What do the options in ConfigProto like allow_soft_placement and log_device_placement mean?

Question

We see this quite often in many of the TensorFlow tutorials:

sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True,                                          log_device_placement=True)) 

What does allow_soft_placement and log_device_placement mean?

Answer 1

If you look at the API of ConfigProto, on line 278, you will see this:

  // Whether soft placement is allowed. If allow_soft_placement is true,   // an op will be placed on CPU if   //   1. there's no GPU implementation for the OP   // or   //   2. no GPU devices are known or registered   // or   //   3. need to co-locate with reftype input(s) which are from CPU.   bool allow_soft_placement = 7; 

What this really means is that if you do something like this without allow_soft_placement=True, TensorFlow will throw an error.

with tf.device('/gpu:0'):     # some op that doesn't have a GPU implementation 

Right below it, you will see on line 281:

  // Whether device placements should be logged.   bool log_device_placement = 8; 

When log_device_placement=True, you will get a verbose output of something like this:

2017-07-03 01:13:59.466748: I tensorflow/core/common_runtime/simple_placer.cc:841] Placeholder_1: (Placeholder)/job:localhost/replica:0/task:0/cpu:0 Placeholder: (Placeholder): /job:localhost/replica:0/task:0/cpu:0 2017-07-03 01:13:59.466765: I tensorflow/core/common_runtime/simple_placer.cc:841] Placeholder: (Placeholder)/job:localhost/replica:0/task:0/cpu:0 Variable/initial_value: (Const): /job:localhost/replica:0/task:0/cpu:0 2017-07-03 01:13:59.466783: I tensorflow/core/common_runtime/simple_placer.cc:841] Variable/initial_value: (Const)/job:localhost/replica:0/task:0/cpu:0 

You can see where each operation is mapped to. For this case, they are all mapped to /cpu:0, but if you're in a distributed setting, there would be many more devices.

Answer 2

In addition to comments in tensorflow/core/protobuf/config.proto (allow_soft_placement, log_device_placement) it is also explained in TF's using GPUs tutorial.

To find out which devices your operations and tensors are assigned to, create the session with log_device_placement configuration option set to True.

Which is helpful for debugging. For each of the nodes of your graph, you will see the device it was assigned to.

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If you would like TensorFlow to automatically choose an existing and supported device to run the operations in case the specified one doesn't exist, you can set allow_soft_placement to True in the configuration option when creating the session.

Which will help you if you accidentally manually specified the wrong device or a device which does not support a particular op. This is useful if you write a code which can be executed in environments you do not know. You still can provide useful defaults, but in the case of failure a graceful fallback.