Will provide in the next few days.

Thanks.

TensorFlow 1.4 (CPU, MKL) is slower than TensorFlow 1.4 (CPU)
inference time:
TensorFlow 1.4 (CPU) about 280ms
TensorFlow 1.4 (CPU, MKL) about 400ms
TensorFlow 1.4 (from pypi use pip) about 800ms

I also noticed that, this version doesn't need MKL. I don't have MKL installed on my system when using this version the first time.

My system information:
OS: debian-sid
CPU: i7 6500U codename skylake.

That's weird. Will take a look tomorrow.

@dwSun @danqing
I would also like to know the result of this as well.

My 1060 is looking to be twice as fast than my Xeon Phi on a different benchmark which makes me believe that I'm doing something drastically wrong with the installation (no MKL). I used the wheel provided by Intel for TF 1.4.

Benchmarks:
tensorflow/tensorflow#8584

I also have issues with the MKL versions provided. It even seems to have an impact on the speed of the computations on the GPU. I had to switch back to an older version of Tensorflow without MKL to be able to continue testing and development.

Please also provide wheels for 1.4.1 without MKL.

Yeah I'm still investigating this issue. The GPU builds with MKL should be fine though - @chricke do you have problem with those or just the CPU-only ones?

I had this issue with the GPU versions while testing a new GPU server we setup at work. Training on the server using one GPU (V100) was slower than training on my lokal GTX 1070.

First i thought it has to do with Keras as switching the optimizer from Keras to native Tensorflow had a big impact (training time down from > 4 hours to 30 minutes per epoc). But that was still slower than on my lokal machine. After checking every step of the setup on another machine it turned out that it was the MKL build of Tensorflow which was causing this. After changing to a build without MKL training on the server was much faster (as expected).

BTW: As i mentioned switching from Keras Optimizer to the Tensorflow version the difference in training time was huge (4hours to 30 minutes) with MKL build. Using non MKL even this difference was much less (only ~5 minutes difference).

That seems to be related to tensorflow/tensorflow#14496. Trying to figure out if there are solutions around this issue.

I would strongly recommend reading through https://www.tensorflow.org/performance/performance_guide#tensorflow_with_intel_mkl_dnn to tune CPU performance. It basically involves tweaking a few variables related to your environment.

On Linux what if you add this line to .profile or .bashrc?

export MKL_THREADING_LAYER=GNU

Not sure if it has to do with TF, but MKL was much slower than openblas for things like matrix multiplication. After adding this it beats openblas.