gpt2.yaml

name: train

resources:
  accelerators: A100:8
  # Use docker image for latest version g++ to enable the compilation of llm.c.
  image_id: docker:nvidia/cuda:12.4.1-cudnn-devel-ubuntu22.04
  any_of:
    # Avoid using docker image for lambda due to the docker is not supported on
    # Lambda yet, but the base image works.
    - cloud: lambda
      image_id: null
    - cloud: aws
    - cloud: gcp
    - cloud: azure
    - cloud: fluidstack
    - cloud: kubernetes
  

setup: |
  cd ~
  pip install tqdm tiktoken requests datasets

  # Training dependencies
  # install cudnn so we can use FlashAttention and run fast (optional)
  # https://developer.nvidia.com/cudnn-downloads
  # for me, CUDA 12 (run `nvcc --version`) running on Linux x86_64 Ubuntu 22.04
  if [ -f ./CUDNN_INSTALLED ]; then
    echo "cudnn already installed"
  else
    system=$(lsb_release -si | tr '[:upper:]' '[:lower:]')
    # Get version and remove the dot
    version=$(lsb_release -sr | tr -d .)
    export system_version="${system}${version}"
    wget https://developer.download.nvidia.com/compute/cudnn/9.1.1/local_installers/cudnn-local-repo-${system_version}-9.1.1_1.0-1_amd64.deb -O cudnn-installer.deb
    sudo dpkg -i cudnn-installer.deb
    sudo cp /var/cudnn-local-repo-${system_version}-9.1.1/cudnn-*-keyring.gpg /usr/share/keyrings/
    # Remove problematic kubernetes.list source
    sudo apt-get update --allow-releaseinfo-change || true

    sudo apt-get -y install cudnn-cuda-12

    touch ./CUDNN_INSTALLED
  fi

  # "install" cudnn-frontend to ~/
  sudo apt -y install git
  git clone https://github.com/NVIDIA/cudnn-frontend.git || true

  # install MPI (optional, if you intend to use multiple GPUs)
  # SkyPilot do not install MPI as that requires NCCL which needs to be manually
  # installed.
  sudo apt install -y openmpi-bin openmpi-doc libopenmpi-dev
  # install nccl
  pip install nvidia-nccl-cu12
  export LIBRARY_PATH=$LIBRARY_PATH:/usr/local/nccl2/lib
  export CPLUS_INCLUDE_PATH=$CPLUS_INCLUDE_PATH:/usr/local/nccl2/include

  git clone https://github.com/karpathy/llm.c.git || true
  cd llm.c

  # add revision to fix the dataset version, as the latest fineweb
  # dataset removed the samples, causing error:
  #   Please pass `features` or at least one example when writing data
  sed -i 's/fw = load_dataset("HuggingFaceFW\/fineweb", name=remote_name, split="train")/fw = load_dataset("HuggingFaceFW\/fineweb", name=remote_name, split="train", revision="9767af12bf8f0f7d3c91e0345b89bc6b9cbe1a94")/' dev/data/fineweb.py

  # compile llm.c (mixed precision, with cuDNN flash-attention)
  # first compilation is ~1 minute, mostly due to cuDNN
  make train_gpt2cu USE_CUDNN=1


run: |
  cd ~/llm.c
  # Processing data
  # tokenize the FineWeb dataset 10B tokens sample (takes ~1 hour, get lunch?)
  # writes ~19GB of raw GPT-2 tokens to dev/data/fineweb10B
  # and ~46GB in ~/.cache/huggingface/datasets/HuggingFaceFW___fineweb
  python dev/data/fineweb.py --version 10B

  # Start training on multiple GPUs
  mpirun -np $SKYPILOT_NUM_GPUS_PER_NODE --allow-run-as-root ./train_gpt2cu \
      -i "dev/data/fineweb10B/fineweb_train_*.bin" \
      -j "dev/data/fineweb10B/fineweb_val_*.bin" \
      -o log124M \
      -e "d12" \
      -b 64 -t 1024 \
      -d 524288 \
      -r 1 \
      -z 1 \
      -c 0.1 \
      -l 0.0006 \
      -q 0.0 \
      -u 700 \
      -n 5000 \
      -v 250 -s 20000 \
      -h 1