feat: add Ampere (A100/A800) support and generalize project naming
- Expand GPU specs database to include A100/A800 with Ampere architecture parameters - Rename h200_tester.py to gpu_tester.py for architecture-neutral branding - Add driver/CUDA compatibility validation per GPU generation - Enhance report module with HTML and Markdown output formats - Improve nvbandwidth binary discovery (system paths, DCGM locations) - Add pyproject.toml with uv for dependency management - Update install_deps.sh, configs, and README for multi-architecture support 🤖 Generated with [Qoder][https://qoder.com]
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README.md
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README.md
@ -1,9 +1,9 @@
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# GPU Training Server Test Suite
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# GPU Training Server Test Suite
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面向 NVIDIA 数据中心 GPU（H100 / H200 / B200 / B300）训练服务器的自动化测试与基准测试工具集。
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面向 NVIDIA 数据中心 GPU（A100 / A800 / H100 / H200 / B200 / B300）训练服务器的自动化测试与基准测试工具集。
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涵盖硬件检测、健康诊断、带宽基准、计算吞吐、多卡通信、压力稳定性、RDMA 网络和训练模拟。
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涵盖硬件检测、健康诊断、带宽基准、计算吞吐、多卡通信、压力稳定性、RDMA 网络和训练模拟。
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> **支持 GPU 架构：** Hopper (H100/H200) · Blackwell (B200/B300)
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> **支持 GPU 架构：** Ampere (A100/A800) · Hopper (H100/H200) · Blackwell (B200/B300)
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> 系统自动检测 GPU 型号并使用对应的规格参数进行基准对比。
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> 系统自动检测 GPU 型号并使用对应的规格参数进行基准对比。
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---
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---
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@ -27,12 +27,12 @@
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```
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```
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servertest/
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servertest/
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├── h200_tester.py # 主入口：CLI + 交互式菜单
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├── gpu_tester.py # 主入口：CLI + 交互式菜单
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├── install_deps.sh # 一键安装三方工具
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├── install_deps.sh # 一键安装三方工具
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├── configs/
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├── configs/
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│ └── default.yaml # 默认配置
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│ └── default.yaml # 默认配置
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├── modules/
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├── modules/
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│ ├── gpu_specs.py # GPU 规格数据库 (H100/H200/B200/B300)
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│ ├── gpu_specs.py # GPU 规格数据库 (A100/A800/H100/H200/B200/B300)
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│ ├── gpu_info.py # GPU 检测 & 信息
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│ ├── gpu_info.py # GPU 检测 & 信息
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│ ├── health_check.py # 健康诊断
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│ ├── health_check.py # 健康诊断
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│ ├── benchmark.py # 内存带宽 + 计算吞吐
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│ ├── benchmark.py # 内存带宽 + 计算吞吐
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@ -55,7 +55,7 @@ servertest/
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|---|---|
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| OS | Ubuntu 22.04 / RHEL 8+ / Rocky 8+ |
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| OS | Ubuntu 22.04 / RHEL 8+ / Rocky 8+ |
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| Python | 3.10+ |
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| Python | 3.10+ |
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| NVIDIA Driver | ≥ 535（H200 支持） |
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| NVIDIA Driver | ≥ 470（Ampere）/ ≥ 535（Hopper）/ ≥ 550（Blackwell） |
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| CUDA | ≥ 12.1 |
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| CUDA | ≥ 12.1 |
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| nvidia-smi | 必须可用 |
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| nvidia-smi | 必须可用 |
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| pip 包 | rich, pyyaml |
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| pip 包 | rich, pyyaml |
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@ -64,7 +64,7 @@ servertest/
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| 项目 | 要求 |
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| 项目 | 要求 |
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|---|---|
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| GPU | ≥ 1 张 NVIDIA 数据中心 GPU（H100/H200/B200/B300 SXM） |
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| GPU | ≥ 1 张 NVIDIA 数据中心 GPU（A100/A800/H100/H200/B200/B300 SXM） |
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| MPI | OpenMPI ≥ 4.1 |
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| MPI | OpenMPI ≥ 4.1 |
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| RDMA | Mellanox ConnectX-7 / BlueField |
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| RDMA | Mellanox ConnectX-7 / BlueField |
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| nvbandwidth | 源码编译安装 |
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| nvbandwidth | 源码编译安装 |
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@ -86,13 +86,13 @@ cd test_gpu_scripts
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sudo bash install_deps.sh
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sudo bash install_deps.sh
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# 3. 运行交互式测试（自动检测 GPU 型号）
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# 3. 运行交互式测试（自动检测 GPU 型号）
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python3 h200_tester.py
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python3 gpu_tester.py
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# 4. 或一键全量测试
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# 4. 或一键全量测试
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python3 h200_tester.py --test all
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python3 gpu_tester.py --test all
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# 5. 手动指定 GPU 型号（跳过自动检测）
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# 5. 手动指定 GPU 型号（跳过自动检测）
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python3 h200_tester.py --gpu-type b200 --test all
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python3 gpu_tester.py --gpu-type b200 --test all
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```
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```
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---
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---
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@ -113,18 +113,18 @@ sudo bash install_deps.sh
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5. 安装 Python 包（rich, pyyaml）
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5. 安装 Python 包（rich, pyyaml）
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6. 检查 DCGM 和 RDMA 工具状态
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6. 检查 DCGM 和 RDMA 工具状态
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默认安装目录 `/opt/h200-test-tools`，可通过环境变量自定义。
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默认安装目录 `/opt/gpu-test-tools`，可通过环境变量自定义。
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### 自定义安装目录
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### 自定义安装目录
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```bash
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```bash
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sudo H200_TOOLS_DIR=/data/tools bash install_deps.sh
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sudo GPU_TOOLS_DIR=/data/tools bash install_deps.sh
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```
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```
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### 手动安装单项
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### 手动安装单项
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```bash
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```bash
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TOOLS=/opt/h200-test-tools
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TOOLS=/opt/gpu-test-tools
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# nvbandwidth
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# nvbandwidth
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git clone https://github.com/NVIDIA/nvbandwidth.git $TOOLS/nvbandwidth
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git clone https://github.com/NVIDIA/nvbandwidth.git $TOOLS/nvbandwidth
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@ -148,7 +148,7 @@ cd $TOOLS/gpu-burn && make
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### 交互式菜单（默认模式）
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### 交互式菜单（默认模式）
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```bash
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```bash
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python3 h200_tester.py
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python3 gpu_tester.py
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```
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```
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显示带编号的测试菜单，输入数字选择测试：
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显示带编号的测试菜单，输入数字选择测试：
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@ -171,25 +171,25 @@ python3 h200_tester.py
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```bash
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```bash
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# 单项测试
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# 单项测试
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python3 h200_tester.py --test gpu-info
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python3 gpu_tester.py --test gpu-info
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python3 h200_tester.py --test health
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python3 gpu_tester.py --test health
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python3 h200_tester.py --test benchmark --type memory
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python3 gpu_tester.py --test benchmark --type memory
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python3 h200_tester.py --test benchmark --type compute --dtype bf16
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python3 gpu_tester.py --test benchmark --type compute --dtype bf16
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python3 h200_tester.py --test nccl
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python3 gpu_tester.py --test nccl
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python3 h200_tester.py --test stress
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python3 gpu_tester.py --test stress
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python3 h200_tester.py --test rdma
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python3 gpu_tester.py --test rdma
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python3 h200_tester.py --test training
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python3 gpu_tester.py --test training
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# 全量测试
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# 全量测试
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python3 h200_tester.py --test all
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python3 gpu_tester.py --test all
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# GPU 型号控制
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# GPU 型号控制
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python3 h200_tester.py --gpu-type auto --test all # 自动检测（默认）
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python3 gpu_tester.py --gpu-type auto --test all # 自动检测（默认）
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python3 h200_tester.py --gpu-type h200 --test all # 强制指定 H200
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python3 gpu_tester.py --gpu-type h200 --test all # 强制指定 H200
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python3 h200_tester.py --gpu-type b300 --test benchmark # 强制指定 B300
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python3 gpu_tester.py --gpu-type b300 --test benchmark # 强制指定 B300
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# 指定自定义配置
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# 指定自定义配置
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python3 h200_tester.py --config /path/to/config.yaml --test all
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python3 gpu_tester.py --config /path/to/config.yaml --test all
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```
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```
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### GPU 自动检测
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### GPU 自动检测
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| GPU 名称关键词 | 识别为 | 使用规格 |
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| GPU 名称关键词 | 识别为 | 使用规格 |
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| `A100` | A100 SXM | Ampere, 80GB HBM2e, 2.0 TB/s |
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| `A800` | A800 SXM | Ampere, 80GB HBM2e, 2.0 TB/s |
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| `H100` | H100 SXM5 | Hopper, 80GB HBM3, 3.4 TB/s |
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| `H100` | H100 SXM5 | Hopper, 80GB HBM3, 3.4 TB/s |
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| `H200` | H200 SXM | Hopper, 141GB HBM3e, 4.8 TB/s |
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| `H200` | H200 SXM | Hopper, 141GB HBM3e, 4.8 TB/s |
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| `B200` | B200 SXM | Blackwell, 180GB HBM3e, 8 TB/s |
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| `B200` | B200 SXM | Blackwell, 180GB HBM3e, 8 TB/s |
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@ -221,12 +223,12 @@ python3 h200_tester.py --config /path/to/config.yaml --test all
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| 指标 | 说明 |
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| 指标 | 说明 |
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| 型号 | 自动检测并确认 GPU 型号（H100/H200/B200/B300） |
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| 型号 | 自动检测并确认 GPU 型号（A100/A800/H100/H200/B200/B300） |
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| VRAM | 总量 / 已用 / 空闲 |
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| VRAM | 总量 / 已用 / 空闲 |
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| 温度 | 实时温度 |
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| 温度 | 实时温度 |
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| 功耗 | 实时功耗 / 功耗上限 |
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| 功耗 | 实时功耗 / 功耗上限 |
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| 时钟频率 | SM 时钟 / 内存时钟 |
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| 时钟频率 | SM 时钟 / 内存时钟 |
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| PCIe | 链路代数和宽度（期望 Gen5 x16） |
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| PCIe | 链路代数和宽度（Ampere: Gen4, Hopper/Blackwell: Gen5） |
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| Persistence Mode | 应开启 |
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| Persistence Mode | 应开启 |
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| ECC 错误 | 单比特 / 双比特计数 |
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| ECC 错误 | 单比特 / 双比特计数 |
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| NVLink 拓扑 | 显示 `nvidia-smi topo -m` 输出 |
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| NVLink 拓扑 | 显示 `nvidia-smi topo -m` 输出 |
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- `device_to_device_memcpy_read_ce` — D2D 读带宽
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- `device_to_device_memcpy_read_ce` — D2D 读带宽
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- `device_to_device_bidirectional_sm` — D2D 双向带宽
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- `device_to_device_bidirectional_sm` — D2D 双向带宽
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**GPU 参考值（D2D 峰值带宽）：** H100: 3,400 GB/s | H200: 4,800 GB/s | B200/B300: 8,000 GB/s
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**GPU 参考值（D2D 峰值带宽）：** A100/A800: 2,039 GB/s | H100: 3,400 GB/s | H200: 4,800 GB/s | B200/B300: 8,000 GB/s
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**效率评级：** ≥ 80% 绿色, 50-80% 黄色, < 50% 红色
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**效率评级：** ≥ 80% 绿色, 50-80% 黄色, < 50% 红色
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使用 PyTorch matmul 测试各精度 GEMM 吞吐量。峰值 TFLOPS 根据 GPU 型号自动匹配。
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使用 PyTorch matmul 测试各精度 GEMM 吞吐量。峰值 TFLOPS 根据 GPU 型号自动匹配。
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| 精度 | H100/H200 峰值 | B200 峰值 | B300 峰值 |
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| 精度 | A100/A800 峰值 | H100/H200 峰值 | B200 峰值 | B300 峰值 |
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| FP32 | 67 TFLOPS | 90 TFLOPS | 125 TFLOPS |
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| FP32 | 19.5 TFLOPS | 67 TFLOPS | 90 TFLOPS | 125 TFLOPS |
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| TF32 | 495 TFLOPS | 1,125 TFLOPS | 1,750 TFLOPS |
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| TF32 | 156 TFLOPS | 495 TFLOPS | 1,125 TFLOPS | 1,750 TFLOPS |
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| FP16 | 990 TFLOPS | 2,250 TFLOPS | 3,500 TFLOPS |
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| FP16 | 312 TFLOPS | 990 TFLOPS | 2,250 TFLOPS | 3,500 TFLOPS |
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| BF16 | 990 TFLOPS | 2,250 TFLOPS | 3,500 TFLOPS |
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| BF16 | 312 TFLOPS | 990 TFLOPS | 2,250 TFLOPS | 3,500 TFLOPS |
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| FP8 | 1,979 TFLOPS | 4,500 TFLOPS | 7,000 TFLOPS |
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| FP8 | N/A | 1,979 TFLOPS | 4,500 TFLOPS | 7,000 TFLOPS |
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默认配置：4096×4096 矩阵，10 次 warmup，100 次迭代。
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默认配置：4096×4096 矩阵，10 次 warmup，100 次迭代。
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默认测试数据量范围 8B ~ 256MB，5 次 warmup，20 次迭代。
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默认测试数据量范围 8B ~ 256MB，5 次 warmup，20 次迭代。
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**NVLink 参考带宽：** H100/H200 ≥ 360 GB/s | B200/B300 ≥ 720 GB/s（40% NVLink 峰值）
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**NVLink 参考带宽：** A100/A800 ≥ 240 GB/s | H100/H200 ≥ 360 GB/s | B200/B300 ≥ 720 GB/s（40% NVLink 峰值）
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### 6. GPU Stress Test（压力测试）
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### 6. GPU Stress Test（压力测试）
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配置文件路径：`configs/default.yaml`
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配置文件路径：`configs/default.yaml`
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```yaml
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```yaml
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# GPU type: auto-detect or override to h100/h200/b200/b300
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# GPU type: auto-detect or override to a100/a800/h100/h200/b200/b300
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gpu_type: auto
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gpu_type: auto
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tools:
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tools:
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install_dir: /opt/h200-test-tools # 三方工具安装目录
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install_dir: /opt/gpu-test-tools # 三方工具安装目录
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benchmark:
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benchmark:
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memory:
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memory:
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### SOP-1: 新服务器到货验收
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### SOP-1: 新服务器到货验收
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**适用场景：** GPU 服务器首次上架，需要确认硬件完整可用。支持 H100/H200/B200/B300。
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**适用场景：** GPU 服务器首次上架，需要确认硬件完整可用。支持 A100/A800/H100/H200/B200/B300。
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```
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```
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步骤 1: 环境准备
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步骤 1: 环境准备
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└── 确认所有工具安装成功
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└── 确认所有工具安装成功
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步骤 2: GPU 信息核对
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步骤 2: GPU 信息核对
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├── python3 h200_tester.py --test gpu-info
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├── python3 gpu_tester.py --test gpu-info
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├── 确认: 系统自动检测到 GPU 型号
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├── 确认: 系统自动检测到 GPU 型号
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├── 核对: GPU 数量是否与采购规格一致
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├── 核对: GPU 数量是否与采购规格一致
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├── 核对: 型号与预期一致（H100/H200/B200/B300）
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├── 核对: 型号与预期一致（A100/A800/H100/H200/B200/B300）
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├── 核对: VRAM 总量符合规格（H100: 80GB, H200: 141GB, B200: 180GB, B300: 288GB）
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├── 核对: VRAM 总量符合规格（A100: 80GB, A800: 80GB, H100: 80GB, H200: 141GB, B200: 180GB, B300: 288GB）
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├── 核对: PCIe Gen5 x16
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├── 核对: PCIe 链路正常（Ampere Gen4 x16, Hopper/Blackwell Gen5 x16）
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└── 核对: NVLink 拓扑显示正确
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└── 核对: NVLink 拓扑显示正确
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步骤 3: 健康诊断
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步骤 3: 健康诊断
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├── python3 h200_tester.py --test health
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├── python3 gpu_tester.py --test health
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├── 确认: 所有检查项 PASS
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├── 确认: 所有检查项 PASS
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├── 重点关注: ECC 双比特错误 = 0
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├── 重点关注: ECC 双比特错误 = 0
|
||||||
├── 重点关注: 温度 < 80°C（空载）
|
├── 重点关注: 温度 < 80°C（空载）
|
||||||
@ -419,29 +421,29 @@ report:
|
|||||||
└── 如有 WARN/FAIL: 记录问题，联系供应商
|
└── 如有 WARN/FAIL: 记录问题，联系供应商
|
||||||
|
|
||||||
步骤 4: 内存带宽基准
|
步骤 4: 内存带宽基准
|
||||||
├── python3 h200_tester.py --test benchmark --type memory
|
├── python3 gpu_tester.py --test benchmark --type memory
|
||||||
├── 确认: D2D 带宽效率 ≥ 90%（自动与 GPU 峰值对比）
|
├── 确认: D2D 带宽效率 ≥ 90%（自动与 GPU 峰值对比）
|
||||||
└── 低于 80%: 检查散热/ECC/固件版本
|
└── 低于 80%: 检查散热/ECC/固件版本
|
||||||
|
|
||||||
步骤 5: 计算吞吐基准
|
步骤 5: 计算吞吐基准
|
||||||
├── python3 h200_tester.py --test benchmark --type compute
|
├── python3 gpu_tester.py --test benchmark --type compute
|
||||||
├── 确认: 各精度 TFLOPS ≥ 峰值的 80%（自动与 GPU 规格对比）
|
├── 确认: 各精度 TFLOPS ≥ 峰值的 80%（自动与 GPU 规格对比）
|
||||||
└── 异常低: 检查功耗限制、时钟频率、驱动版本
|
└── 异常低: 检查功耗限制、时钟频率、驱动版本
|
||||||
|
|
||||||
步骤 6: NCCL 多卡通信
|
步骤 6: NCCL 多卡通信
|
||||||
├── python3 h200_tester.py --test nccl
|
├── python3 gpu_tester.py --test nccl
|
||||||
├── 确认: AllReduce/AllToAll bus bandwidth ≥ 最低阈值（自动根据 NVLink 带宽计算）
|
├── 确认: AllReduce/AllToAll bus bandwidth ≥ 最低阈值（自动根据 NVLink 带宽计算）
|
||||||
└── 异常低: 检查 NVLink 连接、NVSwitch 状态
|
└── 异常低: 检查 NVLink 连接、NVSwitch 状态
|
||||||
|
|
||||||
步骤 7: 压力稳定性
|
步骤 7: 压力稳定性
|
||||||
├── 修改 configs/default.yaml: stress.duration_sec = 600（10分钟）
|
├── 修改 configs/default.yaml: stress.duration_sec = 600（10分钟）
|
||||||
├── python3 h200_tester.py --test stress
|
├── python3 gpu_tester.py --test stress
|
||||||
├── 确认: 所有 GPU PASS
|
├── 确认: 所有 GPU PASS
|
||||||
├── 测试期间观察: 温度不超 90°C
|
├── 测试期间观察: 温度不超 90°C
|
||||||
└── 测试期间观察: 无 ECC 错误增长
|
└── 测试期间观察: 无 ECC 错误增长
|
||||||
|
|
||||||
步骤 8: 生成验收报告
|
步骤 8: 生成验收报告
|
||||||
├── python3 h200_tester.py --test all
|
├── python3 gpu_tester.py --test all
|
||||||
├── 检查 reports/ 目录下的报告文件
|
├── 检查 reports/ 目录下的报告文件
|
||||||
└── 保存报告作为验收依据
|
└── 保存报告作为验收依据
|
||||||
```
|
```
|
||||||
@ -463,7 +465,7 @@ report:
|
|||||||
频率: 每周一次 或 维护窗口
|
频率: 每周一次 或 维护窗口
|
||||||
|
|
||||||
步骤:
|
步骤:
|
||||||
1. python3 h200_tester.py --test health
|
1. python3 gpu_tester.py --test health
|
||||||
2. 重点关注:
|
2. 重点关注:
|
||||||
- ECC 错误是否增长
|
- ECC 错误是否增长
|
||||||
- 温度是否异常升高
|
- 温度是否异常升高
|
||||||
@ -489,7 +491,7 @@ report:
|
|||||||
└── 确保所有单节点测试通过
|
└── 确保所有单节点测试通过
|
||||||
|
|
||||||
步骤 2: RDMA 网络测试
|
步骤 2: RDMA 网络测试
|
||||||
├── python3 h200_tester.py --test rdma
|
├── python3 gpu_tester.py --test rdma
|
||||||
├── 确认: IB 设备被识别
|
├── 确认: IB 设备被识别
|
||||||
├── 确认: 端口状态 Active
|
├── 确认: 端口状态 Active
|
||||||
├── 确认: 写带宽 ≥ 50 GB/s
|
├── 确认: 写带宽 ≥ 50 GB/s
|
||||||
@ -504,12 +506,12 @@ report:
|
|||||||
│ export NCCL_DEBUG=INFO
|
│ export NCCL_DEBUG=INFO
|
||||||
├── 运行 nccl-tests 手动测试:
|
├── 运行 nccl-tests 手动测试:
|
||||||
│ mpirun -np <总GPU数> -hostfile hosts \
|
│ mpirun -np <总GPU数> -hostfile hosts \
|
||||||
│ /opt/h200-test-tools/nccl-tests/build/all_reduce_perf \
|
│ /opt/gpu-test-tools/nccl-tests/build/all_reduce_perf \
|
||||||
│ -b 8 -e 256M -f 2 -g 1 -w 5 -n 20
|
│ -b 8 -e 256M -f 2 -g 1 -w 5 -n 20
|
||||||
└── 确认: 多节点 AllReduce 带宽正常
|
└── 确认: 多节点 AllReduce 带宽正常
|
||||||
|
|
||||||
步骤 4: 训练验证
|
步骤 4: 训练验证
|
||||||
├── python3 h200_tester.py --test training
|
├── python3 gpu_tester.py --test training
|
||||||
├── 可选: 加载更大模型（如 llama 模型）
|
├── 可选: 加载更大模型（如 llama 模型）
|
||||||
└── 确认: 训练 loss 正常下降
|
└── 确认: 训练 loss 正常下降
|
||||||
```
|
```
|
||||||
@ -522,29 +524,29 @@ report:
|
|||||||
|
|
||||||
```
|
```
|
||||||
步骤 1: 快速诊断
|
步骤 1: 快速诊断
|
||||||
├── python3 h200_tester.py --test health
|
├── python3 gpu_tester.py --test health
|
||||||
├── python3 h200_tester.py --test gpu-info
|
├── python3 gpu_tester.py --test gpu-info
|
||||||
└── 记录所有 WARN/FAIL 项
|
└── 记录所有 WARN/FAIL 项
|
||||||
|
|
||||||
步骤 2: 定位故障 GPU
|
步骤 2: 定位故障 GPU
|
||||||
├── 检查 nvidia-smi 输出
|
├── 检查 nvidia-smi 输出
|
||||||
├── 关注: 温度、ECC、功耗异常的 GPU
|
├── 关注: 温度、ECC、功耗异常的 GPU
|
||||||
└── 对故障 GPU 执行:
|
└── 对故障 GPU 执行:
|
||||||
python3 h200_tester.py --test stress
|
python3 gpu_tester.py --test stress
|
||||||
(stress.gpus 设为故障 GPU 编号)
|
(stress.gpus 设为故障 GPU 编号)
|
||||||
|
|
||||||
步骤 3: 通信排查
|
步骤 3: 通信排查
|
||||||
├── python3 h200_tester.py --test nccl
|
├── python3 gpu_tester.py --test nccl
|
||||||
├── 如果 AllReduce 带宽异常低:
|
├── 如果 AllReduce 带宽异常低:
|
||||||
│ - 检查 NVLink 连接: nvidia-smi nvlink -s
|
│ - 检查 NVLink 连接: nvidia-smi nvlink -s
|
||||||
│ - 检查 NVSwitch: nvidia-smi nvswitch -a
|
│ - 检查 NVSwitch: nvidia-smi nvswitch -a
|
||||||
│ - 重置 GPU: nvidia-smi -i <id> -r
|
│ - 重置 GPU: nvidia-smi -i <id> -r
|
||||||
└── 如果多节点异常:
|
└── 如果多节点异常:
|
||||||
python3 h200_tester.py --test rdma
|
python3 gpu_tester.py --test rdma
|
||||||
|
|
||||||
步骤 4: 固件/驱动排查
|
步骤 4: 固件/驱动排查
|
||||||
├── nvidia-smi -q | head -20 (查看驱动/CUDA 版本)
|
├── nvidia-smi -q | head -20 (查看驱动/CUDA 版本)
|
||||||
├── 确认驱动版本满足要求（Hopper ≥ 535, Blackwell ≥ 550）
|
├── 确认驱动版本满足要求（Ampere ≥ 470, Hopper ≥ 535, Blackwell ≥ 550）
|
||||||
├── 确认固件版本与集群一致
|
├── 确认固件版本与集群一致
|
||||||
└── 必要时更新: apt upgrade nvidia-driver-*
|
└── 必要时更新: apt upgrade nvidia-driver-*
|
||||||
```
|
```
|
||||||
@ -560,12 +562,12 @@ report:
|
|||||||
|
|
||||||
步骤:
|
步骤:
|
||||||
1. 变更前运行全量测试，保存基线报告:
|
1. 变更前运行全量测试，保存基线报告:
|
||||||
python3 h200_tester.py --test all
|
python3 gpu_tester.py --test all
|
||||||
|
|
||||||
2. 执行变更（驱动升级/固件更新等）
|
2. 执行变更（驱动升级/固件更新等）
|
||||||
|
|
||||||
3. 变更后再次运行:
|
3. 变更后再次运行:
|
||||||
python3 h200_tester.py --test all
|
python3 gpu_tester.py --test all
|
||||||
|
|
||||||
4. 对比两份报告:
|
4. 对比两份报告:
|
||||||
- 内存带宽偏差 < 5%
|
- 内存带宽偏差 < 5%
|
||||||
@ -587,7 +589,7 @@ report:
|
|||||||
### JSON 格式
|
### JSON 格式
|
||||||
|
|
||||||
```bash
|
```bash
|
||||||
python3 h200_tester.py --test all
|
python3 gpu_tester.py --test all
|
||||||
# 报告位置: ./reports/gpu_report_<timestamp>.json
|
# 报告位置: ./reports/gpu_report_<timestamp>.json
|
||||||
```
|
```
|
||||||
|
|
||||||
@ -596,7 +598,7 @@ python3 h200_tester.py --test all
|
|||||||
### HTML 格式
|
### HTML 格式
|
||||||
|
|
||||||
```bash
|
```bash
|
||||||
python3 h200_tester.py --test all --format html --output report.html
|
python3 gpu_tester.py --test all --format html --output report.html
|
||||||
```
|
```
|
||||||
|
|
||||||
生成深色主题的可视化报告，包含：
|
生成深色主题的可视化报告，包含：
|
||||||
@ -612,7 +614,7 @@ python3 h200_tester.py --test all --format html --output report.html
|
|||||||
|
|
||||||
| 问题 | 原因 | 解决方案 |
|
| 问题 | 原因 | 解决方案 |
|
||||||
|---|---|---|
|
|---|---|---|
|
||||||
| `nvidia-smi not found` | 驱动未安装 | 安装 NVIDIA 驱动（Hopper ≥ 535, Blackwell ≥ 550） |
|
| `nvidia-smi not found` | 驱动未安装 | 安装 NVIDIA 驱动（Ampere ≥ 470, Hopper ≥ 535, Blackwell ≥ 550） |
|
||||||
| `nvbandwidth not found` | 未编译安装 | 运行 `install_deps.sh` 或手动编译 |
|
| `nvbandwidth not found` | 未编译安装 | 运行 `install_deps.sh` 或手动编译 |
|
||||||
| `nccl-tests not found` | 未编译安装 | 运行 `install_deps.sh`，确认 CUDA_HOME 正确 |
|
| `nccl-tests not found` | 未编译安装 | 运行 `install_deps.sh`，确认 CUDA_HOME 正确 |
|
||||||
| `mpirun not found` | MPI 未安装 | `apt install openmpi-bin libopenmpi-dev` |
|
| `mpirun not found` | MPI 未安装 | `apt install openmpi-bin libopenmpi-dev` |
|
||||||
@ -631,18 +633,18 @@ python3 h200_tester.py --test all --format html --output report.html
|
|||||||
|
|
||||||
系统自动检测 GPU 型号，以下为各型号参考规格（dense TFLOPS）：
|
系统自动检测 GPU 型号，以下为各型号参考规格（dense TFLOPS）：
|
||||||
|
|
||||||
| 参数 | H100 SXM5 | H200 SXM | B200 SXM | B300 SXM |
|
| 参数 | A100 SXM | A800 SXM | H100 SXM5 | H200 SXM | B200 SXM | B300 SXM |
|
||||||
|---|---|---|---|---|
|
|---|---|---|---|---|---|---|
|
||||||
| 架构 | Hopper | Hopper | Blackwell | Blackwell Ultra |
|
| 架构 | Ampere | Ampere | Hopper | Hopper | Blackwell | Blackwell Ultra |
|
||||||
| 计算能力 | 9.0 | 9.0 | 10.0 | 10.0 |
|
| 计算能力 | 8.0 | 8.0 | 9.0 | 9.0 | 10.0 | 10.0 |
|
||||||
| HBM 容量 | 80 GB (HBM3) | 141 GB (HBM3e) | 180 GB (HBM3e) | 288 GB (HBM3e) |
|
| HBM 容量 | 80 GB (HBM2e) | 80 GB (HBM2e) | 80 GB (HBM3) | 141 GB (HBM3e) | 180 GB (HBM3e) | 288 GB (HBM3e) |
|
||||||
| 内存带宽 | 3,400 GB/s | 4,800 GB/s | 8,000 GB/s | 8,000 GB/s |
|
| 内存带宽 | 2,039 GB/s | 2,039 GB/s | 3,400 GB/s | 4,800 GB/s | 8,000 GB/s | 8,000 GB/s |
|
||||||
| TDP | 700W | 700W | 1,000W | 1,200W |
|
| TDP | 400W | 400W | 700W | 700W | 1,000W | 1,200W |
|
||||||
| FP32 | 67 TFLOPS | 67 TFLOPS | 90 TFLOPS | 125 TFLOPS |
|
| FP32 | 19.5 TFLOPS | 19.5 TFLOPS | 67 TFLOPS | 67 TFLOPS | 90 TFLOPS | 125 TFLOPS |
|
||||||
| TF32 (dense) | 495 TFLOPS | 495 TFLOPS | 1,125 TFLOPS | 1,750 TFLOPS |
|
| TF32 (dense) | 156 TFLOPS | 156 TFLOPS | 495 TFLOPS | 495 TFLOPS | 1,125 TFLOPS | 1,750 TFLOPS |
|
||||||
| FP16/BF16 (dense) | 990 TFLOPS | 990 TFLOPS | 2,250 TFLOPS | 3,500 TFLOPS |
|
| FP16/BF16 (dense) | 312 TFLOPS | 312 TFLOPS | 990 TFLOPS | 990 TFLOPS | 2,250 TFLOPS | 3,500 TFLOPS |
|
||||||
| FP8 (dense) | 1,979 TFLOPS | 1,979 TFLOPS | 4,500 TFLOPS | 7,000 TFLOPS |
|
| FP8 (dense) | N/A | N/A | 1,979 TFLOPS | 1,979 TFLOPS | 4,500 TFLOPS | 7,000 TFLOPS |
|
||||||
| NVLink | 第 4 代, 900 GB/s | 第 4 代, 900 GB/s | 第 5 代, 1,800 GB/s | 第 5 代, 1,800 GB/s |
|
| NVLink | 第 3 代, 600 GB/s | 第 3 代, 600 GB/s | 第 4 代, 900 GB/s | 第 4 代, 900 GB/s | 第 5 代, 1,800 GB/s | 第 5 代, 1,800 GB/s |
|
||||||
| PCIe | Gen5 x16 | Gen5 x16 | Gen5 x16 | Gen5 x16 |
|
| PCIe | Gen4 x16 | Gen4 x16 | Gen5 x16 | Gen5 x16 | Gen5 x16 | Gen5 x16 |
|
||||||
| 最低驱动 | 535 | 535 | 550 | 550 |
|
| 最低驱动 | 470 | 470 | 535 | 535 | 550 | 550 |
|
||||||
| 最低 CUDA | 12.1 | 12.1 | 12.4 | 12.4 |
|
| 最低 CUDA | 11.0 | 11.0 | 12.1 | 12.1 | 12.4 | 12.4 |
|
||||||
|
|||||||
@ -1,4 +1,4 @@
|
|||||||
# GPU type: auto-detect or override to h100/h200/b200/b300
|
# GPU type: auto-detect or override to a100/a800/h100/h200/b200/b300
|
||||||
gpu_type: auto
|
gpu_type: auto
|
||||||
|
|
||||||
benchmark:
|
benchmark:
|
||||||
@ -21,7 +21,7 @@ benchmark:
|
|||||||
health:
|
health:
|
||||||
temp_warning: 80
|
temp_warning: 80
|
||||||
temp_critical: 90
|
temp_critical: 90
|
||||||
power_limit: null # null = auto-detect from GPU TDP (H100/H200: 700W, B200: 1000W, B300: 1200W)
|
power_limit: null # null = auto-detect from GPU TDP per gpu_specs.py
|
||||||
|
|
||||||
nccl:
|
nccl:
|
||||||
min_bandwidth_gbps: null # null = auto-detect (40% of GPU NVLink BW)
|
min_bandwidth_gbps: null # null = auto-detect (40% of GPU NVLink BW)
|
||||||
@ -59,4 +59,4 @@ report:
|
|||||||
format: json
|
format: json
|
||||||
|
|
||||||
tools:
|
tools:
|
||||||
install_dir: /opt/h200-test-tools
|
install_dir: /opt/gpu-test-tools
|
||||||
|
|||||||
@ -1,5 +1,5 @@
|
|||||||
#!/usr/bin/env python3
|
#!/usr/bin/env python3
|
||||||
"""GPU Training Server Test Suite (H100/H200/B200/B300) - Main CLI Entry Point."""
|
"""GPU Training Server Test Suite (A100/A800/H100/H200/B200/B300) - Main CLI Entry Point."""
|
||||||
|
|
||||||
import argparse
|
import argparse
|
||||||
import json
|
import json
|
||||||
@ -26,7 +26,7 @@ from modules.training_sim import TrainingSim
|
|||||||
from modules.stress_test import StressTest
|
from modules.stress_test import StressTest
|
||||||
from modules.rdma_test import RDMATest
|
from modules.rdma_test import RDMATest
|
||||||
from modules.report import ReportGenerator
|
from modules.report import ReportGenerator
|
||||||
from modules.gpu_specs import detect_gpu_type, get_gpu_specs, get_gpu_label, get_supported_gpus
|
from modules.gpu_specs import detect_gpu_type, get_gpu_specs, get_gpu_label, get_supported_gpus, validate_driver_compatibility
|
||||||
|
|
||||||
DEFAULT_CONFIG = {
|
DEFAULT_CONFIG = {
|
||||||
"benchmark": {
|
"benchmark": {
|
||||||
@ -71,18 +71,18 @@ DEFAULT_CONFIG = {
|
|||||||
"dtype": "bf16",
|
"dtype": "bf16",
|
||||||
},
|
},
|
||||||
"report": {"output_dir": "./reports", "format": "json"},
|
"report": {"output_dir": "./reports", "format": "json"},
|
||||||
"tools": {"install_dir": "/opt/h200-test-tools"},
|
"tools": {"install_dir": "/opt/gpu-test-tools"},
|
||||||
}
|
}
|
||||||
|
|
||||||
BANNER = r"""
|
BANNER = r"""
|
||||||
[bold cyan]
|
[bold cyan]
|
||||||
╔══════════════════════════════════════════════════╗
|
╔══════════════════════════════════════════════════════╗
|
||||||
║ ║
|
║ ║
|
||||||
║ GPU Training Server Test Suite ║
|
║ GPU Training Server Test Suite ║
|
||||||
║ Diagnostics & Benchmarking Tool ║
|
║ Diagnostics & Benchmarking Tool ║
|
||||||
║ Supports: H100 / H200 / B200 / B300 ║
|
║ Supports: A100 / A800 / H100 / H200 / B200 / B300 ║
|
||||||
║ ║
|
║ ║
|
||||||
╚══════════════════════════════════════════════════╝
|
╚══════════════════════════════════════════════════════════╝
|
||||||
[/bold cyan]
|
[/bold cyan]
|
||||||
"""
|
"""
|
||||||
|
|
||||||
@ -121,6 +121,11 @@ def interactive_menu(config: dict):
|
|||||||
else:
|
else:
|
||||||
console.print("[yellow]GPU type could not be auto-detected. Using default thresholds.[/yellow]\n")
|
console.print("[yellow]GPU type could not be auto-detected. Using default thresholds.[/yellow]\n")
|
||||||
|
|
||||||
|
# Driver / CUDA compatibility check
|
||||||
|
compat_warnings = validate_driver_compatibility(gpu_type)
|
||||||
|
for w in compat_warnings:
|
||||||
|
console.print(f"[bold yellow]\u26a0 {w}[/bold yellow]")
|
||||||
|
|
||||||
if not check_prerequisites(console):
|
if not check_prerequisites(console):
|
||||||
return
|
return
|
||||||
|
|
||||||
@ -314,20 +319,20 @@ def _run_full_suite(config: dict, console: Console) -> dict:
|
|||||||
|
|
||||||
def main():
|
def main():
|
||||||
parser = argparse.ArgumentParser(
|
parser = argparse.ArgumentParser(
|
||||||
description="GPU Training Server Test Suite (H100/H200/B200/B300)",
|
description="GPU Training Server Test Suite (A100/A800/H100/H200/B200/B300)",
|
||||||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||||||
epilog="""
|
epilog="""
|
||||||
Examples:
|
Examples:
|
||||||
python h200_tester.py # Interactive menu
|
python gpu_tester.py # Interactive menu
|
||||||
python h200_tester.py --gpu-type h200 # Override GPU type
|
python gpu_tester.py --gpu-type a800 # Override GPU type
|
||||||
python h200_tester.py --test gpu-info # GPU info
|
python gpu_tester.py --test gpu-info # GPU info
|
||||||
python h200_tester.py --test health # Health check
|
python gpu_tester.py --test health # Health check
|
||||||
python h200_tester.py --test benchmark --type memory
|
python gpu_tester.py --test benchmark --type memory
|
||||||
python h200_tester.py --test benchmark --type compute --dtype fp16
|
python gpu_tester.py --test benchmark --type compute --dtype fp16
|
||||||
python h200_tester.py --test nccl # NCCL test
|
python gpu_tester.py --test nccl # NCCL test
|
||||||
python h200_tester.py --test training # Training sim
|
python gpu_tester.py --test training # Training sim
|
||||||
python h200_tester.py --test all # Full suite
|
python gpu_tester.py --test all # Full suite
|
||||||
python h200_tester.py --report --format json --output report.json
|
python gpu_tester.py --report --format json --output report.json
|
||||||
""",
|
""",
|
||||||
)
|
)
|
||||||
parser.add_argument("--test", choices=["gpu-info", "health", "benchmark", "nccl", "stress", "rdma", "training", "all"],
|
parser.add_argument("--test", choices=["gpu-info", "health", "benchmark", "nccl", "stress", "rdma", "training", "all"],
|
||||||
@ -337,11 +342,15 @@ Examples:
|
|||||||
help="Compute benchmark dtype (with --test benchmark --type compute)")
|
help="Compute benchmark dtype (with --test benchmark --type compute)")
|
||||||
parser.add_argument("--interactive", action="store_true", help="Force interactive mode")
|
parser.add_argument("--interactive", action="store_true", help="Force interactive mode")
|
||||||
parser.add_argument("--report", action="store_true", help="Generate report from last results")
|
parser.add_argument("--report", action="store_true", help="Generate report from last results")
|
||||||
parser.add_argument("--format", choices=["json", "html"], default="json", help="Report format")
|
parser.add_argument("--format", choices=["json", "html", "md"], default="json", help="Report format")
|
||||||
parser.add_argument("--output", default=None, help="Report output file path")
|
parser.add_argument("--output", default=None, help="Report output file path")
|
||||||
parser.add_argument("--config", default=None, help="Path to config YAML file")
|
parser.add_argument("--config", default=None, help="Path to config YAML file")
|
||||||
parser.add_argument("--gpu-type", choices=["auto", "h100", "h200", "b200", "b300"],
|
parser.add_argument(
|
||||||
default="auto", help="Override GPU type detection")
|
"--gpu-type",
|
||||||
|
choices=["auto", "a100", "a800", "h100", "h200", "b200", "b300"],
|
||||||
|
default="auto",
|
||||||
|
help="Override GPU type detection",
|
||||||
|
)
|
||||||
|
|
||||||
args = parser.parse_args()
|
args = parser.parse_args()
|
||||||
config = load_config()
|
config = load_config()
|
||||||
@ -359,6 +368,11 @@ Examples:
|
|||||||
|
|
||||||
console = Console()
|
console = Console()
|
||||||
|
|
||||||
|
# Driver / CUDA compatibility check
|
||||||
|
compat_warnings = validate_driver_compatibility(config["gpu_type"])
|
||||||
|
for w in compat_warnings:
|
||||||
|
console.print(f"[bold yellow]\u26a0 {w}[/bold yellow]")
|
||||||
|
|
||||||
# Handle --report standalone
|
# Handle --report standalone
|
||||||
if args.report and not args.test:
|
if args.report and not args.test:
|
||||||
console.print("[yellow]Run tests first to generate a report.[/yellow]")
|
console.print("[yellow]Run tests first to generate a report.[/yellow]")
|
||||||
@ -396,17 +410,19 @@ Examples:
|
|||||||
result = bench.run()
|
result = bench.run()
|
||||||
Benchmark.print_results(result)
|
Benchmark.print_results(result)
|
||||||
if args.report:
|
if args.report:
|
||||||
ReportGenerator(config).generate({"benchmark": result, "timestamp": datetime.now().isoformat()})
|
ReportGenerator(config).generate({"benchmark": result, "timestamp": datetime.now().isoformat()},
|
||||||
|
fmt=args.format, output=args.output)
|
||||||
elif args.test == "all":
|
elif args.test == "all":
|
||||||
results = _run_full_suite(config, console)
|
results = _run_full_suite(config, console)
|
||||||
if args.report:
|
if args.report:
|
||||||
ReportGenerator(config).generate(results)
|
ReportGenerator(config).generate(results, fmt=args.format, output=args.output)
|
||||||
has_errors = any("error" in v for v in results.values() if isinstance(v, dict))
|
has_errors = any("error" in v for v in results.values() if isinstance(v, dict))
|
||||||
sys.exit(1 if has_errors else 0)
|
sys.exit(1 if has_errors else 0)
|
||||||
else:
|
else:
|
||||||
result = _run_test(test_map[args.test], config, console)
|
result = _run_test(test_map[args.test], config, console)
|
||||||
if args.report and result:
|
if args.report and result:
|
||||||
ReportGenerator(config).generate({args.test: result, "timestamp": datetime.now().isoformat()})
|
ReportGenerator(config).generate({args.test: result, "timestamp": datetime.now().isoformat()},
|
||||||
|
fmt=args.format, output=args.output)
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
@ -1,7 +1,7 @@
|
|||||||
#!/usr/bin/env bash
|
#!/usr/bin/env bash
|
||||||
set -euo pipefail
|
set -euo pipefail
|
||||||
|
|
||||||
INSTALL_DIR="${GPU_TOOLS_DIR:-${H200_TOOLS_DIR:-/opt/h200-test-tools}}"
|
INSTALL_DIR="${GPU_TOOLS_DIR:-/opt/gpu-test-tools}"
|
||||||
JOBS="${MAKE_JOBS:-$(nproc)}"
|
JOBS="${MAKE_JOBS:-$(nproc)}"
|
||||||
VERBOSE="${VERBOSE:-0}"
|
VERBOSE="${VERBOSE:-0}"
|
||||||
|
|
||||||
@ -203,8 +203,8 @@ print_summary() {
|
|||||||
|
|
||||||
echo ""
|
echo ""
|
||||||
echo " Usage:"
|
echo " Usage:"
|
||||||
echo " python3 h200_tester.py # Interactive menu"
|
echo " python3 gpu_tester.py # Interactive menu"
|
||||||
echo " python3 h200_tester.py --test all # Full suite"
|
echo " python3 gpu_tester.py --test all # Full suite"
|
||||||
echo ""
|
echo ""
|
||||||
}
|
}
|
||||||
|
|
||||||
|
|||||||
@ -1,6 +1,7 @@
|
|||||||
"""GPU benchmark module — nvbandwidth + PyTorch compute throughput."""
|
"""GPU benchmark module — nvbandwidth + PyTorch compute throughput."""
|
||||||
|
|
||||||
import json
|
import json
|
||||||
|
import os
|
||||||
import shutil
|
import shutil
|
||||||
import subprocess
|
import subprocess
|
||||||
import time
|
import time
|
||||||
@ -11,7 +12,7 @@ from rich.console import Console
|
|||||||
from rich.table import Table
|
from rich.table import Table
|
||||||
from rich.progress import Progress, SpinnerColumn, BarColumn, TextColumn, TimeElapsedColumn
|
from rich.progress import Progress, SpinnerColumn, BarColumn, TextColumn, TimeElapsedColumn
|
||||||
|
|
||||||
from modules.gpu_specs import detect_gpu_type, get_gpu_specs, get_gpu_label
|
from modules.gpu_specs import detect_gpu_type, get_gpu_specs, get_gpu_label, resolve_tools_dir
|
||||||
|
|
||||||
TORCH_AVAILABLE = False
|
TORCH_AVAILABLE = False
|
||||||
try:
|
try:
|
||||||
@ -28,7 +29,7 @@ class Benchmark:
|
|||||||
self.config = config
|
self.config = config
|
||||||
self.console = Console()
|
self.console = Console()
|
||||||
self.bench_cfg = config.get("benchmark", {})
|
self.bench_cfg = config.get("benchmark", {})
|
||||||
self.tools_dir = config.get("tools", {}).get("install_dir", "/opt/h200-test-tools")
|
self.tools_dir = resolve_tools_dir(config)
|
||||||
self.gpu_type = detect_gpu_type()
|
self.gpu_type = detect_gpu_type()
|
||||||
self.specs = get_gpu_specs(self.gpu_type)
|
self.specs = get_gpu_specs(self.gpu_type)
|
||||||
self.gpu_label = get_gpu_label(self.gpu_type)
|
self.gpu_label = get_gpu_label(self.gpu_type)
|
||||||
@ -40,12 +41,24 @@ class Benchmark:
|
|||||||
return results
|
return results
|
||||||
|
|
||||||
def _find_nvbandwidth(self) -> Optional[str]:
|
def _find_nvbandwidth(self) -> Optional[str]:
|
||||||
|
# 1. System PATH
|
||||||
p = shutil.which("nvbandwidth")
|
p = shutil.which("nvbandwidth")
|
||||||
if p:
|
if p:
|
||||||
return p
|
return p
|
||||||
local = shutil.os.path.join(self.tools_dir, "nvbandwidth", "nvbandwidth")
|
# 2. tools_dir
|
||||||
if shutil.os.path.isfile(local) and shutil.os.access(local, shutil.os.X_OK):
|
local = os.path.join(self.tools_dir, "nvbandwidth", "nvbandwidth")
|
||||||
|
if os.path.isfile(local) and os.access(local, os.X_OK):
|
||||||
return local
|
return local
|
||||||
|
# 3. Common DCGM / system locations
|
||||||
|
extra_paths = [
|
||||||
|
"/usr/libexec/datacenter-gpu-manager-4/plugins/cuda12/nvbandwidth",
|
||||||
|
"/usr/libexec/datacenter-gpu-manager/plugins/cuda12/nvbandwidth",
|
||||||
|
"/usr/local/bin/nvbandwidth",
|
||||||
|
"/opt/nvidia/nvbandwidth/nvbandwidth",
|
||||||
|
]
|
||||||
|
for ep in extra_paths:
|
||||||
|
if os.path.isfile(ep) and os.access(ep, os.X_OK):
|
||||||
|
return ep
|
||||||
return None
|
return None
|
||||||
|
|
||||||
def run_memory_benchmark(self) -> dict:
|
def run_memory_benchmark(self) -> dict:
|
||||||
@ -140,7 +153,10 @@ class Benchmark:
|
|||||||
)
|
)
|
||||||
h2d_bw = results_by_test.get("host_to_device_memcpy_read_ce", 0)
|
h2d_bw = results_by_test.get("host_to_device_memcpy_read_ce", 0)
|
||||||
d2h_bw = results_by_test.get("device_to_host_memcpy_write_ce", 0)
|
d2h_bw = results_by_test.get("device_to_host_memcpy_write_ce", 0)
|
||||||
efficiency = (d2d_bw / self.specs["memory_bandwidth_gbps"]) * 100 if d2d_bw else 0
|
peak_bw = self.specs["memory_bandwidth_gbps"]
|
||||||
|
efficiency = (
|
||||||
|
(d2d_bw / peak_bw) * 100 if (d2d_bw and peak_bw) else 0
|
||||||
|
)
|
||||||
|
|
||||||
return {
|
return {
|
||||||
"memory": {
|
"memory": {
|
||||||
@ -216,7 +232,8 @@ class Benchmark:
|
|||||||
progress.advance(task)
|
progress.advance(task)
|
||||||
|
|
||||||
best_d2d = max(v["d2d_gbps"] for v in bandwidth_by_size.values())
|
best_d2d = max(v["d2d_gbps"] for v in bandwidth_by_size.values())
|
||||||
efficiency = (best_d2d / self.specs["memory_bandwidth_gbps"]) * 100
|
peak_bw = self.specs["memory_bandwidth_gbps"]
|
||||||
|
efficiency = (best_d2d / peak_bw) * 100 if peak_bw else 0.0
|
||||||
|
|
||||||
return {
|
return {
|
||||||
"memory": {
|
"memory": {
|
||||||
@ -269,6 +286,14 @@ class Benchmark:
|
|||||||
progress.advance(task)
|
progress.advance(task)
|
||||||
continue
|
continue
|
||||||
|
|
||||||
|
# Skip FP8 if GPU architecture doesn't support it
|
||||||
|
if dtype_name == "fp8" and self.specs.get("fp8_tflops", 0) == 0:
|
||||||
|
arch = self.specs.get("architecture", "unknown")
|
||||||
|
results_by_dtype["fp8"] = f"skipped ({arch} does not support FP8)"
|
||||||
|
self.console.print(f"[dim] fp8: skipped - {arch} architecture has no FP8 support[/dim]")
|
||||||
|
progress.advance(task)
|
||||||
|
continue
|
||||||
|
|
||||||
dtype_val, peak_tflops = dtype_map[dtype_name]
|
dtype_val, peak_tflops = dtype_map[dtype_name]
|
||||||
|
|
||||||
try:
|
try:
|
||||||
@ -321,7 +346,9 @@ class Benchmark:
|
|||||||
efficiency = {}
|
efficiency = {}
|
||||||
for dt, achieved in results_by_dtype.items():
|
for dt, achieved in results_by_dtype.items():
|
||||||
if isinstance(achieved, (int, float)) and dt in dtype_map:
|
if isinstance(achieved, (int, float)) and dt in dtype_map:
|
||||||
efficiency[dt] = round((achieved / dtype_map[dt][1]) * 100, 1)
|
peak_tp = dtype_map[dt][1]
|
||||||
|
if peak_tp:
|
||||||
|
efficiency[dt] = round((achieved / peak_tp) * 100, 1)
|
||||||
|
|
||||||
return {
|
return {
|
||||||
"compute": {
|
"compute": {
|
||||||
@ -382,10 +409,13 @@ class Benchmark:
|
|||||||
t2.add_column("D2D (GB/s)", justify="right")
|
t2.add_column("D2D (GB/s)", justify="right")
|
||||||
for sz, vals in sorted(by_size.items(), key=lambda x: int(x[0])):
|
for sz, vals in sorted(by_size.items(), key=lambda x: int(x[0])):
|
||||||
peak = mem["peak_bandwidth_gbps"]
|
peak = mem["peak_bandwidth_gbps"]
|
||||||
|
if peak:
|
||||||
d2d_eff = (vals["d2d_gbps"] / peak) * 100
|
d2d_eff = (vals["d2d_gbps"] / peak) * 100
|
||||||
ec = "green" if d2d_eff >= 80 else ("yellow" if d2d_eff >= 50 else "red")
|
ec = "green" if d2d_eff >= 80 else ("yellow" if d2d_eff >= 50 else "red")
|
||||||
t2.add_row(sz, f"{vals['h2d_gbps']:.1f}", f"{vals['d2h_gbps']:.1f}",
|
d2d_cell = f"[{ec}]{vals['d2d_gbps']:.1f}[/{ec}]"
|
||||||
f"[{ec}]{vals['d2d_gbps']:.1f}[/{ec}]")
|
else:
|
||||||
|
d2d_cell = f"{vals['d2d_gbps']:.1f}"
|
||||||
|
t2.add_row(sz, f"{vals['h2d_gbps']:.1f}", f"{vals['d2h_gbps']:.1f}", d2d_cell)
|
||||||
c.print(t2)
|
c.print(t2)
|
||||||
|
|
||||||
if "compute" in results and "error" not in results["compute"]:
|
if "compute" in results and "error" not in results["compute"]:
|
||||||
|
|||||||
@ -1,4 +1,4 @@
|
|||||||
"""GPU information detection module for NVIDIA datacenter GPUs (H100/H200/B200/B300)."""
|
"""GPU information detection module for NVIDIA datacenter GPUs (A100/A800/H100/H200/B200/B300)."""
|
||||||
|
|
||||||
import subprocess
|
import subprocess
|
||||||
import shutil
|
import shutil
|
||||||
@ -48,8 +48,8 @@ class GPUInfo:
|
|||||||
names = self._run_smi("name").split("\n") if self._run_smi("name") else []
|
names = self._run_smi("name").split("\n") if self._run_smi("name") else []
|
||||||
uuids = self._run_smi("uuid").split("\n") if self._run_smi("uuid") else []
|
uuids = self._run_smi("uuid").split("\n") if self._run_smi("uuid") else []
|
||||||
pcie_bus = self._run_smi("pci.bus_id").split("\n") if self._run_smi("pci.bus_id") else []
|
pcie_bus = self._run_smi("pci.bus_id").split("\n") if self._run_smi("pci.bus_id") else []
|
||||||
pcie_gen = self._run_smi("pcie_link.gen").split("\n") if self._run_smi("pcie_link.gen") else []
|
pcie_gen = self._run_smi("pcie.link.gen.current").split("\n") if self._run_smi("pcie.link.gen.current") else []
|
||||||
pcie_width = self._run_smi("pcie_link.width").split("\n") if self._run_smi("pcie_link.width") else []
|
pcie_width = self._run_smi("pcie.link.width.current").split("\n") if self._run_smi("pcie.link.width.current") else []
|
||||||
vram_total = self._run_smi("memory.total").split("\n") if self._run_smi("memory.total") else []
|
vram_total = self._run_smi("memory.total").split("\n") if self._run_smi("memory.total") else []
|
||||||
vram_used = self._run_smi("memory.used").split("\n") if self._run_smi("memory.used") else []
|
vram_used = self._run_smi("memory.used").split("\n") if self._run_smi("memory.used") else []
|
||||||
vram_free = self._run_smi("memory.free").split("\n") if self._run_smi("memory.free") else []
|
vram_free = self._run_smi("memory.free").split("\n") if self._run_smi("memory.free") else []
|
||||||
@ -166,7 +166,7 @@ class GPUInfo:
|
|||||||
|
|
||||||
for g in gpus:
|
for g in gpus:
|
||||||
name = g["name"]
|
name = g["name"]
|
||||||
if any(k in name for k in ("H100", "H200", "B200", "B300")):
|
if any(k in name for k in ("A100", "A800", "H100", "H200", "B200", "B300")):
|
||||||
name = f"[bold green]{name}[/bold green]"
|
name = f"[bold green]{name}[/bold green]"
|
||||||
vram = f"{g['vram_used_mb']}/{g['vram_total_mb']} MB"
|
vram = f"{g['vram_used_mb']}/{g['vram_total_mb']} MB"
|
||||||
temp = f"{g['temperature']}°C"
|
temp = f"{g['temperature']}°C"
|
||||||
|
|||||||
@ -1,10 +1,14 @@
|
|||||||
"""GPU specifications database for NVIDIA datacenter GPUs."""
|
"""GPU specifications database for NVIDIA datacenter GPUs (A100/A800/H100/H200/B200/B300)."""
|
||||||
|
|
||||||
|
import os
|
||||||
import shutil
|
import shutil
|
||||||
import subprocess
|
import subprocess
|
||||||
|
from typing import List, Optional
|
||||||
|
|
||||||
# GPU name patterns -> internal key mapping
|
# GPU name patterns -> internal key mapping
|
||||||
GPU_NAME_PATTERNS = {
|
GPU_NAME_PATTERNS = {
|
||||||
|
"A100": "a100",
|
||||||
|
"A800": "a800",
|
||||||
"H100": "h100",
|
"H100": "h100",
|
||||||
"H200": "h200",
|
"H200": "h200",
|
||||||
"B200": "b200",
|
"B200": "b200",
|
||||||
@ -70,6 +74,44 @@ GPU_SPECS = {
|
|||||||
"min_driver_version": "550",
|
"min_driver_version": "550",
|
||||||
"min_cuda_version": "12.4",
|
"min_cuda_version": "12.4",
|
||||||
},
|
},
|
||||||
|
"a100": {
|
||||||
|
"full_name": "NVIDIA A100 SXM",
|
||||||
|
"architecture": "Ampere",
|
||||||
|
"compute_capability": 8.0,
|
||||||
|
"hbm_capacity_gb": 80,
|
||||||
|
"hbm_type": "HBM2e",
|
||||||
|
"memory_bandwidth_gbps": 2039, # GB/s (~2.0 TB/s)
|
||||||
|
"fp32_tflops": 19.5,
|
||||||
|
"tf32_tflops": 156, # dense
|
||||||
|
"fp16_tflops": 312, # dense
|
||||||
|
"bf16_tflops": 312, # dense
|
||||||
|
"fp8_tflops": 0, # Ampere has no FP8
|
||||||
|
"tdp_watts": 400,
|
||||||
|
"nvlink_gen": 3,
|
||||||
|
"nvlink_bandwidth_gbps": 600, # bidirectional
|
||||||
|
"pcie_gen": 4,
|
||||||
|
"min_driver_version": "470",
|
||||||
|
"min_cuda_version": "11.0",
|
||||||
|
},
|
||||||
|
"a800": {
|
||||||
|
"full_name": "NVIDIA A800 SXM",
|
||||||
|
"architecture": "Ampere",
|
||||||
|
"compute_capability": 8.0,
|
||||||
|
"hbm_capacity_gb": 80,
|
||||||
|
"hbm_type": "HBM2e",
|
||||||
|
"memory_bandwidth_gbps": 2039, # GB/s (~2.0 TB/s)
|
||||||
|
"fp32_tflops": 19.5,
|
||||||
|
"tf32_tflops": 156, # dense
|
||||||
|
"fp16_tflops": 312, # dense
|
||||||
|
"bf16_tflops": 312, # dense
|
||||||
|
"fp8_tflops": 0, # Ampere has no FP8
|
||||||
|
"tdp_watts": 400,
|
||||||
|
"nvlink_gen": 3,
|
||||||
|
"nvlink_bandwidth_gbps": 600, # bidirectional (NVLink 3, limited vs A100)
|
||||||
|
"pcie_gen": 4,
|
||||||
|
"min_driver_version": "470",
|
||||||
|
"min_cuda_version": "11.0",
|
||||||
|
},
|
||||||
"b300": {
|
"b300": {
|
||||||
"full_name": "NVIDIA B300 SXM (Blackwell Ultra)",
|
"full_name": "NVIDIA B300 SXM (Blackwell Ultra)",
|
||||||
"architecture": "Blackwell Ultra",
|
"architecture": "Blackwell Ultra",
|
||||||
@ -162,3 +204,92 @@ def get_gpu_label(gpu_type: str) -> str:
|
|||||||
# Strip the "NVIDIA " prefix for display
|
# Strip the "NVIDIA " prefix for display
|
||||||
return full.replace("NVIDIA ", "")
|
return full.replace("NVIDIA ", "")
|
||||||
return "Unknown GPU"
|
return "Unknown GPU"
|
||||||
|
|
||||||
|
|
||||||
|
# ---------------------------------------------------------------------------
|
||||||
|
# Tools path resolution
|
||||||
|
# ---------------------------------------------------------------------------
|
||||||
|
|
||||||
|
def resolve_tools_dir(config: dict) -> str:
|
||||||
|
"""Resolve tools installation directory with smart fallback.
|
||||||
|
|
||||||
|
Priority: GPU_TOOLS_DIR env > config value > /opt/gpu-test-tools > /tmp/gpu-test-tools
|
||||||
|
"""
|
||||||
|
# 1. Env var override
|
||||||
|
env_dir = os.environ.get("GPU_TOOLS_DIR")
|
||||||
|
if env_dir:
|
||||||
|
return env_dir
|
||||||
|
# 2. Config value if explicitly set
|
||||||
|
cfg_dir = config.get("tools", {}).get("install_dir", "")
|
||||||
|
if cfg_dir:
|
||||||
|
return cfg_dir
|
||||||
|
# 3. /opt/gpu-test-tools if it already exists or /opt is writable
|
||||||
|
default = "/opt/gpu-test-tools"
|
||||||
|
if os.path.isdir(default) or os.access("/opt", os.W_OK):
|
||||||
|
return default
|
||||||
|
# 4. Fallback to /tmp
|
||||||
|
return "/tmp/gpu-test-tools"
|
||||||
|
|
||||||
|
|
||||||
|
# ---------------------------------------------------------------------------
|
||||||
|
# Driver / CUDA compatibility validation
|
||||||
|
# ---------------------------------------------------------------------------
|
||||||
|
|
||||||
|
def _query_nvidia_smi(field: str) -> Optional[str]:
|
||||||
|
"""Query a single nvidia-smi field, return value string or None."""
|
||||||
|
nvidia_smi = shutil.which("nvidia-smi")
|
||||||
|
if not nvidia_smi:
|
||||||
|
return None
|
||||||
|
try:
|
||||||
|
r = subprocess.run(
|
||||||
|
[nvidia_smi, f"--query-gpu={field}", "--format=csv,noheader,nounits"],
|
||||||
|
capture_output=True, text=True, timeout=10,
|
||||||
|
)
|
||||||
|
if r.returncode == 0 and r.stdout.strip():
|
||||||
|
return r.stdout.strip().splitlines()[0].strip()
|
||||||
|
except (subprocess.TimeoutExpired, FileNotFoundError, OSError):
|
||||||
|
pass
|
||||||
|
return None
|
||||||
|
|
||||||
|
|
||||||
|
def _version_lt(actual: str, minimum: str) -> bool:
|
||||||
|
"""Return True if actual version < minimum (numeric dotted comparison)."""
|
||||||
|
def to_tuple(v: str):
|
||||||
|
parts = []
|
||||||
|
for p in v.split("."):
|
||||||
|
try:
|
||||||
|
parts.append(int(p))
|
||||||
|
except ValueError:
|
||||||
|
break
|
||||||
|
return tuple(parts) if parts else (0,)
|
||||||
|
return to_tuple(actual) < to_tuple(minimum)
|
||||||
|
|
||||||
|
|
||||||
|
def validate_driver_compatibility(gpu_type: str) -> List[str]:
|
||||||
|
"""Check if current driver/CUDA meets minimum requirements for the detected GPU.
|
||||||
|
|
||||||
|
Returns a list of warning strings (empty if everything is fine).
|
||||||
|
"""
|
||||||
|
specs = get_gpu_specs(gpu_type)
|
||||||
|
warnings: List[str] = []
|
||||||
|
|
||||||
|
min_driver = specs.get("min_driver_version", "")
|
||||||
|
min_cuda = specs.get("min_cuda_version", "")
|
||||||
|
if not min_driver and not min_cuda:
|
||||||
|
return warnings
|
||||||
|
|
||||||
|
actual_driver = _query_nvidia_smi("driver_version")
|
||||||
|
# nvidia-smi reports the highest CUDA version supported by the driver
|
||||||
|
actual_cuda = _query_nvidia_smi("cuda_version")
|
||||||
|
|
||||||
|
gpu_label = get_gpu_label(gpu_type)
|
||||||
|
|
||||||
|
if actual_driver and min_driver and _version_lt(actual_driver, min_driver):
|
||||||
|
warnings.append(
|
||||||
|
f"Driver {actual_driver} < minimum {min_driver} required for {gpu_label}"
|
||||||
|
)
|
||||||
|
if actual_cuda and min_cuda and _version_lt(actual_cuda, min_cuda):
|
||||||
|
warnings.append(
|
||||||
|
f"CUDA {actual_cuda} < minimum {min_cuda} required for {gpu_label}"
|
||||||
|
)
|
||||||
|
return warnings
|
||||||
|
|||||||
@ -1,4 +1,4 @@
|
|||||||
"""Hardware health monitoring module for NVIDIA datacenter GPUs (H100/H200/B200/B300)."""
|
"""Hardware health monitoring module for NVIDIA datacenter GPUs (A100/A800/H100/H200/B200/B300)."""
|
||||||
|
|
||||||
import subprocess
|
import subprocess
|
||||||
import shutil
|
import shutil
|
||||||
@ -72,8 +72,8 @@ class HealthCheck:
|
|||||||
power_limits = query_lines("power.limit")
|
power_limits = query_lines("power.limit")
|
||||||
ecc_single = query_lines("ecc.errors.single_bit.total.volatile")
|
ecc_single = query_lines("ecc.errors.single_bit.total.volatile")
|
||||||
ecc_double = query_lines("ecc.errors.double_bit.total.volatile")
|
ecc_double = query_lines("ecc.errors.double_bit.total.volatile")
|
||||||
pcie_gens = query_lines("pcie_link.gen.current")
|
pcie_gens = query_lines("pcie.link.gen.current")
|
||||||
pcie_widths = query_lines("pcie_link.width.current")
|
pcie_widths = query_lines("pcie.link.width.current")
|
||||||
clock_sms = query_lines("clocks.sm")
|
clock_sms = query_lines("clocks.sm")
|
||||||
clock_mems = query_lines("clocks.mem")
|
clock_mems = query_lines("clocks.mem")
|
||||||
persistence = query_lines("persistence_mode")
|
persistence = query_lines("persistence_mode")
|
||||||
|
|||||||
@ -12,7 +12,7 @@ from rich.console import Console
|
|||||||
from rich.table import Table
|
from rich.table import Table
|
||||||
from rich.progress import Progress, SpinnerColumn, TextColumn, TimeElapsedColumn
|
from rich.progress import Progress, SpinnerColumn, TextColumn, TimeElapsedColumn
|
||||||
|
|
||||||
from modules.gpu_specs import detect_gpu_type, get_gpu_specs
|
from modules.gpu_specs import detect_gpu_type, get_gpu_specs, resolve_tools_dir
|
||||||
|
|
||||||
TORCH_AVAILABLE = False
|
TORCH_AVAILABLE = False
|
||||||
try:
|
try:
|
||||||
@ -29,7 +29,7 @@ class NCCLTest:
|
|||||||
self.config = config
|
self.config = config
|
||||||
self.console = Console()
|
self.console = Console()
|
||||||
self.nccl_cfg = config.get("nccl", {})
|
self.nccl_cfg = config.get("nccl", {})
|
||||||
self.tools_dir = config.get("tools", {}).get("install_dir", "/opt/h200-test-tools")
|
self.tools_dir = resolve_tools_dir(config)
|
||||||
self.gpu_type = detect_gpu_type()
|
self.gpu_type = detect_gpu_type()
|
||||||
self.specs = get_gpu_specs(self.gpu_type)
|
self.specs = get_gpu_specs(self.gpu_type)
|
||||||
|
|
||||||
|
|||||||
@ -1,4 +1,4 @@
|
|||||||
"""Report generation module - export test results to JSON/HTML."""
|
"""Report generation module - export test results to JSON/HTML/Markdown."""
|
||||||
|
|
||||||
import json
|
import json
|
||||||
import os
|
import os
|
||||||
@ -73,6 +73,8 @@ class ReportGenerator:
|
|||||||
return self._generate_json(results, output)
|
return self._generate_json(results, output)
|
||||||
elif fmt == "html":
|
elif fmt == "html":
|
||||||
return self._generate_html(results, output)
|
return self._generate_html(results, output)
|
||||||
|
elif fmt == "md":
|
||||||
|
return self._generate_markdown(results, output)
|
||||||
else:
|
else:
|
||||||
self.console.print(f"[red]Unsupported format: {fmt}[/red]")
|
self.console.print(f"[red]Unsupported format: {fmt}[/red]")
|
||||||
return ""
|
return ""
|
||||||
@ -163,3 +165,314 @@ class ReportGenerator:
|
|||||||
f.write(html)
|
f.write(html)
|
||||||
self.console.print(f"[green]HTML report saved to: {output}[/green]")
|
self.console.print(f"[green]HTML report saved to: {output}[/green]")
|
||||||
return output
|
return output
|
||||||
|
|
||||||
|
# ------------------------------------------------------------------
|
||||||
|
# Markdown report
|
||||||
|
# ------------------------------------------------------------------
|
||||||
|
|
||||||
|
def _generate_markdown(self, results: dict, output: str) -> str:
|
||||||
|
import socket
|
||||||
|
hostname = socket.gethostname()
|
||||||
|
timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
|
||||||
|
|
||||||
|
lines: list[str] = []
|
||||||
|
|
||||||
|
# --- Header ---
|
||||||
|
lines.append("# GPU Test Report\n")
|
||||||
|
lines.append(f"- **Date:** {timestamp}")
|
||||||
|
lines.append(f"- **Host:** {hostname}")
|
||||||
|
|
||||||
|
# Extract GPU info for header
|
||||||
|
gpu_info = results.get("gpu_info")
|
||||||
|
if gpu_info and not gpu_info.get("error"):
|
||||||
|
gpus = gpu_info.get("gpus", [])
|
||||||
|
gpu_name = gpus[0]["name"] if gpus else "Unknown"
|
||||||
|
lines.append(f"- **GPU:** {gpu_name} x{gpu_info.get('gpu_count', len(gpus))}")
|
||||||
|
lines.append(f"- **Driver:** {gpu_info.get('driver_version', 'N/A')} | "
|
||||||
|
f"**CUDA:** {gpu_info.get('cuda_version', 'N/A')}")
|
||||||
|
lines.append("")
|
||||||
|
|
||||||
|
# --- Summary table ---
|
||||||
|
summary_items = self._build_summary(results)
|
||||||
|
if summary_items:
|
||||||
|
lines.append("## Summary\n")
|
||||||
|
lines.append("| Test | Result |")
|
||||||
|
lines.append("|------|--------|")
|
||||||
|
for name, verdict in summary_items:
|
||||||
|
lines.append(f"| {name} | {verdict} |")
|
||||||
|
lines.append("")
|
||||||
|
|
||||||
|
# --- GPU Information ---
|
||||||
|
if gpu_info and not gpu_info.get("error"):
|
||||||
|
lines.append("## GPU Information\n")
|
||||||
|
gpus = gpu_info.get("gpus", [])
|
||||||
|
lines.append("| GPU | Model | VRAM | Temp | Power | SM Clock |")
|
||||||
|
lines.append("|-----|-------|------|------|-------|----------|")
|
||||||
|
for g in gpus:
|
||||||
|
vram = f"{g.get('vram_total_mb', 0)} MB"
|
||||||
|
temp = f"{g.get('temperature', 'N/A')}C"
|
||||||
|
power = f"{g.get('power_draw', 0):.0f}/{g.get('power_limit', 0):.0f}W"
|
||||||
|
clock = f"{g.get('clock_sm', 0)} MHz"
|
||||||
|
lines.append(f"| {g['index']} | {g['name']} | {vram} | {temp} | {power} | {clock} |")
|
||||||
|
lines.append("")
|
||||||
|
|
||||||
|
# --- Health Check ---
|
||||||
|
health = results.get("health")
|
||||||
|
if health and not health.get("error"):
|
||||||
|
lines.append("## Health Check\n")
|
||||||
|
passed = health.get("passed", False)
|
||||||
|
lines.append(f"**Overall: {'PASS' if passed else 'FAIL'}**\n")
|
||||||
|
gpu_health = health.get("gpu_health", [])
|
||||||
|
if gpu_health:
|
||||||
|
lines.append("| GPU | Temp | Power | ECC | PCIe | Throttle | Status |")
|
||||||
|
lines.append("|-----|------|-------|-----|------|----------|--------|")
|
||||||
|
for gh in gpu_health:
|
||||||
|
checks = gh.get("checks", {})
|
||||||
|
temp_c = checks.get("temperature", {})
|
||||||
|
pwr = checks.get("power", {})
|
||||||
|
ecc = checks.get("ecc_errors", {})
|
||||||
|
pcie = checks.get("pcie_link", {})
|
||||||
|
throttle = checks.get("throttling", {})
|
||||||
|
temp_str = f"{temp_c.get('value', '?')}C {temp_c.get('status', '')}"
|
||||||
|
pwr_str = f"{pwr.get('value', 0):.0f}W {pwr.get('status', '')}"
|
||||||
|
ecc_str = f"S:{ecc.get('single', 0)} D:{ecc.get('double', 0)}"
|
||||||
|
pcie_str = f"Gen{pcie.get('gen', '?')}x{pcie.get('width', '?')}"
|
||||||
|
throt_str = throttle.get("status", "?")
|
||||||
|
status = gh.get("status", "?")
|
||||||
|
lines.append(f"| {gh['index']} | {temp_str} | {pwr_str} | "
|
||||||
|
f"{ecc_str} | {pcie_str} | {throt_str} | **{status}** |")
|
||||||
|
lines.append("")
|
||||||
|
|
||||||
|
# --- Memory Bandwidth ---
|
||||||
|
mem_data = self._extract_memory_results(results)
|
||||||
|
if mem_data and not mem_data.get("error"):
|
||||||
|
lines.append("## Memory Bandwidth\n")
|
||||||
|
lines.append(f"Source: {mem_data.get('source', 'unknown')}\n")
|
||||||
|
lines.append("| Metric | Value | Peak | Efficiency |")
|
||||||
|
lines.append("|--------|-------|------|------------|")
|
||||||
|
d2d = mem_data.get("d2d_bandwidth_gbps", 0)
|
||||||
|
h2d = mem_data.get("h2d_bandwidth_gbps", 0)
|
||||||
|
d2h = mem_data.get("d2h_bandwidth_gbps", 0)
|
||||||
|
peak = mem_data.get("peak_bandwidth_gbps", 0)
|
||||||
|
eff = mem_data.get("efficiency_pct", 0)
|
||||||
|
lines.append(f"| D2D (HBM) | {d2d:.1f} GB/s | {peak:.0f} GB/s | {eff:.1f}% |")
|
||||||
|
lines.append(f"| H2D | {h2d:.1f} GB/s | - | - |")
|
||||||
|
lines.append(f"| D2H | {d2h:.1f} GB/s | - | - |")
|
||||||
|
lines.append("")
|
||||||
|
verdict = "PASS" if eff >= 80 else ("WARN" if eff >= 50 else "FAIL")
|
||||||
|
lines.append(f"**Verdict: {verdict}** (D2D efficiency {eff:.1f}%)\n")
|
||||||
|
|
||||||
|
# --- Compute Throughput ---
|
||||||
|
comp_data = self._extract_compute_results(results)
|
||||||
|
if comp_data and not comp_data.get("error"):
|
||||||
|
lines.append("## Compute Throughput\n")
|
||||||
|
per_dtype = comp_data.get("per_dtype_tflops", {})
|
||||||
|
peak_tflops = comp_data.get("peak_tflops", {})
|
||||||
|
eff_pct = comp_data.get("efficiency_pct", {})
|
||||||
|
lines.append("| DType | Achieved (TFLOPS) | Peak | Efficiency | Status |")
|
||||||
|
lines.append("|-------|-------------------|------|------------|--------|")
|
||||||
|
worst_eff = 100.0
|
||||||
|
for dt, val in per_dtype.items():
|
||||||
|
if isinstance(val, str):
|
||||||
|
# skipped or error
|
||||||
|
lines.append(f"| {dt.upper()} | {val} | - | N/A | SKIP |")
|
||||||
|
else:
|
||||||
|
pk = peak_tflops.get(dt, 0)
|
||||||
|
ef = eff_pct.get(dt, 0)
|
||||||
|
if isinstance(ef, (int, float)) and ef > 0:
|
||||||
|
worst_eff = min(worst_eff, ef)
|
||||||
|
status = "PASS" if ef >= 80 else ("WARN" if ef >= 50 else "FAIL")
|
||||||
|
lines.append(f"| {dt.upper()} | {val:.1f} | {pk:.0f} | {ef:.1f}% | {status} |")
|
||||||
|
lines.append("")
|
||||||
|
overall = "PASS" if worst_eff >= 80 else ("WARN" if worst_eff >= 50 else "FAIL")
|
||||||
|
lines.append(f"**Verdict: {overall}** (worst efficiency {worst_eff:.1f}%)\n")
|
||||||
|
|
||||||
|
# --- NCCL ---
|
||||||
|
nccl = results.get("nccl")
|
||||||
|
if nccl and not nccl.get("error"):
|
||||||
|
lines.append("## NCCL Multi-GPU\n")
|
||||||
|
lines.append(f"Source: {nccl.get('source', 'unknown')} | "
|
||||||
|
f"GPUs: {nccl.get('gpu_count', '?')}\n")
|
||||||
|
tests = nccl.get("tests", {})
|
||||||
|
if tests:
|
||||||
|
lines.append("| Operation | Bus BW (GB/s) | Threshold | Status |")
|
||||||
|
lines.append("|-----------|---------------|-----------|--------|")
|
||||||
|
for op, data in tests.items():
|
||||||
|
if isinstance(data, dict) and not data.get("error"):
|
||||||
|
bw = data.get("best_busbw_gbps", 0)
|
||||||
|
req = data.get("min_required_gbps", 0)
|
||||||
|
status = data.get("status", "?")
|
||||||
|
lines.append(f"| {op} | {bw:.1f} | >= {req:.0f} | {status} |")
|
||||||
|
elif isinstance(data, dict) and data.get("error"):
|
||||||
|
lines.append(f"| {op} | - | - | ERROR: {data['error']} |")
|
||||||
|
lines.append("")
|
||||||
|
passed = nccl.get("passed", False)
|
||||||
|
lines.append(f"**Overall: {'PASS' if passed else 'FAIL'}**\n")
|
||||||
|
|
||||||
|
# --- Stress Test ---
|
||||||
|
stress = results.get("stress")
|
||||||
|
if stress and not stress.get("error"):
|
||||||
|
lines.append("## Stress Test\n")
|
||||||
|
passed = stress.get("passed", False)
|
||||||
|
duration = stress.get("duration_sec", 0)
|
||||||
|
elapsed = stress.get("elapsed_sec", 0)
|
||||||
|
source = stress.get("source", "unknown")
|
||||||
|
lines.append(f"- **Source:** {source}")
|
||||||
|
lines.append(f"- **Duration:** {elapsed:.0f}s (requested {duration}s)")
|
||||||
|
lines.append(f"- **Result: {'PASS' if passed else 'FAIL'}**")
|
||||||
|
lines.append("")
|
||||||
|
|
||||||
|
# --- RDMA ---
|
||||||
|
rdma = results.get("rdma")
|
||||||
|
if rdma and not rdma.get("error"):
|
||||||
|
lines.append("## RDMA/InfiniBand\n")
|
||||||
|
bw_tests = rdma.get("bandwidth_tests", [])
|
||||||
|
lat_tests = rdma.get("latency_tests", [])
|
||||||
|
if bw_tests or lat_tests:
|
||||||
|
lines.append("| Test | Value | Threshold | Status |")
|
||||||
|
lines.append("|------|-------|-----------|--------|")
|
||||||
|
for bt in bw_tests:
|
||||||
|
if not bt.get("error"):
|
||||||
|
lines.append(f"| {bt['test']} | {bt.get('bandwidth_gbps', 0):.1f} GB/s | "
|
||||||
|
f">= {bt.get('min_required_gbps', 0)} GB/s | {bt.get('status', '?')} |")
|
||||||
|
for lt in lat_tests:
|
||||||
|
if not lt.get("error"):
|
||||||
|
lines.append(f"| {lt['test']} | {lt.get('latency_us', 0):.2f} us | "
|
||||||
|
f"<= {lt.get('max_allowed_us', 0)} us | {lt.get('status', '?')} |")
|
||||||
|
lines.append("")
|
||||||
|
passed = rdma.get("passed", False)
|
||||||
|
lines.append(f"**Overall: {'PASS' if passed else 'FAIL'}**\n")
|
||||||
|
|
||||||
|
# --- Training ---
|
||||||
|
training = results.get("training")
|
||||||
|
if training and not training.get("error"):
|
||||||
|
lines.append("## Training Simulation\n")
|
||||||
|
lines.append("| Metric | Value |")
|
||||||
|
lines.append("|--------|-------|")
|
||||||
|
lines.append(f"| Model | {training.get('model', 'N/A')} |")
|
||||||
|
lines.append(f"| Params | {training.get('total_params_m', 0):.1f}M |")
|
||||||
|
lines.append(f"| Throughput | {training.get('throughput_tokens_per_sec', 0):.0f} tokens/sec |")
|
||||||
|
lines.append(f"| Avg Step Time | {training.get('avg_step_time_ms', 0):.1f} ms |")
|
||||||
|
lines.append(f"| Peak Memory | {training.get('peak_memory_gb', 0):.1f} GB |")
|
||||||
|
lines.append(f"| Final Loss | {training.get('final_loss', 'N/A')} |")
|
||||||
|
lines.append("")
|
||||||
|
|
||||||
|
# --- Footer ---
|
||||||
|
lines.append("---")
|
||||||
|
lines.append(f"*Generated by GPU Test Suite v0.2.0*")
|
||||||
|
|
||||||
|
content = "\n".join(lines)
|
||||||
|
with open(output, "w") as f:
|
||||||
|
f.write(content)
|
||||||
|
self.console.print(f"[green]Markdown report saved to: {output}[/green]")
|
||||||
|
return output
|
||||||
|
|
||||||
|
def _extract_memory_results(self, results: dict) -> dict:
|
||||||
|
"""Extract memory benchmark data from either full-suite or single-test format."""
|
||||||
|
if "memory_bench" in results:
|
||||||
|
data = results["memory_bench"]
|
||||||
|
return data.get("memory", data) if isinstance(data, dict) else {}
|
||||||
|
if "benchmark" in results:
|
||||||
|
bench = results["benchmark"]
|
||||||
|
if isinstance(bench, dict) and "memory" in bench:
|
||||||
|
return bench["memory"]
|
||||||
|
return {}
|
||||||
|
|
||||||
|
def _extract_compute_results(self, results: dict) -> dict:
|
||||||
|
"""Extract compute benchmark data from either full-suite or single-test format."""
|
||||||
|
if "compute_bench" in results:
|
||||||
|
data = results["compute_bench"]
|
||||||
|
return data.get("compute", data) if isinstance(data, dict) else {}
|
||||||
|
if "benchmark" in results:
|
||||||
|
bench = results["benchmark"]
|
||||||
|
if isinstance(bench, dict) and "compute" in bench:
|
||||||
|
return bench["compute"]
|
||||||
|
return {}
|
||||||
|
|
||||||
|
def _build_summary(self, results: dict) -> list[tuple[str, str]]:
|
||||||
|
"""Build summary verdict list from results."""
|
||||||
|
items = []
|
||||||
|
|
||||||
|
# GPU Info
|
||||||
|
if "gpu_info" in results:
|
||||||
|
gi = results["gpu_info"]
|
||||||
|
if gi.get("error"):
|
||||||
|
items.append(("GPU Info", f"ERROR: {gi['error']}"))
|
||||||
|
else:
|
||||||
|
items.append(("GPU Info", f"PASS ({gi.get('gpu_count', '?')} GPUs detected)"))
|
||||||
|
|
||||||
|
# Health
|
||||||
|
if "health" in results:
|
||||||
|
h = results["health"]
|
||||||
|
if h.get("error"):
|
||||||
|
items.append(("Health Check", f"ERROR: {h['error']}"))
|
||||||
|
elif h.get("passed"):
|
||||||
|
items.append(("Health Check", "PASS"))
|
||||||
|
else:
|
||||||
|
items.append(("Health Check", "FAIL"))
|
||||||
|
|
||||||
|
# Memory Bandwidth
|
||||||
|
mem = self._extract_memory_results(results)
|
||||||
|
if mem:
|
||||||
|
if mem.get("error"):
|
||||||
|
items.append(("Memory Bandwidth", f"ERROR: {mem['error']}"))
|
||||||
|
else:
|
||||||
|
eff = mem.get("efficiency_pct", 0)
|
||||||
|
verdict = "PASS" if eff >= 80 else ("WARN" if eff >= 50 else "FAIL")
|
||||||
|
items.append(("Memory Bandwidth", f"{verdict} ({eff:.1f}%)"))
|
||||||
|
|
||||||
|
# Compute
|
||||||
|
comp = self._extract_compute_results(results)
|
||||||
|
if comp:
|
||||||
|
if comp.get("error"):
|
||||||
|
items.append(("Compute Throughput", f"ERROR: {comp['error']}"))
|
||||||
|
else:
|
||||||
|
eff_pct = comp.get("efficiency_pct", {})
|
||||||
|
valid_effs = [v for v in eff_pct.values() if isinstance(v, (int, float)) and v > 0]
|
||||||
|
if valid_effs:
|
||||||
|
worst = min(valid_effs)
|
||||||
|
verdict = "PASS" if worst >= 80 else ("WARN" if worst >= 50 else "FAIL")
|
||||||
|
items.append(("Compute Throughput", f"{verdict} (worst {worst:.1f}%)"))
|
||||||
|
else:
|
||||||
|
items.append(("Compute Throughput", "N/A"))
|
||||||
|
|
||||||
|
# NCCL
|
||||||
|
if "nccl" in results:
|
||||||
|
n = results["nccl"]
|
||||||
|
if n.get("error"):
|
||||||
|
items.append(("NCCL", f"ERROR: {n['error']}"))
|
||||||
|
elif n.get("passed"):
|
||||||
|
items.append(("NCCL", "PASS"))
|
||||||
|
else:
|
||||||
|
items.append(("NCCL", "FAIL"))
|
||||||
|
|
||||||
|
# Stress
|
||||||
|
if "stress" in results:
|
||||||
|
s = results["stress"]
|
||||||
|
if s.get("error"):
|
||||||
|
items.append(("Stress Test", f"ERROR: {s['error']}"))
|
||||||
|
elif s.get("passed"):
|
||||||
|
items.append(("Stress Test", "PASS"))
|
||||||
|
else:
|
||||||
|
items.append(("Stress Test", "FAIL"))
|
||||||
|
|
||||||
|
# RDMA
|
||||||
|
if "rdma" in results:
|
||||||
|
r = results["rdma"]
|
||||||
|
if r.get("error"):
|
||||||
|
items.append(("RDMA", f"ERROR: {r['error']}"))
|
||||||
|
elif r.get("passed"):
|
||||||
|
items.append(("RDMA", "PASS"))
|
||||||
|
else:
|
||||||
|
items.append(("RDMA", "FAIL"))
|
||||||
|
|
||||||
|
# Training
|
||||||
|
if "training" in results:
|
||||||
|
t = results["training"]
|
||||||
|
if t.get("error"):
|
||||||
|
items.append(("Training", f"ERROR: {t['error']}"))
|
||||||
|
else:
|
||||||
|
tps = t.get("throughput_tokens_per_sec", 0)
|
||||||
|
items.append(("Training", f"PASS ({tps:.0f} tokens/sec)"))
|
||||||
|
|
||||||
|
return items
|
||||||
|
|||||||
@ -12,6 +12,8 @@ from rich.table import Table
|
|||||||
from rich.live import Live
|
from rich.live import Live
|
||||||
from rich.text import Text
|
from rich.text import Text
|
||||||
|
|
||||||
|
from modules.gpu_specs import resolve_tools_dir
|
||||||
|
|
||||||
|
|
||||||
class StressTest:
|
class StressTest:
|
||||||
|
|
||||||
@ -19,7 +21,7 @@ class StressTest:
|
|||||||
self.config = config
|
self.config = config
|
||||||
self.console = Console()
|
self.console = Console()
|
||||||
self.stress_cfg = config.get("stress", {})
|
self.stress_cfg = config.get("stress", {})
|
||||||
self.tools_dir = config.get("tools", {}).get("install_dir", "/opt/h200-test-tools")
|
self.tools_dir = resolve_tools_dir(config)
|
||||||
|
|
||||||
def _find_gpu_burn(self) -> str:
|
def _find_gpu_burn(self) -> str:
|
||||||
p = shutil.which("gpu_burn")
|
p = shutil.which("gpu_burn")
|
||||||
@ -123,7 +125,8 @@ class StressTest:
|
|||||||
tensors = {}
|
tensors = {}
|
||||||
for i in range(gpu_count):
|
for i in range(gpu_count):
|
||||||
with torch.cuda.device(i):
|
with torch.cuda.device(i):
|
||||||
total_mem = torch.cuda.get_device_properties(i).total_mem
|
props = torch.cuda.get_device_properties(i)
|
||||||
|
total_mem = getattr(props, "total_memory", None) or getattr(props, "total_mem", 0)
|
||||||
alloc_size = int(total_mem * 0.9) // 4
|
alloc_size = int(total_mem * 0.9) // 4
|
||||||
tensors[i] = torch.randn(alloc_size, device=f"cuda:{i}", dtype=torch.float32)
|
tensors[i] = torch.randn(alloc_size, device=f"cuda:{i}", dtype=torch.float32)
|
||||||
|
|
||||||
|
|||||||
57
pyproject.toml
Normal file
57
pyproject.toml
Normal file
@ -0,0 +1,57 @@
|
|||||||
|
[project]
|
||||||
|
name = "gpu-server-test-suite"
|
||||||
|
version = "0.2.0"
|
||||||
|
description = "GPU training server diagnostics & benchmarks (A100/A800/H100/H200/B200/B300)."
|
||||||
|
readme = "README.md"
|
||||||
|
requires-python = ">=3.10,<3.13"
|
||||||
|
|
||||||
|
dependencies = [
|
||||||
|
"rich>=13.0",
|
||||||
|
"pyyaml>=6.0",
|
||||||
|
"numpy>=1.24",
|
||||||
|
]
|
||||||
|
|
||||||
|
[project.optional-dependencies]
|
||||||
|
# Install ONE matching your system CUDA version:
|
||||||
|
# uv sync --extra torch-cu118
|
||||||
|
# uv sync --extra torch-cu121
|
||||||
|
# uv sync --extra torch-cu124 (recommended for CUDA 12.x + Driver 535+)
|
||||||
|
# uv sync --extra torch-cu128 (for CUDA 12.8+ / Driver 570+)
|
||||||
|
torch-cu118 = ["torch>=2.1.0"]
|
||||||
|
torch-cu121 = ["torch>=2.1.0"]
|
||||||
|
torch-cu124 = ["torch>=2.1.0"]
|
||||||
|
torch-cu128 = ["torch>=2.1.0"]
|
||||||
|
|
||||||
|
[build-system]
|
||||||
|
requires = ["hatchling"]
|
||||||
|
build-backend = "hatchling.build"
|
||||||
|
|
||||||
|
# PyTorch wheel indexes — one per CUDA version
|
||||||
|
[[tool.uv.index]]
|
||||||
|
name = "pytorch-cu118"
|
||||||
|
url = "https://download.pytorch.org/whl/cu118"
|
||||||
|
explicit = true
|
||||||
|
|
||||||
|
[[tool.uv.index]]
|
||||||
|
name = "pytorch-cu121"
|
||||||
|
url = "https://download.pytorch.org/whl/cu121"
|
||||||
|
explicit = true
|
||||||
|
|
||||||
|
[[tool.uv.index]]
|
||||||
|
name = "pytorch-cu124"
|
||||||
|
url = "https://download.pytorch.org/whl/cu124"
|
||||||
|
explicit = true
|
||||||
|
|
||||||
|
[[tool.uv.index]]
|
||||||
|
name = "pytorch-cu128"
|
||||||
|
url = "https://download.pytorch.org/whl/cu128"
|
||||||
|
explicit = true
|
||||||
|
|
||||||
|
# Default: resolve torch from cu124 index (most broadly compatible with 535+ drivers)
|
||||||
|
# To use a different CUDA version, override:
|
||||||
|
# uv pip install torch --index-url https://download.pytorch.org/whl/cu128
|
||||||
|
[tool.uv.sources]
|
||||||
|
torch = { index = "pytorch-cu124" }
|
||||||
|
|
||||||
|
[tool.hatch.build.targets.wheel]
|
||||||
|
packages = ["modules"]
|
||||||
510
uv.lock
generated
Normal file
510
uv.lock
generated
Normal file
@ -0,0 +1,510 @@
|
|||||||
|
version = 1
|
||||||
|
revision = 3
|
||||||
|
requires-python = ">=3.10, <3.13"
|
||||||
|
resolution-markers = [
|
||||||
|
"python_full_version >= '3.12'",
|
||||||
|
"python_full_version == '3.11.*'",
|
||||||
|
"python_full_version < '3.11'",
|
||||||
|
]
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "filelock"
|
||||||
|
version = "3.29.0"
|
||||||
|
source = { registry = "https://pypi.org/simple" }
|
||||||
|
sdist = { url = "https://files.pythonhosted.org/packages/b5/fe/997687a931ab51049acce6fa1f23e8f01216374ea81374ddee763c493db5/filelock-3.29.0.tar.gz", hash = "sha256:69974355e960702e789734cb4871f884ea6fe50bd8404051a3530bc07809cf90", size = 57571, upload-time = "2026-04-19T15:39:10.068Z" }
|
||||||
|
wheels = [
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/81/47/dd9a212ef6e343a6857485ffe25bba537304f1913bdbed446a23f7f592e1/filelock-3.29.0-py3-none-any.whl", hash = "sha256:96f5f6344709aa1572bbf631c640e4ebeeb519e08da902c39a001882f30ac258", size = 39812, upload-time = "2026-04-19T15:39:08.752Z" },
|
||||||
|
]
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "fsspec"
|
||||||
|
version = "2026.4.0"
|
||||||
|
source = { registry = "https://pypi.org/simple" }
|
||||||
|
sdist = { url = "https://files.pythonhosted.org/packages/d5/8d/1c51c094345df128ca4a990d633fe1a0ff28726c9e6b3c41ba65087bba1d/fsspec-2026.4.0.tar.gz", hash = "sha256:301d8ac70ae90ef3ad05dcf94d6c3754a097f9b5fe4667d2787aa359ec7df7e4", size = 312760, upload-time = "2026-04-29T20:42:38.635Z" }
|
||||||
|
wheels = [
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/d5/0c/043d5e551459da400957a1395e0febbf771446ff34291afcbe3d8be2a279/fsspec-2026.4.0-py3-none-any.whl", hash = "sha256:11ef7bb35dab8a394fde6e608221d5cf3e8499401c249bebaeaad760a1a8dec2", size = 203402, upload-time = "2026-04-29T20:42:36.842Z" },
|
||||||
|
]
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "gpu-server-test-suite"
|
||||||
|
version = "0.2.0"
|
||||||
|
source = { editable = "." }
|
||||||
|
dependencies = [
|
||||||
|
{ name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
|
||||||
|
{ name = "numpy", version = "2.4.4", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
|
||||||
|
{ name = "pyyaml" },
|
||||||
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{ url = "https://files.pythonhosted.org/packages/e1/e3/c164c88b2e5ce7b24d667b9bd83589cf4f3520d97cad01534cd3c4f55fdb/setuptools-81.0.0-py3-none-any.whl", hash = "sha256:fdd925d5c5d9f62e4b74b30d6dd7828ce236fd6ed998a08d81de62ce5a6310d6", size = 1062021, upload-time = "2026-02-06T21:10:37.175Z" },
|
||||||
|
]
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "sympy"
|
||||||
|
version = "1.13.1"
|
||||||
|
source = { registry = "https://pypi.org/simple" }
|
||||||
|
dependencies = [
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||||||
|
{ name = "mpmath" },
|
||||||
|
]
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||||||
|
sdist = { url = "https://files.pythonhosted.org/packages/ca/99/5a5b6f19ff9f083671ddf7b9632028436167cd3d33e11015754e41b249a4/sympy-1.13.1.tar.gz", hash = "sha256:9cebf7e04ff162015ce31c9c6c9144daa34a93bd082f54fd8f12deca4f47515f", size = 7533040, upload-time = "2024-07-19T09:26:51.238Z" }
|
||||||
|
wheels = [
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||||||
|
{ url = "https://files.pythonhosted.org/packages/b2/fe/81695a1aa331a842b582453b605175f419fe8540355886031328089d840a/sympy-1.13.1-py3-none-any.whl", hash = "sha256:db36cdc64bf61b9b24578b6f7bab1ecdd2452cf008f34faa33776680c26d66f8", size = 6189177, upload-time = "2024-07-19T09:26:48.863Z" },
|
||||||
|
]
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "torch"
|
||||||
|
version = "2.6.0+cu124"
|
||||||
|
source = { registry = "https://download.pytorch.org/whl/cu124" }
|
||||||
|
dependencies = [
|
||||||
|
{ name = "filelock" },
|
||||||
|
{ name = "fsspec" },
|
||||||
|
{ name = "jinja2" },
|
||||||
|
{ name = "networkx", version = "3.4.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
|
||||||
|
{ name = "networkx", version = "3.6.1", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11'" },
|
||||||
|
{ name = "nvidia-cublas-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
|
||||||
|
{ name = "nvidia-cuda-cupti-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
|
||||||
|
{ name = "nvidia-cuda-nvrtc-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
|
||||||
|
{ name = "nvidia-cuda-runtime-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
|
||||||
|
{ name = "nvidia-cudnn-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
|
||||||
|
{ name = "nvidia-cufft-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
|
||||||
|
{ name = "nvidia-curand-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
|
||||||
|
{ name = "nvidia-cusolver-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
|
||||||
|
{ name = "nvidia-cusparse-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
|
||||||
|
{ name = "nvidia-cusparselt-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
|
||||||
|
{ name = "nvidia-nccl-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
|
||||||
|
{ name = "nvidia-nvjitlink-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
|
||||||
|
{ name = "nvidia-nvtx-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
|
||||||
|
{ name = "setuptools", marker = "python_full_version >= '3.12'" },
|
||||||
|
{ name = "sympy" },
|
||||||
|
{ name = "triton", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
|
||||||
|
{ name = "typing-extensions" },
|
||||||
|
]
|
||||||
|
wheels = [
|
||||||
|
{ url = "https://download-r2.pytorch.org/whl/cu124/torch-2.6.0%2Bcu124-cp310-cp310-linux_x86_64.whl", hash = "sha256:7f2ba7f7c0459320a521696f6b5bccc187f59890b23c9dfb6c49b0b87c6bfc97", upload-time = "2025-01-30T00:53:02Z" },
|
||||||
|
{ url = "https://download-r2.pytorch.org/whl/cu124/torch-2.6.0%2Bcu124-cp310-cp310-win_amd64.whl", hash = "sha256:7cc45c5b39d74875cfafe908b7f55c544147cc16b01e795feb2fe766583efe78", upload-time = "2025-01-30T00:53:32Z" },
|
||||||
|
{ url = "https://download-r2.pytorch.org/whl/cu124/torch-2.6.0%2Bcu124-cp311-cp311-linux_x86_64.whl", hash = "sha256:d4c3e9a8d31a7c0fcbb9da17c31a1917e1fac26c566a4cfbd8c9568ad7cade79", upload-time = "2025-01-30T00:55:01Z" },
|
||||||
|
{ url = "https://download-r2.pytorch.org/whl/cu124/torch-2.6.0%2Bcu124-cp311-cp311-win_amd64.whl", hash = "sha256:6a1fb2714e9323f11edb6e8abf7aad5f79e45ad25c081cde87681a18d99c29eb", upload-time = "2025-01-30T00:55:31Z" },
|
||||||
|
{ url = "https://download-r2.pytorch.org/whl/cu124/torch-2.6.0%2Bcu124-cp312-cp312-linux_x86_64.whl", hash = "sha256:a393b506844035c0dac2f30ea8478c343b8e95a429f06f3b3cadfc7f53adb597", upload-time = "2025-01-30T00:57:08Z" },
|
||||||
|
{ url = "https://download-r2.pytorch.org/whl/cu124/torch-2.6.0%2Bcu124-cp312-cp312-win_amd64.whl", hash = "sha256:3313061c1fec4c7310cf47944e84513dcd27b6173b72a349bb7ca68d0ee6e9c0", upload-time = "2025-01-30T00:57:40Z" },
|
||||||
|
]
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "triton"
|
||||||
|
version = "3.2.0"
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||||||
|
source = { registry = "https://pypi.org/simple" }
|
||||||
|
wheels = [
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||||||
|
{ url = "https://files.pythonhosted.org/packages/01/65/3ffa90e158a2c82f0716eee8d26a725d241549b7d7aaf7e4f44ac03ebd89/triton-3.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b3e54983cd51875855da7c68ec05c05cf8bb08df361b1d5b69e05e40b0c9bd62", size = 253090354, upload-time = "2025-01-22T19:12:21.872Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/a7/2e/757d2280d4fefe7d33af7615124e7e298ae7b8e3bc4446cdb8e88b0f9bab/triton-3.2.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8009a1fb093ee8546495e96731336a33fb8856a38e45bb4ab6affd6dbc3ba220", size = 253157636, upload-time = "2025-01-22T19:12:51.322Z" },
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/06/00/59500052cb1cf8cf5316be93598946bc451f14072c6ff256904428eaf03c/triton-3.2.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8d9b215efc1c26fa7eefb9a157915c92d52e000d2bf83e5f69704047e63f125c", size = 253159365, upload-time = "2025-01-22T19:13:24.648Z" },
|
||||||
|
]
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "typing-extensions"
|
||||||
|
version = "4.15.0"
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|
source = { registry = "https://pypi.org/simple" }
|
||||||
|
sdist = { url = "https://files.pythonhosted.org/packages/72/94/1a15dd82efb362ac84269196e94cf00f187f7ed21c242792a923cdb1c61f/typing_extensions-4.15.0.tar.gz", hash = "sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466", size = 109391, upload-time = "2025-08-25T13:49:26.313Z" }
|
||||||
|
wheels = [
|
||||||
|
{ url = "https://files.pythonhosted.org/packages/18/67/36e9267722cc04a6b9f15c7f3441c2363321a3ea07da7ae0c0707beb2a9c/typing_extensions-4.15.0-py3-none-any.whl", hash = "sha256:f0fa19c6845758ab08074a0cfa8b7aecb71c999ca73d62883bc25cc018c4e548", size = 44614, upload-time = "2025-08-25T13:49:24.86Z" },
|
||||||
|
]
|
||||||
Loading…
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Reference in New Issue
Block a user