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desde ahora usaremos solo virtualenv, (nunca mas pyenv) | |
1. brew install python3 (will install latest python and pip3 versions) | |
2. pip3 install virtualenv | |
//siguiendo este tutorial: | |
//https://help.dreamhost.com/hc/en-us/articles/115000695551-Installing-and-using-Python-s-virtualenv-using-Python-3 | |
3. revisas que versiones de python tienes instaladas en tu pc con: | |
"python" + TAB |
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//Ya no usaremos "pyenv", tampoco "pyenv-virtualenv" para crear versiones de Python, porque es mala practica | |
//en cambio usaremos solo la libreria "virtualenv": https://virtualenv.pypa.io/en/stable/ | |
//en la que por cada proyecto se tendra una version especifica de python | |
Por ej: | |
paa el proyecto "Gitchain" se inicia el virtualenv con: source .env/bin/activate | |
y para cerrar el virtualenv se usa: deactivate |
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INMUTABLE alternative: | |
array.concat crea un nuevo array, pero adiciona nuevos elementos en el nuevo array | |
array.slice crea copia de un array, | |
object.assign crea copia de obj en el q pueda modificar o agregar elementos en el nuevo objeto | |
en ES6 salio la nueva alternativa SPREAD OPERATOR que afecta tanto a arrays como objects, q hace lo mismo | |
con esto podria usar "shouldComponentUpdate" para mi JSX!! | |
https://developmentarc.gitbooks.io/react-indepth/content/life_cycle/update/using_should_component_update.html |
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export default class App extends Component { | |
constructor(props) { | |
super(props); | |
this.state = { | |
texto: '', | |
saludo: '', | |
cambio: '' | |
}; | |
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Flask Serving | |
This file is a sample flask app that can be used to test your model with an API. | |
This app does the following: | |
- Handles uploads and looks for an image file send as "file" parameter | |
- Stores the image at ./images dir | |
- Invokes ffwd_to_img function from evaluate.py with this image | |
- Returns the output file generated at /output | |
Additional configuration: | |
- You can also choose the checkpoint file name to use as a request parameter | |
- Parameter name: checkpoint |
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INICIANDO TENSORFLOW DENTRO DE DOCKER | |
1ra alterinativa> si quiero solo abrir un container "efimero" uso --rm, haciendo uso de volumenes para grabar mantener mis libros jupyter: | |
sudo docker run --rm -it -p 8888:8888 -v /home/dante/Documents/tensorFlow:/notebooks/huequito tensorflow/tensorflow:1.1.0-rc2-py3 | |
--------------------- | |
2da alternativa> si quiero instalar dependencias de una imagen q inicialize, hare uso de BASH para que me aparezca la linea de comandas e instale lo q necesite. | |
Primero hago RUN: | |
sudo docker run --rm -it -p 8888:8888 -v /home/dante/Documents/tensorFlow:/notebooks/huequito tensorflow/tensorflow:1.1.0-rc2-py3 |
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