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https://github.com/jay-johnson/sci-pype

A Machine Learning API with native redis caching and export + import using S3. Analyze entire datasets using an API for building, training, testing, analyzing, extracting, importing, and archiving. This repository can run from a docker container or from the repository.
https://github.com/jay-johnson/sci-pype

data-science devops-for-data-science docker docker-compose ipython ipython-notebook jupyter jupyter-notebook jupyter-themes machine-learning machine-learning-api predictive python red10 redis s3 seaborn stock-price-prediction xgb xgboost

Last synced: 23 days ago
JSON representation

Host: GitHub
URL: https://github.com/jay-johnson/sci-pype
Owner: jay-johnson
License: apache-2.0
Created: 2016-08-01T05:18:25.000Z (over 8 years ago)
Default Branch: master
Last Pushed: 2022-12-07T23:39:54.000Z (almost 2 years ago)
Last Synced: 2024-09-28T23:40:56.301Z (about 1 month ago)
Topics: data-science, devops-for-data-science, docker, docker-compose, ipython, ipython-notebook, jupyter, jupyter-notebook, jupyter-themes, machine-learning, machine-learning-api, predictive, python, red10, redis, s3, seaborn, stock-price-prediction, xgb, xgboost
Language: Python
Homepage:
Size: 13.3 MB
Stars: 100
Watchers: 14
Forks: 21
Open Issues: 27
Metadata Files:
- Readme: README.rst
- License: LICENSE

Awesome Lists containing this project

README

=======================================================================
Sci-Pype - A Machine Learning Framework for Sharing Models and Analysis
=======================================================================

This is now deployed under the https://redten.io cloud service for analyzing datasets.

Sci-Pype is a framework for analyzing datasets using Python 2.7 and extended from the `Jupyter Scipy-Notebook`_ with a supported command line version (no docker or Jupyter required). It was built to make data analysis easier by providing an API to build, train, test, predict, validate, analyze, extract, archive, and import Models and Analysis datasets with: S3 and redis (Kafka coming soon). After building and training the requested Models with a dataset, they are cached in redis along with their respective Analysis. After they are cached, they can be extracted and shared using S3. From S3, the Models can be imported back into redis for making new predictions using the same API.

.. figure:: ./examples/images/v2/Scipype-Seaborn-Visuals.png
:alt: Sci-Pype - A Machine Learning Framework for Sharing Models and Analysis
:align: center

Analyzing the IRIS dataset with Sci-Pype

Common use cases for this framework are sharing Analysis notebooks and then automating new predictions with email delivery using AWS SES. With this native caching + deployment layer, you can build, train and use the supported Machine Learning Algorithms and Models across multiple environments (including multi-tenant ones). Once trained, you can extract the Models as a compressed, serialized ``Model file`` (like a build artifact) that is uploaded to S3. Importing a ``Model file`` decompresses the file and stores the Pickle-serialized Models + Analysis objects in redis. In production, it might be useful to house larger Models in something like a `load-balanced redis cluster`_ for sharing and making new predictions across a team or by automation.

Please note this is a `large docker container`_ so it may take some time to download and it extracts to ~8.1 GB on disk.

.. _Jupyter Scipy-Notebook: https://github.com/jupyter/docker-stacks/tree/master/scipy-notebook
.. _load-balanced redis cluster: https://github.com/jay-johnson/docker-redis-haproxy-cluster
.. _large docker container: https://hub.docker.com/r/jayjohnson/jupyter/tags/

Notebook Examples
=================

Please refer to the `examples directory`_ for the latest notebooks. Most of the notebooks and command line tools require running with a redis server listening on port 6000 (``/dev-start.sh`` will start one).

.. _examples directory: https://github.com/jay-johnson/sci-pype/tree/master/examples

#. `ML-IRIS-Analysis-Workflow-Classification.ipynb`_

Build a unique Machine Learning Classifier (parameterized XGB by default) for each column in the IRIS dataset. After training and testing the Models, perform a general analysis on each column and save + display images generated during each step. After running, the Models + Analysis are Pickled into a set of objects stored in a set of unique redis cache keys. These leaf nodes are organized into a set of redis keys contained in the ``manifest`` node for retrieval as needed in the future (like a tree of Machine Learning Algorithm Models with their associated pre-computed Analysis in memory).

#. `ML-IRIS-Analysis-Workflow-Regression.ipynb`_

Build a unique Machine Learning Regressor (parameterized XGB by default) for each column in the IRIS dataset. After training and testing the Models, perform a general analysis on each column and save + display images generated during each step. After running, the Models + Analysis are Pickled into a set of objects stored in a set of unique redis cache keys. These leaf nodes are organized into a set of redis keys contained in the ``manifest`` node for retrieval as needed in the future (like a tree of Machine Learning Algorithm Models with their associated pre-computed Analysis in memory).

#. `ML-IRIS-Extract-Models-From-Cache.ipynb`_

Extract all Models and Analysis records from redis and compile a large Pickle-serialized dictionary. Create a ``manifest`` for decoupling Model + Analysis nodes and compress the dictionary object (using zlib) and write it to disk as a ``Model file`` (``*.cache.pickle.zlib``). After creating the file on disk, upload it to the configured S3 Bucket and Key.

Once uploaded to the S3 Bucket you should be able to view, download and share the ``Model files``:

.. figure:: ./examples/images/scipype_s3_bucket_with_xgb_classifier_and_regressor_models_as_pickled_object_files.png

S3 Bucket containing the IRIS ``Model Files``

#. `ML-IRIS-Import-and-Cache-Models-From-S3.ipynb`_

Download the S3 IRIS ``Model file`` from the configured S3 Bucket + Key and decompress the previously-built Analysis and Models using Pickle to store them all in the redis cache according to the ``manifest``. This includes examples from the IRIS sample dataset and requires you to have a valid S3 Bucket storing the Models and are comfortable paying for the download costs to retrieve the ``Model file`` from S3 (https://aws.amazon.com/s3/pricing/).

#. `ML-IRIS-Predict-From-Cache-for-New-Predictions-and-Analysis-Classifier.ipynb`_

This notebook shows how to make new predictions with cached IRIS Classifier Models + Analysis housed in redis.

#. `ML-IRIS-Predict-From-Cache-for-New-Predictions-and-Analysis-Regressor.ipynb`_

This notebook shows how to make new predictions with cached IRIS Regressor Models + Analysis housed in redis.

Command Line Examples
=====================

Most of the notebooks and command line tools require running with a redis server listening on port 6000 (``/dev-start.sh`` will start one). The command line versions that do not require docker or Jupyter can be found:

::

├── bins
│   ├── demo-running-locally.py - Simple validate env is working test
│   ├── ml
│   │   ├── builders - Build and Train Models then Analyze Predictions without display any plotted images (automation examples)
│   │   │   ├── build-classifier-iris.py
│   │   │   ├── build-regressor-iris.py
│   │   │   ├── rl-build-regressor-iris.py
│   │   │   └── secure-rl-build-regressor-iris.py
│   │   ├── demo-ml-classifier-iris.py - Command line version of: ML-IRIS-Analysis-Workflow-Classification.ipynb
│   │   ├── demo-ml-regressor-iris.py - Command line version of: ML-IRIS-Analysis-Workflow-Regression.ipynb
│   │   ├── demo-rl-regressor-iris.py - Command line version of: ML-IRIS-Redis-Labs-Cache-XGB-Regressors.ipynb
│ │   ├── demo-secure-ml-regressor-iris.py - Demo with a Password-Required Redis Server running locally
│ │   ├── demo-secure-rl-regressor-iris.py - Demo with a Password-Required Redis Labs Cloud endpoint
│   │   ├── downloaders
│   │   │   ├── download_boston_house_prices.py
│   │   │   └── download_iris.py - Command line tool for downloading + preparing the IRIS dataset
│   │   ├── extractors
│   │   │   ├── extract_and_upload_iris_classifier.py - Command line version of: ML-IRIS-Extract-Models-From-Cache.ipynb (Classifier)
│   │   │   ├── extract_and_upload_iris_regressor.py - Command line version of: ML-IRIS-Extract-Models-From-Cache.ipynb (Regressor)
│   │   │   ├── rl_extract_and_upload_iris_regressor.py - Command line version of: ML-IRIS-Redis-Labs-Extract-From-Cache.ipynb
│   │   │   └── secure_rl_extract_and_upload_iris_regressor.py - Command line version with a password for: ML-IRIS-Redis-Labs-Extract-From-Cache.ipynb
│   │   ├── importers
│   │   │   ├── import_iris_classifier.py - ML-IRIS-Import-and-Cache-Models-From-S3.ipynb (Classifier)
│   │   │   ├── import_iris_regressor.py - ML-IRIS-Import-and-Cache-Models-From-S3.ipynb (Regressor)
│   │   │   ├── rl_import_iris_regressor.py - Command line version of: ML-IRIS-Redis-Labs-Import-From-S3.ipynb
│   │   │   └── secure_rl_import_iris_regressor.py - Command line version with a password for: ML-IRIS-Redis-Labs-Import-From-S3.ipynb
│   │   └── predictors
│   │   ├── predict-from-cache-iris-classifier.py - ML-IRIS-Predict-From-Cache-for-New-Predictions-and-Analysis-Classifier.ipynb (Classifier)
│   │   ├── predict-from-cache-iris-regressor.py - ML-IRIS-Predict-From-Cache-for-New-Predictions-and-Analysis-Regressor.ipynb (Regressor)
│   │   ├── rl-predict-from-cache-iris-regressor.py - Command line version of: ML-IRIS-Redis-Labs-Predict-From-Cached-XGB.ipynb
│   │   └── secure-rl-predict-from-cache-iris-regressor.py - Command line version with a password for: ML-IRIS-Redis-Labs-Predict-From-Cached-XGB.ipynb

Now you can share, test, and deploy Models and their respective Analysis from a file in S3 for other Sci-Pype users running on different environments.

.. _ML-IRIS-Analysis-Workflow-Classification.ipynb: ./examples/ML-IRIS-Analysis-Workflow-Classification.ipynb
.. _ML-IRIS-Analysis-Workflow-Regression.ipynb: ./examples/ML-IRIS-Analysis-Workflow-Regression.ipynb
.. _ML-IRIS-Extract-Models-From-Cache.ipynb: ./examples/ML-IRIS-Extract-Models-From-Cache.ipynb
.. _ML-IRIS-Import-and-Cache-Models-From-S3.ipynb: ./examples/ML-IRIS-Import-and-Cache-Models-From-S3.ipynb
.. _ML-IRIS-Predict-From-Cache-for-New-Predictions-and-Analysis-Classifier.ipynb: ./examples/ML-IRIS-Predict-From-Cache-for-New-Predictions-and-Analysis-Classifier.ipynb
.. _ML-IRIS-Predict-From-Cache-for-New-Predictions-and-Analysis-Regressor.ipynb: ./examples/ML-IRIS-Predict-From-Cache-for-New-Predictions-and-Analysis-Regressor.ipynb

Overview
========

The docker container runs a Jupyter web application. The web application runs `Jupyter Notebooks`_ as kernels. For now the examples and core included in this repository will only work with Python 2.

.. _Jupyter Notebooks: http://jupyter-notebook.readthedocs.io/en/latest/

This container can run in four modes:

#. Default development

This mode will mount your changes from the repository into the container at runtime for local testing.

To start the local development version run: dev-start.sh_

./dev-start.sh

You can login to the container with: ``./ssh.sh``

.. _dev-start.sh: https://github.com/jay-johnson/sci-pype/blob/s3_and_machine_learning_api/dev-start.sh

#. Docker Run Single Container

To start the local development version run: start.sh_

./start.sh

You can login to the container with: ``./ssh.sh``

.. _start.sh: https://github.com/jay-johnson/sci-pype/blob/master/start.sh

#. Full Stack

To start the full stack mode run: compose-start-full.sh_

::

./compose-start-full.sh

The full-stack-compose.yml_ will deploy three docker containers using docker compose:

- **MySQL Database container with phpMyAdmin** for Stock Data (from the `schemaprototyping repo`_)
- **Jupyter**
- **Redis server** (`jayjohnson/redis-single-node`_) on port 6000

.. _compose-start-full.sh: https://github.com/jay-johnson/sci-pype/blob/master/compose-start-full.sh
.. _full-stack-compose.yml: https://github.com/jay-johnson/sci-pype/blob/master/full-stack-compose.yml
.. _schemaprototyping repo: https://github.com/jay-johnson/docker-schema-prototyping-with-mysql
.. _jayjohnson/redis-single-node: https://hub.docker.com/r/jayjohnson/redis-single-node

#. Standalone Testing

To start the full stack mode run: compose-start-jupyter.sh_

./compose-start-jupyter.sh

The jupyter-docker-compose.yml_ is used to deploy a single Jupyter container.

.. _compose-start-jupyter.sh: https://github.com/jay-johnson/sci-pype/blob/master/compose-start-jupyter.sh
.. _jupyter-docker-compose.yml: https://github.com/jay-johnson/sci-pype/blob/master/jupyter-docker-compose.yml

Running Locally without Docker
==============================

Here is how to run locally without using docker (and Lambda deployments in the future).

#. Clone the repo without the dash character in the name

$ git clone [email protected]:jay-johnson/sci-pype.git scipype

#. Go to the base dir of the repository

dev$ cd scipype

#. Set up a local virtual environment using the installer

This will take some time and may fail due to missing packages on your host. Please refer to the `Coming Soon and Known Issues`_ section for help getting passed these issues.

::

scipype$ ./setup-new-dev.sh

After this finishes you should see the lines:

---------------------------------------------------------
Activate the new Scipype virtualenv with:

source ./dev-properties.sh"
or:
source ./properties.sh

#. Activate the ``scipype`` virtual environment for development:

$ source ./dev-properties.sh

#. Confirm your virtual environment is ready for use

(scipype) scipype$ pip list --format=columns | grep -E -i "tensorflow|pandas|redis|kafka|xgboost|scipy|scikit"
confluent-kafka 0.9.2
kafka-python 1.3.1
pandas 0.19.2
pandas-datareader 0.2.2
pandas-ml 0.4.0
redis 2.10.5
scikit-image 0.12.3
scikit-learn 0.18.1
scikit-neuralnetwork 0.7
scipy 0.18.1
tensorflow 0.12.0
xgboost 0.6a2
(scipype) scipype$

#. Setup the /opt/work symlink

When running outside docker, I find it easiest to just symlink the repo's base dir to ``/opt/work`` to emulate the container's internal directory deployment structure. In a future release, a local-properties.sh file will set all the environment variables relative to the repository, but for now this works.

::

scipype$ ln -s $(pwd) /opt/work

#. Confirm the symlink is setup

scipype$ ll /opt/work
lrwxrwxrwx 1 driver driver 32 Mar 6 22:38 /opt/work -> /home/driver/dev/scipype/
scipype$

#. If you want to always use this virtual environment add this to your ``~/.bashrc``

echo 'source /opt/venv/scipype/bin/activate' >> ~/.bashrc

#. Confirm the Demo downloader works using the Virtual Environment

Please note: this assumes running from a new terminal to validate the virtual environment activation

Activate it

scipype$ source ./dev-properties.sh

Run the Demo

(scipype) scipype$ ./bins/demo-running-locally.py
Downloading(SPY) Dates[Jan, 02 2016 - Jan, 02 2017]
Storing CSV File(/opt/scipype/data/src/spy.csv)
Done Downloading CSV for Ticker(SPY)
Success File exists: /opt/scipype/data/src/spy.csv

Deactivate it

(scipype) scipype$ deactivate
scipype$

#. If you want to automatically load the full Scipype environment ``properties.sh`` for any new shell terminal add this to your user's ``~/.bashrc``

echo 'source /opt/work/properties.sh' >> ~/.bashrc

.. _Coming Soon and Known Issues: https://github.com/jay-johnson/sci-pype/blob/master/README.rst#coming-soon-and-known-issues

Authenticated Redis Examples
============================

You can lock redis down with a password by setting it in the redis.conf before starting the redis server (https://redis.io/topics/security#authentication-feature). Here is how to use the machine learning API with a password-locked Redis Labs endpoint or a local one.

Environment Variables
---------------------

If you are running sci-pype in a docker container it will load the following env vars to ensure the redis application system's clients are setup with the password and database:

# Redis Password where Empty = No Password like:
# ENV_REDIS_PASSWORD=
ENV_REDIS_PASSWORD=2603648a854c4f3ba7c93e8449319380
ENV_REDIS_DB_ID=0

You can run without a password by either not defining the ``ENV_REDIS_PASSWORD`` environment variable or `making it set to an empty string`_.

.. _making it set to an empty string: https://github.com/jay-johnson/sci-pype/blob/d8c750a25d7c46f34d86e8ff833580842b607366/src/connectors/redis/base_redis_application.py#L18-L21

Using a Password-locked Redis Labs Cloud endpoint
-------------------------------------------------

#. Run the Secure Redis Labs Cloud Demo

bins/ml$ ./demo-secure-rl-regressor-iris.py

#. Connect to the Redis Labs Cloud endpoint

After running it you can verify the models were stored on the secured endpoint:

bins/ml$ redis-cli -h pub-redis-12515.us-west-2-1.1.ec2.garantiadata.com -p 12515

#. Verify the server is enforcing the password

pub-redis-12515.us-west-2-1.1.ec2.garantiadata.com:12515> KEYS *
(error) NOAUTH Authentication required

#. Authenticate with the password

pub-redis-12515.us-west-2-1.1.ec2.garantiadata.com:12515> auth 2603648a854c4f3ba7c93e8449319380
OK

#. View the redis keys

pub-redis-12515.us-west-2-1.1.ec2.garantiadata.com:12515> KEYS *
1) "_MD_IRIS_REGRESSOR_PetalWidth"
2) "_MD_IRIS_REGRESSOR_PredictionsDF"
3) "_MD_IRIS_REGRESSOR_SepalWidth"
4) "_MODELS_IRIS_REGRESSOR_LATEST"
5) "_MD_IRIS_REGRESSOR_ResultTargetValue"
6) "_MD_IRIS_REGRESSOR_Accuracy"
7) "_MD_IRIS_REGRESSOR_PetalLength"
8) "_MD_IRIS_REGRESSOR_SepalLength"
pub-redis-12515.us-west-2-1.1.ec2.garantiadata.com:12515> exit
bins/ml$

Local
-----

#. You can run a password-locked, standalone redis server with docker compose using this script:

https://github.com/jay-johnson/sci-pype/blob/master/bins/redis/auth-start.sh

#. Once the redis server is started you can run the local secure demo with the script:

bins/ml$ ./demo-secure-ml-regressor-iris.py

#. After the demo finishes you can authenticate with the local redis server and view the cached models:

bins/ml$ redis-cli -p 6400
127.0.0.1:6400> KEYS *
(error) NOAUTH Authentication required.
127.0.0.1:6400> AUTH 2603648a854c4f3ba7c93e8449319380
OK
127.0.0.1:6400> KEYS *
1) "_MD_IRIS_REGRESSOR_PetalWidth"
2) "_MD_IRIS_REGRESSOR_PetalLength"
3) "_MD_IRIS_REGRESSOR_PredictionsDF"
4) "_MD_IRIS_REGRESSOR_SepalWidth"
5) "_MODELS_IRIS_REGRESSOR_LATEST"
6) "_MD_IRIS_REGRESSOR_Accuracy"
7) "_MD_IRIS_REGRESSOR_ResultTargetValue"
8) "_MD_IRIS_REGRESSOR_SepalLength"
127.0.0.1:6400> exit
bins/ml$

#. If you want to stop the redis server run:

https://github.com/jay-johnson/sci-pype/blob/master/bins/redis/stop.sh

Previous Examples
=================

Version 1 Examples
-------------------

#. `example-core-demo.ipynb`_

How to use the python core from a Jupyter notebook. It also shows how to debug the JSON application configs which are used to connect to external database(s) and redis server(s).

.. figure:: https://jaypjohnson.com/_images/image_2016-08-01_core-integration.png

#. `example-spy-downloader.ipynb`_

**Jupyter + Downloading the SPY Pricing Data**

Download the SPY ETF Pricing Data from Google Finance and store it in the shared ``ENV_PYTHON_SRC_DIR`` directory that is mounted from the host and into the Jupyter container. It uses a script that `downloads the SPY daily pricing data as a csv file`_.

.. figure:: https://jaypjohnson.com/_images/image_2016-08-01_download-spy-pricing-data.png

#. `example-plot-stock-data.ipynb`_

**Download SPY and use Pandas + Matlab to Plot Pricing by the Close**

This shows how to download the SPY daily prices from Google Finance as a csv then load it using Pandas for plotting on the Close prices with Matlab.

.. figure:: https://jaypjohnson.com/_images/image_2016-08-01_plot-spy-by-close-prices.png

#. `example-redis-cache-demo.ipynb`_

**Building a Jupyter + Redis Data Pipeline**

This extends the previous SPY pricing demo and publishes + retreives the pricing data by using a targeted ``CACHE`` redis server (that runs inside the Jupyter container). It stores the Pandas dataframe as JSON in the ``LATEST_SPY_DAILY_STICKS`` redis key.

.. figure:: https://jaypjohnson.com/_images/image_2016-08-01_redis-data-pipeline-with-spy-prices.png

#. `example-db-extract-and-cache.ipynb`_

**Building a Jupyter + MySQL + Redis Data Pipeline**

This requires running the **Full Stack** which uses the https://github.com/jay-johnson/sci-pype/blob/master/full-stack-compose.yml to deploy three docker containers on the same host:

- MySQL (https://hub.docker.com/r/jayjohnson/schemaprototyping/)
- Jupyter (https://hub.docker.com/r/jayjohnson/jupyter/)
- Redis (https://hub.docker.com/r/jayjohnson/redis-single-node/)

**How it works**

.. figure:: https://jaypjohnson.com/_images/image_2016-08-01_using-jupyter-for-stock-analysis.png

1. Extract the IBM stock data from the MySQL dataset and store it as a csv inside the **/opt/work/data/src/ibm.csv** file
2. Load the IBM pricing data with Pandas
3. Plot the pricing data with Matlab
4. Publish the Pandas Dataframe as JSON to Redis
5. Retrieve the Pandas Dataframe from Redis
6. Test the cached pricing data exists outside the Jupyter container with:

$ ./redis.sh
SSH-ing into Docker image(redis-server)
[root@redis-server container]# redis-cli -h localhost -p 6000
localhost:6000> LRANGE LATEST_IBM_DAILY_STICKS 0 0
1) "(dp0\nS'Data'\np1\nS'{\"Date\":{\"49\":971136000000,\"48\":971049600000,\"47\":970790400000,\"46\":970704000000,\"45\":970617600000,\"44\":970531200000,\"43\":970444800000,\"42\":970185600000,\"41\":970099200000,\"40\":970012800000,\"39\":969926400000,\"38\":969

... removed for docs ...

localhost:6000> exit
[root@redis-server container]# exit
exit
$

#. `example-slack-debugging.ipynb`_

**Jupyter + Slack Driven Development**

This example shows how environment variables allow the python core to publish a message into Slack to notify the associated user with a message containing the line number and source code that threw the exception.

.. figure:: https://jaypjohnson.com/_images/image_2016-08-01_slack-debugging.png

.. _example-core-demo.ipynb: https://github.com/jay-johnson/sci-pype/blob/master/examples/example-core-demo.ipynb
.. _example-slack-debugging.ipynb: https://github.com/jay-johnson/sci-pype/blob/master/examples/example-slack-debugging.ipynb
.. _example-spy-downloader.ipynb: https://github.com/jay-johnson/sci-pype/blob/master/examples/example-spy-downloader.ipynb
.. _example-plot-stock-data.ipynb: https://github.com/jay-johnson/sci-pype/blob/master/examples/example-plot-stock-data.ipynb
.. _example-redis-cache-demo.ipynb: https://github.com/jay-johnson/sci-pype/blob/master/examples/example-redis-cache-demo.ipynb
.. _example-db-extract-and-cache.ipynb: https://github.com/jay-johnson/sci-pype/blob/master/examples/example-db-extract-and-cache.ipynb
.. _downloads the SPY daily pricing data as a csv file: https://github.com/jay-johnson/sci-pype/blob/master/bins/download-spy-csv.py

Components
==========

#. Python 2 Core

The PyCore_ uses a `JSON config file`_ for connecting to `redis servers`_ and `configurable databases`_ (MySQL and Postgres) using SQLAlchemy. It has only been tested with the Python 2.7 kernel.

.. _PyCore: https://github.com/jay-johnson/sci-pype/blob/master/src/pycore.py
.. _JSON config file: https://github.com/jay-johnson/sci-pype/blob/master/configs/jupyter.json
.. _redis servers: https://github.com/jay-johnson/sci-pype/blob/master/configs/redis.json
.. _configurable databases: https://github.com/jay-johnson/sci-pype/blob/master/configs/db.json

#. Local Redis Server

When starting the container with ``ENV_DEPLOYMENT_TYPE`` set to anything not ``JustDB``, the container will start a local redis server inside the container on port ``6000`` for iterating on your pipeline analysis, Model deployment and caching strategies.

#. Loading Database and Redis Applications

By default the `jupyter.json`_ config supports multiple environments for integrating notebooks with external resources. Here is table on what they define:

Inside a notebook you can target a different environment before loading the core with:

- Changing to the **JustRedis** Environment:

::

import os
os.environ["ENV_DEPLOYMENT_TYPE"] = "JustRedis"
core = PyCore()

- Changing to the **NoApps** Environment:

::

import os
os.environ["ENV_DEPLOYMENT_TYPE"] = "NoApps"
core = PyCore()

.. _jupyter.json: https://github.com/jay-johnson/sci-pype/blob/master/configs/jupyter.json
.. _local-redis.json: https://github.com/jay-johnson/sci-pype/blob/master/configs/local-redis.json
.. _redis.json: https://github.com/jay-johnson/sci-pype/blob/master/configs/redis.json
.. _db.json: https://github.com/jay-johnson/sci-pype/blob/master/configs/db.json
.. _empty-redis.json: https://github.com/jay-johnson/sci-pype/blob/master/configs/empty-redis.json
.. _empty-db.json: https://github.com/jay-johnson/sci-pype/blob/master/configs/empty-db.json

#. Customize the Jupyter Container Lifecycle

The following environment variables can be used for defining pre-start, start, and post-start Jupyter actions as needed.

+---------------------------+-----------------------------------------------+--------------------------------------------+
| Environment Variable | Default Value | Purpose |
+===========================+===============================================+============================================+
| **ENV_PRESTART_SCRIPT** | `/opt/containerfiles/pre-start-notebook.sh`_ | Run custom actions before starting Jupyter |
+---------------------------+-----------------------------------------------+--------------------------------------------+
| **ENV_START_SCRIPT** | `/opt/containerfiles/start-notebook.sh`_ | Start Jupyter |
+---------------------------+-----------------------------------------------+--------------------------------------------+
| **ENV_POSTSTART_SCRIPT** | `/opt/containerfiles/post-start-notebook.sh`_ | Run custom actions after starting Jupyter |
+---------------------------+-----------------------------------------------+--------------------------------------------+

.. _/opt/containerfiles/pre-start-notebook.sh: https://github.com/jay-johnson/sci-pype/blob/master/containerfiles/pre-start-notebook.sh
.. _/opt/containerfiles/start-notebook.sh: https://github.com/jay-johnson/sci-pype/blob/master/containerfiles/start-notebook.sh
.. _/opt/containerfiles/post-start-notebook.sh: https://github.com/jay-johnson/sci-pype/blob/master/containerfiles/post-start-notebook.sh

#. Slack Debugging

The core supports publishing exceptions into Slack based off the environment variables passed in using docker or docker compose.

#. Tracking Installed Dependencies for Notebook Sharing

This docker container uses these files for tracking Python 2 and Python 3 pips:

- /opt/work/pips/python2-requirements.txt

- /opt/work/pips/python3-requirements.txt

#. Shared Volumes

These are the mounted volumes and directories that can be changed as needed. Also the core uses them as environment variables.

+-------------------------------+---------------------------+-----------------------------------------------------------+
| Host Mount | Container Mount | Purpose |
+===============================+===========================+===========================================================+
| **/opt/project** | /opt/project | Sharing a project from the host machine |
+-------------------------------+---------------------------+-----------------------------------------------------------+
| **/opt/work/data** | /opt/work/data | Sharing a common data dir between host and containers |
+-------------------------------+---------------------------+-----------------------------------------------------------+
| **/opt/work/data/src** | /opt/work/data/src | Passing data source files into the container |
+-------------------------------+---------------------------+-----------------------------------------------------------+
| **/opt/work/data/dst** | /opt/work/data/dst | Passing processed data files outside the container |
+-------------------------------+---------------------------+-----------------------------------------------------------+
| **/opt/work/data/bin** | /opt/work/data/bin | Exchanging data binaries from the host into the container |
+-------------------------------+---------------------------+-----------------------------------------------------------+
| **/opt/work/data/synthesize** | /opt/work/data/synthesize | Sharing files used for synthesizing data |
+-------------------------------+---------------------------+-----------------------------------------------------------+
| **/opt/work/data/tidy** | /opt/work/data/tidy | Sharing files used to tidy and marshall data |
+-------------------------------+---------------------------+-----------------------------------------------------------+
| **/opt/work/data/analyze** | /opt/work/data/analyze | Sharing files used for data analysis and processing |
+-------------------------------+---------------------------+-----------------------------------------------------------+
| **/opt/work/data/output** | /opt/work/data/output | Sharing processed files and analyzed output |
+-------------------------------+---------------------------+-----------------------------------------------------------+

Getting Started
===============

Local Jupyter
-------------

#. Start the Container in Local development mode

$ ./start.sh
Starting new Docker image(docker.io/jayjohnson/jupyter)
4275447ef6a3aa06fb06097837deeb202bd80b15969a9c1269a5ee042d8df13d
$

#. Browse to the local Jupyter website

http://localhost:82/

Full Stack
----------

The full-stack-compose.yml_ patches the Jupyter and redis containers to ensure the MySQL database is listening on port 3306 before starting. It does this by defining a custom `entrypoint`_ wrapper for each in the `wait-for-its tools`_ directory.

.. _full-stack-compose.yml: https://github.com/jay-johnson/sci-pype/blob/master/full-stack-compose.yml
.. _entrypoint: https://github.com/jay-johnson/sci-pype/blob/master/full-stack-compose.yml#L25
.. _wait-for-its tools: https://github.com/jay-johnson/sci-pype/tree/master/wait-for-its

#. Start the Composition

This can take around 20 seconds for MySQL to set up the seed pricing records, and it requires assigning the shared data directory permissions for read/write access from inside the Jupyter container.

$ ./compose-start-full.sh
Before starting changing permissions with:
chown -R driver:users /opt/work/data/*
[sudo] password for driver:
Starting Composition: full-stack-compose.yml
Starting stocksdb
Starting jupyter
Starting redis-server
Done
$

#. Check the Composition

$ docker ps
CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
1fd9bd22987f jayjohnson/redis-single-node:1.0.0 "/wait-for-its/redis-" 12 minutes ago Up 25 seconds 0.0.0.0:6000->6000/tcp redis-server
2bcb6b8d2994 jayjohnson/jupyter:1.0.0 "/wait-for-its/jupyte" 12 minutes ago Up 25 seconds 0.0.0.0:8888->8888/tcp jupyter
b7bce846b9af jayjohnson/schemaprototyping:1.0.0 "/root/start_containe" 25 minutes ago Up 25 seconds 0.0.0.0:81->80/tcp, 0.0.0.0:3307->3306/tcp stocksdb
$

- Optional - Login to the database container

$ ./db.ssh
SSH-ing into Docker image(stocksdb)
[root@stocksdb db-loaders]# ps auwwx | grep mysql | grep -v grep
root 28 0.0 0.0 11648 2752 ? S 17:00 0:00 /bin/sh /usr/bin/mysqld_safe
mysql 656 1.3 12.0 1279736 474276 ? Sl 17:00 0:01 /usr/sbin/mysqld --basedir=/usr --datadir=/var/lib/mysql --plugin-dir=/usr/lib64/mysql/plugin --user=mysql --log-error=/var/log/mysql/error.log --pid-file=/var/lib/mysql/mysqld.pid --socket=/var/lib/mysql/mysqld.sock --port=3306
[root@stocksdb db-loaders]# exit

View the Stocks Database with phpMyAdmin: http://localhost:81/phpmyadmin/sql.php?db=stocks&table=stocks

.. note:: By default the login to this sample db is: ``dbadmin`` / ``dbadmin123`` which can be configured in the db.env_

.. _db.env: https://github.com/jay-johnson/sci-pype/blob/master/test/db.env#L6-L7

- Optional - Login to the Redis container

::

$ ./redis.sh
SSH-ing into Docker image(redis-server)
[root@redis-server container]# ps auwwx | grep redis
root 1 0.0 0.0 11644 2616 ? Ss 17:00 0:00 bash /wait-for-its/redis-wait-for-it.sh
root 28 0.0 0.2 114800 11208 ? Ss 17:00 0:00 /usr/bin/python /usr/bin/supervisord -c /etc/supervisor.d/rediscluster.ini
root 30 0.3 0.0 37268 3720 ? Sl 17:00 0:00 redis-server *:6000
root 47 0.0 0.0 9044 892 ? S+ 17:02 0:00 grep --color=auto redis
[root@redis-server container]# exit

- Optional - Login to the Jupyter container

$ ./ssh.sh
SSH-ing into Docker image(jupyter)
jovyan:/opt/work$ ps auwwx | grep jupyter
jovyan 1 0.0 0.0 13244 2908 ? Ss 17:00 0:00 bash /wait-for-its/jupyter-wait-for-it.sh
jovyan 38 0.3 1.2 180564 48068 ? S 17:00 0:00 /opt/conda/bin/python /opt/conda/bin/jupyter-notebook
jovyan:/opt/work$ exit

#. Run the Database Extraction Jupyter Demo

Open the notebook with this url: http://localhost:82/notebooks/examples/example-db-extract-and-cache.ipynb

#. Click the **Run** Button

This example will connect to the ``stocksdb`` MySQL container and pull 50 records from IBM's pricing data. It will then render plot lines for Open, Close, High, and Low using Pandas and Matlab. Next it will cache the IBM records in the ``redis-server`` container and then verify those records were cached correctly by retrieving it again.

#. From outside the Jupyter container confirm the redis key holds the processed IBM data

::

... removed for docs ...

localhost:6000> exit
[root@redis-server container]# exit
exit
$

#. Stop the Composition

::

$ ./compose-stop-full.sh
Stopping Composition: full-stack-compose.yml
Stopping redis-server ... done
Stopping jupyter ... done
Stopping stocksdb ... done
Done
$

Standalone
----------

#. Start Standalone

Start the standalone Jupyter container using the `jupyter-docker-compose.yml`_ file. This compose file requires access to ``/opt/work/data`` host directory like the Full Stack version for sharing files between the container and the host.

$ ./compose-start-jupyter.sh
Before starting changing permissions with:
chown -R driver:users /opt/work/data/*
[sudo] password for driver:
Starting Composition: jupyter-docker-compose.yml
Starting jupyter
Done
$

#. Stop Standalone

Stop the standalone Jupyter composition with:

$ ./compose-stop-jupyter.sh
Stopping Composition: jupyter-docker-compose.yml
Stopping jupyter ... done
Done
$

Deleting the Containers
-----------------------

Remove the containers with the command:

::

$ docker rm jupyter redis-server stocksdb
jupyter
redis-server
stocksdb
$

Delete them from the host with:

$ docker rmi jayjohnson/schemaprototyping
$ docker rmi jayjohnson/jupyter
$ docker rmi jayjohnson/redis-single-node

Sharing between the Host and the Jupyter Container
--------------------------------------------------

By default, the host will have this directory structure available for passing files in and out of the container:

$ tree /opt/work
/opt/work
└── data
├── analyze
├── bin
├── dst
├── output
├── src
│ └── spy.csv
├── synthesize
└── tidy

8 directories, 1 file

From inside the container here is where the directories are mapped:

$ ./ssh.sh
SSH-ing into Docker image(jupyter)
driver:/opt/work$ tree data/
data/
├── analyze
├── bin
├── dst
├── output
├── src
│ └── spy.csv
├── synthesize
└── tidy

7 directories, 1 file

Coming Soon and Known Issues
============================

#. Missing xattr.h

If you see this error:

xattr.c:29:24: fatal error: attr/xattr.h: No such file or directory

Install RPM:

::

sudo yum install -y libattr-devel

Install Deb:

sudo apt-get install -y libattr1-dev

Retry the install

#. Local Install Confluent:

If you're trying to setup the local development environment and missing the kafka headers:

In file included from confluent_kafka/src/confluent_kafka.c:17:0:
confluent_kafka/src/confluent_kafka.h:21:32: fatal error: librdkafka/rdkafka.h: No such file or directory
#include

Please install Kafka by adding their repository and then installing:

::

$ sudo yum install confluent-platform-oss-2.11
$ sudo yum install librdkafka1 librdkafka-devel

Official RPM Guide: http://docs.confluent.io/3.1.1/installation.html#rpm-packages-via-yum

Official DEB Guide: http://docs.confluent.io/3.1.1/installation.html#deb-packages-via-apt

For ``Fedora 24/RHEL 7/CentOS 7`` users here's a tool to help:

::

scipype/python2$ sudo ./install_confluent_platform.sh

#. Install PyQt4 for ``ImportError: No module named PyQt4`` errors:

(python2) jovyan:/opt/work/bins$ conda install -y pyqt=4.11
Fetching package metadata .........
Solving package specifications: ..........

Package plan for installation in environment /opt/conda/envs/python2:

The following packages will be downloaded:

package | build
---------------------------|-----------------
qt-4.8.7 | 3 31.3 MB conda-forge
pyqt-4.11.4 | py27_2 3.5 MB conda-forge
------------------------------------------------------------
Total: 34.8 MB

The following NEW packages will be INSTALLED:

pyqt: 4.11.4-py27_2 conda-forge
qt: 4.8.7-3 conda-forge (copy)

Pruning fetched packages from the cache ...
Fetching packages ...
qt-4.8.7-3.tar 100% |##########################################################################################################################################| Time: 0:00:06 5.23 MB/s
pyqt-4.11.4-py 100% |##########################################################################################################################################| Time: 0:00:02 1.28 MB/s
Extracting packages ...
[ COMPLETE ]|#############################################################################################################################################################| 100%
Linking packages ...
[ COMPLETE ]|#############################################################################################################################################################| 100%

Now try running a script from the shell:

(python2) jovyan:/opt/work/bins$ ./download-spy-csv.py
Downloading(SPY) Dates[Jan, 02 2016 - Jan, 02 2017]
Storing CSV File(/opt/work/data/src/spy.csv)
Done Downloading CSV for Ticker(SPY)
Success File exists: /opt/work/data/src/spy.csv
(python2) jovyan:/opt/work/bins$

#. How to build a customized Python Core mounted from outside the Jupyter container

#. Fixing the docker compose networking so the stocksdb container does not need to know the compose-generated docker network.

Right now it is defining the `sci-pype_datapype`_ as the expected docker network. This may not work on older versions of docker.

.. _sci-pype_datapype: https://github.com/jay-johnson/sci-pype/blob/master/test/db.env#L1

#. Building Jupyter containers that are smaller and only run one kernel to reduce the overall size of the image

#. Testing on an older docker version

This was tested with ``1.12.1``

$ docker -v
Docker version 1.12.1, build 23cf638
$

#. Setting up the Jupyter wait-for-it to ensure the stocks database is loaded before starting...not just the port is up

For now just shutdown the notebook kernel if you see an error related to the stocks database not being there when running the full stack.

Coming Soon
===========

- Examples for using the Timestamp Forecasting API

- Post Processing Event API support

- Confluent Kafka integration notebooks (the python client is already installed in the virtual env and docker container https://github.com/confluentinc/confluent-kafka-python)

- PySpark integration notebooks

- Tensorflow integration notebooks

- Adding more support and optional third-party mounting for customized Machine Learning Algorithms (like https://github.com/pandas-ml/pandas-ml)

- Odo (http://odo.readthedocs.io/en/latest/) examples

- Lambda deployment integration (http://docs.aws.amazon.com/lambda/latest/dg/lambda-python-how-to-create-deployment-package.html) and likely FPM (https://github.com/jordansissel/fpm) for package building.

License
=======

This project is not related to SciPy.org or the scipy library. It was originally built for exchanging and loading datasets using Redis for creating near-realtime data pipelines for streaming analysis (like a scientific pypeline).

This repo is Apache 2.0 License: https://github.com/jay-johnson/sci-pype/blob/master/LICENSE

Jupyter - BSD: https://github.com/jupyter/jupyter/blob/master/LICENSE

Please refer to the Conda Licenses for individual Python libraries: https://docs.continuum.io/anaconda/pkg-docs

Redis - https://redis.io/topics/license

zlib - https://opensource.org/licenses/zlib-license.php