相关疑难解决方法(0)

如何使用SSE4.2和AVX指令编译Tensorflow？

这是从运行脚本以检查Tensorflow是否正常工作时收到的消息:

I tensorflow/stream_executor/dso_loader.cc:125] successfully opened CUDA library libcublas.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:125] successfully opened CUDA library libcudnn.so.5 locally
I tensorflow/stream_executor/dso_loader.cc:125] successfully opened CUDA library libcufft.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:125] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:125] successfully opened CUDA library libcurand.so.8.0 locally
W tensorflow/core/platform/cpu_feature_guard.cc:95] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:95] The TensorFlow library wasn't compiled to use AVX instructions, but these are available …

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x86 simd compiler-optimization compiler-options tensorflow

Gab*_*Chu

2019 06-29

265
推荐指数

9
解决办法

23万
查看次数

使用带有pip的SSE指令进行Tensorflow安装

我使用此处提供的默认指令在ubuntu 16.04上成功安装了cpu only tensorflow .建议使用virtualenv和pip的指令,所以我没有从源代码构建.我按照这些说明安装没有问题.

我提供的说明验证了我安装的进一步下跌在同一页上,并在程序运行成功,它输出下列警告.

W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
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.
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 …

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python sse pip virtualenv tensorflow

Cur*_*Dan

2017 02-20

11
推荐指数

1
解决办法

1万
查看次数

Keras VGG16 预测速度慢

我正在为这个迁移学习个人项目开发一个特征提取器，Kera 的 VGG16 模型的预测功能似乎很慢（一批 4 张图像需要 31 秒）。我确实希望它会很慢，但不确定预测函数是否比它应该的慢。

data = DataGenerator() 
data = data.from_csv(csv_path=csv_file,
                     img_dir=img_folder,
                     batch_size=batch)

#####################################################
conv_base = VGG16(include_top=False, 
                  weights='imagenet', 
                  input_shape=(480, 640, 3))

model = Sequential()
model.add(conv_base)
model.add(MaxPooling2D(pool_size=(3, 4)))
model.add(Flatten())
######################################################

for inputs, y in data:
    feature_batch = model.predict(inputs)

    yield feature_batch, y

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所以，我的预感是它很慢，原因如下：