dermatologist

const base64 = 'data:image/png;base65,....' // Place your base64 url here.
fetch(base64)
.then(res => res.blob())
.then(blob => {
  const fd = new FormData();
  const file = new File([blob], "filename.jpeg");
  fd.append('image', file)
  
 // Let's upload the file
 // Don't set contentType manually → https://github.com/github/fetch/issues/505#issuecomment-293064470

dermatologist

Public Gist

Create jupyter notebook in vscode

Python: create jupyter notebook

Github access tokens at https://github.com/settings/tokens

vuetify-jsonschema-form demo

• https://koumoul-dev.github.io/vuetify-jsonschema-form/latest/?example=basic

dermatologist

#!/bin/bash

DEVENV=${1:-devopenfaas}

kind create cluster --name "$DEVENV"

export KUBECONFIG="$(kind get kubeconfig-path --name="$DEVENV")"
kubectl rollout status deploy coredns --watch -n kube-system

# INSTALLING HELM

dermatologist

#for not running docker, use save:
docker save <dockernameortag> | gzip > mycontainer.tgz

#for running or paused docker, use export:
docker export <dockernameortag> | gzip > mycontainer.tgz

#load
gunzip -c mycontainer.tgz | docker load

dermatologist

#!/bin/bash
# 
# Download the Large-scale CelebFaces Attributes (CelebA) Dataset
# from their Google Drive link.
# 
# CelebA: http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html
# 
# Google Drive: https://drive.google.com/drive/folders/0B7EVK8r0v71pWEZsZE9oNnFzTm8

python3 get_drive_file.py 0B7EVK8r0v71pZjFTYXZWM3FlRnM celebA.zip

dermatologist

# Below are the dependencies required for installing the common combination of numpy, scipy, pandas and matplotlib 
# in an Alpine based Docker image.
FROM alpine:3.4
RUN echo "http://dl-8.alpinelinux.org/alpine/edge/community" >> /etc/apk/repositories
RUN apk --no-cache --update-cache add gcc gfortran python python-dev py-pip build-base wget freetype-dev libpng-dev openblas-dev
RUN ln -s /usr/include/locale.h /usr/include/xlocale.h
RUN pip install numpy scipy pandas matplotlib

dermatologist

import os

from gensim.models import KeyedVectors
from gensim.scripts.glove2word2vec import glove2word2vec
if os.path.exists('cui2vec_w.txt'):
    wv_from_text = KeyedVectors.load_word2vec_format('cui2vec_w.txt', unicode_errors='ignore')
    print(wv_from_text.most_similar("C0000052", topn=2))
    wv_from_text.save_word2vec_format('cui2vec_w.bin', binary=True)
else:
    with open("cui2vec_pretrained.csv",'r') as f:

dermatologist

#!/usr/bin/env bash
# Fully backup a docker-compose project, including all images, named and unnamed volumes, container filesystems, config, logs, and databases. 

project_dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" && cd .. && pwd )"
cd "$project_dir"

project_name=$(basename "$project_dir")
backup_time=$(date +"%Y-%m-%d_%H-%M")
backup_dir="$project_dir/data/backups/$backup_time"

dermatologist

// Create a Singularity image from a Docker image that is in the Docker hub
// where /tmp/ is the folder where the image will be created and ubuntu:14.04 
// is the docker image used to convert to the Singularity image
docker run \
-v /var/run/docker.sock:/var/run/docker.sock \
-v /tmp/:/output \
--privileged -t --rm \
singularityware/docker2singularity \
ubuntu:14.04
//

dermatologist

import os
import codecs

data_directory = os.path.join('..', 'data',
                              'yelp_dataset_challenge_academic_dataset')

businesses_filepath = os.path.join(data_directory,
                                   'yelp_academic_dataset_business.json')

with codecs.open(businesses_filepath, encoding='utf_8') as f: