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# Release Notes for 2.23.0 | ||
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## New Freatures and Improvements | ||
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* Auto-generated model configuration enables | ||
[dynamic batching](https://github.com/triton-inference-server/server/blob/r22.06/docs/model_configuration.md#default-max-batch-size-and-dynamic-batcher) | ||
in supported models by default. | ||
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* Python backend models now support | ||
[auto-generated model configuration](https://github.com/triton-inference-server/python_backend/tree/r22.06#auto_complete_config). | ||
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* [Decoupled API](https://github.com/triton-inference-server/server/blob/r22.06/docs/decoupled_models.md#python-model-using-python-backend) | ||
support in Python Backend model is out of beta. | ||
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* Updated I/O tensors | ||
[naming convention](https://github.com/triton-inference-server/server/blob/main/docs/model_configuration.md#special-conventions-for-pytorch-backend) | ||
for serving TorchScript models via PyTorch backend. | ||
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* Improvements to Perf Analyzer stability and profiling logic. | ||
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* Refer to the 22.06 column of the | ||
[Frameworks Support Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html) | ||
for container image versions on which the 22.06 inference server container is based. | ||
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## Known Issues | ||
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* Perf Analyzer stability criteria has been changed which may result in | ||
reporting instability for scenarios that were previously considered stable. | ||
This change has been made to improve the accuracy of Perf Analyzer results. | ||
If you observe this message, it can be resolved by increasing the | ||
`--measurement-interval` in the time windows mode or | ||
`--measurement-request-count` in the count windows mode. | ||
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* 22.06 is the last release that defaults to | ||
[TensorFlow version 1](https://github.com/triton-inference-server/tensorflow_backend/tree/r22.06#--backend-configtensorflowversionint). | ||
From 22.07 onwards Triton will change the default TensorFlow version to 2.X. | ||
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* Triton PIP wheels for ARM SBSA are not available from PyPI and pip will | ||
install an incorrect Jetson version of Triton for Arm SBSA. | ||
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The correct 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. | ||
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Refer to issue [pytorch#66930](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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* Running a PyTorch TorchScript model using the PyTorch backend, where multiple | ||
instances of a model are configured can lead to a slowdown in model execution | ||
due to the following PyTorch issue: | ||
[pytorch#27902](https://github.com/pytorch/pytorch/issues/27902). | ||
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* Starting from 22.02, the Triton container, which uses the 22.02 or above | ||
PyTorch container, will report an error during model loading in the PyTorch | ||
backend when using scripted models that were exported in the legacy format | ||
(using our 19.09 or previous PyTorch NGC containers corresponding to | ||
PyTorch 1.2.0 or previous releases). | ||
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To load the model successfully in Triton, you need to export the model again | ||
by using a recent version of PyTorch. |