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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 Forests is not yet available as a Windows Pip package.

Workarounds:

Incompatibility with old or nightly versions 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
1.1.0 - 1.2.0 2.11.0
1.0.0 - 1.0.1 2.10.0 - 2.10.1
0.2.6 - 0.2.7 2.9.1
0.2.5 2.9
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: Wrap your preprocessing function into another function that squeezes its inputs.

Not 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 that it fits on a single machine.

No support for GPU / TPU.

TF-DF does not supports 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.

Loaded models behave differently than Python models.

While abstracted by the Keras API, a model instantiated in Python (e.g., with tfdf.keras.RandomForestModel()) and a model loaded from disk (e.g., with tf.keras.models.load_model()) can behave differently. Notably, a Python instantiated model automatically applies necessary type conversions. For example, if a float64 feature is fed to a model expecting a float32 feature, this conversion is performed implicitly. However, such a conversion is not possible for models loaded from disk. It is therefore important that the training data and the inference data always have the exact same type.

Tensorflow feature name sanitization

Tensorflow sanitizes feature names and might, for instance, convert them to lowercase.