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Known Issues

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:

No support for in-model preprocessing with input of rank 1

Keras expands the dimension of input tensors to rank 2 (using tf.expand_dims). If your preprocessing model argument only support rank 1 tensors, you will get an error complaining about tensor shape.

Workarounds:

  • Solution #1: Apply your preprocessing before the model, for example using the dataset's map method.

  • Solution #2: Wrapps your preprocessing function into another function that squeeze its inputs.

No support for TF distribution strategies.

TF-DF does not yet support distribution strategies or datasets that do not fit in memory. This is because the classical decision forest training algorithms already implemented require the entire dataset to be available in memory.

Workaround

  • Downsample your dataset. A rule of thumb is that TF-DF training uses 4 bytes per input dimension, so a dataset with 100 million examples and 10 numerical/categorical features would be 4 GB in memory.

  • Train a manual ensemble on slices of the dataset, i.e. train N models on N slices of data, and average the predictions.

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.