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The quickest local installation is pip install mlflow. After it finishes, run mlflow server --port 5000 and open http://localhost:5000. That quick self-hosted server uses SQLite as its default backend store. Your Python requirement depends on the workflow: the general environment guide lists Python 3.9 or newer with pip, while the server setup instructions specify Python 3.10 or newer for their uv/pip workflow.
Choose the installation route that fits your use case
| Route | Best for | Persistence and scope | Key requirement |
|---|---|---|---|
| pip | One developer experimenting locally | Local filesystem behavior by default; the quick server uses SQLite | Python 3.9+ in the general environment guidance |
| uv | Running MLflow without first adding it to a project environment | Local server | Python 3.10+ for the documented server workflow |
| Docker Compose | A fuller local stack or team-like testing | PostgreSQL database and MinIO object storage | Docker and the MLflow repository’s Compose files |
| Kubernetes | Cluster deployment and model serving | Cluster-managed services; the tutorial uses KServe | A Kubernetes cluster and the serving dependencies |
| Databricks | A managed tracking service or a local IDE connected to Databricks | Databricks-managed MLflow | Databricks host and authentication variables |
Install MLflow with pip
1. Check the Python environment
Use the Python interpreter in which you intend to run your training code. The general environment documentation lists Python 3.9 or newer with pip. The server setup page uses Python 3.10 or newer for its uv/pip instructions, so check the requirements for the specific workflow before pinning a runtime.
2. Install the package
pip install mlflowRun the command inside your virtual environment when possible. Confirm that the command-line interface is available:
mlflow --version3. Start the local tracking server and UI
mlflow server --port 5000Leave that process running, then browse to http://localhost:5000. The quick self-hosting path starts both the tracking server and its UI and uses SQLite as the default backend store.
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4. Point your MLflow client at the server
A client does not automatically use a server for every command. Set the tracking URI in code:
import mlflow
mlflow.set_tracking_uri("http://localhost:5000")Alternatively, set MLFLOW_TRACKING_URI in the environment before running your program. Without a configured tracking URI, many commands fall back to local filesystem behavior rather than the server you just started.
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Run MLflow with uv
The server setup guide also documents:
uvx mlflow serverThis lets uv invoke MLflow without first installing it into the project environment. The documented uv/pip server workflow specifies Python 3.10 or newer. Add --port 5000 when you want to make the port explicit:
uvx mlflow server --port 5000Open http://localhost:5000 and configure your client with the same tracking URI.
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Use Docker Compose for PostgreSQL and MinIO
Docker Compose is a multi-service local stack, not the shortest beginner installation. The official server setup path uses the MLflow repository’s Compose configuration to start PostgreSQL for metadata and MinIO for object storage, with MLflow exposed on port 5000.
- Clone the MLflow repository using the sparse checkout procedure documented in the server setup guide.
- Change into its
docker-composedirectory. - Copy
.env.dev.exampleto.env. - Start the stack with
docker compose up -d.
Once the containers are healthy, open http://localhost:5000. Use this route when you need database and object-storage services that resemble a shared deployment; use pip or uv when a single local process is enough.
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Connect a local IDE to Databricks MLflow
For local code that should log to Databricks, install the Databricks extra:
pip install --upgrade 'mlflow[databricks]>=3.1'Configure authentication and the tracking destination in the environment:
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export DATABRICKS_TOKEN="your-token"
export DATABRICKS_HOST="https://your-workspace-host"
export MLFLOW_TRACKING_URI="databricks"Use the equivalent environment-variable syntax for your shell or operating system. Databricks runtimes include MLflow, but the environment guidance recommends updating MLflow for the best experience when working with current client features.
Deploy MLflow on Kubernetes
The Kubernetes tutorial is intended for deployment and model serving rather than a first local install. Its serving example installs the MLServer extra:
pip install 'mlflow[mlserver]'Verify the CLI before proceeding:
mlflow --versionThe remainder of that path assumes a Kubernetes cluster and uses KServe for serving. Choose it when cluster orchestration and serving operations are requirements; it adds infrastructure that is unnecessary for local tracking.
Quick Recap
Verify the installation and fix common problems
The command is not found
- Activate the virtual environment where you installed MLflow.
- Run
mlflow --versionwith that environment active. - If you used uv, invoke the server through
uvx mlflow serverrather than expecting a globally installed executable.
The browser cannot open the UI
- Confirm that the
mlflow serverprocess is still running. - Use the exact host and port printed or configured for the server; the quick path uses port 5000.
- For a remote machine or container, ensure that the configured host and port are reachable from your browser.
Runs appear in the wrong place
- Set
MLFLOW_TRACKING_URIor callmlflow.set_tracking_uri(...)before logging. - For the local quick server, use
http://localhost:5000; for Databricks, usedatabrickswith the required host and token variables.
Which setup should you use?
- Learning or a one-machine experiment: install with pip, start the port-5000 server, and keep the default SQLite backend.
- A reproducible local stack: use Docker Compose with PostgreSQL and MinIO.
- Managed tracking: connect your local IDE to Databricks.
- Cluster serving: follow the Kubernetes and KServe path, including the
mlserverextra.
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