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Training a TensorFlow Graph in Determined (via Estimator API)

This example shows how wrap a graph defined in low-level TensorFlow APIs in a custom Estimator, and then run it in Determined.

Files

  • model_def.py: The core code for the model. This includes code for defining the model in low-level TensorFlow APIs, as well as for defining the custom Estimator and the EstimatorTrial.

  • startup-hook.sh: Predownload the dataset in the container. This ensures that the dataset download does not cause conflicts between multiple workers trying to download to the same directory if you were to reconfigure the experiment for distributed training.

Configuration Files

  • const.yaml: Train the model with constant hyperparameter values.

Data

Estimators require tf.data.Datasets as inputs. This examples uses the tensorflow_datasets MNIST dataset as input.

To Run

If you have not yet installed Determined, installation instructions can be found under docs/install-admin.html or at https://docs.determined.ai/latest/index.html

Run the following command: det -m <master host:port> experiment create -f const.yaml .. The other configurations can be run by specifying the appropriate configuration file in place of const.yaml.

Results

Training the model with the hyperparameter settings in const.yaml should yield a validation error of < 2%.