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# Copyright 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. | ||
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# Redistribution and use in source and binary forms, with or without | ||
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# Release Notes for 2.36.0 | ||
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## New Freatures and Improvements | ||
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* ["pytorch_backend"](https://github.com/triton-inference-server/pytorch_backend) | ||
supports implicit state management. | ||
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* ["python_backend"](https://github.com/triton-inference-server/python_backend) | ||
supports | ||
[direct serving of TensorFlow SavedModel](https://github.com/triton-inference-server/python_backend/blob/r23.07/src/resources/platform_handlers/tensorflow_savedmodel/README.md). | ||
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* ["python_backend"](https://github.com/triton-inference-server/python_backend) | ||
supports | ||
[unpacked Conda execution environment](https://github.com/triton-inference-server/python_backend/tree/r23.07#creating-custom-execution-environments). | ||
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* ["python_backend"](https://github.com/triton-inference-server/python_backend) | ||
added the | ||
[model loading APIs](https://github.com/triton-inference-server/python_backend/blob/r23.07/README.md#model-loading-api) | ||
for BLS usage. | ||
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* Triton OpenTelemetry trace mode supports ensemble model tracing. | ||
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* Triton Python client supports | ||
[DLPack tensors in CUDA shared memory utilities](https://github.com/triton-inference-server/client/tree/r23.07#cuda-shared-memory). | ||
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* Triton supports the S3 model repository that contains more than 1000 files. | ||
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* Added | ||
[Java binding](https://github.com/bytedeco/javacpp-presets/pull/1361) | ||
of the Triton in-process C++ API. | ||
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* Refer to the 23.07 column of the | ||
[Frameworks Support Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html) | ||
for container image versions on which the 23.07 inference server container is based. | ||
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## Known Issues | ||
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* The | ||
["fastertransformer_backend"](https://github.com/triton-inference-server/fastertransformer_backend) | ||
build only works with Triton 23.04 and older releases. | ||
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* Some systems which implement `malloc()` may not release memory back to the | ||
operating system right away causing a false memory leak. This can be mitigate | ||
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). | ||
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We recommend experimenting with both `tcmalloc` and `jemalloc` to determine which | ||
one works better for your use case. | ||
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* 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`. | ||
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* 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 | ||
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* 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. | ||
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* Traced models in PyTorch seem to create overflows when int8 tensor values are | ||
transformed to int32 on the GPU. Refer to | ||
https://github.com/pytorch/pytorch/issues/66930 for more information. | ||
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* Triton cannot retrieve GPU metrics with MIG-enabled GPU devices (A100 and | ||
A30). | ||
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* Triton metrics might not work if the host machine is running a separate DCGM | ||
agent on bare-metal or in a container. | ||
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