Unterstützt Google Cloud ML GPU?

Question

Ich teste Google Cloud ML, um mein ML-Modell mit Tensorflow zu beschleunigen.

Leider scheint Google Cloud ML extrem langsam zu sein. Mein Mainstream-Level-PC ist mindestens 10x schneller als Google Cloud ML.

Ich bezweifle, dass es GPU verwendet, also habe ich einen Test gemacht. Ich modifizierte eine Probe code, um die Verwendung von GPU erzwingen.

diff --git a/mnist/trainable/trainer/task.py b/mnist/trainable/trainer/task.py 
index 9acb349..a64a11d 100644 
--- a/mnist/trainable/trainer/task.py 
+++ b/mnist/trainable/trainer/task.py 
@@ -131,11 +131,12 @@ def run_training(): 
    images_placeholder, labels_placeholder = placeholder_inputs(
    FLAGS.batch_size) 

- # Build a Graph that computes predictions from the inference model. 
- logits = mnist.inference(images_placeholder, FLAGS.hidden1, FLAGS.hidden2) 
+ with tf.device("/gpu:0"): 
+  # Build a Graph that computes predictions from the inference model. 
+  logits = mnist.inference(images_placeholder, FLAGS.hidden1, FLAGS.hidden2) 

- # Add to the Graph the Ops for loss calculation. 
- loss = mnist.loss(logits, labels_placeholder) 
+  # Add to the Graph the Ops for loss calculation. 
+  loss = mnist.loss(logits, labels_placeholder) 

    # Add to the Graph the Ops that calculate and apply gradients. 
    train_op = mnist.training(loss, FLAGS.learning_rate) 

Dieses Training Code funktioniert an meinem PC (gcloud beta ml local train ...), aber nicht in der Wolke. Es gibt Fehler wie folgt:

"Traceback (most recent call last): 
    File "/usr/lib/python2.7/runpy.py", line 162, in _run_module_as_main 
    "__main__", fname, loader, pkg_name) 
    File "/usr/lib/python2.7/runpy.py", line 72, in _run_code 
    exec code in run_globals 
    File "/root/.local/lib/python2.7/site-packages/trainer/task.py", line 239, in <module> 
    tf.app.run() 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 43, in run 
    sys.exit(main(sys.argv[:1] + flags_passthrough)) 
    File "/root/.local/lib/python2.7/site-packages/trainer/task.py", line 235, in main 
    run_training() 
    File "/root/.local/lib/python2.7/site-packages/trainer/task.py", line 177, in run_training 
    sess.run(init) 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 766, in run 
    run_metadata_ptr) 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 964, in _run 
    feed_dict_string, options, run_metadata) 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1014, in _do_run 
    target_list, options, run_metadata) 
    File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 1034, in _do_call 
    raise type(e)(node_def, op, message) 
InvalidArgumentError: Cannot assign a device to node 'softmax_linear/biases': Could not satisfy explicit device specification '/device:GPU:0' because no devices matching that specification are registered in this process; available devices: /job:localhost/replica:0/task:0/cpu:0 
Colocation Debug Info: 
Colocation group had the following types and devices: 
ApplyGradientDescent: CPU 
Identity: CPU 
Assign: CPU 
Variable: CPU 
    [[Node: softmax_linear/biases = Variable[container="", dtype=DT_FLOAT, shape=[10], shared_name="", _device="/device:GPU:0"]()]] 

Unterstützt Google Cloud ML GPU?

Answer 1

GPUs sind jetzt in der Betaversion und alle Cloud ML-Kunden haben Zugriff.

Hier sind die docs für die Verwendung von Grafikprozessoren mit Cloud ML.