streaming

Introduction to the Streaming Project

Overview

The Streaming project, developed by MosaicML, offers a revolutionary approach to training large machine learning models with streaming data directly from cloud storage. This tool, named StreamingDataset, is engineered to enhance speed, accuracy, and scalability in data-intensive tasks. By focusing on distributed training across multiple nodes, it assures performance and correctness, making it easy to train large models efficiently, regardless of data location.

Key Features

1. Universal Compatibility: StreamingDataset supports a wide range of data types, including images, text, video, and multimodal data. It integrates smoothly into workflows that use PyTorch IterableDataset, making it a versatile choice for existing projects.

2. Cloud Integration: Compatible with major cloud storage providers such as AWS, Google Cloud, and others, it serves as a bridge between cloud-stored datasets and local machine learning training environments.

3. Determinism: One of its standout features is true determinism—ensuring that data sampling is consistent regardless of the setup like the number of GPUs or nodes involved. This is particularly useful for debugging and reproducing training results.

4. High Throughput and Low Latency: The system is optimized for efficiency, offering high throughput with minimal delay in data access, even when the dataset resides in a remote storage location.

5. Dynamic Data Handling: Users can access any data sample at any time due to its ability to handle random access to the dataset, ensuring the data needed is always available without delays.

6. Cost-Effective and Scalable: With features like instant mid-epoch resumption and seamless data mixing, the solution reduces costs associated with data egress and GPU idle time, while supporting massive scalability.

Getting Started

Installation

Getting started with Streaming is straightforward. Install it via pip:

pip install mosaicml-streaming

Workflow

1. Data Preparation:

   • Convert your datasets into supported formats like MDS, CSV, or JSONL using tools provided by the StreamingDataset package.

2. Upload to Cloud:

   • Upload your datasets to your chosen cloud storage service; for instance, utilising AWS CLI for S3 uploads.

3. Dataset and DataLoader Setup:

   • Setup involves specifying the remote storage location in the StreamingDataset, creating a DataLoader that can manage seamless streaming of data as your model trains.

Advanced Features

• Seamless Data Mixing: Easily combine data from multiple datasets with the Stream function, managing sampling rates on-the-fly during training.

• Instant Resumption: Resume training instantly after interruptions, significantly cutting down on time and costs associated with restarts.

• Disk Usage Management: Implement disk usage limits easily to optimize space with features like least-recently-used shard deletion.

Use Cases and Applications

StreamingDataset has been employed in various breakthrough projects:

• BioMedLM for biomedicine language modeling.
• Mosaic Diffusion Models for training diffusion models cost-effectively.
• Research and innovations around large language models and other high-compute tasks.

Conclusion

Streamlining data access from the cloud to your training environment, StreamingDataset empowers researchers and developers to train large-scale models effectively and efficiently. Its advanced features offer a robust platform for tackling contemporary machine learning challenges, ensuring streamlined operations from data handling to model convergence.

For further exploration, additional resources like detailed documentation, getting started guides, and examples are available through MosaicML's streaming project portal. The adoption of this tool can significantly fine-tune the data handling aspect of model training setups, providing a competitive edge in machine learning research and applications.