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I am working with Keras and have quite limited memory on my GPU (GeForce GTX 970, ~4G). So as a consequence I run out of memory (OOM) working with Keras having a batch size set above a certain level. Lowering the batch size I don't have this issue, but Keras outputs the following warnings:
2019-01-02 09:47:03.173259: W tensorflow/core/common_runtime/bfc_allocator.cc:219] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.57GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-01-02 09:47:03.211139: W tensorflow/core/common_runtime/bfc_allocator.cc:219] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.68GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-01-02 09:47:03.268074: W tensorflow/core/common_runtime/bfc_allocator.cc:219] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.95GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-01-02 09:47:03.685032: W tensorflow/core/common_runtime/bfc_allocator.cc:219] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.39GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-01-02 09:47:03.732304: W tensorflow/core/common_runtime/bfc_allocator.cc:219] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.56GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-01-02 09:47:03.850711: W tensorflow/core/common_runtime/bfc_allocator.cc:219] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.39GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-01-02 09:47:03.879135: W tensorflow/core/common_runtime/bfc_allocator.cc:219] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.48GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-01-02 09:47:03.963522: W tensorflow/core/common_runtime/bfc_allocator.cc:219] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.42GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-01-02 09:47:03.984897: W tensorflow/core/common_runtime/bfc_allocator.cc:219] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.47GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-01-02 09:47:04.058733: W tensorflow/core/common_runtime/bfc_allocator.cc:219] 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.
What do these warnings mean for me as a user? What are those performance gains? Does it mean that it simply computes faster or do I even get better results in terms of a better validation loss?
In my setup I use Keras with Tensorflow backend and tensorflow-gpu==1.8.0.