这个张量流信息是什么意思? 有副作用吗? 安装成功了吗？

我刚刚在 anaconda python 上安装了 tensorflow v2.3

$ python -c "import tensorflow as tf; x = [[2.]]; print('tensorflow version', tf.__version__); print('hello, {}'.format(tf.matmul(x, x)))"

我收到了以下信息

2020-12-15 07:59:12.411952: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations:  AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
hello, [[4.]]

从消息来看，似乎安装成功了。但是 This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)to use the following CPU instructions in performance-critical operations: AVX AVX2到底是什么意思呢？

我使用的张量流版本有一些限制的功能吗? 有什么副作用吗？

我正在使用 Windows10。

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The message

This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN)
to use the following CPU instructions in performance-critical operations:  AVX AVX2

means that in places where performance matters (eg matrix multiplication in deep neural networks), certain optimized compiler instructions will be used. Installation seems to be successful.

The oneDNN GitHub repository says:

oneAPI Deep Neural Network Library (oneDNN) is an open-source cross-platform performance library of basic building blocks for deep learning applications. The library is optimized for Intel Architecture Processors, Intel Processor Graphics and Xe architecture-based Graphics. oneDNN has experimental support for the following architectures:

  • Arm* 64-bit Architecture (AArch64)
  • NVIDIA* GPU
  • OpenPOWER* Power ISA (PPC64)
  • IBMz* (s390x)
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最佳答案

An important part of Tensorflow is that it is supposed to be fast. With a suitable installation, it works with CPUs, GPUs, or TPUs. Part of going fast means that it uses different code depending on your hardware. Some CPUs support operations that other CPUs do not, such as vectorized addition (adding multiple variables at once). Tensorflow is simply telling you that the version you have installed can use the AVX and AVX2 operations and is set to do so by default in certain situations (say inside a forward or back-prop matrix multiply), which can speed things up. This is not an error, it is just telling you that it can and will take advantage of your CPU to get that extra speed out.

Note: AVX stands for Advanced Vector Extensions.

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I did below commands to install keras and tensorflow on CPU and GPU:

conda create --name py36 python==3.6.13
conda install tensorflow
conda install keras
conda install tensorflow-gpu
conda install tensorflow-estimator==2.1.0
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when I used "verbose=0" in Model.fit() it occurred then I remove that and it solved

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I have compiled the Tensorflow library a few times and if you have got something like the following:

kosinkie_l@Fedora ~/project/build $ python -c "import tensorflow as tf; x = [[2.]]; print('tensorflow version', tf.__version__); print('hello, {}'.format(tf.matmul(x, x)))"


2022-08-09 15:31:03.414926: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE3 SSE4.1 SSE4.2 AVX AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
tensorflow version 2.10.0-rc0
hello, Tensor("MatMul:0", shape=(1, 1), dtype=float32)
kosinkie_l@Fedora ~/project/build $

this meant that the cpu can use, but Tensorflow library does not used these.

The messages might be confused - so I have picked to source code (tensorflow/core/platform/cpu_feature_guard.cc:193) and there are the following:

131 #ifndef __AVX__
132     CheckIfFeatureUnused(CPUFeature::AVX, "AVX", missing_instructions);
133 #endif  // __AVX__
134 #ifndef __AVX2__
135     CheckIfFeatureUnused(CPUFeature::AVX2, "AVX2", missing_instructions);
136 #endif  // __AVX2__
...
192     if (!missing_instructions.empty()) {
193       LOG(INFO) << "This TensorFlow binary is optimized with "
194                 << "oneAPI Deep Neural Network Library (oneDNN) "
195                 << "to use the following CPU instructions in performance-"
196                 << "critical operations: " << missing_instructions << std::endl
197                 << "To enable them in other operations, rebuild TensorFlow "
198                 << "with the appropriate compiler flags.";
199     }

The method "CheckIfFeatureUnused(CPUFeature::AVX, "AVX", missing_instructions)" checks if the CPU can execute AVX and puts the "AVX" to missing_instructions collection, what is printed out.

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You have to create new environment or else try to install tensorflow in gpu on currrent base environment, for that use following commands...

creating new environment: conda create --name py36 python==3.6.13 or any latest version

installing tensorflow in CPU: conda install tensorflow conda install keras

installing tensorflow in GPU: conda install tensorflow-gpu conda install tensorflow-estimator==2.1.0 or any latest version

I Hope it will help you, Thank You...


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