Perform Simple Metrics Reporting#

In this tutorial, we’ll walk through how to perform simple metrics reporting using detached mode.

For the full script, visit the GitHub repository.

Objectives#

These step-by-step instructions walk you through the following tasks:

  • Setting up your training environment

  • Importing and initializing the core context

  • Setting the master address and executing your training script

Upon completing this user guide, you will:

  • Grasp the concept and application of detached mode

  • Successfully report metrics in detached mode

  • Navigate and visualize trials using the Determined WebUI

Set Up Your Training Environment#

To begin, you’ll need a Determined cluster. If you are new to Determined, you can install the Determined library and start a cluster locally.

  • Ensure you have Docker running and then run the following command:

    pip install determined

# If your machine has GPUs:
det deploy local cluster-up

# If your machine does not have GPUs:
det deploy local cluster-up --no-gpu

    Note

    When deploying locally, the system prompts you to set a strong password.

    The command, pip install determined, installs the determined library which includes the Determined command-line interface (CLI).

Step 1: Import and Initialize the Core API#

Start by importing the necessary modules for your training code:

import random
from determined.experimental import core_v2

Initialize the core context to recognize the trial with some identifying metadata in the main function:

def main():
    core_v2.init(
        config=core_v2.Config(
            name="detached_mode_example",
        ),
    )

Report your trial and validation metrics:

for i in range(100):
    core_v2.train.report_training_metrics(steps_completed=i, metrics={"loss": random.random()})
    if (i + 1) % 10 == 0:
        loss = random.random()
        print(f"validation loss is: {loss}")
        core_v2.train.report_validation_metrics(
            steps_completed=i, metrics={"loss": loss}
        )
core_v2.close()

if __name__ == "__main__":
    main()

Step 2: Set Master Address and Execute Your Training Script#

Define the Determined master address:

export DET_MASTER=<DET_MASTER_IP:PORT>

Run your training script:

python3 <my_training_script.py>

Visualize the metrics! Navigate to <DET_MASTER_IP:PORT> in your web browser to see the experiment.

Next Steps#

Now that you’ve grasped the essence of simple metrics reporting in detached mode, try more examples using detached mode or learn more about Determined by visiting the tutorials.