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Simvue Python client


Simvue

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.

Metadata

Release files for simvue 2.6.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for simvue 2.6.1
File Size Uploaded
simvue-2.6.1.tar.gz 540.9 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for simvue 2.6.1
File Interpreter ABI Platform
simvue-2.6.1-py3-none-any.whl Python 3 none any Details

Total release size: 725.0 kB

Release files / simvue-2.6.1.tar.gz

Download URL simvue-2.6.1.tar.gz
Size 540.9 kB
Tags Source
SHA-256 checksum
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3e8bfa7fd017d813117d0a4c4e0719379fb65ece243c9b0747f3777b203da34b
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21b6183a1ad03553ef338b2a2af69ac30e268673630e69f22ad028049af3e2cd
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Yes
Uploaded via twine/7.0.0 CPython/3.13.14

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Signed by GitHub Actions, verified by PyPI on Sep 29, 2026.

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Release files / simvue-2.6.1-py3-none-any.whl

Download URL simvue-2.6.1-py3-none-any.whl
Size 184.1 kB
Tags Python 3
SHA-256 checksum
How to use checksums
24b2da7d668fb21c29e0edd8013134c121fdcde27e504469781b3e5dbded5087
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aa43881cc5914d443589781eb50469b8902c3a10244d74cb4c2a52f80cd4a306
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Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/7.0.0 CPython/3.13.14

Provenance

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PyPI Publish Attestation

PyPI verified that this artifact, at this checksum, originated from the publisher listed below.

Signed by GitHub Actions, verified by PyPI on Sep 29, 2026.

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