Let's code on cloud GPUs with VSCode and Jupyter notebooks
In this video I show how to connect VSCode to cloud GPUs for remote development.
This is an extremely simple, and free way to set up a remote development environment that is persistent and scalable. I also show how to run forks of the environment (as jobs) on their own machines to trivially parallelize workloads.
The Studio offers at least 3 ways of coding, 1) connect your local VSCode, 2) Use the native web-based VSCode on the Studio or 3) Run Jupyter notebooks on the browser.
Studio is a much more powerful alternative to Colab that is production grade and highly scalable.
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Chapters (23)
Introduction
0:25
Start a Studio
0:38
VSCode on a cloud CPU machine
0:55
Jupyter notebooks on a cloud CPU machine
1:40
Connect your local VSCode to the cloud machine
2:58
SSH and terminal access
3:05
Install python and system packages
3:40
Explain the persistent cloud environment
4:10
Example 1: Running a Python script for training a model
4:48
The optimal development workflow for GPUs
5:02
Run on a GPU (without code changes)
5:30
Start another Studio for a clean environment (studio.lightning.ai)
6:50
Python script automatically uses the GPU
7:50
Run a hyperparameter sweep from the Studio
11:20
Making code changes from local VSCode to remote server
14:00
Launch async Jobs from the Studio
15:20
Add 4 GPUs to the Studio
16:13
Monitor and interface with the Jobs
17:08
How to speed up model training by scaling to more GPUs
18:23
Profile GPU utilization
19:12
Start Tensorboard to compare models training
20:15
Switch back to CPU to debug
21:15
Summary
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