AIscalator’s documentation¶

AIscalator¶

Docs
Tests
Package

Free software: Apache Software License 2.0
Website: http://www.aiscalate.com
Documentation: https://aiscalator.readthedocs.io/en/latest/
Bugs: https://github.com/aiscalate/aiscalator/issues

Key Features¶

Aiscalator is a toolbox to enable your team streamlining processes from innovation to productization with:

Jupyter workbench
- Explore Data, Prototype Solutions
Docker wrapper tools
- Share Code, Deploy Reproducible Environments
Airflow machinery
- Schedule Tasks, Refine Products
Data Science and Data Engineering best practices

Quick Start¶

Installation¶

Test if prerequisite softwares are installed:

docker --version
docker-compose --version
pip --version

Install AIscalator tool:

git clone https://github.com/Aiscalate/aiscalator.git
cd aiscalator/
make install

Great, we are now ready to use the AIscalator!

The following setup commands are completely optional because they are dealing with prebuilding Docker images. If you choose not to do it at this point, they will get built later on whenever they are required.

However, since producing a Docker image requires a certain amount of time to download, install packages, and sometimes even compiling them, these installation steps can be initiated right away all at once. Thus, you should be free to go enjoy a nice coffee break!

You might want to customize your environment with the AIscalator, this will ask you few questions:

aiscalator setup

Build docker images to run Jupyter environments:

aiscalator jupyter setup

Build docker image to run Airflow:

# aiscalator airflow setup <path-to-workspace-folder>
# for example,
aiscalator airflow setup $PWD

Start working¶

AIscalator commands dealing with jupyter are defining tasks in Airflow jargon; In our case, they are all wrapped inside a Docker container. We also refer to them as Steps.

Whereas AIscalator commands about airflow are made to author, schedule and monitor DAGs (Directed Acyclic Graphs). They define how a workflow is composed of multiple steps, their dependencies and execution times or triggers.

Jupyter¶

Create a new Jupyter notebook to work on, define corresponding AIscalator step:

# aiscalator jupyter new <path-to-store-new-files>
# For example,
aiscalator jupyter new project
# (CTRL + c to kill when done)

Or you can edit an existing AIscalator step:

# aiscalator jupyter edit <aiscalator step>
# For example, if you cloned the git repository:
aiscalator jupyter edit resources/example/example.conf
# (CTRL + c to kill when done)

Run the step without GUI:

# aiscalator jupyter run <aiscalator task>
# For example, if you cloned the git repository:
aiscalator jupyter run resources/example/example.conf

Airflow¶

Start Airflow services:

aiscalator airflow start

Create a new AIscalator DAG, define the airflow job:

# aiscalator airflow new <path-to-store-new-files>
# For example,
aiscalator airflow new project
# (CTRL + c to kill when done)

Or you can edit an existing AIscalator DAG:

# aiscalator airflow edit <aiscalator DAG>
# For example, if you cloned the git repository:
aiscalator airflow edit resources/example/example.conf
# (CTRL + c to kill when done)

Schedule AIscalator DAG into local airflow dags folder:

# aiscalator airflow push <aiscalator DAG>
# For example, if you cloned the git repository:
aiscalator airflow push resources/example/example.conf

Stop Airflow services:

aiscalator airflow stop

Installation¶

Stable release¶

To install aiscalator, run this command in your terminal:

$ pip install aiscalator

This is the preferred method to install aiscalator, as it will always install the most recent stable release.

If you don’t have pip installed, this Python installation guide can guide you through the process.

From sources¶

The sources for aiscalator can be downloaded from the Github repo.

You can either clone the public repository:

$ git clone git://github.com/aiscalate/aiscalator

Or download the tarball:

$ curl  -OL https://codeload.github.com/Aiscalate/aiscalator/legacy.tar.gz/master

Once you have a copy of the source, you can install it with:

$ make install

Development environment on Mac OS¶

First, Install docker: https://docs.docker.com/docker-for-mac/install/

Then, we need to have a proper python environment installed: We would recommend using Homebrew to install pyenv. Best practices would also to be using virtualenv and virtualenvwrapper.

brew install python

# to successfully run all tests with tox, make sure to have
# each version of python installed
brew install pyenv
brew install pyenv-virtualenv
brew install pyenv-virtualenvwrapper

# Follow their documentations to add the proper environment variables to your
# startup scripts (~/,bash_profile etc)

pyenv install 3.4.9 3.5.6 3.6.7 3.7.1 pypy3.5-6.0.0

# in case pyenv runs into errors when running tox because it
# cannot find python3.X (even though python3.X.Y is installed):
git clone git://github.com/concordusapps/pyenv-implict.git ~/.pyenv/plugins/pyenv-implict


pip install -r requirements_dev.txt

# run all tests
tox

Usage¶

The project is still in Alpha version, there is still a lot of work to be done. At the moment, you can install using two methods

Install from PyPI:
```
pip install aiscalator
```

or Download latest version directly from git and install:

git clone https://github.com/Aiscalate/aiscalator.git
cd aiscalator/
make install

After cloning the project, you can start testing the following commands at the moment:

aiscalator jupyter new examples
ls -l examples/
aiscalator jupyter edit resources/example/example.conf
aiscalator jupyter run resources/example/example.conf
aiscalator airflow start

Features¶

Docker docker_image Configuration¶

docker_image support docker_extra_options to be passed as a list of arguments to be appended to the docker execution.

aiscalator¶

aiscalator package¶

Subpackages¶

aiscalator.airflow package¶

Submodules¶

aiscalator.airflow.command module¶

Implementations of commands for Airflow

aiscalator.airflow.command.airflow_cmd(conf: aiscalator.core.config.AiscalatorConfig, service='webserver', cmd=None)[source]¶

Execute an airflow subcommand

Parameters

conf (AiscalatorConfig) – Configuration object for the application
service (string) – service name of the container where to run the command
cmd (list) – subcommands to run

aiscalator.airflow.command.airflow_down(conf: aiscalator.core.config.AiscalatorConfig)[source]¶

Stop an airflow environment

Parameters: conf (AiscalatorConfig) – Configuration object for the application

aiscalator.airflow.command.airflow_edit(conf: aiscalator.core.config.AiscalatorConfig)[source]¶

Starts an airflow environment

Parameters: conf (AiscalatorConfig) – Configuration object for the application

aiscalator.airflow.command.airflow_push(conf: aiscalator.core.config.AiscalatorConfig)[source]¶

Starts an airflow environment

Parameters: conf (AiscalatorConfig) – Configuration object for the application

aiscalator.airflow.command.airflow_setup(conf: aiscalator.core.config.AiscalatorConfig, config_home: str, workspace: list, append: bool = True)[source]¶

Setup the airflow configuration files and environment

Parameters

conf (AiscalatorConfig) – Configuration object for the application
config_home (str) – path to the configuration home directory
workspace (list) – List of path to directories to mount as volumes to airflow workers to use as workspaces
append (bool) – flag to tell if workspace should be appended to the list in the config or replace it.

aiscalator.airflow.command.airflow_up(conf: aiscalator.core.config.AiscalatorConfig)[source]¶

Starts an airflow environment

Parameters: conf (AiscalatorConfig) – Configuration object for the application

aiscalator.airflow.cli module¶

CLI module for Airflow related commands.

Module contents¶

aiscalator.core package¶

Submodules¶

aiscalator.core.config module¶

Handles configurations files for the application

class aiscalator.core.config.AiscalatorConfig(config=None, step_selection=None, dag_selection=None)[source]¶

Bases: object

A configuration object for the Aiscalator application.

This object stores:

global configuration for the whole application
configuration for a particular context specified in a step configuration file.
In this case, we might even focus on a particular step.

…

Variables

_app_conf – global configuration object for the application
_config_path (str) – path to the configuration file (or plain configuration as string)
_step_name (str) – name of the currently processed step
_step – configuration object for the currently processed step
_dag_name (str) – name of the currently processed dag
_dag – configuration object for the currently processed dag

Methods

`airflow_docker_compose_file`()	Return the configuration file to bring airflow services up.
`app_config`()	returns str – the configuration object for the aiscalator application
`app_config_has`(field)	Tests if the applicatin config has a configuration value for the field.
`app_config_home`()	Return the path to the app configuration folder.
`config_path`()	returns str – Returns the path to the step configuration file.
`dag_container_name`()	Return the docker container name to execute this step
`dag_field`(field)	Returns the value associated with the field for the currently focused dag.
`dag_file_path`(string)	Returns absolute path of a file from a field of the currently focused dag.
`dag_name`()	Returns the name of the currently focused dag
`has_dag_field`(field)	Tests if the currently focused dag has a configuration value for the field.
`has_step_field`(field)	Tests if the currently focused step has a configuration value for the field.
`redefine_airflow_workspaces`(workspaces)	Modify the configuration file to change the value of the airflow workspaces
`redefine_app_config_home`(config_home)	Modify the configuration file to change the value of the application configuration home directory.
`root_dir`()	returns str – Returns the path to the folder containing the
`step_container_name`()	Return the docker container name to execute this step
`step_extract_parameters`()	Returns a list of docker parameters
`step_field`(field)	Returns the value associated with the field for the currently focused step.
`step_file_path`(string)	Returns absolute path of a file from a field of the currently focused step.
`step_name`()	Returns the name of the currently focused step
`step_notebook_output_path`(notebook)	Generates the name of the output notebook
`user_env_file`([job])	Find a list of env files to pass to docker containers
`user_id`()	returns str – the user id stored when the application was first setup
`validate_config`()	Check if all the fields in the reference config are defined in focused steps too.

airflow_docker_compose_file()[source]¶: Return the configuration file to bring airflow services up.

app_config()[source]¶

Returns: str – the configuration object for the aiscalator application

app_config_has(field) → bool[source]¶: Tests if the applicatin config has a configuration value for the field.

app_config_home() → str[source]¶: Return the path to the app configuration folder.

config_path()[source]¶

Returns: str – Returns the path to the step configuration file. If it was an URL, it will return the path to the temporary downloaded version of it. If it was a plain string, then returns None

dag_container_name() → str[source]¶: Return the docker container name to execute this step

dag_field(field)[source]¶: Returns the value associated with the field for the currently focused dag.

dag_file_path(string)[source]¶: Returns absolute path of a file from a field of the currently focused dag.

dag_name()[source]¶: Returns the name of the currently focused dag

has_dag_field(field) → bool[source]¶: Tests if the currently focused dag has a configuration value for the field.

has_step_field(field) → bool[source]¶: Tests if the currently focused step has a configuration value for the field.

redefine_airflow_workspaces(workspaces)[source]¶

Modify the configuration file to change the value of the airflow workspaces

Parameters: workspaces (list) – list of workspaces to bind to airflow
Returns: AiscalatorConfig – the new configuration object

redefine_app_config_home(config_home)[source]¶

Modify the configuration file to change the value of the application configuration home directory.

Parameters: config_home (str) – path to the new configuration home
Returns: AiscalatorConfig – the new configuration object

root_dir()[source]¶

Returns: str – Returns the path to the folder containing the configuration file

step_container_name() → str[source]¶: Return the docker container name to execute this step

step_extract_parameters() → list[source]¶: Returns a list of docker parameters

step_field(field)[source]¶: Returns the value associated with the field for the currently focused step.

step_file_path(string)[source]¶: Returns absolute path of a file from a field of the currently focused step.

step_name()[source]¶: Returns the name of the currently focused step

step_notebook_output_path(notebook) → str[source]¶: Generates the name of the output notebook

user_env_file(job=None) → list[source]¶

Find a list of env files to pass to docker containers

Parameters: job – Optional step or dag config
Returns: List – env files

user_id() → str[source]¶

Returns: str – the user id stored when the application was first setup

validate_config()[source]¶: Check if all the fields in the reference config are defined in focused steps too. Otherwise raise an Exception (either pyhocon.ConfigMissingException or pyhocon.ConfigWrongTypeException)

aiscalator.core.config.convert_to_format(file: str, output: str, output_format: str)[source]¶

Converts a HOCON file to another format

Parameters

file (str) – hocon file to convert
output (str) – output file to produce
output_format (str) – format of the output file

Returns

str – the output file

aiscalator.core.config.generate_user_id() → str[source]¶

Returns: str – Returns a string identifying this user when the setup was run first

aiscalator.core.log_regex_analyzer module¶

Class to parse output logs from subprocess and catch particular expressions

class aiscalator.core.log_regex_analyzer.LogRegexAnalyzer(pattern=None, log_level=10)[source]¶

Bases: object

A regular expression analyzer object to parse logs and extract values from patterns in the logs. …

Variables

_artifact (str) – Value of the pattern found in the logs
_pattern (bytes) – Regular expression to search for in the logs

Methods

`artifact`()	Returns the artifact extracted from the logs.
`grep_logs`(pipe)	Reads the logs and extract values defined by the pattern

artifact()[source]¶: Returns the artifact extracted from the logs.

grep_logs(pipe)[source]¶

Reads the logs and extract values defined by the pattern

Parameters: pipe – Stream of logs to analyze

aiscalator.core.utils module¶

Various Utility functions

class aiscalator.core.utils.BackgroundThreadRunner(command, log_function, no_redirect=False)[source]¶

Bases: object

Worker Thread to run logging output in the background

…

Variables

_process – Process object of the command running in the background
_log_function (function(stream -> bool)) – callback function to log the output of the command
_no_redirect (bool) – whether the subprocess STDOUT and STDERR should be redirected to logs
_worker (Thread) – Thread object

Methods

`process`()	Returns the process object.
`run`()	Starts the Thread, process the output of the process.

process()[source]¶: Returns the process object.

run()[source]¶: Starts the Thread, process the output of the process.

aiscalator.core.utils.check_notebook(logger, code_path, from_format='py:percent')[source]¶

Checks existence of notebook file and regenerates using jupytext from associated .py file if possible. Otherwise, create an empty notebook file.

Parameters

code_path (str) – path to the notebook to check
from_format (str) – jupytext format of the .py input file

aiscalator.core.utils.check_notebook_dir(logger, code_path, from_format='py:percent')[source]¶

Check a folder and generate all notebook files that might be required in that folder.

Parameters

code_path (str) – path to a file in the folder
from_format (str) – jupytext format of potential .py files

aiscalator.core.utils.copy_replace(src, dst, pattern=None, replace_value=None)[source]¶

Copies a file from src to dst replacing pattern by replace_value

Parameters

src (string) – Path to the source filename to copy from
dst (string) – Path to the output filename to copy to
pattern – list of Patterns to replace inside the src file
replace_value – list of Values to replace by in the dst file

aiscalator.core.utils.data_file(path)[source]¶

Utility function to find resources data file packaged along with code

Parameters: path (path) – path to the resource file in the package
Returns: absolute path to the resource data file

aiscalator.core.utils.find(collection, item, field='name')[source]¶

Finds an element in a collection which has a field equal to particular item value

Parameters

collection (Set) – Collection of objects
item – value of the item that we are looking for
field (string) – Name of the field from the object to inspect

Returns

object – Corresponding element that has a field matching item in the collection

aiscalator.core.utils.format_file_content(content, prefix='', suffix='')[source]¶

Reformat the content of a file line by line, adding prefix and suffix strings.

Parameters

content (str) – path to the file to reformat its content
prefix (str) – add to each line this prefix string
suffix (str) – add to each line this suffix string

Returns

str – Formatted content of the file

aiscalator.core.utils.log_info(pipe)[source]¶: Default logging function

aiscalator.core.utils.notebook_file(code_path, from_format='py:percent')[source]¶

Parse a path to return both the ipynb and py versions of the file.

Parameters

code_path (str) – path to a file
from_format (str) – jupytext format of potential .py files

Returns

(str, str) – tuple of 2 paths to ipynb and py files

aiscalator.core.utils.sha256(file: str)[source]¶: Reads a file content and returns its sha256 hash.

aiscalator.core.utils.subprocess_run(command, log_function=<function log_info>, no_redirect=False, wait=True)[source]¶

Run command in a subprocess while redirecting output to log_function.

The subprocess either runs synchroneoulsy or in the background depending on the wait parameter.

Parameters

command (List) – Command to run in the subprocess
log_function (function) – Callback function to log the output of the subprocess
no_redirect (bool) – whether the subprocess STDOUT and STDERR should be redirected to logs
wait (bool) – Whether the subprocess should be run synchroneously or in the background

Returns

int – return code of the subprocess
BackgroundThreadRunner – the thread running in the background

aiscalator.core.utils.wait_for_jupyter_lab(commands, logger, notebook, port, folder)[source]¶

Starts jupyter lab and wait for it to start, returning the url it’s running from.

Parameters

commands (list) – List of commands to run to start the process
logger (logging.Logger) – Logger object
notebook (str) – path to the notebook
port – port on which the jupyter lab is listening
folder (str) – path in the container to reach the notebook

Returns

str – url from which it is serving the jupyter lab

Module contents¶

aiscalator.jupyter package¶

Submodules¶

aiscalator.jupyter.command module¶

Implementations of commands for Jupyter

aiscalator.jupyter.command.jupyter_edit(conf: aiscalator.core.config.AiscalatorConfig, param=None, param_raw=None)[source]¶

Starts a Jupyter Lab environment configured to edit the focused step

Parameters

conf (AiscalatorConfig) – Configuration object for the step
param (list) – list of tuples of parameters
param_raw (list) – list of tuples of raw parameters

Returns

string – Url of the running jupyter lab

aiscalator.jupyter.command.jupyter_new(name, path, output_format='hocon')[source]¶

Starts a Jupyter Lab environment configured to edit a brand new step

Parameters

name (str) – name of the new step
path (str) – path to where the new step files should be created
output_format (str) – the format of the new configuration file to produce

Returns

string – Url of the running jupyter lab

aiscalator.jupyter.command.jupyter_run(conf: aiscalator.core.config.AiscalatorConfig, prepare_only=False, param=None, param_raw=None)[source]¶

Executes the step in browserless mode using papermill

Parameters

conf (AiscalatorConfig) – Configuration object for the step
prepare_only (bool) – Indicates if papermill should replace the parameters of the notebook only or it should execute all the cells too

Returns

string – the path to the output notebook resulting from the execution of this step

aiscalator.jupyter.cli module¶

CLI module for Jupyter related commands.

aiscalator.jupyter.cli.prompt_edit(file)[source]¶

When creating a new step, if it is already defined, ask to edit instead

Parameters: file (str) – existing configuration file

aiscalator.jupyter.cli.run_auto_update()[source]¶: Checks and tries to update Aiscalator itself from Pypi if necessary

aiscalator.jupyter.cli.update_aiscalator()[source]¶: Create and run Thread to execute auto update in the background

Module contents¶

Submodules¶

aiscalator.api module¶

API module of the AIscalator tool.

This module presents the entrypoint to use from a python script. It is intended to be used like this, usually from an airflow DAG scripts defining an AIscalator task:

from aiscalator import api

api.jupyter_run("path/to/config.conf", "step_name")

aiscalator.api.jupyter_run(config, notebook=None, prepare_only=False, param=None, param_raw=None)[source]¶

Executes the step in browserless mode using papermill

Parameters

config (str) – path to the configuration file
notebook (str) – name of node to run, if None, then run the first one
parameters (list) – List of parameters and their values
prepare_only (bool) – Indicates if papermill should replace the parameters of the notebook only or it should execute all the cells too

Returns

string – the path to the output notebook resulting from the execution of this step

aiscalator.cli module¶

Command Line Interface of the AIscalator tool.

Using the python click package (https://click.palletsprojects.com/en/7.x/), this module defines all the entry points to the application.

Module contents¶

Aiscalator Module: CLI to AIscalate

Contributing¶

Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given.

You can contribute in many ways:

Types of Contributions¶

Report Bugs¶

Report bugs at https://github.com/aiscalate/aiscalator/issues

If you are reporting a bug, please include:

aiscalator version:
Python version:
Operating System:

Description

Describe what you were trying to get done. Tell us what happened, what went wrong, and what you expected to happen.

What I Did:

Paste the command(s) you ran and the output.
If there was a crash, please include the traceback here.

Screenshots If applicable, add screenshots to help explain your problem.

Additional context Add any other context about the problem here.

Fix Bugs¶

Look through the GitHub issues for bugs. Anything tagged with “bug” and “help wanted” is open to whoever wants to implement it.

Implement Features¶

Look through the GitHub issues for features. Anything tagged with “enhancement” and “help wanted” is open to whoever wants to implement it.

Write Documentation¶

aiscalator could always use more documentation, whether as part of the official aiscalator docs, in docstrings, or even on the web in blog posts, articles, and such.

Submit Feedback¶

The best way to send feedback is to file an issue at https://github.com/aiscalate/aiscalator/issues.

If you are proposing a feature:

Explain in detail how it would work.
Describe alternatives you’ve considered
Keep the scope as narrow as possible, to make it easier to implement.
Remember that this is a volunteer-driven project, and that contributions are welcome :)

Get Started!¶

Ready to contribute? Here’s how to set up aiscalator for local development.

Fork the aiscalator repo on GitHub.

Clone your fork locally:

$ git clone git@github.com:your_name_here/aiscalator.git

Install your local copy into a virtualenv. Assuming you have virtualenvwrapper installed, this is how you set up your fork for local development:
```
$ mkvirtualenv aiscalator
$ cd aiscalator/
$ python setup.py develop
```
Create a branch for local development:
```
$ git checkout -b name-of-your-bugfix-or-feature
```
Now you can make your changes locally.
When you’re done making changes, check that your changes pass flake8 and the tests, including testing other Python versions with tox:
```
$ flake8 aiscalator tests
$ python setup.py test or py.test
$ tox
```
To get flake8 and tox, just pip install them into your virtualenv.

Commit your changes and push your branch to GitHub:

$ git add .
$ git commit -m "Your detailed description of your changes."
$ git push origin name-of-your-bugfix-or-feature

Submit a pull request through the GitHub website.

Pull Request Guidelines¶

Before you submit a pull request, check that it meets these guidelines:

The pull request should include tests.
If the pull request adds functionality, the docs should be updated. Put your new functionality into a function with a docstring, and add the feature to the list in FEATURES.rst.
The pull request should work for Python 3.4, 3.5 and 3.6, and for PyPy. Check https://travis-ci.org/Aiscalate/aiscalator/pull_requests and make sure that the tests pass for all supported Python versions.

Tips¶

To run a subset of tests:

$ py.test tests.test_aiscalator

Deploying¶

A reminder for the maintainers on how to deploy. Make sure all your changes are committed (including an entry in HISTORY.rst). Then run:

$ bumpversion patch # possible: major / minor / patch
$ git push
$ git push --tags

Travis will then deploy to PyPI if tests pass.

Code of Conduct¶

Like the technical community as a whole, the AIscalate team and community is made up of a mixture of professionals and volunteers from all over the world, working on every aspect of the mission - including mentorship, teaching, and connecting people.

Diversity is one of our huge strengths, but it can also lead to communication issues and unhappiness. To that end, we have a few ground rules that we ask people to adhere to. This code applies equally to founders, mentors and those seeking help and guidance.

This isn’t an exhaustive list of things that you can’t do. Rather, take it in the spirit in which it’s intended - a guide to make it easier to enrich all of us and the technical communities in which we participate.

This code of conduct applies to all spaces managed by the Read the Docs project. This includes IRC, the mailing lists, the issue tracker, and any other forums created by the project team which the community uses for communication. In addition, violations of this code outside these spaces may affect a person’s ability to participate within them.

If you believe someone is violating the code of conduct, we ask that you report it by emailing chris@aiscalate.com.

Be friendly and patient.
Be welcoming. We strive to be a community that welcomes and supports people of all backgrounds and identities. This includes, but is not limited to members of any race, ethnicity, culture, national origin, colour, immigration status, social and economic class, educational level, sex, sexual orientation, gender identity and expression, age, size, family status, political belief, religion, and mental and physical ability.
Be considerate. Your work will be used by other people, and you in turn will depend on the work of others. Any decision you take will affect users and colleagues, and you should take those consequences into account when making decisions. Remember that we’re a world-wide community, so you might not be communicating in someone else’s primary language.
Be respectful. Not all of us will agree all the time, but disagreement is no excuse for poor behavior and poor manners. We might all experience some frustration now and then, but we cannot allow that frustration to turn into a personal attack. It’s important to remember that a community where people feel uncomfortable or threatened is not a productive one. Members of the Read the Docs community should be respectful when dealing with other members as well as with people outside the Read the Docs community.
Be careful in the words that you choose. We are a community of professionals, and we conduct ourselves professionally. Be kind to others. Do not insult or put down other participants. Harassment and other exclusionary behavior aren’t acceptable. This includes, but is not limited to:
- Violent threats or language directed against another person.
- Discriminatory jokes and language.
- Posting sexually explicit or violent material.
- Posting (or threatening to post) other people’s personally identifying information (“doxing”).
- Personal insults, especially those using racist or sexist terms.
- Unwelcome sexual attention.
- Advocating for, or encouraging, any of the above behavior.
- Repeated harassment of others. In general, if someone asks you to stop, then stop.
When we disagree, try to understand why. Disagreements, both social and technical, happen all the time and Read the Docs is no exception. It is important that we resolve disagreements and differing views constructively. Remember that we’re different. The strength of Read the Docs comes from its varied community, people from a wide range of backgrounds. Different people have different perspectives on issues. Being unable to understand why someone holds a viewpoint doesn’t mean that they’re wrong. Don’t forget that it is human to err and blaming each other doesn’t get us anywhere. Instead, focus on helping to resolve issues and learning from mistakes.

Original text courtesy of the Speak Up! project http://web.archive.org/web/20141109123859/http://speakup.io/coc.html

This version was adopted from the Django Code of Conduct https://www.djangoproject.com/conduct

Credits¶

Development Lead¶

Christophe Duong <chris@aiscalate.com>

Contributors¶

None yet. Why not be the first?

Components¶

This software uses the following open source components:

Jupyter-Project notebook, lab, etc under BSD-3-Clause license
Jupytext under MIT license
Docker
Jupyter-Docker-Stacks under BSD-3-Clause license
Airflow under Apache-2 license
Docker-Airflow under Apache-2 license
Papermill under BSD-3-Clause license
This project was created with Cookiecutter under BSD-3-Clause license
with the audreyr/cookiecutter-pypackage project template.
with the ionelmc/cookiecutter-pylibrary project template
PyHOCON under Apache-2 license

History¶

0.1.0 (2018-11-07)¶

First Alpha release on PyPI.

0.1.11 (2019-04-26)¶

Added docker_image.docker_extra_options list feature

0.1.13 (2019-06-23)¶

Handle errors in Jupytext conversions

aiscalator run subcommand exit code propagated to cli

Concurrent aiscalator run commands is possible

Navigation

AIscalator’s documentation¶

AIscalator¶

Key Features¶

Quick Start¶

Installation¶

Start working¶

Jupyter¶

Airflow¶

Installation¶

Stable release¶

From sources¶

Development environment on Mac OS¶

Usage¶

Features¶

Docker docker_image Configuration¶

aiscalator¶

aiscalator package¶

Subpackages¶

aiscalator.airflow package¶

Submodules¶

aiscalator.airflow.command module¶

aiscalator.airflow.cli module¶

Module contents¶

aiscalator.core package¶

Submodules¶

aiscalator.core.config module¶

aiscalator.core.log_regex_analyzer module¶

aiscalator.core.utils module¶

Module contents¶

aiscalator.jupyter package¶

Submodules¶

aiscalator.jupyter.command module¶

aiscalator.jupyter.cli module¶

Module contents¶

Submodules¶

aiscalator.api module¶

aiscalator.cli module¶

Module contents¶

Contributing¶

Types of Contributions¶

Report Bugs¶

Fix Bugs¶

Implement Features¶

Write Documentation¶

Submit Feedback¶

Get Started!¶

Pull Request Guidelines¶

Tips¶

Deploying¶

Code of Conduct¶

Credits¶

Development Lead¶

Contributors¶

Components¶

History¶

0.1.0 (2018-11-07)¶

0.1.11 (2019-04-26)¶

0.1.13 (2019-06-23)¶

Indices and tables¶

Navigation