我的 GPU 没有被 Keras/TensorFlow 使用。
为了让我的 GPU 与 tensorflow 一起工作,我通过 pip 安装了 tensorflow-gpu(我在 Windows 上使用 Anaconda)
我有英伟达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')]
我绑
physical_devices = tf.config.experimental.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(physical_devices[0], True)
但没用
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 CPU 99%、RAM 99%,有时 GPU ~7%,但大多数时候是 0
我使用自定义数据生成器,但现在它只选择批处理并调整它们的大小(skimage.io.resize)
1 个纪元 ~ 44s
也有奇怪的行为,每 ~10 个样本在随机点卡住,最后一个样本几乎不卡住(37/38)(~10-15 秒)
编辑:
我发布了我的自定义数据生成 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)
验证是一样的
更新:
所以它是一个引起问题的生成器,但我该如何解决它?
我只使用了 skimage 和 numpy 操作
最佳答案
日志显示确实使用了 GPU。您几乎肯定会遇到 IO 瓶颈:您的 GPU 正在处理 CPU 扔给它的任何东西,速度快于 CPU 加载和预处理它的速度。这在深度学习中很常见,并且有很多方法可以解决它。
如果不详细了解您的数据管道(批处理的字节大小、预处理步骤……)以及数据的存储方式,我们将无法提供很多帮助。加快速度的一种典型方法是将数据存储为二进制格式,例如 TFRecords
,以便 CPU 可以更快地加载它。查看official documentation for this.
编辑:我快速浏览了您的输入管道。这个问题很可能确实是由 IO 造成的:
- 您还应该在 GPU 上运行预处理步骤,您使用的大量增强技术都在
tf.image
中实现。如果可以,您应该考虑使用 Tensorflow 2.0,因为它包含 Keras,并且其中还有很多帮助程序。 - 检查
tf.data.Dataset
API,它有很多帮助程序可以在不同的线程中加载所有数据,这可以根据您拥有的内核数量粗略地加快进程。< - 您应该将图像存储为
TFRecords
。如果您的输入图像较小,这可能会将加载速度提高一个数量级。 - 您也可以尝试更大的批量大小,我认为您的图像可能非常小。
关于python - Keras 看到我的 GPU 但在训练神经网络时不使用它,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/58455765/