55 lines
2.2 KiB
Bash
Executable File
55 lines
2.2 KiB
Bash
Executable File
#!/usr/bin/env bash
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# 远程机 gpu-a800-060 环境搭建（幂等，可重复执行）。
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# 用法：bash scripts/setup_remote.sh
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# 硬约束：根分区仅剩 12G —— 环境、缓存、临时目录一律压到 /data/zym 下。
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set -euo pipefail
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DATA_ROOT=/data/zym
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ENV_PATH=$DATA_ROOT/envs/ars-opd
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REPO_DIR=$DATA_ROOT/ars-opd-rebuild
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# ---- 0. 所有会写盘的路径全部改道 /data（防根分区被写爆）----
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export HF_ENDPOINT=https://hf-mirror.com # huggingface.co 被墙，走镜像
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export HF_HOME=$DATA_ROOT/hf_cache
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export CONDA_PKGS_DIRS=$DATA_ROOT/conda_pkgs # conda 包缓存默认在根分区
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export PIP_CACHE_DIR=$DATA_ROOT/pip_cache # pip 缓存默认在根分区
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export TMPDIR=$DATA_ROOT/tmp # 大 wheel 解压临时目录
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mkdir -p "$DATA_ROOT"/{envs,hf_cache,conda_pkgs,pip_cache,tmp}
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# ---- 1. conda 环境（建在 /data，不建在 ~）----
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if [ ! -d "$ENV_PATH" ]; then
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conda create -p "$ENV_PATH" python=3.11 -y
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fi
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# ---- 2. 代码（远程只读：clone 走 HTTPS 匿名，更新只 git pull）----
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if [ ! -d "$REPO_DIR" ]; then
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git clone https://gitea.iomgaa.online/iomgaa/ars-opd-rebuild.git "$REPO_DIR"
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else
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git -C "$REPO_DIR" pull
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fi
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# ---- 3. 依赖 ----
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conda run -p "$ENV_PATH" pip install -r "$REPO_DIR/requirements.txt" -r "$REPO_DIR/requirements-remote.txt"
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# ---- 4. 环境变量持久化（写入 ~/.bashrc，幂等）----
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if ! grep -q "ars-opd-rebuild env" ~/.bashrc; then
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cat >> ~/.bashrc <<'EOF'
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# --- ars-opd-rebuild env ---
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export HF_ENDPOINT=https://hf-mirror.com
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export HF_HOME=/data/zym/hf_cache
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export CONDA_PKGS_DIRS=/data/zym/conda_pkgs
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export PIP_CACHE_DIR=/data/zym/pip_cache
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alias opd='conda activate /data/zym/envs/ars-opd && cd /data/zym/ars-opd-rebuild'
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EOF
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fi
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# ---- 5. 验证 ----
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echo "=== 验证 torch/CUDA ==="
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conda run -p "$ENV_PATH" python -c "import torch; print('torch', torch.__version__, '| cuda可用:', torch.cuda.is_available(), '| 卡数:', torch.cuda.device_count())"
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echo "=== 验证单元测试 ==="
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conda run -p "$ENV_PATH" python -m pytest "$REPO_DIR/tests" -q
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echo "=== 磁盘检查（根分区不应有明显增长）==="
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df -h / /data | tail -2
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echo "全部完成。日常使用：输入 opd 进入环境与目录。"
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