(install-local-docker)= # Install MLRun locally using Docker You can install and use MLRun and Nuclio locally on your computer. This does not include all the services and elastic scaling capabilities, which you can get with the Kubernetes based deployment, but it is much simpler to start with. ```{admonition} Note Using Docker is limited to local, Nuclio, serving runtimes, and local pipelines. ``` ## Prerequisites - Memory: 8GB - Storage: 7GB ## Overview Use [`docker compose`](https://docs.docker.com/compose/) to install MLRun. It deploys the MLRun service, MLRun UI, Nuclio serverless engine, and optionally the Jupyter server. The MLRun service, MLRun UI, Nuclio, and Jupyter, do not have default resources. This means that they are set with the default cluster/namespace resources limits. These can be modified. There are two installation options: - [**Use MLRun with your own client (PyCharm, VSCode, Jupyter)**](#use-mlrun-with-your-own-client) - [**Use MLRun with MLRun Jupyter image (pre loaded with examples/demos)**](#use-mlrun-with-mlrun-jupyter-image) In both cases you need to set the `SHARED_DIR` environment variable to point to a host path for storing MLRun artifacts and DB, for example `export SHARED_DIR=~/mlrun-data` (or use `set SHARED_DIR=c:\mlrun-data` in windows). Make sure the directory exists. You also need to set the `HOST_IP` variable with your computer IP address (required for Nuclio dashboard). You can select a specific MLRun version with the `TAG` variable and Nuclio version with the `NUCLIO_TAG` variable. ```{admonition} Note Support for running as a non-root user was added in 1.0.5, hence the underlying exposed port was changed. If you want to use previous mlrun versions, modify the mlrun-ui port from 8090 back to 80. If you are running more than one instance of MLRun, change the exposed port. ``` **Watch the installation:** ## Use MLRun with your own client The following commands install MLRun and Nuclio for work with your own IDE or notebook. **{Download}`[Download here]<./compose.yaml>`** the `compose.yaml` file, save it to the working dir and type: ````{dropdown} show the compose.yaml file ```{literalinclude} ./compose.yaml :language: yaml ``` ```` `````{tab-set} ````{tab-item} Linux/Mac ```sh export HOST_IP= export SHARED_DIR=~/mlrun-data mkdir $SHARED_DIR -p docker-compose -f compose.yaml up -d ``` Your `HOST_IP` address can be found using the `ip addr` or `ifconfig` commands (do not use `localhost` or `127.0.0.1`). It is recommended to select an address that does not change dynamically (for example the IP of the bridge interface). ```` ````{tab-item} Windows (cmd) ```sh set HOST_IP= set SHARED_DIR=c:\mlrun-data mkdir %SHARED_DIR% docker-compose -f compose.yaml up -d ``` Your `HOST_IP` address can be found using the `ipconfig` shell command (do not use `localhost` or `127.0.0.1`). It is recommended to select an address that does not change dynamically (for example the IP of the `vEthernet` interface). ```` ````{tab-item} Powershell ```sh $Env:HOST_IP= $Env:SHARED_DIR="~/mlrun-data" mkdir $Env:SHARED_DIR docker-compose -f compose.yaml up -d ``` Your `HOST_IP` address can be found using the `Get-NetIPConfiguration` cmdlet (do not use `localhost` or `127.0.0.1`). It is recommended to select an address that does not change dynamically (for example the IP of the `vEthernet` interface). ```` ````` This creates 3 services: - MLRun API (in [http://localhost:8080](http://localhost:8080)) - MLRun UI (in [http://localhost:8060](http://localhost:8060)) - Nuclio Dashboard/controller (in [http://localhost:8070](http://localhost:8070)) After installing MLRun service, set your client environment to work with the service, by setting the MLRun path env variable to `MLRUN_DBPATH=http://localhost:8080` or using `.env` files (see [setting client environment](./remote.html)). ## Use MLRun with MLRun Jupyter image For the quickest experience with MLRun you can deploy MLRun with a pre-integrated Jupyter server loaded with various ready-to-use MLRun examples. **{Download}`[Download here]<./compose.with-jupyter.yaml>`** the `compose.with-jupyter.yaml` file, save it to the working dir and type: ````{toggle} show the compose.with-jupyter.yaml file ```{literalinclude} ./compose.with-jupyter.yaml :language: yaml ``` ```` `````{tab-set} ````{tab-item} Linux/Mac ```sh export HOST_IP= export SHARED_DIR=~/mlrun-data mkdir -p $SHARED_DIR docker-compose -f compose.with-jupyter.yaml up -d ``` Your `HOST_IP` address can be found using the `ip addr` or `ifconfig` commands (do not use `localhost` or `127.0.0.1`). It is recommended to select an address that does not change dynamically (for example the IP of the bridge interface). ```` ````{tab-item} Windows (cmd) ```sh set HOST_IP= set SHARED_DIR=c:\mlrun-data mkdir %SHARED_DIR% docker-compose -f compose.with-jupyter.yaml up -d ``` Your `HOST_IP` address can be found using the `ipconfig` shell command (do not use `localhost` or `127.0.0.1`). It is recommended to select an address that does not change dynamically (for example the IP of the `vEthernet` interface). ```` ````{tab-item} Powershell ```sh $Env:HOST_IP= $Env:SHARED_DIR="~/mlrun-data" mkdir $Env:SHARED_DIR docker-compose -f compose.with-jupyter.yaml up -d ``` Your `HOST_IP` address can be found using the `Get-NetIPConfiguration` cmdlet (do not use `localhost` or `127.0.0.1`). It is recommended to select an address that does not change dynamically (for example the IP of the `vEthernet` interface). ```` ````` This creates 4 services: - Jupyter lab (in [http://localhost:8888](http://localhost:8888)) - MLRun API (in [http://localhost:8080](http://localhost:8080)), running on the Jupyter container - MLRun UI (in [http://localhost:8060](http://localhost:8060)) - Nuclio Dashboard/controller (in [http://localhost:8070](http://localhost:8070)) After the installation, access the Jupyter server (in [http://localhost:8888](http://localhost:8888)) and run through the [quick-start tutorial](../tutorial/01-mlrun-basics.html) and `demos`. You can see the projects, tasks, and artifacts in MLRun UI (in [http://localhost:8060](http://localhost:8060)). The Jupyter environment is pre-configured to work with the local MLRun and Nuclio services. You can switch to a remote or managed MLRun cluster by editing the `mlrun.env` file in the Jupyter files tree. The artifacts and DB are stored under **/home/jovyan/data** (`/data` in Jupyter tree).