- How to Create an S3 Bucket
- Review the Regions supported by Amazon SageMaker
- Review the Default Limits for Amazon SageMaker Service Limits
- Request a Limit Increase if Needed
- Review the SageMaker Instance Pricing
- Select an Instance Type
- Select “Create a New IAM Role”
- Select the S3 Bucket Created Above
- Select “Jupyter” or “JupyterLab” to Launch the Notebook
- Connect to a Public or Private GitHub or GitLab repo using these instructions.
cd ~/SageMaker
git clone https://github.com/data-science-on-aws/deep-fake-detection-challenge
- Sample TensorFlow Notebook using Distributed TensorFlow and SageMaker.
- Sample PyTorch Notebook using Distributed PyTorch and SageMaker.
- Sample MXNet Notebook using Distributed MXNet and SageMaker.
- To adapt a custom training script to SageMaker, please follow these instructions.