- Install all required software:
docker,nvidia-docker,gitlab-ci-multi-runner - Execute: curl -s http://localhost:3476/docker/cli
- Use that data to fill devices/volumes/volume_driver fields in /etc/gitlab-runner/config.toml
| # -*- 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 |
| 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: |
| [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 |
| #!/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) |
| """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 |
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:
- Create a bare clone of the repository.
(This is temporary and will be removed so just do it wherever.)
git clone --bare [email protected]:usi-systems/easytrace.git
| # 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 |
Here are the simple steps needed to create a deployment from your local GIT repository to a server based on this in-depth tutorial.
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.
These instructions are based on Mistobaan's gist but expanded and updated to work with the latest tensorflow OSX CUDA PR.