Amorphic platform also provides a way to host Jupyter/IPython notebook.
You can set up or create a new notebook instance and use your IPython notebook to perform model training. You can call Python Sagemaker SDK to create a training job. Once a training job is created, you can use the S3 model location information to create a model in the Amorphic portal.
For accessing the Datasets inside the IPython notebooks, you can check the Dataset details for the S3 location information. For example, the exhbhit above shows the dataset details with the respective Dataset S3 location.
For the purpose of creating a Sagemaker model in the Notebook, the user can use the ml-temp bucket. Amorphic Notebookks have write access to ml-temp bucket (For example - s3://cdap-us-west-2-484084523624-develop-ml-temp). Please note that this S3 bucket is almost same as Dataset S3 path except for the “ml-temp” in the end. This “ml-temp” bucket can be used to create a training job and upload a model tar file. This model file location can then be used to create a model using “Artifact Location” of Amorphic model (see model creation section).
You can use the S3 location mentioned here to read the files related to training dataset and save the output sagemaker model tar file for Amorphic model object creation purposes.