.env.example

# GSQ repo-local environment. Copy to `.env` in the repo root (gitignored).
# Loaded by every `scripts/*.sh` bootstrap via `_common.sh` — set only what you need.

# ----- Hugging Face (required for gated models and `scripts/download.sh`) -----
# HF_TOKEN=hf_

# ----- Weights & Biases (omit or leave empty locally; YAML can set wandb: false) -----
# WANDB_API_KEY=
# WANDB_ENTITY=
# WANDB_PROJECT=gsq

# ----- HF cache directories (defaults: ~/.cache/huggingface and HF_HOME/datasets) -----
# Use these on clusters with a shared or fast filesystem.
# HF_HOME=/path/to/huggingface/hub/root
# HF_DATASETS_CACHE=/path/to/writable/datasets/cache
# HF_HUB_OFFLINE=0

# ----- GSQ artifact directory (exported by `_common.sh` as GSQ_RUNTIME) -----
# Defaults to ${REPO_ROOT}/runtime — deliberately **not** the cluster-wide SCRATCH env that many sites set.
# Children are typically checkpoints/, logs/, models/, smoke/, ... (no extra gsq/ prefix).
# Uncomment to relocate artifacts away from <repo>/runtime:
# GSQ_RUNTIME=/path/to/my-gsq-runtime

# ----- Override YAML paths / model ID (optional; beat config file when set in environment) -----
# GSQ_MODEL_NAME=/path/to/model_or_repo_id
# GSQ_CHECKPOINT_DIR=/path/to/checkpoints_root
# GSQ_LOG_DIR=/path/to/logs
# GSQ_ACT_CACHE_DIR=/path/to/act_cache

# ----- Optional: Python venv location -----
# VENV_PATH=/path/to/venv

# ----- Optional: CUDA toolkit (builds, or CUDA_HOME-based tooling) -----
# CUDA_HOME=/usr/local/cuda
# export PATH="${CUDA_HOME}/bin:${PATH}"

# ----- Optional: allocator / NCCL (defaults match `_common.sh` when unset) -----
# PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
# TORCH_NCCL_ASYNC_ERROR_HANDLING=1