Feb. 16, 2018, midnight
By : Eric A. Scuccimarra
After playing with TensorFlow GPU on Windows for a few days I have more information on the errors. I am running TensorFlow 1.6, currently the latest version, with Python 3.6 and Nvidia CUDA 9.0 on an Nvidia GE Force GT 750M.
When the Python Windows process crashes with an error that says CUDA_ERROR_LAUNCH_FAILED, the problem can be solved by reducing the fraction of the GPU memory available with:
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.7
If the Python script fails with an error about exhausted resources or being unable to allocate enough memory, then you need to use a smaller batch size. This problem does not crash the Python process, Python throws an Exception but does not crash.
Once I figured these out, I have had no problems running models on the GPU at all.
Labels: python , machine_learning , tensorflow
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