tensorflow-model-analysis


Nametensorflow-model-analysis JSON
Version 0.47.0 PyPI version JSON
download
home_pagehttps://www.tensorflow.org/tfx/model_analysis/get_started
SummaryA library for analyzing TensorFlow models
upload_time2024-11-05 23:01:11
maintainerNone
docs_urlNone
authorGoogle LLC
requires_python<4,>=3.9
licenseApache 2.0
keywords tensorflow model analysis tfx
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls. 
            <!-- See: www.tensorflow.org/tfx/model_analysis/ -->

# TensorFlow Model Analysis

[![Python](https://img.shields.io/badge/python%20-3.9%7C3.10%7C3.11-blue)](https://github.com/tensorflow/model-analysis)
[![PyPI](https://badge.fury.io/py/tensorflow-model-analysis.svg)](https://badge.fury.io/py/tensorflow-model-analysis)
[![Documentation](https://img.shields.io/badge/api-reference-blue.svg)](https://www.tensorflow.org/tfx/model_analysis/api_docs/python/tfma)

*TensorFlow Model Analysis* (TFMA) is a library for evaluating TensorFlow
models.  It allows users to evaluate their models on large amounts of data in a
distributed manner, using the same metrics defined in their trainer. These
metrics can be computed over different slices of data and visualized in Jupyter
notebooks.

![TFMA Slicing Metrics Browser](https://raw.githubusercontent.com/tensorflow/model-analysis/master/g3doc/images/tfma-slicing-metrics-browser.gif)

Caution: TFMA may introduce backwards incompatible changes before version 1.0.

## Installation

The recommended way to install TFMA is using the
[PyPI package](https://pypi.org/project/tensorflow-model-analysis/):

<pre class="devsite-terminal devsite-click-to-copy">
pip install tensorflow-model-analysis
</pre>

pip install from https://pypi-nightly.tensorflow.org

<pre class="devsite-terminal devsite-click-to-copy">
pip install -i https://pypi-nightly.tensorflow.org/simple tensorflow-model-analysis
</pre>

pip install from the HEAD of the git:

<pre class="devsite-terminal devsite-click-to-copy">
pip install git+https://github.com/tensorflow/model-analysis.git#egg=tensorflow_model_analysis
</pre>

pip install from a released version directly from git:

<pre class="devsite-terminal devsite-click-to-copy">
pip install git+https://github.com/tensorflow/model-analysis.git@v0.21.3#egg=tensorflow_model_analysis
</pre>

If you have cloned the repository locally, and want to test your local change,
pip install from a local folder.

<pre class="devsite-terminal devsite-click-to-copy">
pip install -e $FOLDER_OF_THE_LOCAL_LOCATION
</pre>

Note that protobuf must be installed correctly for the above option since it is
building TFMA from source and it requires protoc and all of its includes
reference-able. Please see [protobuf install instruction](https://github.com/protocolbuffers/protobuf#protocol-compiler-installation)
for see the latest install instructions.

Currently, TFMA requires that TensorFlow is installed but does not have an
explicit dependency on the TensorFlow PyPI package. See the
[TensorFlow install guides](https://www.tensorflow.org/install/) for
instructions.

### Build TFMA from source

To build from source follow the following steps:

Install the protoc as per the link mentioned:
[protoc](https://grpc.io/docs/protoc-installation/#install-pre-compiled-binaries-any-os)

Create a virtual environment by running the commands

```
python3 -m venv <virtualenv_name>
source <virtualenv_name>/bin/activate
pip3 install setuptools wheel
git clone https://github.com/tensorflow/model-analysis.git
cd model-analysis
python3 setup.py bdist_wheel
```
This will build the TFMA wheel in the dist directory. To install the wheel from
dist directory run the commands

```
cd dist
pip3 install tensorflow_model_analysis-<version>-py3-none-any.whl
```

### Jupyter Lab

As of writing, because of https://github.com/pypa/pip/issues/9187, `pip install`
might never finish. In that case, you should revert pip to version 19 instead of
20: `pip install "pip<20"`.

Using a JupyterLab extension requires installing dependencies on the command
line. You can do this within the console in the JupyterLab UI or on the command
line. This includes separately installing any pip package dependencies and
JupyterLab labextension plugin dependencies, and the version numbers must be
compatible.  JupyterLab labextension packages refer to npm packages
(eg, [tensorflow_model_analysis](https://www.npmjs.com/package/tensorflow_model_analysis).

The examples below use 0.32.0. Check available [versions](#compatible-versions)
below to use the latest.


#### Jupyter Lab 3.0.x

```Shell
pip install tensorflow_model_analysis==0.32.0
jupyter labextension install tensorflow_model_analysis@0.32.0
pip install jupyterlab_widgets==1.0.0
```


#### Jupyter Lab 2.2.x

```Shell
pip install tensorflow_model_analysis==0.32.0
jupyter labextension install tensorflow_model_analysis@0.32.0
jupyter labextension install @jupyter-widgets/jupyterlab-manager@2
```

#### Jupyter Lab 1.2.x

```Shell
pip install tensorflow_model_analysis==0.32.0
jupyter labextension install tensorflow_model_analysis@0.32.0
jupyter labextension install @jupyter-widgets/jupyterlab-manager@1.1
```

#### Classic Jupyter Notebook

To enable TFMA visualization in the classic Jupyter Notebook (either through
`jupyter notebook` or
[through the JupyterLab UI](https://jupyterlab.readthedocs.io/en/stable/getting_started/starting.html)),
you'll also need to run:

```shell
jupyter nbextension enable --py widgetsnbextension
jupyter nbextension enable --py tensorflow_model_analysis
```

Note: If Jupyter notebook is already installed in your home directory, add
`--user` to these commands. If Jupyter is installed as root, or using a virtual
environment, the parameter `--sys-prefix` might be required.

#### Building TFMA from source

If you want to build TFMA from source and use the UI in JupyterLab, you'll need
to make sure that the source contains valid version numbers.  Check that the
Python package version number and npm package version number are exactly the
same, and that both valid version numbers (eg, remove the `-dev` suffix).


#### Troubleshooting

Check pip packages:

```Shell
pip list
```

Check JupyterLab extensions:

```Shell
jupyter labextension list  # for JupyterLab
jupyter nbextension list  # for classic Jupyter Notebook
```

### Standalone HTML page with `embed_minimal_html`

TFMA notebook extension can be built into a standalone HTML file that also
bundles data into the HTML file.  See the Jupyter Widgets docs on
[embed_minimal_html](https://ipywidgets.readthedocs.io/en/latest/embedding.html#python-interface).


### Kubeflow Pipelines

[Kubeflow Pipelines](https://www.kubeflow.org/docs/components/pipelines/sdk/output-viewer/)
includes integrations that embed the TFMA notebook extension ([code](https://github.com/kubeflow/pipelines/blob/1.5.0-rc.2/backend/src/apiserver/visualization/types/tfma.py#L17)).
This integration relies on network access at runtime to load a variant of the
JavaScript build published on unpkg.com (see [config](https://github.com/tensorflow/model-analysis/blob/v0.29.0/tensorflow_model_analysis/notebook/jupyter/js/webpack.config.js#L78)
and [loader code](https://github.com/tensorflow/model-analysis/blob/v0.29.0/tensorflow_model_analysis/notebook/jupyter/js/lib/widget.js#L23)).


### Notable Dependencies

TensorFlow is required.

[Apache Beam](https://beam.apache.org/) is required; it's the way that efficient
distributed computation is supported. By default, Apache Beam runs in local
mode but can also run in distributed mode using
[Google Cloud Dataflow](https://cloud.google.com/dataflow/) and other Apache
Beam
[runners](https://beam.apache.org/documentation/runners/capability-matrix/).

[Apache Arrow](https://arrow.apache.org/) is also required. TFMA uses Arrow to
represent data internally in order to make use of vectorized numpy functions.

## Getting Started

For instructions on using TFMA, see the [get started
guide](https://github.com/tensorflow/model-analysis/blob/master/g3doc/get_started.md).

## Compatible Versions

The following table is the TFMA package versions that are compatible with each
other. This is determined by our testing framework, but other *untested*
combinations may also work.

|tensorflow-model-analysis                                                            |apache-beam[gcp]|pyarrow   |tensorflow         |tensorflow-metadata |tfx-bsl   |
|------------------------------------------------------------------------------------ |----------------|----------|-------------------|--------------------|----------|
|[GitHub master](https://github.com/tensorflow/model-analysis/blob/master/RELEASE.md) | 2.60.0         | 10.0.1   | nightly (2.x)     | 1.16.1             | 1.16.1   |
|[0.47.0](https://github.com/tensorflow/model-analysis/blob/v0.47.0/RELEASE.md)       | 2.60.0         | 10.0.1   | 2.16              | 1.16.1             | 1.16.1   |
|[0.46.0](https://github.com/tensorflow/model-analysis/blob/v0.46.0/RELEASE.md)       | 2.47.0         | 10.0.0   | 2.15              | 1.15.0             | 1.15.1   |
|[0.45.0](https://github.com/tensorflow/model-analysis/blob/v0.45.0/RELEASE.md)       | 2.47.0         | 10.0.0   | 2.13              | 1.14.0             | 1.14.0   |
|[0.44.0](https://github.com/tensorflow/model-analysis/blob/v0.44.0/RELEASE.md)       | 2.40.0         | 6.0.0    | 2.12              | 1.13.1             | 1.13.0   |
|[0.43.0](https://github.com/tensorflow/model-analysis/blob/v0.43.0/RELEASE.md)       | 2.40.0         | 6.0.0    | 2.11              | 1.12.0             | 1.12.0   |
|[0.42.0](https://github.com/tensorflow/model-analysis/blob/v0.42.0/RELEASE.md)       | 2.40.0         | 6.0.0    | 1.15.5 / 2.10     | 1.11.0             | 1.11.1   |
|[0.41.0](https://github.com/tensorflow/model-analysis/blob/v0.41.0/RELEASE.md)       | 2.40.0         | 6.0.0    | 1.15.5 / 2.9      | 1.10.0             | 1.10.1   |
|[0.40.0](https://github.com/tensorflow/model-analysis/blob/v0.40.0/RELEASE.md)       | 2.38.0         | 5.0.0    | 1.15.5 / 2.9      | 1.9.0              | 1.9.0    |
|[0.39.0](https://github.com/tensorflow/model-analysis/blob/v0.39.0/RELEASE.md)       | 2.38.0         | 5.0.0    | 1.15.5 / 2.8      | 1.8.0              | 1.8.0    |
|[0.38.0](https://github.com/tensorflow/model-analysis/blob/v0.38.0/RELEASE.md)       | 2.36.0         | 5.0.0    | 1.15.5 / 2.8      | 1.7.0              | 1.7.0    |
|[0.37.0](https://github.com/tensorflow/model-analysis/blob/v0.37.0/RELEASE.md)       | 2.35.0         | 5.0.0    | 1.15.5 / 2.7      | 1.6.0              | 1.6.0    |
|[0.36.0](https://github.com/tensorflow/model-analysis/blob/v0.36.0/RELEASE.md)       | 2.34.0         | 5.0.0    | 1.15.5 / 2.7      | 1.5.0              | 1.5.0    |
|[0.35.0](https://github.com/tensorflow/model-analysis/blob/v0.35.0/RELEASE.md)       | 2.33.0         | 5.0.0    | 1.15 / 2.6        | 1.4.0              | 1.4.0    |
|[0.34.1](https://github.com/tensorflow/model-analysis/blob/v0.34.1/RELEASE.md)       | 2.32.0         | 2.0.0    | 1.15 / 2.6        | 1.2.0              | 1.3.0    |
|[0.34.0](https://github.com/tensorflow/model-analysis/blob/v0.34.0/RELEASE.md)       | 2.31.0         | 2.0.0    | 1.15 / 2.6        | 1.2.0              | 1.3.1    |
|[0.33.0](https://github.com/tensorflow/model-analysis/blob/v0.33.0/RELEASE.md)       | 2.31.0         | 2.0.0    | 1.15 / 2.5        | 1.2.0              | 1.2.0    |
|[0.32.1](https://github.com/tensorflow/model-analysis/blob/v0.32.1/RELEASE.md)       | 2.29.0         | 2.0.0    | 1.15 / 2.5        | 1.1.0              | 1.1.1    |
|[0.32.0](https://github.com/tensorflow/model-analysis/blob/v0.32.0/RELEASE.md)       | 2.29.0         | 2.0.0    | 1.15 / 2.5        | 1.1.0              | 1.1.0    |
|[0.31.0](https://github.com/tensorflow/model-analysis/blob/v0.31.0/RELEASE.md)       | 2.29.0         | 2.0.0    | 1.15 / 2.5        | 1.0.0              | 1.0.0    |
|[0.30.0](https://github.com/tensorflow/model-analysis/blob/v0.30.0/RELEASE.md)       | 2.28.0         | 2.0.0    | 1.15 / 2.4        | 0.30.0             | 0.30.0   |
|[0.29.0](https://github.com/tensorflow/model-analysis/blob/v0.29.0/RELEASE.md)       | 2.28.0         | 2.0.0    | 1.15 / 2.4        | 0.29.0             | 0.29.0   |
|[0.28.0](https://github.com/tensorflow/model-analysis/blob/v0.28.0/RELEASE.md)       | 2.28.0         | 2.0.0    | 1.15 / 2.4        | 0.28.0             | 0.28.0   |
|[0.27.0](https://github.com/tensorflow/model-analysis/blob/v0.27.0/RELEASE.md)       | 2.27.0         | 2.0.0    | 1.15 / 2.4        | 0.27.0             | 0.27.0   |
|[0.26.1](https://github.com/tensorflow/model-analysis/blob/v0.26.1/RELEASE.md)       | 2.28.0         | 0.17.0   | 1.15 / 2.3        | 0.26.0             | 0.26.0   |
|[0.26.0](https://github.com/tensorflow/model-analysis/blob/v0.26.0/RELEASE.md)       | 2.25.0         | 0.17.0   | 1.15 / 2.3        | 0.26.0             | 0.26.0   |
|[0.25.0](https://github.com/tensorflow/model-analysis/blob/v0.25.0/RELEASE.md)       | 2.25.0         | 0.17.0   | 1.15 / 2.3        | 0.25.0             | 0.25.0   |
|[0.24.3](https://github.com/tensorflow/model-analysis/blob/v0.24.3/RELEASE.md)       | 2.24.0         | 0.17.0   | 1.15 / 2.3        | 0.24.0             | 0.24.1   |
|[0.24.2](https://github.com/tensorflow/model-analysis/blob/v0.24.2/RELEASE.md)       | 2.23.0         | 0.17.0   | 1.15 / 2.3        | 0.24.0             | 0.24.0   |
|[0.24.1](https://github.com/tensorflow/model-analysis/blob/v0.24.1/RELEASE.md)       | 2.23.0         | 0.17.0   | 1.15 / 2.3        | 0.24.0             | 0.24.0   |
|[0.24.0](https://github.com/tensorflow/model-analysis/blob/v0.24.0/RELEASE.md)       | 2.23.0         | 0.17.0   | 1.15 / 2.3        | 0.24.0             | 0.24.0   |
|[0.23.0](https://github.com/tensorflow/model-analysis/blob/v0.23.0/RELEASE.md)       | 2.23.0         | 0.17.0   | 1.15 / 2.3        | 0.23.0             | 0.23.0   |
|[0.22.2](https://github.com/tensorflow/model-analysis/blob/v0.22.2/RELEASE.md)       | 2.20.0         | 0.16.0   | 1.15 / 2.2        | 0.22.2             | 0.22.0   |
|[0.22.1](https://github.com/tensorflow/model-analysis/blob/v0.22.1/RELEASE.md)       | 2.20.0         | 0.16.0   | 1.15 / 2.2        | 0.22.2             | 0.22.0   |
|[0.22.0](https://github.com/tensorflow/model-analysis/blob/v0.22.0/RELEASE.md)       | 2.20.0         | 0.16.0   | 1.15 / 2.2        | 0.22.0             | 0.22.0   |
|[0.21.6](https://github.com/tensorflow/model-analysis/blob/v0.21.6/RELEASE.md)       | 2.19.0         | 0.15.0   | 1.15 / 2.1        | 0.21.0             | 0.21.3   |
|[0.21.5](https://github.com/tensorflow/model-analysis/blob/v0.21.5/RELEASE.md)       | 2.19.0         | 0.15.0   | 1.15 / 2.1        | 0.21.0             | 0.21.3   |
|[0.21.4](https://github.com/tensorflow/model-analysis/blob/v0.21.4/RELEASE.md)       | 2.19.0         | 0.15.0   | 1.15 / 2.1        | 0.21.0             | 0.21.3   |
|[0.21.3](https://github.com/tensorflow/model-analysis/blob/v0.21.3/RELEASE.md)       | 2.17.0         | 0.15.0   | 1.15 / 2.1        | 0.21.0             | 0.21.0   |
|[0.21.2](https://github.com/tensorflow/model-analysis/blob/v0.21.2/RELEASE.md)       | 2.17.0         | 0.15.0   | 1.15 / 2.1        | 0.21.0             | 0.21.0   |
|[0.21.1](https://github.com/tensorflow/model-analysis/blob/v0.21.1/RELEASE.md)       | 2.17.0         | 0.15.0   | 1.15 / 2.1        | 0.21.0             | 0.21.0   |
|[0.21.0](https://github.com/tensorflow/model-analysis/blob/v0.21.0/RELEASE.md)       | 2.17.0         | 0.15.0   | 1.15 / 2.1        | 0.21.0             | 0.21.0   |
|[0.15.4](https://github.com/tensorflow/model-analysis/blob/v0.15.4/RELEASE.md)       | 2.16.0         | 0.15.0   | 1.15 / 2.0        | n/a                | 0.15.1   |
|[0.15.3](https://github.com/tensorflow/model-analysis/blob/v0.15.3/RELEASE.md)       | 2.16.0         | 0.15.0   | 1.15 / 2.0        | n/a                | 0.15.1   |
|[0.15.2](https://github.com/tensorflow/model-analysis/blob/v0.15.2/RELEASE.md)       | 2.16.0         | 0.15.0   | 1.15 / 2.0        | n/a                | 0.15.1   |
|[0.15.1](https://github.com/tensorflow/model-analysis/blob/v0.15.1/RELEASE.md)       | 2.16.0         | 0.15.0   | 1.15 / 2.0        | n/a                | 0.15.0   |
|[0.15.0](https://github.com/tensorflow/model-analysis/blob/v0.15.0/RELEASE.md)       | 2.16.0         | 0.15.0   | 1.15              | n/a                | n/a      |
|[0.14.0](https://github.com/tensorflow/model-analysis/blob/v0.14.0/RELEASE.md)       | 2.14.0         | n/a      | 1.14              | n/a                | n/a      |
|[0.13.1](https://github.com/tensorflow/model-analysis/blob/v0.13.1/RELEASE.md)       | 2.11.0         | n/a      | 1.13              | n/a                | n/a      |
|[0.13.0](https://github.com/tensorflow/model-analysis/blob/v0.13.0/RELEASE.md)       | 2.11.0         | n/a      | 1.13              | n/a                | n/a      |
|[0.12.1](https://github.com/tensorflow/model-analysis/blob/v0.12.1/RELEASE.md)       | 2.10.0         | n/a      | 1.12              | n/a                | n/a      |
|[0.12.0](https://github.com/tensorflow/model-analysis/blob/v0.12.0/RELEASE.md)       | 2.10.0         | n/a      | 1.12              | n/a                | n/a      |
|[0.11.0](https://github.com/tensorflow/model-analysis/blob/v0.11.0/RELEASE.md)       | 2.8.0          | n/a      | 1.11              | n/a                | n/a      |
|[0.9.2](https://github.com/tensorflow/model-analysis/blob/v0.9.2/RELEASE.md)         | 2.6.0          | n/a      | 1.9               | n/a                | n/a      |
|[0.9.1](https://github.com/tensorflow/model-analysis/blob/v0.9.1/RELEASE.md)         | 2.6.0          | n/a      | 1.10              | n/a                | n/a      |
|[0.9.0](https://github.com/tensorflow/model-analysis/blob/v0.9.0/RELEASE.md)         | 2.5.0          | n/a      | 1.9               | n/a                | n/a      |
|[0.6.0](https://github.com/tensorflow/model-analysis/blob/v0.6.0/RELEASE.md)         | 2.4.0          | n/a      | 1.6               | n/a                | n/a      |

## Questions

Please direct any questions about working with TFMA to
[Stack Overflow](https://stackoverflow.com) using the
[tensorflow-model-analysis](https://stackoverflow.com/questions/tagged/tensorflow-model-analysis)
tag.

Raw data

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    "description": "<!-- See: www.tensorflow.org/tfx/model_analysis/ -->\n\n# TensorFlow Model Analysis\n\n[![Python](https://img.shields.io/badge/python%20-3.9%7C3.10%7C3.11-blue)](https://github.com/tensorflow/model-analysis)\n[![PyPI](https://badge.fury.io/py/tensorflow-model-analysis.svg)](https://badge.fury.io/py/tensorflow-model-analysis)\n[![Documentation](https://img.shields.io/badge/api-reference-blue.svg)](https://www.tensorflow.org/tfx/model_analysis/api_docs/python/tfma)\n\n*TensorFlow Model Analysis* (TFMA) is a library for evaluating TensorFlow\nmodels.  It allows users to evaluate their models on large amounts of data in a\ndistributed manner, using the same metrics defined in their trainer. These\nmetrics can be computed over different slices of data and visualized in Jupyter\nnotebooks.\n\n![TFMA Slicing Metrics Browser](https://raw.githubusercontent.com/tensorflow/model-analysis/master/g3doc/images/tfma-slicing-metrics-browser.gif)\n\nCaution: TFMA may introduce backwards incompatible changes before version 1.0.\n\n## Installation\n\nThe recommended way to install TFMA is using the\n[PyPI package](https://pypi.org/project/tensorflow-model-analysis/):\n\n<pre class=\"devsite-terminal devsite-click-to-copy\">\npip install tensorflow-model-analysis\n</pre>\n\npip install from https://pypi-nightly.tensorflow.org\n\n<pre class=\"devsite-terminal devsite-click-to-copy\">\npip install -i https://pypi-nightly.tensorflow.org/simple tensorflow-model-analysis\n</pre>\n\npip install from the HEAD of the git:\n\n<pre class=\"devsite-terminal devsite-click-to-copy\">\npip install git+https://github.com/tensorflow/model-analysis.git#egg=tensorflow_model_analysis\n</pre>\n\npip install from a released version directly from git:\n\n<pre class=\"devsite-terminal devsite-click-to-copy\">\npip install git+https://github.com/tensorflow/model-analysis.git@v0.21.3#egg=tensorflow_model_analysis\n</pre>\n\nIf you have cloned the repository locally, and want to test your local change,\npip install from a local folder.\n\n<pre class=\"devsite-terminal devsite-click-to-copy\">\npip install -e $FOLDER_OF_THE_LOCAL_LOCATION\n</pre>\n\nNote that protobuf must be installed correctly for the above option since it is\nbuilding TFMA from source and it requires protoc and all of its includes\nreference-able. Please see [protobuf install instruction](https://github.com/protocolbuffers/protobuf#protocol-compiler-installation)\nfor see the latest install instructions.\n\nCurrently, TFMA requires that TensorFlow is installed but does not have an\nexplicit dependency on the TensorFlow PyPI package. See the\n[TensorFlow install guides](https://www.tensorflow.org/install/) for\ninstructions.\n\n### Build TFMA from source\n\nTo build from source follow the following steps:\n\nInstall the protoc as per the link mentioned:\n[protoc](https://grpc.io/docs/protoc-installation/#install-pre-compiled-binaries-any-os)\n\nCreate a virtual environment by running the commands\n\n```\npython3 -m venv <virtualenv_name>\nsource <virtualenv_name>/bin/activate\npip3 install setuptools wheel\ngit clone https://github.com/tensorflow/model-analysis.git\ncd model-analysis\npython3 setup.py bdist_wheel\n```\nThis will build the TFMA wheel in the dist directory. To install the wheel from\ndist directory run the commands\n\n```\ncd dist\npip3 install tensorflow_model_analysis-<version>-py3-none-any.whl\n```\n\n### Jupyter Lab\n\nAs of writing, because of https://github.com/pypa/pip/issues/9187, `pip install`\nmight never finish. In that case, you should revert pip to version 19 instead of\n20: `pip install \"pip<20\"`.\n\nUsing a JupyterLab extension requires installing dependencies on the command\nline. You can do this within the console in the JupyterLab UI or on the command\nline. This includes separately installing any pip package dependencies and\nJupyterLab labextension plugin dependencies, and the version numbers must be\ncompatible.  JupyterLab labextension packages refer to npm packages\n(eg, [tensorflow_model_analysis](https://www.npmjs.com/package/tensorflow_model_analysis).\n\nThe examples below use 0.32.0. Check available [versions](#compatible-versions)\nbelow to use the latest.\n\n\n#### Jupyter Lab 3.0.x\n\n```Shell\npip install tensorflow_model_analysis==0.32.0\njupyter labextension install tensorflow_model_analysis@0.32.0\npip install jupyterlab_widgets==1.0.0\n```\n\n\n#### Jupyter Lab 2.2.x\n\n```Shell\npip install tensorflow_model_analysis==0.32.0\njupyter labextension install tensorflow_model_analysis@0.32.0\njupyter labextension install @jupyter-widgets/jupyterlab-manager@2\n```\n\n#### Jupyter Lab 1.2.x\n\n```Shell\npip install tensorflow_model_analysis==0.32.0\njupyter labextension install tensorflow_model_analysis@0.32.0\njupyter labextension install @jupyter-widgets/jupyterlab-manager@1.1\n```\n\n#### Classic Jupyter Notebook\n\nTo enable TFMA visualization in the classic Jupyter Notebook (either through\n`jupyter notebook` or\n[through the JupyterLab UI](https://jupyterlab.readthedocs.io/en/stable/getting_started/starting.html)),\nyou'll also need to run:\n\n```shell\njupyter nbextension enable --py widgetsnbextension\njupyter nbextension enable --py tensorflow_model_analysis\n```\n\nNote: If Jupyter notebook is already installed in your home directory, add\n`--user` to these commands. If Jupyter is installed as root, or using a virtual\nenvironment, the parameter `--sys-prefix` might be required.\n\n#### Building TFMA from source\n\nIf you want to build TFMA from source and use the UI in JupyterLab, you'll need\nto make sure that the source contains valid version numbers.  Check that the\nPython package version number and npm package version number are exactly the\nsame, and that both valid version numbers (eg, remove the `-dev` suffix).\n\n\n#### Troubleshooting\n\nCheck pip packages:\n\n```Shell\npip list\n```\n\nCheck JupyterLab extensions:\n\n```Shell\njupyter labextension list  # for JupyterLab\njupyter nbextension list  # for classic Jupyter Notebook\n```\n\n### Standalone HTML page with `embed_minimal_html`\n\nTFMA notebook extension can be built into a standalone HTML file that also\nbundles data into the HTML file.  See the Jupyter Widgets docs on\n[embed_minimal_html](https://ipywidgets.readthedocs.io/en/latest/embedding.html#python-interface).\n\n\n### Kubeflow Pipelines\n\n[Kubeflow Pipelines](https://www.kubeflow.org/docs/components/pipelines/sdk/output-viewer/)\nincludes integrations that embed the TFMA notebook extension ([code](https://github.com/kubeflow/pipelines/blob/1.5.0-rc.2/backend/src/apiserver/visualization/types/tfma.py#L17)).\nThis integration relies on network access at runtime to load a variant of the\nJavaScript build published on unpkg.com (see [config](https://github.com/tensorflow/model-analysis/blob/v0.29.0/tensorflow_model_analysis/notebook/jupyter/js/webpack.config.js#L78)\nand [loader code](https://github.com/tensorflow/model-analysis/blob/v0.29.0/tensorflow_model_analysis/notebook/jupyter/js/lib/widget.js#L23)).\n\n\n### Notable Dependencies\n\nTensorFlow is required.\n\n[Apache Beam](https://beam.apache.org/) is required; it's the way that efficient\ndistributed computation is supported. By default, Apache Beam runs in local\nmode but can also run in distributed mode using\n[Google Cloud Dataflow](https://cloud.google.com/dataflow/) and other Apache\nBeam\n[runners](https://beam.apache.org/documentation/runners/capability-matrix/).\n\n[Apache Arrow](https://arrow.apache.org/) is also required. TFMA uses Arrow to\nrepresent data internally in order to make use of vectorized numpy functions.\n\n## Getting Started\n\nFor instructions on using TFMA, see the [get started\nguide](https://github.com/tensorflow/model-analysis/blob/master/g3doc/get_started.md).\n\n## Compatible Versions\n\nThe following table is the TFMA package versions that are compatible with each\nother. This is determined by our testing framework, but other *untested*\ncombinations may also work.\n\n|tensorflow-model-analysis                                                            |apache-beam[gcp]|pyarrow   |tensorflow         |tensorflow-metadata |tfx-bsl   |\n|------------------------------------------------------------------------------------ |----------------|----------|-------------------|--------------------|----------|\n|[GitHub master](https://github.com/tensorflow/model-analysis/blob/master/RELEASE.md) | 2.60.0         | 10.0.1   | nightly (2.x)     | 1.16.1             | 1.16.1   |\n|[0.47.0](https://github.com/tensorflow/model-analysis/blob/v0.47.0/RELEASE.md)       | 2.60.0         | 10.0.1   | 2.16              | 1.16.1             | 1.16.1   |\n|[0.46.0](https://github.com/tensorflow/model-analysis/blob/v0.46.0/RELEASE.md)       | 2.47.0         | 10.0.0   | 2.15              | 1.15.0             | 1.15.1   |\n|[0.45.0](https://github.com/tensorflow/model-analysis/blob/v0.45.0/RELEASE.md)       | 2.47.0         | 10.0.0   | 2.13              | 1.14.0             | 1.14.0   |\n|[0.44.0](https://github.com/tensorflow/model-analysis/blob/v0.44.0/RELEASE.md)       | 2.40.0         | 6.0.0    | 2.12              | 1.13.1             | 1.13.0   |\n|[0.43.0](https://github.com/tensorflow/model-analysis/blob/v0.43.0/RELEASE.md)       | 2.40.0         | 6.0.0    | 2.11              | 1.12.0             | 1.12.0   |\n|[0.42.0](https://github.com/tensorflow/model-analysis/blob/v0.42.0/RELEASE.md)       | 2.40.0         | 6.0.0    | 1.15.5 / 2.10     | 1.11.0             | 1.11.1   |\n|[0.41.0](https://github.com/tensorflow/model-analysis/blob/v0.41.0/RELEASE.md)       | 2.40.0         | 6.0.0    | 1.15.5 / 2.9      | 1.10.0             | 1.10.1   |\n|[0.40.0](https://github.com/tensorflow/model-analysis/blob/v0.40.0/RELEASE.md)       | 2.38.0         | 5.0.0    | 1.15.5 / 2.9      | 1.9.0              | 1.9.0    |\n|[0.39.0](https://github.com/tensorflow/model-analysis/blob/v0.39.0/RELEASE.md)       | 2.38.0         | 5.0.0    | 1.15.5 / 2.8      | 1.8.0              | 1.8.0    |\n|[0.38.0](https://github.com/tensorflow/model-analysis/blob/v0.38.0/RELEASE.md)       | 2.36.0         | 5.0.0    | 1.15.5 / 2.8      | 1.7.0              | 1.7.0    |\n|[0.37.0](https://github.com/tensorflow/model-analysis/blob/v0.37.0/RELEASE.md)       | 2.35.0         | 5.0.0    | 1.15.5 / 2.7      | 1.6.0              | 1.6.0    |\n|[0.36.0](https://github.com/tensorflow/model-analysis/blob/v0.36.0/RELEASE.md)       | 2.34.0         | 5.0.0    | 1.15.5 / 2.7      | 1.5.0              | 1.5.0    |\n|[0.35.0](https://github.com/tensorflow/model-analysis/blob/v0.35.0/RELEASE.md)       | 2.33.0         | 5.0.0    | 1.15 / 2.6        | 1.4.0              | 1.4.0    |\n|[0.34.1](https://github.com/tensorflow/model-analysis/blob/v0.34.1/RELEASE.md)       | 2.32.0         | 2.0.0    | 1.15 / 2.6        | 1.2.0              | 1.3.0    |\n|[0.34.0](https://github.com/tensorflow/model-analysis/blob/v0.34.0/RELEASE.md)       | 2.31.0         | 2.0.0    | 1.15 / 2.6        | 1.2.0              | 1.3.1    |\n|[0.33.0](https://github.com/tensorflow/model-analysis/blob/v0.33.0/RELEASE.md)       | 2.31.0         | 2.0.0    | 1.15 / 2.5        | 1.2.0              | 1.2.0    |\n|[0.32.1](https://github.com/tensorflow/model-analysis/blob/v0.32.1/RELEASE.md)       | 2.29.0         | 2.0.0    | 1.15 / 2.5        | 1.1.0              | 1.1.1    |\n|[0.32.0](https://github.com/tensorflow/model-analysis/blob/v0.32.0/RELEASE.md)       | 2.29.0         | 2.0.0    | 1.15 / 2.5        | 1.1.0              | 1.1.0    |\n|[0.31.0](https://github.com/tensorflow/model-analysis/blob/v0.31.0/RELEASE.md)       | 2.29.0         | 2.0.0    | 1.15 / 2.5        | 1.0.0              | 1.0.0    |\n|[0.30.0](https://github.com/tensorflow/model-analysis/blob/v0.30.0/RELEASE.md)       | 2.28.0         | 2.0.0    | 1.15 / 2.4        | 0.30.0             | 0.30.0   |\n|[0.29.0](https://github.com/tensorflow/model-analysis/blob/v0.29.0/RELEASE.md)       | 2.28.0         | 2.0.0    | 1.15 / 2.4        | 0.29.0             | 0.29.0   |\n|[0.28.0](https://github.com/tensorflow/model-analysis/blob/v0.28.0/RELEASE.md)       | 2.28.0         | 2.0.0    | 1.15 / 2.4        | 0.28.0             | 0.28.0   |\n|[0.27.0](https://github.com/tensorflow/model-analysis/blob/v0.27.0/RELEASE.md)       | 2.27.0         | 2.0.0    | 1.15 / 2.4        | 0.27.0             | 0.27.0   |\n|[0.26.1](https://github.com/tensorflow/model-analysis/blob/v0.26.1/RELEASE.md)       | 2.28.0         | 0.17.0   | 1.15 / 2.3        | 0.26.0             | 0.26.0   |\n|[0.26.0](https://github.com/tensorflow/model-analysis/blob/v0.26.0/RELEASE.md)       | 2.25.0         | 0.17.0   | 1.15 / 2.3        | 0.26.0             | 0.26.0   |\n|[0.25.0](https://github.com/tensorflow/model-analysis/blob/v0.25.0/RELEASE.md)       | 2.25.0         | 0.17.0   | 1.15 / 2.3        | 0.25.0             | 0.25.0   |\n|[0.24.3](https://github.com/tensorflow/model-analysis/blob/v0.24.3/RELEASE.md)       | 2.24.0         | 0.17.0   | 1.15 / 2.3        | 0.24.0             | 0.24.1   |\n|[0.24.2](https://github.com/tensorflow/model-analysis/blob/v0.24.2/RELEASE.md)       | 2.23.0         | 0.17.0   | 1.15 / 2.3        | 0.24.0             | 0.24.0   |\n|[0.24.1](https://github.com/tensorflow/model-analysis/blob/v0.24.1/RELEASE.md)       | 2.23.0         | 0.17.0   | 1.15 / 2.3        | 0.24.0             | 0.24.0   |\n|[0.24.0](https://github.com/tensorflow/model-analysis/blob/v0.24.0/RELEASE.md)       | 2.23.0         | 0.17.0   | 1.15 / 2.3        | 0.24.0             | 0.24.0   |\n|[0.23.0](https://github.com/tensorflow/model-analysis/blob/v0.23.0/RELEASE.md)       | 2.23.0         | 0.17.0   | 1.15 / 2.3        | 0.23.0             | 0.23.0   |\n|[0.22.2](https://github.com/tensorflow/model-analysis/blob/v0.22.2/RELEASE.md)       | 2.20.0         | 0.16.0   | 1.15 / 2.2        | 0.22.2             | 0.22.0   |\n|[0.22.1](https://github.com/tensorflow/model-analysis/blob/v0.22.1/RELEASE.md)       | 2.20.0         | 0.16.0   | 1.15 / 2.2        | 0.22.2             | 0.22.0   |\n|[0.22.0](https://github.com/tensorflow/model-analysis/blob/v0.22.0/RELEASE.md)       | 2.20.0         | 0.16.0   | 1.15 / 2.2        | 0.22.0             | 0.22.0   |\n|[0.21.6](https://github.com/tensorflow/model-analysis/blob/v0.21.6/RELEASE.md)       | 2.19.0         | 0.15.0   | 1.15 / 2.1        | 0.21.0             | 0.21.3   |\n|[0.21.5](https://github.com/tensorflow/model-analysis/blob/v0.21.5/RELEASE.md)       | 2.19.0         | 0.15.0   | 1.15 / 2.1        | 0.21.0             | 0.21.3   |\n|[0.21.4](https://github.com/tensorflow/model-analysis/blob/v0.21.4/RELEASE.md)       | 2.19.0         | 0.15.0   | 1.15 / 2.1        | 0.21.0             | 0.21.3   |\n|[0.21.3](https://github.com/tensorflow/model-analysis/blob/v0.21.3/RELEASE.md)       | 2.17.0         | 0.15.0   | 1.15 / 2.1        | 0.21.0             | 0.21.0   |\n|[0.21.2](https://github.com/tensorflow/model-analysis/blob/v0.21.2/RELEASE.md)       | 2.17.0         | 0.15.0   | 1.15 / 2.1        | 0.21.0             | 0.21.0   |\n|[0.21.1](https://github.com/tensorflow/model-analysis/blob/v0.21.1/RELEASE.md)       | 2.17.0         | 0.15.0   | 1.15 / 2.1        | 0.21.0             | 0.21.0   |\n|[0.21.0](https://github.com/tensorflow/model-analysis/blob/v0.21.0/RELEASE.md)       | 2.17.0         | 0.15.0   | 1.15 / 2.1        | 0.21.0             | 0.21.0   |\n|[0.15.4](https://github.com/tensorflow/model-analysis/blob/v0.15.4/RELEASE.md)       | 2.16.0         | 0.15.0   | 1.15 / 2.0        | n/a                | 0.15.1   |\n|[0.15.3](https://github.com/tensorflow/model-analysis/blob/v0.15.3/RELEASE.md)       | 2.16.0         | 0.15.0   | 1.15 / 2.0        | n/a                | 0.15.1   |\n|[0.15.2](https://github.com/tensorflow/model-analysis/blob/v0.15.2/RELEASE.md)       | 2.16.0         | 0.15.0   | 1.15 / 2.0        | n/a                | 0.15.1   |\n|[0.15.1](https://github.com/tensorflow/model-analysis/blob/v0.15.1/RELEASE.md)       | 2.16.0         | 0.15.0   | 1.15 / 2.0        | n/a                | 0.15.0   |\n|[0.15.0](https://github.com/tensorflow/model-analysis/blob/v0.15.0/RELEASE.md)       | 2.16.0         | 0.15.0   | 1.15              | n/a                | n/a      |\n|[0.14.0](https://github.com/tensorflow/model-analysis/blob/v0.14.0/RELEASE.md)       | 2.14.0         | n/a      | 1.14              | n/a                | n/a      |\n|[0.13.1](https://github.com/tensorflow/model-analysis/blob/v0.13.1/RELEASE.md)       | 2.11.0         | n/a      | 1.13              | n/a                | n/a      |\n|[0.13.0](https://github.com/tensorflow/model-analysis/blob/v0.13.0/RELEASE.md)       | 2.11.0         | n/a      | 1.13              | n/a                | n/a      |\n|[0.12.1](https://github.com/tensorflow/model-analysis/blob/v0.12.1/RELEASE.md)       | 2.10.0         | n/a      | 1.12              | n/a                | n/a      |\n|[0.12.0](https://github.com/tensorflow/model-analysis/blob/v0.12.0/RELEASE.md)       | 2.10.0         | n/a      | 1.12              | n/a                | n/a      |\n|[0.11.0](https://github.com/tensorflow/model-analysis/blob/v0.11.0/RELEASE.md)       | 2.8.0          | n/a      | 1.11              | n/a                | n/a      |\n|[0.9.2](https://github.com/tensorflow/model-analysis/blob/v0.9.2/RELEASE.md)         | 2.6.0          | n/a      | 1.9               | n/a                | n/a      |\n|[0.9.1](https://github.com/tensorflow/model-analysis/blob/v0.9.1/RELEASE.md)         | 2.6.0          | n/a      | 1.10              | n/a                | n/a      |\n|[0.9.0](https://github.com/tensorflow/model-analysis/blob/v0.9.0/RELEASE.md)         | 2.5.0          | n/a      | 1.9               | n/a                | n/a      |\n|[0.6.0](https://github.com/tensorflow/model-analysis/blob/v0.6.0/RELEASE.md)         | 2.4.0          | n/a      | 1.6               | n/a                | n/a      |\n\n## Questions\n\nPlease direct any questions about working with TFMA to\n[Stack Overflow](https://stackoverflow.com) using the\n[tensorflow-model-analysis](https://stackoverflow.com/questions/tagged/tensorflow-model-analysis)\ntag.\n",
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