diff --git a/Dockerfile.sdk b/Dockerfile.sdk index 8febb7bf39..b2181abe6e 100644 --- a/Dockerfile.sdk +++ b/Dockerfile.sdk @@ -29,7 +29,7 @@ # # Base image on the minimum Triton container -ARG BASE_IMAGE=nvcr.io/nvidia/tritonserver:26.04-py3-min +ARG BASE_IMAGE=nvcr.io/nvidia/tritonserver:26.05-py3-min ARG TRITON_CLIENT_REPO_SUBDIR=clientrepo ARG TRITON_REPO_ORGANIZATION=http://github.com/triton-inference-server diff --git a/README.md b/README.md index b2f3d818b0..6fc36283f3 100644 --- a/README.md +++ b/README.md @@ -27,11 +27,6 @@ --> [![License](https://img.shields.io/badge/License-BSD3-lightgrey.svg)](https://opensource.org/licenses/BSD-3-Clause) ->[!WARNING] ->You are currently on the `main` branch which tracks under-development progress ->towards the next release. The current release is version [2.68.0](https://github.com/triton-inference-server/server/releases/latest) ->and corresponds to the 26.04 container release on NVIDIA GPU Cloud (NGC). - # Triton Inference Server Triton Inference Server is an open source inference serving software that @@ -90,16 +85,16 @@ Inference Server with the ```bash # Step 1: Create the example model repository -git clone -b r26.04 https://github.com/triton-inference-server/server.git +git clone -b r26.05 https://github.com/triton-inference-server/server.git cd server/docs/examples ./fetch_models.sh # Step 2: Launch triton from the NGC Triton container -docker run --gpus=1 --rm --net=host -v ${PWD}/model_repository:/models nvcr.io/nvidia/tritonserver:26.04-py3 tritonserver --model-repository=/models --model-control-mode explicit --load-model densenet_onnx +docker run --gpus=1 --rm --net=host -v ${PWD}/model_repository:/models nvcr.io/nvidia/tritonserver:26.05-py3 tritonserver --model-repository=/models --model-control-mode explicit --load-model densenet_onnx # Step 3: Sending an Inference Request # In a separate console, launch the image_client example from the NGC Triton SDK container -docker run -it --rm --net=host nvcr.io/nvidia/tritonserver:26.04-py3-sdk /workspace/install/bin/image_client -m densenet_onnx -c 3 -s INCEPTION /workspace/images/mug.jpg +docker run -it --rm --net=host nvcr.io/nvidia/tritonserver:26.05-py3-sdk /workspace/install/bin/image_client -m densenet_onnx -c 3 -s INCEPTION /workspace/images/mug.jpg # Inference should return the following Image '/workspace/images/mug.jpg': diff --git a/TRITON_VERSION b/TRITON_VERSION index 6a166a54c5..a740b92f5e 100644 --- a/TRITON_VERSION +++ b/TRITON_VERSION @@ -1 +1 @@ -2.70.0dev +2.69.0 diff --git a/build.py b/build.py index 9baaedbfe3..e80d62f399 100755 --- a/build.py +++ b/build.py @@ -71,8 +71,8 @@ # DEFAULT_TRITON_VERSION_MAP = { - "release_version": "2.70.0dev", - "triton_container_version": "26.06dev", + "release_version": "2.69.0", + "triton_container_version": "26.05", "upstream_container_version": "26.04", "ort_version": "1.24.4", "ort_openvino_version": "2026.1.0", diff --git a/deploy/aws/values.yaml b/deploy/aws/values.yaml index a140611d4f..c94f832aa8 100644 --- a/deploy/aws/values.yaml +++ b/deploy/aws/values.yaml @@ -27,7 +27,7 @@ replicaCount: 1 image: - imageName: nvcr.io/nvidia/tritonserver:26.04-py3 + imageName: nvcr.io/nvidia/tritonserver:26.05-py3 pullPolicy: IfNotPresent modelRepositoryPath: s3://triton-inference-server-repository/model_repository numGpus: 1 diff --git a/deploy/fleetcommand/Chart.yaml b/deploy/fleetcommand/Chart.yaml index e96abde6f5..bd360e7955 100644 --- a/deploy/fleetcommand/Chart.yaml +++ b/deploy/fleetcommand/Chart.yaml @@ -26,7 +26,7 @@ apiVersion: v1 # appVersion is the Triton version; update when changing release -appVersion: 2.68.0 +appVersion: 2.69.0 description: Triton Inference Server (Fleet Command) name: triton-inference-server # version is the Chart version; update when changing anything in the chart diff --git a/deploy/fleetcommand/values.yaml b/deploy/fleetcommand/values.yaml index 6ecf3b351d..b911db4afd 100644 --- a/deploy/fleetcommand/values.yaml +++ b/deploy/fleetcommand/values.yaml @@ -27,7 +27,7 @@ replicaCount: 1 image: - imageName: nvcr.io/nvidia/tritonserver:26.04-py3 + imageName: nvcr.io/nvidia/tritonserver:26.05-py3 pullPolicy: IfNotPresent numGpus: 1 serverCommand: tritonserver @@ -47,13 +47,13 @@ image: # # To set model control mode, uncomment and configure below # TODO: Fix the following url, it is invalid - # See https://github.com/triton-inference-server/server/blob/r26.04/docs/user_guide/model_management.md + # See https://github.com/triton-inference-server/server/blob/r26.05/docs/user_guide/model_management.md # for more details #- --model-control-mode=explicit|poll|none # # Additional server args # - # see https://github.com/triton-inference-server/server/blob/r26.04/README.md + # see https://github.com/triton-inference-server/server/blob/r26.05/README.md # for more details service: diff --git a/deploy/gcp/values.yaml b/deploy/gcp/values.yaml index c9900d68a0..9784c9d252 100644 --- a/deploy/gcp/values.yaml +++ b/deploy/gcp/values.yaml @@ -27,7 +27,7 @@ replicaCount: 1 image: - imageName: nvcr.io/nvidia/tritonserver:26.04-py3 + imageName: nvcr.io/nvidia/tritonserver:26.05-py3 pullPolicy: IfNotPresent modelRepositoryPath: gs://triton-inference-server-repository/model_repository numGpus: 1 diff --git a/deploy/gke-marketplace-app/benchmark/perf-analyzer-script/triton_client.yaml b/deploy/gke-marketplace-app/benchmark/perf-analyzer-script/triton_client.yaml index a732a1da20..0e1347f4fd 100644 --- a/deploy/gke-marketplace-app/benchmark/perf-analyzer-script/triton_client.yaml +++ b/deploy/gke-marketplace-app/benchmark/perf-analyzer-script/triton_client.yaml @@ -33,7 +33,7 @@ metadata: namespace: default spec: containers: - - image: nvcr.io/nvidia/tritonserver:26.04-py3-sdk + - image: nvcr.io/nvidia/tritonserver:26.05-py3-sdk imagePullPolicy: Always name: nv-triton-client securityContext: diff --git a/deploy/gke-marketplace-app/server-deployer/build_and_push.sh b/deploy/gke-marketplace-app/server-deployer/build_and_push.sh index 8e00967f88..4b4468d89d 100755 --- a/deploy/gke-marketplace-app/server-deployer/build_and_push.sh +++ b/deploy/gke-marketplace-app/server-deployer/build_and_push.sh @@ -28,8 +28,8 @@ export REGISTRY=gcr.io/$(gcloud config get-value project | tr ':' '/') export APP_NAME=tritonserver export MAJOR_VERSION=2.67 -export MINOR_VERSION=2.68.0 -export NGC_VERSION=26.04-py3 +export MINOR_VERSION=2.69.0 +export NGC_VERSION=26.05-py3 docker pull nvcr.io/nvidia/$APP_NAME:$NGC_VERSION diff --git a/deploy/gke-marketplace-app/server-deployer/chart/triton/Chart.yaml b/deploy/gke-marketplace-app/server-deployer/chart/triton/Chart.yaml index 18f83cca68..d150f0e8d7 100644 --- a/deploy/gke-marketplace-app/server-deployer/chart/triton/Chart.yaml +++ b/deploy/gke-marketplace-app/server-deployer/chart/triton/Chart.yaml @@ -28,4 +28,4 @@ apiVersion: v1 appVersion: "2.68" description: Triton Inference Server name: triton-inference-server -version: 2.68.0 +version: 2.69.0 diff --git a/deploy/gke-marketplace-app/server-deployer/chart/triton/values.yaml b/deploy/gke-marketplace-app/server-deployer/chart/triton/values.yaml index 8cfd8171b8..362107e71a 100644 --- a/deploy/gke-marketplace-app/server-deployer/chart/triton/values.yaml +++ b/deploy/gke-marketplace-app/server-deployer/chart/triton/values.yaml @@ -31,14 +31,14 @@ maxReplicaCount: 3 tritonProtocol: HTTP # HPA GPU utilization autoscaling target HPATargetAverageValue: 85 -modelRepositoryPath: gs://triton_sample_models/26.04 -publishedVersion: '2.68.0' +modelRepositoryPath: gs://triton_sample_models/26.05 +publishedVersion: '2.69.0' gcpMarketplace: true image: registry: gcr.io repository: nvidia-ngc-public/tritonserver - tag: 26.04-py3 + tag: 26.05-py3 pullPolicy: IfNotPresent # modify the model repository here to match your GCP storage bucket numGpus: 1 diff --git a/deploy/gke-marketplace-app/server-deployer/data-test/schema.yaml b/deploy/gke-marketplace-app/server-deployer/data-test/schema.yaml index 7583068bc6..4c312c9880 100644 --- a/deploy/gke-marketplace-app/server-deployer/data-test/schema.yaml +++ b/deploy/gke-marketplace-app/server-deployer/data-test/schema.yaml @@ -27,7 +27,7 @@ x-google-marketplace: schemaVersion: v2 applicationApiVersion: v1beta1 - publishedVersion: '2.68.0' + publishedVersion: '2.69.0' publishedVersionMetadata: releaseNote: >- Initial release. diff --git a/deploy/gke-marketplace-app/server-deployer/schema.yaml b/deploy/gke-marketplace-app/server-deployer/schema.yaml index 457e13d19d..ccf3b157c4 100644 --- a/deploy/gke-marketplace-app/server-deployer/schema.yaml +++ b/deploy/gke-marketplace-app/server-deployer/schema.yaml @@ -27,7 +27,7 @@ x-google-marketplace: schemaVersion: v2 applicationApiVersion: v1beta1 - publishedVersion: '2.68.0' + publishedVersion: '2.69.0' publishedVersionMetadata: releaseNote: >- Initial release. @@ -89,7 +89,7 @@ properties: modelRepositoryPath: type: string title: Bucket where models are stored. Please make sure the user/service account to create the GKE app has permission to this GCS bucket. Read Triton documentation on configs and formatting details, supporting TensorRT, TensorFlow, Pytorch, Onnx ... etc. - default: gs://triton_sample_models/26.04 + default: gs://triton_sample_models/26.05 image.ldPreloadPath: type: string title: Leave this empty by default. Triton allows users to create custom layers for backend such as TensorRT plugin, the compiled shared library must be provided via LD_PRELOAD environment variable. diff --git a/deploy/gke-marketplace-app/trt-engine/README.md b/deploy/gke-marketplace-app/trt-engine/README.md index 0200987e6f..fff7466da4 100644 --- a/deploy/gke-marketplace-app/trt-engine/README.md +++ b/deploy/gke-marketplace-app/trt-engine/README.md @@ -33,7 +33,7 @@ ``` docker run --gpus all -it --network host \ --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 \ - -v ~:/scripts nvcr.io/nvidia/tensorrt:26.04-py3 + -v ~:/scripts nvcr.io/nvidia/tensorrt:26.05-py3 pip install onnx six torch tf2onnx tensorflow @@ -57,7 +57,7 @@ mkdir -p engines python3 builder.py -m models/fine-tuned/bert_tf_ckpt_large_qa_squad2_amp_128_v19.03.1/model.ckpt -o engines/bert_large_int8_bs1_s128.engine -b 1 -s 128 -c models/fine-tuned/bert_tf_ckpt_large_qa_squad2_amp_128_v19.03.1/ -v models/fine-tuned/bert_tf_ckpt_large_qa_squad2_amp_128_v19.03.1/vocab.txt --int8 --fp16 --strict --calib-num 1 -iln -imh -gsutil cp bert_large_int8_bs1_s128.engine gs://triton_sample_models/26.04/bert/1/model.plan +gsutil cp bert_large_int8_bs1_s128.engine gs://triton_sample_models/26.05/bert/1/model.plan ``` -For each Triton upgrade, container version used to generate the model, and the model path in GCS `gs://triton_sample_models/26.04/` should be updated accordingly with the correct version. +For each Triton upgrade, container version used to generate the model, and the model path in GCS `gs://triton_sample_models/26.05/` should be updated accordingly with the correct version. diff --git a/deploy/k8s-onprem/values.yaml b/deploy/k8s-onprem/values.yaml index 4dc4bf2c15..3d788f3f17 100644 --- a/deploy/k8s-onprem/values.yaml +++ b/deploy/k8s-onprem/values.yaml @@ -30,7 +30,7 @@ tags: openshift: false image: - imageName: nvcr.io/nvidia/tritonserver:26.04-py3 + imageName: nvcr.io/nvidia/tritonserver:26.05-py3 pullPolicy: IfNotPresent modelRepositoryServer: < Replace with the IP Address of your file server > modelRepositoryPath: /srv/models diff --git a/deploy/oci/values.yaml b/deploy/oci/values.yaml index f8867069c1..df5d60066d 100644 --- a/deploy/oci/values.yaml +++ b/deploy/oci/values.yaml @@ -27,7 +27,7 @@ replicaCount: 1 image: - imageName: nvcr.io/nvidia/tritonserver:26.04-py3 + imageName: nvcr.io/nvidia/tritonserver:26.05-py3 pullPolicy: IfNotPresent modelRepositoryPath: s3://https://.compat.objectstorage..oraclecloud.com:443/triton-inference-server-repository numGpus: 1 diff --git a/docs/customization_guide/compose.md b/docs/customization_guide/compose.md index e88f0c90ba..e922d27fbe 100644 --- a/docs/customization_guide/compose.md +++ b/docs/customization_guide/compose.md @@ -46,8 +46,8 @@ The `compose.py` script can be found in the Simply clone the repository and run `compose.py` to create a custom container. Note: Created container version will depend on the branch that was cloned. For example branch - [r26.04](https://github.com/triton-inference-server/server/tree/r26.04) -should be used to create a image based on the NGC 26.04 Triton release. + [r26.05](https://github.com/triton-inference-server/server/tree/r26.05) +should be used to create a image based on the NGC 26.05 Triton release. `compose.py` provides `--backend`, `--repoagent` options that allow you to specify which backends and repository agents to include in the custom image. @@ -78,20 +78,20 @@ For example, running ``` python3 compose.py --backend pytorch --repoagent checksum ``` -on branch [r26.04](https://github.com/triton-inference-server/server/tree/r26.04) pulls: -- `min` container `nvcr.io/nvidia/tritonserver:26.04-py3-min` -- `full` container `nvcr.io/nvidia/tritonserver:26.04-py3` +on branch [r26.05](https://github.com/triton-inference-server/server/tree/r26.05) pulls: +- `min` container `nvcr.io/nvidia/tritonserver:26.05-py3-min` +- `full` container `nvcr.io/nvidia/tritonserver:26.05-py3` Alternatively, users can specify the version of Triton container to pull from any branch by either: 1. Adding flag `--container-version ` to branch ``` -python3 compose.py --backend pytorch --repoagent checksum --container-version 26.04 +python3 compose.py --backend pytorch --repoagent checksum --container-version 26.05 ``` 2. Specifying `--image min, --image full,`. The user is responsible for specifying compatible `min` and `full` containers. ``` -python3 compose.py --backend pytorch --repoagent checksum --image min,nvcr.io/nvidia/tritonserver:26.04-py3-min --image full,nvcr.io/nvidia/tritonserver:26.04-py3 +python3 compose.py --backend pytorch --repoagent checksum --image min,nvcr.io/nvidia/tritonserver:26.05-py3-min --image full,nvcr.io/nvidia/tritonserver:26.05-py3 ``` Method 1 and 2 will result in the same composed container. Furthermore, `--image` flag overrides the `--container-version` flag when both are specified. @@ -102,8 +102,8 @@ Note: 2. vLLM and TensorRT-LLM backends are currently not supported backends for `compose.py`. If you want to build additional backends on top of these backends, it would be better to [build it yourself](#build-it-yourself) by using -`nvcr.io/nvidia/tritonserver:26.04-vllm-python-py3` or -`nvcr.io/nvidia/tritonserver:26.04-trtllm-python-py3` as a `min` container. +`nvcr.io/nvidia/tritonserver:26.05-vllm-python-py3` or +`nvcr.io/nvidia/tritonserver:26.05-trtllm-python-py3` as a `min` container. ### CPU-only container composition diff --git a/docs/getting_started/llm.md b/docs/getting_started/llm.md index 052d7829ca..6ea9a5aa33 100644 --- a/docs/getting_started/llm.md +++ b/docs/getting_started/llm.md @@ -282,7 +282,7 @@ The above needs to be done manually with your favorite editor. Once finished, pl -v $(pwd)/all_models:/opt/all_models \ -v $(pwd)/scripts:/opt/scripts \ -v $(pwd)/Phi-3-mini-4k-instruct:/opt/Phi-3-mini-4k-instruct \ - nvcr.io/nvidia/tritonserver:26.04-trtllm-python-py3 + nvcr.io/nvidia/tritonserver:26.05-trtllm-python-py3 # Launch Server python3 ../scripts/launch_triton_server.py --model_repo ../all_models/inflight_batcher_llm --world_size 1 @@ -308,7 +308,7 @@ The above needs to be done manually with your favorite editor. Once finished, pl - export RELEASE="26.04" + export RELEASE="26.05" docker run -it --net=host --gpus '"device=0"' nvcr.io/nvidia/tritonserver:${RELEASE}-py3-sdk 17. ## Download the Phi-3 tokenizer diff --git a/docs/introduction/release_notes.md b/docs/introduction/release_notes.md index 295734b89f..19fc0f22f3 100644 --- a/docs/introduction/release_notes.md +++ b/docs/introduction/release_notes.md @@ -25,9 +25,9 @@ # (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. --> -# [Triton Inference Server Release 26.04](https://docs.nvidia.com/deeplearning/triton-inference-server/release-notes/rel-26-04.html#rel-26-04) +# [Triton Inference Server Release 26.05](https://docs.nvidia.com/deeplearning/triton-inference-server/release-notes/rel-26-05.html#rel-26-05) -The Triton Inference Server container image, release 26.04, is available +The Triton Inference Server container image, release 26.05, is available on [NGC](https://ngc.nvidia.com/catalog/containers/nvidia:tritonserver) and is open source on [GitHub](https://github.com/triton-inference-server/server). Release notes can diff --git a/docs/user_guide/performance_tuning.md b/docs/user_guide/performance_tuning.md index d51ad5ab2f..ff837a4629 100644 --- a/docs/user_guide/performance_tuning.md +++ b/docs/user_guide/performance_tuning.md @@ -235,7 +235,7 @@ with a `tritonserver` binary. ```bash # Start server container -docker run -ti --rm --gpus=all --network=host -v $PWD:/mnt --name triton-server nvcr.io/nvidia/tritonserver:26.04-py3 +docker run -ti --rm --gpus=all --network=host -v $PWD:/mnt --name triton-server nvcr.io/nvidia/tritonserver:26.05-py3 # Start serving your models tritonserver --model-repository=/mnt/models @@ -284,7 +284,7 @@ by setting the `-u` flag, such as `perf_analyzer -m densenet_onnx -u ```bash # Start the SDK container interactively -docker run -ti --rm --gpus=all --network=host -v $PWD:/mnt --name triton-client nvcr.io/nvidia/tritonserver:26.04-py3-sdk +docker run -ti --rm --gpus=all --network=host -v $PWD:/mnt --name triton-client nvcr.io/nvidia/tritonserver:26.05-py3-sdk # Benchmark model being served from step 3 perf_analyzer -m densenet_onnx --concurrency-range 1:4 diff --git a/python/openai/README.md b/python/openai/README.md index 4598a5a43f..4134e72cb6 100644 --- a/python/openai/README.md +++ b/python/openai/README.md @@ -46,7 +46,7 @@ docker run -it --net=host --gpus all --rm \ -v ${HOME}/.cache/huggingface:/root/.cache/huggingface \ -e HF_TOKEN \ - nvcr.io/nvidia/tritonserver:26.04-vllm-python-py3 + nvcr.io/nvidia/tritonserver:26.05-vllm-python-py3 ``` 2. Launch the OpenAI-compatible Triton Inference Server: @@ -355,7 +355,7 @@ Currently, OpenAI-Compatible Frontend supports loading embedding models and embe docker run -it --net=host --gpus all --rm \ -v ${HOME}/.cache/huggingface:/root/.cache/huggingface \ -e HF_TOKEN \ - nvcr.io/nvidia/tritonserver:26.04-vllm-python-py3 + nvcr.io/nvidia/tritonserver:26.05-vllm-python-py3 ``` 2. Launch the OpenAI-compatible Triton Inference Server: @@ -451,7 +451,7 @@ docker run -it --net=host --gpus all --rm \ -v ${HOME}/.cache/huggingface:/root/.cache/huggingface \ -e HF_TOKEN \ -e TRTLLM_ORCHESTRATOR=1 \ - nvcr.io/nvidia/tritonserver:26.04-trtllm-python-py3 + nvcr.io/nvidia/tritonserver:26.05-trtllm-python-py3 ``` 2. Install dependencies inside the container: diff --git a/qa/common/gen_jetson_trt_models b/qa/common/gen_jetson_trt_models index 7fcdb49823..4d491fa2a1 100755 --- a/qa/common/gen_jetson_trt_models +++ b/qa/common/gen_jetson_trt_models @@ -34,7 +34,7 @@ # Make all generated files accessible outside of container umask 0000 # Set the version of the models -TRITON_VERSION=${TRITON_VERSION:=26.04} +TRITON_VERSION=${TRITON_VERSION:=26.05} # Set the CUDA device to use NVIDIA_VISIBLE_DEVICES=${NVIDIA_VISIBLE_DEVICES:=0} # Set TensorRT image diff --git a/qa/common/gen_qa_model_repository b/qa/common/gen_qa_model_repository index d6ed5738f8..a503b64a10 100755 --- a/qa/common/gen_qa_model_repository +++ b/qa/common/gen_qa_model_repository @@ -66,7 +66,7 @@ log_message.status "Changing working directory to the script directory to: " "${ cd ${TRITON_MDLS_BASE_SCRIPT_DIR} log_message.status "define: default values" -TRITON_VERSION=${TRITON_VERSION:=26.04} +TRITON_VERSION=${TRITON_VERSION:=26.05} ONNX_VERSION=1.20.1 ONNX_OPSET=0 OPENVINO_VERSION=2024.5.0