Skip to main content

neptune.ai MLflow integration library

Reason this release was yanked:

A missing dependency was discovered post-release

Project description

Neptune + MLflow integration

Neptune is a lightweight experiment tracker that offers a single place to track, compare, store, and collaborate on experiments and models.

This integration lets you enjoy tracking and reproducibility of MLflow with the organization and collaboration of Neptune. You can have your MLflow experiment runs hosted in a knowledge repo where you can invite and manage project contributors, while not having to change your MLflow logging code.

Should you wish to switch to Neptune, you can migrate your MLflow data to Neptune with the exporter tool.

What will you get with this integration?

  • A plugin which you can use to send your MLflow-logged metadata to Neptune with the help of a tracking URI.
  • An exporter for migrating existing MLflow experiments to your Neptune project.

Resources

Example

On the command line:

pip install neptune-mlflow

Send your MLflow-logged metadata to Neptune (in Python):

import mlflow
from neptune_mlflow_plugin import create_neptune_tracking_uri

# Create a Neptune tracking URI
neptune_uri = create_neptune_tracking_uri(
    api_token=ANONYMOUS_API_TOKEN,  # Set as environment variable or replace with your own token
    project="common/mlflow-integration",  # Set as environment variable or replace with your own project
    tags=["mlflow", "plugin"],  # (optional) use your own
)

mlflow.set_tracking_uri(neptune_uri)

with mlflow.start_run():
    ...

Export existing MLflow runs to Neptune:

neptune mlflow --project your-neptune-workspace/your-neptune-project

Support

If you got stuck or simply want to talk to us, here are your options:

  • Check our FAQ page
  • You can submit bug reports, feature requests, or contributions directly to the repository
  • Chat! When in the Neptune application click on the blue message icon in the bottom-right corner and send a message. A real person will talk to you ASAP (typically very ASAP)
  • You can just shoot us an email at support@neptune.ai

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

neptune_mlflow-1.1.0.tar.gz (12.9 kB view details)

Uploaded Source

Built Distribution

neptune_mlflow-1.1.0-py3-none-any.whl (19.1 kB view details)

Uploaded Python 3

File details

Details for the file neptune_mlflow-1.1.0.tar.gz.

File metadata

  • Download URL: neptune_mlflow-1.1.0.tar.gz
  • Upload date:
  • Size: 12.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.18

File hashes

Hashes for neptune_mlflow-1.1.0.tar.gz
Algorithm Hash digest
SHA256 4d445abdb87e4a25e70241a823760006d826530552b2a1fc5176bf7cb5a60a70
MD5 28ade0dce13147c5873b6309795299f0
BLAKE2b-256 26b3d774724009c6479ae27c3d5627fe24cc337708b9fc3fe84f3054806af2ed

See more details on using hashes here.

File details

Details for the file neptune_mlflow-1.1.0-py3-none-any.whl.

File metadata

File hashes

Hashes for neptune_mlflow-1.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 661e7a665d18cbe5b7b497d166a895e82dfcf38ba5dd9aedf050690aef51b112
MD5 0f90ad5814a5d39b06ec1777935989ec
BLAKE2b-256 7e529dfaada7e9305dc592c5cfac92eb37f0fb310bad0b2bd6850661b019be10

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page