ClementWalter

'use strict';

const sinon = require('sinon');
const chai = require('chai');
const sinonChai = require('sinon-chai');
const expect = chai.expect;
chai.use(sinonChai);
const utils = require('../../../server/mixins/utils');

describe('mixins utils', () => {

ClementWalter

'use strict';

module.exports = {
  addUserInfoToOptions: function(context, unused, next) {
    if (!context.req.currentUser || !context.req.currentUser.id) {
      const noCurrentUserError = new Error(
        'The request does not contain currentUser information'
      );
      noCurrentUserError.status = 401;
      throw noCurrentUserError;

ClementWalter

'use strict';
const utils = require('./utils');

module.exports = function(Model, bootOptions) {
  const options = Object.assign(
    {
      creatorId: 'creatorId',
      required: true,
    },
    bootOptions

ClementWalter

Editor commands

Commande	Nom JetBrain
Dupliquer une ligne	Duplicate line
Déplacer une ligne	Move line down/up
Déplacer un bloc de code automatiquement sélectionné	Move statement down/up
Ouvrir un fichier	File...
Rechercher dans un fichier / le projet	Find in path

ClementWalter

import itertools
from unittest.mock import sentinel

import requests

if __name__ == '__main__':
    alphabet = ['BD', 'GT', 'IDEAL', 'IDEALWINE', '18', '2018', 'BETTANE', 'DESSAUVE', 'GRAND', 'TASTING', 'ID']
    product_len = 1
    iterator = itertools.product(*(product_len * [alphabet]))
    code = ''.join(next(iterator))

ClementWalter

Pweave cheat sheet

Pweave is a dynamic report generation tool for Python. Its lets write texts (Markdown, reST, Latex...) and code in the same file with the code being evaluated and highlighted on purpose.

Key features:

• Execute python code in the chunks and capture input and output to a report.
• Rich output and support for IPython magics
• Use hidden code chunks, i.e. code is executed, but not printed in the output file.

ClementWalter

The perfect notebook is not a jupyter one

Jupyter notebook has been heavily reported as the perfect prototyping tool for data scientist. Its main features are:

• inline code execution
• easy idea structuring
• nice displays of pictures and dataframe

This overall flexibility has made it a preferred tool compared to the more rustic iPython command line. However it should not be forgotten that this is no more than an REPL where you can navigate efficiently throughout the history.

ClementWalter

ClementWalter

5 things you need to know before starting your machine learning project

and why Jupyter Notebook is not a good tool to do it.

At Sicara, we build machine learning based products for our customers:

• we build products: we need to develop in a production-ready mindset. Algorithms are served in the cloud, served and updated with APIs, etc.
• machine learning products: the customer comes with a business need and we have to deliver a satisfying solution as fast as possible.

ClementWalter

@tf.function(input_signature=(tf.TensorSpec(shape=[None, None, 3], dtype=tf.uint8),))
def preprocessing(input_tensor):
    output_tensor = tf.cast(input_tensor, dtype=tf.float32)
    output_tensor = tf.image.resize_with_pad(output_tensor, target_height=224, target_width=224)
    output_tensor = keras_applications.mobilenet.preprocess_input(output_tensor, data_format="channels_last")
    return output_tensor