我创建了3个虚拟GPU（具有1个GPU），并尝试加速图像的矢量化。但是，将下面提供的代码与来自离文档（here）的手动放置一起使用时，我得到了奇怪的结果：在所有GPU上的训练要比在单个GPU上慢两倍。还要在具有3个物理GPU的机器上检查此代码（并删除虚拟设备初始化）-相同。
环境：Python 3.6，Ubuntu 18.04.3，tensorflow-gpu 1.14.0。
代码（此示例创建了3个虚拟设备，您可以在具有一个GPU的PC上对其进行测试）：
import os
import time
import numpy as np
import tensorflow as tf
start = time.time()
def load_graph(frozen_graph_filename):
# We load the protobuf file from the disk and parse it to retrieve the
# unserialized graph_def
with tf.gfile.GFile(frozen_graph_filename, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
# Then, we import the graph_def into a new Graph and returns it
with tf.Graph().as_default() as graph:
# The name var will prefix every op/nodes in your graph
# Since we load everything in a new graph, this is not needed
tf.import_graph_def(graph_def, name="")
return graph
path_to_graph = '/imagenet/' # Path to imagenet folder where graph file is placed
GRAPH = load_graph(os.path.join(path_to_graph, 'classify_image_graph_def.pb'))
# Create Session
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.9
config.gpu_options.allow_growth = True
session = tf.Session(graph=GRAPH, config=config)
output_dir = '/vectors/' # where to saved vectors from images
# Single GPU vectorization
for image_index, image in enumerate(selected_list):
with Image.open(image) as f:
image_data = f.convert('RGB')
feature_tensor = session.graph.get_tensor_by_name('pool_3:0')
feature_vector = session.run(feature_tensor, {'DecodeJpeg:0': image_data})
feature_vector = np.squeeze(feature_vector)
outfile_name = os.path.basename(image) + ".vc"
out_path = os.path.join(output_dir, outfile_name)
# Save vector
np.savetxt(out_path, feature_vector, delimiter=',')
print(f"Single GPU: {time.time() - start}")
start = time.time()
print("Start calculation on multiple GPU")
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
# Create 3 virtual GPUs with 1GB memory each
try:
tf.config.experimental.set_virtual_device_configuration(
gpus[0],
[tf.config.experimental.VirtualDeviceConfiguration(memory_limit=1024),
tf.config.experimental.VirtualDeviceConfiguration(memory_limit=1024),
tf.config.experimental.VirtualDeviceConfiguration(memory_limit=1024)])
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
print(len(gpus), "Physical GPU,", len(logical_gpus), "Logical GPUs")
except RuntimeError as e:
# Virtual devices must be set before GPUs have been initialized
print(e)
print("Create prepared ops")
start1 = time.time()
gpus = logical_gpus # comment this line to use physical GPU devices for calculations
image_list = ['1.jpg', '2.jpg', '3.jpg'] # list with images to vectorize (tested on 100 and 1000 examples)
# Assign chunk of list to each GPU
# image_list1, image_list2, image_list3 = image_list[:len(image_list)],\
# image_list[len(image_list):2*len(image_list)],\
# image_list[2*len(image_list):]
selected_list = image_list # commit this line if you want to try to assign chunk of list manually to each GPU
output_vectors = []
if gpus:
# Replicate your computation on multiple GPUs
feature_vectors = []
for gpu in gpus: # iterating on a virtual GPU devices, not physical
with tf.device(gpu.name):
print(f"Assign list of images to {gpu.name.split(':', 4)[-1]}")
# Try to assign chunk of list with images to each GPU - work the same time as single GPU
# if gpu.name.split(':', 4)[-1] == "GPU:0":
# selected_list = image_list1
# if gpu.name.split(':', 4)[-1] == "GPU:1":
# selected_list = image_list2
# if gpu.name.split(':', 4)[-1] == "GPU:2":
# selected_list = image_list3
for image_index, image in enumerate(selected_list):
with Image.open(image) as f:
image_data = f.convert('RGB')
feature_tensor = session.graph.get_tensor_by_name('pool_3:0')
feature_vector = session.run(feature_tensor, {'DecodeJpeg:0': image_data})
feature_vectors.append(feature_vector)
print("All images has been assigned to GPU's")
print(f"Time spend on prep ops: {time.time() - start1}")
print("Start calculation on multiple GPU")
start1 = time.time()
for image_index, image in enumerate(image_list):
feature_vector = np.squeeze(feature_vectors[image_index])
outfile_name = os.path.basename(image) + ".vc"
out_path = os.path.join(output_dir, outfile_name)
# Save vector
np.savetxt(out_path, feature_vector, delimiter=',')
# Close session
session.close()
print(f"Calc on GPU's spend: {time.time() - start1}")
print(f"All time, spend on multiple GPU: {time.time() - start}")
提供输出视图（具有100张图像的列表）：
1 Physical GPU, 3 Logical GPUs
Single GPU: 18.76301646232605
Start calculation on multiple GPU
Create prepared ops
Assign list of images to GPU:0
Assign list of images to GPU:1
Assign list of images to GPU:2
All images has been assigned to GPU's
Time spend on prep ops: 18.263537883758545
Start calculation on multiple GPU
Calc on GPU's spend: 11.697082042694092
All time, spend on multiple GPU: 29.960679531097412
我尝试了什么：将包含图像的列表分成3个块，并将每个块分配给GPU（请参阅代码的提交行）。这样可以将多GPU时间减少到17秒，比单GPU运行18秒（〜5％）要快一点。
预期结果：MultiGPU版本比SingleGPU版本快（至少1.5倍加速）。
想法，为什么会发生：我用错误的方式编写了计算
答案 0 :(得分：1)
引起您麻烦的两个基本误会：
with tf.device(...):适用于在范围内创建的图形节点，而不适用于Session.run调用。
Session.run是阻止呼叫。它们不并行运行。 TensorFlow只能并行化单个Session.run的内容。
现代TF可以使这一过程变得更加容易。
主要是您可以停止使用tf.Session和tf.Graph。改用@tf.function，我相信这个基本结构会起作用：
@tf.function
def my_function(inputs, gpus, model):
results = []
for input, gpu in zip(inputs, gpus):
with tf.dvice(gpu):
result.append(model(input))
return results
但是您将要尝试更实际的测试。仅使用3张图像，您根本无法衡量真实性能。
还请注意：
tf.distribute.Strategy分开，@tf.function类可以help simplify some of this。 strategy.experimental_run_v2(my_function, args=(dataset_inputs,)) tf.data.Dataset输入管道将帮助您将加载/预处理与模型执行重叠。但是，如果您真的打算使用tf.Graph和tf.Session来执行此操作，我认为您基本上需要从此重新组织代码：
# Your code:
# Builds a graph
graph = build_graph()
for gpu in gpus():
with tf.device(gpu):
# Calls `gpu` in each device scope.
session.run(...)
对此：
g = tf.Graph()
with g.as_default():
results = []
for gup in gpus:
# Build the graph, on each device
input = iterator.get_next()
with tf.device(gpu):
results.append(my_function(input))
# Use a single `Session.run` call
np_result = session.run(results, feed_dict={inputs: my_inputs})