Introduction to FedML: A Unified and Scalable Machine Learning Library
FedML is an innovative open-source library that provides comprehensive support for machine learning (ML) tasks, enabling seamless training and deployment at any scale, anywhere. Whether you're operating on decentralized GPUs, multi-cloud environments, edge servers, or even smartphones, FedML ensures that developers can easily, economically, and securely manage complex model training and deployment.
The Backbone: TensorOpera AI
FedML operates in conjunction with TensorOpera AI, a next-generation cloud service designed for large language models (LLMs) and Generative AI. TensorOpera AI integrates three key AI infrastructure layers:
- User-Friendly MLOps Tools: Facilitating model operations.
- Efficient Scheduling Systems: Managing resources effectively.
- High-Performance ML Libraries: Accelerating AI job execution across various GPU clouds.
Simplified AI Job Management
TensorOpera AI transforms how developers engage with AI job management. It offers a simplified workflow where developers can run pre-built jobs using the TensorOpera® Launch platform. This service intelligently pairs AI jobs with economical GPU resources, automating job provisioning and execution. This approach removes the complexities of environment setup and management.
Key Features of TensorOpera AI Layers
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MLOps Layer:
- TensorOpera® Studio: Houses popular open-source foundational models. Developers can fine-tune these models with their specific data and deploy them efficiently using GPU resources.
- TensorOpera® Job Store: Contains pre-built jobs for training, deployment, and federated learning, which can be customized using developer-specific datasets and models.
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Scheduler Layer:
- TensorOpera® Launch: Automates the pairing and running of AI jobs with suitable GPU resources. It's ideal for handling intensive tasks like large-scale training and serverless deployments.
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Compute Layer:
- TensorOpera® Deploy: Focused on model serving with high scalability and low latency.
- TensorOpera® Train: Specializes in distributed training of large models, supporting significant computational needs.
- TensorOpera® Federate: Offers platforms for federated learning, allowing for on-device training on smartphones and cross-cloud GPU servers.
Community and Contribution
FedML thrives on community contributions, welcoming input that enhances its open-source framework. It embraces a culture of open collaboration and has adopted a contributor covenant to ensure a positive working environment for all involved. The dedication of its contributors is integral to its ongoing success and innovation.
Visit FedML's GitHub page to learn more about contribution opportunities and join a vibrant community of developers pushing the frontier of machine learning innovation.