Distributed training refers to the process of training a machine learning model across multiple computing resources, such as multiple machines or multiple cores on a single machine. This approach allows for faster training times and can lead to better performance for complex models.
Key Benefits
- Scalability: Distributed training can scale to handle larger datasets and more complex models.
- Performance: By leveraging more computing resources, distributed training can reduce training time.
- Flexibility: It allows for training on different hardware and software configurations.
Common Techniques
- Parameter Server: This technique uses a central server to store model parameters and distribute them to workers.
- All-reduce: This technique combines the gradients from all workers into a single gradient and then averages them.
Challenges
- Communication Overhead: Communicating between workers can introduce significant overhead.
- Synchronization: Ensuring that all workers are in sync can be challenging.
Learn More
For a more in-depth look at distributed training, check out our Distributed Training Guide.
Additional Resources
Distributed Training Architecture