Data compression in TensorFlow

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This project contains data compression ops and layers for TensorFlow.

You can use this library to build your own ML models with end-to-end optimized data compression built in. It’s useful to find storage-efficient representations of your data (images, features, examples, etc.) while only sacrificing a tiny fraction of model performance. It can compress any floating point tensor to a much smaller sequence of bits.

Specifically, the entropy model classes in this library simplify the process of designing rate–distortion optimized codes. During training, they act like likelihood models. Once training is completed, they encode floating point tensors into optimal bit sequences by automating the design of probability tables and calling a range coder implementation behind the scenes.

The main novelty of this method over traditional transform coding is the stochastic minimization of the rate-distortion Lagrangian, and using nonlinear transforms implemented by neural networks. For an introduction to this, consider our paper on nonlinear transform coding, or watch @jonycgn’s talk on learned image compression.

Documentation & getting help

Please post all questions or comments in our Google group. Only file Github issues for actual bugs or feature requests. If you post to the group instead, you may get a faster answer, and you help other people find the question or answer more easily later.

Refer to the API documentation for a complete description of the Keras layers and TensorFlow ops this package implements. Note: the API docs have not been updated for the current beta release yet.


Note: Precompiled packages are currently only provided for Linux and Darwin/Mac OS. To use these packages on Windows, consider using a TensorFlow Docker image and installing tensorflow-compression using pip inside the Docker container.

Set up an environment in which you can install precompiled binary Python packages using the pip command. Refer to the TensorFlow installation instructions for more information on how to set up such a Python environment.

The current stable version of TFC (1.3) requires TensorFlow 1.15. The current beta release of TFC (2.0b2) is built for TensorFlow 2.4. For versions compatible with TensorFlow 1.14 or earlier, see our previous releases.


To install TF and TFC via pip, run the following command:

pip install tensorflow-gpu==1.15 tensorflow-compression==1.3

for the stable release, or

pip install tensorflow-gpu==2.4 tensorflow-probability==0.12.1 tensorflow-compression==2.0b2

for the beta release. If you don’t need GPU support, you can drop the -gpu part.

To test that the installation works correctly, you can run the unit tests with (respectively):

python -m tensorflow_compression.python.all_test


python -m tensorflow_compression.all_tests

Once the command finishes, you should see a message OK (skipped=12) or similar in the last line.


To use a Docker container (e.g. on Windows), be sure to install Docker (e.g., Docker Desktop), use a TensorFlow Docker image, 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:

docker run tensorflow/tensorflow:1.15.0-py3 bash -c \
    "pip install tensorflow-compression==1.3 &&
     python -m tensorflow_compression.python.all_test"

or (for the beta version):

docker run tensorflow/tensorflow:2.4.0 bash -c \
    "pip install tensorflow-probability==0.12.1 tensorflow-compression==2.0b2 &&
     python -m tensorflow_compression.all_tests"

This will fetch the TensorFlow Docker image if it’s not already cached, install the pip package and then run the unit tests to confirm that it works.


It seems that Anaconda 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 3.6 and CUDA libraries, and then installs TensorFlow and tensorflow-compression with GPU support:

conda create --name ENV_NAME python=3.6 cudatoolkit=10.0 cudnn
conda activate ENV_NAME
pip install tensorflow-gpu==1.15 tensorflow-compression==1.3


We recommend importing the library from your Python code as follows:

import tensorflow as tf
import tensorflow_compression as tfc

Using a pre-trained model to compress an image

In the models directory, you’ll find a python script tfci.py. Download the file and run:

python tfci.py -h

This will give you a list of options. Briefly, the command

python tfci.py compress <model> <PNG file>

will compress an image using a pre-trained model and write a file ending in .tfci. Execute python tfci.py models to give you a list of supported pre-trained models. The command

python tfci.py decompress <TFCI file>

will decompress a TFCI file and write a PNG file. By default, an output file will be named like the input file, only with the appropriate file extension appended (any existing extensions will not be removed).

Training your own model

The models directory contains an implementation of the image compression model described in:

“End-to-end optimized image compression”
J. Ballé, V. Laparra, E. P. Simoncelli

To see a list of options, download the file bls2017.py and run:

python bls2017.py -h

To train the model, you need to supply it with a dataset of RGB training images. They should be provided in PNG format. Training can be as simple as the following command:

python bls2017.py --verbose train --train_glob="images/*.png"

This will use the default settings. The most important parameter is --lambda, which controls the trade-off between bitrate and distortion that the model will be optimized for. The number of channels per layer is important, too: models tuned for higher bitrates (or, equivalently, lower distortion) tend to require transforms with a greater approximation capacity (i.e. more channels), so to optimize performance, you want to make sure that the number of channels is large enough (or larger). This is described in more detail in:

“Efficient nonlinear transforms for lossy image compression”
J. Ballé

If you wish, you can monitor progress with Tensorboard. To do this, create a Tensorboard instance in the background before starting the training, then point your web browser to port 6006 on your machine:

tensorboard --logdir=. &

When training has finished, the Python script can be used to compress and decompress images as follows. The same model checkpoint must be accessible to both commands.

python bls2017.py [options] compress original.png compressed.tfci
python bls2017.py [options] decompress compressed.tfci reconstruction.png

Building pip packages

This section describes the necessary steps to build your own pip packages of tensorflow-compression. This may be necessary to install it on platforms for which we don’t provide precompiled binaries (currently only Linux and Darwin).

We use the custom-op Docker images (e.g. tensorflow/tensorflow:nightly-custom-op-ubuntu16) for building pip packages for Linux. Note that this is different from tensorflow/tensorflow:devel. To be compatible with the TensorFlow pip package, the GCC version must match, but tensorflow/tensorflow:devel has a different GCC version installed. For more information, refer to the custom-op instructions.

Inside a Docker container from the image, the following steps need to be taken.

  1. Clone the tensorflow-compression repo from GitHub.
  2. Run :build_pip_pkg inside the cloned repo.

For example:

sudo docker run -v /tmp/tensorflow_compression:/tmp/tensorflow_compression \
    tensorflow/tensorflow:nightly-custom-op-ubuntu16 bash -c \
    "git clone https://github.com/tensorflow/compression.git
         /tensorflow_compression &&
     cd /tensorflow_compression &&
     bazel run -c opt --copt=-mavx :build_pip_pkg"

The wheel file is created inside /tmp/tensorflow_compression. Optimization flags can be passed via --copt to the bazel run command above.

To test the created package, first install the resulting wheel file:

pip install /tmp/tensorflow_compression/tensorflow_compression-*.whl

Then run the unit tests (Do not run the tests in the workspace directory where WORKSPACE of tensorflow_compression repo lives. In that case, the Python interpreter would attempt to import tensorflow_compression packages from the source tree, rather than from the installed package system directory):

pushd /tmp
python -m tensorflow_compression.all_tests

When done, you can uninstall the pip package again:

pip uninstall tensorflow-compression

To build packages for Darwin (and potentially other platforms), you can follow the same steps, but the Docker image should not be necessary.


We provide evaluation results for several image compression methods in terms of different metrics in different colorspaces. Please see the results subdirectory for more information.


Note that this is not an officially supported Google product.