Build AI Models on Any Cloud in Your Secure Environment
In this blog, we discuss how the architecture of the MosaicML platform enables you to easily train large-scale AI models on any cloud provider, while data remains secure on your own private network. Now, both startups and large enterprises can maintain maximum autonomy when training ML workloads.
Introducing Llama2-70B-Chat with MosaicML Inference
Llama2-70B-Chat is a leading AI model for text completion, comparable with ChatGPT in terms of quality. Today, organizations can leverage this state-of-the-art model through a simple API with enterprise-grade reliability, security, and performance by using MosaicML Inference and MLflow AI Gateway.
MosaicML Platform Demo
MosaicML 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.
Farewell, CUDA OOM: Automatic Gradient Accumulation
With automatic gradient accumulation, Composer lets users seamlessly change GPU types and number of GPUs without having to worry about batch size. CUDA out of memory errors are a thing of the past!
MosaicML 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 our platform.
MosaicML StreamingDataset: Fast, Accurate Streaming of Training Data from Cloud Storage
Loading your training data becomes an escalating challenge as datasets grow bigger in size and the number of nodes scales. We built StreamingDataset to make training on large datasets from cloud storage as fast, cheap, and scalable as possible. Specially designed for multi-node, distributed training, StreamingDataset maximizes correctness guarantees, performance, and ease of use.
MosaicML Platform: the Software Infrastructure for Generative AI
The MosaicML platform is designed to tackle the challenges of training large models such as ChatGPT, LaMDA, and Stable Diffusion. Our blog post breaks down the difficulties of training such models, and shows how our platform makes training large AI models easier.
End-to-End Secure Evaluation of Code Generation Models
With MosaicML, you can now evaluate LLMs and Code Generation Models on code generation tasks (such as HumanEval, with MBPP and APPS coming soon), with an effortless, end-to-end secure code evaluation framework. We handle all the requirements of sandboxing serverless compute, making code evaluation a seamless new feature in the MosaicML platform.
New in Composer 0.9: Export for inference APIs, ALiBi for efficient BERT training, TPU beta support, and more!
We’re announcing the 0.9 release of Composer, MosaicML’s open source library for training PyTorch neural networks faster, at lower cost, and to higher accuracy. Composer 0.9 is available as a Python package via Conda or pip, and the source code is on GitHub.
New in Composer 0.11: FSDP Support, Streaming v0.1 Release, Simplified Checkpointing and Distributed Experience
We’re announcing the 0.11 release of Composer, MosaicML’s open-source library for training PyTorch neural networks faster, cheaper, and to higher accuracy. With Composer, we stack and combine speed-up methods into recipes that optimize your training. Composer 0.11 is available as a Python package via pip, and the source code is on GitHub.
New in Composer 0.12: Mid-Epoch Resumption with MosaicML Streaming, CometML ImageVisualizer, HuggingFace Model and Tokenizer Loading, and more!
We’re announcing the 0.12 release of Composer, MosaicML’s open-source library that makes scalable, efficient neural network training easy. Composer 0.12 is available as a Python package via pip, and the source code is on GitHub.
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.
Supercharge Your Model Training with MosaicML Composer
MosaicML was founded in 2020 to address the challenges of growing AI complexity and cost. We want advanced AI to be accessible to a broad set of enterprises and organizations - so we we built Composer, an open-source library that speeds up neural network training.
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.
We have even more exciting things in the works. Get early access to our technology preview
By clicking Sign Up above, you consent to allow Mosaic ML, Inc. to store and process the personal information submitted above to provide you the content requested.