minimal-panel-app:在远程计算机上提供的面板应用程序的教学实现

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  • 2022-05-25 03:14
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最小面板应用 在远程计算机上提供的面板应用程序的教学实现。 在查看完整的应用程序。 为什么这个项目存在 我花了一天的时间弄清楚如何在工作中做到这一点,并决定花一个晚上巩固我的知识。 “如何使用” git clone https://github.com/ericmjl/minimal-panel-app 还有什么有趣的吗? iPad开发 该应用程序的第一个版本完全在iPad上进行了编码,结合使用了Juno和我的家庭远程服务器上的nano (仅是转换后的游戏塔)。 现在可以使用Python开发Web应用程序了,我们可以将现代平板电脑用作瘦客户端! 内存使用情况 将HIV耐药性模型部署到Heroku方面具有挑战性,因为我必须注意内存和存储的使用情况。 有8种模型可以进行预测,将所有模型加载在一起会导致Heroku的免费层上的内存过载。 我通过单独腌制模型并仅在需要时加载它们来解决此问题
minimal-panel-app-master.zip
  • minimal-panel-app-master
  • .gitignore
    1.2KB
  • requirements.txt
    166B
  • Makefile
    78B
  • data
  • models
  • IDV.pkl.gz
    4.4MB
  • LPV.pkl.gz
    3.5MB
  • DRV.pkl.gz
    1.2MB
  • ATV.pkl.gz
    2.7MB
  • SQV.pkl.gz
    4.3MB
  • FPV.pkl.gz
    4.1MB
  • NFV.pkl.gz
    4.6MB
  • TPV.pkl.gz
    1.5MB
  • hiv-protease-data-expanded.csv
    2.3MB
  • scores.pkl.gz
    702B
  • hiv-protease-consensus.txt
    110B
  • model-training.ipynb
    15.6KB
  • LICENSE
    1KB
  • utils.py
    2.3KB
  • README.md
    1.6KB
  • iris.ipynb
    4.9MB
  • test_utils.py
    826B
  • index.html
    149B
  • environment.yml
    311B
  • home.ipynb
    2.3MB
  • Procfile
    175B
  • azure-pipelines.yml
    571B
  • hiv-resistance.ipynb
    5MB
内容介绍
# minimal-panel-app A pedagogical implementation of panel apps served up on a remote machine. See the full app [here](http://minimal-panel-app.herokuapp.com/home). ## why this project exists I spent a day figuring out how to make this happen at work, and decided to spend an evening consolidating my knowledge. ## "how to use" ``` git clone https://github.com/ericmjl/minimal-panel-app ``` ## anything else interesting? ### iPad development The first version of the app was coded up entirely on an iPad, using a combination of [blink](http://blink.sh) [Juno](jhttp://juno.sh), and `nano` on my home remote server (which is nothing more than a converted gaming tower). Web app development in Python is now doable and we can use modern tablets as a thin client! ### memory usage Deploying the HIV drug resistance model to Heroku was challenging because I had to watch out for memory and storage usage. There are 8 models to make predictions on, and loading all of them together causes memory overload on Heroku's free tier. I got around this by pickling the models individually, and only loading them when needed. I also minimized disk usage by using gzip when pickling the files. ### multi-app hosting There are multiple "apps" that are being hosted by a single Panel server here. Each "app" is basically one Jupyter notebook. In each notebook, I define a self-contained, hostable unit that an end-user can interact with. One of them is the homepage, written using Panel's tooling just to prove the point, but the others are actual user-facing interfaces that provide a way to interact with either data or a machine learning model.
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