Addition of ML Registry Functionality (#110)

* Adding ML Registry functionality

* Cleanup whitespace in automl.q

* Removing typo in components section of README

* Fix typo in README

* Fix typo in README

* Remove 'Status' section in registry README

* Cleanup docs folder in mlops, update registry api and examples docs

Author: phagan920

README.md

@@ -104,8 +104,9 @@ This library contains functions that cover the following areas:
 - Utility functions relating to areas including statistical analysis, data preprocessing and array manipulation.
 - A multi-processing framework to parallelize work across many cores or nodes.
 - Functions for seamless integration with PyKX or EmbedPy, which ensure seamless interoperability between Python and kdb+/q in either environment.
+- A location for the storage and versioning of ML models on-prem along with a common model retrieval API allowing models regardless of underlying requirements to be retrieved and used on kdb+ data. This allows for enhanced team collaboration opportunities and management oversight by centralising team work to a common storage location.
 
-These sections are explained in greater depth within the [FRESH](ml/docs/fresh.md), [cross validation](ml/docs/xval.md), [clustering](ml/docs/clustering/algos.md), [timeseries](ml/docs/timeseries/README.md), [optimization](ml/docs/optimize.md), [graph/pipeline](ml/docs/graph/README.md) and [utilities](ml/docs/utilities/metric.md) documentation.
+These sections are explained in greater depth within the [FRESH](ml/docs/fresh.md), [cross validation](ml/docs/xval.md), [clustering](ml/docs/clustering/algos.md), [timeseries](ml/docs/timeseries/README.md), [optimization](ml/docs/optimize.md), [graph/pipeline](ml/docs/graph/README.md), [utilities](ml/docs/utilities/metric.md) and [registry](ml/docs/registry/README.md) documentation.
 
 
 ### nlp
@@ -171,3 +172,4 @@ The Machine Learning Toolkit is provided here under an Apache 2.0 license.
 If you find issues with the interface or have feature requests, please [raise an issue](https://github.com/KxSystems/ml/issues).
 
 To contribute to this project, please follow the [contributing guide](CONTRIBUTING.md).
+

automl/automl.q

@@ -45,4 +45,3 @@ if[all`config`run in commandLineArguments;
   testRun:`test in commandLineArguments;
   runCommandLine[testRun];
   exit 0]
-

docker/Dockerfile

@@ -2,6 +2,11 @@
 
 FROM registry.gitlab.com/kxdev/kxinsights/data-science/ml-tools/automl:embedpy-gcc-deb12
 
+# Java and jq packages required for registry tests
+RUN apt-get update && apt-get install -y openjdk-17-jdk && rm -rf /var/lib/apt/lists/*
+
+ENV JAVA_HOME=/usr/lib/jvm/java-17-openjdk-amd64/
+
 COPY requirements_pinned.txt /opt/kx/automl/
 
 USER kx

ml/docs/registry/README.md

@@ -0,0 +1,112 @@
+# ML Registry
+
+The ML Model Registry defines a centralised location for the storage of the following versioned entities:
+
+1. Machine Learning Models
+2. Model parameters
+3. Performance metrics
+4. Model configuration
+5. Model monitoring information
+
+The ML Registry is intended to allow models and all important metadata information associated with them to be stored locally.
+
+In the context of an MLOps offering the model registry is a collaborative location allowing teams to work together on different stages of a machine learning workflow from model experimentation to publishing a model to production. It is designed to aid in this in the following ways:
+
+1. Provide a solution with which users can store models generated in q/Python to a centralised location on-prem.
+2. A common model retrieval API allowing models regardless of underlying requirements to be retrieved and used on kdb+ data.
+3. The ability to store information related to model training/monitoring requirements, allowing sysadmins to control the promotion of models to production environments.
+4. Enhanced team collaboration opportunities and management oversight by centralising team work to a common storage location.
+
+## Contents
+
+- [Quick start](#quick-start)
+- [Documentation](#documentation)
+- [Testing](#testing)
+- [Status](#status)
+
+
+## Quick start
+
+Start by following the installation step found [here](../../../README.md) or alternatively start a q session using the code below from the `ml` folder
+
+```
+$ q init.q
+q)
+```
+
+Generate a model registry in the current directory and display the contents
+
+```
+q).ml.registry.new.registry[::;::];
+q)\ls
+"CODEOWNERS"
+"CONTRIBUTING.md"
+"KX_ML_REGISTRY"
+...
+q)\ls KX_ML_REGISTRY
+"modelStore"
+"namedExperiments"
+"unnamedExperiments"
+```
+
+Add an experiment folder to the registry
+
+```
+q).ml.registry.new.experiment[::;"test";::];
+q)\ls KX_ML_REGISTRY/namedExperiments/
+"test"
+```
+
+Add a basic q model associated with the experiment
+
+```
+q).ml.registry.set.model[::;{x+1};"mymodel";"q";enlist[`experimentName]!enlist "test"]
+```
+
+Check that the model has been added to the modelStore
+
+```
+q)modelStore
+registrationTime              experimentName modelName uniqueID              ..
+-----------------------------------------------------------------------------..
+2021.08.02D10:27:04.863096000 "test"         "mymodel" 66f12a71-175b-cd56-7d0..
+```
+
+Retrieve the model and model information based on the model name and version
+
+```
+q).ml.registry.get.model[::;::;"mymodel";1 0]
+modelInfo| `major`description`experimentName`folderPath`registryPath`modelSto..
+model    | {x+1}
+```
+
+## Documentation
+
+### Static Documentation
+
+Further information on the breakdown of the API for interacting with the ML-Registry and extended examples can be found in [Registry API](api/setting.md) and [Registry Examples](examples/basic.md).
+
+This provides users with:
+
+1. A breakdown of the API for interacting with the ML-Registry
+2. Examples of interacting with a registry
+
+# Testing
+
+Unit tests are provided for testing the operation of this interface both as a local service. In order to facilitate this users must have embedPy or pykx installed alongside the following additional Python requirements, it is also advisable to have the python requirements_pinned.txt installed before running the below.
+
+```
+$ pip install pyspark xgboost
+```
+
+The local tests are run using a bespoke q script. The local tests can be run standalone using the instructions outlined below.
+
+## Local testing
+
+The below tests are ran from the `ml` directory and test results will output to console
+
+```bash
+$ q ../test.q registry/tests/registry.t
+```
+
+This should present a summary of results of the unit tests.