For Macs, maxOS >= 13.6.6 is required. Apply Silicon devices (M1) must use Rosetta terminal. You can verify, your terminals architecture by running arch. It should print i386 for Rosetta terminal.

Install via pip:

pip install magemaker

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Cloud Account Setup

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AWS Configuration

  • Follow this detailed guide for setting up AWS credentials: AWS Setup Guide

Once you have your AWS credentials, you can configure Magemaker by running:

magemaker --cloud aws

It will prompt you for aws credentials and set up the necessary configurations.

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GCP (Vertex AI) Configuration

  • Follow this detailed guide for setting up GCP credentials: GCP Setup Guide

once you have your GCP credentials, you can configure Magemaker by running:

magemaker --cloud gcp

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Azure Configuration

  • Follow this detailed guide for setting up Azure credentials: GCP Setup Guide

Once you have your Azure credentials, you can configure Magemaker by running:

magemaker --cloud azure

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All three cloud providers

If you have configured all three cloud providers, you can verify your configuration by running:

magemaker --cloud all

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Required Config File

By default, Magemaker will look for a .env file in your project root with the following variables based on which cloud provider(s) you plan to use:

# AWS Configuration
AWS_ACCESS_KEY_ID="your-access-key"      # Required for AWS
AWS_SECRET_ACCESS_KEY="your-secret-key"   # Required for AWS
SAGEMAKER_ROLE="arn:aws:iam::..."        # Required for AWS

# GCP Configuration
PROJECT_ID="your-project-id"             # Required for GCP
GCLOUD_REGION="us-central1"              # Required for GCP

# Azure Configuration
AZURE_SUBSCRIPTION_ID="your-sub-id"      # Required for Azure
AZURE_RESOURCE_GROUP="ml-resources"      # Required for Azure
AZURE_WORKSPACE_NAME="ml-workspace"      # Required for Azure
AZURE_REGION="eastus"                    # Required for Azure

# Optional configurations
HUGGING_FACE_HUB_KEY="your-hf-token"    # Required for gated HF models like llama
Never commit your .env file to version control!

For gated models like llama-3.1 from Meta, you might have to accept terms of use for model on hugging face and adding Hugging face token to the environment are necessary for deployment to go through.

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Best Practices

  1. Resource Management

    • Monitor quota limits
    • Clean up unused resources
    • Set up cost alerts

  2. Environment Management

    • Use separate configurations for dev/prod
    • Regularly rotate access keys
    • Use environment-specific roles

  3. Security

    • Follow principle of least privilege
    • Use service accounts where possible
    • Enable audit logging

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Troubleshooting

Common configuration issues:

  1. AWS Issues

    • Check IAM role permissions
    • Verify SageMaker quota
    • Confirm region settings

  2. GCP Issues

    • Verify service account permissions
    • Check Vertex AI API enablement
    • Confirm project ID

  3. Azure Issues

    • Check resource provider registration status: 
      az provider show -n Microsoft.MachineLearningServices
az provider show -n Microsoft.ContainerRegistry
az provider show -n Microsoft.KeyVault
az provider show -n Microsoft.Storage
az provider show -n Microsoft.Insights
az provider show -n Microsoft.ContainerService
az provider show -n Microsoft.PolicyInsights
az provider show -n Microsoft.Cdn
    • Verify workspace access
    • Confirm subscription status
    • Ensure all required providers are registered
AboutQuick Start