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

#!/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

#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

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

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

# Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>
#         Lars Buitinck <L.J.Buitinck@uva.nl>
# License: Simplified BSD

from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import HashingVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import FeatureHasher
from sklearn.pipeline import Pipeline

dermatologist

Install Multiple Java Versions on macOS High Sierra

Install Homebrew Cask

On Mac, Homebrew is the de-facto package manager, and Homebrew Cask is the app manager. I’m going to use Cask to install Java 7 and 8.

You don't need to install cask anymore, you just need homebrew. Try using any cask command

Install Homebrew Cask first if you haven’t:

dermatologist

Choose archetype:
1: remote -> br.gov.frameworkdemoiselle.archetypes:demoiselle-jsf-jpa (Archetype for web applications (JSF + JPA) using Demoiselle Framework)
2: remote -> br.gov.frameworkdemoiselle.archetypes:demoiselle-minimal (Basic archetype for generic applications using Demoiselle Framework)
3: remote -> co.ntier:spring-mvc-archetype (An extremely simple Spring MVC archetype, configured with NO XML.)
4: remote -> com.agilejava.docbkx:docbkx-quickstart-archetype (-)
5: remote -> com.alibaba.citrus.sample:archetype-webx-quickstart (-)
6: remote -> com.bsb.common.vaadin:com.bsb.common.vaadin.embed-simple-archetype (-)
7: remote -> com.bsb.common.vaadin:com.bsb.common.vaadin7.embed-simple-archetype (-)
8: remote -> com.cedarsoft.open.archetype:multi (-)
9: remote -> com.cedarsoft.open.archetype:simple (-)

dermatologist

# -*- coding: utf-8 -*-

"""From a Query.all(), turn this result to a pandas DataFrame

Table creation and example data come from the official SQLAlchemy ORM
tutorial at http://docs.sqlalchemy.org/en/latest/orm/tutorial.html

Just take a look at the 'query_to_dict' function and the last part of the __main__.
"""

dermatologist

#!/bin/bash
set -u

DOC_DIR=godoc
PKG=github.com/matrix-org/go-neb

# Run a godoc server which we will scrape. Clobber the GOPATH to include
# only our dependencies.
GOPATH=$(pwd):$(pwd)/vendor godoc -http=localhost:6060 &
DOC_PID=$!