Monday 12 February 2018 at 13:50

TensorFlow GPU Errors on Windows

By Eric Antoine Scuccimarra

I have been loving TensorFlow lately and have installed tensorflow-gpu on my Windows 10 laptop. Given that the GPU on my laptop is not a really great one I have run into quite a few issues, most of which I have solved. My GPU is an Nvidia GeForce GT 750M with 2GB of RAM and I am running the latest release of tensorflow as of February 2018, with Python 3.6. 

If you are running into errors I would suggest you try these things in this order:

  1. Try reducing the batch size for training AND validation. I always use batches for training but would evaluate on the validation data all at once. By using batches for validation and averaging the results I am able to avoid most of the memory errors.
  2. If this doesn't work try to restrict the amount of GPU RAM available to tensorflow with config.gpu_options.per_process_gpu_memory_fraction = 0.7
    which restricts the amount  available to 70%. Note that I am unable to ever run the GPU with the memory fraction above 0.7
  3. If all else fails turn the GPU off and use the CPU: 
    config = tf.ConfigProto()
    config = tf.ConfigProto(device_count = {'GPU': 0})

The difference between using the CPU and the GPU is like night and day... With the CPU it takes all day to train through 20 epochs, with the GPU the same can be done in a few hours. I think the main roadblock with my GPU is the amount of RAM, which can easily be managed by controlling the batch size and the config settings above. Just remember to feed the config into the session.

Labels: python, data_science, machine_learning, tensor_flow


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