MLFlowClient.jl

A Julia client for the MLflow REST API. Track experiments, log metrics and parameters, manage models, and more — directly from Julia.

Tested against MLflow 3.11.1.

Installation

using Pkg
Pkg.add("MLFlowClient")

Quick start

using MLFlowClient

# Connect to an MLflow server
mlf = MLFlow("http://localhost:5000")

# Create an experiment and a run
experiment_id = createexperiment(mlf, "my-experiment")
run = createrun(mlf, experiment_id)

# Log parameters, metrics, and tags
logparam(mlf, run, "learning_rate", "0.01")
logmetric(mlf, run, "accuracy", 0.95)
setruntag(mlf, run, "model_type", "linear")

# Complete the run
updaterun(mlf, run; status=RunStatus.FINISHED)

What's covered

MLFlowClient implements the full MLflow REST API (v2.0 and v3.0) and the Authentication REST API:

Area	Operations
Experiments	Create, get, search, update, delete, restore, tags
Runs	Create, get, search, update, delete, restore, tags
Logging	Metrics, parameters, batch, model, inputs
Artifacts	List, upload, download, delete, multipart upload, presigned URLs
Registered models	Create, get, search, rename, update, delete, tags, aliases
Model versions	Create, get, search, update, delete, transition stage, tags
Scorers	Register, list, get, delete (v3.0)
Gateway	Secrets, model definitions, endpoints, bindings, tags, budgets (v3.0)
Prompt optimization	Create, get, search, cancel, delete jobs (v3.0)
Webhooks	Create, get, list, update, delete, test
Users & permissions	Create, get, update, delete users; experiment and model permissions

Authentication

# Basic auth
mlf = MLFlow("http://localhost:5000"; username="admin", password="password")

# Token-based auth
mlf = MLFlow("http://localhost:5000"; headers=Dict("Authorization" => "Bearer <token>"))

Environment variables MLFLOW_TRACKING_URI, MLFLOW_TRACKING_USERNAME, and MLFLOW_TRACKING_PASSWORD are respected when set.

Documentation

See the full documentation for the complete API reference and tutorial.