Lepton AI: Cloud-native AI platform offering free GPU-limited rate AI model deployment

General Introduction

Lepton AI is a leading cloud-native AI platform dedicated to providing developers and enterprises with efficient, reliable and easy-to-use AI solutions. Through its powerful computing capabilities and user-friendly interface, Lepton AI helps users achieve rapid landing and scaling in complex AI projects.

Function List

• Efficient computing: Provides high-performance computing resources to support training and inference of large-scale AI models.
• Cloud Native Experience: Seamlessly integrates with cloud computing technology to simplify the process of developing and deploying AI applications.
• GPU Infrastructure: Provides top-notch GPU hardware support to ensure efficient execution of AI tasks.
• Rapid deployment: Supports native Python development for rapid deployment of models without having to learn containers or Kubernetes.
• Flexible API: Provides a simple and flexible API that facilitates the calling of AI models in any application.
• Horizontal expansion: Supports horizontal scaling to handle large-scale workloads.

Using Help

Installation and use

1. Register for an accountVisit the Lepton AI website, click on the "Register" button, and fill in the relevant information to complete the registration.
2. Create a project: After logging in, go to "Control Panel", click "Create Project", fill in the name and description of the project.
3. Selecting Computing Resources: In Project Settings, select the required computing resources, including GPU type and number.
4. Upload model: In "Model Management", click "Upload Model" and select the local model file for uploading.
5. Configuration environment: In "Environment Configuration", select the required runtime environment and dependency packages.
6. Deployment modelsClick "Deploy", the system will automatically deploy the model and generate the API interface.
7. invoke an API: In "API Documentation", view the generated API interface documentation and use the provided API call model for reasoning.

workflow

1. model training: Train the model locally using Python to ensure that the model performs as expected.
2. model testing: Perform model testing locally to verify the accuracy and stability of the model.
3. Model Upload: Upload the trained model to the Lepton AI platform for online deployment.
4. Environment Configuration: Configure the runtime environment and dependency packages according to the model requirements to ensure that the model runs properly.
5. API call: Use the generated API interface to call the model for inference in the application and get the results in real time.
6. Monitoring and Maintenance: On the "Monitor" page, you can view the model's running status and performance indicators for timely maintenance and optimization.

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