Tensorflow 有没有办法把我所有的GPU设置成XLA,这样我就可以用多个GPU来训练,而不是只训练一个GPU?

Tensorflow 有没有办法把我所有的GPU设置成XLA,这样我就可以用多个GPU来训练,而不是只训练一个GPU?,tensorflow,keras,gpu,nvidia,Tensorflow,Keras,Gpu,Nvidia,我想使用多个GPU来训练keras模型。我的理解是,目前无法使用XLA训练多个GPU。问题是我不知道如何关闭XLA。每个GPU都被列为XLA GPU。 作为参考,我在最新的Ubuntu桌面上使用3个RTX2070。nvidia smi确实显示了所有3个GPU 我已尝试卸载并重新安装tensorflow gpu。这没有帮助 从 值错误: To call `multi_gpu_model` with `gpus=3`, we expect the following devices to be a

我想使用多个GPU来训练keras模型。我的理解是,目前无法使用XLA训练多个GPU。问题是我不知道如何关闭XLA。每个GPU都被列为XLA GPU。 作为参考,我在最新的Ubuntu桌面上使用3个RTX2070。nvidia smi确实显示了所有3个GPU

我已尝试卸载并重新安装
tensorflow gpu
。这没有帮助

值错误:

 To call `multi_gpu_model` with `gpus=3`, we expect the following devices to be available: ['/cpu:0', '/gpu:0', '/gpu:1', '/gpu:2']. However this machine only has: ['/cpu:0', '/xla_cpu:0', '/xla_gpu:0', '/xla_gpu:1', '/xla_gpu:2']. Try reducing `gpus`.
编辑:我使用的是
tensorflow gpu
,实际上我刚刚确认它甚至没有使用一个gpu。我通过将批处理大小增加到10000来确认这一点,并没有看到nvidia smi的任何变化,但我确实通过htop看到了cpu/内存使用的变化

编辑2:

tf.test.gpu_device_name()
只打印一个空字符串

鉴于

    from tensorflow.python.client import device_lib
    print(device_lib.list_local_devices())

prints all of my devices...
[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 7781250607362587360
, name: "/device:XLA_CPU:0"
device_type: "XLA_CPU"
memory_limit: 17179869184
locality {
}
incarnation: 12317810384332135154
physical_device_desc: "device: XLA_CPU device"
, name: "/device:XLA_GPU:0"
device_type: "XLA_GPU"
memory_limit: 17179869184
locality {
}
incarnation: 1761593194774305176
physical_device_desc: "device: XLA_GPU device"
, name: "/device:XLA_GPU:1"
device_type: "XLA_GPU"
memory_limit: 17179869184
locality {
}
incarnation: 11323027499711415341
physical_device_desc: "device: XLA_GPU device"
, name: "/device:XLA_GPU:2"
device_type: "XLA_GPU"
memory_limit: 17179869184
locality {
}
incarnation: 3573490477127930095
physical_device_desc: "device: XLA_GPU device"
]

我也有同样的问题!这个问题有什么解决办法吗?我面临着同样的问题,同样的问题!面临一些问题。不知为什么XLA打开了,我不知道如何关闭它。因此,我以前使用gpu运行的tensorflow程序不再只使用gpu和cpu。。没有人能找到解决办法??
    from tensorflow.python.client import device_lib
    print(device_lib.list_local_devices())

prints all of my devices...
[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 7781250607362587360
, name: "/device:XLA_CPU:0"
device_type: "XLA_CPU"
memory_limit: 17179869184
locality {
}
incarnation: 12317810384332135154
physical_device_desc: "device: XLA_CPU device"
, name: "/device:XLA_GPU:0"
device_type: "XLA_GPU"
memory_limit: 17179869184
locality {
}
incarnation: 1761593194774305176
physical_device_desc: "device: XLA_GPU device"
, name: "/device:XLA_GPU:1"
device_type: "XLA_GPU"
memory_limit: 17179869184
locality {
}
incarnation: 11323027499711415341
physical_device_desc: "device: XLA_GPU device"
, name: "/device:XLA_GPU:2"
device_type: "XLA_GPU"
memory_limit: 17179869184
locality {
}
incarnation: 3573490477127930095
physical_device_desc: "device: XLA_GPU device"
]