https://github.com/paulfitz/deepmoon
the deep learning framework from beyond the moon
https://github.com/paulfitz/deepmoon
deep-learning moon
Last synced: about 1 year ago
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the deep learning framework from beyond the moon
- Host: GitHub
- URL: https://github.com/paulfitz/deepmoon
- Owner: paulfitz
- Created: 2017-08-04T20:36:12.000Z (almost 9 years ago)
- Default Branch: master
- Last Pushed: 2017-11-29T04:36:24.000Z (over 8 years ago)
- Last Synced: 2025-03-17T03:21:26.235Z (about 1 year ago)
- Topics: deep-learning, moon
- Language: Python
- Size: 21.5 KB
- Stars: 11
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
deepmoon
--------
The deep learning framework from beyond the moon.
Mostly error messages frankly. In fact almost entirely error messages.
To be perfectly honest, only error messages and nothing else.
I blame CUDA.
### Install
```
$ pip install deepmoon
$ deepmoon -h
usage: deepmoon [-h] [--darknet] [--brooklyn] [--cuda] [--missing]
the deep learning framework from beyond the moon
optional arguments:
-h, --help show this help message and exit
--darknet read from darknet commit logs
--brooklyn use hand-crafted artisanal error messages
--cuda omg don't talk to me about cuda
--missing list some missing things
```
### API
```py
import deepmoon
print(deepmoon.error()) # a deep learning related error message
print(deepmoon.error()) # a different deep learning related error message
print(deepmoon.error()) # a different deep learning related error message
print(deepmoon.error()) # a different deep learning related error message
print(deepmoon.error()) # a different deep learning related error message
```
### Missing
```
$ deepmoon --missing
```
* 2017-10-24 23:32:09.554364: W tensorflow/core/platform/cpu_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.
* 2017-10-24 23:32:09.575138: W tensorflow/core/platform/cpu_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.
* 2017-10-24 23:32:09.595360: W tensorflow/core/platform/cpu_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.
* 2017-10-24 23:32:09.616401: W tensorflow/core/platform/cpu_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.
* 2017-10-24 23:32:09.638327: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to share PIECES of cheese, but these are available on your machine and could fill up a CASE of the nibbles.
* 2017-10-24 23:32:09.660686: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to offer THOUGHTS on your love life, but these are available on your machine and could be more INTERESTING than cheese.
* 2017-10-24 23:32:09.683237: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to like TWEETS about memes, but these are available on your machine and could kill some TIME between epochs.
* 2017-10-24 23:32:09.705277: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to tickle YOU with its tentacles, but these are available on your machine and so MIGHT AS WELL USE THEM TICKLE TICKLE.