Python: create jupyter notebook
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#!/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 |
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#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 |
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#!/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 |
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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 |
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# 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 |
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.
Install Homebrew Cask first if you haven’t:
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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 (-) |
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# -*- 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__. | |
""" |
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#!/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=$! |