\n
\u4e8c\u3001\u73af\u5883\u4fe1\u606f<\/h3>\n
\u672c\u6b21\u6d4b\u8bd5\u73af\u5883\u5982\u4e0b：<\/p>\n
- \n
- \u670d\u52a1\u5668\u5e73\u53f0：\u98de\u817e S5000C<\/li>\n
- CPU \u578b\u53f7：FTC862<\/li>\n
- GPU \u6570\u91cf：8 \u5f20 NVIDIA GeForce RTX 4090 D<\/li>\n
- NVIDIA \u9a71\u52a8\u7248\u672c：580.126.09<\/li>\n
- \u9a71\u52a8\u652f\u6301\u7684 CUDA \u7248\u672c：13.0<\/li>\n
- Python \u7248\u672c：3.10<\/li>\n
- \u7cfb\u7edf\u67b6\u6784：Linux aarch64 \/ ARM64<\/li>\n
- Conda \u73af\u5883：aq_py310<\/li>\n
- ubuntu \u7248\u672c：Ubuntu 24.04.4 LTS \\\\n \\\\l<\/li>\n<\/ul>\n
\n\u4e09\u3001\u67e5\u770b CPU \u578b\u53f7<\/h3>\n
\u5148\u786e\u8ba4\u5f53\u524d\u670d\u52a1\u5668\u7684 CPU \u578b\u53f7，\u6267\u884c：<\/p>\n
lscpu |<\/span> grep<\/span> "Model name"<\/span><\/p>\n
\u8f93\u51fa\u5982\u4e0b：<\/p>\n
(aq_py310) root@ubuntu-Rack-Server:~# lscpu | grep "Model name"
\nModel name: FTC862
\nBIOS Model name: S5000C\/64 Not Specified CPU @ 2.1GHz<\/p>\n\u53ef\u4ee5\u770b\u5230，\u8fd9\u53f0\u670d\u52a1\u5668\u7684 CPU \u578b\u53f7\u4e3a FTC862，\u5e73\u53f0\u4fe1\u606f\u4e3a S5000C\/64\u3002<\/p>\n
\u8fd9\u4e00\u6b65\u7684\u4e3b\u8981\u4f5c\u7528\u662f\u786e\u8ba4\u5f53\u524d\u786c\u4ef6\u5e73\u53f0，\u65b9\u4fbf\u540e\u7eed\u6392\u67e5\u517c\u5bb9\u6027\u95ee\u9898\u3002<\/p>\n
\n\u56db\u3001\u67e5\u770b GPU\u3001\u9a71\u52a8\u548c CUDA \u4fe1\u606f<\/h3>\n
\u63a5\u7740\u67e5\u770b GPU \u662f\u5426\u88ab\u7cfb\u7edf\u6b63\u786e\u8bc6\u522b，\u4ee5\u53ca\u5f53\u524d NVIDIA \u9a71\u52a8\u548c CUDA \u652f\u6301\u60c5\u51b5\u3002<\/p>\n
\u6267\u884c\u547d\u4ee4：<\/p>\n
nvidia-smi -l<\/span><\/p>\n
\u8f93\u51fa\u5982\u4e0b：<\/p>\n
(aq_py310) root@ubuntu-Rack-Server:~# nvidia-smi -l
\nMon Apr 6 19:23:08 2026
\n+—————————————————————————————–+
\n| NVIDIA-SMI 580.126.09 Driver Version: 580.126.09 CUDA Version: 13.0 |
\n+—————————————–+————————+———————-+
\n| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
\n| Fan Temp Perf Pwr:Usage\/Cap | Memory-Usage | GPU-Util Compute M. |
\n| | | MIG M. |
\n|=========================================+========================+======================|
\n| 0 NVIDIA GeForce RTX 4090 D Off | 00000001:08:00.0 Off | Off |
\n| 30% 27C P8 16W \/ 425W | 15MiB \/ 24564MiB | 0% Default |
\n| | | N\/A |
\n+—————————————–+————————+———————-+
\n| 1 NVIDIA GeForce RTX 4090 D Off | 00000001:09:00.0 Off | Off |
\n| 31% 29C P8 13W \/ 425W | 15MiB \/ 24564MiB | 0% Default |
\n| | | N\/A |
\n+—————————————–+————————+———————-+
\n| 2 NVIDIA GeForce RTX 4090 D Off | 00000001:0C:00.0 Off | Off |
\n| 30% 30C P8 14W \/ 425W | 15MiB \/ 24564MiB | 0% Default |
\n| | | N\/A |
\n+—————————————–+————————+———————-+
\n| 3 NVIDIA GeForce RTX 4090 D Off | 00000001:0D:00.0 Off | Off |
\n| 30% 26C P8 19W \/ 425W | 15MiB \/ 24564MiB | 0% Default |
\n| | | N\/A |
\n+—————————————–+————————+———————-+
\n| 4 NVIDIA GeForce RTX 4090 D Off | 00000004:05:00.0 Off | Off |
\n| 30% 28C P8 14W \/ 425W | 15MiB \/ 24564MiB | 0% Default |
\n| | | N\/A |
\n+—————————————–+————————+———————-+
\n| 5 NVIDIA GeForce RTX 4090 D Off | 00000004:08:00.0 Off | Off |
\n| 30% 28C P8 16W \/ 425W | 15MiB \/ 24564MiB | 0% Default |
\n| | | N\/A |
\n+—————————————–+————————+———————-+
\n| 6 NVIDIA GeForce RTX 4090 D Off | 00000004:09:00.0 Off | Off |
\n| 31% 28C P8 27W \/ 425W | 15MiB \/ 24564MiB | 0% Default |
\n| | | N\/A |
\n+—————————————–+————————+———————-+
\n| 7 NVIDIA GeForce RTX 4090 D Off | 00000004:0C:00.0 Off | Off |
\n| 31% 28C P8 20W \/ 425W | 15MiB \/ 24564MiB | 0% Default |
\n| | | N\/A |
\n+—————————————–+————————+———————-+<\/p>\n\u4ece\u4e0a\u9762\u7684\u4fe1\u606f\u53ef\u4ee5\u786e\u8ba4：<\/p>\n
- \n
- \u5f53\u524d NVIDIA \u9a71\u52a8\u7248\u672c \u4e3a：580.126.09<\/li>\n
- \u9a71\u52a8\u652f\u6301\u7684 CUDA \u7248\u672c \u4e3a：13.0<\/li>\n
- \u5f53\u524d\u673a\u5668\u5171\u8bc6\u522b\u5230 8 \u5f20 RTX 4090 D<\/li>\n
- \u6bcf\u5f20\u663e\u5361\u663e\u5b58\u7ea6 24GB<\/li>\n
- \u5f53\u524d\u6240\u6709\u663e\u5361\u72b6\u6001\u6b63\u5e38<\/li>\n<\/ul>\n
\u8fd9\u91cc\u8865\u5145\u8bf4\u660e\u4e00\u4e0b：<\/p>\n
nvidia-smi \u91cc\u663e\u793a\u7684 CUDA Version \u662f\u9a71\u52a8\u652f\u6301\u7684\u6700\u9ad8 CUDA \u7248\u672c，\u4e0d\u4ee3\u8868\u4f60\u5b89\u88c5\u7684 PyTorch \u5fc5\u987b\u548c\u5b83\u5b8c\u5168\u4e00\u81f4\u3002
\n\u53ea\u8981\u9a71\u52a8\u7248\u672c\u8db3\u591f\u65b0，\u901a\u5e38\u53ef\u4ee5\u5411\u4e0b\u517c\u5bb9\u8f83\u4f4e\u7248\u672c\u7684 CUDA \u8fd0\u884c\u65f6\u3002<\/p>\n\u6bd4\u5982\u672c\u6587\u4e2d，\u9a71\u52a8\u652f\u6301 CUDA 13.0，\u4f46\u5b9e\u9645\u5b89\u88c5\u7684\u662f PyTorch \u5bf9\u5e94 CUDA 12.4，\u540c\u6837\u53ef\u4ee5\u6b63\u5e38\u4f7f\u7528\u3002<\/p>\n
GPU \u4fe1\u606f\u622a\u56fe<\/h4>\n
![\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"](\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/07\/20260725005046-6a64086695f0e.png\")
<\/p>\n
\n\u4e94\u3001\u5b89\u88c5 PyTorch<\/h3>\n
\u7531\u4e8e\u5f53\u524d\u73af\u5883\u662f：<\/p>\n
- \n
- Python 3.10<\/li>\n
- Linux aarch64 \/ ARM64<\/li>\n
- \u9700\u8981\u4f7f\u7528 CUDA 12.4 \u5bf9\u5e94\u7248\u672c<\/li>\n<\/ul>\n
\u8fd9\u91cc\u91c7\u7528 \u76f4\u63a5\u5b89\u88c5\u5b98\u65b9 wheel \u5305 \u7684\u65b9\u5f0f，\u8fd9\u79cd\u65b9\u5f0f\u6bd4\u76f4\u63a5 pip install torch \u66f4\u660e\u786e，\u4e5f\u66f4\u9002\u5408 ARM \u5e73\u53f0\u3002<\/p>\n
\u6267\u884c\u547d\u4ee4：<\/p>\n
python -m<\/span> pip install<\/span> https:\/\/download.pytorch.org\/whl\/cu124\/torch-2.5.1-cp310-cp310-linux_aarch64.whl<\/p>\n
\u8fd9\u6761\u547d\u4ee4\u7684\u542b\u4e49\u5982\u4e0b：<\/p>\n
- \n
- torch-2.5.1：\u5b89\u88c5 PyTorch 2.5.1<\/li>\n
- cp310-cp310：\u5bf9\u5e94 Python 3.10<\/li>\n
- linux_aarch64：\u5bf9\u5e94 Linux ARM64 \u67b6\u6784<\/li>\n
- cu124：\u5bf9\u5e94 CUDA 12.4<\/li>\n<\/ul>\n
\u8fd9\u79cd\u5b89\u88c5\u65b9\u5f0f\u7684\u4f18\u70b9\u662f：<\/p>\n
- \n
- \u7248\u672c\u66f4\u660e\u786e<\/li>\n
- \u4e0d\u5bb9\u6613\u88c5\u9519\u67b6\u6784<\/li>\n
- \u66f4\u9002\u5408\u98de\u817e\u7b49 ARM \u670d\u52a1\u5668\u73af\u5883<\/li>\n
- \u907f\u514d pip \u81ea\u52a8\u89e3\u6790\u65f6\u9009\u5230\u4e0d\u5339\u914d\u7248\u672c<\/li>\n<\/ul>\n
\n\u516d\u3001\u9a8c\u8bc1 PyTorch \u662f\u5426\u5b89\u88c5\u6210\u529f<\/h3>\n
\u5b89\u88c5\u5b8c\u6210\u540e，\u5148\u6267\u884c\u4e00\u4e2a\u7b80\u5355\u7684\u9a8c\u8bc1\u547d\u4ee4：<\/p>\n
python -c<\/span> "import torch; print(torch.__version__); print(torch.version.cuda); print(torch.cuda.is_available()); print(torch.cuda.device_count())"<\/span><\/p>\n
\u8f93\u51fa\u5982\u4e0b：<\/p>\n
\/root\/miniconda3\/envs\/aq_py310\/lib\/python3.10\/site-packages\/torch\/_subclasses\/functional_tensor.py:295: UserWarning: Failed to initialize NumPy: No module named 'numpy' (Triggered internally at \/pytorch\/torch\/csrc\/utils\/tensor_numpy.cpp:84.)
\n cpu = _conversion_method_template(device=torch.device("cpu"))
\n2.5.1
\n12.4
\nTrue
\n8<\/p>\n\u4ece\u7ed3\u679c\u53ef\u4ee5\u770b\u5230：<\/p>\n
- \n
- 2.5.1：PyTorch \u5b89\u88c5\u6210\u529f<\/li>\n
- 12.4：\u5f53\u524d PyTorch \u5bf9\u5e94\u7684 CUDA \u7248\u672c<\/li>\n
- True：\u8bf4\u660e CUDA \u53ef\u4ee5\u6b63\u5e38\u4f7f\u7528<\/li>\n
- 8：\u8bf4\u660e\u6210\u529f\u8bc6\u522b\u5230 8 \u5f20 GPU<\/li>\n<\/ul>\n
\n\u4e03\u3001\u5173\u4e8e NumPy \u8b66\u544a\u8bf4\u660e<\/h3>\n
\u5728\u9a8c\u8bc1\u8f93\u51fa\u4e2d，\u53ef\u4ee5\u770b\u5230\u8fd9\u6837\u4e00\u6761\u8b66\u544a：<\/p>\n
UserWarning: Failed to initialize NumPy: No module named 'numpy'<\/p>\n
\u8fd9\u4e2a\u95ee\u9898\u5e76\u4e0d\u662f PyTorch \u5b89\u88c5\u5931\u8d25，\u4e5f\u4e0d\u662f CUDA \u6709\u95ee\u9898，\u800c\u662f\u5f53\u524d Python \u73af\u5883\u4e2d\u8fd8\u6ca1\u6709\u5b89\u88c5 numpy\u3002<\/p>\n
\u5efa\u8bae\u987a\u624b\u5b89\u88c5\u4e00\u4e0b：<\/p>\n
python -m<\/span> pip install<\/span> numpy<\/p>\n
\u5b89\u88c5\u5b8c\u6210\u540e，\u518d\u6267\u884c\u9a8c\u8bc1\u547d\u4ee4，\u8fd9\u6761\u8b66\u544a\u901a\u5e38\u5c31\u4e0d\u4f1a\u518d\u51fa\u73b0\u4e86\u3002<\/p>\n
\n\u516b\u3001\u8fdb\u4e00\u6b65\u9a8c\u8bc1：\u6d4b\u8bd5 GPU \u5f20\u91cf\u8ba1\u7b97<\/h3>\n
\u4ec5\u4ec5\u770b\u5230 torch.cuda.is_available() \u4e3a True \u8fd8\u4e0d\u591f，\u6700\u597d\u518d\u505a\u4e00\u6b21\u771f\u5b9e\u7684 GPU \u8fd0\u7b97\u6d4b\u8bd5\u3002<\/p>\n
\u6267\u884c\u4e0b\u9762\u811a\u672c：<\/p>\n
python – <<<\/span>'PY'
\nimport torch
\nprint("torch:", torch.__version__)
\nprint("cuda version:", torch.version.cuda)
\nprint("cuda available:", torch.cuda.is_available())
\nprint("device count:", torch.cuda.device_count())
\nif torch.cuda.is_available():
\n x = torch.randn(2, 3).cuda()
\n y = torch.randn(2, 3).cuda()
\n z = x + y
\n print("gpu tensor ok")
\n print(z)
\n print("device 0:", torch.cuda.get_device_name(0))
\nPY<\/span><\/p>\n\u8f93\u51fa\u5982\u4e0b：<\/p>\n
\/root\/miniconda3\/envs\/aq_py310\/lib\/python3.10\/site-packages\/torch\/_subclasses\/functional_tensor.py:295: UserWarning: Failed to initialize NumPy: No module named 'numpy' (Triggered internally at \/pytorch\/torch\/csrc\/utils\/tensor_numpy.cpp:84.)
\n cpu = _conversion_method_template(device=torch.device("cpu"))
\ntorch: 2.5.1
\ncuda version: 12.4
\ncuda available: True
\ndevice count: 8
\ngpu tensor ok
\ntensor([[-1.6702, -1.0425, -1.8767],
\n [-1.3894, -0.3875, 0.3431]], device='cuda:0')
\ndevice 0: NVIDIA GeForce RTX 4090 D<\/p>\n\u4ece\u8fd9\u6bb5\u7ed3\u679c\u53ef\u4ee5\u786e\u8ba4：<\/p>\n
- \n
- PyTorch \u53ef\u4ee5\u6b63\u5e38\u8c03\u7528 CUDA<\/li>\n
- \u5f20\u91cf\u5df2\u7ecf\u6210\u529f\u653e\u5230 GPU \u4e0a\u8ba1\u7b97<\/li>\n
- \u5b9e\u9645\u8fd0\u7b97\u8bbe\u5907\u662f cuda:0<\/li>\n
- \u7b2c 0 \u5f20\u663e\u5361\u540d\u79f0\u8bc6\u522b\u6b63\u5e38：NVIDIA GeForce RTX 4090 D<\/li>\n<\/ul>\n
\u8fd9\u4e00\u6b65\u901a\u8fc7\u540e，\u8bf4\u660e\u6574\u4e2a PyTorch + CUDA \u73af\u5883\u5df2\u7ecf\u642d\u5efa\u6210\u529f，\u53ef\u4ee5\u8fdb\u5165\u540e\u7eed\u6a21\u578b\u8bad\u7ec3\u6216\u63a8\u7406\u9636\u6bb5\u3002<\/p>\n
\u9a8c\u8bc1\u622a\u56fe<\/h4>\n
![\"\u5728\u8fd9\u91cc\u63d2\u5165\u56fe\u7247\u63cf\u8ff0\"](\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/07\/20260725005047-6a6408670245a.png\")
<\/p>\n
\n\u4e5d\u3001\u7ed3\u8bba<\/h3>\n
\u672c\u6b21\u5728 \u98de\u817e S5000C \u670d\u52a1\u5668\u4e0a\u7684\u73af\u5883\u642d\u5efa\u548c\u9a8c\u8bc1\u7ed3\u679c\u5982\u4e0b：<\/p>\n
- \n
- CPU \u5e73\u53f0\u8bc6\u522b\u6b63\u5e38<\/li>\n
- 8 \u5f20 RTX 4090 D \u663e\u5361\u8bc6\u522b\u6b63\u5e38<\/li>\n
- NVIDIA \u9a71\u52a8\u5de5\u4f5c\u6b63\u5e38<\/li>\n
- PyTorch 2.5.1 \u5b89\u88c5\u6210\u529f<\/li>\n
- CUDA 12.4 \u8fd0\u884c\u6b63\u5e38<\/li>\n
- PyTorch \u53ef\u4ee5\u6b63\u786e\u8c03\u7528 GPU \u6267\u884c\u5f20\u91cf\u8ba1\u7b97<\/li>\n<\/ul>\n
\u6574\u4f53\u6765\u770b，\u8fd9\u5957\u73af\u5883\u5df2\u7ecf\u5177\u5907\u6df1\u5ea6\u5b66\u4e60\u8bad\u7ec3\u3001\u6a21\u578b\u63a8\u7406\u3001\u591a\u5361\u4efb\u52a1\u8c03\u5ea6\u7b49\u57fa\u7840\u8fd0\u884c\u6761\u4ef6\u3002<\/p>\n
\n\u5341\u4e00\u3001\u5e38\u89c1\u8bf4\u660e<\/h3>\n
1）\u9a71\u52a8\u7248\u672c\u9ad8\u4e8e PyTorch \u5bf9\u5e94 CUDA \u7248\u672c，\u662f\u6b63\u5e38\u73b0\u8c61<\/h4>\n
\u5f88\u591a\u4eba\u770b\u5230：<\/p>\n
- \n
- nvidia-smi \u91cc\u662f CUDA 13.0<\/li>\n
- torch.version.cuda \u91cc\u662f 12.4<\/li>\n<\/ul>\n
\u4f1a\u62c5\u5fc3\u662f\u4e0d\u662f\u7248\u672c\u4e0d\u4e00\u81f4\u5bfc\u81f4\u6709\u95ee\u9898\u3002<\/p>\n
\u5b9e\u9645\u4e0a\u8fd9\u901a\u5e38\u662f\u6b63\u5e38\u7684\u3002\u56e0\u4e3a\u9a71\u52a8\u652f\u6301\u66f4\u9ad8\u7248\u672c CUDA \u65f6，\u5f80\u5f80\u4e5f\u80fd\u517c\u5bb9\u8f83\u4f4e\u7248\u672c\u7684\u8fd0\u884c\u65f6\u3002<\/p>\n
\n2）\u51fa\u73b0 NumPy \u8b66\u544a，\u4e0d\u5f71\u54cd\u57fa\u7840 CUDA \u9a8c\u8bc1<\/h4>\n
\u5982\u679c\u770b\u5230：<\/p>\n
No module named 'numpy'<\/p>\n
\u8bf4\u660e\u53ea\u662f\u5f53\u524d\u73af\u5883\u6ca1\u88c5 numpy，\u8865\u88c5\u5373\u53ef：<\/p>\n
python -m<\/span> pip install<\/span> numpy<\/p>\n
\n3）\u5efa\u8bae\u4e00\u5b9a\u8981\u505a\u4e00\u6b21\u771f\u5b9e GPU \u8fd0\u7b97\u6d4b\u8bd5<\/h4>\n
\u53ea\u770b torch.cuda.is_available() \u8fd8\u4e0d\u591f，\u6700\u597d\u50cf\u672c\u6587\u4e00\u6837：<\/p>\n
- \n
- \u521b\u5efa CUDA \u5f20\u91cf<\/li>\n
- \u505a\u4e00\u6b21\u52a0\u6cd5\u8fd0\u7b97<\/li>\n
- \u67e5\u770b\u8f93\u51fa\u8bbe\u5907\u4fe1\u606f<\/li>\n<\/ul>\n
\u8fd9\u6837\u66f4\u80fd\u8bf4\u660e\u73af\u5883\u786e\u5b9e\u53ef\u7528\u3002<\/p>\n
\n\u5341\u4e8c\u3001\u547d\u4ee4\u6c47\u603b<\/h3>\n
1. \u67e5\u770b CPU \u578b\u53f7<\/h4>\n
lscpu |<\/span> grep<\/span> "Model name"<\/span><\/p>\n
2. \u67e5\u770b GPU \u548c\u9a71\u52a8\u4fe1\u606f<\/h4>\n
nvidia-smi -l<\/span><\/p>\n
3. \u5b89\u88c5 PyTorch<\/h4>\n
python -m<\/span> pip install<\/span> https:\/\/download.pytorch.org\/whl\/cu124\/torch-2.5.1-cp310-cp310-linux_aarch64.whl<\/p>\n
4. \u5b89\u88c5 NumPy<\/h4>\n
python -m<\/span> pip install<\/span> numpy<\/p>\n
5. \u9a8c\u8bc1 PyTorch \u548c CUDA<\/h4>\n
python -c<\/span> "import torch; print(torch.__version__); print(torch.version.cuda); print(torch.cuda.is_available()); print(torch.cuda.device_count())"<\/span><\/p>\n
6. \u6d4b\u8bd5 GPU \u5f20\u91cf\u8fd0\u7b97<\/h4>\n
python – <<<\/span>'PY'
\nimport torch
\nprint("torch:", torch.__version__)
\nprint("cuda version:", torch.version.cuda)
\nprint("cuda available:", torch.cuda.is_available())
\nprint("device count:", torch.cuda.device_count())
\nif torch.cuda.is_available():
\n x = torch.randn(2, 3).cuda()
\n y = torch.randn(2, 3).cuda()
\n z = x + y
\n print("gpu tensor ok")
\n print(z)
\n print("device 0:", torch.cuda.get_device_name(0))
\nPY<\/span><\/p>\n
\n\u5341\u4e09\u3001\u53c2\u8003\u8bf4\u660e<\/h3>\n
\u672c\u6587\u4e3b\u8981\u8bb0\u5f55\u7684\u662f\u5b9e\u9645\u90e8\u7f72\u8fc7\u7a0b\u4e2d\u7684\u9a8c\u8bc1\u7ed3\u679c，\u9002\u5408\u4f5c\u4e3a\u98de\u817e ARM \u670d\u52a1\u5668\u5b89\u88c5 PyTorch GPU \u73af\u5883\u7684\u53c2\u8003\u3002<\/p>\n
\u5982\u679c\u4f60\u540e\u7eed\u8fd8\u9700\u8981\u7ee7\u7eed\u914d\u7f6e：<\/p>\n
- \n
- torchvision<\/li>\n
- torchaudio<\/li>\n
- \u591a\u5361\u8bad\u7ec3\u73af\u5883<\/li>\n
- NCCL \/ \u5206\u5e03\u5f0f\u8bad\u7ec3<\/li>\n
- Docker \u5bb9\u5668\u73af\u5883<\/li>\n
- Transformers \/ \u5927\u6a21\u578b\u63a8\u7406\u73af\u5883<\/li>\n<\/ul>\n
\u4e5f\u53ef\u4ee5\u5728\u8fd9\u5957\u57fa\u7840\u73af\u5883\u4e0a\u7ee7\u7eed\u6269\u5c55\u3002<\/p>\n
\n\u5982\u679c\u8fd9\u7bc7\u6587\u7ae0\u5bf9\u4f60\u6709\u5e2e\u52a9，\u6b22\u8fce\u70b9\u8d5e\u3001\u6536\u85cf\u3001\u4ea4\u6d41\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
\u98de\u817e S5000C \u670d\u52a1\u5668\u73af\u5883\u642d\u5efa\u5b9e\u6218：PyTorch CUDA RTX 4090D \u5b89\u88c5\u4e0e\u9a8c\u8bc1
\n\u4e00\u3001\u524d\u8a00
\n\u6700\u8fd1\u5728\u98de\u817e S5000C \u670d\u52a1\u5668\u4e0a\u642d\u5efa\u6df1\u5ea6\u5b66\u4e60\u8fd0\u884c\u73af\u5883，\u672c\u6587\u8bb0\u5f55\u4e00\u4e0b\u4ece\u786c\u4ef6\u4fe1\u606f\u786e\u8ba4\u3001GPU \u9a71\u52a8\u68c0\u67e5，\u5230 PyTorch \u5b89\u88c5\u548c CUDA \u9a8c\u8bc1\u7684\u5b8c\u6574\u8fc7\u7a0b\u3002
\n\u5982\u679c\u4f60\u624b\u91cc\u4e5f\u662f\u7c7b\u4f3c\u7684 ARM \u670d\u52a1\u5668，\u6216\u8005\u6b63\u5728\u505a\u98de\u817e\u5e73\u53f0 NVIDIA GPU \u7684\u73af\u5883\u90e8\u7f72，\u5e0c\u671b\u8fd9\u7bc7\u6587\u7ae0\u80fd\u5e2e\u4f60\u5c11\u8d70\u4e00\u4e9b\u5f2f\u8def\u3002
\n\u672c\u6587\u73af\u5883\u5173\u952e<\/p>\n","protected":false},"author":2,"featured_media":82422,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[9201,9200,9199,821,152],"topic":[],"class_list":["post-82424","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-server","tag-rtx4090d","tag-s5000c","tag-9199","tag-cuda","tag-pytorch"],"yoast_head":"\n01 \u98de\u817e S5000C \u670d\u52a1\u5668\u73af\u5883\u642d\u5efa\u5b9e\u6218\uff1aPyTorch + CUDA + RTX 4090D \u5b89\u88c5\u4e0e\u9a8c\u8bc1 - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.wsisp.com\/helps\/82424.html\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"01 \u98de\u817e S5000C \u670d\u52a1\u5668\u73af\u5883\u642d\u5efa\u5b9e\u6218\uff1aPyTorch + CUDA + RTX 4090D \u5b89\u88c5\u4e0e\u9a8c\u8bc1 - 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