SageMaker does not update these libraries when you stop and restart the notebook instance, so you can ensure that your custom environment has specific versions of libraries that you want. The on-start script installs any custom environments that you create as Jupyter kernels, so that they appear in the dropdown list in the Jupyter New menu.
Importing Local Python Modules from Jupyter Notebooks§. If you re-use local modules a lot, you should consider turning them into proper Python packages ...
26.08.2020 · Transforming the Training Data. After you have launched a notebook, you need the following libraries to be imported, we’re taking the example of XGboost here:. import sagemaker import boto3 from sagemaker.predictor import csv_serializer # Converts strings for HTTP POST requests on inference import numpy as np # For performing matrix operations and numerical …
28.09.2020 · Import Packages. Import the necessary packages and specify the role. The key difference here is to specify the arn of the role directly instead of get_execution_role(). Since you are running this from your local machine using your AWS credentials as opposed to a notebook instance with an attached role, get_execution_role() will not work.
17.10.2012 · When you import data from Athena or Amazon Redshift, the imported data is automatically stored in the default SageMaker S3 bucket for the AWS Region in which you are using Studio. Additionally, Athena stores data you preview in Data Wrangler in this bucket.
Sometimes a nice trick to load modules is to modify your sys.path to include a directory you want to import modules from. Unfortunately that doesn't work here because if you appended /home/ec2-user/Sagemaker to the path, firstly that path won't exist on HDFS, and secondly the pyspark context can't search the path on your notebook's EC2 host.
The different Jupyter kernels in Amazon SageMaker notebook instances are separate conda environments. · Install custom environments and kernels on the notebook ...