I configured tensorflow to work with CUDA support on my GPU (GeForce 840M) but the programs are running quite slow in compare to what my CPU used to earlier. Also, I do not get any kind of message that the so and so CUDA library was successfully opened when I run the program. Instead, this is what I get in logs when I run any tensorflow program:
python Neuralnet.py
Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.
Extracting /tmp/data/train-images-idx3-ubyte.gz
Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.
Extracting /tmp/data/train-labels-idx1-ubyte.gz
Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.
Extracting /tmp/data/t10k-images-idx3-ubyte.gz
Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.
Extracting /tmp/data/t10k-labels-idx1-ubyte.gz
2017-03-28 07:53:57.979382: W tensorflow/core/platform/cpu_feature_guard.cc:45]
The TensorFlow library wasn't compiled to use SSE4.1 instructions,
but these are available on your machine and could speed up CPU computations.
2017-03-28 07:53:57.979413: W tensorflow/core/platform/cpu_feature_guard.cc:45]
The TensorFlow library wasn't compiled to use SSE4.2 instructions,
but these are available on your machine and could speed up CPU computations.
2017-03-28 07:53:57.979431: W tensorflow/core/platform/cpu_feature_guard.cc:45]
The TensorFlow library wasn't compiled to use AVX instructions,
but these are available on your machine and could speed up CPU computations.
2017-03-28 07:53:57.979438: W tensorflow/core/platform/cpu_feature_guard.cc:45]
The TensorFlow library wasn't compiled to use AVX2 instructions,
but these are available on your machine and could speed up CPU computations.
2017-03-28 07:53:57.979447: W tensorflow/core/platform/cpu_feature_guard.cc:45]
The TensorFlow library wasn't compiled to use FMA instructions,
but these are available on your machine and could speed up CPU computations.
2017-03-28 07:53:58.233876: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:901]
successful NUMA node read from SysFS had negative value (-1),
but there must be at least one NUMA node, so returning NUMA node zero
2017-03-28 07:53:58.234333: I tensorflow/core/common_runtime/gpu/gpu_device.cc:887]
Found device 0 with properties:
name: GeForce 840M
major: 5 minor: 0 memoryClockRate (GHz) 1.124
pciBusID 0000:08:00.0
Total memory: 1.96GiB
Free memory: 1.75GiB
2017-03-28 07:53:58.234362: I tensorflow/core/common_runtime/gpu/gpu_device.cc:908] DMA: 0
2017-03-28 07:53:58.234372: I tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 0: Y
2017-03-28 07:53:58.234388: I tensorflow/core/common_runtime/gpu/gpu_device.cc:977]
Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce 840M, pci bus id: 0000:08:00.0)
('Epoch', 0, 'completed out of', 15, 'loss:', 115374329.04653475)
And so on the program started runnning but it didn't ran any faster according to my expectations. I installed CUDA from the official documentation, but I did not reset the git master head since it was creating issues and I used the same optimization flags provided bazel build -c opt --config=cuda //tensorflow/tools/pip_package:build_pip_package when building through bazel.
