ b/python/ray/autoscaler/v2/instance_manager/instance_manager.py @@ -58,7 +58,7 @@ def update_instance_manager_state( """ Updates the instance manager state. - If there's a any failure, no updates would be made and the reply + If there's any failure, no updates would be made and the reply would contain the latest version of the instance manager state, and the error info. @@ -80,7 +80,7 @@ def update_instance_manager_state( f"Version mismatch: expected: {request.expected_version}, " f"actual: {version}" ) - logger.warn(err_str) + logger.warning(err_str) return self._get_update_im_state_reply( StatusCode.VERSION_MISMATCH, version, @@ -110,7 +110,7 @@ def update_instance_manager_state( err_str = ( f"Version mismatch: expected: {version}, actual: {result.version}" ) - logger.warn(err_str) + logger.warning(err_str) return self._get_update_im_state_reply( StatusCode.VERSION_MISMATCH, result.version, err_str ) diff --git a/python/ray/autoscaler/v2/monitor.py b/python/ray/autoscaler/v2/monitor.py index 8277addc0017..c9334244aa8c 100644 --- a/python/ray/autoscaler/v2/monitor.py +++ b/python/ray/autoscaler/v2/monitor.py @@ -17,6 +17,7 @@ from ray._private.event.event_logger import get_event_logger from ray._private.ray_logging import setup_component_logger from ray._private.usage.usage_lib import record_extra_usage_tag +from ray._private.worker import SCRIPT_MODE from ray._raylet import GcsClient from ray.autoscaler._private.constants import ( AUTOSCALER_METRIC_PORT, @@ -77,7 +78,7 @@ def __init__( ) self._session_name = self._get_session_name(self.gcs_client) logger.info(f"session_name: {self._session_name}") - worker.mode = 0 + worker.set_mode(SCRIPT_MODE) head_node_ip = self.gcs_address.split(":")[0] self.autoscaler = None From fe52a258417a36d0b4117df43ba6cce16bfce148 Mon Sep 17 00:00:00 2001 From: dentiny Date: Sun, 24 Nov 2024 17:29:23 -0800 Subject: [PATCH 33/54] [core] Use unified timestamp (#48894) @rynewang raised a good point at https://github.com/ray-project/ray/pull/48843#discussion_r1853173542, which points out better to use a unified timestamp instead of multiple. The risk of which is (1) system clock vs steady clock; (2) different algo/impl to get timestamp, etc. Signed-off-by: hjiang --- src/ray/gcs/gcs_server/gcs_health_check_manager.cc | 11 ++++------- 1 file changed, 4 insertions(+), 7 deletions(-) diff --git a/src/ray/gcs/gcs_server/gcs_health_check_manager.cc b/src/ray/gcs/gcs_server/gcs_health_check_manager.cc index efa279daf56a..d6e858482185 100644 --- a/src/ray/gcs/gcs_server/gcs_health_check_manager.cc +++ b/src/ray/gcs/gcs_server/gcs_health_check_manager.cc @@ -90,14 +90,11 @@ void GcsHealthCheckManager::HealthCheckContext::StartHealthCheck() { new (&context_) grpc::ClientContext(); response_.Clear(); - const auto now = std::chrono::system_clock::now(); - auto deadline = now + std::chrono::milliseconds(manager_->timeout_ms_); - context_.set_deadline(deadline); + const auto now = absl::Now(); + const auto deadline = now + absl::Milliseconds(manager_->timeout_ms_); + context_.set_deadline(absl::ToChronoTime(deadline)); stub_->async()->Check( - &context_, - &request_, - &response_, - [this, start = absl::FromChrono(now)](::grpc::Status status) { + &context_, &request_, &response_, [this, start = now](::grpc::Status status) { // This callback is done in gRPC's thread pool. STATS_health_check_rpc_latency_ms.Record( absl::ToInt64Milliseconds(absl::Now() - start)); From ba8674ac946c39487714e49a2a26ed155143b7dd Mon Sep 17 00:00:00 2001 From: Blocka Date: Mon, 25 Nov 2024 13:27:11 +0800 Subject: [PATCH 34/54] [Doc] Logging: Add Fluent Bit DaemonSet and Grafana Loki to Persist KubeRay operator logs (#48725) Signed-off-by: win5923 --- .../cluster/configure-manage-dashboard.md | 2 +- .../cluster/kubernetes/configs/loki.log.yaml | 46 +++++++ .../getting-started/raycluster-quick-start.md | 5 +- doc/source/cluster/kubernetes/user-guides.md | 6 +- .../cluster/kubernetes/user-guides/config.md | 2 +- .../user-guides/images/loki-logs.png | Bin 0 -> 343788 bytes ...> persist-kuberay-custom-resource-logs.md} | 4 +- .../persist-kuberay-operator-logs.md | 116 ++++++++++++++++++ .../user-guides/configure-logging.md | 12 +- .../serve/production-guide/kubernetes.md | 2 +- 10 files changed, 180 insertions(+), 15 deletions(-) create mode 100644 doc/source/cluster/kubernetes/configs/loki.log.yaml create mode 100644 doc/source/cluster/kubernetes/user-guides/images/loki-logs.png rename doc/source/cluster/kubernetes/user-guides/{logging.md => persist-kuberay-custom-resource-logs.md} (99%) create mode 100644 doc/source/cluster/kubernetes/user-guides/persist-kuberay-operator-logs.md diff --git a/doc/source/cluster/configure-manage-dashboard.md b/doc/source/cluster/configure-manage-dashboard.md index ce8eb9c9e941..885357ce38b2 100644 --- a/doc/source/cluster/configure-manage-dashboard.md +++ b/doc/source/cluster/configure-manage-dashboard.md @@ -5,7 +5,7 @@ Dashboard configurations may differ depending on how you launch Ray Clusters (e.g., local Ray Cluster v.s. KubeRay). Integrations with Prometheus and Grafana are optional for enhanced Dashboard experience. :::{note} -Ray Dashboard is only intended for interactive development and debugging because the Dashboard UI and the underlying data are not accessible after Clusters are terminated. For production monitoring and debugging, users should rely on [persisted logs](../cluster/kubernetes/user-guides/logging.md), [persisted metrics](./metrics.md), [persisted Ray states](../ray-observability/user-guides/cli-sdk.rst), and other observability tools. +Ray Dashboard is useful for interactive development and debugging because when clusters terminate, the dashboard UI and the underlying data are no longer accessible. For production monitoring and debugging, you should rely on [persisted logs](../cluster/kubernetes/user-guides/persist-kuberay-custom-resource-logs.md), [persisted metrics](./metrics.md), [persisted Ray states](../ray-observability/user-guides/cli-sdk.rst), and other observability tools. ::: ## Changing the Ray Dashboard port diff --git a/doc/source/cluster/kubernetes/configs/loki.log.yaml b/doc/source/cluster/kubernetes/configs/loki.log.yaml new file mode 100644 index 000000000000..07ab28d13344 --- /dev/null +++ b/doc/source/cluster/kubernetes/configs/loki.log.yaml @@ -0,0 +1,46 @@ +# Fluent Bit Config +config: + inputs: | + [INPUT] + Name tail + Path /var/log/containers/*.log + multiline.parser docker, cri + Tag kube.* + Mem_Buf_Limit 5MB + Skip_Long_Lines On + + filters: | + [FILTER] + Name kubernetes + Match kube.* + Merge_Log On + Keep_Log Off + K8S-Logging.Parser On + K8S-Logging.Exclude On + + outputs: | + [OUTPUT] + Name loki + Match * + Host loki-gateway + Port 80 + Labels job=fluent-bit,namespace=$kubernetes['namespace_name'],pod=$kubernetes['pod_name'],container=$kubernetes['container_name'] + Auto_Kubernetes_Labels Off + tenant_id test +--- +# Grafana Datasource Config +datasources: + datasources.yaml: + apiVersion: 1 + datasources: + - name: Loki + type: loki + access: proxy + editable: true + url: http://loki-gateway.default + jsonData: + timeout: 60 + maxLines: 1000 + httpHeaderName1: "X-Scope-OrgID" + secureJsonData: + httpHeaderValue1: "test" diff --git a/doc/source/cluster/kubernetes/getting-started/raycluster-quick-start.md b/doc/source/cluster/kubernetes/getting-started/raycluster-quick-start.md index 1915081b0717..6275564a9ea7 100644 --- a/doc/source/cluster/kubernetes/getting-started/raycluster-quick-start.md +++ b/doc/source/cluster/kubernetes/getting-started/raycluster-quick-start.md @@ -35,11 +35,12 @@ kubectl get pods # kuberay-operator-7fbdbf8c89-pt8bk 1/1 Running 0 27s ``` -KubeRay offers multiple options for operator installations, such as Helm, Kustomize, and a single-namespaced operator. For further information, please refer to [the installation instructions in the KubeRay documentation](https://ray-project.github.io/kuberay/deploy/installation/). +KubeRay offers multiple options for operator installations, such as Helm, Kustomize, and a single-namespaced operator. For further information, see [the installation instructions in the KubeRay documentation](https://ray-project.github.io/kuberay/deploy/installation/). +(raycluster-deploy)= ## Step 3: Deploy a RayCluster custom resource -Once the KubeRay operator is running, we are ready to deploy a RayCluster. To do so, we create a RayCluster Custom Resource (CR) in the `default` namespace. +Once the KubeRay operator is running, you're ready to deploy a RayCluster. Create a RayCluster Custom Resource (CR) in the `default` namespace. ::::{tab-set} diff --git a/doc/source/cluster/kubernetes/user-guides.md b/doc/source/cluster/kubernetes/user-guides.md index bb8713a51822..0b2b49639949 100644 --- a/doc/source/cluster/kubernetes/user-guides.md +++ b/doc/source/cluster/kubernetes/user-guides.md @@ -15,7 +15,8 @@ user-guides/config user-guides/configuring-autoscaling user-guides/kuberay-gcs-ft user-guides/gke-gcs-bucket -user-guides/logging +user-guides/persist-kuberay-custom-resource-logs +user-guides/persist-kuberay-operator-logs user-guides/gpu user-guides/tpu user-guides/rayserve-dev-doc @@ -45,7 +46,8 @@ at the {ref}`introductory guide ` first. * {ref}`kuberay-gpu` * {ref}`kuberay-tpu` * {ref}`kuberay-gcs-ft` -* {ref}`kuberay-logging` +* {ref}`persist-kuberay-custom-resource-logs` +* {ref}`persist-kuberay-operator-logs` * {ref}`kuberay-dev-serve` * {ref}`kuberay-pod-command` * {ref}`kuberay-pod-security` diff --git a/doc/source/cluster/kubernetes/user-guides/config.md b/doc/source/cluster/kubernetes/user-guides/config.md index 5ca8df8f1c73..e4b18aaa4dea 100644 --- a/doc/source/cluster/kubernetes/user-guides/config.md +++ b/doc/source/cluster/kubernetes/user-guides/config.md @@ -126,7 +126,7 @@ Here are some of the subfields of the pod `template` to pay attention to: #### containers A Ray pod template specifies at minimum one container, namely the container that runs the Ray processes. A Ray pod template may also specify additional sidecar -containers, for purposes such as {ref}`log processing `. However, the KubeRay operator assumes that +containers, for purposes such as {ref}`log processing `. However, the KubeRay operator assumes that the first container in the containers list is the main Ray container. Therefore, make sure to specify any sidecar containers **after** the main Ray container. 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See the instructions for how to process and export logs for {ref}`Log persistence ` and {ref}`KubeRay Clusters `. (logging-directory-structure)= ## Log files in logging directory @@ -131,12 +131,12 @@ ray.get([task.remote() for _ in range(100)]) The output is as follows: ```bash -2023-03-27 15:08:34,195 INFO worker.py:1603 -- Started a local Ray instance. View the dashboard at http://127.0.0.1:8265 +2023-03-27 15:08:34,195 INFO worker.py:1603 -- Started a local Ray instance. View the dashboard at http://127.0.0.1:8265 (task pid=534172) Hello there, I am a task 0.20583517821231412 (task pid=534174) Hello there, I am a task 0.17536720316370757 [repeated 99x across cluster] (Ray deduplicates logs by default. Set RAY_DEDUP_LOGS=0 to disable log deduplication) ``` -This feature is useful when importing libraries such as `tensorflow` or `numpy`, which may emit many verbose warning messages when you import them. +This feature is useful when importing libraries such as `tensorflow` or `numpy`, which may emit many verbose warning messages when you import them. Configure the following environment variables on the driver process **before importing Ray** to customize log deduplication: @@ -247,8 +247,8 @@ ray_tune_logger.addHandler(logging.FileHandler("extra_ray_tune_log.log")) Implement structured logging to enable downstream users and applications to consume the logs efficiently. ### Application logs -A Ray applications include both driver and worker processes. For Python applications, use Python loggers to format and structure your logs. -As a result, Python loggers need to be set up for both driver and worker processes. +A Ray app includes both driver and worker processes. For Python apps, use Python loggers to format and structure your logs. +As a result, you need to set up Python loggers for both driver and worker processes. ::::{tab-set} @@ -472,4 +472,4 @@ The max size of a log file, including its backup, is `RAY_ROTATION_MAX_BYTES * R ## Log persistence -To process and export logs to external stroage or management systems, view {ref}`log persistence on Kubernetes ` and {ref}`log persistence on VMs ` for more details. +To process and export logs to external stroage or management systems, view {ref}`log persistence on Kubernetes ` see {ref}`log persistence on VMs ` for more details. diff --git a/doc/source/serve/production-guide/kubernetes.md b/doc/source/serve/production-guide/kubernetes.md index eab728781a77..5a4914699772 100644 --- a/doc/source/serve/production-guide/kubernetes.md +++ b/doc/source/serve/production-guide/kubernetes.md @@ -238,7 +238,7 @@ Monitor your Serve application using the Ray Dashboard. - Learn more about how to configure and manage Dashboard [here](observability-configure-manage-dashboard). - Learn about the Ray Serve Dashboard [here](serve-monitoring). - Learn how to set up [Prometheus](prometheus-setup) and [Grafana](grafana) for Dashboard. -- Learn about the [Ray Serve logs](serve-logging) and how to [persistent logs](kuberay-logging) on Kubernetes. +- Learn about the [Ray Serve logs](serve-logging) and how to [persistent logs](persist-kuberay-custom-resource-logs) on Kubernetes. :::{note} - To troubleshoot application deployment failures in Serve, you can check the KubeRay operator logs by running `kubectl logs -f ` (e.g., `kubectl logs -f kuberay-operator-7447d85d58-lv7pf`). The KubeRay operator logs contain information about the Serve application deployment event and Serve application health checks. From 415ee19d2ae1006bb11fbdd13ce831ad7547728d Mon Sep 17 00:00:00 2001 From: simonsays1980 Date: Mon, 25 Nov 2024 16:31:08 +0100 Subject: [PATCH 35/54] [RLlib; Offline RL] Allow incomplete `SampleBatch` data and fully compressed observations. (#48699) --- rllib/offline/offline_prelearner.py | 124 +++++++++++++++-------- rllib/offline/tests/test_offline_data.py | 2 +- 2 files changed, 81 insertions(+), 45 deletions(-) diff --git a/rllib/offline/offline_prelearner.py b/rllib/offline/offline_prelearner.py index b000f2c965fc..f5ffca03e75a 100644 --- a/rllib/offline/offline_prelearner.py +++ b/rllib/offline/offline_prelearner.py @@ -1,10 +1,10 @@ import gymnasium as gym import logging import numpy as np -import random +import uuid + from typing import Any, Dict, List, Optional, Union, Set, Tuple, TYPE_CHECKING -import ray from ray.actor import ActorHandle from ray.rllib.core.columns import Columns from ray.rllib.core.learner import Learner @@ -86,8 +86,8 @@ def __init__( self, config: "AlgorithmConfig", learner: Union[Learner, list[ActorHandle]], + locality_hints: Optional[List[str]] = None, spaces: Optional[Tuple[gym.Space, gym.Space]] = None, - locality_hints: Optional[list] = None, module_spec: Optional[MultiRLModuleSpec] = None, module_state: Optional[Dict[ModuleID, Any]] = None, ): @@ -103,24 +103,6 @@ def __init__( self._module = self._learner._module # Otherwise we have remote `Learner`s. else: - # TODO (simon): Check with the data team how to get at - # initialization the data block location. - node_id = ray.get_runtime_context().get_node_id() - # Shuffle indices such that not each data block syncs weights - # with the same learner in case there are multiple learners - # on the same node like the `PreLearner`. - indices = list(range(len(locality_hints))) - random.shuffle(indices) - locality_hints = [locality_hints[i] for i in indices] - learner = [learner[i] for i in indices] - # Choose a learner from the same node. - for i, hint in enumerate(locality_hints): - if hint == node_id: - self._learner = learner[i] - # If no learner has been chosen, there is none on the same node. - if not self._learner: - # Then choose a learner randomly. - self._learner = learner[random.randint(0, len(learner) - 1)] self.learner_is_remote = True # Build the module from spec. Note, this will be a MultiRLModule. self._module = module_spec.build() @@ -525,21 +507,83 @@ def _map_sample_batch_to_episode( # TODO (simon): Add support for multi-agent episodes. NotImplementedError else: - # Unpack observations, if needed. - obs = ( - unpack_if_needed(obs.tolist()) - if schema[Columns.OBS] in input_compress_columns - else obs.tolist() - ) - # Append the last `new_obs` to get the correct length of observations. - obs.append( - unpack_if_needed(batch[schema[Columns.NEXT_OBS]][i][-1]) - if schema[Columns.OBS] in input_compress_columns - else batch[schema[Columns.NEXT_OBS]][i][-1] - ) + # Unpack observations, if needed. Note, observations could + # be either compressed by their entirety (the complete batch + # column) or individually (each column entry). + if isinstance(obs, str): + # Decompress the observations if we have a string, i.e. + # observations are compressed in their entirety. + obs = unpack_if_needed(obs) + # Convert to a list of arrays. This is needed as input by + # the `SingleAgentEpisode`. + obs = [obs[i, ...] for i in range(obs.shape[0])] + # Otherwise observations are only compressed inside of the + # batch column (if at all). + elif isinstance(obs, np.ndarray): + # Unpack observations, if they are compressed otherwise we + # simply convert to a list, which is needed by the + # `SingleAgentEpisode`. + obs = ( + unpack_if_needed(obs.tolist()) + if schema[Columns.OBS] in input_compress_columns + else obs.tolist() + ) + else: + raise TypeError( + f"Unknown observation type: {type(obs)}. When mapping " + "from old recorded `SampleBatches` batched " + "observations should be either of type `np.array` " + "or - if the column is compressed - of `str` type." + ) + + if schema[Columns.NEXT_OBS] in batch: + # Append the last `new_obs` to get the correct length of + # observations. + obs.append( + unpack_if_needed(batch[schema[Columns.NEXT_OBS]][i][-1]) + if schema[Columns.OBS] in input_compress_columns + else batch[schema[Columns.NEXT_OBS]][i][-1] + ) + else: + # Otherwise we duplicate the last observation. + obs.append(obs[-1]) + + # Check, if we have `done`, `truncated`, or `terminated`s in + # the batch. + if ( + schema[Columns.TRUNCATEDS] in batch + and schema[Columns.TERMINATEDS] in batch + ): + truncated = batch[schema[Columns.TRUNCATEDS]][i][-1] + terminated = batch[schema[Columns.TERMINATEDS]][i][-1] + elif ( + schema[Columns.TRUNCATEDS] in batch + and schema[Columns.TERMINATEDS] not in batch + ): + truncated = batch[schema[Columns.TRUNCATEDS]][i][-1] + terminated = False + elif ( + schema[Columns.TRUNCATEDS] not in batch + and schema[Columns.TERMINATEDS] in batch + ): + terminated = batch[schema[Columns.TERMINATEDS]][i][-1] + truncated = False + elif "done" in batch: + terminated = batch["done"][i][-1] + truncated = False + # Otherwise, if no `terminated`, nor `truncated` nor `done` + # is given, we consider the episode as terminated. + else: + terminated = True + truncated = False + # Create a `SingleAgentEpisode`. episode = SingleAgentEpisode( - id_=str(batch[schema[Columns.EPS_ID]][i][0]), + # If the recorded episode has an ID we use this ID, + # otherwise we generate a new one. + id_=str(batch[schema[Columns.EPS_ID]][i][0]) + if schema[Columns.EPS_ID] in batch + else uuid.uuid4().hex, agent_id=agent_id, observations=obs, infos=( @@ -554,16 +598,8 @@ def _map_sample_batch_to_episode( else batch[schema[Columns.ACTIONS]][i] ), rewards=batch[schema[Columns.REWARDS]][i], - terminated=( - any(batch[schema[Columns.TERMINATEDS]][i]) - if schema[Columns.TERMINATEDS] in batch - else any(batch["dones"][i]) - ), - truncated=( - any(batch[schema[Columns.TRUNCATEDS]][i]) - if schema[Columns.TRUNCATEDS] in batch - else False - ), + terminated=terminated, + truncated=truncated, # TODO (simon): Results in zero-length episodes in connector. # t_started=batch[Columns.T if Columns.T in batch else # "unroll_id"][i][0], diff --git a/rllib/offline/tests/test_offline_data.py b/rllib/offline/tests/test_offline_data.py index fad307cc7745..038e9cef383f 100644 --- a/rllib/offline/tests/test_offline_data.py +++ b/rllib/offline/tests/test_offline_data.py @@ -124,7 +124,7 @@ def test_sample_multiple_learners(self): num_samples=10, return_iterator=2, num_shards=2 ) self.assertIsInstance(batch, list) - # Ensure we have indeed two such `SStreamSplitDataIterator` instances. + # Ensure we have indeed two such `StreamSplitDataIterator` instances. self.assertEqual(len(batch), 2) from ray.data._internal.iterator.stream_split_iterator import ( StreamSplitDataIterator, From 93da6b7190ce80d344d45360da03c7d1f15a3cd2 Mon Sep 17 00:00:00 2001 From: Kristian Hartikainen Date: Mon, 25 Nov 2024 11:34:36 -0500 Subject: [PATCH 36/54] Move `shutdown` to upper level to avoid issue with `tmux` (#48786) Fixes #48109 ## Why are these changes needed? See #48109. ## Related issue number Fixes #48109. ## Checks - [x] I've signed off every commit(by using the -s flag, i.e., `git commit -s`) in this PR. - [x] I've run `scripts/format.sh` to lint the changes in this PR. - [x] I've included any doc changes needed for https://docs.ray.io/en/master/. - [x] I've added any new APIs to the API Reference. For example, if I added a method in Tune, I've added it in `doc/source/tune/api/` under the corresponding `.rst` file. - [ ] I've made sure the tests are passing. Note that there might be a few flaky tests, see the recent failures at https://flakey-tests.ray.io/ - Testing Strategy - [ ] Unit tests - [ ] Release tests - [ ] This PR is not tested :( ## Notes I've tested this PR manually and verified that it resolved the issue I reported in #48109. I spent quite a bit of time trying to get the cluster tests to run but couldn't figure out how to do that. ```sh $ ray exec \ --verbose \ --start \ --stop \ --tmux \ --no-config-cache \ ./cluster-config.yml \ 'echo "start" && sleep 10 && echo "done"' [...] Running `tmux new -d bash -c 'echo "start" && sleep 10 && echo "done"; ray stop; ray teardown ~/ray_bootstrap_config.yaml --yes --workers-only; sudo shutdown -h now; exec bash'` bash: warning: setlocale: LC_ALL: cannot change locale (en_US.UTF-8) bash: warning: setlocale: LC_ALL: cannot change locale (en_US.UTF-8) Shared connection to 35.196.24.227 closed. Run `ray attach ./cluster-config.yml --tmux` to check command status. ``` Signed-off-by: Kristian Hartikainen --- python/ray/autoscaler/_private/commands.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/python/ray/autoscaler/_private/commands.py b/python/ray/autoscaler/_private/commands.py index 3c03738854f7..9a9b9d91cc2f 100644 --- a/python/ray/autoscaler/_private/commands.py +++ b/python/ray/autoscaler/_private/commands.py @@ -1153,16 +1153,15 @@ def exec_cluster( }, docker_config=config.get("docker"), ) - shutdown_after_run = False if cmd and stop: cmd = "; ".join( [ cmd, "ray stop", "ray teardown ~/ray_bootstrap_config.yaml --yes --workers-only", + "sudo shutdown -h now", ] ) - shutdown_after_run = True result = _exec( updater, @@ -1172,7 +1171,7 @@ def exec_cluster( port_forward=port_forward, with_output=with_output, run_env=run_env, - shutdown_after_run=shutdown_after_run, + shutdown_after_run=False, extra_screen_args=extra_screen_args, ) if tmux or screen: From 2ae9aa7e3b198ca3dbe5d65f8077e38d537dbe11 Mon Sep 17 00:00:00 2001 From: dentiny Date: Mon, 25 Nov 2024 10:00:20 -0800 Subject: [PATCH 37/54] [core] Minor update on runtime env manager (#48916) Signed-off-by: hjiang --- src/ray/common/runtime_env_manager.cc | 24 ++++++++++++++---------- src/ray/common/runtime_env_manager.h | 8 +++++--- 2 files changed, 19 insertions(+), 13 deletions(-) diff --git a/src/ray/common/runtime_env_manager.cc b/src/ray/common/runtime_env_manager.cc index d765dbc4f1de..08187cf29932 100644 --- a/src/ray/common/runtime_env_manager.cc +++ b/src/ray/common/runtime_env_manager.cc @@ -31,12 +31,13 @@ void RuntimeEnvManager::AddURIReference(const std::string &hex_id, if (!uris.working_dir_uri().empty()) { const auto &uri = uris.working_dir_uri(); uri_reference_[uri]++; - id_to_uris_[hex_id].push_back(uri); + id_to_uris_[hex_id].emplace_back(uri); RAY_LOG(DEBUG) << "[working_dir] Added URI Reference " << uri << " for id " << hex_id; } + for (const auto &uri : uris.py_modules_uris()) { uri_reference_[uri]++; - id_to_uris_[hex_id].push_back(uri); + id_to_uris_[hex_id].emplace_back(uri); RAY_LOG(DEBUG) << "[py_modules] Added URI Reference " << uri << " for id " << hex_id; } PrintDebugString(); @@ -51,21 +52,24 @@ const std::vector &RuntimeEnvManager::GetReferences( void RuntimeEnvManager::RemoveURIReference(const std::string &hex_id) { RAY_LOG(DEBUG) << "Subtracting 1 from URI Reference for id " << hex_id; - if (!id_to_uris_.count(hex_id)) { + auto iter = id_to_uris_.find(hex_id); + if (iter == id_to_uris_.end()) { return; } - for (const auto &uri : id_to_uris_[hex_id]) { - --uri_reference_[uri]; - auto ref_count = uri_reference_[uri]; - RAY_CHECK(ref_count >= 0); - if (ref_count == 0) { - uri_reference_.erase(uri); + for (const auto &uri : iter->second) { + auto uri_ref_iter = uri_reference_.find(uri); + RAY_CHECK(uri_ref_iter != uri_reference_.end()); + --uri_ref_iter->second; + const auto new_ref_count = uri_ref_iter->second; + RAY_CHECK_GE(new_ref_count, 0); + if (new_ref_count == 0) { + uri_reference_.erase(uri_ref_iter); RAY_LOG(DEBUG) << "Deleting URI Reference " << uri; deleter_(uri, [](bool success) {}); } } - id_to_uris_.erase(hex_id); + id_to_uris_.erase(iter); PrintDebugString(); } diff --git a/src/ray/common/runtime_env_manager.h b/src/ray/common/runtime_env_manager.h index a6b282863307..ab58409d8d91 100644 --- a/src/ray/common/runtime_env_manager.h +++ b/src/ray/common/runtime_env_manager.h @@ -12,7 +12,9 @@ // See the License for the specific language governing permissions and // limitations under the License. #pragma once + #include +#include #include "absl/container/flat_hash_map.h" #include "ray/common/id.h" @@ -32,9 +34,9 @@ class RuntimeEnvManager { public: using DeleteFunc = std::function)>; - explicit RuntimeEnvManager(DeleteFunc deleter) : deleter_(deleter) {} + explicit RuntimeEnvManager(DeleteFunc deleter) : deleter_(std::move(deleter)) {} - /// Increase the reference of URI by job or actor ID and runtime_env. + /// Increase the reference count of URI by job or actor ID and runtime_env. /// /// \param[in] hex_id The id of the runtime env. It can be an actor or job id. /// \param[in] runtime_env_info The runtime env used by the id. @@ -53,7 +55,7 @@ class RuntimeEnvManager { /// \return The URIs referenced by the id. const std::vector &GetReferences(const std::string &hex_id) const; - /// Decrease the reference of URI by job_id + /// Decrease the reference count of URI by job_id /// \param[in] hex_id The id of the runtime env. void RemoveURIReference(const std::string &hex_id); From 61b033d7c0cfba03a0f0a44d40c12930c1aec5b7 Mon Sep 17 00:00:00 2001 From: Kai-Hsun Chen Date: Mon, 25 Nov 2024 11:05:38 -0800 Subject: [PATCH 38/54] [core][compiled-graphs] Minimize the overhead of shared memory in NCCL benchmark (#48860) exec_ray_dag_gpu_nccl_static_shape_direct_return had 3079 executions/sec before this PR, and it increased to 5737 executions/sec after this PR. --- .../accelerated_dag_gpu_microbenchmark.py | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/release/microbenchmark/experimental/accelerated_dag_gpu_microbenchmark.py b/release/microbenchmark/experimental/accelerated_dag_gpu_microbenchmark.py index f440e72752fb..895d43bdcdab 100644 --- a/release/microbenchmark/experimental/accelerated_dag_gpu_microbenchmark.py +++ b/release/microbenchmark/experimental/accelerated_dag_gpu_microbenchmark.py @@ -58,13 +58,16 @@ class TorchTensorWorker: def __init__(self): self.device = torch_utils.get_devices()[0] - def send(self, shape, dtype, value: int): - t = torch.ones(shape, dtype=dtype, device=self.device) * value + def send(self, shape, dtype, _): + t = torch.ones(shape, dtype=dtype, device=self.device) * 1 return t def recv(self, tensor): + # This benchmark tests the overhead of sending a tensor between + # actors. To minimize the overhead of shared memory transfer, + # we return only a byte string. assert tensor.device == self.device - return (tensor[0].item(), tensor.shape, tensor.dtype) + return b"x" @ray.remote(num_gpus=1) @@ -139,17 +142,15 @@ def exec_ray_dag( dag = dag.experimental_compile() def _run(): - i = np.random.randint(100) - ref = dag.execute(i) + ref = dag.execute(b"x") result = ray.get(ref) - assert result == (i, SHAPE, DTYPE) + assert result == b"x" else: def _run(): - i = np.random.randint(100) - result = ray.get(dag.execute(i)) - assert result == (i, SHAPE, DTYPE) + result = ray.get(dag.execute(b"x")) + assert result == b"x" results = timeit(label, _run) From 2514aff0353f6c746c5bcb8c85d8f844120e4037 Mon Sep 17 00:00:00 2001 From: Balaji Veeramani Date: Mon, 25 Nov 2024 13:35:30 -0600 Subject: [PATCH 39/54] [Data] Relax type check in `add_column` (#48918) Previously, you could add a column with a list like this: ``` ds.add_column("zeros", lambda batch: [0] * len(batch)) ``` However, after https://github.com/ray-project/ray/pull/48140, this behavior isn't supported. To avoid breaking tests and user code, this PR re-adds support for lists. --------- Signed-off-by: Balaji Veeramani --- python/ray/data/dataset.py | 20 +++++++++----------- python/ray/data/tests/test_map.py | 16 +++++++++------- 2 files changed, 18 insertions(+), 18 deletions(-) diff --git a/python/ray/data/dataset.py b/python/ray/data/dataset.py index 779a5bd3295e..496e29a8dea4 100644 --- a/python/ray/data/dataset.py +++ b/python/ray/data/dataset.py @@ -5,6 +5,7 @@ import logging import time import warnings +from collections.abc import Sequence from typing import ( TYPE_CHECKING, Any, @@ -764,19 +765,20 @@ def add_column( f"got: {batch_format}" ) + def _raise_duplicate_column_error(col: str): + raise ValueError(f"Trying to add an existing column with name {col!r}") + def add_column(batch: DataBatch) -> DataBatch: column = fn(batch) if batch_format == "pandas": import pandas as pd - assert isinstance(column, pd.Series), ( + assert isinstance(column, (pd.Series, Sequence)), ( f"For pandas batch format, the function must return a pandas " - f"Series, got: {type(column)}" + f"Series or sequence, got: {type(column)}" ) if col in batch: - raise ValueError( - f"Trying to add an existing column with name" f" {col}" - ) + _raise_duplicate_column_error(col) batch.loc[:, col] = column return batch elif batch_format == "pyarrow": @@ -798,9 +800,7 @@ def add_column(batch: DataBatch) -> DataBatch: # Append the column to the table return batch.append_column(col, column) else: - raise ValueError( - f"Trying to add an existing column with name {col}" - ) + _raise_duplicate_column_error(col) else: # batch format is assumed to be numpy since we checked at the @@ -810,9 +810,7 @@ def add_column(batch: DataBatch) -> DataBatch: f"numpy.ndarray, got: {type(column)}" ) if col in batch: - raise ValueError( - f"Trying to add an existing column with name" f" {col}" - ) + _raise_duplicate_column_error(col) batch[col] = column return batch diff --git a/python/ray/data/tests/test_map.py b/python/ray/data/tests/test_map.py index d4e7e2c374de..41100f4b8a2c 100644 --- a/python/ray/data/tests/test_map.py +++ b/python/ray/data/tests/test_map.py @@ -353,7 +353,7 @@ def test_add_column(ray_start_regular_shared): # Adding a column that is already there should result in an error with pytest.raises( ray.exceptions.UserCodeException, - match="Trying to add an existing column with name id", + match="Trying to add an existing column with name 'id'", ): ds = ray.data.range(5).add_column( "id", lambda x: pc.add(x["id"], 1), batch_format="pyarrow" @@ -362,7 +362,7 @@ def test_add_column(ray_start_regular_shared): # Adding a column in the wrong format should result in an error with pytest.raises( - ray.exceptions.UserCodeException, match="For pyarrow batch " "format" + ray.exceptions.UserCodeException, match="For pyarrow batch format" ): ds = ray.data.range(5).add_column("id", lambda x: [1], batch_format="pyarrow") assert ds.take(2) == [{"id": 1}, {"id": 2}] @@ -381,7 +381,7 @@ def test_add_column(ray_start_regular_shared): # Adding a column that is already there should result in an error with pytest.raises( ray.exceptions.UserCodeException, - match="Trying to add an existing column with name id", + match="Trying to add an existing column with name 'id'", ): ds = ray.data.range(5).add_column( "id", lambda x: np.add(x["id"], 1), batch_format="numpy" @@ -390,7 +390,7 @@ def test_add_column(ray_start_regular_shared): # Adding a column in the wrong format should result in an error with pytest.raises( - ray.exceptions.UserCodeException, match="For numpy batch " "format" + ray.exceptions.UserCodeException, match="For numpy batch format" ): ds = ray.data.range(5).add_column("id", lambda x: [1], batch_format="numpy") assert ds.take(2) == [{"id": 1}, {"id": 2}] @@ -405,16 +405,18 @@ def test_add_column(ray_start_regular_shared): # Adding a column that is already there should result in an error with pytest.raises( ray.exceptions.UserCodeException, - match="Trying to add an existing column with name id", + match="Trying to add an existing column with name 'id'", ): ds = ray.data.range(5).add_column("id", lambda x: x["id"] + 1) assert ds.take(2) == [{"id": 1}, {"id": 2}] # Adding a column in the wrong format should result in an error with pytest.raises( - ray.exceptions.UserCodeException, match="For pandas batch " "format" + ray.exceptions.UserCodeException, match="For pandas batch format" ): - ds = ray.data.range(5).add_column("id", lambda x: [1], batch_format="pandas") + ds = ray.data.range(5).add_column( + "id", lambda x: np.array([1]), batch_format="pandas" + ) assert ds.take(2) == [{"id": 1}, {"id": 2}] with pytest.raises(ValueError): From 8dbc1230a0cc787fe7b47d7e5e8086a8a3db9a6f Mon Sep 17 00:00:00 2001 From: Jiajun Yao Date: Mon, 25 Nov 2024 11:52:22 -0800 Subject: [PATCH 40/54] [Core] Only publish WORKER_OBJECT_EVICTION when the object is out of scope or manually freed (#47990) Signed-off-by: Jiajun Yao --- src/mock/ray/core_worker/reference_count.h | 2 +- src/ray/core_worker/actor_manager.cc | 2 +- src/ray/core_worker/core_worker.cc | 2 +- src/ray/core_worker/reference_count.cc | 37 +++++++++---------- src/ray/core_worker/reference_count.h | 19 ++++++---- .../core_worker/test/actor_manager_test.cc | 6 +-- .../core_worker/test/reference_count_test.cc | 28 +++++++------- .../transport/actor_task_submitter.cc | 2 +- 8 files changed, 50 insertions(+), 48 deletions(-) diff --git a/src/mock/ray/core_worker/reference_count.h b/src/mock/ray/core_worker/reference_count.h index c0679dec135f..c9f7a1d0b415 100644 --- a/src/mock/ray/core_worker/reference_count.h +++ b/src/mock/ray/core_worker/reference_count.h @@ -41,7 +41,7 @@ class MockReferenceCounter : public ReferenceCounterInterface { bool add_local_ref, const absl::optional &pinned_at_raylet_id)); - MOCK_METHOD2(AddObjectPrimaryCopyDeleteCallback, + MOCK_METHOD2(AddObjectOutOfScopeOrFreedCallback, bool(const ObjectID &object_id, const std::function callback)); diff --git a/src/ray/core_worker/actor_manager.cc b/src/ray/core_worker/actor_manager.cc index 02a89a7c65c9..a31c402fae11 100644 --- a/src/ray/core_worker/actor_manager.cc +++ b/src/ray/core_worker/actor_manager.cc @@ -172,7 +172,7 @@ bool ActorManager::AddActorHandle(std::unique_ptr actor_handle, } if (inserted && owned) { - RAY_CHECK(reference_counter_->AddObjectPrimaryCopyDeleteCallback( + RAY_CHECK(reference_counter_->AddObjectOutOfScopeOrFreedCallback( actor_creation_return_id, [this, actor_id](const ObjectID &object_id) { MarkActorKilledOrOutOfScope(GetActorHandle(actor_id)); })); diff --git a/src/ray/core_worker/core_worker.cc b/src/ray/core_worker/core_worker.cc index 5dcd799b8936..e04ade96a0fe 100644 --- a/src/ray/core_worker/core_worker.cc +++ b/src/ray/core_worker/core_worker.cc @@ -3860,7 +3860,7 @@ void CoreWorker::ProcessSubscribeForObjectEviction( // Returns true if the object was present and the callback was added. It might have // already been evicted by the time we get this request, in which case we should // respond immediately so the raylet unpins the object. - if (!reference_counter_->AddObjectPrimaryCopyDeleteCallback(object_id, unpin_object)) { + if (!reference_counter_->AddObjectOutOfScopeOrFreedCallback(object_id, unpin_object)) { // If the object is already evicted (callback cannot be set), unregister the // subscription & publish the message so that the subscriber knows it. unpin_object(object_id); diff --git a/src/ray/core_worker/reference_count.cc b/src/ray/core_worker/reference_count.cc index eb5abe2ea387..6dd4c8bf6b7d 100644 --- a/src/ray/core_worker/reference_count.cc +++ b/src/ray/core_worker/reference_count.cc @@ -543,7 +543,7 @@ int64_t ReferenceCounter::ReleaseLineageReferences(ReferenceTable::iterator ref) RAY_LOG(DEBUG) << "Releasing lineage internal for argument " << argument_id; arg_it->second.lineage_ref_count--; if (arg_it->second.OutOfScope(lineage_pinning_enabled_)) { - DeleteObjectPrimaryCopy(arg_it); + OnObjectOutOfScopeOrFreed(arg_it); } if (arg_it->second.ShouldDelete(lineage_pinning_enabled_)) { RAY_CHECK(arg_it->second.on_ref_removed == nullptr); @@ -663,7 +663,7 @@ void ReferenceCounter::FreePlasmaObjects(const std::vector &object_ids } // Free only the plasma value. We must keep the reference around so that we // have the ownership information. - DeleteObjectPrimaryCopy(it); + OnObjectOutOfScopeOrFreed(it); } } @@ -700,8 +700,7 @@ void ReferenceCounter::DeleteReferenceInternal(ReferenceTable::iterator it, DeleteReferenceInternal(inner_it, deleted); } } - // Perform the deletion. - DeleteObjectPrimaryCopy(it); + OnObjectOutOfScopeOrFreed(it); if (deleted) { deleted->push_back(id); } @@ -764,20 +763,20 @@ int64_t ReferenceCounter::EvictLineage(int64_t min_bytes_to_evict) { return lineage_bytes_evicted; } -void ReferenceCounter::DeleteObjectPrimaryCopy(ReferenceTable::iterator it) { - RAY_LOG(DEBUG) << "Calling on_object_primary_copy_delete for object " << it->first - << " num callbacks: " - << it->second.on_object_primary_copy_delete_callbacks.size(); - for (const auto &callback : it->second.on_object_primary_copy_delete_callbacks) { +void ReferenceCounter::OnObjectOutOfScopeOrFreed(ReferenceTable::iterator it) { + RAY_LOG(DEBUG) << "Calling on_object_out_of_scope_or_freed_callbacks for object " + << it->first << " num callbacks: " + << it->second.on_object_out_of_scope_or_freed_callbacks.size(); + for (const auto &callback : it->second.on_object_out_of_scope_or_freed_callbacks) { callback(it->first); } - it->second.on_object_primary_copy_delete_callbacks.clear(); + it->second.on_object_out_of_scope_or_freed_callbacks.clear(); + UnsetObjectPrimaryCopy(it); +} + +void ReferenceCounter::UnsetObjectPrimaryCopy(ReferenceTable::iterator it) { it->second.pinned_at_raylet_id.reset(); if (it->second.spilled && !it->second.spilled_node_id.IsNil()) { - // The spilled copy of the object should get deleted during the - // on_object_primary_copy_delete callback, so reset the spill location metadata here. - // NOTE(swang): Spilled copies in cloud storage are not GCed, so we do not - // reset the spilled metadata. it->second.spilled = false; it->second.spilled_url = ""; it->second.spilled_node_id = NodeID::Nil(); @@ -795,7 +794,7 @@ bool ReferenceCounter::SetObjectRefDeletedCallback( return true; } -bool ReferenceCounter::AddObjectPrimaryCopyDeleteCallback( +bool ReferenceCounter::AddObjectOutOfScopeOrFreedCallback( const ObjectID &object_id, const std::function callback) { absl::MutexLock lock(&mutex_); auto it = object_id_refs_.find(object_id); @@ -812,7 +811,7 @@ bool ReferenceCounter::AddObjectPrimaryCopyDeleteCallback( return false; } - it->second.on_object_primary_copy_delete_callbacks.emplace_back(callback); + it->second.on_object_out_of_scope_or_freed_callbacks.emplace_back(callback); return true; } @@ -822,7 +821,7 @@ void ReferenceCounter::ResetObjectsOnRemovedNode(const NodeID &raylet_id) { const auto &object_id = it->first; if (it->second.pinned_at_raylet_id.value_or(NodeID::Nil()) == raylet_id || it->second.spilled_node_id == raylet_id) { - DeleteObjectPrimaryCopy(it); + UnsetObjectPrimaryCopy(it); if (!it->second.OutOfScope(lineage_pinning_enabled_)) { objects_to_recover_.push_back(object_id); } @@ -862,7 +861,7 @@ void ReferenceCounter::UpdateObjectPinnedAtRaylet(const ObjectID &object_id, if (check_node_alive_(raylet_id)) { it->second.pinned_at_raylet_id = raylet_id; } else { - DeleteObjectPrimaryCopy(it); + UnsetObjectPrimaryCopy(it); objects_to_recover_.push_back(object_id); } } @@ -1429,7 +1428,7 @@ bool ReferenceCounter::HandleObjectSpilled(const ObjectID &object_id, } else { RAY_LOG(DEBUG).WithField(spilled_node_id).WithField(object_id) << "Object spilled to dead node "; - DeleteObjectPrimaryCopy(it); + UnsetObjectPrimaryCopy(it); objects_to_recover_.push_back(object_id); } return true; diff --git a/src/ray/core_worker/reference_count.h b/src/ray/core_worker/reference_count.h index 4ef6e14a0016..5eb228301c34 100644 --- a/src/ray/core_worker/reference_count.h +++ b/src/ray/core_worker/reference_count.h @@ -49,7 +49,7 @@ class ReferenceCounterInterface { bool is_reconstructable, bool add_local_ref, const absl::optional &pinned_at_raylet_id = absl::optional()) = 0; - virtual bool AddObjectPrimaryCopyDeleteCallback( + virtual bool AddObjectOutOfScopeOrFreedCallback( const ObjectID &object_id, const std::function callback) = 0; virtual bool SetObjectRefDeletedCallback( @@ -320,7 +320,7 @@ class ReferenceCounter : public ReferenceCounterInterface, /// Adds the callback that will be run when the object goes out of scope /// (Reference.OutOfScope() returns true). /// Returns true if the object was in scope and the callback was added, else false. - bool AddObjectPrimaryCopyDeleteCallback( + bool AddObjectOutOfScopeOrFreedCallback( const ObjectID &object_id, const std::function callback) ABSL_LOCKS_EXCLUDED(mutex_); @@ -783,13 +783,13 @@ class ReferenceCounter : public ReferenceCounterInterface, /// Metadata related to borrowing. std::unique_ptr borrow_info; - /// Callback that will be called when this Object's primary copy - /// should be deleted: out of scope or internal_api.free + /// Callback that will be called when this object + /// is out of scope or manually freed. /// Note: when an object is out of scope, it can still /// have lineage ref count and on_object_ref_delete /// will be called when lineage ref count is also 0. std::vector> - on_object_primary_copy_delete_callbacks; + on_object_out_of_scope_or_freed_callbacks; /// Callback that will be called when the object ref is deleted /// from the reference table (all refs including lineage ref count go to 0). std::function on_object_ref_delete; @@ -847,9 +847,12 @@ class ReferenceCounter : public ReferenceCounterInterface, rpc::Address *owner_address = nullptr) const ABSL_EXCLUSIVE_LOCKS_REQUIRED(mutex_); - /// Delete the object primary copy, if any. Also unsets the raylet address - /// that the object was pinned at, if the address was set. - void DeleteObjectPrimaryCopy(ReferenceTable::iterator it); + /// Unsets the raylet address + /// that the object was pinned at or spilled at, if the address was set. + void UnsetObjectPrimaryCopy(ReferenceTable::iterator it); + + /// This should be called whenever the object is out of scope or manually freed. + void OnObjectOutOfScopeOrFreed(ReferenceTable::iterator it); /// Shutdown if all references have gone out of scope and shutdown /// is scheduled. diff --git a/src/ray/core_worker/test/actor_manager_test.cc b/src/ray/core_worker/test/actor_manager_test.cc index b5c938c6c0ce..8f68f28cd845 100644 --- a/src/ray/core_worker/test/actor_manager_test.cc +++ b/src/ray/core_worker/test/actor_manager_test.cc @@ -168,7 +168,7 @@ class ActorManagerTest : public ::testing::Test { ray_namespace, -1, false); - EXPECT_CALL(*reference_counter_, AddObjectPrimaryCopyDeleteCallback(_, _)) + EXPECT_CALL(*reference_counter_, AddObjectOutOfScopeOrFreedCallback(_, _)) .WillRepeatedly(testing::Return(true)); actor_manager_->AddNewActorHandle(std::move(actor_handle), call_site, @@ -207,7 +207,7 @@ TEST_F(ActorManagerTest, TestAddAndGetActorHandleEndToEnd) { "", -1, false); - EXPECT_CALL(*reference_counter_, AddObjectPrimaryCopyDeleteCallback(_, _)) + EXPECT_CALL(*reference_counter_, AddObjectOutOfScopeOrFreedCallback(_, _)) .WillRepeatedly(testing::Return(true)); // Add an actor handle. @@ -284,7 +284,7 @@ TEST_F(ActorManagerTest, RegisterActorHandles) { "", -1, false); - EXPECT_CALL(*reference_counter_, AddObjectPrimaryCopyDeleteCallback(_, _)) + EXPECT_CALL(*reference_counter_, AddObjectOutOfScopeOrFreedCallback(_, _)) .WillRepeatedly(testing::Return(true)); ObjectID outer_object_id = ObjectID::Nil(); diff --git a/src/ray/core_worker/test/reference_count_test.cc b/src/ray/core_worker/test/reference_count_test.cc index 4351692284e2..67423a3ed75a 100644 --- a/src/ray/core_worker/test/reference_count_test.cc +++ b/src/ray/core_worker/test/reference_count_test.cc @@ -572,9 +572,9 @@ TEST_F(ReferenceCountTest, TestUnreconstructableObjectOutOfScope) { // The object goes out of scope once it has no more refs. std::vector out; - ASSERT_FALSE(rc->AddObjectPrimaryCopyDeleteCallback(id, callback)); + ASSERT_FALSE(rc->AddObjectOutOfScopeOrFreedCallback(id, callback)); rc->AddOwnedObject(id, {}, address, "", 0, false, /*add_local_ref=*/true); - ASSERT_TRUE(rc->AddObjectPrimaryCopyDeleteCallback(id, callback)); + ASSERT_TRUE(rc->AddObjectOutOfScopeOrFreedCallback(id, callback)); ASSERT_FALSE(*out_of_scope); rc->RemoveLocalReference(id, &out); ASSERT_TRUE(*out_of_scope); @@ -582,9 +582,9 @@ TEST_F(ReferenceCountTest, TestUnreconstructableObjectOutOfScope) { // Unreconstructable objects go out of scope even if they have a nonzero // lineage ref count. *out_of_scope = false; - ASSERT_FALSE(rc->AddObjectPrimaryCopyDeleteCallback(id, callback)); + ASSERT_FALSE(rc->AddObjectOutOfScopeOrFreedCallback(id, callback)); rc->AddOwnedObject(id, {}, address, "", 0, false, /*add_local_ref=*/false); - ASSERT_TRUE(rc->AddObjectPrimaryCopyDeleteCallback(id, callback)); + ASSERT_TRUE(rc->AddObjectOutOfScopeOrFreedCallback(id, callback)); rc->UpdateSubmittedTaskReferences({}, {id}); ASSERT_FALSE(*out_of_scope); rc->UpdateFinishedTaskReferences({}, {id}, false, empty_borrower, empty_refs, &out); @@ -2437,9 +2437,9 @@ TEST_F(ReferenceCountLineageEnabledTest, TestUnreconstructableObjectOutOfScope) // The object goes out of scope once it has no more refs. std::vector out; - ASSERT_FALSE(rc->AddObjectPrimaryCopyDeleteCallback(id, callback)); + ASSERT_FALSE(rc->AddObjectOutOfScopeOrFreedCallback(id, callback)); rc->AddOwnedObject(id, {}, address, "", 0, false, /*add_local_ref=*/true); - ASSERT_TRUE(rc->AddObjectPrimaryCopyDeleteCallback(id, callback)); + ASSERT_TRUE(rc->AddObjectOutOfScopeOrFreedCallback(id, callback)); ASSERT_FALSE(*out_of_scope); ASSERT_FALSE(*out_of_scope); rc->RemoveLocalReference(id, &out); @@ -2450,9 +2450,9 @@ TEST_F(ReferenceCountLineageEnabledTest, TestUnreconstructableObjectOutOfScope) // Unreconstructable objects stay in scope if they have a nonzero lineage ref // count. *out_of_scope = false; - ASSERT_FALSE(rc->AddObjectPrimaryCopyDeleteCallback(id, callback)); + ASSERT_FALSE(rc->AddObjectOutOfScopeOrFreedCallback(id, callback)); rc->AddOwnedObject(id, {}, address, "", 0, false, /*add_local_ref=*/false); - ASSERT_TRUE(rc->AddObjectPrimaryCopyDeleteCallback(id, callback)); + ASSERT_TRUE(rc->AddObjectOutOfScopeOrFreedCallback(id, callback)); rc->UpdateSubmittedTaskReferences({return_id}, {id}); ASSERT_TRUE(rc->IsObjectPendingCreation(return_id)); ASSERT_FALSE(*out_of_scope); @@ -2541,7 +2541,7 @@ TEST_F(ReferenceCountLineageEnabledTest, TestPinLineageRecursive) { rc->UpdateFinishedTaskReferences({}, {id}, false, empty_borrower, empty_refs, &out); // We should fail to set the deletion callback because the object has // already gone out of scope. - ASSERT_FALSE(rc->AddObjectPrimaryCopyDeleteCallback( + ASSERT_FALSE(rc->AddObjectOutOfScopeOrFreedCallback( id, [&](const ObjectID &object_id) { ASSERT_FALSE(true); })); ASSERT_EQ(out.size(), 1); @@ -2658,7 +2658,7 @@ TEST_F(ReferenceCountLineageEnabledTest, TestPlasmaLocation) { ObjectID id = ObjectID::FromRandom(); NodeID node_id = NodeID::FromRandom(); rc->AddOwnedObject(id, {}, rpc::Address(), "", 0, true, /*add_local_ref=*/true); - ASSERT_TRUE(rc->AddObjectPrimaryCopyDeleteCallback(id, callback)); + ASSERT_TRUE(rc->AddObjectOutOfScopeOrFreedCallback(id, callback)); ASSERT_TRUE(rc->IsPlasmaObjectPinnedOrSpilled(id, &owned_by_us, &pinned_at, &spilled)); ASSERT_TRUE(owned_by_us); ASSERT_TRUE(pinned_at.IsNil()); @@ -2674,7 +2674,7 @@ TEST_F(ReferenceCountLineageEnabledTest, TestPlasmaLocation) { deleted->clear(); rc->AddOwnedObject(id, {}, rpc::Address(), "", 0, true, /*add_local_ref=*/true); - ASSERT_TRUE(rc->AddObjectPrimaryCopyDeleteCallback(id, callback)); + ASSERT_TRUE(rc->AddObjectOutOfScopeOrFreedCallback(id, callback)); rc->UpdateObjectPinnedAtRaylet(id, node_id); rc->ResetObjectsOnRemovedNode(node_id); auto objects = rc->FlushObjectsToRecover(); @@ -2683,7 +2683,7 @@ TEST_F(ReferenceCountLineageEnabledTest, TestPlasmaLocation) { ASSERT_TRUE(rc->IsPlasmaObjectPinnedOrSpilled(id, &owned_by_us, &pinned_at, &spilled)); ASSERT_TRUE(owned_by_us); ASSERT_TRUE(pinned_at.IsNil()); - ASSERT_TRUE(deleted->count(id) > 0); + ASSERT_TRUE(deleted->empty()); deleted->clear(); } @@ -2699,7 +2699,7 @@ TEST_F(ReferenceCountTest, TestFree) { ASSERT_FALSE(rc->IsPlasmaObjectFreed(id)); rc->FreePlasmaObjects({id}); ASSERT_TRUE(rc->IsPlasmaObjectFreed(id)); - ASSERT_FALSE(rc->AddObjectPrimaryCopyDeleteCallback(id, callback)); + ASSERT_FALSE(rc->AddObjectOutOfScopeOrFreedCallback(id, callback)); ASSERT_EQ(deleted->count(id), 0); rc->UpdateObjectPinnedAtRaylet(id, node_id); bool owned_by_us; @@ -2714,7 +2714,7 @@ TEST_F(ReferenceCountTest, TestFree) { // Test free after receiving information about where the object is pinned. rc->AddOwnedObject(id, {}, rpc::Address(), "", 0, true, /*add_local_ref=*/true); - ASSERT_TRUE(rc->AddObjectPrimaryCopyDeleteCallback(id, callback)); + ASSERT_TRUE(rc->AddObjectOutOfScopeOrFreedCallback(id, callback)); rc->UpdateObjectPinnedAtRaylet(id, node_id); ASSERT_FALSE(rc->IsPlasmaObjectFreed(id)); rc->FreePlasmaObjects({id}); diff --git a/src/ray/core_worker/transport/actor_task_submitter.cc b/src/ray/core_worker/transport/actor_task_submitter.cc index c54b9deb16ec..af2600e0a6b8 100644 --- a/src/ray/core_worker/transport/actor_task_submitter.cc +++ b/src/ray/core_worker/transport/actor_task_submitter.cc @@ -44,7 +44,7 @@ void ActorTaskSubmitter::NotifyGCSWhenActorOutOfScope( })); }; - if (!reference_counter_->AddObjectPrimaryCopyDeleteCallback( + if (!reference_counter_->AddObjectOutOfScopeOrFreedCallback( actor_creation_return_id, [actor_out_of_scope_callback](const ObjectID &object_id) { actor_out_of_scope_callback(object_id); From cb384454b7943e42b63684c765e0a2cda6631ea0 Mon Sep 17 00:00:00 2001 From: Lonnie Liu <95255098+aslonnie@users.noreply.github.com> Date: Mon, 25 Nov 2024 16:38:37 -0800 Subject: [PATCH 41/54] [release tests] update byod dependencies (#48896) - upgrade cryptography and pyopenssl - remove dataset Signed-off-by: Lonnie Liu --- .../byod/requirements_byod_3.9.txt | 69 +- .../byod/requirements_ml_byod_3.9.in | 1 - .../byod/requirements_ml_byod_3.9.txt | 1132 ++++++++--------- 3 files changed, 596 insertions(+), 606 deletions(-) diff --git a/release/ray_release/byod/requirements_byod_3.9.txt b/release/ray_release/byod/requirements_byod_3.9.txt index adff2b611647..c5b98cebe695 100644 --- a/release/ray_release/byod/requirements_byod_3.9.txt +++ b/release/ray_release/byod/requirements_byod_3.9.txt @@ -144,6 +144,7 @@ ale-py==0.10.1 \ # via # -c release/ray_release/byod/requirements_compiled.txt # -r release/ray_release/byod/requirements_byod_3.9.in + # gymnasium annotated-types==0.6.0 \ --hash=sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43 \ --hash=sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d @@ -574,33 +575,39 @@ crcmod==1.7 \ # via # -c release/ray_release/byod/requirements_compiled.txt # gsutil -cryptography==38.0.1 \ - --hash=sha256:0297ffc478bdd237f5ca3a7dc96fc0d315670bfa099c04dc3a4a2172008a405a \ - --hash=sha256:10d1f29d6292fc95acb597bacefd5b9e812099d75a6469004fd38ba5471a977f \ - --hash=sha256:16fa61e7481f4b77ef53991075de29fc5bacb582a1244046d2e8b4bb72ef66d0 \ - --hash=sha256:194044c6b89a2f9f169df475cc167f6157eb9151cc69af8a2a163481d45cc407 \ - --hash=sha256:1db3d807a14931fa317f96435695d9ec386be7b84b618cc61cfa5d08b0ae33d7 \ - --hash=sha256:3261725c0ef84e7592597606f6583385fed2a5ec3909f43bc475ade9729a41d6 \ - --hash=sha256:3b72c360427889b40f36dc214630e688c2fe03e16c162ef0aa41da7ab1455153 \ - --hash=sha256:3e3a2599e640927089f932295a9a247fc40a5bdf69b0484532f530471a382750 \ - --hash=sha256:3fc26e22840b77326a764ceb5f02ca2d342305fba08f002a8c1f139540cdfaad \ - --hash=sha256:5067ee7f2bce36b11d0e334abcd1ccf8c541fc0bbdaf57cdd511fdee53e879b6 \ - --hash=sha256:52e7bee800ec869b4031093875279f1ff2ed12c1e2f74923e8f49c916afd1d3b \ - --hash=sha256:64760ba5331e3f1794d0bcaabc0d0c39e8c60bf67d09c93dc0e54189dfd7cfe5 \ - --hash=sha256:765fa194a0f3372d83005ab83ab35d7c5526c4e22951e46059b8ac678b44fa5a \ - --hash=sha256:79473cf8a5cbc471979bd9378c9f425384980fcf2ab6534b18ed7d0d9843987d \ - --hash=sha256:896dd3a66959d3a5ddcfc140a53391f69ff1e8f25d93f0e2e7830c6de90ceb9d \ - --hash=sha256:89ed49784ba88c221756ff4d4755dbc03b3c8d2c5103f6d6b4f83a0fb1e85294 \ - --hash=sha256:ac7e48f7e7261207d750fa7e55eac2d45f720027d5703cd9007e9b37bbb59ac0 \ - --hash=sha256:ad7353f6ddf285aeadfaf79e5a6829110106ff8189391704c1d8801aa0bae45a \ - --hash=sha256:b0163a849b6f315bf52815e238bc2b2346604413fa7c1601eea84bcddb5fb9ac \ - --hash=sha256:b6c9b706316d7b5a137c35e14f4103e2115b088c412140fdbd5f87c73284df61 \ - --hash=sha256:c2e5856248a416767322c8668ef1845ad46ee62629266f84a8f007a317141013 \ - --hash=sha256:ca9f6784ea96b55ff41708b92c3f6aeaebde4c560308e5fbbd3173fbc466e94e \ - --hash=sha256:d1a5bd52d684e49a36582193e0b89ff267704cd4025abefb9e26803adeb3e5fb \ - --hash=sha256:d3971e2749a723e9084dd507584e2a2761f78ad2c638aa31e80bc7a15c9db4f9 \ - --hash=sha256:d4ef6cc305394ed669d4d9eebf10d3a101059bdcf2669c366ec1d14e4fb227bd \ - --hash=sha256:d9e69ae01f99abe6ad646947bba8941e896cb3aa805be2597a0400e0764b5818 +cryptography==42.0.5 \ + --hash=sha256:0270572b8bd2c833c3981724b8ee9747b3ec96f699a9665470018594301439ee \ + --hash=sha256:111a0d8553afcf8eb02a4fea6ca4f59d48ddb34497aa8706a6cf536f1a5ec576 \ + --hash=sha256:16a48c23a62a2f4a285699dba2e4ff2d1cff3115b9df052cdd976a18856d8e3d \ + --hash=sha256:1b95b98b0d2af784078fa69f637135e3c317091b615cd0905f8b8a087e86fa30 \ + --hash=sha256:1f71c10d1e88467126f0efd484bd44bca5e14c664ec2ede64c32f20875c0d413 \ + --hash=sha256:2424ff4c4ac7f6b8177b53c17ed5d8fa74ae5955656867f5a8affaca36a27abb \ + --hash=sha256:2bce03af1ce5a5567ab89bd90d11e7bbdff56b8af3acbbec1faded8f44cb06da \ + --hash=sha256:329906dcc7b20ff3cad13c069a78124ed8247adcac44b10bea1130e36caae0b4 \ + --hash=sha256:37dd623507659e08be98eec89323469e8c7b4c1407c85112634ae3dbdb926fdd \ + --hash=sha256:3eaafe47ec0d0ffcc9349e1708be2aaea4c6dd4978d76bf6eb0cb2c13636c6fc \ + --hash=sha256:5e6275c09d2badf57aea3afa80d975444f4be8d3bc58f7f80d2a484c6f9485c8 \ + --hash=sha256:6fe07eec95dfd477eb9530aef5bead34fec819b3aaf6c5bd6d20565da607bfe1 \ + --hash=sha256:7367d7b2eca6513681127ebad53b2582911d1736dc2ffc19f2c3ae49997496bc \ + --hash=sha256:7cde5f38e614f55e28d831754e8a3bacf9ace5d1566235e39d91b35502d6936e \ + --hash=sha256:9481ffe3cf013b71b2428b905c4f7a9a4f76ec03065b05ff499bb5682a8d9ad8 \ + --hash=sha256:98d8dc6d012b82287f2c3d26ce1d2dd130ec200c8679b6213b3c73c08b2b7940 \ + --hash=sha256:a011a644f6d7d03736214d38832e030d8268bcff4a41f728e6030325fea3e400 \ + --hash=sha256:a2913c5375154b6ef2e91c10b5720ea6e21007412f6437504ffea2109b5a33d7 \ + --hash=sha256:a30596bae9403a342c978fb47d9b0ee277699fa53bbafad14706af51fe543d16 \ + --hash=sha256:b03c2ae5d2f0fc05f9a2c0c997e1bc18c8229f392234e8a0194f202169ccd278 \ + --hash=sha256:b6cd2203306b63e41acdf39aa93b86fb566049aeb6dc489b70e34bcd07adca74 \ + --hash=sha256:b7ffe927ee6531c78f81aa17e684e2ff617daeba7f189f911065b2ea2d526dec \ + --hash=sha256:b8cac287fafc4ad485b8a9b67d0ee80c66bf3574f655d3b97ef2e1082360faf1 \ + --hash=sha256:ba334e6e4b1d92442b75ddacc615c5476d4ad55cc29b15d590cc6b86efa487e2 \ + --hash=sha256:ba3e4a42397c25b7ff88cdec6e2a16c2be18720f317506ee25210f6d31925f9c \ + --hash=sha256:c41fb5e6a5fe9ebcd58ca3abfeb51dffb5d83d6775405305bfa8715b76521922 \ + --hash=sha256:cd2030f6650c089aeb304cf093f3244d34745ce0cfcc39f20c6fbfe030102e2a \ + --hash=sha256:cd65d75953847815962c84a4654a84850b2bb4aed3f26fadcc1c13892e1e29f6 \ + --hash=sha256:e4985a790f921508f36f81831817cbc03b102d643b5fcb81cd33df3fa291a1a1 \ + --hash=sha256:e807b3188f9eb0eaa7bbb579b462c5ace579f1cedb28107ce8b48a9f7ad3679e \ + --hash=sha256:f12764b8fffc7a123f641d7d049d382b73f96a34117e0b637b80643169cec8ac \ + --hash=sha256:f8837fe1d6ac4a8052a9a8ddab256bc006242696f03368a4009be7ee3075cdb7 # via # -c release/ray_release/byod/requirements_compiled.txt # pyopenssl @@ -1264,7 +1271,7 @@ gsutil==5.27 \ # via # -c release/ray_release/byod/requirements_compiled.txt # -r release/ray_release/byod/requirements_byod_3.9.in -gymnasium==1.0.0 \ +gymnasium[atari]==1.0.0 \ --hash=sha256:9d2b66f30c1b34fe3c2ce7fae65ecf365d0e9982d2b3d860235e773328a3b403 \ --hash=sha256:b6f40e1e24c5bd419361e1a5b86a9117d2499baecc3a660d44dfff4c465393ad # via @@ -2203,9 +2210,9 @@ pygments==2.18.0 \ # via # -c release/ray_release/byod/requirements_compiled.txt # rich -pyopenssl==23.0.0 \ - --hash=sha256:c1cc5f86bcacefc84dada7d31175cae1b1518d5f60d3d0bb595a67822a868a6f \ - --hash=sha256:df5fc28af899e74e19fccb5510df423581047e10ab6f1f4ba1763ff5fde844c0 +pyopenssl==24.2.1 \ + --hash=sha256:4247f0dbe3748d560dcbb2ff3ea01af0f9a1a001ef5f7c4c647956ed8cbf0e95 \ + --hash=sha256:967d5719b12b243588573f39b0c677637145c7a1ffedcd495a487e58177fbb8d # via # -c release/ray_release/byod/requirements_compiled.txt # gcs-oauth2-boto-plugin diff --git a/release/ray_release/byod/requirements_ml_byod_3.9.in b/release/ray_release/byod/requirements_ml_byod_3.9.in index 7ef915a8a698..69ca2cc2c734 100644 --- a/release/ray_release/byod/requirements_ml_byod_3.9.in +++ b/release/ray_release/byod/requirements_ml_byod_3.9.in @@ -6,7 +6,6 @@ bitsandbytes boto3 cmake crc32c -dataset datasets decord deepspeed>=0.12.3 diff --git a/release/ray_release/byod/requirements_ml_byod_3.9.txt b/release/ray_release/byod/requirements_ml_byod_3.9.txt index 7eeec76c2886..8d9e5a044f47 100644 --- a/release/ray_release/byod/requirements_ml_byod_3.9.txt +++ b/release/ray_release/byod/requirements_ml_byod_3.9.txt @@ -1,4 +1,3 @@ - # # This file is autogenerated by pip-compile with python 3.9 # To update, run: @@ -118,12 +117,6 @@ aiosignal==1.3.1 \ # via # -c release/ray_release/byod/requirements_compiled.txt # aiohttp -alembic==1.12.1 \ - --hash=sha256:47d52e3dfb03666ed945becb723d6482e52190917fdb47071440cfdba05d92cb \ - --hash=sha256:bca5877e9678b454706347bc10b97cb7d67f300320fa5c3a94423e8266e2823f - # via - # -c release/ray_release/byod/requirements_compiled.txt - # dataset annotated-types==0.6.0 \ --hash=sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43 \ --hash=sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d @@ -175,17 +168,12 @@ attrs==21.4.0 \ # aiohttp # jsonlines # jsonschema - # markdown-it-py backcall==0.2.0 \ --hash=sha256:5cbdbf27be5e7cfadb448baf0aa95508f91f2bbc6c6437cd9cd06e2a4c215e1e \ --hash=sha256:fbbce6a29f263178a1f7915c1940bde0ec2b2a967566fe1c65c1dfb7422bd255 # via # -c release/ray_release/byod/requirements_compiled.txt # ipython -banal==1.0.6 \ - --hash=sha256:2fe02c9305f53168441948f4a03dfbfa2eacc73db30db4a93309083cb0e250a5 \ - --hash=sha256:877aacb16b17f8fa4fd29a7c44515c5a23dc1a7b26078bc41dd34829117d85e1 - # via dataset bitsandbytes==0.43.1 \ --hash=sha256:52c1c7189a6ca006555a9663e544e75f40520a97a26e075411f9f9aca0771fcd \ --hash=sha256:a81c826d576d6d691c7b4a7491c8fdc0f37f769795d6ca2e54afa605d2c260a3 @@ -510,12 +498,6 @@ comm==0.2.0 \ # via # -c release/ray_release/byod/requirements_compiled.txt # ipywidgets -commonmark==0.9.1 \ - --hash=sha256:452f9dc859be7f06631ddcb328b6919c67984aca654e5fefb3914d54691aed60 \ - --hash=sha256:da2f38c92590f83de410ba1a3cbceafbc74fee9def35f9251ba9a971d6d66fd9 - # via - # -c release/ray_release/byod/requirements_compiled.txt - # rich configargparse==1.7 \ --hash=sha256:d249da6591465c6c26df64a9f73d2536e743be2f244eb3ebe61114af2f94f86b \ --hash=sha256:e7067471884de5478c58a511e529f0f9bd1c66bfef1dea90935438d6c23306d1 @@ -652,33 +634,39 @@ crcmod==1.7 \ # via # -c release/ray_release/byod/requirements_compiled.txt # gsutil -cryptography==38.0.1 \ - --hash=sha256:0297ffc478bdd237f5ca3a7dc96fc0d315670bfa099c04dc3a4a2172008a405a \ - --hash=sha256:10d1f29d6292fc95acb597bacefd5b9e812099d75a6469004fd38ba5471a977f \ - --hash=sha256:16fa61e7481f4b77ef53991075de29fc5bacb582a1244046d2e8b4bb72ef66d0 \ - --hash=sha256:194044c6b89a2f9f169df475cc167f6157eb9151cc69af8a2a163481d45cc407 \ - --hash=sha256:1db3d807a14931fa317f96435695d9ec386be7b84b618cc61cfa5d08b0ae33d7 \ - --hash=sha256:3261725c0ef84e7592597606f6583385fed2a5ec3909f43bc475ade9729a41d6 \ - --hash=sha256:3b72c360427889b40f36dc214630e688c2fe03e16c162ef0aa41da7ab1455153 \ - --hash=sha256:3e3a2599e640927089f932295a9a247fc40a5bdf69b0484532f530471a382750 \ - --hash=sha256:3fc26e22840b77326a764ceb5f02ca2d342305fba08f002a8c1f139540cdfaad \ - --hash=sha256:5067ee7f2bce36b11d0e334abcd1ccf8c541fc0bbdaf57cdd511fdee53e879b6 \ - --hash=sha256:52e7bee800ec869b4031093875279f1ff2ed12c1e2f74923e8f49c916afd1d3b \ - --hash=sha256:64760ba5331e3f1794d0bcaabc0d0c39e8c60bf67d09c93dc0e54189dfd7cfe5 \ - --hash=sha256:765fa194a0f3372d83005ab83ab35d7c5526c4e22951e46059b8ac678b44fa5a \ - --hash=sha256:79473cf8a5cbc471979bd9378c9f425384980fcf2ab6534b18ed7d0d9843987d \ - --hash=sha256:896dd3a66959d3a5ddcfc140a53391f69ff1e8f25d93f0e2e7830c6de90ceb9d \ - --hash=sha256:89ed49784ba88c221756ff4d4755dbc03b3c8d2c5103f6d6b4f83a0fb1e85294 \ - --hash=sha256:ac7e48f7e7261207d750fa7e55eac2d45f720027d5703cd9007e9b37bbb59ac0 \ - --hash=sha256:ad7353f6ddf285aeadfaf79e5a6829110106ff8189391704c1d8801aa0bae45a \ - --hash=sha256:b0163a849b6f315bf52815e238bc2b2346604413fa7c1601eea84bcddb5fb9ac \ - --hash=sha256:b6c9b706316d7b5a137c35e14f4103e2115b088c412140fdbd5f87c73284df61 \ - --hash=sha256:c2e5856248a416767322c8668ef1845ad46ee62629266f84a8f007a317141013 \ - --hash=sha256:ca9f6784ea96b55ff41708b92c3f6aeaebde4c560308e5fbbd3173fbc466e94e \ - --hash=sha256:d1a5bd52d684e49a36582193e0b89ff267704cd4025abefb9e26803adeb3e5fb \ - --hash=sha256:d3971e2749a723e9084dd507584e2a2761f78ad2c638aa31e80bc7a15c9db4f9 \ - --hash=sha256:d4ef6cc305394ed669d4d9eebf10d3a101059bdcf2669c366ec1d14e4fb227bd \ - --hash=sha256:d9e69ae01f99abe6ad646947bba8941e896cb3aa805be2597a0400e0764b5818 +cryptography==42.0.5 \ + --hash=sha256:0270572b8bd2c833c3981724b8ee9747b3ec96f699a9665470018594301439ee \ + --hash=sha256:111a0d8553afcf8eb02a4fea6ca4f59d48ddb34497aa8706a6cf536f1a5ec576 \ + --hash=sha256:16a48c23a62a2f4a285699dba2e4ff2d1cff3115b9df052cdd976a18856d8e3d \ + --hash=sha256:1b95b98b0d2af784078fa69f637135e3c317091b615cd0905f8b8a087e86fa30 \ + --hash=sha256:1f71c10d1e88467126f0efd484bd44bca5e14c664ec2ede64c32f20875c0d413 \ + --hash=sha256:2424ff4c4ac7f6b8177b53c17ed5d8fa74ae5955656867f5a8affaca36a27abb \ + --hash=sha256:2bce03af1ce5a5567ab89bd90d11e7bbdff56b8af3acbbec1faded8f44cb06da \ + --hash=sha256:329906dcc7b20ff3cad13c069a78124ed8247adcac44b10bea1130e36caae0b4 \ + --hash=sha256:37dd623507659e08be98eec89323469e8c7b4c1407c85112634ae3dbdb926fdd \ + --hash=sha256:3eaafe47ec0d0ffcc9349e1708be2aaea4c6dd4978d76bf6eb0cb2c13636c6fc \ + --hash=sha256:5e6275c09d2badf57aea3afa80d975444f4be8d3bc58f7f80d2a484c6f9485c8 \ + --hash=sha256:6fe07eec95dfd477eb9530aef5bead34fec819b3aaf6c5bd6d20565da607bfe1 \ + --hash=sha256:7367d7b2eca6513681127ebad53b2582911d1736dc2ffc19f2c3ae49997496bc \ + --hash=sha256:7cde5f38e614f55e28d831754e8a3bacf9ace5d1566235e39d91b35502d6936e \ + --hash=sha256:9481ffe3cf013b71b2428b905c4f7a9a4f76ec03065b05ff499bb5682a8d9ad8 \ + --hash=sha256:98d8dc6d012b82287f2c3d26ce1d2dd130ec200c8679b6213b3c73c08b2b7940 \ + --hash=sha256:a011a644f6d7d03736214d38832e030d8268bcff4a41f728e6030325fea3e400 \ + --hash=sha256:a2913c5375154b6ef2e91c10b5720ea6e21007412f6437504ffea2109b5a33d7 \ + --hash=sha256:a30596bae9403a342c978fb47d9b0ee277699fa53bbafad14706af51fe543d16 \ + --hash=sha256:b03c2ae5d2f0fc05f9a2c0c997e1bc18c8229f392234e8a0194f202169ccd278 \ + --hash=sha256:b6cd2203306b63e41acdf39aa93b86fb566049aeb6dc489b70e34bcd07adca74 \ + --hash=sha256:b7ffe927ee6531c78f81aa17e684e2ff617daeba7f189f911065b2ea2d526dec \ + --hash=sha256:b8cac287fafc4ad485b8a9b67d0ee80c66bf3574f655d3b97ef2e1082360faf1 \ + --hash=sha256:ba334e6e4b1d92442b75ddacc615c5476d4ad55cc29b15d590cc6b86efa487e2 \ + --hash=sha256:ba3e4a42397c25b7ff88cdec6e2a16c2be18720f317506ee25210f6d31925f9c \ + --hash=sha256:c41fb5e6a5fe9ebcd58ca3abfeb51dffb5d83d6775405305bfa8715b76521922 \ + --hash=sha256:cd2030f6650c089aeb304cf093f3244d34745ce0cfcc39f20c6fbfe030102e2a \ + --hash=sha256:cd65d75953847815962c84a4654a84850b2bb4aed3f26fadcc1c13892e1e29f6 \ + --hash=sha256:e4985a790f921508f36f81831817cbc03b102d643b5fcb81cd33df3fa291a1a1 \ + --hash=sha256:e807b3188f9eb0eaa7bbb579b462c5ace579f1cedb28107ce8b48a9f7ad3679e \ + --hash=sha256:f12764b8fffc7a123f641d7d049d382b73f96a34117e0b637b80643169cec8ac \ + --hash=sha256:f8837fe1d6ac4a8052a9a8ddab256bc006242696f03368a4009be7ee3075cdb7 # via # -c release/ray_release/byod/requirements_compiled.txt # pyopenssl @@ -694,10 +682,6 @@ dataproperty==1.0.1 \ # via # pytablewriter # tabledata -dataset==1.6.2 \ - --hash=sha256:77d362118f67a8cbb4848dbd30ab362b9fa7cfebdbfaf426c9c500cb38969a99 \ - --hash=sha256:dcca9ba7658473d3082b1adf87a650252a1cd665705b73fa7d4ee32116a107b9 - # via -r release/ray_release/byod/requirements_ml_byod_3.9.in datasets==2.14.0 \ --hash=sha256:1bb3d1c992a593949a8d3e445b358ac1db4ead00e6619ea2e5e7b6dfc222dde1 \ --hash=sha256:93081cc3d9d0ce860c81f950a3ba23d24704da2eacbe2722092ef4f6ae0ada96 @@ -867,68 +851,84 @@ fonttools==4.45.1 \ # via # -c release/ray_release/byod/requirements_compiled.txt # matplotlib -frozenlist==1.4.0 \ - --hash=sha256:007df07a6e3eb3e33e9a1fe6a9db7af152bbd8a185f9aaa6ece10a3529e3e1c6 \ - --hash=sha256:008eb8b31b3ea6896da16c38c1b136cb9fec9e249e77f6211d479db79a4eaf01 \ - --hash=sha256:09163bdf0b2907454042edb19f887c6d33806adc71fbd54afc14908bfdc22251 \ - --hash=sha256:0c7c1b47859ee2cac3846fde1c1dc0f15da6cec5a0e5c72d101e0f83dcb67ff9 \ - --hash=sha256:0e5c8764c7829343d919cc2dfc587a8db01c4f70a4ebbc49abde5d4b158b007b \ - --hash=sha256:10ff5faaa22786315ef57097a279b833ecab1a0bfb07d604c9cbb1c4cdc2ed87 \ - --hash=sha256:17ae5cd0f333f94f2e03aaf140bb762c64783935cc764ff9c82dff626089bebf \ - --hash=sha256:19488c57c12d4e8095a922f328df3f179c820c212940a498623ed39160bc3c2f \ - --hash=sha256:1a0848b52815006ea6596c395f87449f693dc419061cc21e970f139d466dc0a0 \ - --hash=sha256:1e78fb68cf9c1a6aa4a9a12e960a5c9dfbdb89b3695197aa7064705662515de2 \ - --hash=sha256:261b9f5d17cac914531331ff1b1d452125bf5daa05faf73b71d935485b0c510b \ - --hash=sha256:2b8bcf994563466db019fab287ff390fffbfdb4f905fc77bc1c1d604b1c689cc \ - --hash=sha256:38461d02d66de17455072c9ba981d35f1d2a73024bee7790ac2f9e361ef1cd0c \ - --hash=sha256:490132667476f6781b4c9458298b0c1cddf237488abd228b0b3650e5ecba7467 \ - --hash=sha256:491e014f5c43656da08958808588cc6c016847b4360e327a62cb308c791bd2d9 \ - --hash=sha256:515e1abc578dd3b275d6a5114030b1330ba044ffba03f94091842852f806f1c1 \ - --hash=sha256:556de4430ce324c836789fa4560ca62d1591d2538b8ceb0b4f68fb7b2384a27a \ - --hash=sha256:5833593c25ac59ede40ed4de6d67eb42928cca97f26feea219f21d0ed0959b79 \ - --hash=sha256:6221d84d463fb110bdd7619b69cb43878a11d51cbb9394ae3105d082d5199167 \ - --hash=sha256:6918d49b1f90821e93069682c06ffde41829c346c66b721e65a5c62b4bab0300 \ - --hash=sha256:6c38721585f285203e4b4132a352eb3daa19121a035f3182e08e437cface44bf \ - --hash=sha256:71932b597f9895f011f47f17d6428252fc728ba2ae6024e13c3398a087c2cdea \ - --hash=sha256:7211ef110a9194b6042449431e08c4d80c0481e5891e58d429df5899690511c2 \ - --hash=sha256:764226ceef3125e53ea2cb275000e309c0aa5464d43bd72abd661e27fffc26ab \ - --hash=sha256:7645a8e814a3ee34a89c4a372011dcd817964ce8cb273c8ed6119d706e9613e3 \ - --hash=sha256:76d4711f6f6d08551a7e9ef28c722f4a50dd0fc204c56b4bcd95c6cc05ce6fbb \ - --hash=sha256:7f4f399d28478d1f604c2ff9119907af9726aed73680e5ed1ca634d377abb087 \ - --hash=sha256:88f7bc0fcca81f985f78dd0fa68d2c75abf8272b1f5c323ea4a01a4d7a614efc \ - --hash=sha256:8d0edd6b1c7fb94922bf569c9b092ee187a83f03fb1a63076e7774b60f9481a8 \ - --hash=sha256:901289d524fdd571be1c7be054f48b1f88ce8dddcbdf1ec698b27d4b8b9e5d62 \ - --hash=sha256:93ea75c050c5bb3d98016b4ba2497851eadf0ac154d88a67d7a6816206f6fa7f \ - --hash=sha256:981b9ab5a0a3178ff413bca62526bb784249421c24ad7381e39d67981be2c326 \ - --hash=sha256:9ac08e601308e41eb533f232dbf6b7e4cea762f9f84f6357136eed926c15d12c \ - --hash=sha256:a02eb8ab2b8f200179b5f62b59757685ae9987996ae549ccf30f983f40602431 \ - --hash=sha256:a0c6da9aee33ff0b1a451e867da0c1f47408112b3391dd43133838339e410963 \ - --hash=sha256:a6c8097e01886188e5be3e6b14e94ab365f384736aa1fca6a0b9e35bd4a30bc7 \ - --hash=sha256:aa384489fefeb62321b238e64c07ef48398fe80f9e1e6afeff22e140e0850eef \ - --hash=sha256:ad2a9eb6d9839ae241701d0918f54c51365a51407fd80f6b8289e2dfca977cc3 \ - --hash=sha256:b206646d176a007466358aa21d85cd8600a415c67c9bd15403336c331a10d956 \ - --hash=sha256:b826d97e4276750beca7c8f0f1a4938892697a6bcd8ec8217b3312dad6982781 \ - --hash=sha256:b89ac9768b82205936771f8d2eb3ce88503b1556324c9f903e7156669f521472 \ - --hash=sha256:bd7bd3b3830247580de99c99ea2a01416dfc3c34471ca1298bccabf86d0ff4dc \ - --hash=sha256:bdf1847068c362f16b353163391210269e4f0569a3c166bc6a9f74ccbfc7e839 \ - --hash=sha256:c11b0746f5d946fecf750428a95f3e9ebe792c1ee3b1e96eeba145dc631a9672 \ - --hash=sha256:c5374b80521d3d3f2ec5572e05adc94601985cc526fb276d0c8574a6d749f1b3 \ - --hash=sha256:ca265542ca427bf97aed183c1676e2a9c66942e822b14dc6e5f42e038f92a503 \ - --hash=sha256:ce31ae3e19f3c902de379cf1323d90c649425b86de7bbdf82871b8a2a0615f3d \ - --hash=sha256:ceb6ec0a10c65540421e20ebd29083c50e6d1143278746a4ef6bcf6153171eb8 \ - --hash=sha256:d081f13b095d74b67d550de04df1c756831f3b83dc9881c38985834387487f1b \ - --hash=sha256:d5655a942f5f5d2c9ed93d72148226d75369b4f6952680211972a33e59b1dfdc \ - --hash=sha256:d5a32087d720c608f42caed0ef36d2b3ea61a9d09ee59a5142d6070da9041b8f \ - --hash=sha256:d6484756b12f40003c6128bfcc3fa9f0d49a687e171186c2d85ec82e3758c559 \ - --hash=sha256:dd65632acaf0d47608190a71bfe46b209719bf2beb59507db08ccdbe712f969b \ - --hash=sha256:de343e75f40e972bae1ef6090267f8260c1446a1695e77096db6cfa25e759a95 \ - --hash=sha256:e29cda763f752553fa14c68fb2195150bfab22b352572cb36c43c47bedba70eb \ - --hash=sha256:e41f3de4df3e80de75845d3e743b3f1c4c8613c3997a912dbf0229fc61a8b963 \ - --hash=sha256:e66d2a64d44d50d2543405fb183a21f76b3b5fd16f130f5c99187c3fb4e64919 \ - --hash=sha256:e74b0506fa5aa5598ac6a975a12aa8928cbb58e1f5ac8360792ef15de1aa848f \ - --hash=sha256:f0ed05f5079c708fe74bf9027e95125334b6978bf07fd5ab923e9e55e5fbb9d3 \ - --hash=sha256:f61e2dc5ad442c52b4887f1fdc112f97caeff4d9e6ebe78879364ac59f1663e1 \ - --hash=sha256:fec520865f42e5c7f050c2a79038897b1c7d1595e907a9e08e3353293ffc948e +frozenlist==1.4.1 \ + --hash=sha256:04ced3e6a46b4cfffe20f9ae482818e34eba9b5fb0ce4056e4cc9b6e212d09b7 \ + --hash=sha256:0633c8d5337cb5c77acbccc6357ac49a1770b8c487e5b3505c57b949b4b82e98 \ + --hash=sha256:068b63f23b17df8569b7fdca5517edef76171cf3897eb68beb01341131fbd2ad \ + --hash=sha256:0c250a29735d4f15321007fb02865f0e6b6a41a6b88f1f523ca1596ab5f50bd5 \ + --hash=sha256:1979bc0aeb89b33b588c51c54ab0161791149f2461ea7c7c946d95d5f93b56ae \ + --hash=sha256:1a4471094e146b6790f61b98616ab8e44f72661879cc63fa1049d13ef711e71e \ + --hash=sha256:1b280e6507ea8a4fa0c0a7150b4e526a8d113989e28eaaef946cc77ffd7efc0a \ + --hash=sha256:1d0ce09d36d53bbbe566fe296965b23b961764c0bcf3ce2fa45f463745c04701 \ + --hash=sha256:20b51fa3f588ff2fe658663db52a41a4f7aa6c04f6201449c6c7c476bd255c0d \ + --hash=sha256:23b2d7679b73fe0e5a4560b672a39f98dfc6f60df63823b0a9970525325b95f6 \ + --hash=sha256:23b701e65c7b36e4bf15546a89279bd4d8675faabc287d06bbcfac7d3c33e1e6 \ + --hash=sha256:2471c201b70d58a0f0c1f91261542a03d9a5e088ed3dc6c160d614c01649c106 \ + --hash=sha256:27657df69e8801be6c3638054e202a135c7f299267f1a55ed3a598934f6c0d75 \ + --hash=sha256:29acab3f66f0f24674b7dc4736477bcd4bc3ad4b896f5f45379a67bce8b96868 \ + --hash=sha256:32453c1de775c889eb4e22f1197fe3bdfe457d16476ea407472b9442e6295f7a \ + --hash=sha256:3a670dc61eb0d0eb7080890c13de3066790f9049b47b0de04007090807c776b0 \ + --hash=sha256:3e0153a805a98f5ada7e09826255ba99fb4f7524bb81bf6b47fb702666484ae1 \ + --hash=sha256:410478a0c562d1a5bcc2f7ea448359fcb050ed48b3c6f6f4f18c313a9bdb1826 \ + --hash=sha256:442acde1e068288a4ba7acfe05f5f343e19fac87bfc96d89eb886b0363e977ec \ + --hash=sha256:48f6a4533887e189dae092f1cf981f2e3885175f7a0f33c91fb5b7b682b6bab6 \ + --hash=sha256:4f57dab5fe3407b6c0c1cc907ac98e8a189f9e418f3b6e54d65a718aaafe3950 \ + --hash=sha256:4f9c515e7914626b2a2e1e311794b4c35720a0be87af52b79ff8e1429fc25f19 \ + --hash=sha256:55fdc093b5a3cb41d420884cdaf37a1e74c3c37a31f46e66286d9145d2063bd0 \ + --hash=sha256:5667ed53d68d91920defdf4035d1cdaa3c3121dc0b113255124bcfada1cfa1b8 \ + --hash=sha256:590344787a90ae57d62511dd7c736ed56b428f04cd8c161fcc5e7232c130c69a \ + --hash=sha256:5a7d70357e7cee13f470c7883a063aae5fe209a493c57d86eb7f5a6f910fae09 \ + --hash=sha256:5c3894db91f5a489fc8fa6a9991820f368f0b3cbdb9cd8849547ccfab3392d86 \ + --hash=sha256:5c849d495bf5154cd8da18a9eb15db127d4dba2968d88831aff6f0331ea9bd4c \ + --hash=sha256:64536573d0a2cb6e625cf309984e2d873979709f2cf22839bf2d61790b448ad5 \ + --hash=sha256:693945278a31f2086d9bf3df0fe8254bbeaef1fe71e1351c3bd730aa7d31c41b \ + --hash=sha256:6db4667b187a6742b33afbbaf05a7bc551ffcf1ced0000a571aedbb4aa42fc7b \ + --hash=sha256:6eb73fa5426ea69ee0e012fb59cdc76a15b1283d6e32e4f8dc4482ec67d1194d \ + --hash=sha256:722e1124aec435320ae01ee3ac7bec11a5d47f25d0ed6328f2273d287bc3abb0 \ + --hash=sha256:7268252af60904bf52c26173cbadc3a071cece75f873705419c8681f24d3edea \ + --hash=sha256:74fb4bee6880b529a0c6560885fce4dc95936920f9f20f53d99a213f7bf66776 \ + --hash=sha256:780d3a35680ced9ce682fbcf4cb9c2bad3136eeff760ab33707b71db84664e3a \ + --hash=sha256:82e8211d69a4f4bc360ea22cd6555f8e61a1bd211d1d5d39d3d228b48c83a897 \ + --hash=sha256:89aa2c2eeb20957be2d950b85974b30a01a762f3308cd02bb15e1ad632e22dc7 \ + --hash=sha256:8aefbba5f69d42246543407ed2461db31006b0f76c4e32dfd6f42215a2c41d09 \ + --hash=sha256:96ec70beabbd3b10e8bfe52616a13561e58fe84c0101dd031dc78f250d5128b9 \ + --hash=sha256:9750cc7fe1ae3b1611bb8cfc3f9ec11d532244235d75901fb6b8e42ce9229dfe \ + --hash=sha256:9acbb16f06fe7f52f441bb6f413ebae6c37baa6ef9edd49cdd567216da8600cd \ + --hash=sha256:9d3e0c25a2350080e9319724dede4f31f43a6c9779be48021a7f4ebde8b2d742 \ + --hash=sha256:a06339f38e9ed3a64e4c4e43aec7f59084033647f908e4259d279a52d3757d09 \ + --hash=sha256:a0cb6f11204443f27a1628b0e460f37fb30f624be6051d490fa7d7e26d4af3d0 \ + --hash=sha256:a7496bfe1da7fb1a4e1cc23bb67c58fab69311cc7d32b5a99c2007b4b2a0e932 \ + --hash=sha256:a828c57f00f729620a442881cc60e57cfcec6842ba38e1b19fd3e47ac0ff8dc1 \ + --hash=sha256:a9b2de4cf0cdd5bd2dee4c4f63a653c61d2408055ab77b151c1957f221cabf2a \ + --hash=sha256:b46c8ae3a8f1f41a0d2ef350c0b6e65822d80772fe46b653ab6b6274f61d4a49 \ + --hash=sha256:b7e3ed87d4138356775346e6845cccbe66cd9e207f3cd11d2f0b9fd13681359d \ + --hash=sha256:b7f2f9f912dca3934c1baec2e4585a674ef16fe00218d833856408c48d5beee7 \ + --hash=sha256:ba60bb19387e13597fb059f32cd4d59445d7b18b69a745b8f8e5db0346f33480 \ + --hash=sha256:beee944ae828747fd7cb216a70f120767fc9f4f00bacae8543c14a6831673f89 \ + --hash=sha256:bfa4a17e17ce9abf47a74ae02f32d014c5e9404b6d9ac7f729e01562bbee601e \ + --hash=sha256:c037a86e8513059a2613aaba4d817bb90b9d9b6b69aace3ce9c877e8c8ed402b \ + --hash=sha256:c302220494f5c1ebeb0912ea782bcd5e2f8308037b3c7553fad0e48ebad6ad82 \ + --hash=sha256:c6321c9efe29975232da3bd0af0ad216800a47e93d763ce64f291917a381b8eb \ + --hash=sha256:c757a9dd70d72b076d6f68efdbb9bc943665ae954dad2801b874c8c69e185068 \ + --hash=sha256:c99169d4ff810155ca50b4da3b075cbde79752443117d89429595c2e8e37fed8 \ + --hash=sha256:c9c92be9fd329ac801cc420e08452b70e7aeab94ea4233a4804f0915c14eba9b \ + --hash=sha256:cc7b01b3754ea68a62bd77ce6020afaffb44a590c2289089289363472d13aedb \ + --hash=sha256:db9e724bebd621d9beca794f2a4ff1d26eed5965b004a97f1f1685a173b869c2 \ + --hash=sha256:dca69045298ce5c11fd539682cff879cc1e664c245d1c64da929813e54241d11 \ + --hash=sha256:dd9b1baec094d91bf36ec729445f7769d0d0cf6b64d04d86e45baf89e2b9059b \ + --hash=sha256:e02a0e11cf6597299b9f3bbd3f93d79217cb90cfd1411aec33848b13f5c656cc \ + --hash=sha256:e6a20a581f9ce92d389a8c7d7c3dd47c81fd5d6e655c8dddf341e14aa48659d0 \ + --hash=sha256:e7004be74cbb7d9f34553a5ce5fb08be14fb33bc86f332fb71cbe5216362a497 \ + --hash=sha256:e774d53b1a477a67838a904131c4b0eef6b3d8a651f8b138b04f748fccfefe17 \ + --hash=sha256:edb678da49d9f72c9f6c609fbe41a5dfb9a9282f9e6a2253d5a91e0fc382d7c0 \ + --hash=sha256:f146e0911cb2f1da549fc58fc7bcd2b836a44b79ef871980d605ec392ff6b0d2 \ + --hash=sha256:f56e2333dda1fe0f909e7cc59f021eba0d2307bc6f012a1ccf2beca6ba362439 \ + --hash=sha256:f9a3ea26252bd92f570600098783d1371354d89d5f6b7dfd87359d669f2109b5 \ + --hash=sha256:f9aa1878d1083b276b0196f2dfbe00c9b7e752475ed3b682025ff20c1c1f51ac \ + --hash=sha256:fb3c2db03683b5767dedb5769b8a40ebb47d6f7f45b1b3e3b4b51ec8ad9d9825 \ + --hash=sha256:fbeb989b5cc29e8daf7f976b421c220f1b8c731cbf22b9130d8815418ea45887 \ + --hash=sha256:fde5bd59ab5357e3853313127f4d3565fc7dad314a74d7b5d43c22c6a5ed2ced \ + --hash=sha256:fe1a06da377e3a1062ae5fe0926e12b84eceb8a50b350ddca72dc85015873f74 # via # -c release/ray_release/byod/requirements_compiled.txt # aiohttp @@ -964,8 +964,9 @@ fugue-sql-antlr==0.2.0 \ # via # -c release/ray_release/byod/requirements_compiled.txt # fugue -future==0.18.3 \ - --hash=sha256:34a17436ed1e96697a86f9de3d15a3b0be01d8bc8de9c1dffd59fb8234ed5307 +future==1.0.0 \ + --hash=sha256:929292d34f5872e70396626ef385ec22355a1fae8ad29e1a734c3e43f9fbc216 \ + --hash=sha256:bd2968309307861edae1458a4f8a4f3598c03be43b97521076aebf5d94c07b05 # via # -c release/ray_release/byod/requirements_compiled.txt # petastorm @@ -1295,7 +1296,6 @@ greenlet==3.0.1 \ # via # -c release/ray_release/byod/requirements_compiled.txt # gevent - # sqlalchemy gsutil==5.27 \ --hash=sha256:681a2d844acdf05fac989da6dd406944ae11cb27a4cf3c9edef74d2585ab5f05 # via @@ -1431,9 +1431,9 @@ jupyterlab-widgets==3.0.11 \ # via # -c release/ray_release/byod/requirements_compiled.txt # ipywidgets -jupytext==1.13.6 \ - --hash=sha256:2160774e30587fb427213231f0267ed070ba4ede41cf6121dbb2b14225eb83ba \ - --hash=sha256:c6c25918ddb6403d0d8504e08d35f6efc447baf0dbeb6a28b73adf39e866a0c4 +jupytext==1.16.3 \ + --hash=sha256:1ebac990461dd9f477ff7feec9e3003fa1acc89f3c16ba01b73f79fd76f01a98 \ + --hash=sha256:870e0d7a716dcb1303df6ad1cec65e3315a20daedd808a55cb3dae2d56e4ed20 # via # -c release/ray_release/byod/requirements_compiled.txt # -r release/ray_release/byod/requirements_ml_byod_3.9.in @@ -1680,19 +1680,14 @@ lxml==4.9.4 \ # via # -c release/ray_release/byod/requirements_compiled.txt # sacrebleu -mako==1.3.0 \ - --hash=sha256:57d4e997349f1a92035aa25c17ace371a4213f2ca42f99bee9a602500cfd54d9 \ - --hash=sha256:e3a9d388fd00e87043edbe8792f45880ac0114e9c4adc69f6e9bfb2c55e3b11b - # via - # -c release/ray_release/byod/requirements_compiled.txt - # alembic -markdown-it-py==1.1.0 \ - --hash=sha256:36be6bb3ad987bfdb839f5ba78ddf094552ca38ccbd784ae4f74a4e1419fc6e3 \ - --hash=sha256:98080fc0bc34c4f2bcf0846a096a9429acbd9d5d8e67ed34026c03c61c464389 +markdown-it-py==2.2.0 \ + --hash=sha256:5a35f8d1870171d9acc47b99612dc146129b631baf04970128b568f190d0cc30 \ + --hash=sha256:7c9a5e412688bc771c67432cbfebcdd686c93ce6484913dccf06cb5a0bea35a1 # via # -c release/ray_release/byod/requirements_compiled.txt # jupytext # mdit-py-plugins + # rich markupsafe==2.1.3 \ --hash=sha256:05fb21170423db021895e1ea1e1f3ab3adb85d1c2333cbc2310f2a26bc77272e \ --hash=sha256:0a4e4a1aff6c7ac4cd55792abf96c915634c2b97e3cc1c7129578aa68ebd754e \ @@ -1757,7 +1752,6 @@ markupsafe==2.1.3 \ # via # -c release/ray_release/byod/requirements_compiled.txt # jinja2 - # mako # werkzeug matplotlib==3.7.4 \ --hash=sha256:0037d066cca1f4bda626c507cddeb6f7da8283bc6a214da2db13ff2162933c52 \ @@ -1829,6 +1823,12 @@ mdit-py-plugins==0.3.5 \ # via # -c release/ray_release/byod/requirements_compiled.txt # jupytext +mdurl==0.1.2 \ + --hash=sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8 \ + --hash=sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba + # via + # -c release/ray_release/byod/requirements_compiled.txt + # markdown-it-py memray==1.10.0 ; platform_system != "Windows" and sys_platform != "darwin" and platform_machine != "aarch64" \ --hash=sha256:0a21745fb516b7a6efcd40aa7487c59e9313fcfc782d0193fcfcf00b48426874 \ --hash=sha256:22f2a47871c172a0539bd72737bb6b294fc10c510464066b825d90fcd3bb4916 \ @@ -1868,7 +1868,7 @@ memray==1.10.0 ; platform_system != "Windows" and sys_platform != "darwin" and p # via # -c release/ray_release/byod/requirements_compiled.txt # -r release/ray_release/byod/requirements_ml_byod_3.9.in -modin==0.22.2 \ +modin==0.22.2 ; python_version < "3.12" \ --hash=sha256:532fe0bfb2dcf06c0ad2d467721ef489fd58bb3ef7150bcf4a7ddd1069be1e4d \ --hash=sha256:fa897dc59d5b9a8496be044185689fdd337b9f26cc81c4144b217a2a94d029bc # via @@ -1952,81 +1952,97 @@ msgpack==1.0.7 \ # via # -c release/ray_release/byod/requirements_compiled.txt # locust -multidict==6.0.4 \ - --hash=sha256:01a3a55bd90018c9c080fbb0b9f4891db37d148a0a18722b42f94694f8b6d4c9 \ - --hash=sha256:0b1a97283e0c85772d613878028fec909f003993e1007eafa715b24b377cb9b8 \ - --hash=sha256:0dfad7a5a1e39c53ed00d2dd0c2e36aed4650936dc18fd9a1826a5ae1cad6f03 \ - --hash=sha256:11bdf3f5e1518b24530b8241529d2050014c884cf18b6fc69c0c2b30ca248710 \ - --hash=sha256:1502e24330eb681bdaa3eb70d6358e818e8e8f908a22a1851dfd4e15bc2f8161 \ - --hash=sha256:16ab77bbeb596e14212e7bab8429f24c1579234a3a462105cda4a66904998664 \ - --hash=sha256:16d232d4e5396c2efbbf4f6d4df89bfa905eb0d4dc5b3549d872ab898451f569 \ - --hash=sha256:21a12c4eb6ddc9952c415f24eef97e3e55ba3af61f67c7bc388dcdec1404a067 \ - 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--hash=sha256:fe5d7785250541f7f5019ab9cba2c71169dc7d74d0f45253f8313f436458a4ef # via # -c release/ray_release/byod/requirements_compiled.txt # aiohttp @@ -2219,6 +2235,12 @@ numpy==1.26.4 \ # triad # utilsforecast # xgboost +nvidia-nccl-cu12==2.20.5 \ + --hash=sha256:057f6bf9685f75215d0c53bf3ac4a10b3e6578351de307abad9e18a99182af56 \ + --hash=sha256:1fc150d5c3250b170b29410ba682384b14581db722b2531b0d8d33c595f33d01 + # via + # -c release/ray_release/byod/requirements_compiled.txt + # xgboost oauth2client==4.1.3 \ --hash=sha256:b8a81cc5d60e2d364f0b1b98f958dbd472887acaf1a5b05e21c28c31a2d6d3ac \ --hash=sha256:d486741e451287f69568a4d26d70d9acd73a2bbfa275746c535b4209891cccc6 @@ -2250,6 +2272,7 @@ packaging==23.0 \ # evaluate # fugue-sql-antlr # huggingface-hub + # jupytext # lightning-utilities # matplotlib # modin @@ -2263,7 +2286,7 @@ packaging==23.0 \ # transformers # typepy # utilsforecast -pandas==1.5.3 \ +pandas==1.5.3 ; python_version < "3.12" \ --hash=sha256:14e45300521902689a81f3f41386dc86f19b8ba8dd5ac5a3c7010ef8d2932813 \ --hash=sha256:26d9c71772c7afb9d5046e6e9cf42d83dd147b5cf5bcb9d97252077118543792 \ --hash=sha256:3749077d86e3a2f0ed51367f30bf5b82e131cc0f14260c4d3e499186fccc4406 \ @@ -2710,9 +2733,9 @@ pynvml==11.5.0 \ # via # -c release/ray_release/byod/requirements_compiled.txt # deepspeed -pyopenssl==23.0.0 \ - --hash=sha256:c1cc5f86bcacefc84dada7d31175cae1b1518d5f60d3d0bb595a67822a868a6f \ - --hash=sha256:df5fc28af899e74e19fccb5510df423581047e10ab6f1f4ba1763ff5fde844c0 +pyopenssl==24.2.1 \ + --hash=sha256:4247f0dbe3748d560dcbb2ff3ea01af0f9a1a001ef5f7c4c647956ed8cbf0e95 \ + --hash=sha256:967d5719b12b243588573f39b0c677637145c7a1ffedcd495a487e58177fbb8d # via # -c release/ray_release/byod/requirements_compiled.txt # gcs-oauth2-boto-plugin @@ -2963,95 +2986,86 @@ qpd==0.4.4 \ # via # -c release/ray_release/byod/requirements_compiled.txt # fugue -regex==2023.10.3 \ - 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--hash=sha256:c0c18345010870e58238790a6779a1219b4d97bd2e77e1140e8ee5d14df071aa \ + --hash=sha256:c3bea0ba8b73b71b37ac833a7f3fd53825924165da6a924aec78c13032f20850 \ + --hash=sha256:c486b4106066d502495b3025a0a7251bf37ea9540433940a23419461ab9f2a80 \ + --hash=sha256:c49e15eac7c149f3670b3e27f1f28a2c1ddeccd3a2812cba953e01be2ab9b5fe \ + --hash=sha256:c6a2b494a76983df8e3d3feea9b9ffdd558b247e60b92f877f93a1ff43d26656 \ + --hash=sha256:cab12877a9bdafde5500206d1020a584355a97884dfd388af3699e9137bf7388 \ + --hash=sha256:cac27dcaa821ca271855a32188aa61d12decb6fe45ffe3e722401fe61e323cd1 \ + --hash=sha256:cdd09d47c0b2efee9378679f8510ee6955d329424c659ab3c5e3a6edea696294 \ + --hash=sha256:cf2430df4148b08fb4324b848672514b1385ae3807651f3567871f130a728cc3 \ + --hash=sha256:d0a3d8d6acf0c78a1fff0e210d224b821081330b8524e3e2bc5a68ef6ab5803d \ + --hash=sha256:d0c0c0003c10f54a591d220997dd27d953cd9ccc1a7294b40a4be5312be8797b \ + --hash=sha256:d1f059a4d795e646e1c37665b9d06062c62d0e8cc3c511fe01315973a6542e40 \ + --hash=sha256:d347a741ea871c2e278fde6c48f85136c96b8659b632fb57a7d1ce1872547600 \ + --hash=sha256:d3ee02d9e5f482cc8309134a91eeaacbdd2261ba111b0fef3748eeb4913e6a2c \ + --hash=sha256:d99ceffa25ac45d150e30bd9ed14ec6039f2aad0ffa6bb87a5936f5782fc1569 \ + --hash=sha256:e38a7d4e8f633a33b4c7350fbd8bad3b70bf81439ac67ac38916c4a86b465456 \ + --hash=sha256:e4682f5ba31f475d58884045c1a97a860a007d44938c4c0895f41d64481edbc9 \ + --hash=sha256:e5bb9425fe881d578aeca0b2b4b3d314ec88738706f66f219c194d67179337cb \ + --hash=sha256:e64198f6b856d48192bf921421fdd8ad8eb35e179086e99e99f711957ffedd6e \ + --hash=sha256:e6662686aeb633ad65be2a42b4cb00178b3fbf7b91878f9446075c404ada552f \ + --hash=sha256:ec54d5afa89c19c6dd8541a133be51ee1017a38b412b1321ccb8d6ddbeb4cf7d \ + --hash=sha256:f5b1dff3ad008dccf18e652283f5e5339d70bf8ba7c98bf848ac33db10f7bc7a \ + --hash=sha256:f8ec0c2fea1e886a19c3bee0cd19d862b3aa75dcdfb42ebe8ed30708df64687a \ + --hash=sha256:f9ebd0a36102fcad2f03696e8af4ae682793a5d30b46c647eaf280d6cfb32796 # via # -c release/ray_release/byod/requirements_compiled.txt # diffusers @@ -3080,9 +3094,9 @@ requests==2.31.0 \ # torchtext # transformers # wandb -requests-oauthlib==1.3.1 \ - --hash=sha256:2577c501a2fb8d05a304c09d090d6e47c306fef15809d102b327cf8364bddab5 \ - --hash=sha256:75beac4a47881eeb94d5ea5d6ad31ef88856affe2332b9aafb52c6452ccf0d7a +requests-oauthlib==2.0.0 \ + --hash=sha256:7dd8a5c40426b779b0868c404bdef9768deccf22749cde15852df527e6269b36 \ + --hash=sha256:b3dffaebd884d8cd778494369603a9e7b58d29111bf6b41bdc2dcd87203af4e9 # via # -c release/ray_release/byod/requirements_compiled.txt # google-auth-oauthlib @@ -3098,12 +3112,13 @@ retry-decorator==1.1.1 \ # -c release/ray_release/byod/requirements_compiled.txt # gcs-oauth2-boto-plugin # gsutil -rich==12.6.0 \ - --hash=sha256:a4eb26484f2c82589bd9a17c73d32a010b1e29d89f1604cd9bf3a2097b81bb5e \ - --hash=sha256:ba3a3775974105c221d31141f2c116f4fd65c5ceb0698657a11e9f295ec93fd0 +rich==13.3.2 \ + --hash=sha256:91954fe80cfb7985727a467ca98a7618e5dd15178cc2da10f553b36a93859001 \ + --hash=sha256:a104f37270bf677148d8acb07d33be1569eeee87e2d1beb286a4e9113caf6f2f # via # -c release/ray_release/byod/requirements_compiled.txt # memray + # typer rouge-score==0.1.2 \ --hash=sha256:c7d4da2683e68c9abf0135ef915d63a46643666f848e558a1b9f7ead17ff0f04 # via lm-eval @@ -3127,105 +3142,107 @@ s3transfer==0.6.2 \ sacrebleu==2.4.2 \ --hash=sha256:611a581d205828912f0b05f806b110180087184d3be2dc650fda7a729d6ecb89 # via lm-eval -safetensors==0.4.1 \ - --hash=sha256:04157d008385bea66d12fe90844a80d4a76dc25ec5230b5bd9a630496d1b7c03 \ - --hash=sha256:04dd14f53f5500eb4c4149674216ba1000670efbcf4b1b5c2643eb244e7882ea \ - --hash=sha256:097e9af2efa8778cd2f0cba451784253e62fa7cc9fc73c0744d27212f7294e25 \ - --hash=sha256:0bd0afd95c1e497f520e680ea01e0397c0868a3a3030e128438cf6e9e3fcd671 \ - --hash=sha256:0ddd050e01f3e843aa8c1c27bf68675b8a08e385d0045487af4d70418c3cb356 \ - 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--hash=sha256:e375d975159ac534c7161269de24ddcd490df2157b55c1a6eeace6cbb56903f0 \ + --hash=sha256:e4119532cd10dba04b423e0f86aecb96cfa5a602238c0aa012f70c3a40c44b50 \ + --hash=sha256:e7dbbde64b6c534548696808a0e01276d28ea5773bc9a2dfb97a88cd3dffe3df \ + --hash=sha256:e9afd5358719f1b2cf425fad638fc3c887997d6782da317096877e5b15b2ce93 \ + --hash=sha256:ec4b52ce9a396260eb9731eb6aea41a7320de22ed73a1042c2230af0212758ce \ + --hash=sha256:edb5698a7bc282089f64c96c477846950358a46ede85a1c040e0230344fdde10 \ + --hash=sha256:ee463219d9ec6c2be1d331ab13a8e0cd50d2f32240a81d498266d77d07b7e71e \ + --hash=sha256:efcc860be094b8d19ac61b452ec635c7acb9afa77beb218b1d7784c6d41fe8ad \ + --hash=sha256:f5e6883af9a68c0028f70a4c19d5a6ab6238a379be36ad300a22318316c00cb0 \ + --hash=sha256:f9650713b2cfa9537a2baf7dd9fee458b24a0aaaa6cafcea8bdd5fb2b8efdc34 \ + --hash=sha256:faefeb3b81bdfb4e5a55b9bbdf3d8d8753f65506e1d67d03f5c851a6c87150e9 \ + --hash=sha256:fb9c65bd82f9ef3ce4970dc19ee86be5f6f93d032159acf35e663c6bea02b237 \ + --hash=sha256:fe746d03ed8d193674a26105e4f0fe6c726f5bb602ffc695b409eaf02f04763d \ + --hash=sha256:fef5d70683643618244a4f5221053567ca3e77c2531e42ad48ae05fae909f542 # via # -c release/ray_release/byod/requirements_compiled.txt # accelerate @@ -3346,9 +3363,9 @@ sentencepiece==0.1.96 \ # via # -c release/ray_release/byod/requirements_compiled.txt # -r release/ray_release/byod/requirements_ml_byod_3.9.in -sentry-sdk==1.37.1 \ - --hash=sha256:7cd324dd2877fdc861f75cba4242bce23a58272a6fea581fcb218bb718bd9cc5 \ - --hash=sha256:a249c7364827ee89daaa078bb8b56ece0b3d52d9130961bef2302b79bdf7fe70 +sentry-sdk==2.10.0 \ + --hash=sha256:545fcc6e36c335faa6d6cda84669b6e17025f31efbf3b2211ec14efe008b75d1 \ + --hash=sha256:87b3d413c87d8e7f816cc9334bff255a83d8b577db2b22042651c30c19c09190 # via # -c release/ray_release/byod/requirements_compiled.txt # wandb @@ -3444,6 +3461,12 @@ setproctitle==1.3.3 \ # via # -c release/ray_release/byod/requirements_compiled.txt # wandb +shellingham==1.5.4 \ + --hash=sha256:7ecfff8f2fd72616f7481040475a65b2bf8af90a56c89140852d1120324e8686 \ + --hash=sha256:8dbca0739d487e5bd35ab3ca4b36e11c4078f3a234bfce294b0a0291363404de + # via + # -c release/ray_release/byod/requirements_compiled.txt + # typer six==1.16.0 \ --hash=sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926 \ --hash=sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254 @@ -3470,50 +3493,15 @@ smmap==5.0.1 \ # via # -c release/ray_release/byod/requirements_compiled.txt # gitdb -sniffio==1.3.0 \ - --hash=sha256:e60305c5e5d314f5389259b7f22aaa33d8f7dee49763119234af3755c55b9101 \ - --hash=sha256:eecefdce1e5bbfb7ad2eeaabf7c1eeb404d7757c379bd1f7e5cce9d8bf425384 +sniffio==1.3.1 \ + --hash=sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2 \ + --hash=sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc # via # -c release/ray_release/byod/requirements_compiled.txt # anyio -sqlalchemy==1.4.17 \ - --hash=sha256:196fb6bb2733834e506c925d7532f8eabad9d2304deef738a40846e54c31e236 \ - --hash=sha256:1dd77acbc19bee9c0ba858ff5e4e5d5c60895495c83b4df9bcdf4ad5e9b74f21 \ - --hash=sha256:216ff28fe803885ceb5b131dcee6507d28d255808dd5bcffcb3b5fa75be2e102 \ - --hash=sha256:461a4ea803ce0834822f372617a68ac97f9fa1281f2a984624554c651d7c3ae1 \ - --hash=sha256:4b09191ed22af149c07a880f309b7740f3f782ff13325bae5c6168a6aa57e715 \ - --hash=sha256:4c5e20666b33b03bf7f14953f0deb93007bf8c1342e985bd7c7cf25f46fac579 \ - --hash=sha256:4d93b62e98248e3e1ac1e91c2e6ee1e7316f704be1f734338b350b6951e6c175 \ - --hash=sha256:5732858e56d32fa7e02468f4fd2d8f01ddf709e5b93d035c637762890f8ed8b6 \ - --hash=sha256:58c02d1771bb0e61bc9ced8f3b36b5714d9ece8fd4bdbe2a44a892574c3bbc3c \ - --hash=sha256:651cdb3adcee13624ba22d5ff3e96f91e16a115d2ca489ddc16a8e4c217e8509 \ - --hash=sha256:6fe1c8dc26bc0005439cb78ebc78772a22cccc773f5a0e67cb3002d791f53f0f \ - --hash=sha256:7222f3236c280fab3a2d76f903b493171f0ffc29667538cc388a5d5dd0216a88 \ - --hash=sha256:7dc3d3285fb682316d580d84e6e0840fdd8ffdc05cb696db74b9dd746c729908 \ - --hash=sha256:7e45043fe11d503e1c3f9dcf5b42f92d122a814237cd9af68a11dae46ecfcae1 \ - --hash=sha256:7eb55d5583076c03aaf1510473fad2a61288490809049cb31028af56af7068ee \ - --hash=sha256:82922a320d38d7d6aa3a8130523ec7e8c70fa95f7ca7d0fd6ec114b626e4b10b \ - --hash=sha256:8e133e2551fa99c75849848a4ac08efb79930561eb629dd7d2dc9b7ee05256e6 \ - --hash=sha256:949ac299903d2ed8419086f81847381184e2264f3431a33af4679546dcc87f01 \ - --hash=sha256:a2d225c8863a76d15468896dc5af36f1e196b403eb9c7e0151e77ffab9e7df57 \ - --hash=sha256:a5f00a2be7d777119e15ccfb5ba0b2a92e8a193959281089d79821a001095f80 \ - --hash=sha256:b0ad951a6e590bbcfbfeadc5748ef5ec8ede505a8119a71b235f7481cc08371c \ - --hash=sha256:b59b2c0a3b1d93027f6b6b8379a50c354483fe1ebe796c6740e157bb2e06d39a \ - --hash=sha256:bc89e37c359dcd4d75b744e5e81af128ba678aa2ecea4be957e80e6e958a1612 \ - --hash=sha256:bde055c019e6e449ebc4ec61abd3e08690abeb028c7ada2a3b95d8e352b7b514 \ - --hash=sha256:c367ed95d41df584f412a9419b5ece85b0d6c2a08a51ae13ae47ef74ff9a9349 \ - --hash=sha256:dde05ae0987e43ec84e64d6722ce66305eda2a5e2b7d6fda004b37aabdfbb909 \ - --hash=sha256:ee6e7ca09ff274c55d19a1e15ee6f884fa0230c0d9b8d22a456e249d08dee5bf \ - --hash=sha256:f1c68f7bd4a57ffdb85eab489362828dddf6cd565a4c18eda4c446c1d5d3059d \ - --hash=sha256:f63e1f531a8bf52184e2afb53648511f3f8534decb7575b483a583d3cd8d13ed \ - --hash=sha256:fdad4a33140b77df61d456922b7974c1f1bb2c35238f6809f078003a620c4734 - # via - # -c release/ray_release/byod/requirements_compiled.txt - # alembic - # dataset -sqlglot==20.4.0 \ - --hash=sha256:401a2933298cf66901704cf2029272d8243ee72ac47b9fd8784254401b43ee43 \ - --hash=sha256:9a42135d0530de8150a2c5106e0c52abd3396d92501ebe97df7b371d20de5dc9 +sqlglot==25.6.1 \ + --hash=sha256:c1fcbaa00429979f16fb8cea20279a8b3f5312e76d97abb8f8c6a9b21be450d7 \ + --hash=sha256:ea40f3bf8452e2c1a696fe120163190bd67e49b346336e7db6d34400b57b7601 # via # -c release/ray_release/byod/requirements_compiled.txt # fugue @@ -3571,9 +3559,9 @@ statsmodels==0.14.0 \ # via # -c release/ray_release/byod/requirements_compiled.txt # statsforecast -sympy==1.12 \ - --hash=sha256:c3588cd4295d0c0f603d0f2ae780587e64e2efeedb3521e46b9bb1d08d184fa5 \ - --hash=sha256:ebf595c8dac3e0fdc4152c51878b498396ec7f30e7a914d6071e674d49420fb8 +sympy==1.13.1 \ + --hash=sha256:9cebf7e04ff162015ce31c9c6c9144daa34a93bd082f54fd8f12deca4f47515f \ + --hash=sha256:db36cdc64bf61b9b24578b6f7bab1ecdd2452cf008f34faa33776680c26d66f8 # via # -c release/ray_release/byod/requirements_compiled.txt # torch @@ -3764,17 +3752,12 @@ tokenizers==0.15.2 \ # via # -c release/ray_release/byod/requirements_compiled.txt # transformers -toml==0.10.2 \ - --hash=sha256:806143ae5bfb6a3c6e736a764057db0e6a0e05e338b5630894a5f779cabb4f9b \ - --hash=sha256:b3bda1d108d5dd99f4a20d24d9c348e91c4db7ab1b749200bded2f839ccbe68f - # via - # -c release/ray_release/byod/requirements_compiled.txt - # jupytext tomli==2.0.1 \ --hash=sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc \ --hash=sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f # via # -c release/ray_release/byod/requirements_compiled.txt + # jupytext # pytest torch==2.3.0 \ --hash=sha256:09c81c5859a5b819956c6925a405ef1cdda393c9d8a01ce3851453f699d3358c \ @@ -3881,9 +3864,9 @@ tqdm-multiprocess==0.0.11 \ --hash=sha256:3ebdf03e7a675150fa0bbceaa9c3c64b8cb556e9ffafa4fe6c078e51820524aa \ --hash=sha256:a74002a1222ea9cbe8cdc9bd460108c6009be359621fbee9b92d0515d4d180f7 # via lm-eval -traitlets==5.14.0 \ - --hash=sha256:f14949d23829023013c47df20b4a76ccd1a85effb786dc060f34de7948361b33 \ - --hash=sha256:fcdaa8ac49c04dfa0ed3ee3384ef6dfdb5d6f3741502be247279407679296772 +traitlets==5.14.3 \ + --hash=sha256:9ed0579d3502c94b4b3732ac120375cda96f923114522847de4b3bb98b96b6b7 \ + --hash=sha256:b74e89e397b1ed28cc831db7aea759ba6640cb3de13090ca145426688ff1ac4f # via # -c release/ray_release/byod/requirements_compiled.txt # comm @@ -3900,9 +3883,9 @@ transformers==4.36.2 \ # -r release/ray_release/byod/requirements_ml_byod_3.9.in # lm-eval # peft -triad==0.9.3 \ - --hash=sha256:1862b5a78deb9d475c7747b605f2b32457e96c6719f8cbc4e7e95147f34f6f64 \ - --hash=sha256:e4dff41ffbb98bad4d9741c9dd632890cdfe0b873f23d76d2b5f9ca41d4440a7 +triad==0.9.8 \ + --hash=sha256:2c0ba7d83977c6d4e7b59e3cc70727f858014ef7676c62d184aa8e63f7bef5de \ + --hash=sha256:5b67673124891981daf8afbab44b2e6358932ca35ef3ff38a25bc3e0f6f03f17 # via # -c release/ray_release/byod/requirements_compiled.txt # adagio @@ -3930,9 +3913,9 @@ typepy[datetime]==1.3.2 \ # dataproperty # pytablewriter # tabledata -typer==0.9.0 \ - --hash=sha256:50922fd79aea2f4751a8e0408ff10d2662bd0c8bbfa84755a699f3bada2978b2 \ - --hash=sha256:5d96d986a21493606a358cae4461bd8cdf83cbf33a5aa950ae629ca3b51467ee +typer==0.12.3 \ + --hash=sha256:070d7ca53f785acbccba8e7d28b08dcd88f79f1fbda035ade0aecec71ca5c914 \ + --hash=sha256:49e73131481d804288ef62598d97a1ceef3058905aa536a1134f90891ba35482 # via # -c release/ray_release/byod/requirements_compiled.txt # -r release/ray_release/byod/requirements_ml_byod_3.9.in @@ -3942,7 +3925,6 @@ typing-extensions==4.8.0 \ # via # -c release/ray_release/byod/requirements_compiled.txt # -r release/ray_release/byod/requirements_ml_byod_3.9.in - # alembic # fastapi # huggingface-hub # ipython @@ -3954,9 +3936,9 @@ typing-extensions==4.8.0 \ # torch # typer # wandb -urllib3==1.26.18 \ - --hash=sha256:34b97092d7e0a3a8cf7cd10e386f401b3737364026c45e622aa02903dffe0f07 \ - --hash=sha256:f8ecc1bba5667413457c529ab955bf8c67b45db799d159066261719e328580a0 +urllib3==1.26.19 \ + --hash=sha256:37a0344459b199fce0e80b0d3569837ec6b6937435c5244e7fd73fa6006830f3 \ + --hash=sha256:3e3d753a8618b86d7de333b4223005f68720bcd6a7d2bcb9fbd2229ec7c1e429 # via # -c release/ray_release/byod/requirements_compiled.txt # -r release/ray_release/byod/requirements_ml_byod_3.9.in @@ -3965,9 +3947,9 @@ urllib3==1.26.18 \ # requests # responses # sentry-sdk -utilsforecast==0.0.23 \ - --hash=sha256:188daa121c528965e26a3a38f409b66a15f9eef2b44684cc9426f3ddb1146841 \ - --hash=sha256:290882da47ebc7887663c05c46c67e19bc63898220be444ca6173d0a5fdeee4a +utilsforecast==0.2.0 \ + --hash=sha256:3db4245da4e361f26c8eaeef216c2d1206b20defbb033bf11d3e66ce2b1d6ef8 \ + --hash=sha256:a4825bf8da547e3dc552f9b9a7a8159341a118c3a5d122191f09bc3683cba433 # via # -c release/ray_release/byod/requirements_compiled.txt # statsforecast @@ -3992,9 +3974,9 @@ wandb==0.17.0 \ # via # -c release/ray_release/byod/requirements_compiled.txt # -r release/ray_release/byod/requirements_ml_byod_3.9.in -wcwidth==0.2.12 \ - --hash=sha256:f01c104efdf57971bcb756f054dd58ddec5204dd15fa31d6503ea57947d97c02 \ - --hash=sha256:f26ec43d96c8cbfed76a5075dac87680124fa84e0855195a6184da9c187f133c +wcwidth==0.2.13 \ + --hash=sha256:3da69048e4540d84af32131829ff948f1e022c1c6bdb8d6102117aac784f6859 \ + --hash=sha256:72ea0c06399eb286d978fdedb6923a9eb47e1c486ce63e9b4e64fc18303972b5 # via # -c release/ray_release/byod/requirements_compiled.txt # prompt-toolkit @@ -4011,13 +3993,15 @@ widgetsnbextension==4.0.11 \ # via # -c release/ray_release/byod/requirements_compiled.txt # ipywidgets -xgboost==1.7.6 \ - --hash=sha256:127cf1f5e2ec25cd41429394c6719b87af1456ce583e89f0bffd35d02ad18bcb \ - --hash=sha256:1c527554a400445e0c38186039ba1a00425dcdb4e40b37eed0e74cb39a159c47 \ - --hash=sha256:281c3c6f4fbed2d36bf95cd02a641afa95e72e9abde70064056da5e76233e8df \ - --hash=sha256:4c34675b4d2678c624ddde5d45361e7e16046923e362e4e609b88353e6b87124 \ - --hash=sha256:59b4b366d2cafc7f645e87d897983a5b59be02876194b1d213bd8d8b811d8ce8 \ - --hash=sha256:b1d5db49b199152d62bd9217c98760207d3de86d2b9d243260c573ffe638f80a +xgboost==2.1.0 \ + --hash=sha256:19d145eb847b070c32342b1bf2d7331c102783e07a484f8b13b7d759d707c6b0 \ + --hash=sha256:43b16205689249d7509daf7a6ab00ad0e6c570b3a9c263cb32b26e39d9477bb3 \ + --hash=sha256:7144980923e76ce741c7b03a14d3bd7514db6de5c7cabe96ba95b229d274f5ca \ + --hash=sha256:73673c9bb85927db7fe2e3aed6df6d35dba708cfd6767cc63d4ea11dda2dede5 \ + --hash=sha256:74904b91c42524a6c32147fe5718569e78fb65911ff4499b053f81d0964514d4 \ + --hash=sha256:840a0c6e2119d8c8f260a5dace996ea064a267f62b301a25d7d452488a7ac860 \ + --hash=sha256:b2a456eb0f3d3e8fd8ab37e44ac288292bf8ea8744c294be9fd88713d27af810 \ + --hash=sha256:cedc2e386e686795735448fd4597533acacc5ba6fb47dd910c204c468b80bb96 # via # -c release/ray_release/byod/requirements_compiled.txt # -r release/ray_release/byod/requirements_ml_byod_3.9.in @@ -4134,103 +4118,103 @@ xxhash==3.4.1 \ # -c release/ray_release/byod/requirements_compiled.txt # datasets # evaluate -yarl==1.9.3 \ - --hash=sha256:09c19e5f4404574fcfb736efecf75844ffe8610606f3fccc35a1515b8b6712c4 \ - --hash=sha256:0ab5baaea8450f4a3e241ef17e3d129b2143e38a685036b075976b9c415ea3eb \ - --hash=sha256:0d155a092bf0ebf4a9f6f3b7a650dc5d9a5bbb585ef83a52ed36ba46f55cc39d \ - --hash=sha256:126638ab961633f0940a06e1c9d59919003ef212a15869708dcb7305f91a6732 \ - --hash=sha256:1a0a4f3aaa18580038cfa52a7183c8ffbbe7d727fe581300817efc1e96d1b0e9 \ - --hash=sha256:1d93461e2cf76c4796355494f15ffcb50a3c198cc2d601ad8d6a96219a10c363 \ - --hash=sha256:26a1a8443091c7fbc17b84a0d9f38de34b8423b459fb853e6c8cdfab0eacf613 \ - --hash=sha256:271d63396460b6607b588555ea27a1a02b717ca2e3f2cf53bdde4013d7790929 \ - --hash=sha256:28a108cb92ce6cf867690a962372996ca332d8cda0210c5ad487fe996e76b8bb \ - --hash=sha256:29beac86f33d6c7ab1d79bd0213aa7aed2d2f555386856bb3056d5fdd9dab279 \ - --hash=sha256:2c757f64afe53a422e45e3e399e1e3cf82b7a2f244796ce80d8ca53e16a49b9f \ - --hash=sha256:2dad8166d41ebd1f76ce107cf6a31e39801aee3844a54a90af23278b072f1ccf \ - --hash=sha256:2dc72e891672343b99db6d497024bf8b985537ad6c393359dc5227ef653b2f17 \ - --hash=sha256:2f3c8822bc8fb4a347a192dd6a28a25d7f0ea3262e826d7d4ef9cc99cd06d07e \ - --hash=sha256:32435d134414e01d937cd9d6cc56e8413a8d4741dea36af5840c7750f04d16ab \ - --hash=sha256:3cfa4dbe17b2e6fca1414e9c3bcc216f6930cb18ea7646e7d0d52792ac196808 \ - --hash=sha256:3d5434b34100b504aabae75f0622ebb85defffe7b64ad8f52b8b30ec6ef6e4b9 \ - --hash=sha256:4003f380dac50328c85e85416aca6985536812c082387255c35292cb4b41707e \ - --hash=sha256:44e91a669c43f03964f672c5a234ae0d7a4d49c9b85d1baa93dec28afa28ffbd \ - --hash=sha256:4a14907b597ec55740f63e52d7fee0e9ee09d5b9d57a4f399a7423268e457b57 \ - --hash=sha256:4ce77d289f8d40905c054b63f29851ecbfd026ef4ba5c371a158cfe6f623663e \ - --hash=sha256:4d6d74a97e898c1c2df80339aa423234ad9ea2052f66366cef1e80448798c13d \ - --hash=sha256:51382c72dd5377861b573bd55dcf680df54cea84147c8648b15ac507fbef984d \ - --hash=sha256:525cd69eff44833b01f8ef39aa33a9cc53a99ff7f9d76a6ef6a9fb758f54d0ff \ - --hash=sha256:53ec65f7eee8655bebb1f6f1607760d123c3c115a324b443df4f916383482a67 \ - --hash=sha256:5f74b015c99a5eac5ae589de27a1201418a5d9d460e89ccb3366015c6153e60a \ - --hash=sha256:6280353940f7e5e2efaaabd686193e61351e966cc02f401761c4d87f48c89ea4 \ - 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--hash=sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0 +zipp==3.19.2 \ + --hash=sha256:bf1dcf6450f873a13e952a29504887c89e6de7506209e5b1bcc3460135d4de19 \ + --hash=sha256:f091755f667055f2d02b32c53771a7a6c8b47e1fdbc4b72a8b9072b3eef8015c # via # -c release/ray_release/byod/requirements_compiled.txt # importlib-metadata From d0ae2d31dbe4dafb1d0d89f108a82d5cef08a199 Mon Sep 17 00:00:00 2001 From: dentiny Date: Mon, 25 Nov 2024 16:54:38 -0800 Subject: [PATCH 42/54] [core] Fix gcs server using shared pointer (#48888) Current implementation for shared pointer usage is completely a mess; Shared pointer should **ONLY** be used where lifecycle is impossible to judge, for example, async operations or multi-threaded cases; abusing would lead to memory leak (i.e. circular dependency), and makes code hard to justify. --------- Signed-off-by: hjiang --- BUILD.bazel | 40 +++++++++++++++++-- .../ray/gcs/gcs_server/gcs_node_manager.h | 6 ++- src/ray/gcs/gcs_server/gcs_actor_manager.h | 2 +- src/ray/gcs/gcs_server/gcs_actor_scheduler.cc | 31 +++++++------- src/ray/gcs/gcs_server/gcs_actor_scheduler.h | 10 ++--- .../gcs_autoscaler_state_manager.cc | 6 +-- .../gcs_server/gcs_autoscaler_state_manager.h | 13 +++--- src/ray/gcs/gcs_server/gcs_node_manager.cc | 11 +++-- src/ray/gcs/gcs_server/gcs_node_manager.h | 4 +- .../gcs_server/gcs_placement_group_manager.cc | 8 ++-- .../gcs_server/gcs_placement_group_manager.h | 6 +-- .../gcs_placement_group_scheduler.cc | 6 +-- .../gcs_placement_group_scheduler.h | 13 +++--- src/ray/gcs/gcs_server/gcs_resource_manager.h | 2 +- src/ray/gcs/gcs_server/gcs_server.cc | 36 ++++++++--------- src/ray/gcs/gcs_server/gcs_server.h | 18 +++++---- .../gcs_node_manager_export_event_test.cc | 8 ++-- .../test/gcs_actor_scheduler_mock_test.cc | 14 ++++--- .../test/gcs_actor_scheduler_test.cc | 9 +++-- .../test/gcs_autoscaler_state_manager_test.cc | 4 +- .../gcs_server/test/gcs_node_manager_test.cc | 8 ++-- .../gcs_placement_group_manager_mock_test.cc | 2 +- .../test/gcs_placement_group_manager_test.cc | 2 +- .../gcs_placement_group_scheduler_test.cc | 8 ++-- .../raylet/scheduling/cluster_task_manager.h | 1 + 25 files changed, 155 insertions(+), 113 deletions(-) diff --git a/BUILD.bazel b/BUILD.bazel index dd31e976d6c2..f30037472d39 100644 --- a/BUILD.bazel +++ b/BUILD.bazel @@ -2403,11 +2403,43 @@ ray_cc_test( ) ray_cc_test( - name = "gcs_export_event_test", + name = "gcs_job_manager_export_event_test", size = "small", - srcs = glob([ - "src/ray/gcs/gcs_server/test/export_api/*.cc", - ]), + srcs = ["src/ray/gcs/gcs_server/test/export_api/gcs_job_manager_export_event_test.cc"], + tags = [ + "no_windows", + "team:core" + ], + deps = [ + ":gcs_server_lib", + ":gcs_server_test_util", + ":gcs_test_util_lib", + ":ray_mock", + "@com_google_googletest//:gtest_main", + ], +) + +ray_cc_test( + name = "gcs_actor_manager_export_event_test", + size = "small", + srcs = ["src/ray/gcs/gcs_server/test/export_api/gcs_actor_manager_export_event_test.cc"], + tags = [ + "no_windows", + "team:core" + ], + deps = [ + ":gcs_server_lib", + ":gcs_server_test_util", + ":gcs_test_util_lib", + ":ray_mock", + "@com_google_googletest//:gtest_main", + ], +) + +ray_cc_test( + name = "gcs_node_manager_export_event_test", + size = "small", + srcs = ["src/ray/gcs/gcs_server/test/export_api/gcs_node_manager_export_event_test.cc"], tags = [ "no_windows", "team:core" diff --git a/src/mock/ray/gcs/gcs_server/gcs_node_manager.h b/src/mock/ray/gcs/gcs_server/gcs_node_manager.h index 3a8f22949fae..7a3efe197529 100644 --- a/src/mock/ray/gcs/gcs_server/gcs_node_manager.h +++ b/src/mock/ray/gcs/gcs_server/gcs_node_manager.h @@ -18,7 +18,11 @@ namespace gcs { class MockGcsNodeManager : public GcsNodeManager { public: - MockGcsNodeManager() : GcsNodeManager(nullptr, nullptr, nullptr, ClusterID::Nil()) {} + MockGcsNodeManager() + : GcsNodeManager(/*gcs_publisher=*/nullptr, + /*gcs_table_storage=*/nullptr, + /*raylet_client_pool=*/nullptr, + /*cluster_id=*/ClusterID::Nil()) {} MOCK_METHOD(void, HandleRegisterNode, (rpc::RegisterNodeRequest request, diff --git a/src/ray/gcs/gcs_server/gcs_actor_manager.h b/src/ray/gcs/gcs_server/gcs_actor_manager.h index dadb90498379..afdf55be80d6 100644 --- a/src/ray/gcs/gcs_server/gcs_actor_manager.h +++ b/src/ray/gcs/gcs_server/gcs_actor_manager.h @@ -321,7 +321,7 @@ class GcsActorManager : public rpc::ActorInfoHandler { std::function destroy_owned_placement_group_if_needed, const rpc::CoreWorkerClientFactoryFn &worker_client_factory = nullptr); - ~GcsActorManager() = default; + ~GcsActorManager() override = default; void HandleRegisterActor(rpc::RegisterActorRequest request, rpc::RegisterActorReply *reply, diff --git a/src/ray/gcs/gcs_server/gcs_actor_scheduler.cc b/src/ray/gcs/gcs_server/gcs_actor_scheduler.cc index f562c0f9034e..5daa1a992257 100644 --- a/src/ray/gcs/gcs_server/gcs_actor_scheduler.cc +++ b/src/ray/gcs/gcs_server/gcs_actor_scheduler.cc @@ -27,17 +27,17 @@ GcsActorScheduler::GcsActorScheduler( instrumented_io_context &io_context, GcsActorTable &gcs_actor_table, const GcsNodeManager &gcs_node_manager, - std::shared_ptr cluster_task_manager, + ClusterTaskManager &cluster_task_manager, GcsActorSchedulerFailureCallback schedule_failure_handler, GcsActorSchedulerSuccessCallback schedule_success_handler, - std::shared_ptr raylet_client_pool, + rpc::NodeManagerClientPool &raylet_client_pool, rpc::CoreWorkerClientFactoryFn client_factory, std::function normal_task_resources_changed_callback) : io_context_(io_context), gcs_actor_table_(gcs_actor_table), gcs_node_manager_(gcs_node_manager), - cluster_task_manager_(std::move(cluster_task_manager)), + cluster_task_manager_(cluster_task_manager), schedule_failure_handler_(std::move(schedule_failure_handler)), schedule_success_handler_(std::move(schedule_success_handler)), raylet_client_pool_(raylet_client_pool), @@ -97,11 +97,11 @@ void GcsActorScheduler::ScheduleByGcs(std::shared_ptr actor) { const auto &owner_node = gcs_node_manager_.GetAliveNode(actor->GetOwnerNodeID()); RayTask task(actor->GetCreationTaskSpecification(), owner_node.has_value() ? actor->GetOwnerNodeID().Binary() : std::string()); - cluster_task_manager_->QueueAndScheduleTask(task, - /*grant_or_reject*/ false, - /*is_selected_based_on_locality*/ false, - /*reply*/ reply.get(), - send_reply_callback); + cluster_task_manager_.QueueAndScheduleTask(task, + /*grant_or_reject*/ false, + /*is_selected_based_on_locality*/ false, + /*reply*/ reply.get(), + send_reply_callback); } void GcsActorScheduler::ScheduleByRaylet(std::shared_ptr actor) { @@ -218,7 +218,7 @@ std::vector GcsActorScheduler::CancelOnNode(const NodeID &node_id) { } } - raylet_client_pool_->Disconnect(node_id); + raylet_client_pool_.Disconnect(node_id); return actor_ids; } @@ -531,7 +531,7 @@ void GcsActorScheduler::DoRetryCreatingActorOnWorker( std::shared_ptr GcsActorScheduler::GetOrConnectLeaseClient( const rpc::Address &raylet_address) { - return raylet_client_pool_->GetOrConnectByAddress(raylet_address); + return raylet_client_pool_.GetOrConnectByAddress(raylet_address); } bool GcsActorScheduler::KillActorOnWorker(const rpc::Address &worker_address, @@ -664,13 +664,13 @@ void GcsActorScheduler::HandleWorkerLeaseRejectedReply( void GcsActorScheduler::OnActorDestruction(std::shared_ptr actor) { if (!actor->GetAcquiredResources().IsEmpty()) { ReturnActorAcquiredResources(actor); - cluster_task_manager_->ScheduleAndDispatchTasks(); + cluster_task_manager_.ScheduleAndDispatchTasks(); } } void GcsActorScheduler::ReturnActorAcquiredResources(std::shared_ptr actor) { auto &cluster_resource_manager = - cluster_task_manager_->GetClusterResourceScheduler()->GetClusterResourceManager(); + cluster_task_manager_.GetClusterResourceScheduler()->GetClusterResourceManager(); cluster_resource_manager.AddNodeAvailableResources( scheduling::NodeID(actor->GetNodeID().Binary()), actor->GetAcquiredResources().GetResourceSet()); @@ -678,14 +678,13 @@ void GcsActorScheduler::ReturnActorAcquiredResources(std::shared_ptr a } size_t GcsActorScheduler::GetPendingActorsCount() const { - return cluster_task_manager_->GetInfeasibleQueueSize() + - cluster_task_manager_->GetPendingQueueSize(); + return cluster_task_manager_.GetInfeasibleQueueSize() + + cluster_task_manager_.GetPendingQueueSize(); } bool GcsActorScheduler::CancelInFlightActorScheduling( const std::shared_ptr &actor) { - return cluster_task_manager_->CancelTask( - actor->GetCreationTaskSpecification().TaskId()); + return cluster_task_manager_.CancelTask(actor->GetCreationTaskSpecification().TaskId()); } } // namespace gcs diff --git a/src/ray/gcs/gcs_server/gcs_actor_scheduler.h b/src/ray/gcs/gcs_server/gcs_actor_scheduler.h index 1ea66d0ddbe0..048d1da8939c 100644 --- a/src/ray/gcs/gcs_server/gcs_actor_scheduler.h +++ b/src/ray/gcs/gcs_server/gcs_actor_scheduler.h @@ -129,14 +129,14 @@ class GcsActorScheduler : public GcsActorSchedulerInterface { instrumented_io_context &io_context, GcsActorTable &gcs_actor_table, const GcsNodeManager &gcs_node_manager, - std::shared_ptr cluster_task_manager_, + ClusterTaskManager &cluster_task_manager_, GcsActorSchedulerFailureCallback schedule_failure_handler, GcsActorSchedulerSuccessCallback schedule_success_handler, - std::shared_ptr raylet_client_pool, + rpc::NodeManagerClientPool &raylet_client_pool, rpc::CoreWorkerClientFactoryFn client_factory = nullptr, std::function normal_task_resources_changed_callback = nullptr); - virtual ~GcsActorScheduler() = default; + ~GcsActorScheduler() override = default; /// Schedule the specified actor. /// If there is no available nodes then the actor would be queued in the @@ -377,7 +377,7 @@ class GcsActorScheduler : public GcsActorSchedulerInterface { /// Reference of GcsNodeManager. const GcsNodeManager &gcs_node_manager_; /// The cluster task manager. - std::shared_ptr cluster_task_manager_; + ClusterTaskManager &cluster_task_manager_; /// The handler to handle the scheduling failures. GcsActorSchedulerFailureCallback schedule_failure_handler_; /// The handler to handle the successful scheduling. @@ -385,7 +385,7 @@ class GcsActorScheduler : public GcsActorSchedulerInterface { /// The nodes which are releasing unused workers. absl::flat_hash_set nodes_of_releasing_unused_workers_; /// The cached raylet clients used to communicate with raylet. - std::shared_ptr raylet_client_pool_; + rpc::NodeManagerClientPool &raylet_client_pool_; /// The cached core worker clients which are used to communicate with leased worker. rpc::CoreWorkerClientPool core_worker_clients_; diff --git a/src/ray/gcs/gcs_server/gcs_autoscaler_state_manager.cc b/src/ray/gcs/gcs_server/gcs_autoscaler_state_manager.cc index e4ef13371b53..c166dbbf6398 100644 --- a/src/ray/gcs/gcs_server/gcs_autoscaler_state_manager.cc +++ b/src/ray/gcs/gcs_server/gcs_autoscaler_state_manager.cc @@ -28,12 +28,12 @@ GcsAutoscalerStateManager::GcsAutoscalerStateManager( GcsNodeManager &gcs_node_manager, GcsActorManager &gcs_actor_manager, const GcsPlacementGroupManager &gcs_placement_group_manager, - std::shared_ptr raylet_client_pool) + rpc::NodeManagerClientPool &raylet_client_pool) : session_name_(session_name), gcs_node_manager_(gcs_node_manager), gcs_actor_manager_(gcs_actor_manager), gcs_placement_group_manager_(gcs_placement_group_manager), - raylet_client_pool_(std::move(raylet_client_pool)), + raylet_client_pool_(raylet_client_pool), last_cluster_resource_state_version_(0), last_seen_autoscaler_state_version_(0) {} @@ -396,7 +396,7 @@ void GcsAutoscalerStateManager::HandleDrainNode( raylet_address.set_ip_address(node->node_manager_address()); raylet_address.set_port(node->node_manager_port()); - const auto raylet_client = raylet_client_pool_->GetOrConnectByAddress(raylet_address); + const auto raylet_client = raylet_client_pool_.GetOrConnectByAddress(raylet_address); raylet_client->DrainRaylet( request.reason(), request.reason_message(), diff --git a/src/ray/gcs/gcs_server/gcs_autoscaler_state_manager.h b/src/ray/gcs/gcs_server/gcs_autoscaler_state_manager.h index c00d8d465202..c592a7a484d6 100644 --- a/src/ray/gcs/gcs_server/gcs_autoscaler_state_manager.h +++ b/src/ray/gcs/gcs_server/gcs_autoscaler_state_manager.h @@ -29,12 +29,11 @@ class GcsResourceManager; class GcsAutoscalerStateManager : public rpc::autoscaler::AutoscalerStateHandler { public: - GcsAutoscalerStateManager( - const std::string &session_name, - GcsNodeManager &gcs_node_manager, - GcsActorManager &gcs_actor_manager, - const GcsPlacementGroupManager &gcs_placement_group_manager, - std::shared_ptr raylet_client_pool); + GcsAutoscalerStateManager(const std::string &session_name, + GcsNodeManager &gcs_node_manager, + GcsActorManager &gcs_actor_manager, + const GcsPlacementGroupManager &gcs_placement_group_manager, + rpc::NodeManagerClientPool &raylet_client_pool); void HandleGetClusterResourceState( rpc::autoscaler::GetClusterResourceStateRequest request, @@ -152,7 +151,7 @@ class GcsAutoscalerStateManager : public rpc::autoscaler::AutoscalerStateHandler const GcsPlacementGroupManager &gcs_placement_group_manager_; /// Raylet client pool. - std::shared_ptr raylet_client_pool_; + rpc::NodeManagerClientPool &raylet_client_pool_; // The default value of the last seen version for the request is 0, which indicates // no version has been reported. So the first reported version should be 1. diff --git a/src/ray/gcs/gcs_server/gcs_node_manager.cc b/src/ray/gcs/gcs_server/gcs_node_manager.cc index a232ecf10903..c57e8ea2fd08 100644 --- a/src/ray/gcs/gcs_server/gcs_node_manager.cc +++ b/src/ray/gcs/gcs_server/gcs_node_manager.cc @@ -29,14 +29,13 @@ namespace ray { namespace gcs { ////////////////////////////////////////////////////////////////////////////////////////// -GcsNodeManager::GcsNodeManager( - std::shared_ptr gcs_publisher, - std::shared_ptr gcs_table_storage, - std::shared_ptr raylet_client_pool, - const ClusterID &cluster_id) +GcsNodeManager::GcsNodeManager(std::shared_ptr gcs_publisher, + std::shared_ptr gcs_table_storage, + rpc::NodeManagerClientPool *raylet_client_pool, + const ClusterID &cluster_id) : gcs_publisher_(std::move(gcs_publisher)), gcs_table_storage_(std::move(gcs_table_storage)), - raylet_client_pool_(std::move(raylet_client_pool)), + raylet_client_pool_(raylet_client_pool), cluster_id_(cluster_id) {} void GcsNodeManager::WriteNodeExportEvent(rpc::GcsNodeInfo node_info) const { diff --git a/src/ray/gcs/gcs_server/gcs_node_manager.h b/src/ray/gcs/gcs_server/gcs_node_manager.h index db258d4cb00c..b924fec264c9 100644 --- a/src/ray/gcs/gcs_server/gcs_node_manager.h +++ b/src/ray/gcs/gcs_server/gcs_node_manager.h @@ -50,7 +50,7 @@ class GcsNodeManager : public rpc::NodeInfoHandler { /// \param gcs_table_storage GCS table external storage accessor. explicit GcsNodeManager(std::shared_ptr gcs_publisher, std::shared_ptr gcs_table_storage, - std::shared_ptr raylet_client_pool, + rpc::NodeManagerClientPool *raylet_client_pool, const ClusterID &cluster_id); /// Handle register rpc request come from raylet. @@ -248,7 +248,7 @@ class GcsNodeManager : public rpc::NodeInfoHandler { /// Storage for GCS tables. std::shared_ptr gcs_table_storage_; /// Raylet client pool. - std::shared_ptr raylet_client_pool_; + rpc::NodeManagerClientPool *raylet_client_pool_ = nullptr; /// Cluster ID to be shared with clients when connecting. const ClusterID cluster_id_; diff --git a/src/ray/gcs/gcs_server/gcs_placement_group_manager.cc b/src/ray/gcs/gcs_server/gcs_placement_group_manager.cc index c60bcd43cc45..1aec60e9603c 100644 --- a/src/ray/gcs/gcs_server/gcs_placement_group_manager.cc +++ b/src/ray/gcs/gcs_server/gcs_placement_group_manager.cc @@ -14,6 +14,8 @@ #include "ray/gcs/gcs_server/gcs_placement_group_manager.h" +#include + #include "ray/common/asio/asio_util.h" #include "ray/common/asio/instrumented_io_context.h" #include "ray/common/ray_config.h" @@ -181,15 +183,15 @@ rpc::PlacementGroupStats *GcsPlacementGroup::GetMutableStats() { GcsPlacementGroupManager::GcsPlacementGroupManager( instrumented_io_context &io_context, - std::shared_ptr scheduler, + GcsPlacementGroupSchedulerInterface *scheduler, std::shared_ptr gcs_table_storage, GcsResourceManager &gcs_resource_manager, std::function get_ray_namespace) : io_context_(io_context), - gcs_placement_group_scheduler_(std::move(scheduler)), + gcs_placement_group_scheduler_(scheduler), gcs_table_storage_(std::move(gcs_table_storage)), gcs_resource_manager_(gcs_resource_manager), - get_ray_namespace_(get_ray_namespace) { + get_ray_namespace_(std::move(get_ray_namespace)) { placement_group_state_counter_.reset( new CounterMap()); placement_group_state_counter_->SetOnChangeCallback( diff --git a/src/ray/gcs/gcs_server/gcs_placement_group_manager.h b/src/ray/gcs/gcs_server/gcs_placement_group_manager.h index d90fdccf3a8a..a7d91388e264 100644 --- a/src/ray/gcs/gcs_server/gcs_placement_group_manager.h +++ b/src/ray/gcs/gcs_server/gcs_placement_group_manager.h @@ -237,7 +237,7 @@ class GcsPlacementGroupManager : public rpc::PlacementGroupInfoHandler { /// \param gcs_resource_manager Reference of GcsResourceManager. /// \param get_ray_namespace A callback to get the ray namespace. GcsPlacementGroupManager(instrumented_io_context &io_context, - std::shared_ptr scheduler, + GcsPlacementGroupSchedulerInterface *scheduler, std::shared_ptr gcs_table_storage, GcsResourceManager &gcs_resource_manager, std::function get_ray_namespace); @@ -480,8 +480,8 @@ class GcsPlacementGroupManager : public rpc::PlacementGroupInfoHandler { std::deque> infeasible_placement_groups_; /// The scheduler to schedule all registered placement_groups. - std::shared_ptr - gcs_placement_group_scheduler_; + /// Scheduler's lifecycle lies in [GcsServer]. + gcs::GcsPlacementGroupSchedulerInterface *gcs_placement_group_scheduler_ = nullptr; /// Used to update placement group information upon creation, deletion, etc. std::shared_ptr gcs_table_storage_; diff --git a/src/ray/gcs/gcs_server/gcs_placement_group_scheduler.cc b/src/ray/gcs/gcs_server/gcs_placement_group_scheduler.cc index 6bc2737c14a6..85a94f863598 100644 --- a/src/ray/gcs/gcs_server/gcs_placement_group_scheduler.cc +++ b/src/ray/gcs/gcs_server/gcs_placement_group_scheduler.cc @@ -26,13 +26,13 @@ GcsPlacementGroupScheduler::GcsPlacementGroupScheduler( std::shared_ptr gcs_table_storage, const gcs::GcsNodeManager &gcs_node_manager, ClusterResourceScheduler &cluster_resource_scheduler, - std::shared_ptr raylet_client_pool) + rpc::NodeManagerClientPool &raylet_client_pool) : io_context_(io_context), return_timer_(io_context), gcs_table_storage_(std::move(gcs_table_storage)), gcs_node_manager_(gcs_node_manager), cluster_resource_scheduler_(cluster_resource_scheduler), - raylet_client_pool_(std::move(raylet_client_pool)) {} + raylet_client_pool_(raylet_client_pool) {} void GcsPlacementGroupScheduler::ScheduleUnplacedBundles( const SchedulePgRequest &request) { @@ -279,7 +279,7 @@ void GcsPlacementGroupScheduler::CancelResourceReserve( std::shared_ptr GcsPlacementGroupScheduler::GetOrConnectLeaseClient(const rpc::Address &raylet_address) { - return raylet_client_pool_->GetOrConnectByAddress(raylet_address); + return raylet_client_pool_.GetOrConnectByAddress(raylet_address); } std::shared_ptr diff --git a/src/ray/gcs/gcs_server/gcs_placement_group_scheduler.h b/src/ray/gcs/gcs_server/gcs_placement_group_scheduler.h index ec7ac53941bd..df16f025d082 100644 --- a/src/ray/gcs/gcs_server/gcs_placement_group_scheduler.h +++ b/src/ray/gcs/gcs_server/gcs_placement_group_scheduler.h @@ -290,12 +290,11 @@ class GcsPlacementGroupScheduler : public GcsPlacementGroupSchedulerInterface { /// \param cluster_resource_scheduler The resource scheduler which is used when /// scheduling. /// \param lease_client_factory Factory to create remote lease client. - GcsPlacementGroupScheduler( - instrumented_io_context &io_context, - std::shared_ptr gcs_table_storage, - const GcsNodeManager &gcs_node_manager, - ClusterResourceScheduler &cluster_resource_scheduler, - std::shared_ptr raylet_client_pool); + GcsPlacementGroupScheduler(instrumented_io_context &io_context, + std::shared_ptr gcs_table_storage, + const GcsNodeManager &gcs_node_manager, + ClusterResourceScheduler &cluster_resource_scheduler, + rpc::NodeManagerClientPool &raylet_client_pool); virtual ~GcsPlacementGroupScheduler() = default; @@ -502,7 +501,7 @@ class GcsPlacementGroupScheduler : public GcsPlacementGroupSchedulerInterface { placement_group_leasing_in_progress_; /// The cached raylet clients used to communicate with raylets. - std::shared_ptr raylet_client_pool_; + rpc::NodeManagerClientPool &raylet_client_pool_; /// The nodes which are releasing unused bundles. absl::flat_hash_set nodes_of_releasing_unused_bundles_; diff --git a/src/ray/gcs/gcs_server/gcs_resource_manager.h b/src/ray/gcs/gcs_server/gcs_resource_manager.h index dff95380cd21..47ecf9fff3a3 100644 --- a/src/ray/gcs/gcs_server/gcs_resource_manager.h +++ b/src/ray/gcs/gcs_server/gcs_resource_manager.h @@ -67,7 +67,7 @@ class GcsResourceManager : public rpc::NodeResourceInfoHandler, NodeID local_node_id, std::shared_ptr cluster_task_manager = nullptr); - virtual ~GcsResourceManager() {} + virtual ~GcsResourceManager() = default; /// Handle the resource update. void ConsumeSyncMessage(std::shared_ptr message) override; diff --git a/src/ray/gcs/gcs_server/gcs_server.cc b/src/ray/gcs/gcs_server/gcs_server.cc index c51c14bbb0dd..a8ad05ff3c9d 100644 --- a/src/ray/gcs/gcs_server/gcs_server.cc +++ b/src/ray/gcs/gcs_server/gcs_server.cc @@ -64,7 +64,7 @@ GcsServer::GcsServer(const ray::gcs::GcsServerConfig &config, ClusterID::Nil(), RayConfig::instance().gcs_server_rpc_client_thread_num()), raylet_client_pool_( - std::make_shared(client_call_manager_)), + std::make_unique(client_call_manager_)), pubsub_periodical_runner_(io_context_provider_.GetIOContext()), periodical_runner_(io_context_provider_.GetDefaultIOContext()), is_started_(false), @@ -289,7 +289,7 @@ void GcsServer::InitGcsNodeManager(const GcsInitData &gcs_init_data) { RAY_CHECK(gcs_table_storage_ && gcs_publisher_); gcs_node_manager_ = std::make_unique(gcs_publisher_, gcs_table_storage_, - raylet_client_pool_, + raylet_client_pool_.get(), rpc_server_.GetClusterId()); // Initialize by gcs tables data. gcs_node_manager_->Initialize(gcs_init_data); @@ -323,7 +323,7 @@ void GcsServer::InitGcsHealthCheckManager(const GcsInitData &gcs_init_data) { void GcsServer::InitGcsResourceManager(const GcsInitData &gcs_init_data) { RAY_CHECK(cluster_resource_scheduler_ && cluster_task_manager_); - gcs_resource_manager_ = std::make_shared( + gcs_resource_manager_ = std::make_unique( io_context_provider_.GetDefaultIOContext(), cluster_resource_scheduler_->GetClusterResourceManager(), *gcs_node_manager_, @@ -446,25 +446,25 @@ void GcsServer::InitGcsActorManager(const GcsInitData &gcs_init_data) { const rpc::PushTaskReply &reply) { gcs_actor_manager_->OnActorCreationSuccess(std::move(actor), reply); }; - auto client_factory = [this](const rpc::Address &address) { - return std::make_shared(address, client_call_manager_); - }; RAY_CHECK(gcs_resource_manager_ && cluster_task_manager_); scheduler = std::make_unique( io_context_provider_.GetDefaultIOContext(), gcs_table_storage_->ActorTable(), *gcs_node_manager_, - cluster_task_manager_, + *cluster_task_manager_, schedule_failure_handler, schedule_success_handler, - raylet_client_pool_, - client_factory, + *raylet_client_pool_, + /*factory=*/ + [this](const rpc::Address &address) { + return std::make_shared(address, client_call_manager_); + }, /*normal_task_resources_changed_callback=*/ [this](const NodeID &node_id, const rpc::ResourcesData &resources) { gcs_resource_manager_->UpdateNodeNormalTaskResources(node_id, resources); }); - gcs_actor_manager_ = std::make_shared( + gcs_actor_manager_ = std::make_unique( std::move(scheduler), gcs_table_storage_, gcs_publisher_, @@ -480,23 +480,23 @@ void GcsServer::InitGcsActorManager(const GcsInitData &gcs_init_data) { // Initialize by gcs tables data. gcs_actor_manager_->Initialize(gcs_init_data); // Register service. - actor_info_service_.reset(new rpc::ActorInfoGrpcService( - io_context_provider_.GetDefaultIOContext(), *gcs_actor_manager_)); + actor_info_service_ = std::make_unique( + io_context_provider_.GetDefaultIOContext(), *gcs_actor_manager_); rpc_server_.RegisterService(*actor_info_service_); } void GcsServer::InitGcsPlacementGroupManager(const GcsInitData &gcs_init_data) { RAY_CHECK(gcs_table_storage_ && gcs_node_manager_); - gcs_placement_group_scheduler_ = std::make_shared( + gcs_placement_group_scheduler_ = std::make_unique( io_context_provider_.GetDefaultIOContext(), gcs_table_storage_, *gcs_node_manager_, *cluster_resource_scheduler_, - raylet_client_pool_); + *raylet_client_pool_); - gcs_placement_group_manager_ = std::make_shared( + gcs_placement_group_manager_ = std::make_unique( io_context_provider_.GetDefaultIOContext(), - gcs_placement_group_scheduler_, + gcs_placement_group_scheduler_.get(), gcs_table_storage_, *gcs_resource_manager_, [this](const JobID &job_id) { @@ -671,7 +671,7 @@ void GcsServer::InitGcsAutoscalerStateManager(const GcsInitData &gcs_init_data) *gcs_node_manager_, *gcs_actor_manager_, *gcs_placement_group_manager_, - raylet_client_pool_); + *raylet_client_pool_); gcs_autoscaler_state_manager_->Initialize(gcs_init_data); autoscaler_state_service_.reset(new rpc::autoscaler::AutoscalerStateGrpcService( @@ -826,7 +826,7 @@ std::shared_ptr GcsServer::GetOrConnectRedis() { RAY_CHECK(status.ok()) << "Failed to init redis gcs client as " << status; // Init redis failure detector. - gcs_redis_failure_detector_ = std::make_shared( + gcs_redis_failure_detector_ = std::make_unique( io_context_provider_.GetDefaultIOContext(), redis_client_, []() { RAY_LOG(FATAL) << "Redis connection failed. Shutdown GCS."; }); diff --git a/src/ray/gcs/gcs_server/gcs_server.h b/src/ray/gcs/gcs_server/gcs_server.h index 22ece4dda229..6c37e9d8210c 100644 --- a/src/ray/gcs/gcs_server/gcs_server.h +++ b/src/ray/gcs/gcs_server/gcs_server.h @@ -14,6 +14,8 @@ #pragma once +#include + #include "ray/common/asio/asio_util.h" #include "ray/common/asio/instrumented_io_context.h" #include "ray/common/ray_syncer/ray_syncer.h" @@ -218,9 +220,9 @@ class GcsServer { /// The `ClientCallManager` object that is shared by all `NodeManagerWorkerClient`s. rpc::ClientCallManager client_call_manager_; /// Node manager client pool. - std::shared_ptr raylet_client_pool_; + std::unique_ptr raylet_client_pool_; /// The gcs resource manager. - std::shared_ptr gcs_resource_manager_; + std::unique_ptr gcs_resource_manager_; /// The cluster resource scheduler. std::shared_ptr cluster_resource_scheduler_; /// The cluster task manager. @@ -230,15 +232,17 @@ class GcsServer { /// The gcs node manager. std::unique_ptr gcs_node_manager_; /// The health check manager. - std::shared_ptr gcs_healthcheck_manager_; + std::unique_ptr gcs_healthcheck_manager_; /// The gcs redis failure detector. - std::shared_ptr gcs_redis_failure_detector_; + std::unique_ptr gcs_redis_failure_detector_; /// The gcs actor manager. - std::shared_ptr gcs_actor_manager_; + std::unique_ptr gcs_actor_manager_; /// The gcs placement group scheduler. - std::shared_ptr gcs_placement_group_scheduler_; + /// [gcs_placement_group_scheduler_] depends on [raylet_client_pool_]. + std::unique_ptr gcs_placement_group_scheduler_; /// The gcs placement group manager. - std::shared_ptr gcs_placement_group_manager_; + /// [gcs_placement_group_manager_] depends on [gcs_placement_group_scheduler_]. + std::unique_ptr gcs_placement_group_manager_; /// Job info handler and service. std::unique_ptr gcs_job_manager_; std::unique_ptr job_info_service_; diff --git a/src/ray/gcs/gcs_server/test/export_api/gcs_node_manager_export_event_test.cc b/src/ray/gcs/gcs_server/test/export_api/gcs_node_manager_export_event_test.cc index 742716da4b45..61d2d0e8b932 100644 --- a/src/ray/gcs/gcs_server/test/export_api/gcs_node_manager_export_event_test.cc +++ b/src/ray/gcs/gcs_server/test/export_api/gcs_node_manager_export_event_test.cc @@ -41,7 +41,7 @@ class GcsNodeManagerExportAPITest : public ::testing::Test { public: GcsNodeManagerExportAPITest() { raylet_client_ = std::make_shared(); - client_pool_ = std::make_shared( + client_pool_ = std::make_unique( [this](const rpc::Address &) { return raylet_client_; }); gcs_publisher_ = std::make_shared( std::make_unique()); @@ -72,7 +72,7 @@ class GcsNodeManagerExportAPITest : public ::testing::Test { protected: std::shared_ptr gcs_table_storage_; std::shared_ptr raylet_client_; - std::shared_ptr client_pool_; + std::unique_ptr client_pool_; std::shared_ptr gcs_publisher_; instrumented_io_context io_service_; std::string log_dir_; @@ -81,7 +81,7 @@ class GcsNodeManagerExportAPITest : public ::testing::Test { TEST_F(GcsNodeManagerExportAPITest, TestExportEventRegisterNode) { // Test export event is written when a node is added with HandleRegisterNode gcs::GcsNodeManager node_manager( - gcs_publisher_, gcs_table_storage_, client_pool_, ClusterID::Nil()); + gcs_publisher_, gcs_table_storage_, client_pool_.get(), ClusterID::Nil()); auto node = Mocker::GenNodeInfo(); rpc::RegisterNodeRequest register_request; @@ -103,7 +103,7 @@ TEST_F(GcsNodeManagerExportAPITest, TestExportEventRegisterNode) { TEST_F(GcsNodeManagerExportAPITest, TestExportEventUnregisterNode) { // Test export event is written when a node is removed with HandleUnregisterNode gcs::GcsNodeManager node_manager( - gcs_publisher_, gcs_table_storage_, client_pool_, ClusterID::Nil()); + gcs_publisher_, gcs_table_storage_, client_pool_.get(), ClusterID::Nil()); auto node = Mocker::GenNodeInfo(); auto node_id = NodeID::FromBinary(node->node_id()); node_manager.AddNode(node); diff --git a/src/ray/gcs/gcs_server/test/gcs_actor_scheduler_mock_test.cc b/src/ray/gcs/gcs_server/test/gcs_actor_scheduler_mock_test.cc index 6beeb8b7504c..aca66ca39c09 100644 --- a/src/ray/gcs/gcs_server/test/gcs_actor_scheduler_mock_test.cc +++ b/src/ray/gcs/gcs_server/test/gcs_actor_scheduler_mock_test.cc @@ -42,7 +42,7 @@ class GcsActorSchedulerMockTest : public Test { std::make_unique(nullptr, nullptr, nullptr, ClusterID::Nil()); raylet_client = std::make_shared(); core_worker_client = std::make_shared(); - client_pool = std::make_shared( + client_pool = std::make_unique( [this](const rpc::Address &) { return raylet_client; }); local_node_id = NodeID::FromRandom(); auto cluster_resource_scheduler = std::make_shared( @@ -52,7 +52,7 @@ class GcsActorSchedulerMockTest : public Test { /*is_node_available_fn=*/ [](auto) { return true; }, /*is_local_node_with_raylet=*/false); - auto cluster_task_manager = std::make_shared( + cluster_task_manager = std::make_unique( local_node_id, cluster_resource_scheduler, /*get_node_info=*/ @@ -70,10 +70,10 @@ class GcsActorSchedulerMockTest : public Test { io_context, *actor_table, *gcs_node_manager, - cluster_task_manager, + *cluster_task_manager, [this](auto a, auto b, auto c) { schedule_failure_handler(a); }, [this](auto a, const rpc::PushTaskReply) { schedule_success_handler(a); }, - client_pool, + *client_pool, [this](const rpc::Address &) { return core_worker_client; }); auto node_info = std::make_shared(); node_info->set_state(rpc::GcsNodeInfo::ALIVE); @@ -82,14 +82,16 @@ class GcsActorSchedulerMockTest : public Test { worker_id = WorkerID::FromRandom(); gcs_node_manager->AddNode(node_info); } + std::shared_ptr raylet_client; instrumented_io_context io_context; std::shared_ptr store_client; std::unique_ptr actor_table; - std::unique_ptr actor_scheduler; std::unique_ptr gcs_node_manager; + std::unique_ptr cluster_task_manager; + std::unique_ptr actor_scheduler; std::shared_ptr core_worker_client; - std::shared_ptr client_pool; + std::unique_ptr client_pool; std::shared_ptr>> counter; MockCallback schedule_failure_handler; diff --git a/src/ray/gcs/gcs_server/test/gcs_actor_scheduler_test.cc b/src/ray/gcs/gcs_server/test/gcs_actor_scheduler_test.cc index 48a0c0becbe9..6302ee02ed63 100644 --- a/src/ray/gcs/gcs_server/test/gcs_actor_scheduler_test.cc +++ b/src/ray/gcs/gcs_server/test/gcs_actor_scheduler_test.cc @@ -12,10 +12,11 @@ // See the License for the specific language governing permissions and // limitations under the License. +#include + #include // clang-format off -#include "gtest/gtest.h" #include "ray/common/asio/instrumented_io_context.h" #include "ray/gcs/gcs_server/gcs_actor_scheduler.h" #include "ray/gcs/gcs_server/test/gcs_server_test_util.h" @@ -39,7 +40,7 @@ class GcsActorSchedulerTest : public ::testing::Test { store_client_ = std::make_shared(io_service_); gcs_table_storage_ = std::make_shared(io_service_); gcs_node_manager_ = std::make_shared( - gcs_publisher_, gcs_table_storage_, raylet_client_pool_, ClusterID::Nil()); + gcs_publisher_, gcs_table_storage_, raylet_client_pool_.get(), ClusterID::Nil()); gcs_actor_table_ = std::make_shared(store_client_); local_node_id_ = NodeID::FromRandom(); @@ -73,7 +74,7 @@ class GcsActorSchedulerTest : public ::testing::Test { io_service_, *gcs_actor_table_, *gcs_node_manager_, - cluster_task_manager_, + *cluster_task_manager_, /*schedule_failure_handler=*/ [this](std::shared_ptr actor, const rpc::RequestWorkerLeaseReply::SchedulingFailureType failure_type, @@ -84,7 +85,7 @@ class GcsActorSchedulerTest : public ::testing::Test { [this](std::shared_ptr actor, const rpc::PushTaskReply &reply) { success_actors_.emplace_back(std::move(actor)); }, - raylet_client_pool_, + *raylet_client_pool_, /*client_factory=*/ [this](const rpc::Address &address) { return worker_client_; }, /*normal_task_resources_changed_callback=*/ diff --git a/src/ray/gcs/gcs_server/test/gcs_autoscaler_state_manager_test.cc b/src/ray/gcs/gcs_server/test/gcs_autoscaler_state_manager_test.cc index 856261b032e4..2f281fa31844 100644 --- a/src/ray/gcs/gcs_server/test/gcs_autoscaler_state_manager_test.cc +++ b/src/ray/gcs/gcs_server/test/gcs_autoscaler_state_manager_test.cc @@ -61,7 +61,7 @@ class GcsAutoscalerStateManagerTest : public ::testing::Test { void SetUp() override { raylet_client_ = std::make_shared(); - client_pool_ = std::make_shared( + client_pool_ = std::make_unique( [this](const rpc::Address &) { return raylet_client_; }); cluster_resource_manager_ = std::make_unique(io_service_); gcs_node_manager_ = std::make_shared(); @@ -86,7 +86,7 @@ class GcsAutoscalerStateManagerTest : public ::testing::Test { *gcs_node_manager_, *gcs_actor_manager_, *gcs_placement_group_manager_, - client_pool_)); + *client_pool_)); } public: diff --git a/src/ray/gcs/gcs_server/test/gcs_node_manager_test.cc b/src/ray/gcs/gcs_server/test/gcs_node_manager_test.cc index fcc273aa4f9f..eb12d59dbdb3 100644 --- a/src/ray/gcs/gcs_server/test/gcs_node_manager_test.cc +++ b/src/ray/gcs/gcs_server/test/gcs_node_manager_test.cc @@ -28,7 +28,7 @@ class GcsNodeManagerTest : public ::testing::Test { public: GcsNodeManagerTest() { raylet_client_ = std::make_shared(); - client_pool_ = std::make_shared( + client_pool_ = std::make_unique( [this](const rpc::Address &) { return raylet_client_; }); gcs_publisher_ = std::make_shared( std::make_unique()); @@ -37,13 +37,13 @@ class GcsNodeManagerTest : public ::testing::Test { protected: std::shared_ptr gcs_table_storage_; std::shared_ptr raylet_client_; - std::shared_ptr client_pool_; + std::unique_ptr client_pool_; std::shared_ptr gcs_publisher_; }; TEST_F(GcsNodeManagerTest, TestManagement) { gcs::GcsNodeManager node_manager( - gcs_publisher_, gcs_table_storage_, client_pool_, ClusterID::Nil()); + gcs_publisher_, gcs_table_storage_, client_pool_.get(), ClusterID::Nil()); // Test Add/Get/Remove functionality. auto node = Mocker::GenNodeInfo(); auto node_id = NodeID::FromBinary(node->node_id()); @@ -58,7 +58,7 @@ TEST_F(GcsNodeManagerTest, TestManagement) { TEST_F(GcsNodeManagerTest, TestListener) { gcs::GcsNodeManager node_manager( - gcs_publisher_, gcs_table_storage_, client_pool_, ClusterID::Nil()); + gcs_publisher_, gcs_table_storage_, client_pool_.get(), ClusterID::Nil()); // Test AddNodeAddedListener. int node_count = 1000; std::vector> added_nodes; diff --git a/src/ray/gcs/gcs_server/test/gcs_placement_group_manager_mock_test.cc b/src/ray/gcs/gcs_server/test/gcs_placement_group_manager_mock_test.cc index 6cfd689ac168..1e3ef61060c8 100644 --- a/src/ray/gcs/gcs_server/test/gcs_placement_group_manager_mock_test.cc +++ b/src/ray/gcs/gcs_server/test/gcs_placement_group_manager_mock_test.cc @@ -47,7 +47,7 @@ class GcsPlacementGroupManagerMockTest : public Test { gcs_placement_group_manager_ = std::make_unique(io_context_, - gcs_placement_group_scheduler_, + gcs_placement_group_scheduler_.get(), gcs_table_storage_, *resource_manager_, [](auto &) { return ""; }); diff --git a/src/ray/gcs/gcs_server/test/gcs_placement_group_manager_test.cc b/src/ray/gcs/gcs_server/test/gcs_placement_group_manager_test.cc index d407332d8021..ad808b644b67 100644 --- a/src/ray/gcs/gcs_server/test/gcs_placement_group_manager_test.cc +++ b/src/ray/gcs/gcs_server/test/gcs_placement_group_manager_test.cc @@ -89,7 +89,7 @@ class GcsPlacementGroupManagerTest : public ::testing::Test { io_service_, cluster_resource_manager_, *gcs_node_manager_, NodeID::FromRandom()); gcs_placement_group_manager_.reset(new gcs::GcsPlacementGroupManager( io_service_, - mock_placement_group_scheduler_, + mock_placement_group_scheduler_.get(), gcs_table_storage_, *gcs_resource_manager_, [this](const JobID &job_id) { return job_namespace_table_[job_id]; })); diff --git a/src/ray/gcs/gcs_server/test/gcs_placement_group_scheduler_test.cc b/src/ray/gcs/gcs_server/test/gcs_placement_group_scheduler_test.cc index 8bbcaedefc63..093bdaf13fcc 100644 --- a/src/ray/gcs/gcs_server/test/gcs_placement_group_scheduler_test.cc +++ b/src/ray/gcs/gcs_server/test/gcs_placement_group_scheduler_test.cc @@ -55,21 +55,21 @@ class GcsPlacementGroupSchedulerTest : public ::testing::Test { [](auto) { return true; }, /*is_local_node_with_raylet=*/false); gcs_node_manager_ = std::make_shared( - gcs_publisher_, gcs_table_storage_, raylet_client_pool_, ClusterID::Nil()); + gcs_publisher_, gcs_table_storage_, raylet_client_pool_.get(), ClusterID::Nil()); gcs_resource_manager_ = std::make_shared( io_service_, cluster_resource_scheduler_->GetClusterResourceManager(), *gcs_node_manager_, local_node_id); store_client_ = std::make_shared(io_service_); - raylet_client_pool_ = std::make_shared( + raylet_client_pool_ = std::make_unique( [this](const rpc::Address &addr) { return raylet_clients_[addr.port()]; }); scheduler_ = std::make_shared( io_service_, gcs_table_storage_, *gcs_node_manager_, *cluster_resource_scheduler_, - raylet_client_pool_); + *raylet_client_pool_); counter_.reset(new CounterMap()); } @@ -296,7 +296,7 @@ class GcsPlacementGroupSchedulerTest : public ::testing::Test { ABSL_GUARDED_BY(placement_group_requests_mutex_); std::shared_ptr gcs_publisher_; std::shared_ptr gcs_table_storage_; - std::shared_ptr raylet_client_pool_; + std::unique_ptr raylet_client_pool_; std::shared_ptr> counter_; }; diff --git a/src/ray/raylet/scheduling/cluster_task_manager.h b/src/ray/raylet/scheduling/cluster_task_manager.h index 058c40f97fcf..752cbd6b3e98 100644 --- a/src/ray/raylet/scheduling/cluster_task_manager.h +++ b/src/ray/raylet/scheduling/cluster_task_manager.h @@ -161,6 +161,7 @@ class ClusterTaskManager : public ClusterTaskManagerInterface { const NodeID &self_node_id_; /// Responsible for resource tracking/view of the cluster. + /// TODO(hjiang): Use reference instead of shared pointer. std::shared_ptr cluster_resource_scheduler_; /// Function to get the node information of a given node id. From 3496c5b4b1176919722501ab1b39ce42dcc31638 Mon Sep 17 00:00:00 2001 From: Lonnie Liu <95255098+aslonnie@users.noreply.github.com> Date: Mon, 25 Nov 2024 19:21:37 -0800 Subject: [PATCH 43/54] [data] use dask 2022.10.2 (#48898) 2022.10.1 is yanked Signed-off-by: Lonnie Liu --- python/requirements/ml/data-requirements.txt | 4 ++-- python/requirements_compiled.txt | 4 ++-- release/ray_release/byod/requirements_byod_3.9.txt | 12 ++++++------ 3 files changed, 10 insertions(+), 10 deletions(-) diff --git a/python/requirements/ml/data-requirements.txt b/python/requirements/ml/data-requirements.txt index 6e2baa5592fe..de91b5010c7c 100644 --- a/python/requirements/ml/data-requirements.txt +++ b/python/requirements/ml/data-requirements.txt @@ -1,8 +1,8 @@ # Used by CI for datasets and docs. # https://github.com/ray-project/ray/pull/29448#discussion_r1006256498 -dask[complete]==2022.10.1; python_version < '3.12' -distributed==2022.10.1; python_version < '3.12' +dask[complete]==2022.10.2; python_version < '3.12' +distributed==2022.10.2; python_version < '3.12' dask[complete]==2024.6.0; python_version >= '3.12' distributed==2024.6.0; python_version >= '3.12' aioboto3==11.2.0 diff --git a/python/requirements_compiled.txt b/python/requirements_compiled.txt index 9dd3caa3ecab..f3b39647b0e8 100644 --- a/python/requirements_compiled.txt +++ b/python/requirements_compiled.txt @@ -360,7 +360,7 @@ cython==0.29.37 # via # -r /ray/ci/../python/requirements/test-requirements.txt # gpy -dask==2022.10.1 ; python_version < "3.12" +dask==2022.10.2 ; python_version < "3.12" # via # -r /ray/ci/../python/requirements/ml/data-requirements.txt # distributed @@ -398,7 +398,7 @@ dill==0.3.7 # multiprocess distlib==0.3.7 # via virtualenv -distributed==2022.10.1 ; python_version < "3.12" +distributed==2022.10.2 ; python_version < "3.12" # via # -r /ray/ci/../python/requirements/ml/data-requirements.txt # dask diff --git a/release/ray_release/byod/requirements_byod_3.9.txt b/release/ray_release/byod/requirements_byod_3.9.txt index c5b98cebe695..f1dcf9ee13a8 100644 --- a/release/ray_release/byod/requirements_byod_3.9.txt +++ b/release/ray_release/byod/requirements_byod_3.9.txt @@ -657,9 +657,9 @@ cython==0.29.37 \ # via # -c release/ray_release/byod/requirements_compiled.txt # -r release/ray_release/byod/requirements_byod_3.9.in -dask[complete]==2022.10.1 ; python_version < "3.12" \ - --hash=sha256:2e6765bb6011c97c59fd4792540df679c703100443fcd99c82b98d8697295822 \ - --hash=sha256:79d283326045700af0de7e2be57fd663499958c63638bf5076839cbcde64aa3f +dask[complete]==2022.10.2 ; python_version < "3.12" \ + --hash=sha256:42cb43f601709575fa46ce09e74bea83fdd464187024f56954e09d9b428ceaab \ + --hash=sha256:928003a97b890a14c8a09a01f15320d261053bda530a8bf191d84f33db4a63b8 # via # -c release/ray_release/byod/requirements_compiled.txt # -r release/ray_release/byod/requirements_byod_3.9.in @@ -680,9 +680,9 @@ diskcache==5.6.3 \ --hash=sha256:2c3a3fa2743d8535d832ec61c2054a1641f41775aa7c556758a109941e33e4fc \ --hash=sha256:5e31b2d5fbad117cc363ebaf6b689474db18a1f6438bc82358b024abd4c2ca19 # via petastorm -distributed==2022.10.1 ; python_version < "3.12" \ - --hash=sha256:31abab8ecc76951875828a3689d47dc4f20226b3ec99a0dc1af6183d02dbe5fe \ - --hash=sha256:42c6fe7d3bea491e23ce020879c411f2ecfecdb4914a6cb6b4a63530a7b0fa70 +distributed==2022.10.2 ; python_version < "3.12" \ + --hash=sha256:53f0a5bf6efab9a5ab3345cd913f6d3f3d4ea444ee2edbea331c7fef96fd67d0 \ + --hash=sha256:ae4fffdb55c6cb510ba1cbdf2856563af80ebf93e5ceacb91c1ce79e7da108d8 # via # -c release/ray_release/byod/requirements_compiled.txt # dask From 824c29fe0569750f4a73969d386df8943dcc3f3e Mon Sep 17 00:00:00 2001 From: Kai-Hsun Chen Date: Mon, 25 Nov 2024 20:47:23 -0800 Subject: [PATCH 44/54] [core][autoscaler] Remove unused deprecated flags for AutoscalerMonitor (#48919) Signed-off-by: kaihsun --- python/ray/autoscaler/_private/monitor.py | 10 ---------- python/ray/autoscaler/v2/monitor.py | 10 ---------- 2 files changed, 20 deletions(-) diff --git a/python/ray/autoscaler/_private/monitor.py b/python/ray/autoscaler/_private/monitor.py index a641652615df..90b5610f59ae 100644 --- a/python/ray/autoscaler/_private/monitor.py +++ b/python/ray/autoscaler/_private/monitor.py @@ -604,16 +604,6 @@ def log_resource_batch_data_if_desired( parser.add_argument( "--gcs-address", required=False, type=str, help="The address (ip:port) of GCS." ) - parser.add_argument( - "--redis-address", required=False, type=str, help="This is deprecated" - ) - parser.add_argument( - "--redis-password", - required=False, - type=str, - default=None, - help="This is deprecated", - ) parser.add_argument( "--autoscaling-config", required=False, diff --git a/python/ray/autoscaler/v2/monitor.py b/python/ray/autoscaler/v2/monitor.py index c9334244aa8c..558725f3e78f 100644 --- a/python/ray/autoscaler/v2/monitor.py +++ b/python/ray/autoscaler/v2/monitor.py @@ -198,16 +198,6 @@ def record_autoscaler_v2_usage(gcs_client: GcsClient) -> None: parser.add_argument( "--gcs-address", required=False, type=str, help="The address (ip:port) of GCS." ) - parser.add_argument( - "--redis-address", required=False, type=str, help="This is deprecated" - ) - parser.add_argument( - "--redis-password", - required=False, - type=str, - default=None, - help="This is deprecated", - ) parser.add_argument( "--autoscaling-config", required=False, From 104b2bce51933e1c9360c70461f5fea40663190a Mon Sep 17 00:00:00 2001 From: Balaji Veeramani Date: Mon, 25 Nov 2024 22:58:46 -0600 Subject: [PATCH 45/54] [CI] Add "defaults" feature for release test configs (#48880) The Ray Data release test config contains lots of duplicate configuration (e.g., the working directory and team name). To reduce the maintenance cost and improve readability, this PR adds a feature where you can specify defaults. --------- Signed-off-by: Balaji Veeramani --- release/ray_release/config.py | 11 ++++++++ release/ray_release/tests/test_config.py | 36 ++++++++++++++++++++++++ release/release_data_tests.yaml | 33 ++++------------------ 3 files changed, 53 insertions(+), 27 deletions(-) diff --git a/release/ray_release/config.py b/release/ray_release/config.py index 884292545f1d..c38fa4b2f7e4 100644 --- a/release/ray_release/config.py +++ b/release/ray_release/config.py @@ -81,11 +81,22 @@ def _test_definition_invariant( def parse_test_definition(test_definitions: List[TestDefinition]) -> List[Test]: + default_definition = {} tests = [] for test_definition in test_definitions: + if test_definition["name"] == "DEFAULTS": + default_definition = copy.deepcopy(test_definition) + continue + + # Add default values to the test definition. + test_definition = deep_update( + copy.deepcopy(default_definition), test_definition + ) + if "variations" not in test_definition: tests.append(Test(test_definition)) continue + variations = test_definition.pop("variations") _test_definition_invariant( test_definition, diff --git a/release/ray_release/tests/test_config.py b/release/ray_release/tests/test_config.py index 9b1f5adbb929..c7884b116880 100644 --- a/release/ray_release/tests/test_config.py +++ b/release/ray_release/tests/test_config.py @@ -93,6 +93,42 @@ def test_parse_test_definition(): parse_test_definition([invalid_test_definition]) +def test_parse_test_definition_with_defaults(): + test_definitions = yaml.safe_load( + """ + - name: DEFAULTS + working_dir: default_working_dir + - name: sample_test_with_default_working_dir + frequency: nightly + team: sample + cluster: + byod: + type: gpu + cluster_compute: compute.yaml + run: + timeout: 100 + script: python script.py + - name: sample_test_with_overridden_working_dir + working_dir: overridden_working_dir + frequency: nightly + team: sample + cluster: + byod: + type: gpu + cluster_compute: compute.yaml + run: + timeout: 100 + script: python script.py + """ + ) + test_with_default, test_with_override = parse_test_definition(test_definitions) + schema = load_schema_file() + assert not validate_test(test_with_default, schema) + assert not validate_test(test_with_override, schema) + assert test_with_default["working_dir"] == "default_working_dir" + assert test_with_override["working_dir"] == "overridden_working_dir" + + def test_schema_validation(): test = VALID_TEST.copy() diff --git a/release/release_data_tests.yaml b/release/release_data_tests.yaml index 752aaacb4fb0..1a7adae918be 100644 --- a/release/release_data_tests.yaml +++ b/release/release_data_tests.yaml @@ -1,8 +1,4 @@ -############### -# Reading tests -############### - -- name: read_parquet +- name: DEFAULTS group: data-tests working_dir: nightly_tests/dataset @@ -13,23 +9,17 @@ byod: type: gpu cluster_compute: multi_node_autoscaling_compute.yaml + +############### +# Reading tests +############### +- name: read_parquet run: timeout: 3600 script: python read_and_consume_benchmark.py s3://ray-benchmark-data/parquet/10TiB --format parquet --iterate - name: read_images - group: data-tests - working_dir: nightly_tests/dataset - - frequency: nightly - team: data - - cluster: - byod: - type: gpu - cluster_compute: multi_node_autoscaling_compute.yaml - run: timeout: 3600 script: python read_and_consume_benchmark.py s3://air-example-data-2/300G-image-data-synthetic-raw --format image --iterate @@ -39,17 +29,6 @@ ############### - name: count_parquet - group: data-tests - working_dir: nightly_tests/dataset - - frequency: nightly - team: data - - cluster: - byod: - type: gpu - cluster_compute: multi_node_autoscaling_compute.yaml - run: timeout: 600 script: python read_and_consume_benchmark.py s3://ray-benchmark-data/parquet/10TiB --format parquet --count From 5e661d2adec29bf9b638ad2d795e41bb03b62977 Mon Sep 17 00:00:00 2001 From: Kai-Hsun Chen Date: Mon, 25 Nov 2024 21:25:45 -0800 Subject: [PATCH 46/54] [core] Make `include_log_monitor` configurable in the `ray start` command (#48891) Signed-off-by: kaihsun --- python/ray/scripts/scripts.py | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/python/ray/scripts/scripts.py b/python/ray/scripts/scripts.py index 1f26a483a7aa..eed702bb7438 100644 --- a/python/ray/scripts/scripts.py +++ b/python/ray/scripts/scripts.py @@ -622,6 +622,15 @@ def debug(address: str, verbose: bool): type=str, help="a JSON serialized dictionary mapping label name to label value.", ) +@click.option( + "--include-log-monitor", + default=None, + type=bool, + help="If set to True or left unset, a log monitor will start monitoring " + "the log files of all processes on this node and push their contents to GCS. " + "Only one log monitor should be started per physical host to avoid log " + "duplication on the driver process.", +) @add_click_logging_options @PublicAPI def start( @@ -668,6 +677,7 @@ def start( ray_debugger_external, disable_usage_stats, labels, + include_log_monitor, ): """Start Ray processes manually on the local machine.""" @@ -757,6 +767,7 @@ def start( no_monitor=no_monitor, tracing_startup_hook=tracing_startup_hook, ray_debugger_external=ray_debugger_external, + include_log_monitor=include_log_monitor, ) if ray_constants.RAY_START_HOOK in os.environ: From 58425b9ff03989d5a50cdfb59ac66742b512dab6 Mon Sep 17 00:00:00 2001 From: Mengjin Yan Date: Mon, 25 Nov 2024 21:36:51 -0800 Subject: [PATCH 47/54] [Core] Add task_name, task_function_name and actor_name in Structured Logging (#48703) Signed-off-by: Mengjin Yan --- python/ray/_private/ray_logging/constants.py | 3 + python/ray/_private/ray_logging/filters.py | 9 ++ python/ray/_private/worker.py | 8 ++ python/ray/_raylet.pyx | 55 +++++++- python/ray/includes/libcoreworker.pxd | 2 + python/ray/runtime_context.py | 124 ++++++++++++++++-- python/ray/serve/tests/test_logging.py | 12 ++ python/ray/tests/test_logging_2.py | 6 + python/ray/tests/test_runtime_context.py | 130 +++++++++++++++++++ src/ray/core_worker/core_worker.h | 13 ++ 10 files changed, 350 insertions(+), 12 deletions(-) diff --git a/python/ray/_private/ray_logging/constants.py b/python/ray/_private/ray_logging/constants.py index de84d510c16c..54552bdfe1d7 100644 --- a/python/ray/_private/ray_logging/constants.py +++ b/python/ray/_private/ray_logging/constants.py @@ -41,6 +41,9 @@ class LogKey(str, Enum): NODE_ID = "node_id" ACTOR_ID = "actor_id" TASK_ID = "task_id" + ACTOR_NAME = "actor_name" + TASK_NAME = "task_name" + TASK_FUNCTION_NAME = "task_func_name" # Logger built-in context ASCTIME = "asctime" diff --git a/python/ray/_private/ray_logging/filters.py b/python/ray/_private/ray_logging/filters.py index e7003022040a..91233a2b11c6 100644 --- a/python/ray/_private/ray_logging/filters.py +++ b/python/ray/_private/ray_logging/filters.py @@ -20,4 +20,13 @@ def filter(self, record): task_id = runtime_context.get_task_id() if task_id is not None: setattr(record, LogKey.TASK_ID.value, task_id) + task_name = runtime_context.get_task_name() + if task_name is not None: + setattr(record, LogKey.TASK_NAME.value, task_name) + task_function_name = runtime_context.get_task_function_name() + if task_function_name is not None: + setattr(record, LogKey.TASK_FUNCTION_NAME.value, task_function_name) + actor_name = runtime_context.get_actor_name() + if actor_name is not None: + setattr(record, LogKey.ACTOR_NAME.value, actor_name) return True diff --git a/python/ray/_private/worker.py b/python/ray/_private/worker.py index e62375c5e523..d2b0cf3b013d 100644 --- a/python/ray/_private/worker.py +++ b/python/ray/_private/worker.py @@ -531,6 +531,14 @@ def actor_name(self): def current_task_id(self): return self.core_worker.get_current_task_id() + @property + def current_task_name(self): + return self.core_worker.get_current_task_name() + + @property + def current_task_function_name(self): + return self.core_worker.get_current_task_function_name() + @property def current_node_id(self): return self.core_worker.get_current_node_id() diff --git a/python/ray/_raylet.pyx b/python/ray/_raylet.pyx index 944adba8fc33..f3d93dce33ba 100644 --- a/python/ray/_raylet.pyx +++ b/python/ray/_raylet.pyx @@ -261,6 +261,9 @@ cdef optional[ObjectIDIndexType] NULL_PUT_INDEX = nullopt # https://docs.python.org/3/library/contextvars.html#contextvars.ContextVar # It is thread-safe. async_task_id = contextvars.ContextVar('async_task_id', default=None) +async_task_name = contextvars.ContextVar('async_task_name', default=None) +async_task_function_name = contextvars.ContextVar('async_task_function_name', + default=None) class DynamicObjectRefGenerator: @@ -1815,7 +1818,8 @@ cdef void execute_task( return core_worker.run_async_func_or_coro_in_event_loop( async_function, function_descriptor, name_of_concurrency_group_to_execute, task_id=task_id, - func_args=(actor, *arguments), func_kwargs=kwarguments) + task_name=task_name, func_args=(actor, *arguments), + func_kwargs=kwarguments) return function(actor, *arguments, **kwarguments) @@ -1927,7 +1931,8 @@ cdef void execute_task( execute_streaming_generator_async(context), function_descriptor, name_of_concurrency_group_to_execute, - task_id=task_id) + task_id=task_id, + task_name=task_name) else: execute_streaming_generator_sync(context) @@ -3415,6 +3420,48 @@ cdef class CoreWorker: with nogil: CCoreWorkerProcess.GetCoreWorker().Exit(c_exit_type, detail, null_ptr) + def get_current_task_name(self) -> str: + """Return the current task name. + + If it is a normal task, it returns the task name from the main thread. + If it is a threaded actor, it returns the task name for the current thread. + If it is async actor, it returns the task name stored in contextVar for + the current asyncio task. + """ + # We can only obtain the correct task name within asyncio task + # via async_task_name contextvar. We try this first. + # It is needed because the core worker's GetCurrentTask API + # doesn't have asyncio context, thus it cannot return the + # correct task name. + task_name = async_task_name.get() + if task_name is None: + # if it is not within asyncio context, fallback to TaskName + # obtainable from core worker. + task_name = CCoreWorkerProcess.GetCoreWorker().GetCurrentTaskName() \ + .decode("utf-8") + return task_name + + def get_current_task_function_name(self) -> str: + """Return the current task function. + + If it is a normal task, it returns the task function from the main thread. + If it is a threaded actor, it returns the task function for the current thread. + If it is async actor, it returns the task function stored in contextVar for + the current asyncio task. + """ + # We can only obtain the correct task function within asyncio task + # via async_task_function_name contextvar. We try this first. + # It is needed because the core Worker's GetCurrentTask API + # doesn't have asyncio context, thus it cannot return the + # correct task function. + task_function_name = async_task_function_name.get() + if task_function_name is None: + # if it is not within asyncio context, fallback to TaskName + # obtainable from core worker. + task_function_name = CCoreWorkerProcess.GetCoreWorker() \ + .GetCurrentTaskFunctionName().decode("utf-8") + return task_function_name + def get_current_task_id(self) -> TaskID: """Return the current task ID. @@ -4822,6 +4869,7 @@ cdef class CoreWorker: specified_cgname: str, *, task_id: Optional[TaskID] = None, + task_name: Optional[str] = None, func_args: Optional[Tuple] = None, func_kwargs: Optional[Dict] = None, ): @@ -4868,6 +4916,9 @@ cdef class CoreWorker: try: if task_id: async_task_id.set(task_id) + if task_name is not None: + async_task_name.set(task_name) + async_task_function_name.set(function_descriptor.repr) if inspect.isawaitable(func_or_coro): coroutine = func_or_coro diff --git a/python/ray/includes/libcoreworker.pxd b/python/ray/includes/libcoreworker.pxd index 242c5f10dd49..87f5d59a8583 100644 --- a/python/ray/includes/libcoreworker.pxd +++ b/python/ray/includes/libcoreworker.pxd @@ -191,6 +191,8 @@ cdef extern from "ray/core_worker/core_worker.h" nogil: CJobID GetCurrentJobId() CTaskID GetCurrentTaskId() + const c_string GetCurrentTaskName() + const c_string GetCurrentTaskFunctionName() void UpdateTaskIsDebuggerPaused( const CTaskID &task_id, const c_bool is_debugger_paused) diff --git a/python/ray/runtime_context.py b/python/ray/runtime_context.py index 78f7cee9502d..5cacae69371b 100644 --- a/python/ray/runtime_context.py +++ b/python/ray/runtime_context.py @@ -124,7 +124,7 @@ def get_worker_id(self) -> str: @property @Deprecated(message="Use get_task_id() instead", warning=True) def task_id(self): - """Get current task ID for this worker or driver. + """Get current task ID for this worker. Task ID is the id of a Ray task. This shouldn't be used in a driver process. @@ -155,7 +155,7 @@ def f(): Returns: The current worker's task id. None if there's no task id. """ - # only worker mode has actor_id + # only worker mode has task_id assert ( self.worker.mode == ray._private.worker.WORKER_MODE ), f"This method is only available when the process is a\ @@ -165,7 +165,7 @@ def f(): return task_id if not task_id.is_nil() else None def get_task_id(self) -> Optional[str]: - """Get current task ID for this worker or driver. + """Get current task ID for this worker. Task ID is the id of a Ray task. The ID will be in hex format. This shouldn't be used in a driver process. @@ -201,7 +201,7 @@ def get_task_id(): Returns: The current worker's task id in hex. None if there's no task id. """ - # only worker mode has actor_id + # only worker mode has task_id if self.worker.mode != ray._private.worker.WORKER_MODE: logger.warning( "This method is only available when the process is a " @@ -212,12 +212,116 @@ def get_task_id(): return task_id.hex() if not task_id.is_nil() else None def _get_current_task_id(self) -> TaskID: - async_task_id = ray._raylet.async_task_id.get() - if async_task_id is None: - task_id = self.worker.current_task_id - else: - task_id = async_task_id - return task_id + return self.worker.current_task_id + + def get_task_name(self) -> Optional[str]: + """Get current task name for this worker. + + Task name by default is the task's funciton call string. It can also be + specified in options when triggering a task. + + Example: + + .. testcode:: + + import ray + + @ray.remote + class Actor: + def get_task_name(self): + return ray.get_runtime_context().get_task_name() + + @ray.remote + class AsyncActor: + async def get_task_name(self): + return ray.get_runtime_context().get_task_name() + + @ray.remote + def get_task_name(): + return ray.get_runtime_context().get_task_name() + + a = Actor.remote() + b = AsyncActor.remote() + # Task names are available for actor tasks. + print(ray.get(a.get_task_name.remote())) + # Task names are avaiable for async actor tasks. + print(ray.get(b.get_task_name.remote())) + # Task names are available for normal tasks. + # Get default task name + print(ray.get(get_task_name.remote())) + # Get specified task name + print(ray.get(get_task_name.options(name="task_name").remote())) + + .. testoutput:: + :options: +MOCK + + Actor.get_task_name + AsyncActor.get_task_name + get_task_name + task_nams + + Returns: + The current worker's task name + """ + # only worker mode has task_name + if self.worker.mode != ray._private.worker.WORKER_MODE: + logger.warning( + "This method is only available when the process is a " + f"worker. Current mode: {self.worker.mode}" + ) + return None + return self.worker.current_task_name + + def get_task_function_name(self) -> Optional[str]: + """Get current task function name string for this worker. + + Example: + + .. testcode:: + + import ray + + @ray.remote + class Actor: + def get_task_function_name(self): + return ray.get_runtime_context().get_task_function_name() + + @ray.remote + class AsyncActor: + async def get_task_function_name(self): + return ray.get_runtime_context().get_task_function_name() + + @ray.remote + def get_task_function_name(): + return ray.get_runtime_context().get_task_function_name() + + a = Actor.remote() + b = AsyncActor.remote() + # Task functions are available for actor tasks. + print(ray.get(a.get_task_function_name.remote())) + # Task functions are available for async actor tasks. + print(ray.get(b.get_task_function_name.remote())) + # Task functions are available for normal tasks. + print(ray.get(get_task_function_name.remote())) + + .. testoutput:: + :options: +MOCK + + [python modual name].Actor.get_task_function_name + [python modual name].AsyncActor.get_task_function_name + [python modual name].get_task_function_name + + Returns: + The current worker's task function call string + """ + # only worker mode has task_function_name + if self.worker.mode != ray._private.worker.WORKER_MODE: + logger.warning( + "This method is only available when the process is a " + f"worker. Current mode: {self.worker.mode}" + ) + return None + return self.worker.current_task_function_name @property @Deprecated(message="Use get_actor_id() instead", warning=True) diff --git a/python/ray/serve/tests/test_logging.py b/python/ray/serve/tests/test_logging.py index 061dafd75fe0..24f9a47a01a1 100644 --- a/python/ray/serve/tests/test_logging.py +++ b/python/ray/serve/tests/test_logging.py @@ -351,6 +351,9 @@ def fn(*args): "actor_id": ray.get_runtime_context().get_actor_id(), "worker_id": ray.get_runtime_context().get_worker_id(), "node_id": ray.get_runtime_context().get_node_id(), + "task_name": ray.get_runtime_context().get_task_name(), + "task_func_name": ray.get_runtime_context().get_task_function_name(), + "actor_name": ray.get_runtime_context().get_actor_name(), } @serve.deployment( @@ -369,6 +372,9 @@ def __call__(self, req: starlette.requests.Request): "actor_id": ray.get_runtime_context().get_actor_id(), "worker_id": ray.get_runtime_context().get_worker_id(), "node_id": ray.get_runtime_context().get_node_id(), + "task_name": ray.get_runtime_context().get_task_name(), + "task_func_name": ray.get_runtime_context().get_task_function_name(), + "actor_name": ray.get_runtime_context().get_actor_name(), } serve.run(fn.bind(), name="app1", route_prefix="/fn") @@ -418,6 +424,9 @@ def check_log(): f'"worker_id": "{resp["worker_id"]}", ' f'"node_id": "{resp["node_id"]}", ' f'"actor_id": "{resp["actor_id"]}", ' + f'"task_name": "{resp["task_name"]}", ' + f'"task_func_name": "{resp["task_func_name"]}", ' + f'"actor_name": "{resp["actor_name"]}", ' f'"deployment": "{resp["app_name"]}_fn", ' f'"replica": "{method_replica_id}", ' f'"component_name": "replica".*' @@ -430,6 +439,9 @@ def check_log(): f'"worker_id": "{resp2["worker_id"]}", ' f'"node_id": "{resp2["node_id"]}", ' f'"actor_id": "{resp2["actor_id"]}", ' + f'"task_name": "{resp2["task_name"]}", ' + f'"task_func_name": "{resp2["task_func_name"]}", ' + f'"actor_name": "{resp2["actor_name"]}", ' f'"deployment": "{resp2["app_name"]}_Model", ' f'"replica": "{class_method_replica_id}", ' f'"component_name": "replica".*' diff --git a/python/ray/tests/test_logging_2.py b/python/ray/tests/test_logging_2.py index b48b04e44a59..9d5be165f9ac 100644 --- a/python/ray/tests/test_logging_2.py +++ b/python/ray/tests/test_logging_2.py @@ -54,11 +54,14 @@ def f(): "worker_id": runtime_context.get_worker_id(), "node_id": runtime_context.get_node_id(), "task_id": runtime_context.get_task_id(), + "task_name": runtime_context.get_task_name(), + "task_func_name": runtime_context.get_task_function_name(), } for attr in should_exist: assert hasattr(record, attr) assert getattr(record, attr) == expected_values[attr] assert not hasattr(record, "actor_id") + assert not hasattr(record, "actor_name") obj_ref = f.remote() ray.get(obj_ref) @@ -77,7 +80,10 @@ def f(self): "worker_id": runtime_context.get_worker_id(), "node_id": runtime_context.get_node_id(), "actor_id": runtime_context.get_actor_id(), + "actor_name": runtime_context.get_actor_name(), "task_id": runtime_context.get_task_id(), + "task_name": runtime_context.get_task_name(), + "task_func_name": runtime_context.get_task_function_name(), } for attr in should_exist: assert hasattr(record, attr) diff --git a/python/ray/tests/test_runtime_context.py b/python/ray/tests/test_runtime_context.py index 3835a7e7eb3a..23bb39c6ab4d 100644 --- a/python/ray/tests/test_runtime_context.py +++ b/python/ray/tests/test_runtime_context.py @@ -264,6 +264,136 @@ def test_auto_init(shutdown_only): assert ray.is_initialized() +def test_get_task_name(shutdown_only): + ray.init() + + # for a normal task + @ray.remote + def get_task_name_for_normal_task(): + return ray.get_runtime_context().get_task_name() + + expected_task_name = "normal_task_name" + task_name = ray.get( + get_task_name_for_normal_task.options(name=expected_task_name).remote() + ) + assert ( + task_name == expected_task_name + ), f"Check normal task name failed. expected={expected_task_name}, \ +actual={task_name}" + + # for an actor task + @ray.remote + class Actor: + def get_task_name_for_actor_task(self): + return ray.get_runtime_context().get_task_name() + + expected_task_name = "Actor.get_task_name_for_actor_task" + actor = Actor.remote() + task_name = ray.get(actor.get_task_name_for_actor_task.remote()) + assert ( + task_name == expected_task_name + ), f"Check actor task name failed. expected={expected_task_name}, \ +actual={task_name}" + + # for a threaded actor task + @ray.remote + class ThreadedActor: + def get_task_name_for_threaded_actor_task(self): + return ray.get_runtime_context().get_task_name() + + expected_task_name = "ThreadedActor.get_task_name_for_threaded_actor_task" + threaded_actor = ThreadedActor.options(max_concurrency=2).remote() + task_name = ray.get(threaded_actor.get_task_name_for_threaded_actor_task.remote()) + assert ( + task_name == expected_task_name + ), f"Check actor task name failed. expected={expected_task_name}, \ +actual={task_name}" + + # for a async actor task + @ray.remote + class AsyncActor: + async def get_task_name_for_async_actor_task(self): + return ray.get_runtime_context().get_task_name() + + expected_task_name = "AsyncActor.get_task_name_for_async_actor_task" + async_actor = AsyncActor.remote() + task_name = ray.get(async_actor.get_task_name_for_async_actor_task.remote()) + assert ( + task_name == expected_task_name + ), f"Check actor task name failed. expected={expected_task_name}, \ +actual={task_name}" + + +def test_get_task_function_name(shutdown_only): + ray.init() + + # for a normal task + @ray.remote + def get_task_function_name_for_normal_task(): + return ray.get_runtime_context().get_task_function_name() + + expected_task_function_name = __name__ + ".get_task_function_name_for_normal_task" + task_function_name = ray.get(get_task_function_name_for_normal_task.remote()) + assert ( + task_function_name == expected_task_function_name + ), f"Check normal task function failed. expected={expected_task_function_name}, \ +actual={task_function_name}" + + # for an actor task + @ray.remote + class Actor: + def get_task_function_name_for_actor_task(self): + return ray.get_runtime_context().get_task_function_name() + + expected_task_function_name = ( + __name__ + ".Actor.get_task_function_name_for_actor_task" + ) + actor = Actor.remote() + task_function_name = ray.get(actor.get_task_function_name_for_actor_task.remote()) + assert ( + task_function_name == expected_task_function_name + ), f"Check actor task function failed. expected={expected_task_function_name}, \ +actual={task_function_name}" + + # for a threaded actor task + @ray.remote + class ThreadedActor: + def get_task_function_name_for_threaded_actor_task(self): + return ray.get_runtime_context().get_task_function_name() + + expected_task_function_name = ( + __name__ + ".ThreadedActor.get_task_function_name_for_threaded_actor_task" + ) + threaded_actor = ThreadedActor.options(max_concurrency=2).remote() + task_function_name = ray.get( + threaded_actor.get_task_function_name_for_threaded_actor_task.remote() + ) + assert ( + task_function_name == expected_task_function_name + ), f"Check actor task function failed. expected={expected_task_function_name}, \ +actual={task_function_name}" + + # for a async actor task + @ray.remote + class AsyncActor: + async def get_task_function_name_for_async_actor_task(self): + return ray.get_runtime_context().get_task_function_name() + + expected_task_function_name = ( + __name__ + + ".test_get_task_function_name..AsyncActor.\ +get_task_function_name_for_async_actor_task" + ) + async_actor = AsyncActor.remote() + task_function_name = ray.get( + async_actor.get_task_function_name_for_async_actor_task.remote() + ) + assert ( + task_function_name == expected_task_function_name + ), f"Check actor task function failed. expected={expected_task_function_name}, \ +actual={task_function_name}" + + def test_async_actor_task_id(shutdown_only): ray.init() diff --git a/src/ray/core_worker/core_worker.h b/src/ray/core_worker/core_worker.h index bdf7c13f15d5..4ab8a5cd0f3c 100644 --- a/src/ray/core_worker/core_worker.h +++ b/src/ray/core_worker/core_worker.h @@ -227,6 +227,19 @@ class CoreWorker : public rpc::CoreWorkerServiceHandler { const TaskID &GetCurrentTaskId() const { return worker_context_.GetCurrentTaskID(); } + const std::string GetCurrentTaskName() const { + return worker_context_.GetCurrentTask() != nullptr + ? worker_context_.GetCurrentTask()->GetName() + : ""; + } + + const std::string GetCurrentTaskFunctionName() const { + return (worker_context_.GetCurrentTask() != nullptr && + worker_context_.GetCurrentTask()->FunctionDescriptor() != nullptr) + ? worker_context_.GetCurrentTask()->FunctionDescriptor()->CallSiteString() + : ""; + } + /// Controls the is debugger paused flag. /// /// \param task_id The task id of the task to update. From 49e3061e356ed6e81e3a31547deb3e4028023b54 Mon Sep 17 00:00:00 2001 From: dentiny Date: Tue, 26 Nov 2024 09:13:04 -0800 Subject: [PATCH 48/54] [core] Fix process start type annotation (#48930) Signed-off-by: hjiang --- python/ray/_private/services.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/python/ray/_private/services.py b/python/ray/_private/services.py index 0de0ba78405f..23461e950286 100644 --- a/python/ray/_private/services.py +++ b/python/ray/_private/services.py @@ -834,8 +834,8 @@ def start_ray_process( use_valgrind_profiler: bool = False, use_perftools_profiler: bool = False, use_tmux: bool = False, - stdout_file: Optional[str] = None, - stderr_file: Optional[str] = None, + stdout_file: Optional[IO[AnyStr]] = None, + stderr_file: Optional[IO[AnyStr]] = None, pipe_stdin: bool = False, ): """Start one of the Ray processes. @@ -1443,8 +1443,8 @@ def start_gcs_server( redis_address: str, log_dir: str, session_name: str, - stdout_file: Optional[str] = None, - stderr_file: Optional[str] = None, + stdout_file: Optional[IO[AnyStr]] = None, + stderr_file: Optional[IO[AnyStr]] = None, redis_password: Optional[str] = None, config: Optional[dict] = None, fate_share: Optional[bool] = None, From 0f2c62c09c6e57cfd5afbda9f8b72205fec2dea7 Mon Sep 17 00:00:00 2001 From: Edward Oakes Date: Tue, 26 Nov 2024 11:14:02 -0600 Subject: [PATCH 49/54] [serve] Add FF to run sync methods in a threadpool (#48897) ## Why are these changes needed? Adds a feature flag to run sync user-defined methods in a threadpool by default. This matches the existing behavior when using a FastAPI ingress. This should address a lot of user confusion and make it easier to write performant code by default. For example, just sticking a torch model call in a sync method will now provide reasonable performance out of the box. However, there may be some existing user code that is not thread safe, so we need to do a gentle migration. This PR introduces the behavior behind a feature flag and warns users about the upcoming change and how to opt into the new behavior or maintain existing behavior once it does (just adding `async def` will do it). I've opted to set the max thread pool size to `max_ongoing_requests`, which seems like a reasonable policy. If needed we can add a user-facing API for this in the future. TODO before merging: - [x] Get it working for sync generators. - [x] Add warning for default change (people can keep behavior by changing to async def). - [x] Add/update UserCallableWrapper tests. - [x] Add/update some integration tests (verify that request context is set correctly!). - [x] Set maximum thread pool size. ## Related issue number Closes https://github.com/ray-project/ray/issues/44354 Closes https://github.com/ray-project/ray/issues/44403 Closes https://github.com/ray-project/ray/issues/48903 --------- Signed-off-by: Edward Oakes --- python/ray/serve/_private/constants.py | 14 ++ .../ray/serve/_private/local_testing_mode.py | 6 +- python/ray/serve/_private/replica.py | 145 +++++++++++++++--- python/ray/serve/tests/BUILD | 22 +++ .../serve/tests/test_replica_sync_methods.py | 127 +++++++++++++++ .../tests/unit/test_user_callable_wrapper.py | 101 +++++++++--- 6 files changed, 374 insertions(+), 41 deletions(-) create mode 100644 python/ray/serve/tests/test_replica_sync_methods.py diff --git a/python/ray/serve/_private/constants.py b/python/ray/serve/_private/constants.py index f4c2adac37d1..6e45bae19ceb 100644 --- a/python/ray/serve/_private/constants.py +++ b/python/ray/serve/_private/constants.py @@ -355,3 +355,17 @@ RAY_SERVE_FORCE_LOCAL_TESTING_MODE = ( os.environ.get("RAY_SERVE_FORCE_LOCAL_TESTING_MODE", "0") == "1" ) + +# Run sync methods defined in the replica in a thread pool by default. +RAY_SERVE_RUN_SYNC_IN_THREADPOOL = ( + os.environ.get("RAY_SERVE_RUN_SYNC_IN_THREADPOOL", "0") == "1" +) + +RAY_SERVE_RUN_SYNC_IN_THREADPOOL_WARNING = ( + "Calling sync method '{method_name}' directly on the " + "asyncio loop. In a future version, sync methods will be run in a " + "threadpool by default. Ensure your sync methods are thread safe " + "or keep the existing behavior by making them `async def`. Opt " + "into the new behavior by setting " + "RAY_SERVE_RUN_SYNC_IN_THREADPOOL=1." +) diff --git a/python/ray/serve/_private/local_testing_mode.py b/python/ray/serve/_private/local_testing_mode.py index 6ccc16cd3628..84525625a938 100644 --- a/python/ray/serve/_private/local_testing_mode.py +++ b/python/ray/serve/_private/local_testing_mode.py @@ -10,7 +10,10 @@ import ray from ray import cloudpickle from ray.serve._private.common import DeploymentID, RequestMetadata -from ray.serve._private.constants import SERVE_LOGGER_NAME +from ray.serve._private.constants import ( + RAY_SERVE_RUN_SYNC_IN_THREADPOOL, + SERVE_LOGGER_NAME, +) from ray.serve._private.replica import UserCallableWrapper from ray.serve._private.replica_result import ReplicaResult from ray.serve._private.router import Router @@ -66,6 +69,7 @@ def make_local_deployment_handle( deployment.init_args, deployment.init_kwargs, deployment_id=deployment_id, + run_sync_methods_in_threadpool=RAY_SERVE_RUN_SYNC_IN_THREADPOOL, ) try: logger.info(f"Initializing local replica class for {deployment_id}.") diff --git a/python/ray/serve/_private/replica.py b/python/ray/serve/_private/replica.py index 079e3039d5d0..23fc7d237f94 100644 --- a/python/ray/serve/_private/replica.py +++ b/python/ray/serve/_private/replica.py @@ -1,5 +1,6 @@ import asyncio import concurrent.futures +import functools import inspect import logging import os @@ -7,9 +8,9 @@ import threading import time import traceback +import warnings from abc import ABC, abstractmethod from contextlib import contextmanager -from functools import wraps from importlib import import_module from typing import ( Any, @@ -23,6 +24,7 @@ ) import starlette.responses +from anyio import to_thread from starlette.types import ASGIApp, Message import ray @@ -47,6 +49,8 @@ HEALTH_CHECK_METHOD, RAY_SERVE_COLLECT_AUTOSCALING_METRICS_ON_HANDLE, RAY_SERVE_REPLICA_AUTOSCALING_METRIC_RECORD_PERIOD_S, + RAY_SERVE_RUN_SYNC_IN_THREADPOOL, + RAY_SERVE_RUN_SYNC_IN_THREADPOOL_WARNING, RECONFIGURE_METHOD, SERVE_CONTROLLER_NAME, SERVE_LOGGER_NAME, @@ -274,6 +278,7 @@ def __init__( init_args, init_kwargs, deployment_id=self._deployment_id, + run_sync_methods_in_threadpool=RAY_SERVE_RUN_SYNC_IN_THREADPOOL, ) # Guards against calling the user's callable constructor multiple times. @@ -602,6 +607,11 @@ async def initialize(self, deployment_config: DeploymentConfig): self._user_callable_initialized = True if deployment_config: + await asyncio.wrap_future( + self._user_callable_wrapper.set_sync_method_threadpool_limit( + deployment_config.max_ongoing_requests + ) + ) await asyncio.wrap_future( self._user_callable_wrapper.call_reconfigure( deployment_config.user_config @@ -635,6 +645,11 @@ async def reconfigure(self, deployment_config: DeploymentConfig): if logging_config_changed: self._configure_logger_and_profilers(deployment_config.logging_config) + await asyncio.wrap_future( + self._user_callable_wrapper.set_sync_method_threadpool_limit( + deployment_config.max_ongoing_requests + ) + ) if user_config_changed: await asyncio.wrap_future( self._user_callable_wrapper.call_reconfigure( @@ -990,6 +1005,7 @@ def __init__( init_kwargs: Dict, *, deployment_id: DeploymentID, + run_sync_methods_in_threadpool: bool, ): if not (inspect.isfunction(deployment_def) or inspect.isclass(deployment_def)): raise TypeError( @@ -1003,6 +1019,8 @@ def __init__( self._is_function = inspect.isfunction(deployment_def) self._deployment_id = deployment_id self._destructor_called = False + self._run_sync_methods_in_threadpool = run_sync_methods_in_threadpool + self._warned_about_sync_method_change = False # Will be populated in `initialize_callable`. self._callable = None @@ -1033,7 +1051,7 @@ def _run_on_user_code_event_loop(f: Callable) -> Callable: f ), "_run_on_user_code_event_loop can only be used on coroutine functions." - @wraps(f) + @functools.wraps(f) def wrapper(self, *args, **kwargs) -> concurrent.futures.Future: return asyncio.run_coroutine_threadsafe( f(self, *args, **kwargs), @@ -1042,6 +1060,12 @@ def wrapper(self, *args, **kwargs) -> concurrent.futures.Future: return wrapper + @_run_on_user_code_event_loop + async def set_sync_method_threadpool_limit(self, limit: int): + # NOTE(edoakes): the limit is thread local, so this must + # be run on the user code event loop. + to_thread.current_default_thread_limiter().total_tokens = limit + def _get_user_callable_method(self, method_name: str) -> Callable: if self._is_function: return self._callable @@ -1082,17 +1106,89 @@ async def _send_user_result_over_asgi( else: await Response(result).send(scope, receive, send) - async def _call_func_or_gen(self, callable: Callable, *args, **kwargs) -> Any: + async def _call_func_or_gen( + self, + callable: Callable, + *, + args: Optional[Tuple[Any]] = None, + kwargs: Optional[Dict[str, Any]] = None, + request_metadata: Optional[RequestMetadata] = None, + generator_result_callback: Optional[Callable] = None, + run_sync_methods_in_threadpool_override: Optional[bool] = None, + ) -> Tuple[Any, bool]: """Call the callable with the provided arguments. This is a convenience wrapper that will work for `def`, `async def`, generator, and async generator functions. + + Returns the result and a boolean indicating if the result was a sync generator + that has already been consumed. """ - result = callable(*args, **kwargs) - if inspect.iscoroutine(result): - result = await result + sync_gen_consumed = False + args = args if args is not None else tuple() + kwargs = kwargs if kwargs is not None else dict() + run_sync_in_threadpool = ( + self._run_sync_methods_in_threadpool + if run_sync_methods_in_threadpool_override is None + else run_sync_methods_in_threadpool_override + ) + is_sync_method = ( + inspect.isfunction(callable) or inspect.ismethod(callable) + ) and not ( + inspect.iscoroutinefunction(callable) + or inspect.isasyncgenfunction(callable) + ) - return result + if is_sync_method and run_sync_in_threadpool: + is_generator = inspect.isgeneratorfunction(callable) + if is_generator: + sync_gen_consumed = True + if request_metadata and not request_metadata.is_streaming: + # TODO(edoakes): make this check less redundant with the one in + # _handle_user_method_result. + raise TypeError( + f"Method '{callable.__name__}' returned a generator. " + "You must use `handle.options(stream=True)` to call " + "generators on a deployment." + ) + + def run_callable(): + result = callable(*args, **kwargs) + if is_generator: + for r in result: + # TODO(edoakes): make this less redundant with the handling in + # _handle_user_method_result. + if request_metadata and request_metadata.is_grpc_request: + r = (request_metadata.grpc_context, r.SerializeToString()) + generator_result_callback(r) + + result = None + + return result + + # NOTE(edoakes): we use anyio.to_thread here because it's what Starlette + # uses (and therefore FastAPI too). The max size of the threadpool is + # set to max_ongoing_requests in the replica wrapper. + # anyio.to_thread propagates ContextVars to the worker thread automatically. + result = await to_thread.run_sync(run_callable) + else: + if ( + is_sync_method + and not self._warned_about_sync_method_change + and run_sync_methods_in_threadpool_override is None + ): + self._warned_about_sync_method_change = True + warnings.warn( + RAY_SERVE_RUN_SYNC_IN_THREADPOOL_WARNING.format( + method_name=callable.__name__, + ) + ) + + result = callable(*args, **kwargs) + if inspect.iscoroutine(result): + result = await result + + return result, sync_gen_consumed @property def user_callable(self) -> Optional[Callable]: @@ -1129,8 +1225,10 @@ async def initialize_callable(self) -> Optional[ASGIApp]: self._callable = self._deployment_def.__new__(self._deployment_def) await self._call_func_or_gen( self._callable.__init__, - *self._init_args, - **self._init_kwargs, + args=self._init_args, + kwargs=self._init_kwargs, + # Always run the constructor on the main user code thread. + run_sync_methods_in_threadpool_override=False, ) if isinstance(self._callable, ASGIAppReplicaWrapper): @@ -1192,7 +1290,7 @@ async def call_reconfigure(self, user_config: Any): ) await self._call_func_or_gen( getattr(self._callable, RECONFIGURE_METHOD), - user_config, + args=(user_config,), ) def _prepare_args_for_http_request( @@ -1264,6 +1362,7 @@ async def _handle_user_method_result( user_method_name: str, request_metadata: RequestMetadata, *, + sync_gen_consumed: bool, generator_result_callback: Optional[Callable], is_asgi_app: bool, asgi_args: Optional[ASGIArgs], @@ -1297,7 +1396,7 @@ async def _handle_user_method_result( # For the FastAPI codepath, the response has already been sent over # ASGI, but for the vanilla deployment codepath we need to send it. await self._send_user_result_over_asgi(result, asgi_args) - elif not request_metadata.is_http_request: + elif not request_metadata.is_http_request and not sync_gen_consumed: # If a unary method is called with stream=True for anything EXCEPT # an HTTP request, raise an error. # HTTP requests are always streaming regardless of if the method @@ -1382,12 +1481,20 @@ async def call_user_method( request_args[0], request_metadata, user_method_params ) - result = await self._handle_user_method_result( - await self._call_func_or_gen( - user_method, *request_args, **request_kwargs - ), + result, sync_gen_consumed = await self._call_func_or_gen( + user_method, + args=request_args, + kwargs=request_kwargs, + request_metadata=request_metadata, + generator_result_callback=generator_result_callback + if request_metadata.is_streaming + else None, + ) + return await self._handle_user_method_result( + result, user_method_name, request_metadata, + sync_gen_consumed=sync_gen_consumed, generator_result_callback=generator_result_callback, is_asgi_app=is_asgi_app, asgi_args=asgi_args, @@ -1412,8 +1519,6 @@ async def call_user_method( if receive_task is not None and not receive_task.done(): receive_task.cancel() - return result - @_run_on_user_code_event_loop async def call_destructor(self): """Explicitly call the `__del__` method of the user callable. @@ -1437,7 +1542,11 @@ async def call_destructor(self): try: if hasattr(self._callable, "__del__"): # Make sure to accept `async def __del__(self)` as well. - await self._call_func_or_gen(self._callable.__del__) + await self._call_func_or_gen( + self._callable.__del__, + # Always run the destructor on the main user callable thread. + run_sync_methods_in_threadpool_override=False, + ) if hasattr(self._callable, "__serve_multiplex_wrapper"): await getattr(self._callable, "__serve_multiplex_wrapper").shutdown() diff --git a/python/ray/serve/tests/BUILD b/python/ray/serve/tests/BUILD index 369b9a339c6f..9f3208084538 100644 --- a/python/ray/serve/tests/BUILD +++ b/python/ray/serve/tests/BUILD @@ -467,3 +467,25 @@ py_test_module_list( "//python/ray/serve:serve_lib", ], ) + + +# Test currently off-by-default behavior to run replica sync methods in a threadpool. +# TODO(edoakes): remove this once the FF is flipped on by default. +py_test_module_list( + size = "small", + env = {"RAY_SERVE_RUN_SYNC_IN_THREADPOOL": "1"}, + files = [ + "test_replica_sync_methods.py", + ], + name_suffix = "_with_run_sync_in_threadpool", + tags = [ + "exclusive", + "no_windows", + "team:serve", + ], + deps = [ + ":common", + ":conftest", + "//python/ray/serve:serve_lib", + ], +) diff --git a/python/ray/serve/tests/test_replica_sync_methods.py b/python/ray/serve/tests/test_replica_sync_methods.py new file mode 100644 index 000000000000..d6485704138f --- /dev/null +++ b/python/ray/serve/tests/test_replica_sync_methods.py @@ -0,0 +1,127 @@ +import asyncio +import sys + +import pytest +import requests +from anyio import to_thread +from fastapi import FastAPI +from starlette.responses import PlainTextResponse + +import ray +from ray import serve +from ray._private.test_utils import SignalActor, wait_for_condition +from ray.serve._private.constants import RAY_SERVE_RUN_SYNC_IN_THREADPOOL + + +@pytest.mark.skipif( + not RAY_SERVE_RUN_SYNC_IN_THREADPOOL, + reason="Run sync method in threadpool FF disabled.", +) +@pytest.mark.parametrize("use_fastapi", [False, True]) +def test_not_running_in_asyncio_loop(serve_instance, use_fastapi: bool): + if use_fastapi: + fastapi_app = FastAPI() + + @serve.deployment + @serve.ingress(fastapi_app) + class D: + @fastapi_app.get("/") + def root(self): + with pytest.raises(RuntimeError, match="no running event loop"): + asyncio.get_running_loop() + + else: + + @serve.deployment + class D: + def __call__(self) -> str: + with pytest.raises(RuntimeError, match="no running event loop"): + asyncio.get_running_loop() + + serve.run(D.bind()) + # Would error if the check fails. + requests.get("http://localhost:8000/").raise_for_status() + + +@pytest.mark.skipif( + not RAY_SERVE_RUN_SYNC_IN_THREADPOOL, + reason="Run sync method in threadpool FF disabled.", +) +def test_concurrent_execution(serve_instance): + signal_actor = SignalActor.remote() + + @serve.deployment + class D: + def do_sync(self): + ray.get(signal_actor.wait.remote()) + + async def do_async(self): + await signal_actor.wait.remote() + + h = serve.run(D.bind()) + + sync_results = [h.do_sync.remote(), h.do_sync.remote()] + async_results = [h.do_async.remote(), h.do_async.remote()] + + wait_for_condition(lambda: ray.get(signal_actor.cur_num_waiters.remote()) == 4) + ray.get(signal_actor.send.remote()) + [r.result() for r in sync_results + async_results] + + +@pytest.mark.skipif( + not RAY_SERVE_RUN_SYNC_IN_THREADPOOL, + reason="Run sync method in threadpool FF disabled.", +) +@pytest.mark.parametrize("use_fastapi", [False, True]) +def test_context_vars_propagated(serve_instance, use_fastapi: bool): + if use_fastapi: + fastapi_app = FastAPI() + + @serve.deployment + @serve.ingress(fastapi_app) + class D: + @fastapi_app.get("/") + def root(self): + return PlainTextResponse( + serve.context._serve_request_context.get().request_id + ) + + else: + + @serve.deployment + class D: + def __call__(self) -> str: + return PlainTextResponse( + serve.context._serve_request_context.get().request_id + ) + + serve.run(D.bind()) + + r = requests.get("http://localhost:8000/", headers={"X-Request-Id": "TEST-ID"}) + r.raise_for_status() + # If context vars weren't propagated, the request ID would be empty. + assert r.text == "TEST-ID" + + +@pytest.mark.skipif( + not RAY_SERVE_RUN_SYNC_IN_THREADPOOL, + reason="Run sync method in threadpool FF disabled.", +) +def test_thread_limit_set_to_max_ongoing_requests(serve_instance): + @serve.deployment + class D: + async def __call__(self): + return to_thread.current_default_thread_limiter().total_tokens + + h = serve.run(D.bind()) + + # Check that it's set if max_ongoing_requests is defaulted. + assert h.remote().result() == 5 + + # Update to a custom value, check again. + h = serve.run(D.options(max_ongoing_requests=10).bind()) + assert h.remote().result() == 10 + + +if __name__ == "__main__": + sys.exit(pytest.main(["-v", "-s", __file__])) diff --git a/python/ray/serve/tests/unit/test_user_callable_wrapper.py b/python/ray/serve/tests/unit/test_user_callable_wrapper.py index b03c9ca7e39e..39188f32c421 100644 --- a/python/ray/serve/tests/unit/test_user_callable_wrapper.py +++ b/python/ray/serve/tests/unit/test_user_callable_wrapper.py @@ -4,7 +4,7 @@ import sys import threading from dataclasses import dataclass -from typing import AsyncGenerator, Callable, Generator, Optional +from typing import Any, AsyncGenerator, Callable, Dict, Generator, Optional, Tuple import pytest from fastapi import FastAPI @@ -90,13 +90,18 @@ async def basic_async_generator(n: int, raise_exception: bool = False): def _make_user_callable_wrapper( - callable: Optional[Callable] = None, *init_args, **init_kwargs + callable: Optional[Callable] = None, + *, + init_args: Optional[Tuple[Any]] = None, + init_kwargs: Optional[Dict[str, Any]] = None, + run_sync_methods_in_threadpool: bool = False, ) -> UserCallableWrapper: return UserCallableWrapper( callable if callable is not None else BasicClass, - init_args, - init_kwargs, + init_args or tuple(), + init_kwargs or dict(), deployment_id=DeploymentID(name="test_name"), + run_sync_methods_in_threadpool=run_sync_methods_in_threadpool, ) @@ -144,8 +149,11 @@ def test_calling_methods_before_initialize(): user_callable_wrapper.call_reconfigure(None).result() -def test_basic_class_callable(): - user_callable_wrapper = _make_user_callable_wrapper() +@pytest.mark.parametrize("run_sync_methods_in_threadpool", [False, True]) +def test_basic_class_callable(run_sync_methods_in_threadpool: bool): + user_callable_wrapper = _make_user_callable_wrapper( + run_sync_methods_in_threadpool=run_sync_methods_in_threadpool + ) user_callable_wrapper.initialize_callable().result() @@ -215,8 +223,11 @@ def test_basic_class_callable(): ).result() -def test_basic_class_callable_generators(): - user_callable_wrapper = _make_user_callable_wrapper() +@pytest.mark.parametrize("run_sync_methods_in_threadpool", [False, True]) +def test_basic_class_callable_generators(run_sync_methods_in_threadpool: bool): + user_callable_wrapper = _make_user_callable_wrapper( + run_sync_methods_in_threadpool=run_sync_methods_in_threadpool + ) user_callable_wrapper.initialize_callable().result() result_list = [] @@ -291,9 +302,12 @@ def test_basic_class_callable_generators(): assert result_list == [0] +@pytest.mark.parametrize("run_sync_methods_in_threadpool", [False, True]) @pytest.mark.parametrize("fn", [basic_sync_function, basic_async_function]) -def test_basic_function_callable(fn: Callable): - user_callable_wrapper = _make_user_callable_wrapper(fn) +def test_basic_function_callable(fn: Callable, run_sync_methods_in_threadpool: bool): + user_callable_wrapper = _make_user_callable_wrapper( + fn, run_sync_methods_in_threadpool=run_sync_methods_in_threadpool + ) user_callable_wrapper.initialize_callable().result() # Call non-generator function with is_streaming. @@ -325,9 +339,14 @@ def test_basic_function_callable(fn: Callable): ).result() +@pytest.mark.parametrize("run_sync_methods_in_threadpool", [False, True]) @pytest.mark.parametrize("fn", [basic_sync_generator, basic_async_generator]) -def test_basic_function_callable_generators(fn: Callable): - user_callable_wrapper = _make_user_callable_wrapper(fn) +def test_basic_function_callable_generators( + fn: Callable, run_sync_methods_in_threadpool: bool +): + user_callable_wrapper = _make_user_callable_wrapper( + fn, run_sync_methods_in_threadpool=run_sync_methods_in_threadpool + ) user_callable_wrapper.initialize_callable().result() result_list = [] @@ -366,36 +385,68 @@ def test_basic_function_callable_generators(fn: Callable): @pytest.mark.asyncio -async def test_user_code_runs_on_separate_loop(): +@pytest.mark.parametrize("run_sync_methods_in_threadpool", [False, True]) +async def test_user_code_runs_on_separate_loop(run_sync_methods_in_threadpool: bool): main_loop = asyncio.get_running_loop() class GetLoop: def __init__(self): self._constructor_loop = asyncio.get_running_loop() - def check_health(self): + async def check_health(self): check_health_loop = asyncio.get_running_loop() assert ( check_health_loop == self._constructor_loop ), "User constructor and health check should run on the same loop." return check_health_loop - def __call__(self) -> asyncio.AbstractEventLoop: + async def call_async(self) -> Optional[asyncio.AbstractEventLoop]: user_method_loop = asyncio.get_running_loop() assert ( user_method_loop == self._constructor_loop ), "User constructor and other methods should run on the same loop." + return user_method_loop - user_callable_wrapper = _make_user_callable_wrapper(GetLoop) + def call_sync(self): + if run_sync_methods_in_threadpool: + with pytest.raises(RuntimeError, match="no running event loop"): + asyncio.get_running_loop() + + user_method_loop = None + else: + user_method_loop = asyncio.get_running_loop() + assert ( + user_method_loop == self._constructor_loop + ), "User constructor and other methods should run on the same loop." + + return user_method_loop + + user_callable_wrapper = _make_user_callable_wrapper( + GetLoop, run_sync_methods_in_threadpool=run_sync_methods_in_threadpool + ) user_callable_wrapper.initialize_callable().result() - request_metadata = _make_request_metadata() + + # Async methods should all run on the same loop. + request_metadata = _make_request_metadata(call_method="call_async") user_code_loop = user_callable_wrapper.call_user_method( request_metadata, tuple(), dict() ).result() assert isinstance(user_code_loop, asyncio.AbstractEventLoop) assert user_code_loop != main_loop + # Sync methods should run on the same loop if run_sync_methods_in_threadpool is off, + # else run in no asyncio loop. + request_metadata = _make_request_metadata(call_method="call_sync") + user_code_loop = user_callable_wrapper.call_user_method( + request_metadata, tuple(), dict() + ).result() + if run_sync_methods_in_threadpool: + assert user_code_loop is None + else: + assert isinstance(user_code_loop, asyncio.AbstractEventLoop) + assert user_code_loop != main_loop + # `check_health` method asserts that it runs on the correct loop. user_callable_wrapper.call_user_health_check().result() @@ -412,7 +463,7 @@ def __call__(self) -> str: msg = "hello world" user_callable_wrapper = _make_user_callable_wrapper( AsyncInitializer, - msg, + init_args=(msg,), ) user_callable_wrapper.initialize_callable().result() request_metadata = _make_request_metadata() @@ -498,8 +549,11 @@ def stream(self, msg: serve_pb2.UserDefinedMessage): yield serve_pb2.UserDefinedResponse(greeting=f"Hello {msg.greeting} {i}!") -def test_grpc_unary_request(): - user_callable_wrapper = _make_user_callable_wrapper(gRPCClass) +@pytest.mark.parametrize("run_sync_methods_in_threadpool", [False, True]) +def test_grpc_unary_request(run_sync_methods_in_threadpool: bool): + user_callable_wrapper = _make_user_callable_wrapper( + gRPCClass, run_sync_methods_in_threadpool=run_sync_methods_in_threadpool + ) user_callable_wrapper.initialize_callable().result() grpc_request = gRPCRequest( @@ -518,8 +572,11 @@ def test_grpc_unary_request(): @pytest.mark.asyncio -def test_grpc_streaming_request(): - user_callable_wrapper = _make_user_callable_wrapper(gRPCClass) +@pytest.mark.parametrize("run_sync_methods_in_threadpool", [False, True]) +def test_grpc_streaming_request(run_sync_methods_in_threadpool: bool): + user_callable_wrapper = _make_user_callable_wrapper( + gRPCClass, run_sync_methods_in_threadpool=run_sync_methods_in_threadpool + ) user_callable_wrapper.initialize_callable() grpc_request = gRPCRequest( From ed3d48c552e0ac2f235773322686c6ad5c521cd5 Mon Sep 17 00:00:00 2001 From: Kai-Hsun Chen Date: Tue, 26 Nov 2024 10:09:05 -0800 Subject: [PATCH 50/54] [core][autoscaler] Autoscaler doesn't scale up correctly when the KubeRay RayCluster is not in the goal state (#48909) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit ## Why are these changes needed? ### Issue * Create a Autoscaler V2 RayCluster CR. * head Pod: `num-cpus: 0` * worker Pod: Each worker Pod has 1 CPU, and the `maxReplicas` of the worker group is 10. * Run the following script in the head Pod: https://gist.github.com/kevin85421/6f09368ba48572e28f53654dca854b57 * There are 10 scale requests to add a new node. However, only some of them will be created (e.g., 5). ### Reason In the reproduction script above, the `cloud_instance_updater` will send a request to scale up one worker Pod 10 times because the `maxReplicas` of the worker group is set to 10. However, the construction of the scale_request depends on the Pods in the Kubernetes cluster. For example, * cluster state: RayCluster Replicas: 2, Ray Pods: 1 * 1st scale request: launch 1 node --> goal state: RayCluster Replicas: 2 (Ray Pods + 1) * 2nd scale request: launch 1 node --> goal state: RayCluster Replicas: 2 (Ray Pods + 1) --> **this should be 3!** The above example is expected to create 3 Pods. However, it will ultimately create only 2 Pods. ### Solution Use RayCluster CR instead of Ray Pods to build scale requests. ## Related issue number Closes #46473 ## Checks 10 worker Pods are created successfully. Screenshot 2024-11-24 at 2 11 39 AM - [ ] I've signed off every commit(by using the -s flag, i.e., `git commit -s`) in this PR. - [ ] I've run `scripts/format.sh` to lint the changes in this PR. - [ ] I've included any doc changes needed for https://docs.ray.io/en/master/. - [ ] I've added any new APIs to the API Reference. For example, if I added a method in Tune, I've added it in `doc/source/tune/api/` under the corresponding `.rst` file. - [ ] I've made sure the tests are passing. Note that there might be a few flaky tests, see the recent failures at https://flakey-tests.ray.io/ - Testing Strategy - [ ] Unit tests - [ ] Release tests - [ ] This PR is not tested :( --------- Signed-off-by: kaihsun --- .../kuberay/ray-cluster.complete.yaml | 2 +- .../cloud_providers/kuberay/cloud_provider.py | 42 +++++-- .../autoscaler/v2/tests/test_node_provider.py | 118 ++++++++++++++++++ .../tests/kuberay/test_autoscaling_config.py | 6 +- 4 files changed, 152 insertions(+), 16 deletions(-) diff --git a/python/ray/autoscaler/kuberay/ray-cluster.complete.yaml b/python/ray/autoscaler/kuberay/ray-cluster.complete.yaml index f51ac9d4c242..d57f5d6f23b9 100644 --- a/python/ray/autoscaler/kuberay/ray-cluster.complete.yaml +++ b/python/ray/autoscaler/kuberay/ray-cluster.complete.yaml @@ -108,7 +108,7 @@ spec: workerGroupSpecs: # the pod replicas in this group typed worker - replicas: 1 - minReplicas: 1 + minReplicas: 0 maxReplicas: 300 # logical group name, for this called small-group, also can be functional groupName: small-group diff --git a/python/ray/autoscaler/v2/instance_manager/cloud_providers/kuberay/cloud_provider.py b/python/ray/autoscaler/v2/instance_manager/cloud_providers/kuberay/cloud_provider.py index 19e236cb4d19..c1b8ddc2a31b 100644 --- a/python/ray/autoscaler/v2/instance_manager/cloud_providers/kuberay/cloud_provider.py +++ b/python/ray/autoscaler/v2/instance_manager/cloud_providers/kuberay/cloud_provider.py @@ -209,21 +209,25 @@ def _initialize_scale_request( cur_instances = self.instances # Get the worker groups that have pending deletes and the worker groups that - # have finished deletes. + # have finished deletes, and the set of workers included in the workersToDelete + # field of any worker group. ( worker_groups_with_pending_deletes, worker_groups_without_pending_deletes, - ) = self._get_workers_groups_with_deletes( - ray_cluster, set(cur_instances.keys()) - ) + worker_to_delete_set, + ) = self._get_workers_delete_info(ray_cluster, set(cur_instances.keys())) # Calculate the desired number of workers by type. num_workers_dict = defaultdict(int) - for _, cur_instance in cur_instances.items(): - if cur_instance.node_kind == NodeKind.HEAD: - # Only track workers. - continue - num_workers_dict[cur_instance.node_type] += 1 + worker_groups = ray_cluster["spec"].get("workerGroupSpecs", []) + for worker_group in worker_groups: + node_type = worker_group["groupName"] + # Handle the case where users manually increase `minReplicas` + # to scale up the number of worker Pods. In this scenario, + # `replicas` will be smaller than `minReplicas`. + num_workers_dict[node_type] = max( + worker_group["replicas"], worker_group["minReplicas"] + ) # Add to launch nodes. for node_type, count in to_launch.items(): @@ -242,6 +246,11 @@ def _initialize_scale_request( # Not possible to delete head node. continue + if to_delete_instance.cloud_instance_id in worker_to_delete_set: + # If the instance is already in the workersToDelete field of + # any worker group, skip it. + continue + num_workers_dict[to_delete_instance.node_type] -= 1 assert num_workers_dict[to_delete_instance.node_type] >= 0 to_delete_instances_by_type[to_delete_instance.node_type].append( @@ -321,6 +330,7 @@ def _submit_scale_request( # No patch required. return + logger.info(f"Submitting a scale request: {scale_request}") self._patch(f"rayclusters/{self._cluster_name}", patch_payload) def _add_launch_errors( @@ -392,9 +402,9 @@ def instances(self) -> Dict[CloudInstanceId, CloudInstance]: return copy.deepcopy(self._cached_instances) @staticmethod - def _get_workers_groups_with_deletes( + def _get_workers_delete_info( ray_cluster_spec: Dict[str, Any], node_set: Set[CloudInstanceId] - ) -> Tuple[Set[NodeType], Set[NodeType]]: + ) -> Tuple[Set[NodeType], Set[NodeType], Set[CloudInstanceId]]: """ Gets the worker groups that have pending deletes and the worker groups that have finished deletes. @@ -404,10 +414,13 @@ def _get_workers_groups_with_deletes( deletes. worker_groups_with_finished_deletes: The worker groups that have finished deletes. + worker_to_delete_set: A set of Pods that are included in the workersToDelete + field of any worker group. """ worker_groups_with_pending_deletes = set() worker_groups_with_deletes = set() + worker_to_delete_set = set() worker_groups = ray_cluster_spec["spec"].get("workerGroupSpecs", []) for worker_group in worker_groups: @@ -422,6 +435,7 @@ def _get_workers_groups_with_deletes( worker_groups_with_deletes.add(node_type) for worker in workersToDelete: + worker_to_delete_set.add(worker) if worker in node_set: worker_groups_with_pending_deletes.add(node_type) break @@ -429,7 +443,11 @@ def _get_workers_groups_with_deletes( worker_groups_with_finished_deletes = ( worker_groups_with_deletes - worker_groups_with_pending_deletes ) - return worker_groups_with_pending_deletes, worker_groups_with_finished_deletes + return ( + worker_groups_with_pending_deletes, + worker_groups_with_finished_deletes, + worker_to_delete_set, + ) def _fetch_instances(self) -> Dict[CloudInstanceId, CloudInstance]: """ diff --git a/python/ray/autoscaler/v2/tests/test_node_provider.py b/python/ray/autoscaler/v2/tests/test_node_provider.py index 02d84e376b8d..47483d3f61fa 100644 --- a/python/ray/autoscaler/v2/tests/test_node_provider.py +++ b/python/ray/autoscaler/v2/tests/test_node_provider.py @@ -492,6 +492,124 @@ def test_pending_deletes(self): }, ] + def test_increase_min_replicas_to_scale_up(self): + # Simulate the case where users manually increase the `minReplicas` field + # from 0 to $num_pods. KubeRay will create $num_pods worker Pods to meet the new + # `minReplicas`, even though the `replicas` field is still 0. + small_group = "small-group" + num_pods = 0 + assert ( + self.mock_client._ray_cluster["spec"]["workerGroupSpecs"][0]["groupName"] + == small_group + ) + for pod in self.mock_client._pod_list["items"]: + if pod["metadata"]["labels"]["ray.io/group"] == small_group: + num_pods += 1 + assert num_pods > 0 + self.mock_client._ray_cluster["spec"]["workerGroupSpecs"][0]["replicas"] = 0 + self.mock_client._ray_cluster["spec"]["workerGroupSpecs"][0][ + "minReplicas" + ] = num_pods + + # Launching a new node and `replicas` should be + # `max(replicas, minReplicas) + 1`. + self.provider.launch(shape={small_group: 1}, request_id="launch-1") + patches = self.mock_client.get_patches( + f"rayclusters/{self.provider._cluster_name}" + ) + assert len(patches) == 1 + assert patches[0] == { + "op": "replace", + "path": "/spec/workerGroupSpecs/0/replicas", + "value": num_pods + 1, + } + + def test_inconsistent_pods_raycr_scale_up(self): + """ + Test the case where the cluster state has not yet reached the desired state. + Specifically, the replicas field in the RayCluster CR does not match the actual + number of Pods. + """ + # Check the assumptions of the test + small_group = "small-group" + num_pods = 0 + for pod in self.mock_client._pod_list["items"]: + if pod["metadata"]["labels"]["ray.io/group"] == small_group: + num_pods += 1 + + assert ( + self.mock_client._ray_cluster["spec"]["workerGroupSpecs"][0]["groupName"] + == small_group + ) + desired_replicas = num_pods + 1 + self.mock_client._ray_cluster["spec"]["workerGroupSpecs"][0][ + "replicas" + ] = desired_replicas + + # Launch a new node. The replicas field should be incremented by 1, even though + # the cluster state has not yet reached the goal state. + launch_request = {"small-group": 1} + self.provider.launch(shape=launch_request, request_id="launch-1") + + patches = self.mock_client.get_patches( + f"rayclusters/{self.provider._cluster_name}" + ) + assert len(patches) == 1 + assert patches[0] == { + "op": "replace", + "path": "/spec/workerGroupSpecs/0/replicas", + "value": desired_replicas + 1, + } + + def test_inconsistent_pods_raycr_scale_down(self): + """ + Test the case where the cluster state has not yet reached the desired state. + Specifically, the replicas field in the RayCluster CR does not match the actual + number of Pods. + """ + # Check the assumptions of the test + small_group = "small-group" + num_pods = 0 + pod_to_delete = None + for pod in self.mock_client._pod_list["items"]: + if pod["metadata"]["labels"]["ray.io/group"] == small_group: + num_pods += 1 + pod_to_delete = pod["metadata"]["name"] + assert pod_to_delete is not None + + assert ( + self.mock_client._ray_cluster["spec"]["workerGroupSpecs"][0]["groupName"] + == small_group + ) + desired_replicas = num_pods + 1 + self.mock_client._ray_cluster["spec"]["workerGroupSpecs"][0][ + "replicas" + ] = desired_replicas + + # Terminate a node. The replicas field should be decremented by 1, even though + # the cluster state has not yet reached the goal state. + self.provider.terminate(ids=[pod_to_delete], request_id="term-1") + patches = self.mock_client.get_patches( + f"rayclusters/{self.provider._cluster_name}" + ) + assert len(patches) == 2 + assert patches == [ + { + "op": "replace", + "path": "/spec/workerGroupSpecs/0/replicas", + "value": desired_replicas - 1, + }, + { + "op": "replace", + "path": "/spec/workerGroupSpecs/0/scaleStrategy", + "value": { + "workersToDelete": [ + pod_to_delete, + ] + }, + }, + ] + if __name__ == "__main__": if os.environ.get("PARALLEL_CI"): diff --git a/python/ray/tests/kuberay/test_autoscaling_config.py b/python/ray/tests/kuberay/test_autoscaling_config.py index 6ae9ab7fc4fe..12b5c239f4a6 100644 --- a/python/ray/tests/kuberay/test_autoscaling_config.py +++ b/python/ray/tests/kuberay/test_autoscaling_config.py @@ -82,7 +82,7 @@ def _get_basic_autoscaling_config() -> dict: }, "small-group": { "max_workers": 300, - "min_workers": 1, + "min_workers": 0, "node_config": {}, "resources": { "CPU": 1, @@ -95,7 +95,7 @@ def _get_basic_autoscaling_config() -> dict: # and modified max_workers. "gpu-group": { "max_workers": 200, - "min_workers": 1, + "min_workers": 0, "node_config": {}, "resources": { "CPU": 1, @@ -109,7 +109,7 @@ def _get_basic_autoscaling_config() -> dict: # and modified max_workers and node_config. "tpu-group": { "max_workers": 4, - "min_workers": 1, + "min_workers": 0, "node_config": {}, "resources": { "CPU": 1, From 5896b3fa4c7e07a7d7cfc8f248bd8d50802cbb4c Mon Sep 17 00:00:00 2001 From: dentiny Date: Tue, 26 Nov 2024 12:20:19 -0800 Subject: [PATCH 51/54] [core] Fix error info timestamp unit (#48763) Addresses https://github.com/ray-project/ray/issues/48760 Reading through the initial PR (https://github.com/ray-project/ray/pull/2256/files), the timestamp unit for error information is actually seconds. After refactor PR (https://github.com/ray-project/ray/pull/5024), it's refactored from python class into protobuf. In this PR, I tried to use `absl::Time` instead of integer as argument, to avoid confusion on units. --------- Signed-off-by: dentiny Signed-off-by: hjiang Co-authored-by: Ruiyang Wang <56065503+rynewang@users.noreply.github.com> --- src/ray/gcs/gcs_server/gcs_actor_manager.cc | 7 +-- src/ray/gcs/gcs_server/gcs_node_manager.cc | 4 +- src/ray/gcs/pb_util.h | 23 ++-------- src/ray/gcs/pb_utils.cc | 47 +++++++++++++++++++++ src/ray/protobuf/gcs.proto | 2 + src/ray/raylet/node_manager.cc | 14 +++--- src/ray/raylet/worker_pool.cc | 38 ++++++++--------- src/ray/raylet/worker_pool.h | 14 +++--- src/ray/raylet/worker_pool_test.cc | 17 ++++---- 9 files changed, 100 insertions(+), 66 deletions(-) create mode 100644 src/ray/gcs/pb_utils.cc diff --git a/src/ray/gcs/gcs_server/gcs_actor_manager.cc b/src/ray/gcs/gcs_server/gcs_actor_manager.cc index aa609bacb445..8bf3f3d484f1 100644 --- a/src/ray/gcs/gcs_server/gcs_actor_manager.cc +++ b/src/ray/gcs/gcs_server/gcs_actor_manager.cc @@ -749,11 +749,8 @@ Status GcsActorManager::RegisterActor(const ray::rpc::RegisterActorRequest &requ "explicitly connect to this namespace with ray.init(namespace=\"" << actor->GetRayNamespace() << "\", ...)"; - auto error_data_ptr = - gcs::CreateErrorTableData("detached_actor_anonymous_namespace", - stream.str(), - absl::GetCurrentTimeNanos(), - job_id); + auto error_data_ptr = gcs::CreateErrorTableData( + "detached_actor_anonymous_namespace", stream.str(), absl::Now(), job_id); RAY_LOG(WARNING) << error_data_ptr->SerializeAsString(); RAY_CHECK_OK( diff --git a/src/ray/gcs/gcs_server/gcs_node_manager.cc b/src/ray/gcs/gcs_server/gcs_node_manager.cc index c57e8ea2fd08..15aa488cb65f 100644 --- a/src/ray/gcs/gcs_server/gcs_node_manager.cc +++ b/src/ray/gcs/gcs_server/gcs_node_manager.cc @@ -392,8 +392,8 @@ std::shared_ptr GcsNodeManager::RemoveNode( .WithField("ip", removed_node->node_manager_address()) << error_message.str(); RAY_LOG(WARNING) << error_message.str(); - auto error_data_ptr = - gcs::CreateErrorTableData(type, error_message.str(), current_time_ms()); + auto error_data_ptr = gcs::CreateErrorTableData( + type, error_message.str(), absl::FromUnixMillis(current_time_ms())); RAY_CHECK_OK(gcs_publisher_->PublishError(node_id.Hex(), *error_data_ptr, nullptr)); } diff --git a/src/ray/gcs/pb_util.h b/src/ray/gcs/pb_util.h index cb3c518072b2..0bdd056b0b8d 100644 --- a/src/ray/gcs/pb_util.h +++ b/src/ray/gcs/pb_util.h @@ -16,6 +16,7 @@ #include +#include "absl/time/time.h" #include "ray/common/constants.h" #include "ray/common/id.h" #include "ray/common/ray_config.h" @@ -61,27 +62,11 @@ inline std::shared_ptr CreateJobTableData( } /// Helper function to produce error table data. -inline std::shared_ptr CreateErrorTableData( +std::shared_ptr CreateErrorTableData( const std::string &error_type, const std::string &error_msg, - double timestamp, - const JobID &job_id = JobID::Nil()) { - uint32_t max_error_msg_size_bytes = RayConfig::instance().max_error_msg_size_bytes(); - auto error_info_ptr = std::make_shared(); - error_info_ptr->set_type(error_type); - if (error_msg.length() > max_error_msg_size_bytes) { - std::ostringstream stream; - stream << "The message size exceeds " << std::to_string(max_error_msg_size_bytes) - << " bytes. Find the full log from the log files. Here is abstract: " - << error_msg.substr(0, max_error_msg_size_bytes); - error_info_ptr->set_error_message(stream.str()); - } else { - error_info_ptr->set_error_message(error_msg); - } - error_info_ptr->set_timestamp(timestamp); - error_info_ptr->set_job_id(job_id.Binary()); - return error_info_ptr; -} + absl::Time timestamp, + const JobID &job_id = JobID::Nil()); /// Helper function to produce worker failure data. inline std::shared_ptr CreateWorkerFailureData( diff --git a/src/ray/gcs/pb_utils.cc b/src/ray/gcs/pb_utils.cc new file mode 100644 index 000000000000..9330f80c27ce --- /dev/null +++ b/src/ray/gcs/pb_utils.cc @@ -0,0 +1,47 @@ +// Copyright 2024 The Ray Authors. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +// TODO(hjiang): Move all functions from `pb_utils.h` to this implementation file. + +#include + +#include "absl/strings/str_format.h" +#include "ray/gcs/pb_util.h" + +namespace ray::gcs { + +std::shared_ptr CreateErrorTableData( + const std::string &error_type, + const std::string &error_msg, + absl::Time timestamp, + const JobID &job_id) { + uint32_t max_error_msg_size_bytes = RayConfig::instance().max_error_msg_size_bytes(); + auto error_info_ptr = std::make_shared(); + error_info_ptr->set_type(error_type); + if (error_msg.length() > max_error_msg_size_bytes) { + std::string formatted_error_message = absl::StrFormat( + "The message size exceeds %d bytes. Find the full log from the log files. Here " + "is abstract: %s", + max_error_msg_size_bytes, + std::string_view{error_msg}.substr(0, max_error_msg_size_bytes)); + error_info_ptr->set_error_message(std::move(formatted_error_message)); + } else { + error_info_ptr->set_error_message(error_msg); + } + error_info_ptr->set_timestamp(absl::ToUnixMillis(timestamp)); + error_info_ptr->set_job_id(job_id.Binary()); + return error_info_ptr; +} + +} // namespace ray::gcs diff --git a/src/ray/protobuf/gcs.proto b/src/ray/protobuf/gcs.proto index b34f5bd9f39b..2d0ff6dfac4a 100644 --- a/src/ray/protobuf/gcs.proto +++ b/src/ray/protobuf/gcs.proto @@ -165,6 +165,8 @@ message ErrorTableData { // The error message. string error_message = 3; // The timestamp of the error message. + // Unit: millisecond. + // TODO(hjiang): Update field naming from `timestamp` to `timestamp_millisec`. double timestamp = 4; } diff --git a/src/ray/raylet/node_manager.cc b/src/ray/raylet/node_manager.cc index 879edff0bb6c..18384711de58 100644 --- a/src/ray/raylet/node_manager.cc +++ b/src/ray/raylet/node_manager.cc @@ -143,7 +143,7 @@ NodeManager::NodeManager( /*starting_worker_timeout_callback=*/ [this] { cluster_task_manager_->ScheduleAndDispatchTasks(); }, config.ray_debugger_external, - /*get_time=*/[]() { return absl::GetCurrentTimeNanos() / 1e6; }), + /*get_time=*/[]() { return absl::Now(); }), client_call_manager_(io_service), worker_rpc_pool_(client_call_manager_), core_worker_subscriber_(std::make_unique( @@ -1562,8 +1562,8 @@ void NodeManager::DisconnectClient(const std::shared_ptr &clie .WithField("node_id", self_node_id_.Hex()) .WithField("job_id", worker->GetAssignedJobId().Hex()) << error_message_str; - auto error_data_ptr = - gcs::CreateErrorTableData(type, error_message_str, current_time_ms(), job_id); + auto error_data_ptr = gcs::CreateErrorTableData( + type, error_message_str, absl::FromUnixMillis(current_time_ms()), job_id); RAY_CHECK_OK(gcs_client_->Errors().AsyncReportJobError(error_data_ptr, nullptr)); } } @@ -1762,9 +1762,11 @@ void NodeManager::ProcessPushErrorRequestMessage(const uint8_t *message_data) { auto const &type = string_from_flatbuf(*message->type()); auto const &error_message = string_from_flatbuf(*message->error_message()); + // TODO(hjiang): Figure out what's the unit for `PushErrorRequest`. double timestamp = message->timestamp(); JobID job_id = from_flatbuf(*message->job_id()); - auto error_data_ptr = gcs::CreateErrorTableData(type, error_message, timestamp, job_id); + auto error_data_ptr = gcs::CreateErrorTableData( + type, error_message, absl::FromUnixMillis(timestamp), job_id); RAY_CHECK_OK(gcs_client_->Errors().AsyncReportJobError(error_data_ptr, nullptr)); } @@ -2126,8 +2128,8 @@ void NodeManager::MarkObjectsAsFailed( << " object may hang forever."; std::string error_message = stream.str(); RAY_LOG(ERROR) << error_message; - auto error_data_ptr = - gcs::CreateErrorTableData("task", error_message, current_time_ms(), job_id); + auto error_data_ptr = gcs::CreateErrorTableData( + "task", error_message, absl::FromUnixMillis(current_time_ms()), job_id); RAY_CHECK_OK(gcs_client_->Errors().AsyncReportJobError(error_data_ptr, nullptr)); } } diff --git a/src/ray/raylet/worker_pool.cc b/src/ray/raylet/worker_pool.cc index eda893e8efbe..5964063a3205 100644 --- a/src/ray/raylet/worker_pool.cc +++ b/src/ray/raylet/worker_pool.cc @@ -89,10 +89,10 @@ WorkerPool::WorkerPool(instrumented_io_context &io_service, const std::vector &worker_ports, std::shared_ptr gcs_client, const WorkerCommandMap &worker_commands, - const std::string &native_library_path, + std::string native_library_path, std::function starting_worker_timeout_callback, int ray_debugger_external, - const std::function get_time) + std::function get_time) : worker_startup_token_counter_(0), io_service_(&io_service), node_id_(node_id), @@ -105,15 +105,15 @@ WorkerPool::WorkerPool(instrumented_io_context &io_service, RayConfig::instance().worker_maximum_startup_concurrency() : maximum_startup_concurrency), gcs_client_(std::move(gcs_client)), - native_library_path_(native_library_path), - starting_worker_timeout_callback_(starting_worker_timeout_callback), + native_library_path_(std::move(native_library_path)), + starting_worker_timeout_callback_(std::move(starting_worker_timeout_callback)), ray_debugger_external(ray_debugger_external), first_job_registered_python_worker_count_(0), first_job_driver_wait_num_python_workers_( std::min(num_prestarted_python_workers, maximum_startup_concurrency_)), num_prestart_python_workers(num_prestarted_python_workers), periodical_runner_(io_service), - get_time_(get_time) { + get_time_(std::move(get_time)) { RAY_CHECK_GT(maximum_startup_concurrency_, 0); // We need to record so that the metric exists. This way, we report that 0 // processes have started before a task runs on the node (as opposed to the @@ -1043,7 +1043,7 @@ void WorkerPool::PushWorker(const std::shared_ptr &worker) { } void WorkerPool::TryKillingIdleWorkers() { - int64_t now = get_time_(); + const auto now = get_time_(); // Filter out all idle workers that are already dead and/or associated with // jobs that have already finished. @@ -1056,14 +1056,14 @@ void WorkerPool::TryKillingIdleWorkers() { } const auto &job_id = idle_worker->GetAssignedJobId(); - if (finished_jobs_.count(job_id) > 0) { + if (finished_jobs_.contains(job_id)) { // The job has finished, so we should kill the worker immediately. KillIdleWorker(idle_worker, it->second); it = idle_of_all_languages_.erase(it); } else { - if (it->second == -1 || - now - it->second > - RayConfig::instance().idle_worker_killing_time_threshold_ms()) { + if (now - it->second > + absl::Milliseconds( + RayConfig::instance().idle_worker_killing_time_threshold_ms())) { // The job has not yet finished and the worker has been idle for longer // than the timeout. num_killable_idle_workers++; @@ -1085,9 +1085,9 @@ void WorkerPool::TryKillingIdleWorkers() { auto it = idle_of_all_languages_.begin(); while (num_killable_idle_workers > num_desired_idle_workers && it != idle_of_all_languages_.end()) { - if (it->second == -1 || - now - it->second > - RayConfig::instance().idle_worker_killing_time_threshold_ms()) { + if (now - it->second > + absl::Milliseconds( + RayConfig::instance().idle_worker_killing_time_threshold_ms())) { RAY_LOG(DEBUG) << "Number of idle workers " << num_killable_idle_workers << " is larger than the number of desired workers " << num_desired_idle_workers << " killing idle worker with PID " @@ -1102,7 +1102,7 @@ void WorkerPool::TryKillingIdleWorkers() { } void WorkerPool::KillIdleWorker(std::shared_ptr idle_worker, - int64_t last_time_used_ms) { + absl::Time last_time_used) { // To avoid object lost issue caused by forcibly killing, send an RPC request to the // worker to allow it to do cleanup before exiting. We kill it anyway if the driver // is already exited. @@ -1122,8 +1122,8 @@ void WorkerPool::KillIdleWorker(std::shared_ptr idle_worker, } rpc_client->Exit( request, - [this, idle_worker, last_time_used_ms](const ray::Status &status, - const rpc::ExitReply &r) { + [this, idle_worker, last_time_used](const ray::Status &status, + const rpc::ExitReply &r) { RAY_CHECK(pending_exit_idle_workers_.erase(idle_worker->WorkerId())); if (!status.ok()) { RAY_LOG(ERROR) << "Failed to send exit request: " << status.ToString(); @@ -1149,8 +1149,7 @@ void WorkerPool::KillIdleWorker(std::shared_ptr idle_worker, // kill the worker (e.g., when the worker owns the object). Without this, // if the first N workers own objects, it can't kill idle workers that are // >= N+1. - idle_of_all_languages_.push_back( - std::make_pair(idle_worker, last_time_used_ms)); + idle_of_all_languages_.emplace_back(idle_worker, last_time_used); } }); } @@ -1311,7 +1310,7 @@ void WorkerPool::PopWorker(const TaskSpecification &task_spec, auto worker_fits_for_task_fn = [this, &pop_worker_request, &skip_reason_count]( - const std::pair, int64_t> &pair) -> bool { + const std::pair, absl::Time> &pair) -> bool { const auto &worker = pair.first; WorkerUnfitForTaskReason reason = WorkerFitsForTask(*worker, *pop_worker_request); if (reason == WorkerUnfitForTaskReason::NONE) { @@ -1544,6 +1543,7 @@ void WorkerPool::WarnAboutSize() { << "some discussion of workarounds)."; std::string warning_message_str = warning_message.str(); RAY_LOG(WARNING) << warning_message_str; + auto error_data_ptr = gcs::CreateErrorTableData( "worker_pool_large", warning_message_str, get_time_()); RAY_CHECK_OK(gcs_client_->Errors().AsyncReportJobError(error_data_ptr, nullptr)); diff --git a/src/ray/raylet/worker_pool.h b/src/ray/raylet/worker_pool.h index ef2e1e048635..3d7f456f82cb 100644 --- a/src/ray/raylet/worker_pool.h +++ b/src/ray/raylet/worker_pool.h @@ -25,6 +25,7 @@ #include #include +#include "absl/time/time.h" #include "ray/common/asio/instrumented_io_context.h" #include "ray/common/asio/periodical_runner.h" #include "ray/common/client_connection.h" @@ -200,7 +201,7 @@ class WorkerPool : public WorkerPoolInterface, public IOWorkerPoolInterface { /// it times out to start a worker. /// \param ray_debugger_external Ray debugger in workers will be started in a way /// that they are accessible from outside the node. - /// \param get_time A callback to get the current time. + /// \param get_time A callback to get the current time in milliseconds. WorkerPool(instrumented_io_context &io_service, const NodeID node_id, const std::string node_address, @@ -212,10 +213,10 @@ class WorkerPool : public WorkerPoolInterface, public IOWorkerPoolInterface { const std::vector &worker_ports, std::shared_ptr gcs_client, const WorkerCommandMap &worker_commands, - const std::string &native_library_path, + std::string native_library_path, std::function starting_worker_timeout_callback, int ray_debugger_external, - const std::function get_time); + std::function get_time); /// Destructor responsible for freeing a set of workers owned by this class. virtual ~WorkerPool() override; @@ -472,7 +473,7 @@ class WorkerPool : public WorkerPoolInterface, public IOWorkerPoolInterface { /// TODO(scv119): replace dynamic options by runtime_env. const std::vector &LookupWorkerDynamicOptions(StartupToken token) const; - void KillIdleWorker(std::shared_ptr worker, int64_t last_time_used_ms); + void KillIdleWorker(std::shared_ptr worker, absl::Time last_time_used); /// Gloabl startup token variable. Incremented once assigned /// to a worker process and is added to @@ -587,7 +588,8 @@ class WorkerPool : public WorkerPoolInterface, public IOWorkerPoolInterface { /// The pool of idle non-actor workers of all languages. This is used to kill idle /// workers in FIFO order. The second element of std::pair is the time a worker becomes /// idle. - std::list, int64_t>> idle_of_all_languages_; + std::list, absl::Time>> + idle_of_all_languages_; private: /// A helper function that returns the reference of the pool state @@ -792,7 +794,7 @@ class WorkerPool : public WorkerPoolInterface, public IOWorkerPoolInterface { PeriodicalRunner periodical_runner_; /// A callback to get the current time. - const std::function get_time_; + const std::function get_time_; /// Runtime env manager client. std::shared_ptr runtime_env_agent_client_; /// Stats diff --git a/src/ray/raylet/worker_pool_test.cc b/src/ray/raylet/worker_pool_test.cc index 0da18903fdfe..022c5055522a 100644 --- a/src/ray/raylet/worker_pool_test.cc +++ b/src/ray/raylet/worker_pool_test.cc @@ -14,8 +14,10 @@ #include "ray/raylet/worker_pool.h" -#include "gmock/gmock.h" -#include "gtest/gtest.h" +#include +#include + +#include "absl/time/time.h" #include "nlohmann/json.hpp" #include "ray/common/asio/asio_util.h" #include "ray/common/asio/instrumented_io_context.h" @@ -26,9 +28,8 @@ #include "src/ray/protobuf/runtime_env_agent.pb.h" using json = nlohmann::json; -namespace ray { -namespace raylet { +namespace ray::raylet { int MAXIMUM_STARTUP_CONCURRENCY = 15; int PYTHON_PRESTART_WORKERS = 15; @@ -140,7 +141,7 @@ class WorkerPoolMock : public WorkerPool { "", []() {}, 0, - [this]() { return current_time_ms_; }), + [this]() { return absl::FromUnixMillis(current_time_ms_); }), last_worker_process_(), instrumented_io_service_(io_service), error_message_type_(1), @@ -241,7 +242,7 @@ class WorkerPoolMock : public WorkerPool { size_t GetIdleWorkerSize() { return idle_of_all_languages_.size(); } - std::list, int64_t>> &GetIdleWorkers() { + std::list, absl::Time>> &GetIdleWorkers() { return idle_of_all_languages_; } @@ -2151,9 +2152,7 @@ TEST_F(WorkerPoolTest, RegisterFirstJavaDriverCallbackImmediately) { ASSERT_TRUE(callback_called); } -} // namespace raylet - -} // namespace ray +} // namespace ray::raylet int main(int argc, char **argv) { InitShutdownRAII ray_log_shutdown_raii( From 62d59ba60823ceb7b2fb9a121c40ff58042be166 Mon Sep 17 00:00:00 2001 From: Alexey Kudinkin Date: Tue, 26 Nov 2024 21:53:53 -0800 Subject: [PATCH 52/54] [Data] Rebased PA overflow tests onto standalone Ray cluster (#48887) Tidying up Signed-off-by: Alexey Kudinkin --- python/ray/data/tests/test_arrow_block.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/python/ray/data/tests/test_arrow_block.py b/python/ray/data/tests/test_arrow_block.py index 22fe9f7ef7a0..00ed13363f20 100644 --- a/python/ray/data/tests/test_arrow_block.py +++ b/python/ray/data/tests/test_arrow_block.py @@ -91,7 +91,7 @@ def binary_dataset_single_file_gt_2gb(): ], ) def test_single_row_gt_2gb( - ray_start_regular_shared, + ray_start_regular, restore_data_context, binary_dataset_single_file_gt_2gb, col_name, @@ -124,7 +124,7 @@ def _id(row): ], ) def test_arrow_batch_gt_2gb( - ray_start_regular_shared, + ray_start_regular, parquet_dataset_single_column_gt_2gb, restore_data_context, op, @@ -158,10 +158,10 @@ def _id(x): print( f">>> Batch:\n" f"------\n" - "Column: 'id'" + "Column: 'id'\n" f"Values: {batch['id']}\n" f"------\n" - "Column: 'bin'" + "Column: 'bin'\n" f"Total: {total_binary_column_size / GiB} GiB\n" f"Values: {[str(v)[:3] + ' x ' + str(len(v)) for v in batch['bin']]}\n" ) From 3d7fc8f60aea371cba4af7a5ac021222a88763d1 Mon Sep 17 00:00:00 2001 From: Blocka Date: Wed, 27 Nov 2024 14:28:57 +0800 Subject: [PATCH 53/54] [Doc] Logging: Specify the Helm chart version in the Persist KubeRay Operator Logs doc (#48937) Signed-off-by: win5923 --- .../kubernetes/user-guides/persist-kuberay-operator-logs.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/doc/source/cluster/kubernetes/user-guides/persist-kuberay-operator-logs.md b/doc/source/cluster/kubernetes/user-guides/persist-kuberay-operator-logs.md index 496afd34e4a5..3cadfbb81938 100644 --- a/doc/source/cluster/kubernetes/user-guides/persist-kuberay-operator-logs.md +++ b/doc/source/cluster/kubernetes/user-guides/persist-kuberay-operator-logs.md @@ -19,7 +19,7 @@ helm repo add grafana https://grafana.github.io/helm-charts helm repo update # Install Loki with single replica mode -helm install loki grafana/loki -f https://raw.githubusercontent.com/grafana/loki/refs/heads/main/production/helm/loki/single-binary-values.yaml +helm install loki grafana/loki --version 6.21.0 -f https://raw.githubusercontent.com/grafana/loki/refs/heads/main/production/helm/loki/single-binary-values.yaml ``` ### Configure log processing @@ -48,7 +48,7 @@ Deploy the Fluent Bit deployment with the [Helm chart repository](https://github helm repo add fluent https://fluent.github.io/helm-charts helm repo update -helm install fluent-bit fluent/fluent-bit -f fluent-bit-config.yaml +helm install fluent-bit fluent/fluent-bit --version 0.48.2 -f fluent-bit-config.yaml ``` ### Install the KubeRay Operator @@ -75,7 +75,7 @@ Deploy the Grafana deployment with the [Helm chart repository](https://github.co helm repo add grafana https://grafana.github.io/helm-charts helm repo update -helm install grafana grafana/grafana -f datasource-config.yaml +helm install grafana grafana/grafana --version 8.6.2 -f datasource-config.yaml ``` ### Check the Grafana Dashboard From 3bd3a02449f9a90443a7a35e63ab962da2f69e81 Mon Sep 17 00:00:00 2001 From: Balaji Veeramani Date: Wed, 27 Nov 2024 11:45:17 -0600 Subject: [PATCH 54/54] [Data] Clarify schema validation error (#48882) ```python ray.data.range(1).groupby("does_not_exist").count().materialize() ``` **Before** ``` ValueError: The column 'does_not_exist' does not exist in the schema 'Column Type ------ ---- id int64'. ``` **After** ``` ValueError: You specified the column 'does_not_exist', but there's no such column in the dataset. The dataset has columns: {'id'} ``` --------- Signed-off-by: Balaji Veeramani --- .../_internal/planner/exchange/sort_task_spec.py | 5 +++-- python/ray/data/tests/test_execution_optimizer.py | 15 ++++----------- 2 files changed, 7 insertions(+), 13 deletions(-) diff --git a/python/ray/data/_internal/planner/exchange/sort_task_spec.py b/python/ray/data/_internal/planner/exchange/sort_task_spec.py index 827c4a2c7a51..7c67b3dbdefe 100644 --- a/python/ray/data/_internal/planner/exchange/sort_task_spec.py +++ b/python/ray/data/_internal/planner/exchange/sort_task_spec.py @@ -81,8 +81,9 @@ def validate_schema(self, schema: Optional[Union[type, "pyarrow.lib.Schema"]]): for column in self._columns: if column not in schema_names_set: raise ValueError( - "The column '{}' does not exist in the " - "schema '{}'.".format(column, schema) + f"You specified the column '{column}', but there's no such " + "column in the dataset. The dataset has columns: " + f"{schema_names_set}" ) @property diff --git a/python/ray/data/tests/test_execution_optimizer.py b/python/ray/data/tests/test_execution_optimizer.py index d657ce1c9d98..af7af855b187 100644 --- a/python/ray/data/tests/test_execution_optimizer.py +++ b/python/ray/data/tests/test_execution_optimizer.py @@ -1145,9 +1145,7 @@ def test_sort_validate_keys(ray_start_regular_shared): assert extract_values("id", ds.sort("id").take_all()) == list(range(10)) invalid_col_name = "invalid_column" - with pytest.raises( - ValueError, match=f"The column '{invalid_col_name}' does not exist" - ): + with pytest.raises(ValueError, match="there's no such column in the dataset"): ds.sort(invalid_col_name).take_all() ds_named = ray.data.from_items( @@ -1165,10 +1163,7 @@ def test_sort_validate_keys(ray_start_regular_shared): assert [d["col1"] for d in r1] == [7, 5, 3, 1] assert [d["col2"] for d in r2] == [8, 6, 4, 2] - with pytest.raises( - ValueError, - match=f"The column '{invalid_col_name}' does not exist in the schema", - ): + with pytest.raises(ValueError, match="there's no such column in the dataset"): ds_named.sort(invalid_col_name).take_all() @@ -1279,9 +1274,7 @@ def test_aggregate_e2e(ray_start_regular_shared, use_push_based_shuffle): def test_aggregate_validate_keys(ray_start_regular_shared): ds = ray.data.range(10) invalid_col_name = "invalid_column" - with pytest.raises( - ValueError, match=f"The column '{invalid_col_name}' does not exist" - ): + with pytest.raises(ValueError): ds.groupby(invalid_col_name).count() ds_named = ray.data.from_items( @@ -1308,7 +1301,7 @@ def test_aggregate_validate_keys(ray_start_regular_shared): with pytest.raises( ValueError, - match=f"The column '{invalid_col_name}' does not exist in the schema", + match="there's no such column in the dataset", ): ds_named.groupby(invalid_col_name).count()