forked from ccf-ai-infra/Intro-ops
165 lines
4.5 KiB
Plaintext
165 lines
4.5 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 00 — 环境配置与验证\n",
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"\n",
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"本 Notebook 帮助你验证 intro-ops 开发环境是否正确配置。\n",
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"\n",
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"## 检查清单\n",
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"\n",
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"1. GPU 是否可用\n",
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"2. CUDA Toolkit 版本\n",
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"3. PyTorch 是否正确安装\n",
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"4. intro-ops 是否成功构建\n",
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"5. 运行一个简单的基线测试"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Step 1: 检查 GPU 和 CUDA"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"\n",
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"print(f\"PyTorch 版本: {torch.__version__}\")\n",
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"print(f\"CUDA 可用: {torch.cuda.is_available()}\")\n",
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"\n",
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"if torch.cuda.is_available():\n",
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" print(f\"GPU 型号: {torch.cuda.get_device_name(0)}\")\n",
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" print(f\"CUDA 版本: {torch.version.cuda}\")\n",
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" print(f\"GPU 数量: {torch.cuda.device_count()}\")\n",
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" print(f\"当前设备: {torch.cuda.current_device()}\")\n",
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"else:\n",
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" print(\"⚠️ 未检测到 CUDA GPU。intro-ops 需要 NVIDIA GPU 才能运行。\")\n",
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" print(\" 请确认 CUDA Toolkit 和 NVIDIA 驱动已正确安装。\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Step 2: 检查 intro-ops 构建产物"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import sys\n",
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"\n",
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"# 检查 CAMP_BUILD_DIR 环境变量\n",
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"build_dir = os.environ.get(\"CAMP_BUILD_DIR\", \"build-nvidia\")\n",
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"lib_path = os.path.join(build_dir, \"libcamp_ops.so\")\n",
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"\n",
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"print(f\"构建目录: {build_dir}\")\n",
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"print(f\"库文件路径: {lib_path}\")\n",
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"print(f\"库文件存在: {os.path.exists(lib_path)}\")\n",
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"\n",
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"if not os.path.exists(lib_path):\n",
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" print(\"\\n⚠️ libcamp_ops.so 未找到。请先构建项目：\")\n",
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" print(\" bash scripts/build_nvidia.sh configure\")\n",
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" print(\" bash scripts/build_nvidia.sh build\")\n",
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" print(f\"\\n 然后设置环境变量: export CAMP_BUILD_DIR={build_dir}\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Step 3: 验证 Python 依赖"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import importlib\n",
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"\n",
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"deps = [\"torch\", \"pytest\", \"cmake\", \"ninja\"]\n",
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"for dep in deps:\n",
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" try:\n",
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" mod = importlib.import_module(dep)\n",
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" version = getattr(mod, \"__version__\", \"unknown\")\n",
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" print(f\"✓ {dep}: {version}\")\n",
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" except ImportError:\n",
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" print(f\"✗ {dep}: 未安装\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Step 4: 运行基线测试\n",
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"\n",
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"如果以上检查全部通过，运行一个简单的 copy 算子测试验证端到端流程。"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import subprocess\n",
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"import sys\n",
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"\n",
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"result = subprocess.run([\n",
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" sys.executable, \"-m\", \"pytest\",\n",
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" \"tests/op_tests/test_copy.py\", \"-v\",\n",
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" \"--backend\", \"nvidia\",\n",
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" \"-x\", # 遇到第一个失败就停止\n",
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"], capture_output=True, text=True)\n",
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"\n",
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"print(result.stdout)\n",
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"if result.returncode != 0:\n",
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" print(result.stderr)\n",
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" print(\"\\n⚠️ 基线测试失败。请检查上面的错误信息。\")\n",
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"else:\n",
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" print(\"\\n✓ 环境配置正确，可以开始学习了！\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 常见问题\n",
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"\n",
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"- **`torch.cuda.is_available()` 返回 False**：检查 NVIDIA 驱动和 CUDA Toolkit 安装\n",
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"- **`libcamp_ops.so` 不存在**：运行 `bash scripts/build_nvidia.sh configure && bash scripts/build_nvidia.sh build`\n",
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"- **ImportError**：运行 `pip install -r requirements.txt`\n",
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"\n",
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"更多帮助见 `docs/FAQ.md` 和 `docs/troubleshooting.md`。"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"name": "python",
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"version": "3.12.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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