2019-05-06 14:02:27.264629: I tensorflow/stream_executor/dso_loader.cc:152] successfully opened CUDA library libcublas.so.10.0 locally
2019-05-06 14:02:30.554284: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.45GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-05-06 14:02:30.555624: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.02GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-05-06 14:02:30.884528: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.72GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-05-06 14:02:32.482456: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.72GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-05-06 14:02:32.482999: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.54GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-05-06 14:02:34.231668: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.32GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-05-06 14:02:35.367897: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.27GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-05-06 14:02:35.949636: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.09GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-05-06 14:05:05.327823: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.11GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-05-06 14:05:05.629307: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.08GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
Does this mean that GPU is not being used? I am using a tensorflow:latest-gpu-py3 image