tensorflow_gpu相關接口及多gpu訓練及相關注意

多gpu訓練使用keras

multi_gpu_model(model, gpus, cpu_merge=True, cpu_relocation=False)

這個函數有個極其重要的參數，cpu_merge和cpu_relocation，這個參數會影響到你訓練成功或失敗，因爲比如你用了否定的，然後你訓練卻用了cpu存模型，或者相反，則會導致你更新參數失敗。

正確的案例應該模仿官方，我的方式是

with tf.device('/cpu:0'):
    predict_model = keras.models.Model(inputs=input_images, outputs=cls_pred)

muti_gpu_model = keras.utils.multi_gpu_model(model=predict_model, gpus=2)

freeze測試：

現在有個問題就是我要凍結參數時，我是凍結多gpu模型還是凍結原來的模型還是都凍結？

 

 

查看設備名字：
 

tf.test.gpu_device_name()

Returns the name of a GPU device if available or the empty string.

 

tf.contrib.eager.list_devices()

Names of the available devices, as a list.

 

使用具體設備的函數

# Place the operations on device "GPU:0" in the "ps" job.
device_spec = DeviceSpec(job="ps", device_type="GPU", device_index=0)
with tf.device(device_spec):
  # Both my_var and squared_var will be placed on /job:ps/device:GPU:0.
  my_var = tf.Variable(..., name="my_variable")
  squared_var = tf.square(my_var)

If a DeviceSpec is partially specified, it will be merged with other DeviceSpecs according to the scope in which it is defined. DeviceSpec components defined in inner scopes take precedence over those defined in outer scopes.

with tf.device(DeviceSpec(job="train", )):
  with tf.device(DeviceSpec(job="ps", device_type="GPU", device_index=0):
    # Nodes created here will be assigned to /job:ps/device:GPU:0.
  with tf.device(DeviceSpec(device_type="GPU", device_index=1):
    # Nodes created here will be assigned to /job:train/device:GPU:1.

A DeviceSpec consists of 5 components -- each of which is optionally specified:

• Job: The job name.
• Replica: The replica index.
• Task: The task index.
• Device type: The device type string (e.g. "CPU" or "GPU").
• Device index: The device index.

__init__

__init__(
    job=None,
    replica=None,
    task=None,
    device_type=None,
    device_index=None
)

Create a new DeviceSpec object.

Args:

• job: string. Optional job name.
• replica: int. Optional replica index.
• task: int. Optional task index.
• device_type: Optional device type string (e.g. "CPU" or "GPU")
• device_index: int. Optional device index. If left unspecified, device represents 'any' device_index.

 

封裝好的一個分支

tf.distribute