gschivley

# -*- coding: utf-8 -*-
import dash
import dash_core_components as dcc
import dash_html_components as html
from dash.dependencies import Input, Output
import pandas as pd
import sqlalchemy
import altair as alt
import io
from vega_datasets import data

alexcjohnson

The MIT License (MIT)

Copyright (c) Plotly, Inc

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

Hopobcn

Use Gitlab-CI with GPU support

Since gitlab-runner cannot be forced to use nvidia-docker wrapper, follow this steps:

1. Install all required software: docker, nvidia-docker, gitlab-ci-multi-runner
2. Execute: curl -s http://localhost:3476/docker/cli
3. Use that data to fill devices/volumes/volume_driver fields in /etc/gitlab-runner/config.toml

estahn

[Unit]
Description=Metabase server
After=syslog.target
After=network.target

[Service]
WorkingDirectory=/home/data/tools/metabase
ExecStart=/usr/bin/java -jar /home/data/tools/metabase/metabase.jar
Environment=MB_JETTY_PORT=8080
User=data

eddieantonio

#!/usr/bin/env python3
# -*- coding: UTF-8 -*-

import socket
import json

server_address = '/tmp/example.sock'

sock = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)
sock.connect(server_address)

low-ghost

"""Creates a python socket client that will interact with javascript."""
import socket

socket_path = '/tmp/node-python-sock'
# connect to the unix local socket with a stream type
client = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)
client.connect(socket_path)
# send an initial message (as bytes)
client.send(b'python connected')
# start a loop

0xjac

The repository for the assignment is public and Github does not allow the creation of private forks for public repositories.

The correct way of creating a private frok by duplicating the repo is documented here.

For this assignment the commands are:

1. Create a bare clone of the repository. (This is temporary and will be removed so just do it wherever.)

git clone --bare git@github.com:usi-systems/easytrace.git

dirko

# Keras==1.0.6
from keras.models import Sequential
import numpy as np
from keras.layers.recurrent import LSTM
from keras.layers.core import TimeDistributedDense, Activation
from keras.preprocessing.sequence import pad_sequences
from keras.layers.embeddings import Embedding
from sklearn.cross_validation import train_test_split
from keras.layers import Merge
from keras.backend import tf

noelboss

Simple automated GIT Deployment using GIT Hooks

Here are the simple steps needed to create a deployment from your local GIT repository to a server based on this in-depth tutorial.

How it works

You are developing in a working-copy on your local machine, lets say on the master branch. Most of the time, people would push code to a remote server like github.com or gitlab.com and pull or export it to a production server. Or you use a service like deepl.io to act upon a Web-Hook that's triggered that service.

ageitgey

Build tensorflow on OSX with NVIDIA CUDA support (GPU acceleration)

These instructions are based on Mistobaan's gist but expanded and updated to work with the latest tensorflow OSX CUDA PR.

Requirements

OS X 10.10 (Yosemite) or newer