Skip to main content

deep-learning-mlflow

Local MLflow integration layer for deep-learning-core.

deep-learning-mlflow adds local MLflow tracking on top of deep-learning-core without Azure dependencies. It is the public MLflow variant behind deep-learning-core[mlflow].

Current release: deep-learning-mlflow==0.0.13. Requires deep-learning-core>=0.0.34,<0.1.

What's New in 0.0.13?

  • MLflow now implements the public RL callback hooks on_episode_end(), on_update_end(), and on_evaluation_end()
  • custom callbacks can subclass the integration without relying on private underscore methods

Previous versions are recorded in the release history.

Install

Install from PyPI through the core extra:

pip install "deep-learning-core[mlflow]"

Install the package directly:

pip install deep-learning-mlflow

Install in a uv project:

uv add "deep-learning-core[mlflow]"

Quick Start

uv init
uv add deep-learning-mlflow
uv run dl-init --root-dir . --with-mlflow
uv run dl-run --config configs/base.yaml
uv run dl-sweep experiments/lr_sweep.yaml

The scaffold points MLflow at a local ./mlruns directory by default. Generated repositories ignore mlruns/ so local tracking data is not accidentally committed. Tracker experiment naming defaults to the repository root name unless tracking.experiment_name overrides it. Run artifacts are uploaded from config.yaml, per-epoch epoch_<n>/ directories after checkpointing, and final/ at the end of training.

Concrete local tracking flow:

uv run dl-init --root-dir . --with-mlflow
uv run dl-run --config configs/base.yaml
uv run dl-analyze --sweep experiments/lr_sweep.yaml

What You Get

  • the mlflow callback for local training runs
  • epoch, RL episode, algorithm-update, and evaluation metric logging
  • dl-init --with-mlflow scaffold support
  • local ./mlruns tracking defaults for generated experiment repositories
  • automatic upload of epoch_<n>/, final/, and config.yaml artifacts

Azure-backed MLflow wiring remains part of dl-azure.

Companion Packages

Docs

License

MIT. See LICENSE.

Release files for deep-learning-mlflow 0.0.13

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

Source distribution (sdist)

Source distribution for deep-learning-mlflow 0.0.13
File Size Uploaded
deep_learning_mlflow-0.0.13.tar.gz 230.3 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for deep-learning-mlflow 0.0.13
File Interpreter ABI Platform
deep_learning_mlflow-0.0.13-py3-none-any.whl Python 3 none any Details

Total release size: 242.8 kB

Release files / deep_learning_mlflow-0.0.13.tar.gz

Download URL deep_learning_mlflow-0.0.13.tar.gz
Size 230.3 kB
Tags Source
SHA-256 checksum
How to use checksums
87b4bfa9d4eaec93af4eb396f67ce668b1056f517fde97d23e5e79ce3d5d30b4
BLAKE2b-256 checksum
How to use checksums
3c96d95d814901785a52da0595cc2f9f0d3a24a9f8ad50ac35f9c5022e6625f0
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

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 Jul 27, 2026.

Transparency log

Release files / deep_learning_mlflow-0.0.13-py3-none-any.whl

Download URL deep_learning_mlflow-0.0.13-py3-none-any.whl
Size 12.5 kB
Tags Python 3
SHA-256 checksum
How to use checksums
d4ff1e7614d4ff0aaad4e6f830de8bac2f6477605481a0595dd2f2fc903e3a65
BLAKE2b-256 checksum
How to use checksums
a9433433828c04e2542854130c682d8e269a19f2ae26773a19c0619a56883633
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
Yes
Uploaded via twine/6.1.0 CPython/3.13.14

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

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 Jul 27, 2026.

Transparency log

Release history Release notifications | RSS feed

0.0.15

2 release files

0.0.14

2 release files

This release

0.0.13 This release

2 release files

0.0.12

2 release files

0.0.11

2 release files

0.0.10

2 release files

0.0.9

2 release files

0.0.8

2 release files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page