docs: Fix Triton release_notes.md (#8031)

Author: nv-kmcgill53

docs/introduction/compatibility.md

@@ -37,6 +37,7 @@
 
 | Triton release version	 | NGC Tag	 | Python version	 | Torch version | TensorRT version | TensorRT-LLM version | CUDA version | CUDA Driver version | Size |
 | --- | ---  | --- | --- | --- | --- | --- | --- | --- |
+| 25.02 | nvcr.io/nvidia/tritonserver:25.02-trtllm-python-py3 | Python 3.12.3 | 2.6.0a0%2Becf3bae40a.nv25.1 | 10.8.0.43 | 0.17.0.post1 | 12.8.0.038 | 570.86.10 | 28G |
 | 25.01 | nvcr.io/nvidia/tritonserver:25.01-trtllm-python-py3 | Python 3.12.3  | 2.6.0a0%2Becf3bae40a.nv25.1 | 10.8.0.43 | 0.17.0 | 12.8.0.038 | 570.86.10 | 30G |
 | 24.12 | nvcr.io/nvidia/tritonserver:24.12-trtllm-python-py3 | Python 3.12.3  | 2.6.0a0%2Bdf5bbc09d1.nv24.11 | 10.7.0 | 0.16.0 | 12.6.3 | 560.35.05 | 22G |
 | 24.11 | nvcr.io/nvidia/tritonserver:24.11-trtllm-python-py3 | Python 3.10.12  | 2.5.0a0%2Be000cf0ad9.nv24.10 | 10.6.0 | 0.15.0 | 12.6.3 | 555.42.06 | 24.8G |
@@ -52,6 +53,7 @@
 
 | Triton release version	 | NGC Tag	 | Python version	 | vLLM version | CUDA version | CUDA Driver version | Size |
 | --- | --- | --- | --- | --- | --- | --- |
+| 25.02 | nvcr.io/nvidia/tritonserver:25.02-vllm-python-py3 | Python 3.12.3  | 0.7.0+5e800e3d.nv25.2.cu128 | 12.8.0.038 | 570.86.10 | 22G |
 | 25.01 | nvcr.io/nvidia/tritonserver:25.01-vllm-python-py3 | Python 3.12.3  | 0.6.3.post1 | 12.8.0.038 | 570.86.10 | 23G |
 | 24.12 | nvcr.io/nvidia/tritonserver:24.12-vllm-python-py3 | Python 3.12.3 |  0.5.5 | 12.6.3.004 | 560.35.05 | 20G |
 | 24.11 | nvcr.io/nvidia/tritonserver:24.11-vllm-python-py3 | Python 3.12.3 |  0.5.5 | 12.6.3.001 | 560.35.05 | 22.1G |
@@ -67,6 +69,7 @@
 
 | Triton release version	 | ONNX Runtime	 |
 | --- | --- |
+| 25.02 | 1.20.1 |
 | 25.01 | 1.20.1 |
 | 24.12 | 1.20.1 |
 | 24.11 | 1.19.2 |

docs/introduction/release_notes.md

@@ -1,5 +1,5 @@
 <!--
-# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+# Copyright (c) 2024-2025, NVIDIA CORPORATION. All rights reserved.
 #
 # Redistribution and use in source and binary forms, with or without
 # modification, are permitted provided that the following conditions
@@ -25,256 +25,10 @@
 # (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 25.01](https://docs.nvidia.com/deeplearning/triton-inference-server/release-notes/rel-25-01.html#rel-25-01)
+# [Triton Inference Server Release 25.02](https://docs.nvidia.com/deeplearning/triton-inference-server/release-notes/rel-25-02.html#rel-25-02)
 
-The Triton Inference Server container image, release 25.01, is available
+The Triton Inference Server container image, release 25.02, 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).
-
-## Contents of the Triton Inference Server container
-
-The [Triton Inference
-Server](https://github.com/triton-inference-server/server) Docker image
-contains the inference server executable and related shared libraries
-in /opt/tritonserver.
-
-For a complete list of what the container includes, refer to [Deep
-Learning Frameworks Support
-Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html).
-
-The container also includes the following:
-
--   [Ubuntu 24.04](https://releases.ubuntu.com/24.04/) including [Python
-    3.12](https://www.python.org/downloads/release/python-3120/)
-
--   [NVIDIA CUDA
-    12.8.0.038](https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html)
-
--   [NVIDIA cuBLAS
-    12.8.3.14](https://docs.nvidia.com/cuda/cublas/index.html)
-
--   [cuDNN
-    9.7.0.66](https://docs.nvidia.com/deeplearning/cudnn/release-notes/)
-
--   [NVIDIA NCCL
-    2.25.1](https://docs.nvidia.com/deeplearning/nccl/release-notes/) (optimized
-    for [NVIDIA NVLink](http://www.nvidia.com/object/nvlink.html)®)
-
--   [NVIDIA TensorRT™
-    10.8.0.43](https://docs.nvidia.com/deeplearning/tensorrt/release-notes/index.html)
-
--   OpenUCX 1.15.0
-
--   GDRCopy 2.4.1
-
--   NVIDIA HPC-X 2.21
-
--   OpenMPI 4.1.7
-
--   [FIL](https://github.com/triton-inference-server/fil_backend)
-
--   [NVIDIA DALI®
-    1.45](https://docs.nvidia.com/deeplearning/dali/release-notes/index.html)
-
--   [nvImageCodec
-    0.2.0.7](https://docs.nvidia.com/cuda/nvimagecodec/release_notes_v0.2.0.html)
-
--   ONNX Runtime 1.20.1
-
--   Intel[ OpenVINO ](https://github.com/openvinotoolkit/openvino/tree/2022.1.0)2024.05.0
-
--   DCGM 3.3.6
-
--   [TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM/) version [release/0.17.0](https://github.com/NVIDIA/TensorRT-LLM/tree/v0.17.0)
-
--   [vLLM](https://github.com/vllm-project/vllm) version 0.6.3.1
-
-## Driver Requirements
-
-Release 25.01 is based on [CUDA
-12.8.0.038](https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html) which
-requires [NVIDIA
-Driver](http://www.nvidia.com/Download/index.aspx?lang=en-us) release
-560 or later. However, if you are running on a data center GPU (for
-example, T4 or any other data center GPU), you can use NVIDIA driver
-release 470.57 (or later R470), 525.85 (or later R525), 535.86 (or later
-R535), or 545.23 (or later R545).
-
-The CUDA driver\'s compatibility package only supports particular
-drivers. Thus, users should upgrade from all R418, R440, R450, R460,
-R510, R520, R530, R545 and R555 drivers, which are not
-forward-compatible with CUDA 12.6. For a complete list of supported
-drivers, see the [CUDA Application
-Compatibility](https://docs.nvidia.com/deploy/cuda-compatibility/index.html#use-the-right-compat-package) topic.
-For more information, see [CUDA Compatibility and
-Upgrades](https://docs.nvidia.com/cuda/cuda-c-best-practices-guide/index.html#cuda-compatibility-and-upgrades).
-
-## GPU Requirements
-
-Release 25.01 supports CUDA compute capability 7.5 and later. This
-corresponds to GPUs in the NVIDIA Turing™, NVIDIA Ampere architecture,
-NVIDIA Hopper™, NVIDIA Ada Lovelace, and NVIDIA Blackwell architecture
-families. For a list of GPUs to which this compute capability
-corresponds, see [CUDA GPUs](https://developer.nvidia.com/cuda-gpus).
-For additional support details, see [Deep Learning Frameworks Support
-Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html).
-
-## Key Features and Enhancements
-
-This Inference Server release includes the following key features and
-enhancements.
-
--   Starting with the 25.01 release, Triton Inference Server supports
-    Blackwell GPU architectures.
-
--   Fixed a bug when passing the correlation ID of string type to
-    python_backend. Added datatype checks to correlation ID values.
-
--   vLLM backend can now take advantage of the [vLLM
-    v0.6](https://blog.vllm.ai/2024/09/05/perf-update.html) performance
-    improvement by communicating with the vLLM engine via ZMQ.
-
--   GenAI-Perf now provides the exact input sequence length requested
-    for synthetic text generation.
-
--   GenAI-Perf supports the creation of a prefix pool to emulate system
-    prompts via \--num-system-prompts and \--system-prompt-length.
-
--   GenAI-Perf improved error visibility via returning more detailed
-    errors when OpenAI frontends return an error or metric generation
-    fails.
-
--   GenAI-Perf reports time-to-second-token and request count in its
-    metrics.
-
--   GenAI-Perf allows the use of a custom tokenizer in its "compare"
-    subcommand for comparing multiple profiles.
-
--   GenAI-Perf natively supports \--request-count for sending a specific
-    number of requests and \--header for sending a list of headers with
-    every request.
-
--   Model Analyzer functionality has been migrated to GenAI-Perf via the
-    "analyze" subcommand, enabling the tool to sweep and find the
-    optimal model configuration.
-
--   A bytes appending bug was fixed in GenAI-Perf, resulting in more
-    accurate output sequence lengths for Triton.
-
-
-## Known Issues
-
--   A segmentation fault related to DCGM and NSCQ may be encountered
-    during server shutdown on NVSwitch systems. A possible workaround
-    for this issue is to disable the collection of GPU metrics
-    \`tritonserver \--allow-gpu-metrics false \...\`
-
--   vLLM backend currently does not take advantage of the [vLLM
-    v0.6](https://blog.vllm.ai/2024/09/05/perf-update.html) performance
-    improvement when metrics are enabled.
-
--   Please note, that the vllm version provided in 25.01 container is
-    0.6.3.post1. Due to some issues with vllm library versioning,
-    \`vllm.\_\_version\_\_\` displays \`0.6.3\`.
-
--   Incorrect results are known to occur when using TensorRT (TRT)
-    Backend for inference using int8 data type for I/O on the Blackwell
-    GPU architecture.
-
--   When running Torch TRT models, the output may differ from running
-    the same model on a previous release.
-
--   When using TensorRT models, if auto-complete configuration is
-    disabled and is_non_linear_format_io:true for reformat-free tensors
-    is not provided in the model configuration, the model may not load
-    successfully.
-
--   When using Python models in[decoupled
-    mode](https://github.com/triton-inference-server/python_backend/tree/main?tab=readme-ov-file#decoupled-mode),
-    users need to ensure that the ResponseSender goes out of scope or is
-    properly cleaned up before unloading the model to guarantee that the
-    unloading process executes correctly.
-
--   Restart support was temporarily removed for Python models.
-
--   The Triton Inference Server with vLLM backend currently does not
-    support running vLLM models with tensor parallelism sizes greater
-    than 1 and default \"distributed_executor_backend\" setting when
-    using explicit model control mode. In attempt to load a vllm model
-    (tp \> 1) in explicit mode, users could potentially see failure at
-    the \`initialize\` step: \`could not acquire lock for
-    \<\_io.BufferedWriter name=\'\<stdout\>\'\> at interpreter shutdown,
-    possibly due to daemon threads\`. For the default model control
-    mode, after server shutdown, vllm related sub-processes are not
-    killed. Related vllm
-    issue: <https://github.com/vllm-project/vllm/issues/6766> . Please
-    specify distributed_executor_backend:ray in the model.json when
-    deploying vllm models with tensor parallelism \> 1.
-
--   When loading models with file override, multiple model configuration
-    files are not supported. Users must provide the model configuration
-    by setting parameter config : \<JSON\> instead of custom
-    configuration file in the following
-    format: file:configs/\<model-config-name\>.pbtxt :
-    \<base64-encoded-file-content\>.
-
--   TensorRT-LLM [backend](https://github.com/triton-inference-server/tensorrtllm_backend) provides
-    limited support of Triton extensions and features.
-
--   The TensorRT-LLM backend may core dump on server shutdown. This
-    impacts server teardown only and will not impact inferencing.
-
--   The Java CAPI is known to have intermittent segfaults.
-
--   Some systems which implement malloc() may not release memory back to
-    the operating system right away causing a false memory leak. This
-    can be mitigated by using a different malloc implementation.
-    Tcmalloc and jemalloc are installed in the Triton container and can
-    be [used by specifying the library in
-    LD_PRELOAD](https://github.com/triton-inference-server/server/blob/r22.12/docs/user_guide/model_management.md).
-    NVIDIA recommends experimenting with both tcmalloc and jemalloc to
-    determine which one works better for your use case.
-
--   Auto-complete may cause an increase in server start time. To avoid a
-    start time increase, users can provide the full model configuration
-    and launch the server with \--disable-auto-complete-config.
-
--   Auto-complete does not support PyTorch models due to lack of
-    metadata in the model. It can only verify that the number of inputs
-    and the input names matches what is specified in the model
-    configuration. There is no model metadata about the number of
-    outputs and datatypes. Related PyTorch
-    bug:<https://github.com/pytorch/pytorch/issues/38273>.
-
--   Triton Client PIP wheels for ARM SBSA are not available from PyPI
-    and pip will install an incorrect Jetson version of Triton Client
-    library for Arm SBSA. The correct client wheel file can be pulled
-    directly from the Arm SBSA SDK image and manually installed.
-
--   Traced models in PyTorch seem to create overflows when int8 tensor
-    values are transformed to int32 on the GPU. Refer
-    to [pytorch/pytorch#66930](https://github.com/pytorch/pytorch/issues/66930) for
-    more information.
-
--   Triton cannot retrieve GPU metrics with [MIG-enabled GPU
-    devices](https://docs.nvidia.com/datacenter/tesla/mig-user-guide/index.html#supported-gpus).
-
--   Triton metrics might not work if the host machine is running a
-    separate DCGM agent on bare-metal or in a container.
-
--   When cloud storage (AWS, GCS, AZURE) is used as a model repository
-    and a model has multiple versions, Triton creates an extra local
-    copy of the cloud model's folder in the temporary directory, which
-    is deleted upon server's shutdown.
-
--   Python backend support for Windows is limited and does not currently
-    support the following features:
-
-    -   GPU tensors
-
-    -   CPU and GPU-related metrics
-
-    -   Custom execution environments
-
-    -   The model load/unload APIs
+on [GitHub](https://github.com/triton-inference-server/server). Release notes can
+be found on the [GitHub Release Page](https://github.com/triton-inference-server/server/releases)