Tensorflow 在被 Ray worker 调用时无法检测到 GPU
Tensorflow can't detect GPU when invoked by Ray worker
当我尝试使用以下代码示例将 Tensorflow 与 Ray 结合使用时，Tensorflow 在 "remote" worker 调用时无法检测到我机器上的 GPU，但它在调用 "locally" 时确实找到了 GPU .我把 "remote" 和 "locally" 放在引号中，因为我的桌面上的所有东西都是 运行，它有两个 GPU，并且是 运行 Ubuntu 16.04，我使用tensorflow-gpu Anaconda 包。
local_network 似乎对日志中的这些消息负责：
2018-01-26 17:24:33.149634: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Quadro M5000, pci bus id: 0000:03:00.0)
2018-01-26 17:24:33.149642: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:1) -> (device: 1, name: Quadro M5000, pci bus id: 0000:04:00.0)
而 remote_network 似乎对这条消息负责：
2018-01-26 17:24:34.309270: E tensorflow/stream_executor/cuda/cuda_driver.cc:406] failed call to cuInit: CUDA_ERROR_NO_DEVICE
为什么 Tensorflow 在一种情况下能够检测到 GPU 而在另一种情况下却不能？
import tensorflow as tf
import numpy as np
import ray
ray.init()
BATCH_SIZE = 100
NUM_BATCHES = 1
NUM_ITERS = 201
class Network(object):
def __init__(self, x, y):
# Seed TensorFlow to make the script deterministic.
tf.set_random_seed(0)
# Define the inputs.
x_data = tf.constant(x, dtype=tf.float32)
y_data = tf.constant(y, dtype=tf.float32)
# Define the weights and computation.
w = tf.Variable(tf.random_uniform([1], -1.0, 1.0))
b = tf.Variable(tf.zeros([1]))
y = w * x_data + b
# Define the loss.
self.loss = tf.reduce_mean(tf.square(y - y_data))
optimizer = tf.train.GradientDescentOptimizer(0.5)
self.grads = optimizer.compute_gradients(self.loss)
self.train = optimizer.apply_gradients(self.grads)
# Define the weight initializer and session.
init = tf.global_variables_initializer()
self.sess = tf.Session()
# Additional code for setting and getting the weights
self.variables = ray.experimental.TensorFlowVariables(self.loss, self.sess)
# Return all of the data needed to use the network.
self.sess.run(init)
# Define a remote function that trains the network for one step and returns the
# new weights.
def step(self, weights):
# Set the weights in the network.
self.variables.set_weights(weights)
# Do one step of training. We only need the actual gradients so we filter over the list.
actual_grads = self.sess.run([grad[0] for grad in self.grads])
return actual_grads
def get_weights(self):
return self.variables.get_weights()
# Define a remote function for generating fake data.
@ray.remote(num_return_vals=2)
def generate_fake_x_y_data(num_data, seed=0):
# Seed numpy to make the script deterministic.
np.random.seed(seed)
x = np.random.rand(num_data)
y = x * 0.1 + 0.3
return x, y
# Generate some training data.
batch_ids = [generate_fake_x_y_data.remote(BATCH_SIZE, seed=i) for i in range(NUM_BATCHES)]
x_ids = [x_id for x_id, y_id in batch_ids]
y_ids = [y_id for x_id, y_id in batch_ids]
# Generate some test data.
x_test, y_test = ray.get(generate_fake_x_y_data.remote(BATCH_SIZE, seed=NUM_BATCHES))
# Create actors to store the networks.
remote_network = ray.remote(Network)
actor_list = [remote_network.remote(x_ids[i], y_ids[i]) for i in range(NUM_BATCHES)]
local_network = Network(x_test, y_test)
# Get initial weights of local network.
weights = local_network.get_weights()
# Do some steps of training.
for iteration in range(NUM_ITERS):
# Put the weights in the object store. This is optional. We could instead pass
# the variable weights directly into step.remote, in which case it would be
# placed in the object store under the hood. However, in that case multiple
# copies of the weights would be put in the object store, so this approach is
# more efficient.
weights_id = ray.put(weights)
# Call the remote function multiple times in parallel.
gradients_ids = [actor.step.remote(weights_id) for actor in actor_list]
# Get all of the weights.
gradients_list = ray.get(gradients_ids)
# Take the mean of the different gradients. Each element of gradients_list is a list
# of gradients, and we want to take the mean of each one.
mean_grads = [sum([gradients[i] for gradients in gradients_list]) / len(gradients_list) for i in range(len(gradients_list[0]))]
feed_dict = {grad[0]: mean_grad for (grad, mean_grad) in zip(local_network.grads, mean_grads)}
local_network.sess.run(local_network.train, feed_dict=feed_dict)
weights = local_network.get_weights()
# Print the current weights. They should converge to roughly to the values 0.1
# and 0.3 used in generate_fake_x_y_data.
if iteration % 20 == 0:
print("Iteration {}: weights are {}".format(iteration, weights))
GPU 被 ray.remote 装饰器本身切断。来自其源代码：
def remote(*args, **kwargs):
...
num_cpus = kwargs["num_cpus"] if "num_cpus" in kwargs else 1
num_gpus = kwargs["num_gpus"] if "num_gpus" in kwargs else 0 # !!!
...
所以下面的调用有效地设置了 num_gpus=0:
remote_network = ray.remote(Network)
Ray API 有点奇怪，你不能简单地说 ray.remote(Network, num_gpus=2)（尽管这正是你想要的）。这是我所做的，它似乎可以在我的机器上运行：
ray.init(num_gpus=2)
...
@ray.remote(num_gpus=2)
class RemoteNetwork(Network):
pass
actor_list = [RemoteNetwork.remote(x_ids[i],y_ids[i]) for i in range(NUM_BATCHES)]
当我尝试使用以下代码示例将 Tensorflow 与 Ray 结合使用时，Tensorflow 在 "remote" worker 调用时无法检测到我机器上的 GPU，但它在调用 "locally" 时确实找到了 GPU .我把 "remote" 和 "locally" 放在引号中，因为我的桌面上的所有东西都是 运行，它有两个 GPU，并且是 运行 Ubuntu 16.04，我使用tensorflow-gpu Anaconda 包。
local_network 似乎对日志中的这些消息负责：
2018-01-26 17:24:33.149634: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Quadro M5000, pci bus id: 0000:03:00.0)
2018-01-26 17:24:33.149642: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:1) -> (device: 1, name: Quadro M5000, pci bus id: 0000:04:00.0)
而 remote_network 似乎对这条消息负责：
2018-01-26 17:24:34.309270: E tensorflow/stream_executor/cuda/cuda_driver.cc:406] failed call to cuInit: CUDA_ERROR_NO_DEVICE
为什么 Tensorflow 在一种情况下能够检测到 GPU 而在另一种情况下却不能？
import tensorflow as tf
import numpy as np
import ray
ray.init()
BATCH_SIZE = 100
NUM_BATCHES = 1
NUM_ITERS = 201
class Network(object):
def __init__(self, x, y):
# Seed TensorFlow to make the script deterministic.
tf.set_random_seed(0)
# Define the inputs.
x_data = tf.constant(x, dtype=tf.float32)
y_data = tf.constant(y, dtype=tf.float32)
# Define the weights and computation.
w = tf.Variable(tf.random_uniform([1], -1.0, 1.0))
b = tf.Variable(tf.zeros([1]))
y = w * x_data + b
# Define the loss.
self.loss = tf.reduce_mean(tf.square(y - y_data))
optimizer = tf.train.GradientDescentOptimizer(0.5)
self.grads = optimizer.compute_gradients(self.loss)
self.train = optimizer.apply_gradients(self.grads)
# Define the weight initializer and session.
init = tf.global_variables_initializer()
self.sess = tf.Session()
# Additional code for setting and getting the weights
self.variables = ray.experimental.TensorFlowVariables(self.loss, self.sess)
# Return all of the data needed to use the network.
self.sess.run(init)
# Define a remote function that trains the network for one step and returns the
# new weights.
def step(self, weights):
# Set the weights in the network.
self.variables.set_weights(weights)
# Do one step of training. We only need the actual gradients so we filter over the list.
actual_grads = self.sess.run([grad[0] for grad in self.grads])
return actual_grads
def get_weights(self):
return self.variables.get_weights()
# Define a remote function for generating fake data.
@ray.remote(num_return_vals=2)
def generate_fake_x_y_data(num_data, seed=0):
# Seed numpy to make the script deterministic.
np.random.seed(seed)
x = np.random.rand(num_data)
y = x * 0.1 + 0.3
return x, y
# Generate some training data.
batch_ids = [generate_fake_x_y_data.remote(BATCH_SIZE, seed=i) for i in range(NUM_BATCHES)]
x_ids = [x_id for x_id, y_id in batch_ids]
y_ids = [y_id for x_id, y_id in batch_ids]
# Generate some test data.
x_test, y_test = ray.get(generate_fake_x_y_data.remote(BATCH_SIZE, seed=NUM_BATCHES))
# Create actors to store the networks.
remote_network = ray.remote(Network)
actor_list = [remote_network.remote(x_ids[i], y_ids[i]) for i in range(NUM_BATCHES)]
local_network = Network(x_test, y_test)
# Get initial weights of local network.
weights = local_network.get_weights()
# Do some steps of training.
for iteration in range(NUM_ITERS):
# Put the weights in the object store. This is optional. We could instead pass
# the variable weights directly into step.remote, in which case it would be
# placed in the object store under the hood. However, in that case multiple
# copies of the weights would be put in the object store, so this approach is
# more efficient.
weights_id = ray.put(weights)
# Call the remote function multiple times in parallel.
gradients_ids = [actor.step.remote(weights_id) for actor in actor_list]
# Get all of the weights.
gradients_list = ray.get(gradients_ids)
# Take the mean of the different gradients. Each element of gradients_list is a list
# of gradients, and we want to take the mean of each one.
mean_grads = [sum([gradients[i] for gradients in gradients_list]) / len(gradients_list) for i in range(len(gradients_list[0]))]
feed_dict = {grad[0]: mean_grad for (grad, mean_grad) in zip(local_network.grads, mean_grads)}
local_network.sess.run(local_network.train, feed_dict=feed_dict)
weights = local_network.get_weights()
# Print the current weights. They should converge to roughly to the values 0.1
# and 0.3 used in generate_fake_x_y_data.
if iteration % 20 == 0:
print("Iteration {}: weights are {}".format(iteration, weights))
GPU 被 ray.remote 装饰器本身切断。来自其源代码：
def remote(*args, **kwargs):
...
num_cpus = kwargs["num_cpus"] if "num_cpus" in kwargs else 1
num_gpus = kwargs["num_gpus"] if "num_gpus" in kwargs else 0 # !!!
...
所以下面的调用有效地设置了 num_gpus=0:
remote_network = ray.remote(Network)
Ray API 有点奇怪，你不能简单地说 ray.remote(Network, num_gpus=2)（尽管这正是你想要的）。这是我所做的，它似乎可以在我的机器上运行：
ray.init(num_gpus=2)
...
@ray.remote(num_gpus=2)
class RemoteNetwork(Network):
pass
actor_list = [RemoteNetwork.remote(x_ids[i],y_ids[i]) for i in range(NUM_BATCHES)]