Simvue Python client

Collect metadata, metrics and artifacts from simulations, processing and AI/ML training tasks running on any platform, in real time.

Configuration

The service URL and token can be defined as environment variables:

export SIMVUE_URL=...
export SIMVUE_TOKEN=...

or a file simvue.toml can be created containing:

[server]
url = "..."
token = "..."

The exact contents of both of the above options can be obtained directly by clicking the Create new run button on the web UI. Note that the environment variables have preference over the config file.

Usage example

from simvue import Run

...

if __name__ == "__main__":
    ...

    # Using a context manager means that the status will be set to completed automatically,
    # and also means that if the code exits with an exception this will be reported to Simvue
    with Run() as run:
        # Specify a run name, metadata (dict), tags (list), description, folder
        run.init(
            "example-run-name",
            {
                "learning_rate": 0.001,
                "training_steps": 2000,
                "batch_size": 32,
            },  # Metadaata
            ["tensorflow"],  # Tags
            "This is a test.",  # Description
            "/Project-A/part1",
        )  # Folder full path

        # Set folder details if necessary
        run.set_folder_details(
            "/Project-A/part1",  # Folder full path
            metadata={},  # Metadata
            tags=["tensorflow"],  # Tags
            description="This is part 1 of a test",
        )  # Description

        # Upload the code
        run.save_file("training.py", "code")

        # Upload an input file
        run.save_file("params.in", "input")

        # Add an alert (the alert definition will be created if necessary)
        run.create_metric_threshold_alert(
            name="loss-too-high",  # Name
            rule="is above",  # Rule
            metric="loss",  # Metric
            frequency=1,  # Frequency
            window=1,  # Window
            threshold=10,  # Threshold
            notification="email",  # Notification type
        )

        ...

        while not converged:
            ...

            # Send metrics inside main application loop
            run.log_metrics({"loss": 0.5, "density": 34.4})

            ...

        # Upload an output file
        run.save_file("output.cdf", "output")

        # If we weren't using a context manager we'd need to end the run
        # run.close()

License

Released under the terms of the Apache 2 license.