pythonの最初の環境構築メモ

Python3 
conda init zsh
sh -c "$(curl -fsSL https://raw.github.com/ohmyzsh/ohmyzsh/master/tools/install.sh)"


# conda create (GUIで操作しても良い)
conda create --name [環境名] python=3.7 --prefix=[prefix] -y
conda activate [環境名]
jupyter kernelspec list
ipython kernel install --user --name=[環境名] --display-name=[環境名]


setting.json(remote)
{
    "remote.autoForwardPortsSource": "output"
}

setting.json(workspace)
{
    "remote.autoForwardPortsSource": "output",
    "python.pythonPath": "/root/anaconda3/bin/python(pyenv path)"
}

# kedro new
kedro new
cd [project名]
git init
…
git push -u origin main


conda env update --file environment.yml
pip list --format freeze

name: [環境名]
channels:
  - conda-forge
  - anaconda
  - default
dependencies:
  - kedro
  - pre-commit
  - pytest
  - pytest-cov
  - tox
  - black
  - yapf
  - pylint
  - mypy
  - pip

git の subdirectoryをlocalにinstallする方法

深層学習 Python3

stackoverflow.com

pytorch の referenceをinstallする方法を探してたら、これが便利そう

$ svn ls https://github.com/pytorch/vision.git/trunk/references/detection
README.md
coco_eval.py
coco_utils.py
engine.py
group_by_aspect_ratio.py
train.py
transforms.py
utils.py
$ svn ls https://github.com/pytorch/vision.git/trunk/references
classification/
detection/
segmentation/
similarity/
video_classification/
$ svn export https://github.com/pytorch/vision.git/trunk/references
A    references
A    references/classification
A    references/classification/README.md
A    references/classification/train.py
A    references/classification/train_quantization.py
...
  • utils の importができない場合のrename

stackoverflow.com

deep learning を mobileで実装するときのリンク集

深層学習

エッジやmobileでdeep を使用するときに参考にするサイト。

github.com

hub.packtpub.com

www.mobindustry.net

https://heartbeat.fritz.ai/intro-to-machine-learning-on-android-how-to-convert-a-custom-model-to-tensorflow-lite-e07d2d9d50e3

https://heartbeat.fritz.ai/machine-learning-on-android-computer-vision-c38c4072acd8

www.slideshare.net

www.slideshare.net

www.slideshare.net

google colab で動画が再生できなかった。

Python3 
import base64
import io

def play(file_path):
    video = io.open(file_path, 'r+b').read()
    encoded = base64.b64encode(video)
    return(HTML(data='''<video width="320" height="240" controls><source src="data:video/mp4;base64,{0}" type="video/mp4" /></video>'''.format(encoded.decode('ascii'))))

walkingmask.hatenablog.com