8

我认为它应该与 一起使用with tf.device("/gpu:0")，但我应该把它放在哪里？我不认为它是：

with tf.device("/gpu:0"):
    tf.app.run()

那么我应该把它放在 的main()函数中tf.app，还是我用于估计器的模型函数中？

编辑：如果这有帮助，这是我的main()功能：

def main(unused_argv):
  """Code to load training folds data pickle or generate one if not present"""

  # Create the Estimator
  mnist_classifier = tf.estimator.Estimator(
      model_fn=cnn_model_fn2, model_dir="F:/python_machine_learning_codes/tmp/custom_age_adience_1")

  # Set up logging for predictions
  # Log the values in the "Softmax" tensor with label "probabilities"
  tensors_to_log = {"probabilities": "softmax_tensor"}
  logging_hook = tf.train.LoggingTensorHook(
      tensors=tensors_to_log, every_n_iter=100)

  # Train the model
  train_input_fn = tf.estimator.inputs.numpy_input_fn(
      x={"x": train_data},
      y=train_labels,
      batch_size=64,
      num_epochs=None,
      shuffle=True)
  mnist_classifier.train(
      input_fn=train_input_fn,
      steps=500,
      hooks=[logging_hook])

  # Evaluate the model and print results
  """Code to load eval fold data pickle or generate one if not present"""

  eval_logs = {"probabilities": "softmax_tensor"}
  eval_hook = tf.train.LoggingTensorHook(
      tensors=eval_logs, every_n_iter=100)
  eval_input_fn = tf.estimator.inputs.numpy_input_fn(
      x={"x": eval_data},
      y=eval_labels,
      num_epochs=1,
      shuffle=False)
  eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn, hooks=[eval_hook])

正如你所看到的，我在这里没有明确的会话声明，那么我到底应该把with tf.device("/gpu:0")?

4

3 回答 3

1

你可以把它放在你的模型函数的开头，也就是说，当你定义你的模型时，你应该写：

def cnn_model_fn2(...):
    with tf.device('/gpu:0'):
        ...

但是，我希望 tensorflow 会自动将 gpu 用于您的模型。您可能需要检查它是否被正确检测到：

from tensorflow.python.client import device_lib
device_lib.list_local_devices()
于 2018-03-25T12:28:18.363 回答
0

我想知道使用tf.contrib.distribute来指定设备放置策略是否有效。

def main(unused_argv):
    """Code to load training folds data pickle or generate one if not present"""

    strategy = tf.contrib.distribute.OneDeviceStrategy(device='/gpu:0')
    config = tf.estimator.RunConfig(train_distribute=strategy)

    # Create the Estimator
    mnist_classifier = tf.estimator.Estimator(
        model_fn=cnn_model_fn2,
        config=config,
        model_dir="F:/python_machine_learning_codes/tmp/custom_age_adience_1")

    ......
于 2018-11-10T09:55:59.840 回答
0

with estimator there isn't any statement like

sess = tf.Session(config = xxxxxxxxxxxxx)

neither a statement as

sess.run()

So...unfortunately there isn't a full documentation in the tensorflow web. I'm trying with the different options of RunConfig

# Create a tf.estimator.RunConfig to ensure the model is run on CPU, which
# trains faster than GPU for this model.
run_config = tf.estimator.RunConfig().replace(
        session_config=tf.ConfigProto(log_device_placement=True,
                                      device_count={'GPU': 0}))

Try to work with this...Actually I'm working with something like your task so if I get some advances I will post it here.

Take a look here: https://github.com/tensorflow/models/blob/master/official/wide_deep/wide_deep.py In this example they are using the code showed above with the .replace statement to ensure that the model is running on CPU.

于 2017-12-20T11:17:05.913 回答