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Quick Demo
Colossal-AI is an integrated large-scale deep learning system with efficient parallelization techniques. The system can accelerate model training on distributed systems with multiple GPUs by applying parallelization techniques. The system can also run on systems with only one GPU. Quick demos showing how to use Colossal-AI are given below.
Single GPU
Colossal-AI can be used to train deep learning models on systems with only one GPU and achieve baseline
performances. We provided an example to train ResNet on CIFAR10 dataset
with only one GPU. You can find the example in ColossalAI-Examples.
Detailed instructions can be found in its README.md.
Multiple GPUs
Colossal-AI can be used to train deep learning models on distributed systems with multiple GPUs and accelerate the training process drastically by applying efficient parallelization techniques. When we have several parallelism for you to try out.
1. data parallel
You can use the same ResNet example as the
single-GPU demo above. By setting --nproc_per_node to be the number of GPUs you have on your machine, the example
is turned into a data parallel example.
2. hybrid parallel
Hybrid parallel includes data, tensor, and pipeline parallelism. In Colossal-AI, we support different types of tensor
parallelism (i.e. 1D, 2D, 2.5D and 3D). You can switch between different tensor parallelism by simply changing the configuration
in the config.py. You can follow the GPT example.
Detailed instructions can be found in its README.md.
3. MoE parallel
We provided an example of ViT-MoE to demonstrate MoE parallelism. WideNet uses mixture of experts (MoE) to achieve better performance. More details can be found in Tutorial: Integrate Mixture-of-Experts Into Your Model
4. sequence parallel
Sequence parallel is designed to tackle memory efficiency and sequence length limit problems in NLP tasks. We provided
an example of BERT in
ColossalAI-Examples. You can follow the README.md to execute the code.