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Update README for 2.37.0/23.08 (#6232)
* Update README for 2.37.0/23.08 * 23.07 -> 23.08 replacement
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<!-- | ||
# Copyright 2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved. | ||
# | ||
# Redistribution and use in source and binary forms, with or without | ||
# modification, are permitted provided that the following conditions | ||
# are met: | ||
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# notice, this list of conditions and the following disclaimer. | ||
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# Release Notes for 2.37.0 | ||
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## New Freatures and Improvements | ||
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* Triton can load model instances in parallel for supporting backends. See [TRITONBACKEND_BackendAttributeSetParallelModelInstanceLoading](https://github.com/triton-inference-server/backend/tree/r23.08#tritonbackend_backendattribute) for more details. As of 23.08, only [python](https://github.com/triton-inference-server/python_backend/tree/r23.08) and [onnxruntime](https://github.com/triton-inference-server/onnxruntime_backend/tree/r23.08) backends support loading model instances in parallel. | ||
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* Python backend models can capture [trace for composing child](https://github.com/triton-inference-server/server/blob/r23.08/docs/user_guide/trace.md#tracing-for-bls-models) models when executing BLS requests. | ||
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* Triton OpenTelemetry Tracing exposes [resource settings](https://github.com/triton-inference-server/server/blob/r23.08/docs/user_guide/trace.md#opentelemetry-trace-apis-settings) which can be used to configure the service name and version. | ||
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* Python backend supports directly [loading and serving PyTorch models](https://github.com/triton-inference-server/python_backend/tree/r23.08#pytorch-platform-experimental) with torch.compile(). | ||
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* Exposed [preserve_ordering](https://github.com/triton-inference-server/common/blob/r23.08/protobuf/model_config.proto#L1461-L1481) field to oldest strategy sequence batcher. The default behavior of the oldest strategy sequence batcher to preserve response order across the independent requests belonging to different sequences is changed from True to False. Note: This setting does not impact order of responses within a sequence. | ||
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* Refer to the 23.08 column of the | ||
[Frameworks Support Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html) | ||
for container image versions on which the 23.08 inference server container is | ||
based. | ||
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## Known Issues | ||
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* Triton uses OpenTelemetry CPP library version, which can cause Triton to [crash](https://github.com/triton-inference-server/server/issues/6202), when OpenTelemetry’s exporter timeouts. | ||
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* When using decoupled models, there is a possibility that response order as sent from the backend may not match with the order in which these responses are received by the streaming gRPC client. | ||
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* The | ||
["fastertransformer_backend"](https://github.com/triton-inference-server/fastertransformer_backend) is only officially supported for 22.12, though it can be built for Triton container versions up to 23.07. | ||
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* The Java CAPI is known to have intermittent segfaults we’re looking for a root cause. | ||
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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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