Update README for 22.06 release

RELEASE.md

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+
+# Release Notes for 2.23.0
+
+## New Freatures and Improvements
+
+* 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.
+
+* Python backend models now support 
+  [auto-generated model configuration](https://github.com/triton-inference-server/python_backend/tree/r22.06#auto_complete_config).
+
+* [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.
+
+* 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.
+
+* Improvements to Perf Analyzer stability and profiling logic.
+
+* 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.
+
+
+## Known Issues
+
+* 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.
+
+* 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.
+
+* Triton PIP wheels for ARM SBSA are not available from PyPI and pip will 
+  install an incorrect Jetson version of Triton for Arm SBSA. 
+
+  The correct 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 issue [pytorch#66930](https://github.com/pytorch/pytorch/issues/66930) 
+  for more information.
+
+* Triton cannot retrieve GPU metrics with MIG-enabled GPU devices (A100 and A30).
+
+* Triton metrics might not work if the host machine is running a separate DCGM 
+  agent on bare-metal or in a container.
+
+* 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).
+
+* 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). 
+
+  To load the model successfully in Triton, you need to export the model again 
+  by using a recent version of PyTorch.