How do I check if keras is using gpu version of tensorflow?

When I run a keras script, I get the following output:

    Using TensorFlow backend.
    2017-06-14 17:40:44.621761: 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.
    2017-06-14 17:40:44.621783: 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.
    2017-06-14 17:40:44.621788: 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.
    2017-06-14 17:40:44.621791: 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.
    2017-06-14 17:40:44.621795: 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.
    2017-06-14 17:40:44.721911: 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
    2017-06-14 17:40:44.722288: I 
    tensorflow/core/common_runtime/gpu/gpu_device.cc:887] Found device 0 
    with properties: 
    name: GeForce GTX 850M
    major: 5 minor: 0 memoryClockRate (GHz) 0.9015
    pciBusID 0000:0a:00.0
    Total memory: 3.95GiB
    Free memory: 3.69GiB
    2017-06-14 17:40:44.722302: I 
    tensorflow/core/common_runtime/gpu/gpu_device.cc:908] DMA: 0 
    2017-06-14 17:40:44.722307: I 
    tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 0:   Y 
    2017-06-14 17:40:44.722312: I 
    tensorflow/core/common_runtime/gpu/gpu_device.cc:977] Creating 
    TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 850M, 
    pci bus id: 0000:0a:00.0)

What does this mean? Am I using GPU or CPU version of tensorflow?

Before installing keras, I was working with the GPU version of tensorflow.

Also sudo pip3 list shows tensorflow-gpu(1.1.0) and nothing like tensorflow-cpu.

Running the command mentioned on [this stackoverflow question], gives the following:

    The TensorFlow library wasn't compiled to use SSE4.1 instructions, 
    but these are available on your machine and could speed up CPU 
    computations.
    2017-06-14 17:53:31.424793: 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.
    2017-06-14 17:53:31.424803: 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.
    2017-06-14 17:53:31.424812: 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.
    2017-06-14 17:53:31.424820: 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.
    2017-06-14 17:53:31.540959: 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
    2017-06-14 17:53:31.541359: I 
    tensorflow/core/common_runtime/gpu/gpu_device.cc:887] Found device 0 
    with properties: 
    name: GeForce GTX 850M
    major: 5 minor: 0 memoryClockRate (GHz) 0.9015
    pciBusID 0000:0a:00.0
    Total memory: 3.95GiB
    Free memory: 128.12MiB
    2017-06-14 17:53:31.541407: I 
    tensorflow/core/common_runtime/gpu/gpu_device.cc:908] DMA: 0 
    2017-06-14 17:53:31.541420: I 
    tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 0:   Y 
    2017-06-14 17:53:31.541441: I 
    tensorflow/core/common_runtime/gpu/gpu_device.cc:977] Creating 
    TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 850M, 
    pci bus id: 0000:0a:00.0)
    2017-06-14 17:53:31.547902: E 
    tensorflow/stream_executor/cuda/cuda_driver.cc:893] failed to 
    allocate 128.12M (134348800 bytes) from device: 
    CUDA_ERROR_OUT_OF_MEMORY
    Device mapping:
    /job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce 
    GTX 850M, pci bus id: 0000:0a:00.0
    2017-06-14 17:53:31.549482: I 
    tensorflow/core/common_runtime/direct_session.cc:257] Device 
    mapping:
    /job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce 
    GTX 850M, pci bus id: 0000:0a:00.0

You are using the GPU version. You can list the available tensorflow devices with (also check this question):

    from tensorflow.python.client import device_lib
    print(device_lib.list_local_devices()) # list of DeviceAttributes

EDIT: With tensorflow >= 1.4 you can run the following function:

    import tensorflow as tf
    tf.test.is_gpu_available() # True/False

    # Or only check for gpu's with cuda support
    tf.test.is_gpu_available(cuda_only=True)

NOTE:

In your case both the cpu and gpu are available, if you use the cpu version of tensorflow the gpu will not be listed. In your case, without setting your tensorflow device (with tf.device("..")), tensorflow will automatically pick your gpu!

In addition, your sudo pip3 list clearly shows you are using tensorflow-gpu. If you would have the tensoflow cpu version the name would be something like tensorflow(1.1.0).

Check this issue for information about the warnings.

From: stackoverflow.com/q/44544766