TensorFlow wasn't compiled to use SSE (etc.) instructions, but these are available
Question:
I am running TensorFlow for the first time using some example code. I got the following warnings when running my code. Does anybody know why this happened, and how to fix it?
2017-03-31 02:12:59.346109: W c:tf_jenkinshomeworkspacerelease-windevicecpuoswindowstensorflowcoreplatformcpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE instructions, but these are available on your machine and could speed up CPU computations.
2017-03-31 02:12:59.346968: W c:tf_jenkinshomeworkspacerelease-windevicecpuoswindowstensorflowcoreplatformcpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE2 instructions, but these are available on your machine and could speed up CPU computations.
2017-03-31 02:12:59.346975: W c:tf_jenkinshomeworkspacerelease-windevicecpuoswindowstensorflowcoreplatformcpu_feature_guard.cc:45] The TensorFlow libbrary wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
2017-03-31 02:12:59.346979: W c:tf_jenkinshomeworkspacerelease-windevicecpuoswindowstensorflowcoreplatformcpu_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.
2017-03-31 02:12:59.346983: W c:tf_jenkinshomeworkspacerelease-windevicecpuoswindowstensorflowcoreplatformcpu_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.
2017-03-31 02:12:59.346987: W c:tf_jenkinshomeworkspacerelease-windevicecpuoswindowstensorflowcoreplatformcpu_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.
2017-03-31 02:12:59.346991: W c:tf_jenkinshomeworkspacerelease-windevicecpuoswindowstensorflowcoreplatformcpu_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.
2017-03-31 02:12:59.346995: W c:tf_jenkinshomeworkspacerelease-windevicecpuoswindowstensorflowcoreplatformcpu_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.
Answers:
Those are warnings (as indicated by the W after the colon. Errors have an E there).
The warnings refer to the fact that your CPU supports SSE Instructions, which allow some fast in-hardware-parallel operations. Enabling these operations is a compile-time operation (i.e. to use SSE you need to build the library from the source enabling the specific SSE version you’re targeting), in which case you might take a look at this question.
Note, however, that SSE support influences only the computation speed. Tensorflow will work with or without SSE, but it might take longer for your code to run.
Note, also, that this influences only the CPU. If you’re using the GPU build of Tensorflow, all the operations run on the GPU will not benefit of SSE instructions.
To hide those warnings, you could do this before your actual code.
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf
for detailed discussion, please refer here https://github.com/tensorflow/tensorflow/issues/7778
I hope, it can be a help for the other. 🙂
This isn’t an error, just warnings saying if you build TensorFlow from the source it can be faster on your machine.
And just like the warnings say, you should only compile TF with these flags if you need to make TF faster.
You can use TF environment variable TF_CPP_MIN_LOG_LEVEL and it works as follows:
- It defaults to 0, displaying all logs
- To filter out
INFO logs set it to 1
WARNINGS additionally, 2- and to additionally filter out
ERROR logs set it to 3
So you can do the following to silence the warnings:
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf
For more detail discussion you see How to compile tensorflow using SSE4.1, SSE4.2, and AVX.
I am running TensorFlow for the first time using some example code. I got the following warnings when running my code. Does anybody know why this happened, and how to fix it?
2017-03-31 02:12:59.346109: W c:tf_jenkinshomeworkspacerelease-windevicecpuoswindowstensorflowcoreplatformcpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE instructions, but these are available on your machine and could speed up CPU computations.
2017-03-31 02:12:59.346968: W c:tf_jenkinshomeworkspacerelease-windevicecpuoswindowstensorflowcoreplatformcpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE2 instructions, but these are available on your machine and could speed up CPU computations.
2017-03-31 02:12:59.346975: W c:tf_jenkinshomeworkspacerelease-windevicecpuoswindowstensorflowcoreplatformcpu_feature_guard.cc:45] The TensorFlow libbrary wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
2017-03-31 02:12:59.346979: W c:tf_jenkinshomeworkspacerelease-windevicecpuoswindowstensorflowcoreplatformcpu_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.
2017-03-31 02:12:59.346983: W c:tf_jenkinshomeworkspacerelease-windevicecpuoswindowstensorflowcoreplatformcpu_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.
2017-03-31 02:12:59.346987: W c:tf_jenkinshomeworkspacerelease-windevicecpuoswindowstensorflowcoreplatformcpu_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.
2017-03-31 02:12:59.346991: W c:tf_jenkinshomeworkspacerelease-windevicecpuoswindowstensorflowcoreplatformcpu_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.
2017-03-31 02:12:59.346995: W c:tf_jenkinshomeworkspacerelease-windevicecpuoswindowstensorflowcoreplatformcpu_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.
Those are warnings (as indicated by the W after the colon. Errors have an E there).
The warnings refer to the fact that your CPU supports SSE Instructions, which allow some fast in-hardware-parallel operations. Enabling these operations is a compile-time operation (i.e. to use SSE you need to build the library from the source enabling the specific SSE version you’re targeting), in which case you might take a look at this question.
Note, however, that SSE support influences only the computation speed. Tensorflow will work with or without SSE, but it might take longer for your code to run.
Note, also, that this influences only the CPU. If you’re using the GPU build of Tensorflow, all the operations run on the GPU will not benefit of SSE instructions.
To hide those warnings, you could do this before your actual code.
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf
for detailed discussion, please refer here https://github.com/tensorflow/tensorflow/issues/7778
I hope, it can be a help for the other. 🙂
This isn’t an error, just warnings saying if you build TensorFlow from the source it can be faster on your machine.
And just like the warnings say, you should only compile TF with these flags if you need to make TF faster.
You can use TF environment variable TF_CPP_MIN_LOG_LEVEL and it works as follows:
- It defaults to 0, displaying all logs
- To filter out
INFOlogs set it to 1 WARNINGSadditionally, 2- and to additionally filter out
ERRORlogs set it to 3
So you can do the following to silence the warnings:
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf
For more detail discussion you see How to compile tensorflow using SSE4.1, SSE4.2, and AVX.