Installation Tutorial


Supported Linux Distributions

Masterful is supported on most Linux distributions that are supported by Tensorflow and PyTorch.


Masterful supports Python 3.6+, which is installed by default in most Linux distributions.


Masterful is currently distributed on the Python Package Index here. While Python 3.x is installed by default on most Linux distributions, pip is not installed by default, so make sure to install it if it is not already installed.

sudo apt install python3-pip

Tensorflow 2

Installing the Masterful package does not automatically install Tensorflow, so as not to overwrite the existing version of TF. Therefore Tensorflow, Tensorflow Datasets, and Tensorflow Addons must be installed separately from Masterful. Note that the tensorflow-datasets and tensorflow-addons packages are built for specific versions of the tensorflow package, so make sure to install the appropriate version of each according to their compatibility matrices.

Masterful requires Tensorflow versions 2.4.1 or greater.

pip install --upgrade pip
pip install tensorflow
pip install tensorflow-datasets
pip install tensorflow-addons

Masterful Installation

Install Masterful with Python’s pip package manager.

pip install --upgrade pip
pip install masterful

Verify your installation by running:

python -c "import masterful; print(masterful.__version__)"

If this returns a version number like 0.5, your installation was successful.

Next Steps

Now it’s time to get started! Go to the quickstart train your first model with Masterful.


For the quickest access to help, support, and advice, please join the masterful-community slack.

I see a warning from Tensorflow Addons that my version of tensorflow is not supported

You may see the following warning if there is a version mismatch between Tensorflow and Tensorflow Addons. Typically this happens if you install a specific version of Tensorflow, but install the latest version of Tensorflow Addons without looking at the compatibility matrix. If this happens, you will see a warning in the console that looks like this:

... site-packages/tensorflow_addons/utils/ UserWarning: Tensorflow Addons supports using Python ops for all Tensorflow versions above or equal to 2.5.0 and strictly below 2.8.0 (nightly versions are not supported).
 The versions of TensorFlow you are currently using is 2.4.1 and is not supported.
Some things might work, some things might not.
If you were to encounter a bug, do not file an issue.
If you want to make sure you're using a tested and supported configuration, either change the TensorFlow version or the TensorFlow Addons's version.
You can find the compatibility matrix in TensorFlow Addon's readme:

In order to fix this, you can uninstall Tensorflow Addons, and re-install the specific version of Tensorflow Addons that is compatible with your version of Tensorflow. The latest compatibility matrix can be found at

For convenience, here is the compatibility matrix for Tensorflow Addons:

TensorFlow Addons




2.5, 2.6, 2.7

3.7, 3.8, 3.9


2.5, 2.6, 2.7

3.7, 3.8, 3.9


2.4, 2.5, 2.6

3.6, 3.7, 3.8, 3.9


2.3, 2.4, 2.5

3.6, 3.7, 3.8, 3.9


2.3, 2.4

3.6, 3.7, 3.8


2.2, 2.3

3.5, 3.6, 3.7, 3.8



3.5, 3.6, 3.7, 3.8


2.1, 2.2

3.5, 3.6, 3.7



3.5, 3.6, 3.7



2.7, 3.5, 3.6, 3.7



2.7, 3.5, 3.6, 3.7

Registering Masterful raises an AlreadyExistsError when using TensorFlow 2.6

Ensure the Keras version matches the Tensorflow version. Starting in Tensorflow 2.6, Keras was moved out of Tensorflow and back into a separate package, leaving a stale copy of Keras packages under tensorflow/python/keras, which have been removed in Tensorflow 2.7. Because of this TensorFlow 2.6 is known to erroneously use Keras 2.7 as a dependency. Although this bug is not related to Masterful, registering Masterful will expose the incompatibility during their import. Downgrading Keras to 2.6 resolves this issue.

I see too much Tensorflow logging cluttering up my console

By default, Tensorflow sets the C++ logging level to INFO, so you will see lots of console output like:

2022-01-17 11:16:11.651649: I tensorflow/stream_executor/platform/default/] Successfully opened dynamic library
2022-01-17 11:16:11.651685: I tensorflow/stream_executor/platform/default/] Successfully opened dynamic library
2022-01-17 11:16:11.651710: I tensorflow/stream_executor/platform/default/] Successfully opened dynamic library
2022-01-17 11:16:11.651734: I tensorflow/stream_executor/platform/default/] Successfully opened dynamic library
2022-01-17 11:16:11.651757: I tensorflow/stream_executor/platform/default/] Successfully opened dynamic library
2022-01-17 11:16:11.651781: I tensorflow/stream_executor/platform/default/] Successfully opened dynamic library

You can set the environment variable to 1 in your shell, before running your code, to eliminate these INFO logs.


If setting directly in your python script, make sure to set this variable before importing Tensorflow. Otherwise, it won’t be picked up.

# Must set BEFORE importing Tensorflow (including dependencies that might also
# import Tensorflow).
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'

# Now we can import Tensorflow for the first time.
import tensorflow

I receive an AttributeError trying to access masterful.XXX

If you try to access any Masterful APIs before registering with your account ID and authorization key, you will receive an AttributeError similar to the following:

Traceback (most recent call last):
  File "", line 38, in <module>
    model_spec, data_spec = masterful.spec.create_model_and_data_specs(
AttributeError: module 'masterful' has no attribute 'spec'

This is expected, and can be fixed by registering before accessing any Masterful APIs.

masterful = masterful.register()

# Now you access all Masterful API calls.

I receive a NotImplementedError about symbolic Tensors

If you see an error similar to the one below in the stack trace:

NotImplementedError: Cannot convert a symbolic Tensor (model/masterful_rotate/rotate/_angles_to_projective_transforms/strided_slice:0) to a numpy array. This error may indicate that you're trying to pass a Tensor to a NumPy call, which is not supported

then your numpy version is incompatible with your Tensorflow version. Typically this means your numpy version is too high, and you should try downgrading your numpy version to a previous version. For example, with Tensorflow 2.4.1, numpy==1.19.5 is known to work.