Train and host MindsDB models
The following section explaines how to train and host models using Amazon SageMaker console.
Create Train Job
Follow the steps below to successfully start a train job and use MindsDB to create the models: 1. Open the Amazon SageMaker console at https://console.aws.amazon.com/sagemaker/.
- From the left panel choose Create Training Job and provide the following information
- Job name
- IAM role - it’s best if you provide AmazonSageMakeFullAccess IAM policy
Algorithm source - Your own algorithm container in ECR
Provide container ECR path
- Container - the ECR registry Image URI that we have pushed
Input mode - File
Resource configuration - leave the default instance type and count
Hyperparameters - MindsDB requires to_predict column name, so it knows which column we want to predict, e.g.
- Key - to_predict
Value - Class(the column in diabetes dataset)
Input data configuration
- Channel name - training
- Data source - s3
- S3 location - path to the s3 bucket where the dataset is located
7.Output data configuration - path to the s3 where the models will be saved
Create model and add the required settings: 1. Model name - must be unique
IAM role - it’s best if you provide AmazonSageMakeFullAccess IAM policy
Container input options
- Provide model artifacts and inference image location
- Use a single model
- Location of the inference code - 846763053924.dkr.ecr.us-east-1.amazonaws.com/mindsdb_impl:latest
- Location of model artifacts - path to model.tar.gz inside s3 bucket.
In the endpoint configuration, add the models to deploy, and the hardware requirements:
- Endpoint configuration name.
- Add model - select the previously created model.
- Choose Create endpoint configuration.
The last step is to create endpoint and provide endpoint configuration that specify which models to deploy and the requirements:
- Endpoint name.
- Attach endpoint configuration - select the previously created endpoint configuration.
- Choose Create endpoint.
After finishing the above steps, SageMaker will create a new instance and start the inference code.