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Easy Parallel Library (EPL) is a general and efficient library for distributed model training.
- Usability - Users can implement different parallelism strategies with a few lines of annotations, including data parallelism, pipeline parallelism, tensor model parallelism, and their hybrids.
- Memory Efficient - EPL provides various memory-saving techniques, including gradient checkpoint, ZERO, CPU Offload, etc. Users are able to train larger models with fewer computing resources.
- High Performance - EPL provides an optimized communication library to achieve high scalability and efficiency.
For more information, you may read the docs.
EPL Model Zoo provides end-to-end parallel training examples.
To install EPL, please refer to the following instructions.
Here are a few examples of different parallelism strategies by changing only annotations. Please refer to API documentation for API details and tutorials for more examples.
The following example shows a basic data parallelism annotation. The data parallelism degree is determined by the allocated GPU number.
+ import epl
+ epl.init()
+ with epl.replicate(device_count=1):
model()
The following example shows pipeline parallelism with two pipeline stages, each stage is computed with one GPU. If the total GPU number is 4, EPL will automatically apply two-degree data parallelism over the model pipeline.
+ import epl
+
+ config = epl.Config({"pipeline.num_micro_batch": 4})
+ epl.init(config)
+ with epl.replicate(device_count=1, name="stage_0"):
model_part1()
+ with epl.replicate(device_count=1, name="stage_1"):
model_part2()
The following example shows a tensor model parallelism annotation.
We apply data parallelism to the ResNet part, and apply tensor model parallelism to classification part.
+ import epl
+ config = epl.Config({"cluster.colocate_split_and_replicate": True})
+ epl.init(config)
+ with epl.replicate(8):
ResNet()
+ with epl.split(8):
classification()
If you use EPL in your publication, please cite it by using the following BibTeX entry.
@inproceedings {jia2022whale,
author = {Xianyan Jia and Le Jiang and Ang Wang and Wencong Xiao and Ziji Shi and Jie Zhang and Xinyuan Li and Langshi Chen and Yong Li and Zhen Zheng and Xiaoyong Liu and Wei Lin},
title = {Whale: Efficient Giant Model Training over Heterogeneous {GPUs}},
booktitle = {2022 USENIX Annual Technical Conference (USENIX ATC 22)},
year = {2022},
isbn = {978-1-939133-29-57},
address = {Carlsbad, CA},
pages = {673--688},
url = {https://www.usenix.org/conference/atc22/presentation/jia-xianyan},
publisher = {USENIX Association},
month = jul,
}
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