tensorflow-gpu

I want to specify the gpu to run my process. And I set it as follows: import tensorflow as tf with tf.device('/gpu:0'): a = tf.constant(3.0) with tf.Session() as sess: while True: print sess.run(a) However it still allocate memory in both my two gpus. | 0 7479 C python 5437MiB | 1 7479 C python 5437MiB I believe that you need to set CUDA_VISIBLE_DEVICES=1 . Or which ever GPU you want to use. If you make only one GPU visible, you will refer to it as /gpu:0 in tensorflow regardless of what you set the environment variable to. More info on that environment variable: https://devblogs.nvidia.com

We are running multi GPU jobs on Tensorflow and evaluating a migration from the queue based model (using the string_input_producer interface) to the new Tensorflow Dataset API. The latter appears to offer an easier way to switch between Train and Validation, concurrently. A snippet of code below shows how we are doing this. train_dataset, train_iterator = get_dataset(train_files, batch_size, epochs) val_dataset, val_iterator = get_dataset(val_files, batch_size, epochs) is_validating = tf.placeholder(dtype=bool, shape=()) next_batch = tf.cond(is_validating, lambda: val_iterator.get_next(),

There seem to be several threads/issues on this already but it doesn't appear to me that this has been solved: How can I use tensorflow metric function within keras models? https://github.com/fchollet/keras/issues/6050 https://github.com/fchollet/keras/issues/3230 People seem to either run into problems around variable initialization or the metric being 0. I need to calculate different segmentation metrics and would like to include tf.metric.mean_iou in my Keras model. This is the best I have been able to come up with so far: def mean_iou(y_true, y_pred): score, up_opt = tf.metrics.mean_iou(y

I built the gpu version of the docker image https://github.com/floydhub/dl-docker with keras version 2.0.0 and tensorflow version 0.12.1. I then ran the mnist tutorial https://github.com/fchollet/keras/blob/master/examples/mnist_cnn.py but realized that keras is not using GPU. Below is the output that I have root@b79b8a57fb1f:~/sharedfolder# python test.py Using TensorFlow backend. Downloading data from https://s3.amazonaws.com/img-datasets/mnist.npz x_train shape: (60000, 28, 28, 1) 60000 train samples 10000 test samples Train on 60000 samples, validate on 10000 samples Epoch 1/12 2017-09-06

As per TensorFlow documentation , the prefetch and map methods of tf.contrib.data.Dataset class, both have a parameter called buffer_size . For prefetch method, the parameter is known as buffer_size and according to documentation : buffer_size: A tf.int64 scalar tf.Tensor, representing the maximum number elements that will be buffered when prefetching. For the map method, the parameter is known as output_buffer_size and according to documentation : output_buffer_size: (Optional.) A tf.int64 scalar tf.Tensor, representing the maximum number of processed elements that will be buffered. Similarly