BeS 53 Жалоба Опубликовано October 14, 2016 Всем привет, А у кого-то есть опыт развертывания Tensorflow с поддержкой CUDA на картах последнего поколения (1080/TitanX) с CUDA 8.0? Что-то у меня при вызове ./configure вообще ничего про наличие CUDA не сообщается, хотя карта стоит и в том-же caffe она подцепилась вообще без проблем. Поделиться сообщением Ссылка на сообщение Поделиться на других сайтах
Smorodov 579 Жалоба Опубликовано October 16, 2016 GTX1070 работает в TF с CUDA 8.0 на Ubuntu 16.04. Как ставил, если честно не помню уже, вроде проблем не было. TF особо не использую, тестил на немного модифицированном скрипте из примеров (добавил установку девайса): # Copyright 2015 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """A very simple MNIST classifier. See extensive documentation at http://tensorflow.org/tutorials/mnist/beginners/index.md """ from __future__ import absolute_import from __future__ import division from __future__ import print_function # Import data from tensorflow.examples.tutorials.mnist import input_data import tensorflow as tf with tf.device('/gpu:0'): flags = tf.app.flags FLAGS = flags.FLAGS flags.DEFINE_string('data_dir', '/tmp/data/', 'Directory for storing data') mnist = input_data.read_data_sets(FLAGS.data_dir, one_hot=True) sess = tf.InteractiveSession() # Create the model x = tf.placeholder(tf.float32, [None, 784]) W = tf.Variable(tf.zeros([784, 10])) b = tf.Variable(tf.zeros([10])) y = tf.nn.softmax(tf.matmul(x, W) + b) # Define loss and optimizer y_ = tf.placeholder(tf.float32, [None, 10]) cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1])) train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy) # Train tf.initialize_all_variables().run() for i in range(1000): batch_xs, batch_ys = mnist.train.next_batch(100) train_step.run({x: batch_xs, y_: batch_ys}) # Test trained model correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) print(accuracy.eval({x: mnist.test.images, y_: mnist.test.labels})) Лог скрипта: I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcublas.so.8.0 locally I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcudnn.so.5 locally I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcufft.so.8.0 locally I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcuda.so.1 locally I tensorflow/stream_executor/dso_loader.cc:111] successfully opened CUDA library libcurand.so.8.0 locally Extracting /tmp/data/train-images-idx3-ubyte.gz Extracting /tmp/data/train-labels-idx1-ubyte.gz Extracting /tmp/data/t10k-images-idx3-ubyte.gz Extracting /tmp/data/t10k-labels-idx1-ubyte.gz I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:925] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero I tensorflow/core/common_runtime/gpu/gpu_init.cc:118] Found device 0 with properties: name: GeForce GTX 1070 major: 6 minor: 1 memoryClockRate (GHz) 1.683 pciBusID 0000:01:00.0 Total memory: 7.92GiB Free memory: 7.42GiB W tensorflow/stream_executor/cuda/cuda_driver.cc:572] creating context when one is currently active; existing: 0x2cc5c00 I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:925] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero I tensorflow/core/common_runtime/gpu/gpu_init.cc:118] Found device 1 with properties: name: GeForce GTX 970 major: 5 minor: 2 memoryClockRate (GHz) 1.2155 pciBusID 0000:07:00.0 Total memory: 3.94GiB Free memory: 3.88GiB I tensorflow/core/common_runtime/gpu/gpu_init.cc:61] cannot enable peer access from device ordinal 0 to device ordinal 1 I tensorflow/core/common_runtime/gpu/gpu_init.cc:61] cannot enable peer access from device ordinal 1 to device ordinal 0 I tensorflow/core/common_runtime/gpu/gpu_init.cc:138] DMA: 0 1 I tensorflow/core/common_runtime/gpu/gpu_init.cc:148] 0: Y N I tensorflow/core/common_runtime/gpu/gpu_init.cc:148] 1: N Y I tensorflow/core/common_runtime/gpu/gpu_device.cc:867] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0) I tensorflow/core/common_runtime/gpu/gpu_device.cc:867] Creating TensorFlow device (/gpu:1) -> (device: 1, name: GeForce GTX 970, pci bus id: 0000:07:00.0) 0.9169 В configure вроде явно спрашивают про GPU. configure Еще наткнулся на похожие вопроы на SO: http://stackoverflow.com/questions/39817645/cuda-cudnn-installed-but-tensorflow-cant-use-the-gpu и http://stackoverflow.com/questions/38794497/tensorflow-bazel-0-3-0-build-cuda-8-0-gtx-1070-fails Поделиться сообщением Ссылка на сообщение Поделиться на других сайтах