What are some effective scaling techniques for deep learning?

What are some effective scaling techniques for deep learning?

Some effective scaling techniques include data parallelism, model parallelism, and distributed training.

Can you explain data parallelism in more detail?

Data parallelism involves splitting the dataset into smaller batches and processing them across multiple GPUs or machines.

What about model parallelism? How does that work?

Model parallelism divides the model itself across different devices, allowing larger models to be trained that wouldn't fit in memory on a single device.

Are there any tools or frameworks that support these techniques?

Yes, frameworks like TensorFlow, PyTorch, and Horovod provide built-in support for both data and model parallelism.