Updates for 2.2 release.

PiperOrigin-RevId: 373692337
Change-Id: Ifc039a21d10d16941a782ca273c3164a31391430

Author: Johannes Ballé

README.md

@@ -37,7 +37,7 @@ for a complete description of the classes and functions this package implements.
 ## Installation
 
 ***Note: Precompiled packages are currently only provided for Linux and
-Darwin/Mac OS and Python 3.6-3.8. To use these packages on Windows, consider
+Darwin/Mac OS and Python 3.6-3.9. To use these packages on Windows, consider
 using a [TensorFlow Docker image](https://www.tensorflow.org/install/docker) and
 installing TensorFlow Compression using pip inside the Docker container.***
 
@@ -49,18 +49,13 @@ for more information on how to set up such a Python environment.
 The current version of TensorFlow Compression requires TensorFlow 2. For
 versions compatible with TensorFlow 1, see our [previous
 releases](https://github.com/tensorflow/compression/releases).
-***Note: Because TFC currently relies on features and fixes designated for
-TF 2.5, the pip package currently depends on tf-nightly packages. Once TF 2.5 is
-released (likely in April 2021), we will resume depending on the stable version
-of TF.***
 
 ### pip
 
 To install TFC via `pip`, run the following command:
 
 ```bash
-pip uninstall -y tensorflow
-pip install tf-nightly==2.5.0.dev20210312 tensorflow-compression
+pip install tensorflow-compression
 ```
 
 To test that the installation works correctly, you can run the unit tests with:
@@ -81,8 +76,8 @@ and then run the `pip install` command inside the Docker container, not on the
 host. For instance, you can use a command line like this:
 
 ```bash
-docker run tensorflow/tensorflow:nightly bash -c \
-    "pip install tf-nightly==2.5.0.dev20210312 tensorflow-compression &&
+docker run tensorflow/tensorflow:2.5.0 bash -c \
+    "pip install tensorflow-compression &&
      python -m tensorflow_compression.all_tests"
 ```
 
@@ -94,15 +89,19 @@ the pip package and then run the unit tests to confirm that it works.
 It seems that [Anaconda](https://www.anaconda.com/distribution/) ships its own
 binary version of TensorFlow which is incompatible with our pip package. To
 solve this, always install TensorFlow via `pip` rather than `conda`. For
-example, this creates an Anaconda environment with Python 3.8 and CUDA
-libraries, and then installs TensorFlow and TensorFlow Compression:
+example, this creates an Anaconda environment with CUDA libraries, and then
+installs TensorFlow and TensorFlow Compression:
 
 ```bash
-conda create --name ENV_NAME python=3.8 cudatoolkit=10.0 cudnn
+conda create --name ENV_NAME python cudatoolkit cudnn
 conda activate ENV_NAME
-pip install tf-nightly==2.5.0.dev20210312 tensorflow-compression
+pip install tensorflow-compression
 ```
 
+Depending on the requirements of the `tensorflow` pip package, you may need to
+pin the CUDA libraries to specific versions. If you aren't using a GPU, CUDA is
+of course not necessary.
+
 ## Usage
 
 We recommend importing the library from your Python code as follows:

build_pip_pkg.py

@@ -78,9 +78,6 @@ def main(srcdir):
           "Intended Audience :: Science/Research",
           "License :: OSI Approved :: Apache Software License",
           "Programming Language :: Python :: 3",
-          "Programming Language :: Python :: 3.6",
-          "Programming Language :: Python :: 3.7",
-          "Programming Language :: Python :: 3.8",
           "Topic :: Scientific/Engineering :: Mathematics",
           "Topic :: Software Development :: Libraries :: Python Modules",
           "Topic :: Software Development :: Libraries",

docs/api_docs/python/tfc.md

@@ -90,12 +90,12 @@ Data compression in TensorFlow.
 
 [`class UniversalIndexedEntropyModel`](./tfc/UniversalIndexedEntropyModel.md): Indexed entropy model model which implements Universal Quantization.
 
+[`class Y4MDataset`](./tfc/Y4MDataset.md): A `tf.Dataset` of Y'CbCr video frames from '.y4m' files.
+
 ## Functions
 
 [`estimate_tails(...)`](./tfc/estimate_tails.md): Estimates approximate tail quantiles.
 
-[`irdft_matrix(...)`](./tfc/irdft_matrix.md): Matrix for implementing kernel reparameterization with `tf.matmul`.
-
 [`lower_bound(...)`](./tfc/lower_bound.md): Same as `tf.maximum`, but with helpful gradient for `inputs < bound`.
 
 [`lower_tail(...)`](./tfc/lower_tail.md): Approximates lower tail quantile for range coding.