Setup for Docker
Install Docker
If you haven’t done that already, install Docker on your machine following the instructions. To make sure Docker is successfully installed on your machine, run a test container as follows:
docker run hello-world
You should see the Hello from Docker! message. Otherwise, check the
Docker’s Get Started documentation.
Docker for Mac users - RAM allocation issues
By default, Docker for Mac allocates 2 GB of RAM, which is insufficient for deploying MindsDB with Docker. We recommend increasing the default RAM limit to 8 GB. Please refer to the Docker Desktop for Mac users manual for more information on how to increase the allocated memory.
Install & Start MindsDB
Please note that this method of MindsDB installation requires a minimum of 8 GB RAM and 10 GB free storage.
Run the command below to start MindsDB in Docker.
docker run -p 47334:47334 -p 47335:47335 mindsdb/mindsdb
If you wish to simply spin up the container without the logs, run the following command:
docker run -d -p 47334:47334 -p 47335:47335 mindsdb/mindsdb
What’s the difference?
- On running the previous command, docker will print out all the mindsdb logs in your shell.
- Using Docker’s
detachflag, the CLI will simply return the container ID, such that you can use the same shell again for different things.
If you would like to persist your models & configurations in the host machine, run the following commands:
mkdir mdb_data
docker run -p 47334:47334 -p 47335:47335 -v $(pwd)/mdb_data:/root/mdb_storage mindsdb/mindsdb
Let’s analyze this command part by part:
docker runis a native Docker command used to start a container-p 47334:47334publishes port 47334 to access MindsDB GUI and HTTP API-p 47335:47335publishes port 47335 to access MindsDB MySQL API-v $(pwd)/mdb_data:/root/mdb_storagemaps the newly created foldermdb_dataon the host machine to the/root/mdb_storageinside the containermindsdb/mindsdbis the container we want to start
Now you can access the MindsDB editor by going to 127.0.0.1:47334 in your browser.
Configuration Options
Default Configuration
The default configuration for MindsDB’s Docker image is stored as a JSON code, as below.
{
"config_version": "1.4",
"storage_dir": "/root/mdb_storage",
"log": {
"level": {
"console": "ERROR",
"file": "WARNING",
"db": "WARNING"
}
},
"debug": false,
"integrations": {},
"auth": {
"username": "mindsdb",
"password": "123"
},
"api": {
"http": {
"host": "127.0.0.1",
"port": "47334"
},
"mysql": {
"host": "127.0.0.1",
"port": "47335",
"database": "mindsdb",
"ssl": true
},
"mongodb": {
"host": "127.0.0.1",
"port": "47336",
"database": "mindsdb"
}
}
}
Custom Configuration
To override the default configuration, you can provide values via the MDB_CONFIG_CONTENT environment variable, as below.
docker run -e MDB_CONFIG_CONTENT='{"api":{"http": {"host": "0.0.0.0","port": "8080"}}}' mindsdb/mindsdb
Known Issues
#1
If you experience any issues related to MKL or your training process does not complete, please add the MKL_SERVICE_FORCE_INTEL environment variable, as below.
docker run -e MKL_SERVICE_FORCE_INTEL=1 -p 47334:47334 -p 47335:47335 mindsdb/mindsdb
What’s Next
Now that you installed and started MindsDB locally in your Docker container, go ahead and find out how to create and train a model using the CREATE MODEL statement. In the MindsDB SQL section, you’ll find a comprehensive overview of the SQL syntax offered by MindsDB. We also provide No-SQL syntax documented in the MindsDB No-SQL section.
You can connect MindsDB to different clients, including PostgreSQL CLI and MySQL CLI.
Check out the Use Cases section to follow tutorials that cover Large Language Models, Natural Language Processing, Time Series, Classification, and Regression models.