Floyd
Train, optimize, access libraries, and track ML models with real-time monitoring.
What is Floyd?
Floyd is a platform designed to help machine learning practitioners train, optimise, and manage models without managing infrastructure themselves. It provides GPU access, model tracking, and real-time monitoring capabilities in one place. The platform is aimed at data scientists, ML engineers, and teams who want to focus on model development rather than server setup and maintenance. Floyd handles the computational resources and logging, allowing you to experiment faster and keep organised records of your work.
Key Features
GPU access
Run training jobs on remote GPUs without setting up your own hardware
Model tracking
Log experiments, parameters, and results to compare model versions
Real-time monitoring
Watch training progress and resource usage as jobs run
Library access
Pre-installed ML frameworks and libraries ready to use
Job management
Queue, schedule, and organise multiple training runs
Integration
Connect with version control and notebook environments
Pros & Cons
Advantages
- Removes infrastructure setup overhead; start training immediately
- Built-in experiment tracking makes it easy to compare model performance across runs
- Pay only for compute used on the free tier, scaling up as needed
- Real-time monitoring helps you catch issues early in training
Limitations
- Limited to the platforms and frameworks Floyd supports; less flexibility than self-hosted solutions
- Costs can accumulate quickly with large-scale or long-running experiments
- Smaller community and fewer integrations compared to larger ML platforms
Use Cases
Training deep learning models on datasets too large for local machines
Running multiple hyperparameter experiments and comparing results systematically
Prototyping ML projects without investing in on-premises GPU hardware
Collaborating with team members on shared model development
Monitoring long-running training jobs remotely