安装与验证
- 安装TensorFlow 2.21.0 GPU版本。
1python3 -m pip install "tensorflow[and-cuda]==2.21.0"
- 配置NVIDIA pip包安装的CUDA运行库路径。
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export LD_LIBRARY_PATH=/usr/lib64:\ /usr/local/lib/python3.11/site-packages/nvidia/cublas/lib:\ /usr/local/lib/python3.11/site-packages/nvidia/cuda_cupti/lib:\ /usr/local/lib/python3.11/site-packages/nvidia/cuda_nvrtc/lib:\ /usr/local/lib/python3.11/site-packages/nvidia/cuda_runtime/lib:\ /usr/local/lib/python3.11/site-packages/nvidia/cudnn/lib:\ /usr/local/lib/python3.11/site-packages/nvidia/cufft/lib:\ /usr/local/lib/python3.11/site-packages/nvidia/curand/lib:\ /usr/local/lib/python3.11/site-packages/nvidia/cusolver/lib:\ /usr/local/lib/python3.11/site-packages/nvidia/cusparse/lib:\ /usr/local/lib/python3.11/site-packages/nvidia/nccl/lib:\ /usr/local/lib/python3.11/site-packages/nvidia/nvjitlink/lib:${LD_LIBRARY_PATH:-}
- 执行以下命令验证TensorFlow版本、GPU设备可见性和GPU张量计算。
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python3 - <<'PY' import tensorflow as tf print("tensorflow_version=" + tf.__version__) print("physical_gpus=" + repr(tf.config.list_physical_devices("GPU"))) print("built_with_cuda=" + str(tf.test.is_built_with_cuda())) assert tf.__version__ == "2.21.0" assert tf.config.list_physical_devices("GPU") assert tf.test.is_built_with_cuda() with tf.device("/GPU:0"): a = tf.constant([[1.0, 2.0], [3.0, 4.0]]) b = tf.constant([[1.0, 2.0], [3.0, 4.0]]) c = tf.matmul(a, b) print("tensorflow_gpu_matmul=" + str(c.numpy().tolist())) PY
预期输出如下信息。
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tensorflow_version=2.21.0 physical_gpus=[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')] built_with_cuda=True tensorflow_gpu_matmul=[[7.0, 10.0], [15.0, 22.0]]
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