| Test that IREE has the correct model and optimizer state after doing one train | |
| step and after initialization of parameters. The ground truth is extracted from | |
| a JAX model. The MLIR model is generated with IREE JAX. | |
| To regenerate the model together with the test data use | |
| ```shell | |
| python -m venv generate_mnist.venv | |
| source generate_mnist.venv/bin/activate | |
| # Add IREE Python to your PYTHONPATH, following | |
| # https://openxla.github.io/iree/building-from-source/getting-started/#python-bindings | |
| pip install -r generate_test_data_requirements.txt | |
| python ./generate_test_data.py | |
| ``` | |
| Upload to gcs | |
| ```shell | |
| tar --remove-files -v -c -f mnist_train.tar *.npz *.mlirbc | |
| DIGEST="$(sha256sum mnist_train.tar | awk '{print $1}')" | |
| gcloud storage mv mnist_train.tar "gs://iree-model-artifacts/mnist_train.${DIGEST}.tar" | |
| sed -i \ | |
| "s|MODEL_ARTIFACTS_URL =.*|MODEL_ARTIFACTS_URL = \"https://storage.googleapis.com/iree-model-artifacts/mnist_train.${DIGEST}.tar\"|" \ | |
| mnist_train_test.py | |
| ``` |