File size: 3,736 Bytes
0185029 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 | #!/usr/bin/env bash
set -euo pipefail
SCRIPT_DIR="$(cd -- "$(dirname -- "${BASH_SOURCE[0]}")" && pwd)"
ORARL_ROOT="$(cd -- "${SCRIPT_DIR}/.." && pwd)"
ENV_NAME="${ORARL_CONDA_ENV:-orarl}"
PYTORCH_INDEX="https://download.pytorch.org/whl/cu129"
EVALUATION_ONLY=0
usage() {
cat <<'EOF'
Usage: bash scripts/create_conda_env.sh [options]
Create the pinned OraRL CUDA 12.9 environment. This checkout ships both the
training runtime and the evaluators, so no external runtime is required. Use
--evaluation-only to skip the training-side environment validation.
Options:
--name ENV_NAME Conda environment name (default: orarl).
--evaluation-only Validate the environment for evaluation only.
EOF
}
while [[ $# -gt 0 ]]; do
case "$1" in
--name)
if [[ $# -lt 2 || -z "$2" ]]; then
echo "ERROR: --name requires a non-empty environment name." >&2
exit 2
fi
ENV_NAME="$2"
shift 2
;;
--evaluation-only)
EVALUATION_ONLY=1
shift
;;
-h|--help)
usage
exit 0
;;
*)
echo "ERROR: unknown argument: $1" >&2
usage >&2
exit 2
;;
esac
done
if ! command -v conda >/dev/null 2>&1; then
echo "ERROR: conda is not available in PATH." >&2
exit 1
fi
if [[ ! -d "${ORARL_ROOT}/verl" ]]; then
echo "ERROR: the bundled training runtime is missing at ${ORARL_ROOT}/verl." >&2
echo "Clone the full repository instead of copying individual directories." >&2
exit 1
fi
if command -v nvidia-smi >/dev/null 2>&1; then
echo "Detected cluster GPU(s):"
nvidia-smi --query-gpu=name,driver_version --format=csv,noheader || true
echo "CUDA 12.9 GA officially requires Linux driver 575.51.03 or newer."
else
echo "WARNING: nvidia-smi is unavailable; run the GPU check on an allocated H20 node." >&2
fi
if conda run -n "${ENV_NAME}" python -c "pass" >/dev/null 2>&1; then
echo "ERROR: conda environment '${ENV_NAME}' already exists." >&2
echo "Choose another name with --name or remove/update it explicitly." >&2
exit 1
fi
conda env create \
--name "${ENV_NAME}" \
--file "${ORARL_ROOT}/environment.yml"
run_in_env() {
conda run --no-capture-output -n "${ENV_NAME}" "$@"
}
run_in_env python -m pip install --upgrade \
"pip==26.0.1" \
"setuptools==82.0.1" \
"wheel==0.46.3"
# Install the CUDA build explicitly. Generic PyPI resolves PyTorch 2.10 to the
# CUDA 12.8 wheel, which is not the stack validated with vLLM 0.19.1 here.
run_in_env python -m pip install \
"torch==2.10.0+cu129" \
"torchvision==0.25.0+cu129" \
"torchaudio==2.10.0+cu129" \
--index-url "${PYTORCH_INDEX}"
# PyTorch must be importable before building FlashAttention.
run_in_env python -m pip install \
"flash-attn==2.8.3" \
--no-build-isolation
run_in_env python -m pip install \
"vllm==0.19.1" \
--extra-index-url "${PYTORCH_INDEX}"
run_in_env python -m pip install \
--requirement "${ORARL_ROOT}/requirements-cu129.txt"
# One editable install covers the CLIs, the trainer runtime, and the evaluators.
# --no-deps keeps it from replacing the GPU stack pinned above.
run_in_env python -m pip install --no-deps --editable "${ORARL_ROOT}"
run_in_env python -m pip install "pytest" "ruff"
run_in_env bash "${ORARL_ROOT}/scripts/install_conda_runtime_hook.sh"
CHECK_ARGUMENTS=()
if [[ "${EVALUATION_ONLY}" -eq 1 ]]; then
CHECK_ARGUMENTS+=(--evaluation-only)
fi
run_in_env bash -c \
'source "${CONDA_PREFIX}/etc/conda/activate.d/orarl-runtime.sh"; shift; exec python "$@"' \
_ "${ORARL_ROOT}/scripts/check_environment.py" "${CHECK_ARGUMENTS[@]}"
cat <<EOF
OraRL environment created successfully.
conda activate ${ENV_NAME}
python ${ORARL_ROOT}/scripts/check_environment.py --require-gpu
EOF
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