Update README and versions for 20.02 release

Dockerfile

@@ -151,8 +151,8 @@ FROM ${TENSORFLOW_IMAGE} AS trtserver_tf
 ############################################################################
 FROM ${BASE_IMAGE} AS trtserver_build
 
-ARG TRTIS_VERSION=1.11.0dev
-ARG TRTIS_CONTAINER_VERSION=20.02dev
+ARG TRTIS_VERSION=1.11.0
+ARG TRTIS_CONTAINER_VERSION=20.02
 
 # libgoogle-glog0v5 is needed by caffe2 libraries.
 # libcurl4-openSSL-dev is needed for GCS
@@ -334,8 +334,8 @@ ENTRYPOINT ["/opt/tensorrtserver/nvidia_entrypoint.sh"]
 ############################################################################
 FROM ${BASE_IMAGE}
 
-ARG TRTIS_VERSION=1.11.0dev
-ARG TRTIS_CONTAINER_VERSION=20.02dev
+ARG TRTIS_VERSION=1.11.0
+ARG TRTIS_CONTAINER_VERSION=20.02
 
 ENV TENSORRT_SERVER_VERSION ${TRTIS_VERSION}
 ENV NVIDIA_TENSORRT_SERVER_VERSION ${TRTIS_CONTAINER_VERSION}

README.rst

@@ -30,13 +30,239 @@
 NVIDIA TensorRT Inference Server
 ================================
 
-    **NOTE: You are currently on the 20.02 branch which tracks
-    stabilization towards the next release. This branch is not usable
-    during stabilization.**
-
 .. overview-begin-marker-do-not-remove
 
+The NVIDIA TensorRT Inference Server provides a cloud inferencing
+solution optimized for NVIDIA GPUs. The server provides an inference
+service via an HTTP or GRPC endpoint, allowing remote clients to
+request inferencing for any model being managed by the server. 
+
+What's New in 1.11.0
+--------------------
+* The TensorRT backend is improved to have significantly better performance. 
+  Improvements include reducing thread contention, using pinned memory for 
+  faster CPU<->GPU transfers, and increasing compute and memory copy overlap 
+  on GPUs.
+
+* Reduce memory usage of TensorRT models in many cases by sharing weights 
+  across multiple model instances.
+
+* Boolean data-type and shape tensors are now supported for TensorRT models.
+
+* A new model configuration option allows the dynamic batcher to create 
+  “ragged” batches for custom backend models. A ragged batch is a batch where
+  one or more of the input/output tensors have different shapes in different 
+  batch entries.
+
+* Local S3 storage endpoints are now supported for model repositories. A 
+  local S3 endpoint is specified as `s3://host:port/path/to/repository`.
+
+* The Helm chart showing an example Kubernetes deployment is updated to 
+  include Prometheus and Grafana support so that inference server metrics can
+  be collected and visualized.
+
+* The inference server container no longer sets `LD_LIBRARY_PATH`, instead the
+  server uses `RUNPATH` to locate its shared libraries.
+
+* Python 2 is end-of-life so all support has been removed. Python 3 is still 
+  supported.
+
+Features
+--------
+
+* `Multiple framework support
+  <https://docs.nvidia.com/deeplearning/sdk/tensorrt-inference-server-guide/docs/model_repository.html#framework-model-definition>`_. The
+  server can manage any number and mix of models (limited by system
+  disk and memory resources). Supports TensorRT, TensorFlow GraphDef,
+  TensorFlow SavedModel, ONNX, PyTorch, and Caffe2 NetDef model
+  formats. Also supports TensorFlow-TensorRT integrated
+  models. Variable-size input and output tensors are allowed if
+  supported by the framework. See `Capabilities
+  <https://docs.nvidia.com/deeplearning/sdk/tensorrt-inference-server-guide/docs/capabilities.html#capabilities>`_
+  for detailed support information for each framework.
+
+* `Concurrent model execution support
+  <https://docs.nvidia.com/deeplearning/sdk/tensorrt-inference-server-guide/docs/model_configuration.html#instance-groups>`_. Multiple
+  models (or multiple instances of the same model) can run
+  simultaneously on the same GPU.
+
+* Batching support. For models that support batching, the server can
+  accept requests for a batch of inputs and respond with the
+  corresponding batch of outputs. The inference server also supports
+  multiple `scheduling and batching
+  <https://docs.nvidia.com/deeplearning/sdk/tensorrt-inference-server-guide/docs/model_configuration.html#scheduling-and-batching>`_
+  algorithms that combine individual inference requests together to
+  improve inference throughput. These scheduling and batching
+  decisions are transparent to the client requesting inference.
+
+* `Custom backend support
+  <https://docs.nvidia.com/deeplearning/sdk/tensorrt-inference-server-guide/docs/model_repository.html#custom-backends>`_. The inference server
+  allows individual models to be implemented with custom backends
+  instead of by a deep-learning framework. With a custom backend a
+  model can implement any logic desired, while still benefiting from
+  the GPU support, concurrent execution, dynamic batching and other
+  features provided by the server.
+
+* `Ensemble support
+  <https://docs.nvidia.com/deeplearning/sdk/tensorrt-inference-server-guide/docs/models_and_schedulers.html#ensemble-models>`_. An
+  ensemble represents a pipeline of one or more models and the
+  connection of input and output tensors between those models. A
+  single inference request to an ensemble will trigger the execution
+  of the entire pipeline.
+
+* Multi-GPU support. The server can distribute inferencing across all
+  system GPUs.
+
+* The inference server provides `multiple modes for model management
+  <https://docs.nvidia.com/deeplearning/sdk/tensorrt-inference-server-guide/docs/model_management.html>`_. These
+  model management modes allow for both implicit and explicit loading
+  and unloading of models without requiring a server restart.
+
+* `Model repositories
+  <https://docs.nvidia.com/deeplearning/sdk/tensorrt-inference-server-guide/docs/model_repository.html#>`_
+  may reside on a locally accessible file system (e.g. NFS), in Google
+  Cloud Storage or in Amazon S3.
+
+* Readiness and liveness `health endpoints
+  <https://docs.nvidia.com/deeplearning/sdk/tensorrt-inference-server-guide/docs/http_grpc_api.html#health>`_
+  suitable for any orchestration or deployment framework, such as
+  Kubernetes.
+
+* `Metrics
+  <https://docs.nvidia.com/deeplearning/sdk/tensorrt-inference-server-guide/docs/metrics.html>`_
+  indicating GPU utilization, server throughput, and server latency.
+
+* `C library inferface
+  <https://docs.nvidia.com/deeplearning/sdk/tensorrt-inference-server-guide/docs/library_api.html>`_
+  allows the full functionality of the inference server to be included
+  directly in an application.
+
 .. overview-end-marker-do-not-remove
 
+The current release of the TensorRT Inference Server is 1.11.0 and
+corresponds to the 20.02 release of the tensorrtserver container on
+`NVIDIA GPU Cloud (NGC) <https://ngc.nvidia.com>`_. The branch for
+this release is `r20.02
+<https://github.com/NVIDIA/tensorrt-inference-server/tree/r20.02>`_.
+
+Backwards Compatibility
+-----------------------
+
+Continuing in the latest version the following interfaces maintain
+backwards compatibilty with the 1.0.0 release. If you have model
+configuration files, custom backends, or clients that use the
+inference server HTTP or GRPC APIs (either directly or through the
+client libraries) from releases prior to 1.0.0 you should edit
+and rebuild those as necessary to match the version 1.0.0 APIs.
+
+The following inferfaces will maintain backwards compatibility for all
+future 1.x.y releases (see below for exceptions):
+
+* Model configuration as defined in `model_config.proto
+  <https://github.com/NVIDIA/tensorrt-inference-server/blob/master/src/core/model_config.proto>`_.
+
+* The inference server HTTP and GRPC APIs as defined in `api.proto
+  <https://github.com/NVIDIA/tensorrt-inference-server/blob/master/src/core/api.proto>`_
+  and `grpc_service.proto
+  <https://github.com/NVIDIA/tensorrt-inference-server/blob/master/src/core/grpc_service.proto>`_,
+  except as noted below.
+
+* The V1 custom backend interface as defined in `custom.h
+  <https://github.com/NVIDIA/tensorrt-inference-server/blob/master/src/backends/custom/custom.h>`_.
+
+As new features are introduced they may temporarily have beta status
+where they are subject to change in non-backwards-compatible
+ways. When they exit beta they will conform to the
+backwards-compatibility guarantees described above. Currently the
+following features are in beta:
+
+* The inference server library API as defined in `trtserver.h
+  <https://github.com/NVIDIA/tensorrt-inference-server/blob/master/src/core/trtserver.h>`_
+  is currently in beta and may undergo non-backwards-compatible
+  changes.
+
+* The inference server HTTP and GRPC APIs related to system and CUDA
+  shared memory are currently in beta and may undergo
+  non-backwards-compatible changes.
+
+* The V2 custom backend interface as defined in `custom.h
+  <https://github.com/NVIDIA/tensorrt-inference-server/blob/master/src/backends/custom/custom.h>`_
+  is currently in beta and may undergo non-backwards-compatible
+  changes.
+
+* The C++ and Python client libraries are not stictly included in the
+  inference server compatibility guarantees and so should be
+  considered as beta status.
+
+Documentation
+-------------
+
+The User Guide, Developer Guide, and API Reference `documentation for
+the current release
+<https://docs.nvidia.com/deeplearning/sdk/tensorrt-inference-server-guide/docs/index.html>`_
+provide guidance on installing, building, and running the TensorRT
+Inference Server.
+
+You can also view the `documentation for the master branch
+<https://docs.nvidia.com/deeplearning/sdk/tensorrt-inference-server-master-branch-guide/docs/index.html>`_
+and for `earlier releases
+<https://docs.nvidia.com/deeplearning/sdk/inference-server-archived/index.html>`_.
+
+An `FAQ
+<https://docs.nvidia.com/deeplearning/sdk/tensorrt-inference-server-guide/docs/faq.html>`_
+provides answers for frequently asked questions.
+
+READMEs for deployment examples can be found in subdirectories of
+deploy/, for example, `deploy/single_server/README.rst
+<https://github.com/NVIDIA/tensorrt-inference-server/tree/master/deploy/single_server/README.rst>`_.
+
+The `Release Notes
+<https://docs.nvidia.com/deeplearning/sdk/inference-release-notes/index.html>`_
+and `Support Matrix
+<https://docs.nvidia.com/deeplearning/dgx/support-matrix/index.html>`_
+indicate the required versions of the NVIDIA Driver and CUDA, and also
+describe which GPUs are supported by the inference server.
+
+Other Documentation
+^^^^^^^^^^^^^^^^^^^
+
+* `Maximizing Utilization for Data Center Inference with TensorRT
+  Inference Server
+  <https://on-demand-gtc.gputechconf.com/gtcnew/sessionview.php?sessionName=s9438-maximizing+utilization+for+data+center+inference+with+tensorrt+inference+server>`_.
+
+* `NVIDIA TensorRT Inference Server Boosts Deep Learning Inference
+  <https://devblogs.nvidia.com/nvidia-serves-deep-learning-inference/>`_.
+
+* `GPU-Accelerated Inference for Kubernetes with the NVIDIA TensorRT
+  Inference Server and Kubeflow
+  <https://www.kubeflow.org/blog/nvidia_tensorrt/>`_.
+
+Contributing
+------------
+
+Contributions to TensorRT Inference Server are more than welcome. To
+contribute make a pull request and follow the guidelines outlined in
+the `Contributing <CONTRIBUTING.md>`_ document.
+
+Reporting problems, asking questions
+------------------------------------
+
+We appreciate any feedback, questions or bug reporting regarding this
+project. When help with code is needed, follow the process outlined in
+the Stack Overflow (https://stackoverflow.com/help/mcve)
+document. 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 dependency 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.
+
 .. |License| image:: https://img.shields.io/badge/License-BSD3-lightgrey.svg
    :target: https://opensource.org/licenses/BSD-3-Clause

VERSION

@@ -1 +1 @@
-1.11.0dev
+1.11.0