The underlying engine behind the decision forests algorithms used by TensorFlow Decision Forests have been extensively production-tested. But this API to TensorFlow and Keras is new, and some issues are expected -- we are trying to fix them as quickly as possible.
See also the known issues of Yggdrasil Decision Forests and the migration guide for behavior that is different from other algorithms.
Windows Pip package is not available
TensorFlow Decision Forest is not yet available as a Windows Pip package.
Workarounds:
- Solution #1: Install Windows Subsystem for Linux (WSL) on your Windows machine and follow the Linux instructions.
Incompatibility with old or nightly version of TensorFlow
TensorFlow ABI is not compatible in between releases. Because TF-DF relies on custom TensorFlow C++ ops, each version of TF-DF is tied to a specific version of TensorFlow. The last released version of TF-DF is always tied to the last released version of TensorFlow.
For reasons, the current version of TF-DF might not be compatible with older versions or with the nightly build of TensorFlow.
If using incompatible versions of TF and TF-DF, you will see cryptic errors such as:
tensorflow_decision_forests/tensorflow/ops/training/training.so: undefined symbol: _ZN10tensorflow11GetNodeAttrERKNS_9AttrSliceEN4absl14lts_2020_09_2311string_viewEPSs
- Use the version of TF-DF that is compatible with your version of TensorFlow.
Compatibility table
The following table shows the compatibility between
tensorflow_decision_forests and its dependencies:
| tensorflow_decision_forests | tensorflow |
|---|---|
| 0.2.4 | 2.8 |
| 0.2.1 - 0.2.3 | 2.7 |
| 0.1.9 - 0.2.0 | 2.6 |
| 0.1.1 - 0.1.8 | 2.5 |
| 0.1.0 | 2.4 |
- Solution #2: Wrapps your preprocessing function into another function that squeeze its inputs.
No all models support distributed training and distribute strategies
Unless specified, models are trained on a single machine and are not compatible
with distribution strategies. For example the GradientBoostedTreesModel does
not support distributed training while DistributedGradientBoostedTreesModel
does.
Workarounds:
- Use a model that support distribution strategies (e.g.
DistributedGradientBoostedTreesModel), or downsample your dataset so it fits on a single machine.
No support for GPU / TPU.
TF-DF does not support GPU or TPU training. Compiling with AVX instructions, however, may speed up serving.
No support for model_to_estimator
TF-DF does not implement the APIs required to convert a trained/untrained model to the estimator format.