vram

TensorFlow always (pre-)allocates all free memory (VRAM) on my graphics card, which is ok since I want my simulations to run as fast as possible on my workstation. However, I would like to log how much memory (in sum) TensorFlow really uses. Additionally it would be really nice, if I could also log how much memory single tensors use. This information is important to measure and compare the memory size that different ML/AI architectures need. Any tips? Update, can use TensorFlow ops to query allocator: # maximum across all sessions and .run calls so far sess.run(tf.contrib.memory_stats

So I installed the GPU version of TensorFlow on a Windows 10 machine with a GeForce GTX 980 graphics card on it. Admittedly, I know very little about graphics cards, but according to dxdiag it does have: 4060MB of dedicated memory (VRAM) and; 8163MB of shared memory for a total of about 12224MB . What I noticed, though, is that this "shared" memory seems to be pretty much useless. When I start training a model, the VRAM will fill up and if the memory requirement exceeds these 4GB , TensorFlow will crash with a "resource exhausted" error message. I CAN, of course, prevent reaching that point by

i'm training some Music Data on a LSTM-RNN in Tensorflow and encountered some Problem with GPU-Memory-Allocation which i don't understand: I encounter an OOM when there actually seems to be just about enough VRAM still available. Some background: I'm working on Ubuntu Gnome 16.04, using a GTX1060 6GB, Intel Xeon E3-1231V3 and 8GB RAM. So now first the part of the error-message which i can understand, in the and i will add the whole error message in the end again for anyone who might ask for it to help: I tensorflow/core/common_runtime/bfc_allocator.cc:696] 8 Chunks of size 256 totalling 2.0KiB