Cloudflare R2 and MosaicML: Train LLMs on Any Compute with Zero Switching Costs
Read the complete blog post to learn more!
Building generative AI models requires massive compute AND data storage infrastructure. Training huge datasets means that terabytes of data must be read in parallel by thousands of processes. In addition, model checkpoints need to be saved frequently throughout a training run, and these checkpoints alone can be hundreds of gigabytes in size.
In a recent blog post, Cloudflare and MosaicML engineers discuss how their tools work together to address these challenges. MosaicML’s open source StreamingDataset and Composer libraries let users easily stream in training data and read/write model checkpoints back to Cloudflare R2. And thanks to R2’s zero-egress pricing and MosaicML’s cloud-agnostic platform, users can start/stop/move/resize jobs in response to GPU availability and prices across compute providers, without paying any data transfer fees. By eliminating egress fees, R2’s storage is an exceptionally cost-effective complement to MosaicML training, providing maximum autonomy and control.
What’s a Rich Text element?
The rich text element allows you to create and format headings, paragraphs, blockquotes, images, and video all in one place instead of having to add and format them individually. Just double-click and easily create content.
Static and dynamic content editing
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How to customize formatting for each rich text
Headings, paragraphs, blockquotes, figures, images, and figure captions can all be styled after a class is added to the rich text element using the "When inside of" nested selector system.