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TensorFlow Integration

IREE supports compiling and running TensorFlow programs represented as tf.Module classes or stored in the SavedModel format.


Install TensorFlow by following the official documentation:

python -m pip install tf-nightly

Install IREE pip packages, either from pip or by building from source:

python -m pip install \
  iree-compiler \
  iree-runtime \


The TensorFlow package is currently only available on Linux and macOS. It is not available on Windows yet (see this issue).

Importing models

IREE compilers transform a model into its final deployable format in several sequential steps. The first step for a TensorFlow model is to use either the iree-import-tf command-line tool or IREE's Python APIs to import the model into a format (i.e., MLIR) compatible with the generic IREE compilers.

From SavedModel on TensorFlow Hub

IREE supports importing and using SavedModels from TensorFlow Hub.

Using the command-line tool

First download the SavedModel and load it to get the serving signature, which is used as the entry point for IREE compilation flow:

import tensorflow.compat.v2 as tf
loaded_model = tf.saved_model.load('/path/to/downloaded/model/')


If there are no serving signatures in the original SavedModel, you may add them by yourself by following "Missing serving signature in SavedModel".

Then you can import the model with iree-import-tf. You can read the options supported via iree-import-tf -help. Using MobileNet v2 as an example and assuming the serving signature is predict:

  --tf-import-type=savedmodel_v1 \
  --tf-savedmodel-exported-names=predict \
  /path/to/savedmodel -o iree_input.mlir


iree-import-tf is installed as /path/to/python/site-packages/iree/tools/tf/iree-import-tf. You can find out the full path to the site-packages directory via the python -m site command.

-tf-import-type needs to match the SavedModel version. You can try both v1 and v2 if you see one of them gives an empty dump.

Afterwards you can further compile the model in iree_input.mlir for CPU or GPU.



Discuss training


Colab notebooks
Training an MNIST digits classifier Open in Colab
Edge detection module Open In Colab
Pretrained ResNet50 inference Open In Colab
TensorFlow Hub Import Open In Colab

End-to-end execution tests can be found in IREE's integrations/tensorflow/e2e/ directory.


Missing serving signature in SavedModel

Sometimes SavedModels are exported without explicit serving signatures. This happens by default for TensorFlow Hub SavedModels. However, serving signatures are required as entry points for IREE compilation flow. You can use Python to load and re-export the SavedModel to give it serving signatures. For example, for MobileNet v2, assuming we want the serving signature to be predict and operating on a 224x224 RGB image:

import tensorflow.compat.v2 as tf
loaded_model = tf.saved_model.load('/path/to/downloaded/model/')
call = loaded_model.__call__.get_concrete_function(
         tf.TensorSpec([1, 224, 224, 3], tf.float32))
signatures = {'predict': call},
  '/path/to/resaved/model/', signatures=signatures)

The above will create a new SavedModel with a serving signature, predict, and save it to /path/to/resaved/model/.