InternLM/doc/en/install.md

InternLM Installation

Environment Preparation

The required packages and corresponding version are shown as follows:

• Python == 3.10
• GCC == 10.2.0
• MPFR == 4.1.0
• CUDA >= 11.7
• Pytorch >= 1.13.1
• Transformers >= 4.28.0
• Flash-Attention >= v1.0.5
• Apex == 23.05
• GPU with Ampere or Hopper architecture (such as H100, A100)
• Linux OS

After installing the above dependencies, some system environment variables need to be updated:

export CUDA_PATH={path_of_cuda_11.7}
export GCC_HOME={path_of_gcc_10.2.0}
export MPFR_HOME={path_of_mpfr_4.1.0}
export LD_LIBRARY_PATH=${GCC_HOME}/lib64:${MPFR_HOME}/lib:${CUDA_PATH}/lib64:$LD_LIBRARY_PATH
export PATH=${GCC_HOME}/bin:${CUDA_PATH}/bin:$PATH
export CC=${GCC_HOME}/bin/gcc
export CXX=${GCC_HOME}/bin/c++

Environment Installation

Clone the project internlm and its dependent submodules from the github repository, as follows:

git clone git@github.com:InternLM/InternLM.git --recurse-submodules

It is recommended to build a Python-3.10 virtual environment using conda and install the required dependencies based on the requirements/ files:

conda create --name internlm-env python=3.10 -y
conda activate internlm-env
cd internlm
pip install -r requirements/torch.txt 
pip install -r requirements/runtime.txt 

Install flash-attention (version v1.0.5):

cd ./third_party/flash-attention
python setup.py install
cd ./csrc
cd fused_dense_lib && pip install -v .
cd ../xentropy && pip install -v .
cd ../rotary && pip install -v .
cd ../layer_norm && pip install -v .
cd ../../../../

Install Apex (version 23.05):

cd ./third_party/apex
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
cd ../../

Environment Image

Users can obtain an image with the InternLM runtime environment installed from https://hub.docker.com/r/sunpengsdu/internlm. The commands for pulling the image and starting the container are as follows:

# pull image
docker pull sunpengsdu/internlm:torch1.13-cuda11.7-flashatten1.0.5-centos
# start container
docker run --gpus all -d -it --shm-size=2gb --name myinternlm sunpengsdu/internlm:torch1.13-cuda11.7-flashatten1.0.5-centos
docker exec -it myinternlm bash