Technology
How to Train, Deploy & Scale AI Models with Lightning AI | Full Tutorial with Studio + AI Hub
Added by: Sree Sankaran
What You'll Learn
- How to use Lightning Studios for developing and training AI models with GPU acceleration.
- How to deploy AI models using AI Hub, including serverless deployments and autoscaling.
- How to publish and share your deployed models via APIs for internal or external use.
Video Breakdown
This video provides a comprehensive tutorial on using Lightning AI's platform, covering both Lightning Studios for model development and AI Hub for model deployment. It demonstrates how to train, deploy, and scale AI models, including using GPUs, running jobs, and publishing models via APIs.
Key Topics
Lightning AI Platform
AI Hub Deployment
Lightning Studios IDE
GPU Model Training
Serverless Deployments
Model Publishing
Video Index
Introduction to Lightning AI Platform
This module introduces the Lightning AI platform, focusing on AI Hub for model deployment and Lightn...
This module introduces the Lightning AI platform, focusing on AI Hub for model deployment and Lightning Studios for model development. It outlines the workflow from development to deployment and publishing.
Overview of AI Hub
0:34 - 1:02
This chapter provides a quick view of the AI Hub, showcasing API templates for deploying various models, including text embedding and Hugging Face models.
API Templates
Model Deployment
Hugging Face
Llama 3
Workflow from Studios to AI Hub
1:26 - 1:42
This chapter explains the process of developing models in Studios, deploying them, and publishing them via APIs for internal and external use.
Development
Deployment
API Publishing
Internal/External Use
Developing and Training Models in Lightning Studios
This module covers the use of Lightning Studios for model development, including switching between C...
This module covers the use of Lightning Studios for model development, including switching between CPU and GPU instances, running jobs, and using the Lightning SDK for asynchronous tasks.
Studio Environment and GPU Switching
1:42 - 2:51
This chapter explains the persistent cloud environment of Studios and demonstrates how to switch from CPU to GPU instances for model training.
Cloud Environment
GPU Switching
Spot Instances
Interruptible Machines
Monitoring GPU Utilization
3:53 - 4:31
This chapter highlights the GPU utilization metrics available in Studios, which aid in hyperparameter tuning and memory management.
GPU Metrics
Hyperparameter Tuning
Memory Management
CPU Utilization
Running Jobs and Hyperparameter Sweeps
5:30 - 7:34
This chapter demonstrates how to run jobs in Lightning, both through the Jobs plugin and programmatically using the Lightning SDK, especially for hyperparameter sweeps.
Jobs Plugin
Lightning SDK
Hyperparameter Sweep
Asynchronous Jobs
Deploying and Publishing Models with AI Hub
This module focuses on deploying models using AI Hub, including packaging models in Docker container...
This module focuses on deploying models using AI Hub, including packaging models in Docker containers, serverless deployments, autoscaling, and publishing models via APIs.
Packaging and Serving Models
7:34 - 8:24
This chapter explains how to package models using LitServe or other serving libraries in Docker containers for deployment.
Litserve
Docker Containers
Model Serving
Open Source Libraries
Serverless Deployment and Autoscaling
8:36 - 9:37
This chapter demonstrates how to deploy models serverlessly with autoscaling parameters, including advanced features like authentication and health checks.
Serverless Deployment
Autoscaling
Authentication
Health Checks
Monitoring and Testing Deployments
9:38 - 10:41
This chapter covers monitoring deployment metrics like traffic, latency, and cold start time, as well as testing the deployment.
Deployment Monitoring
Traffic Metrics
Latency
Cold Start Time
Publishing to AI Hub
12:47 - 13:31
This chapter explains the process of publishing deployments to the AI Hub, making them available for internal or external developers.
AI Hub Publishing
Internal/External Access
Metadata
Deployment Parameters
Questions This Video Answers
What are Lightning Studios and how are they used?
Lightning Studios are cloud-based development environments for teams to prepare data, train, and fine-tune models on GPUs, functioning like a 'laptop in the cloud'.
What is AI Hub and how does it facilitate model deployment?
AI Hub is a platform for teams to deploy, share, and publish models via APIs, both internally and externally, streamlining the model serving process.
How can I deploy a model using a Docker container in Lightning AI?
You can package your model server in a Docker container and use Lightning AI's Jobs feature to deploy it, specifying the Docker image, port, and autoscaling parameters.
What is LitServe and how does it help in serving models?
LitServe is Lightning AI's open-source framework for serving models, allowing you to define a server in a few lines of code and expose your model via a port.
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