Deploying Your First Model with GPUDeploy
Last updated July 30, 2024
Ready to experience the power of GPUDeploy? This guide takes you through the process of deploying your first machine learning model on the platform. We'll use a simple example to illustrate the steps involved, providing you with a hands-on experience that sets you on the path to efficient model deployment.
Deploying a Basic Model
Let's assume you have a trained machine learning model that you want to deploy for predictions. Here's a step-by-step breakdown of the process:
- 1. Prepare Your Model: Make sure your model is in a format compatible with GPUDeploy (e.g., PyTorch or TensorFlow). You might need to save it as a file or package it into a container. For this example, let's assume your model is a PyTorch model saved as a `.pt` file.
- 2. Log In to GPUDeploy: Access your GPUDeploy account using your credentials.
- 3. Upload Your Model: Navigate to the "Models" section of the dashboard. Click on "Upload Model" and select the `.pt` file containing your PyTorch model.
- 4. Configure Deployment: After uploading, you'll be redirected to the model details page. Here, configure the deployment settings:
- Instance Type: Choose the appropriate instance type based on your model's requirements (e.g., CPU, GPU).
- Instance Count: Specify the number of instances you want to deploy.
- Resource Allocation: Configure memory and disk space for each instance according to your model's needs.
- 5. Create Endpoint: After configuring the settings, click "Deploy." GPUDeploy will create a unique endpoint for your model, making it accessible for predictions. The endpoint URL will be available on the dashboard.
- 6. Test the Endpoint: Once the deployment is complete, you can test the endpoint by using the provided URL to send requests and receive predictions from your deployed model.
This example demonstrates the basic workflow for deploying your first model. GPUDeploy makes the process straightforward, allowing you to focus on building and improving your models while it handles the deployment complexities.
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