Training Machine Learning models requires expensive hardware that is not affordable to anyone. An NVIDIA RTX4090 costs around $1600 and comes with bar

Building AI Applications in the Cloud

submited by

Style Pass

2024-07-27 13:00:03

Training Machine Learning models requires expensive hardware that is not affordable to anyone. An NVIDIA RTX4090 costs around $1600 and comes with barely enough memory to run Llama 7B on degraded precision. However, with cloud computing, this hardware can be rented at a fraction of the cost, for however long you need it, and be upgraded at the click of a button. And you can get started right away without installing hardware, drivers, or software. This guide will teach you how to get started building AI applications in the cloud.

First, you need to rent a GPU, which takes a few clicks on gpudeploy.com. Once you have launched your GPU instance, you will usually get access via SSH (Secure Shell), which allows you to run commands over an encrypted connection. Alternatively, gpudeploy enables you to develop and run Python programs on a Jupyter Notebook server, accessible from within your browser.

SSH uses key pairs for authentication. The private key stays on your computer, while the public key is stored on the server (you upload it during instance creation). Make sure to never share your private key.