Largely based on the Tensorflow 1.6 gist, this should hopefully simplify things a bit. Mixing homebrew python2/python3 with pip ends up being a mess, so here's an approach to uses the built-in python27.
| import tensorflow as tf | |
| from tensorflow.contrib.eager.python import tfe | |
| tfe.enable_eager_execution() | |
| context = tf.device('/gpu:0') | |
| context.__enter__() | |
| # download resnet_model | |
| import sys, os, urllib.request | |
| resnet_model_url="https://raw.githubusercontent.com/tensorflow/models/master/official/resnet/resnet_model.py" | |
| response = urllib.request.urlopen(resnet_model_url) |
These instructions were inspired by Mistobaan's gist, ageitgey's gist, and mattiasarro's tutorial.
I always encountered the following warnings when running my scripts using the precompiled TensorFlow Python package:
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] Th
These instructions were inspired by Mistobaan's gist, ageitgey's gist, and mattiasarro's tutorial.
I always encountered the following warnings when running my scripts using the precompiled TensorFlow Python package:
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] Th