Update README and versions for 1.40.0 / 24.05 (#872)

Dockerfile

@@ -12,11 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-ARG BASE_IMAGE=nvcr.io/nvidia/tritonserver:24.04-py3
-ARG TRITONSDK_BASE_IMAGE=nvcr.io/nvidia/tritonserver:24.04-py3-sdk
+ARG BASE_IMAGE=nvcr.io/nvidia/tritonserver:24.05-py3
+ARG TRITONSDK_BASE_IMAGE=nvcr.io/nvidia/tritonserver:24.05-py3-sdk
 
-ARG MODEL_ANALYZER_VERSION=1.40.0dev
-ARG MODEL_ANALYZER_CONTAINER_VERSION=24.05dev
+ARG MODEL_ANALYZER_VERSION=1.40.0
+ARG MODEL_ANALYZER_CONTAINER_VERSION=24.05
 FROM ${TRITONSDK_BASE_IMAGE} as sdk
 
 FROM $BASE_IMAGE

README.md

@@ -19,105 +19,4 @@ limitations under the License.
 # Triton Model Analyzer
 
 > [!Warning]
->
-> ##### LATEST RELEASE
->
-> You are currently on the `main` branch which tracks under-development progress towards the next release. <br>
-> The latest release of the Triton Model Analyzer is 1.39.0 and is available on branch
-> [r24.04](https://github.com/triton-inference-server/model_analyzer/tree/r24.04).
-
-Triton Model Analyzer is a CLI tool which can help you find a more optimal configuration, on a given piece of hardware, for single, multiple, ensemble, or BLS models running on a [Triton Inference Server](https://github.com/triton-inference-server/server/). Model Analyzer will also generate reports to help you better understand the trade-offs of the different configurations along with their compute and memory requirements.
-<br><br>
-
-# Features
-
-### Search Modes
-
-- [Quick Search](docs/config_search.md#quick-search-mode) will **sparsely** search the [Max Batch Size](https://github.com/triton-inference-server/server/blob/main/docs/user_guide/model_configuration.md#maximum-batch-size),
-  [Dynamic Batching](https://github.com/triton-inference-server/server/blob/main/docs/user_guide/model_configuration.md#dynamic-batcher), and
-  [Instance Group](https://github.com/triton-inference-server/server/blob/main/docs/user_guide/model_configuration.md#instance-groups) spaces by utilizing a heuristic hill-climbing algorithm to help you quickly find a more optimal configuration
-
-- [Automatic Brute Search](docs/config_search.md#automatic-brute-search) will **exhaustively** search the
-  [Max Batch Size](https://github.com/triton-inference-server/server/blob/main/docs/user_guide/model_configuration.md#maximum-batch-size),
-  [Dynamic Batching](https://github.com/triton-inference-server/server/blob/main/docs/user_guide/model_configuration.md#dynamic-batcher), and
-  [Instance Group](https://github.com/triton-inference-server/server/blob/main/docs/user_guide/model_configuration.md#instance-groups)
-  parameters of your model configuration
-
-- [Manual Brute Search](docs/config_search.md#manual-brute-search) allows you to create manual sweeps for every parameter that can be specified in the model configuration
-
-### Model Types
-
-- [Ensemble Model Search](docs/config_search.md#ensemble-model-search): Model Analyzer can help you find the optimal
-  settings when profiling an ensemble model, utilizing the [Quick Search](docs/config_search.md#quick-search-mode) algorithm
-
-- [BLS Model Search](docs/config_search.md#bls-model-search): Model Analyzer can help you find the optimal
-  settings when profiling a BLS model, utilizing the [Quick Search](docs/config_search.md#quick-search-mode) algorithm
-
-- [Multi-Model Search](docs/config_search.md#multi-model-search-mode): Model Analyzer can help you
-  find the optimal settings when profiling multiple concurrent models, utilizing the [Quick Search](docs/config_search.md#quick-search-mode) algorithm
-
-- [LLM Search](docs/config_search.md#llm-search-mode): Model Analyzer can help you
-  find the optimal settings when profiling large language models, utilizing the [Quick Search](docs/config_search.md#quick-search-mode) algorithm
-
-### Other Features
-
-- [Detailed and summary reports](docs/report.md): Model Analyzer is able to generate
-  summarized and detailed reports that can help you better understand the trade-offs
-  between different model configurations that can be used for your model.
-
-- [QoS Constraints](docs/config.md#constraint): Constraints can help you
-  filter out the Model Analyzer results based on your QoS requirements. For
-  example, you can specify a latency budget to filter out model configurations
-  that do not satisfy the specified latency threshold.
-  <br><br>
-
-# Examples and Tutorials
-
-### **Single Model**
-
-See the [Single Model Quick Start](docs/quick_start.md) for a guide on how to use Model Analyzer to profile, analyze and report on a simple PyTorch model.
-
-### **Multi Model**
-
-See the [Multi-model Quick Start](docs/mm_quick_start.md) for a guide on how to use Model Analyzer to profile, analyze and report on two models running concurrently on the same GPU.
-
-### **Ensemble Model**
-
-See the [Ensemble Model Quick Start](docs/ensemble_quick_start.md) for a guide on how to use Model Analyzer to profile, analyze and report on a simple Ensemble model.
-
-### **BLS Model**
-
-See the [BLS Model Quick Start](docs/bls_quick_start.md) for a guide on how to use Model Analyzer to profile, analyze and report on a simple BLS model.
-<br><br>
-
-# Documentation
-
-- [Installation](docs/install.md)
-- [Model Analyzer CLI](docs/cli.md)
-- [Launch Modes](docs/launch_modes.md)
-- [Configuring Model Analyzer](docs/config.md)
-- [Model Analyzer Metrics](docs/metrics.md)
-- [Model Config Search](docs/config_search.md)
-- [Checkpointing](docs/checkpoints.md)
-- [Model Analyzer Reports](docs/report.md)
-- [Deployment with Kubernetes](docs/kubernetes_deploy.md)
-  <br><br>
-
-# Reporting problems, asking questions
-
-We appreciate any feedback, questions or bug reporting regarding this
-project. When help with code is needed, follow the process outlined in
-the Stack Overflow (https://stackoverflow.com/help/mcve)
-document. Ensure posted examples are:
-
-- minimal – use as little code as possible that still produces the
-  same problem
-
-- complete – provide all parts needed to reproduce the problem. Check
-  if you can strip external dependency and still show the problem. The
-  less time we spend on reproducing problems the more time we have to
-  fix it
-
-- verifiable – test the code you're about to provide to make sure it
-  reproduces the problem. Remove all other problems that are not
-  related to your request/question.
+> You are currently on the `24.05` branch which tracks under-development and unreleased features.

VERSION

@@ -1 +1 @@
-1.40.0dev
+1.40.0

docs/bls_quick_start.md

@@ -49,7 +49,7 @@ git pull origin main
 **1. Pull the SDK container:**
 
 ```
-docker pull nvcr.io/nvidia/tritonserver:24.04-py3-sdk
+docker pull nvcr.io/nvidia/tritonserver:24.05-py3-sdk
 ```
 
 **2. Run the SDK container**
@@ -59,7 +59,7 @@ docker run -it --gpus 1 \
       --shm-size 2G \
       -v /var/run/docker.sock:/var/run/docker.sock \
       -v $(pwd)/examples/quick-start:$(pwd)/examples/quick-start \
-      --net=host nvcr.io/nvidia/tritonserver:24.04-py3-sdk
+      --net=host nvcr.io/nvidia/tritonserver:24.05-py3-sdk
 ```
 
 **Important:** The example above uses a single GPU. If you are running on multiple GPUs, you may need to increase the shared memory size accordingly<br><br>

docs/config.md

@@ -153,7 +153,7 @@ cpu_only_composing_models: <comma-delimited-string-list>
 [ reload_model_disable: <bool> | default: false]
 
 # Triton Docker image tag used when launching using Docker mode
-[ triton_docker_image: <string> | default: nvcr.io/nvidia/tritonserver:24.04-py3 ]
+[ triton_docker_image: <string> | default: nvcr.io/nvidia/tritonserver:24.05-py3 ]
 
 # Triton Server HTTP endpoint url used by Model Analyzer client"
 [ triton_http_endpoint: <string> | default: localhost:8000 ]

docs/ensemble_quick_start.md

@@ -55,7 +55,7 @@ mkdir examples/quick/ensemble_add_sub/1
 **1. Pull the SDK container:**
 
 ```
-docker pull nvcr.io/nvidia/tritonserver:24.04-py3-sdk
+docker pull nvcr.io/nvidia/tritonserver:24.05-py3-sdk
 ```
 
 **2. Run the SDK container**
@@ -65,7 +65,7 @@ docker run -it --gpus 1 \
       --shm-size 1G \
       -v /var/run/docker.sock:/var/run/docker.sock \
       -v $(pwd)/examples/quick-start:$(pwd)/examples/quick-start \
-      --net=host nvcr.io/nvidia/tritonserver:24.04-py3-sdk
+      --net=host nvcr.io/nvidia/tritonserver:24.05-py3-sdk
 ```
 
 **Important:** The example above uses a single GPU. If you are running on multiple GPUs, you may need to increase the shared memory size accordingly<br><br>

docs/kubernetes_deploy.md

@@ -79,7 +79,7 @@ images:
 
   triton:
     image: nvcr.io/nvidia/tritonserver
-    tag: 24.04-py3
+    tag: 24.05-py3
 ```
 
 The model analyzer executable uses the config file defined in `helm-chart/templates/config-map.yaml`. This config can be modified to supply arguments to model analyzer. Only the content under the `config.yaml` section of the file should be modified.

docs/mm_quick_start.md

@@ -49,7 +49,7 @@ git pull origin main
 **1. Pull the SDK container:**
 
 ```
-docker pull nvcr.io/nvidia/tritonserver:24.04-py3-sdk
+docker pull nvcr.io/nvidia/tritonserver:24.05-py3-sdk
 ```
 
 **2. Run the SDK container**
@@ -58,7 +58,7 @@ docker pull nvcr.io/nvidia/tritonserver:24.04-py3-sdk
 docker run -it --gpus all \
       -v /var/run/docker.sock:/var/run/docker.sock \
       -v $(pwd)/examples/quick-start:$(pwd)/examples/quick-start \
-      --net=host nvcr.io/nvidia/tritonserver:24.04-py3-sdk
+      --net=host nvcr.io/nvidia/tritonserver:24.05-py3-sdk
 ```
 
 ## `Step 3:` Profile both models concurrently

docs/quick_start.md

@@ -49,7 +49,7 @@ git pull origin main
 **1. Pull the SDK container:**
 
 ```
-docker pull nvcr.io/nvidia/tritonserver:24.04-py3-sdk
+docker pull nvcr.io/nvidia/tritonserver:24.05-py3-sdk
 ```
 
 **2. Run the SDK container**
@@ -58,7 +58,7 @@ docker pull nvcr.io/nvidia/tritonserver:24.04-py3-sdk
 docker run -it --gpus all \
       -v /var/run/docker.sock:/var/run/docker.sock \
       -v $(pwd)/examples/quick-start:$(pwd)/examples/quick-start \
-      --net=host nvcr.io/nvidia/tritonserver:24.04-py3-sdk
+      --net=host nvcr.io/nvidia/tritonserver:24.05-py3-sdk
 ```
 
 ## `Step 3:` Profile the `add_sub` model

helm-chart/values.yaml

@@ -41,4 +41,4 @@ images:
 
   triton:
     image: nvcr.io/nvidia/tritonserver
-    tag: 24.04-py3
+    tag: 24.05-py3

model_analyzer/config/input/config_defaults.py

@@ -56,7 +56,7 @@
 DEFAULT_RUN_CONFIG_PROFILE_MODELS_CONCURRENTLY_ENABLE = False
 DEFAULT_REQUEST_RATE_SEARCH_ENABLE = False
 DEFAULT_TRITON_LAUNCH_MODE = "local"
-DEFAULT_TRITON_DOCKER_IMAGE = "nvcr.io/nvidia/tritonserver:24.04-py3"
+DEFAULT_TRITON_DOCKER_IMAGE = "nvcr.io/nvidia/tritonserver:24.05-py3"
 DEFAULT_TRITON_HTTP_ENDPOINT = "localhost:8000"
 DEFAULT_TRITON_GRPC_ENDPOINT = "localhost:8001"
 DEFAULT_TRITON_METRICS_URL = "http://localhost:8002/metrics"