Training a model using Neptune ML - Amazon Neptune
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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.