使用Dask时，tensorflow只能从安装了2个GPU的机器检测1个GPU

我们的HPC节点有2个K80 GPU。当我使用python在HPC节点上运行以下代码时，代码将检测2个gpu并显示“gpu设备类型：['TeslaK80'，'TeslaK80']”

但当我用DASK运行相同的代码时，它只能检测1个GPU。它显示“gpu设备类型：['TeslaK80']”

以下是检测GPU的代码 导入tensorflow作为tf

def init_gpu()
    print("\n\n\n ... tensorflow version = ", tf.__version__)
    from tensorflow.python.client import device_lib

    local_device_protos = device_lib.list_local_devices()
    print("local device protos:{0}".format(local_device_protos))
    _gpu_raw_info =  [(x.name,x.physical_device_desc) for x in local_device_protos if x.device_type == 'GPU']
    print("gpu raw info:{0}".format(_gpu_raw_info))
    _gpu_names =  [x[0] for x in _gpu_raw_info]
    _gpu_devices =  [x[1] for x in _gpu_raw_info]
    _gpu_device_types =  [x.split(':')[2].split(',')[0].replace(' ','') for x in _gpu_devices]
     print("gpu device types:{0}".format(_gpu_device_types))

以下是在群集上启动作业的DASK LSF群集代码：

cluster = LSFCluster(queue=queue_name, project=hpc_project, alltime='80:00', cores=1, processes=1, local_directory='dask-worker-space', memory='250GB', job_extra=['-gpu "num=2"'], log_directory='scheduler_log', dashboard_address=':8787'))
cluster.scale(1* 1)
client = Client(cluster.scheduler_address, timeout=60)
wbsd_results = []
r = dask.delayed(init_gpu)()
wbsd_results.append(r)
client.compute(wbsd, sync=True)

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请帮忙。谢谢。

您能否确认，当您在没有dask的情况下运行LSF作业时，作业可以同时看到两个GPU？有一些例子，这可能是有用的如果我使用LSF作业没有DASK，我得到了1个GPU以及。它必须是LSF设置。谢谢