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994182c | 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 | #!/usr/bin/env bash
# Core training/eval deps for the Phase-0 go/no-go (NO flash-attn build, NO vllm).
# Run on the GPU host from the repo root. vLLM is installed separately afterward
# so a vLLM dependency conflict can't invalidate the Phase-0 result.
set -euo pipefail
export HF_HOME=${HF_HOME:-/workspace/hf-cache}
export HF_HUB_CACHE=${HF_HUB_CACHE:-/workspace/hf-cache/hub}
export PIP_ROOT_USER_ACTION=ignore
# Image ships torch in the system (PEP-668 externally-managed) Python; allow installs there.
export PIP_BREAK_SYSTEM_PACKAGES=1
mkdir -p "$HF_HOME" /workspace/reports /workspace/tmp
python -m pip install --upgrade pip setuptools wheel packaging >/dev/null
echo "== installing core deps =="
python -m pip install -U \
"transformers>=5.12.1" "accelerate>=1.14.0" "datasets>=5.0.0" "peft>=0.19.1" \
"huggingface_hub>=1.1.0" "safetensors>=0.6.0" "tokenizers>=0.22.0" \
"sentencepiece>=0.2.0" "protobuf>=6.0.0" "einops>=0.8.0" "pyyaml>=6.0.0"
# Hybrid (qwen3_5 / Gated-DeltaNet) kernels. Triton-based FLA + causal-conv1d are
# usually wheels; tolerate failure and fall back to sdpa for the smoke.
echo "== installing hybrid-arch kernels (best effort) =="
python -m pip install -U "causal-conv1d>=1.6.2.post1" || echo "WARN: causal-conv1d failed"
python -m pip install -U "flash-linear-attention>=0.5.1" || echo "WARN: flash-linear-attention failed"
echo "== versions =="
python - <<'PY'
import importlib
for m in ["torch","transformers","accelerate","datasets","peft","tokenizers"]:
try:
mod=importlib.import_module(m); print(m, getattr(mod,"__version__","?"))
except Exception as e:
print(m, "MISSING", repr(e))
import torch
print("cuda_available", torch.cuda.is_available(), "devices", torch.cuda.device_count())
if torch.cuda.is_available():
print("gpu0", torch.cuda.get_device_name(0))
PY
echo "core setup done."
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