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Update README and add RELEASE notes for 23.06 (#5991)
* Update README.md for 23.06 * Update documentation structure * Update RELEASE.md Co-authored-by: Tanmay Verma <tanmay2592@gmail.com> --------- Co-authored-by: Misha Chornyi <mchornyi@nvidia.com> Co-authored-by: Tanmay Verma <tanmay2592@gmail.com>
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# Copyright 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. | ||
# | ||
# Redistribution and use in source and binary forms, with or without | ||
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# Release Notes for 2.35.0 | ||
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## New Freatures and Improvements | ||
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* Support for | ||
[KIND\_MODEL instance type](https://github.com/triton-inference-server/pytorch_backend/tree/r23.06#model-instance-group-kind) | ||
has been extended to the PyTorch backend. | ||
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* The gRPC clients can now indicate whether they want to receive the flags | ||
associated with each response. This can help the clients to | ||
[programmatically determine](https://github.com/triton-inference-server/server/blob/r23.06/docs/user_guide/decoupled_models.md#knowing-when-a-decoupled-inference-request-is-complete) | ||
when all the responses for a given request have been received on the client | ||
side for decoupled models. | ||
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* Added beta support for using | ||
[Redis](https://github.com/triton-inference-server/redis_cache/tree/r23.06) as | ||
a cache for inference requests. | ||
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* The | ||
[statistics extension](https://github.com/triton-inference-server/server/blob/r23.06/docs/protocol/extension_statistics.md) | ||
now includes the memory usage of the loaded models. This statistics is | ||
currently implemented only for TensorRT and ONNXRuntime backends. | ||
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* Added support for batch inputs in ragged batching for PyTorch backend. | ||
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* Added | ||
[serial sequences](https://github.com/triton-inference-server/client/blob/main/src/c%2B%2B/perf_analyzer/docs/cli.md#--serial-sequences) | ||
mode for Perf Analyzer. | ||
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* Refer to the 23.06 column of the | ||
[Frameworks Support Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html) | ||
for container image versions on which the 23.06 inference server container is | ||
based. | ||
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## Known Issues | ||
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* The Fastertransfer backend build only works with Triton 23.04 and older | ||
releases. | ||
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* OpenVINO 2022.1 is used in the OpenVINO backend and the OpenVINO execution | ||
provider for the Onnxruntime Backend. OpenVINO 2022.1 is not officially | ||
supported on Ubuntu 22.04 and should be treated as beta. | ||
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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. | ||
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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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