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我最近开始使用 azure for ML 并尝试使用机器学习服务工作区。因为我需要使用 GPU 上的图像训练 NN，所以我已经设置了一个工作区，其中计算集为 NC6s-V2 机器。

问题是训练仍然在 CPU 上进行——日志说它无法找到 CUDA。这是运行我的脚本时的警告日志。任何线索如何解决这个问题？

我还在估计器的 conda packages 选项中明确提到了 tensorflow-gpu 包。

这是我的估算器代码，

script_params = {
         '--input_data_folder': ds.path('dataset').as_mount(),
         '--zip_file_name': 'train.zip',
         '--run_mode': 'train'
    }


est = Estimator(source_directory='./scripts',
                     script_params=script_params,
                     compute_target=compute_target,
                     entry_script='main.py',
                     conda_packages=['scikit-image', 'keras', 'tqdm', 'pillow', 'matplotlib', 'scipy', 'tensorflow-gpu']
                     )

run = exp.submit(config=est)

run.wait_for_completion(show_output=True)

计算目标是根据 github 上的示例代码制作的：

compute_name = "P100-NC6s-V2"
compute_min_nodes = 0
compute_max_nodes = 4

vm_size = "STANDARD_NC6S_V2"

if compute_name in ws.compute_targets:
    compute_target = ws.compute_targets[compute_name]
    if compute_target and type(compute_target) is AmlCompute:
        print('found compute target. just use it. ' + compute_name)
else:
    print('creating a new compute target...')
    provisioning_config = AmlCompute.provisioning_configuration(vm_size=vm_size,
                                                                min_nodes=compute_min_nodes,
                                                                max_nodes=compute_max_nodes)

    # create the cluster
    compute_target = ComputeTarget.create(
        ws, compute_name, provisioning_config)

    # can poll for a minimum number of nodes and for a specific timeout.
    # if no min node count is provided it will use the scale settings for the cluster
    compute_target.wait_for_completion(
        show_output=True, min_node_count=None, timeout_in_minutes=20)

    # For a more detailed view of current AmlCompute status, use get_status()
    print(compute_target.get_status().serialize())

这是它无法使用 GPU 的警告：

2019-08-12 14:50:16.961247: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55a7ce570830 executing computations on platform Host. Devices:
2019-08-12 14:50:16.961278: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): <undefined>, <undefined>
2019-08-12 14:50:16.971025: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /opt/intel/compilers_and_libraries_2018.3.222/linux/mpi/intel64/lib:/opt/intel/compilers_and_libraries_2018.3.222/linux/mpi/mic/lib:/opt/intel/compilers_and_libraries_2018.3.222/linux/mpi/intel64/lib:/opt/intel/compilers_and_libraries_2018.3.222/linux/mpi/mic/lib:/azureml-envs/azureml_5fdf05c5671519f307e0f43128b8610e/lib:
2019-08-12 14:50:16.971054: E tensorflow/stream_executor/cuda/cuda_driver.cc:318] failed call to cuInit: UNKNOWN ERROR (303)
2019-08-12 14:50:16.971081: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: 4bd815dfb0e74e3da901861a4746184f000000
2019-08-12 14:50:16.971089: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: 4bd815dfb0e74e3da901861a4746184f000000
2019-08-12 14:50:16.971164: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:200] libcuda reported version is: Not found: was unable to find libcuda.so DSO loaded into this program
2019-08-12 14:50:16.971202: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:204] kernel reported version is: 418.40.4
Device mapping:
/job:localhost/replica:0/task:0/device:XLA_CPU:0 -> device: XLA_CPU device
2019-08-12 14:50:16.973301: I tensorflow/core/common_runtime/direct_session.cc:296] Device mapping:
/job:localhost/replica:0/task:0/device:XLA_CPU:0 -> device: XLA_CPU device

根据日志，它当前正在使用 CPU。任何线索如何解决这里的问题？

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3 回答 3

1

您可以将 Tensorflow Estimator 与 Keras 和其他分层库一起使用，而不是基本 Estimator。这样您就不必担心设置和配置 GPU 库，因为 Tensorflow Estimator 使用预先配置了 GPU 库的 Docker 映像。

有关文档，请参见此处：

API 参考您可以使用conda_packages参数来指定其他库。还设置参数use_gpu = True。

示例笔记本

于 2019-08-13T14:10:12.680 回答
0

错误的第 3 行似乎表明您尚未安装在 GPU 上运行 NN 所需的所有 CUDA 库。确保安装了所有 CUDA 依赖项。如果您不确定，请参阅此堆栈溢出问题：

如何使用 TensorFlow GPU？

欢迎来到 SO！

于 2019-08-13T04:40:36.207 回答
0

之前和你有同样的问题，我解决了

env = Environment.from_pip_requirements(
            name="hello",
            file_path=f'projects/requirements.txt'
        )
env.docker.enabled = True
env.docker.base_image = 'mcr.microsoft.com/azureml/openmpi3.1.2-cuda10.1-cudnn7-ubuntu18.04'

您必须指定正确的泊坞窗图像

于 2020-10-10T18:50:36.730 回答