https://towardsdatascience.com/deploy-mlflow-with-docker-compose-8059f16b6039
Start a Local MLflow Server
Create environment and install mlflow
mamba create -n mlflow jupyterlab -c conda-forge
mamba activate mlflow
pip install mlflowstart a local MLflow server with UI by running the command below
mlflow uiOr,
mlflow server --host 127.0.0.1 --port 8080There are many options to configure the server, refer to https://mlflow.org/docs/latest/tracking/server.html#configure-server
Run jupyter lab
jupyter labCreate a new notebook
Connect MLflow Session to Your Server
Now that the server is spun up, let’s connect our MLflow session to the local server.
import mlflow
mlflow.set_tracking_uri("http://127.0.0.1:5000")Next, let’s try logging some dummy metrics. We can view these test metrics on the local hosted UI. Test connection
mlflow.set_experiment("/check-localhost-connection")
with mlflow.start_run():
mlflow.log_metric("foo", 1)
mlflow.log_metric("bar", 2)Another, example for python
import mlflow
remote_server_uri = "..." # set to your server URI
mlflow.set_tracking_uri(remote_server_uri)
mlflow.set_experiment("/my-experiment")
with mlflow.start_run():
mlflow.log_param("a", 1)
mlflow.log_metric("b", 2)Another, example for r language
library(mlflow)
install_mlflow()
remote_server_uri = "..." # set to your server URI
mlflow_set_tracking_uri(remote_server_uri)
mlflow_set_experiment("/my-experiment")
mlflow_log_param("a", "1")View Experiment on Your MLflow Server
Now let’s view your experiment on the local server. Open the URL in your browser, which is http://localhost:5000 in our case. In the UI, inside the left sidebar you should see the experiment with name “check-localhost-connection”. Clicking on this experiment name should bring you to the experiment view