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.
The recommended way to install TFMA is using the PyPI package:
pip install tensorflow-model-analysis
Currently, TFMA requires that TensorFlow is installed but does not have an explicit dependency on the TensorFlow PyPI package. See the TensorFlow install guides for instructions.
To enable TFMA visualization in Jupyter Notebook:
jupyter nbextension enable --py widgetsnbextension
jupyter nbextension enable --py tensorflow_model_analysis
TensorFlow is required.
Apache Beam 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 and other Apache Beam runners.
Apache Arrow is also required. TFMA uses Arrow to represent data internally in order to make use of vectorized numpy functions.
For instructions on using TFMA, see the get started guide.
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.
|GitHub master||nightly (1.x/2.x)||2.19.0|
|0.21.6||1.15 / 2.1||2.19.0|
|0.21.5||1.15 / 2.1||2.19.0|
|0.21.4||1.15 / 2.1||2.17.0|
|0.21.3||1.15 / 2.1||2.17.0|
|0.21.2||1.15 / 2.1||2.17.0|
|0.21.1||1.15 / 2.1||2.17.0|
|0.21.0||1.15 / 2.1||2.17.0|
|0.15.4||1.15 / 2.0||2.16.0|
|0.15.3||1.15 / 2.0||2.16.0|
|0.15.2||1.15 / 2.0||2.16.0|
|0.15.1||1.15 / 2.0||2.16.0|