MosaicML Platform Demo
In this video, we show you our mcli command-line tool for interfacing with our platform, and demo training a ResNet-50 model in a few phases:
- We run training on a single GPU.
- We scale up training to multiple GPUs within a single node.
- We use GPUs across multiple nodes - and show how we eliminate all of the complexity for our customers to make it simple and magical.
- Lastly, we show the power of our algorithmic optimizations, and how they are applied through MosaicML Cloud.
Training Orchestration Made Easy
When you submit a job to MosaicML's platform, here's what's going on under the hood:
- Pulling the container image in which training takes place, where all of the drivers and libraries are installed and pre-configured
- Setting up configured integrations, such as GitHub for cloning the exact version of the training code you want to run, and WandB/Comet/Tensorboard for experiment tracking
- Orchestrating the jobs: configuring parallelism and inter-node communication
- Streaming your data directly from remote data stores with no impact on training performance and no persistent local storage
All of this is done with cloud-native technologies that keep you in control of your data. Now that you’ve seen how easy it is, contact us to try it out yourself!
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
A rich text element can be used with static or dynamic content. For static content, just drop it into any page and begin editing. For dynamic content, add a rich text field to any collection and then connect a rich text element to that field in the settings panel. Voila!
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.