explora

async explora.start_new_session(self, repo_id, model, hyperparameters, percentage_averaged=0.75, max_rounds=5, library_type='PYTHON', checkpoint_frequency=1)

Validate arguments and then start a new session by sending a message to the server with the given configuration. Designed to be called in Explora.ipynb.

Parameters

  • repo_id (str) – The repo ID associated with the current dataset.

  • model (keras.engine.Model) – The initial Keras model to train with. The model must be compiled!

  • hyperparams (dict) – The hyperparameters to be used during training. Must include batch_size!

  • percentage_averaged (float) – Percentage of nodes to be averaged before moving on to the next round.

  • max_rounds (int) – Maximum number of rounds to train for.

  • library_type (str) – The type of library to train with. Must be either PYTHON or JAVASCRIPT.

  • checkpoint_frequency (int) – Save the model in S3 every checkpoint_frequency rounds.

Examples

>>> start_new_session(
...     repo_id="c9bf9e57-1685-4c89-bafb-ff5af830be8a",
...     model=keras.models.load_model("model.h5"),
...     hyperparameters={"batch_size": 100},
...     percentage_averaged=0.75,
...     max_rounds=5,
...     library_type="PYTHON",
...     checkpoint_frequency=1,
... )
Starting session!
Waiting...
Session complete! Check dashboard for final model!