Maybe my question is a bit naive, but I really didn't find anything in the tensorflow documentation.
I have a trained tensorflow model where the variables of it was placed in the GPU. Now I would like to restore this model and test it using the CPU.
If I do this via 'tf.train.Saver.restore` as in the example:
saver = tf.train.import_meta_graph("/tmp/graph.meta")
saver.restore(session, "/tmp/model.ckp")
I have the following excpetion:
InvalidArgumentError: Cannot assign a device to node 'b_fc8/b_fc8/Adam_1': 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
How can I make restore these variables in the CPU?
Thanks
Use clear_devices flag, ie
saver = tf.train.import_meta_graph("/tmp/graph.meta", clear_devices=True)
I'm using tensorflow 0.12 and clear_devices=True and tf.device('/cpu:0') was not working with me (saver.restore was still trying to assign variables to /gpu:0).
I really needed to force everything to /cpu:0 since I was loading several models which wouldn't fit in GPU memory anyways. Here are two alternatives to force everything to /cpu:0
Set os.environ['CUDA_VISIBLE_DEVICES']=''
Use the device_count of ConfigProto like tf.Session(config=tf.ConfigProto(device_count={"GPU": 0, "CPU": 1}))
Related
I'm trying to run the model scoring (inference graph) from tensorflow objec detection API to run it on multiple GPU's, tried specifying the GPU number in the main, but it runs only on single GPU.placed GPU utilization snapshot here
Using tensorflow-gpu==1.13.1, can you kindly point me what I'm missing here.
for i in range(2):
with tf.device('/gpu:{}' . format(i)):
tf_init()
init = tf.global_variables_initializer
with detection_graph.as_default():
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as session:
call to #run_inference_multiple_images function
The responses to this question should give you a few options for fixing this.
Usually TensorFlow will occupy all visible GPUs unless told otherwise. So if you haven't already tried, you could just remove the with tf.device line (assuming you only have the two GPUs) and TensorFlow should use them both.
Otherwise, I think the easiest is setting the environment variables with os.environ["CUDA_VISIBLE_DEVICES"] = "0,1".
I need to force TPUEstimator to use the CPU. I have a rented google machine and the GPU is already running training. Since the CPUs are idle, I want to start a second Tensorflow session for evaluation but I want to force the evaluation cycle to use CPUs only so that it does not steal GPU time.
I am assuming there is a flag in the run_config or similar for doing this but am struggling to find one in the TF documentation.
run_config = tf.contrib.tpu.RunConfig(
cluster=tpu_cluster_resolver,
master=FLAGS.master,
model_dir=FLAGS.output_dir,
save_checkpoints_steps=FLAGS.save_checkpoints_steps,
tpu_config=tf.contrib.tpu.TPUConfig(
iterations_per_loop=FLAGS.iterations_per_loop,
num_shards=FLAGS.num_tpu_cores,
per_host_input_for_training=is_per_host))
You can run a TPUEstimator locally by including two arguments: (1) use_tpu should be set to False, and (2) tf.contrib.tpu.RunConfig should be passed as the config argument.
my_tpu_estimator = tf.contrib.tpu.TPUEstimator(
model_fn=my_model_fn,
config=tf.contrib.tpu.RunConfig()
use_tpu=False)
The majority of example TPU models can be run in local mode by setting the command line flags:
$> python mnist_tpu.py --use_tpu=false --master=''
More documentation can be found here.
Here is part of my code.
with tf.Graph().as_default(), tf.device('/cpu:0'):
global_step = tf.get_variable(
'global_step',
[],
initializer = tf.constant_initializer(0),
writer = tf.summary.FileWriter(logs_path,graph=tf.get_default_graph())
with tf.device('/gpu:0'):
tf.summary.scalar('learning_rate', INITIAL_LEARNING_RATE)
summary_op = tf.summary.merge_all()
when I run it. I will get following error:
InvalidArgumentError (see above for traceback): Cannot assign a device for operation 'learning_rate': Could not satisfy explicit device specification '/device:GPU:0' because no
supported kernel for GPU devices is available.
[[Node: learning_rate = ScalarSummary[T=DT_FLOAT, _device="/device:GPU:0"](learning_rate/tags, learning_rate/values)]]
if I move these 2 ops into tf.device("/cpu:0") device scope, It will work.
tf.summary.scalar('learning_rate', INITIAL_LEARNING_RATE)
summary_op = tf.summary.merge_all()
I google it. there are many suggestiones about using "allow_soft_placement=True". But I think this solution is basically change device scope automatically. So my question is:
why these 2 ops can not assign to gpu? Is there any documents I can look at to figure out what ops can or cannot assign to gpu?
any suggestion is welcome.
You can't assign a summary operation to a GPU because is meaningless.
In short, a GPU executes parallel operations. A summary is nothing but a file in which you append new lines every time you write on it. It's a sequential operation that has nothing in common with the operation that GPUs are capable to do.
Your error says it all:
Could not satisfy explicit device specification '/device:GPU:0' because no supported kernel for GPU devices is available.
That operation (in the tensorflow version you're using) has no GPU implementation and thus must be sent to a CPU device.
I have a fine-tuned Inception v3 on a 2-GPU machine.
Now I am trying to run the trained model on another machine with 1 GPU, but I got an error like this:
Cannot assign a device to node 'tower_1/gradients/tower_1/conv0/BatchNorm/moments/sufficient_statistics/Sub_grad/BroadcastGradientArgs': Could not satisfy explicit device specification '/device:GPU:1' because no devices matching that specification are registered in this process; available devices: /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/gpu:0
It seems that the model wants a 2-GPU environment like the one it was trained on. Can I convert this model so that it uses only 1 GPU?
I changed two things and it worked.
Turn on allow_soft_placement option in Session:
config = tf.ConfigProto(allow_soft_placement=True)
sess = tf.Session(config=config)
Rename model file name from model.ckpt-50000.data-00000-of-00001 to model.ckpt-50000
In order to use the contrib.learn.Estimator for multi-GPU training, I am attempting to specify GPU assignments in my model_fn.
In pseudo-code:
def model_fn(X, y):
with tf.device('/gpu:1'):
... various tensorflow ops for model ...
return predictions, loss, train_op
Everything works fine without the tf.device('/gpu:1') call, but with it I encounter the following error:
InvalidArgumentError (see above for traceback): Cannot assign a device to
node 'save/ShardedFilename_1': Could not satisfy explicit device
specification '/device:GPU:1' because no supported kernel
for GPU devices is available.
I do not believe that I am adding the offending op to the graph myself, but rather that it is injected through the Estimator's snapshot functionality.
I believe that the solution is to set allow_soft_placement=True so that non GPU functions will fall to CPU, but it's not obvious to me how that exposed when dealing with contrib.learn.Estimator.
I see that the option is usually set in ConfigProto & passed to the session, but I've been using the Estimator's functionality to manage the session for me. Should I be taking control of the session creation, or am I missing a parameter somewhere to accomplish this?
Many thanks in advance for any advice.
Along with Estimator leaving contrib in Tensorflow 1.0 this is fixed.