Update README for 22.07 release

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 # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 -->
 
-**NOTE: You are currently on the r22.07 branch which tracks stabilization
-towards the next release. This branch is not usable during stabilization.**
+# Triton Inference Server
+
+[![License](https://img.shields.io/badge/License-BSD3-lightgrey.svg)](https://opensource.org/licenses/BSD-3-Clause)
+
+**LATEST RELEASE: You are currently on the main branch which tracks
+under-development progress towards the next release. The current release is 
+version [2.24.0](https://github.com/triton-inference-server/server/tree/r22.07)
+and corresponds to the 22.07 container release on 
+[NVIDIA GPU Cloud (NGC)](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tritonserver).**
+
+----
+Triton Inference Server is an open source inference serving software that 
+streamlines AI inferencing. Triton enables teams to deploy any AI model from 
+multiple deep learning and machine learning frameworks, including TensorRT, 
+TensorFlow, PyTorch, ONNX, OpenVINO, Python, RAPIDS FIL, and more. Triton 
+supports inference across cloud, data center,edge and embedded devices on NVIDIA 
+GPUs, x86 and ARM CPU, or AWS Inferentia. Triton delivers optimized performance 
+for many query types, including real time, batched, ensembles and audio/video 
+streaming.
+
+Major features include:
+
+- [Supports multiple deep learning
+  frameworks](https://github.com/triton-inference-server/backend#where-can-i-find-all-the-backends-that-are-available-for-triton)
+- [Supports multiple machine learning
+  frameworks](https://github.com/triton-inference-server/fil_backend)
+- [Concurrent model
+  execution](docs/architecture.md#concurrent-model-execution)
+- [Dynamic batching](docs/model_configuration.md#dynamic-batcher)
+- [Sequence batching](docs/model_configuration.md#sequence-batcher) and 
+  [implicit state management](docs/architecture.md#implicit-state-management)
+  for stateful models
+- Provides [Backend API](https://github.com/triton-inference-server/backend) that
+  allows adding custom backends and pre/post processing operations
+- Model pipelines using
+  [Ensembling](docs/architecture.md#ensemble-models) or [Business
+  Logic Scripting
+  (BLS)](https://github.com/triton-inference-server/python_backend#business-logic-scripting)
+- [HTTP/REST and GRPC inference
+  protocols](docs/inference_protocols.md) based on the community
+  developed [KServe
+  protocol](https://github.com/kserve/kserve/tree/master/docs/predict-api/v2)
+- A [C API](docs/inference_protocols.md#in-process-triton-server-api) and
+  [Java API](docs/inference_protocols.md#java-bindings-for-in-process-triton-server-api)
+  allow Triton to link directly into your application for edge and other in-process use cases
+- [Metrics](docs/metrics.md) indicating GPU utilization, server
+  throughput, server latency, and more
+
+Need enterprise support?  NVIDIA global support is available for Triton 
+Inference Server with the 
+[NVIDIA AI Enterprise software suite](https://www.nvidia.com/en-us/data-center/products/ai-enterprise/). 
+
+## Serve a Model in 3 Easy Steps
+
+```bash
+# Step 1: Create the example model repository 
+git clone -b r22.07 https://github.com/triton-inference-server/server.git
+
+cd server/docs/examples
+
+./fetch_models.sh
+
+# Step 2: Launch triton from the NGC Triton container
+docker run --gpus=1 --rm --net=host -v /full/path/to/docs/examples/model_repository:/models nvcr.io/nvidia/tritonserver:22.07-py3 tritonserver --model-repository=/models
+
+# Step 3: In a separate console, launch the image_client example from the NGC Triton SDK container
+docker run -it --rm --net=host nvcr.io/nvidia/tritonserver:22.07-py3-sdk
+
+/workspace/install/bin/image_client -m densenet_onnx -c 3 -s INCEPTION /workspace/images/mug.jpg
+
+# Inference should return the following
+Image '/workspace/images/mug.jpg':
+    15.346230 (504) = COFFEE MUG
+    13.224326 (968) = CUP
+    10.422965 (505) = COFFEEPOT
+```
+Please read the [QuickStart](docs/quickstart.md) guide for additional information
+regarding this example. The quickstart guide also contains an example of how to launch Triton on [CPU-only systems](docs/quickstart.md#run-on-cpu-only-system).
+
+## Examples and Tutorials
+
+Check out [NVIDIA LaunchPad](https://www.nvidia.com/en-us/data-center/products/ai-enterprise-suite/trial/)
+for free access to a set of hands-on labs with Triton Inference Server hosted on
+NVIDIA infrastructure.
+
+Specific end-to-end examples for popular models, such as ResNet, BERT, and DLRM 
+are located in the 
+[NVIDIA Deep Learning Examples](https://github.com/NVIDIA/DeepLearningExamples)
+page on GitHub. The 
+[NVIDIA Developer Zone](https://developer.nvidia.com/nvidia-triton-inference-server) 
+contains additional documentation, presentations, and examples.
+
+## Documentation
+
+### Build and Deploy
+
+The recommended way to build and use Triton Inference Server is with Docker
+images.
+
+- [Install Triton Inference Server with Docker containers](docs/build.md#building-triton-with-docker) (*Recommended*)
+- [Install Triton Inference Server without Docker containers](docs/build.md#building-triton-without-docker)
+- [Build a custom Triton Inference Server Docker container](docs/compose.md)
+- [Build Triton Inference Server from source](docs/build.md#building-on-unsupported-platforms)
+- [Build Triton Inference Server for Windows 10](docs/build.md#building-for-windows-10)
+- Examples for deploying Triton Inference Server with Kubernetes and Helm on [GCP](deploy/gcp/README.md), 
+  [AWS](deploy/aws/README.md), and [NVIDIA FleetCommand](deploy/fleetcommand/README.md)
+
+### Using Triton
+
+#### Preparing Models for Triton Inference Server
+
+The first step in using Triton to serve your models is to place one or
+more models into a [model repository](docs/model_repository.md). Depending on 
+the type of the model and on what Triton capabilities you want to enable for
+the model, you may need to create a [model
+configuration](docs/model_configuration.md) for the model.  
+
+- [Add custom operations to Triton if needed by your model](docs/custom_operations.md)
+- Enable model pipelining with [Model Ensemble](docs/architecture.md#ensemble-models)
+  and [Business Logic Scripting (BLS)](https://github.com/triton-inference-server/python_backend#business-logic-scripting)
+- Optimize your models setting [scheduling and batching](docs/architecture.md#models-and-schedulers)
+  parameters and [model instances](docs/model_configuration.md#instance-groups).
+- Use the [Model Analyzer tool](https://github.com/triton-inference-server/model_analyzer)
+  to help optimize your model configuration with profiling
+- Learn how to [explicitly manage what models are available by loading and 
+  unloading models](docs/model_management.md)
+
+#### Configure and Use Triton Inference Server
+
+- Read the [Quick Start Guide](docs/quickstart.md) to run Triton Inference 
+  Server on both GPU and CPU
+- Triton supports multiple execution engines, called 
+  [backends](https://github.com/triton-inference-server/backend#where-can-i-find-all-the-backends-that-are-available-for-triton), including 
+  [TensorRT](https://github.com/triton-inference-server/tensorrt_backend), 
+  [TensorFlow](https://github.com/triton-inference-server/tensorflow_backend), 
+  [PyTorch](https://github.com/triton-inference-server/pytorch_backend), 
+  [ONNX](https://github.com/triton-inference-server/onnxruntime_backend), 
+  [OpenVINO](https://github.com/triton-inference-server/openvino_backend), 
+  [Python](https://github.com/triton-inference-server/python_backend), and more
+- Learn how to [optimize performance](docs/optimization.md) using the 
+  [Performance Analyzer](docs/perf_analyzer.md) and 
+  [Model Analyzer](https://github.com/triton-inference-server/model_analyzer)
+- Learn how to [manage loading and unloading models](docs/model_management.md) in 
+  Triton
+- Send requests directly to Triton with the [HTTP/REST JSON-based
+  or gRPC protocols](docs/inference_protocols.md#httprest-and-grpc-protocols)
+
+#### Client Support and Examples
+
+A Triton *client* application sends inference and other requests to Triton. The 
+[Python and C++ client libraries](https://github.com/triton-inference-server/client)
+provide APIs to simplify this communication.
+
+- Review client examples for [C++](https://github.com/triton-inference-server/client/blob/main/src/c%2B%2B/examples),
+  [Python](https://github.com/triton-inference-server/client/blob/main/src/python/examples),
+  and [Java](https://github.com/triton-inference-server/client/blob/main/src/java/src/main/java/triton/client/examples)
+- Configure [HTTP](https://github.com/triton-inference-server/client#http-options)
+  and [gRPC](https://github.com/triton-inference-server/client#grpc-options)
+  client options
+- Send input data (e.g. a jpeg image) directly to Triton in the [body of an HTTP 
+  request without any additional metadata](https://github.com/triton-inference-server/server/blob/main/docs/protocol/extension_binary_data.md#raw-binary-request)
+
+### Extend Triton
+
+[Triton Inference Server's architecture](docs/architecture.md) is specifically 
+designed for modularity and flexibility
+
+- [Customize Triton Inference Server container](docs/compose.md) for your use case
+- [Create custom backends](https://github.com/triton-inference-server/backend)
+  in either [C/C++](https://github.com/triton-inference-server/backend/blob/main/README.md#triton-backend-api)
+  or [Python](https://github.com/triton-inference-server/python_backend)
+- Create [decouple backends and models](docs/decoupled_models.md) that can send 
+  multiple responses for a request or not send any responses for a request
+- Use a [Triton repository agent](docs/repository_agents.md) to add functionality
+  that operates when a model is loaded and unloaded, such as authentication, 
+  decryption, or conversion
+- Deploy Triton on [Jetson and JetPack](docs/jetson.md)
+- [Use Triton on AWS 
+   Inferentia](https://github.com/triton-inference-server/python_backend/tree/main/inferentia)
+
+### Additional Documentation
+
+- [FAQ](docs/faq.md)
+- [User Guide](docs#user-guide)
+- [Developer Guide](docs#developer-guide)
+- [Release Notes](https://docs.nvidia.com/deeplearning/triton-inference-server/release-notes/index.html)
+- [GPU, Driver, and CUDA Support
+Matrix](https://docs.nvidia.com/deeplearning/dgx/support-matrix/index.html)
+
+## Contributing
+
+Contributions to Triton Inference Server are more than welcome. To
+contribute please review the [contribution 
+guidelines](CONTRIBUTING.md). If you have a backend, client,
+example or similar contribution that is not modifying the core of
+Triton, then you should file a PR in the [contrib
+repo](https://github.com/triton-inference-server/contrib).
+
+## Reporting problems, asking questions
+
+We appreciate any feedback, questions or bug reporting regarding this project. 
+When posting [issues in GitHub](https://github.com/triton-inference-server/server/issues),
+follow the process outlined in the [Stack Overflow document](https://stackoverflow.com/help/mcve).
+Ensure posted examples are:
+- minimal – use as little code as possible that still produces the
+  same problem
+- complete – provide all parts needed to reproduce the problem. Check
+  if you can strip external dependencies and still show the problem. The
+  less time we spend on reproducing problems the more time we have to
+  fix it
+- verifiable – test the code you're about to provide to make sure it
+  reproduces the problem. Remove all other problems that are not
+  related to your request/question.
+
+## For more information
+
+Please refer to the [NVIDIA Developer Triton page](https://developer.nvidia.com/nvidia-triton-inference-server)
+for more information.
diff --git a/RELEASE.md b/RELEASE.md
@@ -0,0 +1,122 @@
+<!--
+# Copyright 2018-2022, 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:
+#  * Redistributions of source code must retain the above copyright
+#    notice, this list of conditions and the following disclaimer.
+#  * Redistributions in binary form must reproduce the above copyright
+#    notice, this list of conditions and the following disclaimer in the
+#    documentation and/or other materials provided with the distribution.
+#  * Neither the name of NVIDIA CORPORATION nor the names of its
+#    contributors may be used to endorse or promote products derived
+#    from this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
+# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+# PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
+# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
+# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
+# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
+# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
+# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+-->
+
+# Release Notes for 2.24.0
+
+## New Freatures and Improvements
+
+* [Auto-Complete](https://github.com/triton-inference-server/server/blob/main/docs/model_configuration.md#auto-generated-model-configuration) 
+  is enabled by default. The `--strict-model-config` option has been soft 
+  deprecated, use the new  `--disable-auto-complete-config` CLI option instead.
+
+* New example backend demonstrating 
+  [Business Logic Scripting in C++](https://github.com/triton-inference-server/backend/blob/r22.07/examples/backends/bls/README.md).
+
+* Users can provide values for 
+  ["init_ops"](https://github.com/triton-inference-server/tensorflow_backend/tree/r22.07#parameters) 
+  in Tensorflow TF1.x GraphDef models through json file.
+
+* New 
+  [asyncio compatible API](https://github.com/triton-inference-server/client#python-asyncio-support-beta) 
+  to the Python GRPC/HTTP APIs.
+
+* Added  thread pool to reduce service downtime for concurrently loading models. 
+  The thread pool size is configurable with the new `--model-load-thread-count`
+  tritonserver option. You can find more information 
+  [here](https://github.com/triton-inference-server/server/blob/main/docs/model_management.md#concurrently-loading-models). 
+
+* Model Analyzer now doesn't require `config.pbtxt` file for models that can be 
+  auto-completed in Triton.
+
+* Refer to the 22.07 column of the 
+  [Frameworks Support Matrix](https://docs.nvidia.com/deeplearning/frameworks/support-matrix/index.html) 
+  for container image versions on which the 22.07 inference server container is 
+  based.
+
+## Known Issues
+
+* JetPack release will be published later in the month in order to align with 
+  JetPack SDK public availability.
+
+* Auto-complete could cause an increase in server start time. To avoid a start 
+  time increase, users should provide the full model configuration.
+
+* When auto-completing some model configs, backends may generate a model config 
+  even though there is not enough metadata (ex. Graphdef models for Tensorflow 
+  Backend). The user will see the model successfully load but fail to inference.
+  In this case the user should provide the full model configuration for these 
+  models or use the `--disable-auto-complete-config` CLI option to show which 
+  models fail to load.
+
+* Can't do autocomplete for PyTorch models, not enough metadata. Can only verify
+  that the number of inputs is correct and the input names match what is 
+  specified in the model configuration. No info about number of outputs and 
+  datatypes. Related pytorch bug: 
+  https://github.com/pytorch/pytorch/issues/38273.
+
+* Running inference on multiple TensorRT model instances in Triton may fail 
+  with signal(6). The issue is expected to be fixed in a future release. Details 
+  can be found at https://github.com/triton-inference-server/server/issues/4566.
+
+* 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.
+
+* Unlike previously noted, 22.07 is the last release that defaults to 
+  [TensorFlow version 1](https://github.com/triton-inference-server/tensorflow_backend/tree/r22.06#--backend-configtensorflowversionint).
+  From 22.08 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 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.
+
+* 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.