Linux command
mlflow 命令
文本
复制后可按需替换文件名、目录或参数。
常用示例
Start MLflow tracking server
mlflow server --host [0.0.0.0] --port [5000]
Start UI
mlflow ui
Run an MLflow project
mlflow run [project-uri] -P [param=value]
Run project from Git
mlflow run https://github.com/[user]/[repo]
Create a new experiment
mlflow experiments create -n [experiment-name]
List experiments
mlflow experiments search
Serve a model
mlflow models serve -m [models:/model-name/version] -p [5001]
Build Docker image for model
mlflow models build-docker -m [models:/model-name/version] -n [image-name]
说明
mlflow is the CLI for MLflow, an open-source platform for machine learning lifecycle management. It tracks experiments, packages code, and deploys models. The tracking server stores experiment metadata, parameters, metrics, and artifacts. Use mlflow ui for local development or mlflow server for team deployment. mlflow run executes MLflow Projects—directories or Git repos with MLproject files defining entry points, parameters, and environments. It ensures reproducibility. Model serving with models serve creates REST endpoints for predictions. models build-docker packages models as containers. The Models component supports multiple ML frameworks. Artifacts include datasets, models, and outputs. The tracking server stores references; actual files go to configured storage (local, S3, GCS, Azure Blob).
FAQ
What is the mlflow command used for?
mlflow is the CLI for MLflow, an open-source platform for machine learning lifecycle management. It tracks experiments, packages code, and deploys models. The tracking server stores experiment metadata, parameters, metrics, and artifacts. Use mlflow ui for local development or mlflow server for team deployment. mlflow run executes MLflow Projects—directories or Git repos with MLproject files defining entry points, parameters, and environments. It ensures reproducibility. Model serving with models serve creates REST endpoints for predictions. models build-docker packages models as containers. The Models component supports multiple ML frameworks. Artifacts include datasets, models, and outputs. The tracking server stores references; actual files go to configured storage (local, S3, GCS, Azure Blob).
How do I run a basic mlflow example?
Run `mlflow server --host [0.0.0.0] --port [5000]` in a terminal, then adjust file names, paths, flags, or remote targets for your system.
Where can I find more mlflow examples?
This page includes 8 examples for mlflow, plus related commands for nearby Linux tasks.