How To Train an AI Model
- By Paul Mah
- October 08, 2024
How do you train an AI model using 10,000 Nvidia H100 GPUs? It’s simple, just follow these three steps.
While this knowledge is common among engineers working on large-scale training, it isn’t widely known among other technologists. So Meta AI expert Soumith Chintala wrote a blog to help the rest of us understand the magic of training generative AI.
Parallelize things first
The first step is to fit as large a network and as large a batch-size as possible onto the 10,000 H100s using memory-saving tricks and parallelizing jobs.
For AI model efficiency, Chintala suggests techniques like checkpointing to optimize memory usage by selectively saving or discarding data during training. This enables larger batch sizes and more efficient multi-GPU utilization for enhanced AI performance.
The next step is to communicate state between the GPUs as quickly as possible. This will require network switches with a lot of expensive, high-speed HBM memory for adequate performance so data packets don’t get dropped due to delays.
Amazingly, he described the use of RDMA to bypass the CPU for performance, and the use of sophisticated network libraries to properly map the network and efficiently transmit data over the network.
According to Chintala, adjusting the packet routing algorithm in the network switches and network adapters in the servers is also necessary to adequately load-balance the data flow.
Mind the failures
Finally, with 10,000 GPUs working together, expect frequent failures. And this is where things get tricky, as these can happen in hardware or software and must be detected as quickly as possible. This is “quite hard” says Chintala, who explained how Meta developed various tools for this purpose.
One way to counter node failures is to save the model state frequently, ensuring the ability to recover and continue as quickly as possible. Distributed checkpointing shards model weights across GPUs, allowing each to save only a portion, he notes. This optimizes storage and enables efficient recovery from other GPU checkpoints.
So there you have it. Expert tips in case you have half a billion dollars in spare change – the cheapest H100 GPU costs more than USD 30,000 each, excluding data center infrastructure and servers.
You can read the full blog here.
Image credit: iStock/peshkov
Paul Mah
Paul Mah is the editor of DSAITrends, where he report on the latest developments in data science and AI. A former system administrator, programmer, and IT lecturer, he enjoys writing both code and prose.