MosaicML Cloud Demo
The MosaicML Cloud makes it easy to train any size model on any number of GPUs. Achieve more accurate results faster and seamlessly scale your workloads with our distributed training methods. In this video, we show how to easily run and monitor ML training jobs, scale training across multiple GPUs and multiple nodes, and lastly speed up training with algorithmic and system efficiency methods.
MosaicML Cloud Delivers Leading NLP Performance in MLPerf v2.1
MosaicML leads the MLPerf NLP results, delivering a score of 7.9 minutes on 8x NVIDIA A100 GPUs in the Open Division, thanks to algorithmic and systems optimizations delivered through the MosaicML Cloud.
MosaicML Satisfies the Need for Speed with MLPerf Results
MosaicML’s Open Division submission to the MLPerf Image Classification benchmark delivers a score of 23.8 minutes (4.5x speed-up relative to our baseline) on 8x NVIDIA A100 GPUs. Our results show how algorithmic speedups written in PyTorch deliver ML innovation that truly can benefit everyone, from academic researchers to enterprise practitioners.
Train Faster & Cheaper on AWS with MosaicML Composer
Use Composer, our open-source training library, to reduce deep learning training time and cost on AWS. Composer makes it easy to use the latest and greatest training algorithms, composing them together to speed up training and improve model quality.
New in Composer 0.10: CometML integration, Auto evaluation batch size selection, Streaming dataset preview, and API improvements!
Mosaic LLMs (Part 1): Billion-Parameter GPT Training Made Easy
In Part 1 of this LLM blog post series, we use the MosaicML platform to train vanilla GPT-3 models up to 1.3B params, and show how to cut training times down to hours with strong multi-node scaling. We also discover that larger models can train more efficiently than smaller models on modern hardware, and that a 10x in parameter count may only result in ~5x the training time.
We have even more exciting things in the works. Get early access to our technology preview
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