Neptune Client is the Python library for interacting with Neptune.ai, an MLOps platform for experiment tracking and model management. It allows users to log, organize, and visualize machine learning metadata, including hyperparameters, metrics, and artifacts. The current version is 1.14.0.post2, and the library is actively maintained with frequent releases.
pip install neptune-clientVerified import paths — ran on the pinned version, not inferred.
This quickstart demonstrates how to initialize a Neptune run, log hyperparameters, track metrics over time, and upload an artifact. It assumes `NEPTUNE_API_TOKEN` and `NEPTUNE_PROJECT` are set as environment variables for authentication and project selection. If not, replace the placeholders with your actual token and project name. The code creates a local 'sample_output.txt' file and uploads it as an artifact.
Run `pip uninstall neptune-client neptune` then `pip install neptune`.
Update import statements from `neptune.new` to `neptune`. If using the old API, consult migration guides.
Explicitly convert unsupported types to strings using `str(value)` or use `neptune.types.stringify_unsupported(value)` if you intend to log them as strings.
Ensure all arguments to package-level functions are passed as keyword arguments (e.g., `neptune.init_run(project='...', api_token='...')` instead of `neptune.init_run('...', '...')`).Upgrade your Python environment to version 3.8 or higher.
Consult Neptune's official documentation for alternative ways to manage models and model versions, or continue using older versions if these endpoints are critical to your workflow.
Pass `include_plotlyjs='cdn'` to the `upload()` or `File.as_html()` methods to link to Plotly.js from a CDN instead of embedding it directly.