Problem statement first: How does one properly setup tensorflow for running on a DSVM using a remote Docker environment? Can this be done in aml_config/*.runconfig?
I receive the following message and I would like to be able to utilize the increased speeds of the extended FMA operations.
tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
Background: I utilize a local docker environment managed through Azure ML Workbench for initial testing and code validation so that I'm not running an expensive DSVM constantly. Once I assess that my code is to my liking, I then run it on a remote docker instance on an Azure DSVM.
I want a consistent conda environment across my compute environments, so this works out extremely well. However, I cannot figure out how to control the tensorflow build to optimize for the hardware at hand (i.e. my local docker on macOS vs. remote docker on Ubuntu DSVM)