A library for analyzing TensorFlow models
Project description
<!-- See: www.tensorflow.org/tfx/model_analysis/ -->
# TensorFlow Model Analysis
[](https://github.com/tensorflow/model-analysis)
[](https://badge.fury.io/py/tensorflow-model-analysis)
[](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.
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-hypernode.com/project/tensorflow-model-analysis/):
<pre class="devsite-terminal devsite-click-to-copy">
pip install tensorflow-model-analysis
</pre>
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.
To enable TFMA visualization in Jupyter Notebook:
<pre class="prettyprint">
<code class="devsite-terminal">jupyter nbextension enable --py widgetsnbextension</code>
<code class="devsite-terminal">jupyter nbextension install --py --symlink tensorflow_model_analysis</code>
<code class="devsite-terminal">jupyter nbextension enable --py tensorflow_model_analysis</code>
</pre>
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.
### Dependencies
[Apache Beam](https://beam.apache.org/) is required to run distributed analysis.
By default, Apache Beam runs in local mode but can also run in distributed mode
using [Google Cloud Dataflow](https://cloud.google.com/dataflow/). TFMA is
designed to be extensible for other Apache Beam runners.
## Getting Started
For instructions on using TFMA, see the [get started
guide](https://github.com/tensorflow/model-analysis/blob/master/g3doc/get_started.md) and try out
the extensive [end-to-end example](https://github.com/tensorflow/tfx/blob/master/examples/chicago_taxi/README.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 |tensorflow |apache-beam[gcp]|
|---------------------------|--------------------|----------------|
|GitHub master |1.12 |2.10.0 |
|0.12.0 |1.12 |2.10.0 |
|0.11.0 |1.11 |2.8.0 |
|0.9.2 |1.9 |2.6.0 |
|0.9.1 |1.10 |2.6.0 |
|0.9.0 |1.9 |2.5.0 |
|0.6.0 |1.6 |2.4.0 |
## 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.
# TensorFlow Model Analysis
[](https://github.com/tensorflow/model-analysis)
[](https://badge.fury.io/py/tensorflow-model-analysis)
[](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.
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-hypernode.com/project/tensorflow-model-analysis/):
<pre class="devsite-terminal devsite-click-to-copy">
pip install tensorflow-model-analysis
</pre>
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.
To enable TFMA visualization in Jupyter Notebook:
<pre class="prettyprint">
<code class="devsite-terminal">jupyter nbextension enable --py widgetsnbextension</code>
<code class="devsite-terminal">jupyter nbextension install --py --symlink tensorflow_model_analysis</code>
<code class="devsite-terminal">jupyter nbextension enable --py tensorflow_model_analysis</code>
</pre>
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.
### Dependencies
[Apache Beam](https://beam.apache.org/) is required to run distributed analysis.
By default, Apache Beam runs in local mode but can also run in distributed mode
using [Google Cloud Dataflow](https://cloud.google.com/dataflow/). TFMA is
designed to be extensible for other Apache Beam runners.
## Getting Started
For instructions on using TFMA, see the [get started
guide](https://github.com/tensorflow/model-analysis/blob/master/g3doc/get_started.md) and try out
the extensive [end-to-end example](https://github.com/tensorflow/tfx/blob/master/examples/chicago_taxi/README.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 |tensorflow |apache-beam[gcp]|
|---------------------------|--------------------|----------------|
|GitHub master |1.12 |2.10.0 |
|0.12.0 |1.12 |2.10.0 |
|0.11.0 |1.11 |2.8.0 |
|0.9.2 |1.9 |2.6.0 |
|0.9.1 |1.10 |2.6.0 |
|0.9.0 |1.9 |2.5.0 |
|0.6.0 |1.6 |2.4.0 |
## 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.
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