Update main post-23.10 release

Dockerfile

@@ -12,8 +12,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-ARG BASE_IMAGE=nvcr.io/nvidia/tritonserver:23.09-py3
-ARG TRITONSDK_BASE_IMAGE=nvcr.io/nvidia/tritonserver:23.09-py3-sdk
+ARG BASE_IMAGE=nvcr.io/nvidia/tritonserver:23.10-py3
+ARG TRITONSDK_BASE_IMAGE=nvcr.io/nvidia/tritonserver:23.10-py3-sdk
 
 ARG MODEL_ANALYZER_VERSION=1.34.0dev
 ARG MODEL_ANALYZER_CONTAINER_VERSION=23.11dev

README.md

@@ -23,7 +23,7 @@ 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 1.32.0 and is available on
 branch
-[r23.09](https://github.com/triton-inference-server/model_analyzer/tree/r23.09).
+[r23.10](https://github.com/triton-inference-server/model_analyzer/tree/r23.10).
 
 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>

docs/bls_quick_start.md

@@ -49,7 +49,7 @@ git pull origin main
 **1. Pull the SDK container:**
 
 ```
-docker pull nvcr.io/nvidia/tritonserver:23.09-py3-sdk
+docker pull nvcr.io/nvidia/tritonserver:23.10-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:23.09-py3-sdk
+      --net=host nvcr.io/nvidia/tritonserver:23.10-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:23.09-py3 ]
+[ triton_docker_image: <string> | default: nvcr.io/nvidia/tritonserver:23.10-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:23.09-py3-sdk
+docker pull nvcr.io/nvidia/tritonserver:23.10-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:23.09-py3-sdk
+      --net=host nvcr.io/nvidia/tritonserver:23.10-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: 23.09-py3
+    tag: 23.10-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:23.09-py3-sdk
+docker pull nvcr.io/nvidia/tritonserver:23.10-py3-sdk
 ```
 
 **2. Run the SDK container**
@@ -58,7 +58,7 @@ docker pull nvcr.io/nvidia/tritonserver:23.09-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:23.09-py3-sdk
+      --net=host nvcr.io/nvidia/tritonserver:23.10-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:23.09-py3-sdk
+docker pull nvcr.io/nvidia/tritonserver:23.10-py3-sdk
 ```
 
 **2. Run the SDK container**
@@ -58,7 +58,7 @@ docker pull nvcr.io/nvidia/tritonserver:23.09-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:23.09-py3-sdk
+      --net=host nvcr.io/nvidia/tritonserver:23.10-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: 23.09-py3
+    tag: 23.10-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:23.09-py3"
+DEFAULT_TRITON_DOCKER_IMAGE = "nvcr.io/nvidia/tritonserver:23.10-py3"
 DEFAULT_TRITON_HTTP_ENDPOINT = "localhost:8000"
 DEFAULT_TRITON_GRPC_ENDPOINT = "localhost:8001"
 DEFAULT_TRITON_METRICS_URL = "http://localhost:8002/metrics"