Training a model using Neptune ML

After you have processed the data that you exported from Neptune for model training, you can start a model-training job using a curl (or awscurl) command like the following:

curl \
  -X POST https://(your Neptune endpoint)/ml/modeltraining
  -H 'Content-Type: application/json' \
  -d '{
        "id" : "(a unique model-training job ID)",
        "dataProcessingJobId" : "(the data-processing job-id of a completed job)",
        "trainModelS3Location" : "s3://(your Amazon S3 bucket)/neptune-model-graph-autotrainer"
      }'

The details of how to use this command are explained in The modeltraining command, along with information about how to get the status of a running job, how to stop a running job, and how to list all running jobs.

You can also supply a previousModelTrainingJobId to use information from a completed Neptune ML model training job to accelerate the hyperparameter search in a new training job. This is useful during model retraining on new graph data, as well as incremental training on the same graph data. Use a command like this one:

curl \
  -X POST https://(your Neptune endpoint)/ml/modeltraining
  -H 'Content-Type: application/json' \
  -d '{
        "id" : "(a unique model-training job ID)",
        "dataProcessingJobId" : "(the data-processing job-id of a completed job)",
        "trainModelS3Location" : "s3://(your Amazon S3 bucket)/neptune-model-graph-autotrainer"
        "previousModelTrainingJobId" : "(the model-training job-id of a completed job)"
      }'

You can train your own model implementation on the Neptune ML training infrastructure by supplying a customModelTrainingParameters object, like this:

curl \
  -X POST https://(your Neptune endpoint)/ml/modeltraining
  -H 'Content-Type: application/json' \
  -d '{
        "id" : "(a unique model-training job ID)",
        "dataProcessingJobId" : "(the data-processing job-id of a completed job)",
        "trainModelS3Location" : "s3://(your Amazon S3 bucket)/neptune-model-graph-autotrainer"
        "modelName": "custom",
        "customModelTrainingParameters" : {
          "sourceS3DirectoryPath": "s3://(your Amazon S3 bucket)/(path to your Python module)",
          "trainingEntryPointScript": "(your training script entry-point name in the Python module)",
          "transformEntryPointScript": "(your transform script entry-point name in the Python module)"
        }
      }'

See The modeltraining command for more information, such as about how to get the status of a running job, how to stop a running job, and how to list all running jobs. See Custom models in Neptune ML for information about how to implement and use a custom model.