Quickstart
Installing MLflow
You install MLflow by running:
pip install mlflow
install.packages("mlflow")
mlflow::install_mlflow()
Note
MLflow works on MacOS. If you run into issues with the default system Python on MacOS, try
installing Python 3 through the Homebrew package manager using
brew install python. (In this case, installing MLflow is now pip3 install mlflow).
At this point we recommend you follow the tutorial for a walk-through on how you can leverage MLflow in your daily workflow.
Downloading the Quickstart
Download the quickstart code by cloning MLflow via git clone https://github.com/mlflow/mlflow,
and cd into the examples subdirectory of the repository. We’ll use this working directory for
running the quickstart.
We avoid running directly from our clone of MLflow as doing so would cause the tutorial to use MLflow from source, rather than your PyPi installation of MLflow.
Using the Tracking API
The MLflow Tracking API lets you log metrics and artifacts (files) from your data
science code and see a history of your runs. You can try it out by writing a simple Python script
as follows (this example is also included in quickstart/mlflow_tracking.py):
import os
from mlflow import log_metric, log_param, log_artifact
if __name__ == "__main__":
# Log a parameter (key-value pair)
log_param("param1", 5)
# Log a metric; metrics can be updated throughout the run
log_metric("foo", 1)
log_metric("foo", 2)
log_metric("foo", 3)
# Log an artifact (output file)
with open("output.txt", "w") as f:
f.write("Hello world!")
log_artifact("output.txt")
library(mlflow)
# Log a parameter (key-value pair)
mlflow_log_param("param1", 5)
# Log a metric; metrics can be updated throughout the run
mlflow_log_metric("foo", 1)
mlflow_log_metric("foo", 2)
mlflow_log_metric("foo", 3)
# Log an artifact (output file)
writeLines("Hello world!", "output.txt")
mlflow_log_artifact("output.txt")
Viewing the Tracking UI
By default, wherever you run your program, the tracking API writes data into files into a local
./mlruns directory. You can then run MLflow’s Tracking UI:
mlflow ui
mlflow_ui()
and view it at http://localhost:5000.
Note
If you see message [CRITICAL] WORKER TIMEOUT in the MLflow UI or error logs, try using http://localhost:5000 instead of http://127.0.0.1:5000.
Running MLflow Projects
MLflow allows you to package code and its dependencies as a project that can be run in a
reproducible fashion on other data. Each project includes its code and a MLproject file that
defines its dependencies (for example, Python environment) as well as what commands can be run into the
project and what arguments they take.
You can easily run existing projects with the mlflow run command, which runs a project from
either a local directory or a GitHub URI:
mlflow run sklearn_elasticnet_wine -P alpha=0.5
mlflow run https://github.com/mlflow/mlflow-example.git -P alpha=5
There’s a sample project in tutorial, including a MLproject file that
specifies its dependencies. if you haven’t configured a tracking server,
projects log their Tracking API data in the local mlruns directory so you can see these
runs using mlflow ui.
Note
By default mlflow run installs all dependencies using conda.
To run a project without using conda, you can provide the --no-conda option to
mlflow run. In this case, you must ensure that the necessary dependencies are already installed
in your Python environment.
For more information, see MLflow Projects.
Saving and Serving Models
MLflow includes a generic MLmodel format for saving models from a variety of tools in diverse
flavors. For example, many models can be served as Python functions, so an MLmodel file can
declare how each model should be interpreted as a Python function in order to let various tools
serve it. MLflow also includes tools for running such models locally and exporting them to Docker
containers or commercial serving platforms.
To illustrate this functionality, the mlflow.sklearn package can log scikit-learn models as
MLflow artifacts and then load them again for serving. There is an example training application in
sklearn_logistic_regression/train.py that you can run as follows:
python sklearn_logistic_regression/train.py
When you run the example, it outputs an MLflow run ID for that experiment. If you look at
mlflow ui, you will also see that the run saved a model folder containing an MLmodel
description file and a pickled scikit-learn model. You can pass the run ID and the path of the model
within the artifacts directory (here “model”) to various tools. For example, MLflow includes a
simple REST server for python-based models:
mlflow models serve -m runs:/<RUN_ID>/model
Note
By default the server runs on port 5000. If that port is already in use, use the –port option to
specify a different port. For example: mlflow models serve -m runs:/<RUN_ID>/model --port 1234
Once you have started the server, you can pass it some sample data and see the predictions.
The following example uses curl to send a JSON-serialized pandas DataFrame with the split
orientation to the model server. For more information about the input data formats accepted by
the pyfunc model server, see the MLflow deployment tools documentation.
curl -d '{"columns":["x"], "data":[[1], [-1]]}' -H 'Content-Type: application/json; format=pandas-split' -X POST localhost:5000/invocations
which returns:
{"predictions": [1, 0]}
For more information, see MLflow Models.
Logging to a Remote Tracking Server
In the examples above, MLflow logs data to the local filesystem of the machine it’s running on. To manage results centrally or share them across a team, you can configure MLflow to log to a remote tracking server. To get access to a remote tracking server:
Launch a Tracking Server on a Remote Machine
Launch a tracking server on a remote machine.
You can then log to the remote tracking server by
setting the MLFLOW_TRACKING_URI environment variable to your server’s URI, or
by adding the following to the start of your program:
import mlflow mlflow.set_tracking_uri("http://YOUR-SERVER:4040") mlflow.set_experiment("my-experiment")library(mlflow) install_mlflow() mlflow_set_tracking_uri("http://YOUR-SERVER:4040") mlflow_set_experiment("/my-experiment")
Log to Databricks Community Edition
Alternatively, sign up for Databricks Community Edition,
a free service that includes a hosted tracking server. Note that
Community Edition is intended for quick experimentation rather than production use cases.
After signing up, run databricks configure to create a credentials file for MLflow, specifying
https://community.cloud.databricks.com as the host.
To log to the Community Edition server, set the MLFLOW_TRACKING_URI environment variable
to “databricks”, or add the following to the start of your program:
import mlflow mlflow.set_tracking_uri("databricks") # Note: on Databricks, the experiment name passed to set_experiment must be a valid path # in the workspace, like '/Users/<your-username>/my-experiment'. See # https://docs.databricks.com/user-guide/workspace.html for more info. mlflow.set_experiment("/my-experiment")library(mlflow) install_mlflow() mlflow_set_tracking_uri("databricks") # Note: on Databricks, the experiment name passed to mlflow_set_experiment must be a # valid path in the workspace, like '/Users/<your-username>/my-experiment'. See # https://docs.databricks.com/user-guide/workspace.html for more info. mlflow_set_experiment("/my-experiment")