Keras / TensorFlow不使用我的GPU。
为了尝试使我的GPU与tensorflow一起使用，我通过pip安装了tensorflow-gpu（我在Windows上使用的是Anaconda）
我有nvidia 1080ti
print(tf.test.is_gpu_available())
True
print(tf.config.experimental.list_physical_devices())
[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'),
PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
我被绑住
physical_devices = tf.config.experimental.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(physical_devices[0], True)
但没有帮助
sess = tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(log_device_placement=True))
print(sess)
Device mapping:
/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1
<tensorflow.python.client.session.Session object at 0x000001A2A3BBACF8>
仅来自tf的警告：
W tensorflow/stream_executor/cuda/redzone_allocator.cc:312] Internal: Invoking ptxas not supported on Windows
整个日志：
2019-10-18 20:06:26.094049: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudart64_100.dll
2019-10-18 20:06:35.078225: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2019-10-18 20:06:35.090832: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll
2019-10-18 20:06:35.180744: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.683
pciBusID: 0000:01:00.0
2019-10-18 20:06:35.185505: I tensorflow/stream_executor/platform/default/dlopen_checker_stub.cc:25] GPU libraries are statically linked, skip dlopen check.
2019-10-18 20:06:35.189328: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
2019-10-18 20:06:35.898592: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-10-18 20:06:35.901683: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165] 0
2019-10-18 20:06:35.904235: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0: N
2019-10-18 20:06:35.906687: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/device:GPU:0 with 8784 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)
2019-10-18 20:06:38.694481: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.683
pciBusID: 0000:01:00.0
2019-10-18 20:06:38.700482: I tensorflow/stream_executor/platform/default/dlopen_checker_stub.cc:25] GPU libraries are statically linked, skip dlopen check.
2019-10-18 20:06:38.704020: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
[I 20:06:47.324 NotebookApp] Saving file at /Untitled.ipynb
2019-10-18 20:07:22.227110: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.683
pciBusID: 0000:01:00.0
2019-10-18 20:07:22.246012: I tensorflow/stream_executor/platform/default/dlopen_checker_stub.cc:25] GPU libraries are statically linked, skip dlopen check.
2019-10-18 20:07:22.261643: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
2019-10-18 20:07:22.272150: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-10-18 20:07:22.275457: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165] 0
2019-10-18 20:07:22.277980: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0: N
2019-10-18 20:07:22.316260: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 8784 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)
Device mapping:
/job:localhost/replica:0/task:0/device:GPU:0 -> device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1
2019-10-18 20:07:32.986802: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.683
pciBusID: 0000:01:00.0
2019-10-18 20:07:32.990509: I tensorflow/stream_executor/platform/default/dlopen_checker_stub.cc:25] GPU libraries are statically linked, skip dlopen check.
2019-10-18 20:07:32.993763: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0
2019-10-18 20:07:32.995570: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-10-18 20:07:32.997920: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165] 0
2019-10-18 20:07:32.999435: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0: N
2019-10-18 20:07:33.001380: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 8784 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)
2019-10-18 20:07:36.048204: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cudnn64_7.dll
2019-10-18 20:07:37.971703: W tensorflow/stream_executor/cuda/redzone_allocator.cc:312] Internal: Invoking ptxas not supported on Windows
Relying on driver to perform ptx compilation. This message will be only logged once.
2019-10-18 20:07:38.576861: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library cublas64_100.dll
还尝试使用pip重新安装tensorflow-gpu
为什么我认为GPU不起作用？ -因为我的python内核使用的CPU为99％，RAM为99％，有时GPU为7％，但大多数情况下为0
我使用自定义数据生成器，但现在它仅选择批次并调整其大小（skimage.io.resize）
1个时期〜44秒
还具有每10个样本随机出现一次冻结的奇怪行为，几乎不会冻结最后一个样本（37/38）（〜10-15秒）
我发布了自定义数据源here
train_gen = DataGenerator(x = x_train,
y = y_train,
batch_size = 128,
target_shape = (100, 100, 3),
sample_std = False,
feature_std = False,
proj_parameters = None,
blur_parameters = None,
nois_parameters = None,
flip_parameters = None,
gamm_parameters = None)
验证相同
因此，它是一个解决问题的生成器，但是我该如何解决呢？
我只使用了skimage和numpy操作
答案 0 :(得分：4)
日志显示GPU确实已使用。几乎可以肯定，您会遇到IO瓶颈：您的GPU处理CPU所抛出的异常的速度快于CPU可以加载和预处理它的速度。这在深度学习中非常常见，并且有解决方法。
如果不了解您的数据管道（批处理的字节大小，预处理步骤等）以及如何存储数据，我们将无法提供很多帮助。一种加快速度的典型方法是存储数据，是一种二进制格式，例如TFRecords，以便CPU可以更快地加载它。参见official documentation for this.
编辑：我很快通过了您的输入管道。 IO确实很可能会出现此问题：
tf.image中实现了许多您使用的增强技术。如果可以的话，您应该考虑使用Tensorflow 2.0，因为它包含Keras，并且那里也有很多帮助程序。tf.data.Dataset API，它具有大量的帮助程序，可将所有数据加载到不同的线程中，从而可以根据您拥有的内核数来大致加快该过程。TFRecords。如果您输入的图像很小，这可能会将加载速度提高一个数量级。