Mini-Lightning is a lightweight machine learning training library, which is a mini version of Pytorch-Lightning with only 1k lines of code. It has the advantages of faster, more concise and more flexible.
Project description
Mini-Lightning
Introduction
- Mini-Lightning is a lightweight machine learning training library, which is a mini version of Pytorch-Lightning with only 1k lines of code. It has the advantages of faster, more concise and more flexible.
- Existing features: support for DDP(multi-node and multi-gpu), Sync-BN, DP, AMP, gradient accumulation, warmup and lr_scheduler, grad clip, tensorboard, model and result saving, beautiful console log, torchmetrics, etc.
- Only the minimal interfaces are exposed, keeping the features of simplicity, easy to read, use and extend.
- examples can be found in
examples/
- If you have any problems or bug finding, please raise issue, Thank you.
Installation
- Create a virtual environment and install Python (>= 3.8)
- Download the latest version (>=1.12) of Torch(corresponding CUDA version) from the official website of PyTorch.
- Install mini-lightning
# from pypi
pip install mini-lightning -U
# Or download the files from the repository to local,
# and go to the folder where setup.py is located, and run the following command
# (Recommended) You can enjoy the latest features and functions (including bug fixes)
pip install .
Examples
- First, you need to install the Mini-Lightning
- Run the following examples
### test environment
python examples/test_env.py
### cv.py
pip install "torchvision>=0.13"
python examples/cv.py
# Using DP (not recommended, please use DDP)
python examples/cv.py # setting device_ids=[0, 1]
### nlp.py
pip install "transformers>=4.25" "datasets>=2.7"
python examples/nlp.py
### dqn.py
pip install "gym>=0.26.2" "pygame>=2.1.2"
python examples/dqn.py
### gan.py
pip install "torchvision>=0.13"
python examples/gan.py
### cl.py (contrastive_learning)
pip install "torchvision>=0.13" "scikit-learn>=1.2"
python examples/cl.py
### cl_ddp.py
torchrun --nproc_per_node 2 examples/cl_ddp.py --device_ids 0 1
### gnn.py gnn2.py
# download torch_geometric
# Ref: https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html
python examples/gnn.py
python examples/gnn2.py
python examples/gnn3.py
### ae.py
pip install "torchvision>=0.13" "scikit-learn>=1.2"
python examples/ae.py
### vae.py
pip install "torchvision>=0.13"
python examples/vae.py
### meta_learning.py
pip install "torchvision>=0.13"
python examples/meta_learning.py
########## ddp
### cv_ddp.py; cv_ddp_spawn.py
# torchrun (Recommended)
# Ref: https://pytorch.org/docs/stable/elastic/run.html
# spawn
# Ref: https://pytorch.org/docs/stable/notes/ddp.html
## single-node, multi-gpu
torchrun --nproc_per_node 2 examples/cv_ddp.py --device_ids 0 1
python cv_ddp_spawn.py # setting device_ids=[0, 1]
## multi-node
# default: --master_port 29500, or set master_port to prevents port conflicts.
torchrun --nnodes 2 --node_rank 0 --master_addr 127.0.0.1 --nproc_per_node 4 examples/cv_ddp.py --device_ids 0 1 2 3
torchrun --nnodes 2 --node_rank 1 --master_addr xxx.xxx.xxx.xxx --nproc_per_node 4 examples/cv_ddp.py --device_ids 0 1 2 3
TODO
- Automatic parameter adjustment
- Examples: Audio, Meta-learning, Diffusion, Auto-regressive, Reinforcement Learning
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