Alpa
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About
Alpa is a system for training and serving large-scale neural networks. It offers features such as distributed training with both shard and pipeline parallelism, allowing users to train and serve very large models like OPT-175B, BLOOM-176B, and CodeGen-16B. Alpa provides tutorials, a performance tuning guide, and documentation covering its architecture and compiler. It also includes FAQs and a developer guide for more in-depth understanding and contribution. The system integrates with Slurm for cluster management and offers different methods for controlling GPU device usage.
Platform
Task
Features
• distributed training
• large model serving
• shard parallelism
• pipeline parallelism
• slurm integration
• performance tuning
FAQs
How to control the GPU devices used by Alpa?
CUDA_VISIBLE_DEVICES works for alpa, but there are some caveats. If you use Ray cluster, you should not put CUDA_VISIBLE_DEVICES before the python script you run. You should apply this environment variable to ray start --head. For example, CUDA_VISIBLE_DEVICES=0,1 ray start --head.
Method 2: Use arguments in alpa.init
You can use the arguments of alpa.init to configure the number of devices to use. See the docstring.
Method 3: Use other Ray features
If you are familiar with Ray, you can use advanced Ray features like placement group.
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