# 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 -e .  # -e: editable mode

### test environment
python examples/test_env.py

### cv
pip install "torchvision>=0.13"
python examples/cv.py
# cv+dp (not recommended, please use DDP)
python examples/cv.py  # setting device_ids=[0, 1]

### nlp: bert gpt
pip install "transformers>=4.25" "datasets>=2.7" "peft>=0.3"
python examples/nlp_bert_mlm.py
python examples/nlp_bert_seq_cls.py
python examples/nlp_gpt_lm.py
python examples/nlp_gpt_seq_cls.py
# sft
python examples/nlp_gpt_zh_sft_adapter.py
python examples/nlp_gpt_zh_sft_lora.py
# llm (model parallelism)
#   Ref: https://modelscope.cn/models/baichuan-inc/baichuan-7B/summary
python examples/nlp_baichuan_sft_lora.py
#   Ref: https://modelscope.cn/models/ZhipuAI/chatglm2-6b/summary
python examples/nlp_chatglm2_sft_lora.py

### dqn
pip install "gym>=0.26.2" "pygame>=2.1.2"
python examples/dqn.py

### gan
pip install "torchvision>=0.13"
python examples/gan.py

### contrastive learning
pip install "torchvision>=0.13" "scikit-learn>=1.2"
python examples/cl.py
# cl+ddp
torchrun --nproc_per_node 2 examples/cl_ddp.py --device 0,1

### gnn
# download torch_geometric
#   Ref: https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html
python examples/gnn_node.py
python examples/gnn_edge.py
python examples/gnn_graph.py

### ae
pip install "torchvision>=0.13" "scikit-learn>=1.2"
python examples/ae.py

### vae
pip install "torchvision>=0.13"
python examples/vae.py

### meta learning
pip install "torchvision>=0.13"
python examples/meta_learning.py


########## ddp
# 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 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 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 0,1,2,3