Keras ignores GPU when using TensorFlow
The docs say that when using TensorFlow Server, Keras will automatically launch on the GPU if detected. I went into a remote GPU and I am trying to run the Keras program, but for some reason I am only using CPUs. How do I get Keras to run on the GPU to speed things up?
If it helps, it looks like this:
model = Sequential()
model.add(SimpleRNN(out_dim, input_shape = (X_train.shape[1], X_train.shape[2]), return_sequences = False))
model.add(Dense(num_classes, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer = "adam", metrics = ['accuracy'])
hist = model.fit(X_train, dummy_y, validation_data=(X_test, dummy_y_test), nb_epoch = epochs, batch_size = b_size)
and here's the conclusion which python
and proof that Keras is using the TensorFlow backend:
user@GPU6:~$ which python
/mnt/data/user/pkgs/anaconda2/bin/python
user@GPU6:~$ python
Python 2.7.12 |Anaconda custom (64-bit)| (default, Jul 2 2016, 17:42:40)
[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
Anaconda is brought to you by Continuum Analytics.
Please check out: http://continuum.io/thanks and https://anaconda.org
>>> import keras
Using TensorFlow backend.
and here's the conclusion nvidia-smi
. I have several processes similar to the ones above, but they only use the processor:
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 367.57 Driver Version: 367.57 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce GTX TIT... Off | 0000:03:00.0 Off | N/A |
| 26% 27C P8 13W / 250W | 9MiB / 6082MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 1 GeForce GTX TIT... Off | 0000:83:00.0 Off | N/A |
| 26% 31C P8 13W / 250W | 9MiB / 6082MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
| 2 GeForce GTX TIT... Off | 0000:84:00.0 Off | N/A |
| 26% 31C P8 14W / 250W | 9MiB / 6082MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 2408 G Xorg 9MiB |
| 1 2408 G Xorg 9MiB |
| 2 2408 G Xorg 9MiB |
+-----------------------------------------------------------------------------+
None of my processes are running on GPU. How can I fix this?
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Perhaps you have tensor version of processor installed.
Since it seems like you are using Anaconda and py2.7: follow these steps to set the GPU version for tensorflow in conda env using py2.7
conda create -n tensorflow
source activate tensorflow
pip install --ignore-installed --upgrade https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.0.1-cp27-none-linux_x86_64.whl
see this github issue
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