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.

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.

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 (<repo base dir>/dev-start.sh will start one).

1. 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).

2. 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).

3. 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:

   S3 Bucket containing the IRIS Model Files

4. 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/).

5. 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.

6. 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 (<repo base dir>/dev-start.sh will start one). The command line versions that do not require docker or Jupyter can be found:

<repo base dir>
├── 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.

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.

This container can run in four modes:

1. 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

2. Docker Run Single Container

   To start the local development version run: start.sh

   ./start.sh

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

3. 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

4. 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.

Running Locally without Docker

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

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

   $ git clone git@github.com:jay-johnson/sci-pype.git scipype

2. Go to the base dir of the repository

   dev$ cd scipype

3. 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

4. Activate the scipype virtual environment for development:

   $ source ./dev-properties.sh

5. 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$

6. 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

7. 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$

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

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

9. 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$

10. 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

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.

Using a Password-locked Redis Labs Cloud endpoint

1. Run the Secure Redis Labs Cloud Demo

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

2. 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

3. Verify the server is enforcing the password

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

4. Authenticate with the password

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

5. 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

1. 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

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

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

3. 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$

4. 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

1. 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).

   

2. 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.

   

3. 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.

   

4. 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.

   

5. 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

   

   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
      $

6. 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.

   

Components

1. 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.

2. 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.

3. 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:

   Name	Purpose	Redis Applications	Database Applications
   Local	Use the internal redis server with the stock db	local-redis.json	db.json
   NoApps	Run the core without redis servers or databases	empty-redis.json	empty-db.json
   JustRedis	Run with just the redis servers and no databases	local-redis.json	empty-db.json
   JustDB	Run without redis servers and load the databases	empty-redis.json	db.json
   Test	Connect to external redis servers and databases	redis.json	db.json
   Live	Connect to external redis servers and databases	redis.json	db.json

   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()

4. 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

5. Slack Debugging

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

6. 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

7. 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

1. Start the Container in Local development mode

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

2. 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.

1. 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
   $

2. 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

   • 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

3. Run the Database Extraction Jupyter Demo

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

4. 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.

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

   $ ./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
   $

6. 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

1. 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
   $

2. 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

1. 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

2. 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 <librdkafka/rdkafka.h>

   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

3. 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$

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

5. 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.

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

7. Testing on an older docker version

   This was tested with 1.12.1

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

8. 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