Python SDK Example Workflows

Walk through how to use the Python SDK in these basic and advanced workflow examples.

Find the Top Performing Checkpoint

In this example, we’ll walk through the most basic workflow for creating an experiment, waiting for it to complete, and finding the top-performing checkpoint.

The first step is to import the client module and possibly to call login():

from determined.experimental import client

# We will assume that you have called `det user login`, so this is unnecessary:
# client.login(master=..., user=..., password=...)

The next step is to call create_experiment():

# Config can be a path to a config file or a Python dict of the config.
exp = client.create_experiment(config="my_config.yaml", model_dir=".")
print(f"started experiment {exp.id}")

The returned object is an Experiment object, which offers methods to manage the experiment’s lifecycle. In the following example, we simply await the experiment’s completion.

exit_status = exp.wait()
print(f"experiment completed with status {exit_status}")

Now that the experiment has completed, you can grab the top-performing checkpoint from training:

best_checkpoint = exp.list_checkpoints()[0]
print(f"best checkpoint was {best_checkpoint.uuid}")

Create an Experiment and Follow its Logs

Using det CLI, you can create an experiment and print its logs until completion using:

det e create --follow ...

The same behavior can be replicated with the Python SDK:

exp = client.create_experiment(...)
for logline in exp.await_first_trial().iter_logs():
   print(logline)

Run and Administer Experiments

Visit the det-python-sdk-demo to learn how to run and administer experiments using the Python SDK.