mirror of https://github.com/hpcaitech/ColossalAI
aibig-modeldata-parallelismdeep-learningdistributed-computingfoundation-modelsheterogeneous-traininghpcinferencelarge-scalemodel-parallelismpipeline-parallelism
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
922 B
922 B
Multi-dimensional Parallelism with Colossal-AI
Table of contents
📚 Overview
This example lets you to quickly try out the hybrid parallelism provided by Colossal-AI.
You can change the parameters below to try out different settings in the config.py
.
# parallel setting
TENSOR_PARALLEL_SIZE = 2
TENSOR_PARALLEL_MODE = '1d'
parallel = dict(
pipeline=2,
tensor=dict(mode=TENSOR_PARALLEL_MODE, size=TENSOR_PARALLEL_SIZE),
)
🚀 Quick Start
-
Install PyTorch
-
Install the dependencies.
pip install -r requirements.txt
- Run the training scripts with synthetic data.
colossalai run --nproc_per_node 4 train.py --config config.py
- Modify the config file to play with different types of tensor parallelism, for example, change tensor parallel size to be 4 and mode to be 2d and run on 8 GPUs.