tensorflow-gpu

I've made this neural net to figure out whether a house is a good buy or a bad buy. For some reasons the code is not updating weights and biases. My loss stays same. This is my code: I've made this neural net to figure out whether a house is a good buy or a bad buy. For some reasons the code is not updating weights and biases. My loss stays same. This is my code: import pandas as pd import tensorflow as tf data = pd.read_csv("E:/workspace_py/datasets/good_bad_buy.csv") features = data.drop(['index', 'good buy'], axis = 1) lbls = data.drop(['index', 'area', 'bathrooms', 'price', 'sq_price'],

I have used an AWS p3 instance to train the following model using GPU acceleration: x = CuDNNLSTM(128, return_sequences=True)(inputs) x = Dropout(0.2)(x) x = CuDNNLSTM(128, return_sequences=False)(x) x = Dropout(0.2)(x) predictions = Dense(1, activation='tanh')(x) model = Model(inputs=inputs, outputs=predictions) After training I saved the model with Keras' save_model function and moved it to a separate production server that doesn't have a GPU. When I attempt to predict using the model on the production server it fails with the following error: No OpKernel was registered to support Op

I am running keras with tensorflow backend on linux. First, I installed tensorflow GPU version by itself, and run the following code to check and found out that it's running on GPU and shows the GPU it's running on, device mapping, etc. The tensorflow I use was from https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow- 0.11.0-cp27-none-linux_x86_64.whl a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a') b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b') c = tf.matmul(a, b) # Creates a session with log_device_placement set to True. sess = tf

I am working on my master's project which uses keras and tensorflow backend .I have intel(r) hd graphics 520 ,So I am not able to use tensorflow-gpu. The cpu version is working fine .Is there any way to use tensorflow-gpu with the intel(r) hd graphics 520? Tensorflow GPU support needs Nvidia Cuda and CuDNN packages installed. For GPU accelerated training you will need a dedicated GPU . Intel onboard graphics can't be used for that purpose. You can see full requirements for tensorflow-gpu here 来源： https://stackoverflow.com/questions/47399802/is-there-anyway-to-use-tensorflow-gpu-with-intelr-hd

I am trying to train model in synchronous distributed fashion for data parallelism. There are 4 gpus in my machine. Each gpu should should run a worker to train on separate non-overlapping subset of the data (between graph replication). The main data file is separated into 16 smaller TFRecord files. Each worker is supposed to process 4 different files. The problem is that training freezes independently and at different times in each worker process. They freeze at some point. One of the 'ps' reports following error related to grpc: 2017-09-21 16:45:55.606842: I tensorflow/core/distributed