What are the best practices for model parallelism in AI?

What are the best practices for model parallelism in AI?

Model parallelism involves distributing a model across multiple devices. Best practices include careful partitioning of the model, ensuring efficient communication between devices, and optimizing data transfer.

Can you explain how to partition a model effectively?

Effective partitioning involves analyzing the model's architecture and identifying layers that can be split across devices. Aim for balanced workloads to minimize idle time.

What tools can help with implementing model parallelism?

Tools like TensorFlow, PyTorch, and Horovod provide built-in support for model parallelism, making it easier to implement.

Are there any common pitfalls to avoid?

Yes, common pitfalls include uneven load distribution, excessive communication overhead, and not considering the latency of data transfer.

How do I measure the performance of a model parallelism setup?

You can measure performance by tracking metrics like training time, throughput, and resource utilization across devices.