Please enable JavaScript.

Coggle requires JavaScript to display documents.

Amazon SageMaker workflow (Deploy (RealTime, Batch Processing, Neo, Model…

- - - - A lifecycle configuration provides shell scripts that run only when you create the notebook instance or whenever you start one. When you create a notebook instance, you can create a new lifecycle configuration and the scripts it uses or apply one that you already have.
      - Scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started. To help decrease the run time of scripts, try the following: Cut down on necessary steps. For example, limit which conda environments in which to install large packages. Run tasks in parallel processes. Use the nohup command in your script.
      - Lifecycle configurations run as the root user. If your script makes any changes within the /home/ec2-user/SageMaker directory, (for example, installing a package with pip), use the command sudo -u ec2-user to run as the ec2-user user. This is the same user that Amazon SageMaker runs as.
      - Amazon SageMaker notebook instances use conda environments to implement different kernels for Jupyter notebooks. If you want to install packages that are available to one or more notebook kernels, enclose the commands to install the packages with conda environment commands that activate the conda environment that contains the kernel where you want to install the packages.
    - - Kernel updates
        Security patches
        AWS SDK updates
        Amazon SageMaker Python SDK updates
        Open source software updates
- - - - When you start the configuration steps, please note that the warm start and early stopping features are not available with multi-algorithm HPO. If you want to use these features, you can only tune a single algorithm at a time.
- - - - Protecting Data in-transit/at-rest using aws-kms
        
        You can choose to encrypt internode training communications. Enabling inter-container traffic encryption can increase training time, especially if you are using distributed deep learning algorithms. For affected algorithms, adding this additional level of security also increases cost. The training time for most Amazon SageMaker built-in algorithms, such as XGBoost, DeepAR, and linear learner, typically aren't affected.
        
        Amazon SageMaker does not support resource-based policies
        
        You can attach tags to Amazon SageMaker resources or pass tags in a request to Amazon SageMaker. To control access based on tags, you provide tag information in the condition element of a policy using the sagemaker:ResourceTag/key-name, aws:RequestTag/key-name, or aws:TagKeys condition keys.
    - - Network isolation is not supported by the following managed Amazon SageMaker containers as they require access to Amazon S3:
        Chainer
        PyTorch
        Scikit-learn
        Amazon SageMaker Reinforcement Learning
      - The Amazon SageMaker API and Runtime support Amazon Virtual Private Cloud (Amazon VPC) interface endpoints that are powered by AWS PrivateLink. Each VPC endpoint is represented by one or more Elastic Network Interfaces (ENIs) with private IP addresses in your VPC subnets. The VPC interface endpoint connects your VPC directly to the Amazon SageMaker API or Runtime without an internet gateway, NAT device, VPN connection, or AWS Direct Connect connection. The instances in your VPC don't need public IP addresses to communicate with the Amazon SageMaker API or Runtime.
        
        If your processing job needs access to an AWS service that supports interface VPC endpoints, create an endpoint to connect to that service. For a list of services that support interface endpoints, see VPC Endpoints in the Amazon VPC User Guide. For information about creating an interface VPC endpoint, see Interface VPC Endpoints (AWS PrivateLink) in the Amazon VPC User Guide. If your processing job needs access to an AWS service that doesn't support interface VPC endpoints or to a resource outside of AWS, create a NAT gateway and configure your security groups to allow outbound connections. For information about setting up a NAT gateway for your VPC, see Scenario 2: VPC with Public and Private Subnets (NAT) in the Amazon Virtual Private Cloud User Guide.