<!-- See: www.tensorflow.org/tfx/model_analysis/ -->
# TensorFlow Model Analysis [![PyPI](https://img.shields.io/pypi/pyversions/tensorflow-model-analysis.svg?style=plastic)](https://github.com/tensorflow/model-analysis)
*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](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>
Currently, TFMA requires that TensorFlow is installed but does not have an
explicit dependency on the TensorFlow PyPI package. See the
[TensorFlow install guides](/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](g3doc/get_started.md) and try out the extensive [end-to-end example](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 |1.11 |2.8.0 |
|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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tensorflow_model_analysis-0.11.0.tar.gz
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tensorflow_model_analysis-0.11.0.tar.gz (109个子文件)
setup.cfg 38B
vulcanized_template.html 819KB
MANIFEST.in 28B
index.js 538KB
extension.js 4KB
index.js.map 662KB
README.md 3KB
not-zip-safe 1B
PKG-INFO 1KB
PKG-INFO 1KB
post_export_metrics_test.py 40KB
integration_test.py 35KB
post_export_metrics.py 33KB
load.py 23KB
model_eval_lib.py 16KB
util.py 16KB
evaluate_test.py 15KB
serialization_test.py 15KB
tfma_unit.py 14KB
util_test.py 12KB
serialization.py 12KB
export.py 12KB
metrics.py 11KB
aggregate.py 11KB
slicer.py 10KB
fake_sequence_to_prediction.py 10KB
model_eval_lib_test.py 10KB
util_test.py 10KB
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setup.py 9KB
exporter.py 9KB
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util.py 8KB
tfma_unit_test.py 7KB
graph_ref_test.py 7KB
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