diff --git a/concepts/deployment.mdx b/concepts/deployment.mdx index 66ca7a9..00e9a28 100644 --- a/concepts/deployment.mdx +++ b/concepts/deployment.mdx @@ -202,10 +202,9 @@ Choose your instance type based on your model's requirements: 4. Set up monitoring and alerting for your endpoints -Make sure you setup budget monitory and alerts to avoid unexpected charges. +Make sure you set up budget monitoring and alerts to avoid unexpected charges. - ## Troubleshooting Deployments Common issues and their solutions: @@ -225,4 +224,44 @@ Common issues and their solutions: - Verify model ID and version - Check instance memory requirements - Validate Hugging Face token if required - - Endpoing deployed but deployment failed. Check the logs, and do report this to us if you see this issue. + - Endpoint deployed but deployment failed. Check the logs, and do report this to us if you see this issue. + +--- + +### Additional Notes on Command-Line Options + +The Magemaker CLI supports both interactive and YAML-based deployments. Here’s a breakdown of relevant options: + +#### Cloud selection +```sh +magemaker --cloud [aws|gcp|azure|all] +``` +- Launches the interactive deployment flow, walking you through options and configuration for your selected cloud. + +#### YAML-based deployment +```sh +magemaker --deploy path/to/deployment.yaml +``` +- Deploys using the specified YAML file. The `destination` field in the YAML chooses the cloud provider. + +#### Advanced options + +| Flag | Description | +|--------------|------------------------------------------------| +| `--hf` | Deploy a Hugging Face Model (and select instance interactively) | +| `--instance` | Specify cloud instance type (overrides YAML if interactive) | +| `--deploy` | Path to deployment YAML file | +| `--train` | Path to training YAML file | +| `--cloud` | Specify provider for interactive mode | +| `-v` / `--verbose` | Increase output verbosity | +| `--version` | Show Magemaker version | + +> **Note:** If you specify both `--cloud` and `--deploy`, Magemaker will use the `destination` from the YAML and issue a warning if both are provided. + + +If you run Magemaker with no arguments, you must specify a cloud provider using `--cloud`. Examples: +- `magemaker --cloud gcp` +- `magemaker --cloud aws` +- `magemaker --cloud azure` +- `magemaker --cloud all` + \ No newline at end of file diff --git a/concepts/models.mdx b/concepts/models.mdx index 0161380..a1a5eb6 100644 --- a/concepts/models.mdx +++ b/concepts/models.mdx @@ -43,6 +43,7 @@ We plan to add support for the following model sources: Models from Azure ML Model Catalog and Azure OpenAI + ## Model Requirements ### Instance Type Recommendations by Cloud Provider @@ -131,22 +132,20 @@ deployment: !Deployment The model ids for Azure are different from AWS and GCP. Make sure to use the one provided by Azure in the Azure Model Catalog. - To find the relevnt model id, follow the following steps + To find the relevant model id, follow these steps: - - Find the workpsace in the Azure portal and click on the studio url provided. Click on the `Model Catalog` on the left side bar + + Find the workspace in the Azure portal and click on the studio URL provided. Click on the `Model Catalog` on the left sidebar. ![Azure ML Creation](../Images/workspace-studio.png) - - Select Hugging-Face from the collections list. The id of the model card is the id you need to use in the yaml file + + Select Hugging-Face from the collections list. The id of the model card is the id you need to use in the yaml file. ![Azure ML Creation](../Images/hugging-face.png) - - ## Model Configuration ### Basic Parameters @@ -181,7 +180,7 @@ models: - Consider data residency requirements - Test latency from different regions -3. **Cost Management** +2. **Cost Management** - Compare instance pricing - Make sure you set up the relevant alerting @@ -202,4 +201,4 @@ Common model-related issues: 3. **Authentication Issues** - Verify cloud credentials - Check model access permissions - - Validate API keys \ No newline at end of file + - Validate API keys diff --git a/quick-start.mdx b/quick-start.mdx index 5853ef8..7ebc9f7 100644 --- a/quick-start.mdx +++ b/quick-start.mdx @@ -128,7 +128,6 @@ models: - ### Model Fine-tuning Fine-tune models using the `train` command: @@ -150,7 +149,8 @@ training: !Training per_device_train_batch_size: 32 learning_rate: 2e-5 ``` -{/* + + Remember to deactivate unused endpoints to avoid unnecessary charges! @@ -184,3 +185,32 @@ You can reach us, faizan & jneid, at [support@slashml.com](mailto:support@slashm If anything doesn't make sense or you have suggestions, do point them out at [magemaker.featurebase.app](https://magemaker.featurebase.app/). We'd love to hear from you! We're excited to learn how we can make this more valuable for the community and welcome any and all feedback and suggestions. + +--- + +### v0.1.2+ CLI Update + +**New:** You can now specify `--cloud all` to configure AWS, GCP, and Azure interactively, or use YAML with a `deployment.destination` field for explicit cloud targeting. + +#### Cloud CLI flags + +- `--cloud aws` — configure for AWS SageMaker +- `--cloud gcp` — configure for Google Cloud Vertex AI +- `--cloud azure` — configure for Azure ML +- `--cloud all` — configure all three providers at once + +If you do not specify `--cloud`, the CLI will require you to choose one, and prints a help message. + +If you provide a YAML deployment file with `--deploy`, the CLI uses the `deployment.destination` field to determine which provider to target. + +**Legacy:** Previous CLI usage with e.g. `magemaker --cloud=gcp` is still supported, but the preferred usage is now: + +```sh +magemaker --cloud gcp +``` +or +```sh +magemaker --deploy .magemaker_config/my-deploy.yaml +``` + +If you run just `magemaker` with no arguments, you’ll now see a colored error indicating you must specify a cloud provider, with suggested commands. \ No newline at end of file