CODEWITHSUNDEEP
pythontensorflowgpuinferencemultiple-gpu
leonard
leonard

Reputation: 845

Place loaded frozen model on specific gpu device in Tensorflow

I have a frozen model and 4 gpus. I would like to perform inference on as much data as fast as possible. I basically want to execute data parallelism where the same model is performing inference on 4 batches: one batch for each gpu.

This is what I am roughly trying to do

def return_ops():
    # load the graph
    with tf.Graph().as_default() as graph:
        od_graph_def = tf.GraphDef()
        with tf.gfile.GFile(model_path, 'rb') as fid:
            serialized_graph = fid.read()
            od_graph_def.ParseFromString(serialized_graph)
            tf.import_graph_def(od_graph_def, name='')

    inputs = []
    outputs = []
    with graph.as_default() as g:
        for gpu in ['/gpu:0', '/gpu:1', '/gpu:2', '/gpu:3']:
            with tf.device(gpu):
                image_tensor = g.get_tensor_by_name('input:0')
                get_embeddings = g.get_tensor_by_name('embeddings:0')
            inputs.append(image_tensor)
            outputs.append(get_embeddings)

    return inputs, outputs, g

However, when I run

#sample batch
x = np.ones((100,160,160,3))
# get ops
image_tensor_list, pt_list, emb_list, graph = return_ops()
# construct feed dict
feed_dict = {it: x for it in image_tensor_list}

# run the ops
with tf.Session(graph=graph, config=tf.ConfigProto(allow_soft_placement=True)) as sess:
    inf = sess.run(emb_list, feed_dict=feed_dict)

Everything is running on /gpu:0 when inspecting using nvidia-smi.

I can, however, run 

with tf.device("/gpu:1"):
    t = tf.range(1000)

with tf.Session() as sess:
    sess.run(t)

and there is activity on the second gpu...

How can I implement this data parallelism task properly?

Upvotes: 1

Views: 958

Answers (1)

leonard
leonard

Reputation: 845

I learned that the placement of tensors on GPU needs to occur when importing the graph_def. The code below returns ops that I can then run with sess.run([output1, ..., outputk], feed_dict). It will place all operations on the gpu, which is not ideal, therefore I pass allow_soft_placement to be true for the session config. 

class MultiGPUNet(object):

    def __init__(self, model_path, n_gpu):

        self.model_path = model_path
        self.n_gpu = n_gpu
        self.graph = tf.Graph()

        # specify device for n_gpu copies of model
        # during graphdef parsing
        for i in range(self.n_gpu):
            self._init_models(i, self.graph)

    def _init_models(self, i, graph):

        with self.graph.as_default():
            od_graph_def = tf.GraphDef()

            with tf.gfile.GFile(model_path, 'rb') as fid:
                serialized_graph = fid.read()
                od_graph_def.ParseFromString(serialized_graph)

                with tf.device('/device:GPU:{}'.format(i)):
                    tf.import_graph_def(od_graph_def, name='{}'.format(i))

    def get_tensors(self):

        output_tensors = []
        input_tensors = []
        train_tensors = []

        for i in range(self.n_gpu):
            input_tensors.append(
                self.graph.get_tensor_by_name('{}/<input_name>:0'.format(i)))
            output_tensors.append(
                self.graph.get_tensor_by_name('{}/<out_name>:0'.format(i)))
            train_tensors.append(
                self.graph.get_tensor_by_name('{}/<train_name>:0'.format(i)))

        def make_feed_dict(x):
            """x will be a list of batches"""
            assert len(x)==len(input_tensors)
            input_data = zip(input_tensors, x)
            train_bool = zip(train_tensors, [False]*len(train_tensors))
            return dict(input_data + train_bool)

        return output_tensors, make_feed_dict

Upvotes: 3

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