TraceML is the client-side engine for ML/Data tracking, visualization, dashboards, and model UI, designed specifically for integration with the Polyaxon MLOps platform. It allows users to log metrics, parameters, artifacts, and manage experiment runs programmatically. The current version is 1.3.0, and it maintains a relatively frequent release cadence, often aligning with Polyaxon platform updates.
pip install tracemlVerified import paths — ran on the pinned version, not inferred.
Demonstrates how to initialize a TraceML run, log a metric, a parameter, and declare an artifact kind. By default, it runs in offline mode for local execution without a Polyaxon server. To track to a remote Polyaxon instance, ensure authentication is configured (e.g., via `POLYAXON_TOKEN` environment variable) and set `is_offline=False`.
Ensure the `run.start()` context manager is used for automatic run finalization and artifact uploading. Verify Polyaxon server is running and accessible if tracking remotely. If `is_offline=True` is used, data is stored locally and will not appear in the Polyaxon UI. Check `polyaxon config show` for current client configuration.
`traceml.tracking.run` provides a higher-level, context-managed interface ideal for logging metrics, parameters, and artifacts within an experiment. Use `PolyaxonClient` for lower-level API interactions like managing projects, models, or users directly. For most experiment tracking tasks, `run` is the preferred entry point.
Ensure the `POLYAXON_TOKEN` environment variable is set with a valid API token or `~/.polyaxon/config.yaml` is correctly configured with valid credentials and a reachable host. Verify network connectivity to the Polyaxon API endpoint.
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