Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.
For easy editor integration and command-line usage, we'd like to be able to specify a Python version per project, with its own virtualenv to isolate its libraries from those of other projects.
We're willing to change $PATH globally once, but not per project. And we'd like to avoid having to run every python command invocation in a special subshell created by a shell wrapper. Instead, simply invoking "python" or "pip" etc. should do the right thing, based on the directory in which it is invoked.
It turns out this is possible!
| # Working example for my blog post at: | |
| # https://danijar.github.io/structuring-your-tensorflow-models | |
| import functools | |
| import tensorflow as tf | |
| from tensorflow.examples.tutorials.mnist import input_data | |
| def doublewrap(function): | |
| """ | |
| A decorator decorator, allowing to use the decorator to be used without |