Using the GPUDeploy API for Programmatic Deployment
Last updated July 30, 2024
GPUDeploy's powerful API allows you to automate and programmatically control your model deployments, integrating them seamlessly into your existing workflows and systems. This programmatic approach offers greater flexibility, efficiency, and scalability compared to manual deployment methods. This guide explores how to use the GPUDeploy API for managing your model deployments.
API Access and Authentication
- Obtain API Credentials: Sign in to your GPUDeploy account and navigate to the "API Keys" section. Generate a new API key with appropriate permissions for your intended actions.
- API Documentation: Refer to the GPUDeploy API documentation for complete details about available endpoints, request parameters, and response formats.
- Authentication: Use your generated API key to authenticate your API requests. Typically, this involves including the API key in the request headers.
Common API Operations
Here are some common operations you can perform using the GPUDeploy API:
- Model Upload: Programmatically upload your trained machine learning model to GPUDeploy, either as a container image or a serialized file.
- Deployment Creation: Create new deployments based on your uploaded model, specifying the desired instance type, instance count, and resource allocation.
- Deployment Management: Start, stop, delete, and update your deployments, including scaling instances, adjusting resource allocation, and rolling back to previous deployments.
- Endpoint Retrieval: Obtain the unique endpoint URL for your deployed model, enabling programmatic access for inference tasks.
- Monitoring Data Retrieval: Retrieve performance metrics for your deployed models, such as latency, throughput, resource utilization, and error rates.
Example: Python with `requests` Library
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