Borrowed heavily from https://github.com/jpangburn/tensorflowmemorytest
USAGE: resolve.sh TF_REV TF_TYPE
example:
resolve.sh 1.3.0 gpu
GPU
./resolve.sh 1.3.0 gpu
CPU
./resolve.sh 1.3.0 cpu
./build.sh
export CUDA_VISIBLE_DEVICES=0
./run.sh
Open system monitor and watch the memory.
To reproduce this issue, run the following:
# resolve
./resolve.sh 1.3.0 gpu
# build
./build.sh
# run
export CUDA_VISIBLE_DEVICES=0
./run.sh
Processing 1 floats.
2017-08-29 14:30:27.963729: 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.
2017-08-29 14:30:27.963779: 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.
2017-08-29 14:30:27.963788: 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.
2017-08-29 14:30:27.963795: 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.
2017-08-29 14:30:27.963802: 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.
2017-08-29 14:30:29.569904: I tensorflow/core/common_runtime/gpu/gpu_device.cc:955] Found device 0 with properties:
name: GeForce GTX 1080
major: 6 minor: 1 memoryClockRate (GHz) 1.7335
pciBusID 0000:01:00.0
Total memory: 7.92GiB
Free memory: 7.81GiB
2017-08-29 14:30:29.569957: I tensorflow/core/common_runtime/gpu/gpu_device.cc:976] DMA: 0
2017-08-29 14:30:29.569965: I tensorflow/core/common_runtime/gpu/gpu_device.cc:986] 0: Y
2017-08-29 14:30:29.569981: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0)
valgrind output was generated with the following command:
valgrind --leak-check=yes java -Djava.compiler=NONE -cp libtensorflow.jar:./out/ -Djava.library.path=./jni/ test.MemoryTest 1000 2>&1 | tee valgrind.out