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https://github.com/thoth-station/adviser

The recommendation engine for Python software stacks and Dependency Monkey in project Thoth.
https://github.com/thoth-station/adviser

artificial-intelligence hacktoberfest machine-learning machinelearning monte-carlo-tree-search python python3 reinforcement-learning resolver temporal-difference thoth

Last synced: 11 months ago
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The recommendation engine for Python software stacks and Dependency Monkey in project Thoth.

Awesome Lists containing this project

README 

          
Thoth Adviser

-------------



Welcome to Thoth's adviser repository.



This repository provides sources for a component called "thoth-adviser" which

serves the following purposes:



1. Recommendation engine for `project Thoth `__.



2. A `tool called "Dependency Monkey" `__

   that can generate all the possible software stacks for a project respecting

   dependency resolution in the Python ecosystem following programmable rules.



3. Check provenance of installed Python artifacts based on package source

   indexes used.



If you would like to interact with Thoth from user's perspective, check

`Thamos repository `__.



If you would like to browse technical documentation, visit

`thoth-adviser section

`__ at

`thoth-station.ninja `_.



.. image:: https://github.com/thoth-station/adviser/blob/master/docs/source/_static/rl_video.png?raw=true

   :alt: A reinforcement learning based dependency resolution.

   :align: center

   :target:  https://www.youtube.com/watch?v=WEJ65Rvj3lc



Software stack resolution pipeline

==================================



The software stack generation is shared for `Dependency Monkey

`__

as well as for the recommendation engine. The core principle of the software

stack generation lies in an abstraction called "software stack resolution

pipeline". This pipeline is made out of multiple units of different type that

form atomic pieces to score packages that can occur in a software stack based

on the dependency resolution.



.. image:: https://github.com/thoth-station/adviser/blob/master/docs/source/_static/pipeline_builder.gif?raw=true

   :alt: Building a resolution pipeline.

   :align: center



As can be seen in the animation shown above, the pipeline units that form the

software stack resolution pipeline are included in the pipeline dynamically on

the pipeline creation. A set of units included form a pipeline configuration.



The pipeline configuration is built by "pipeline builder" which asks each

pipeline unit for inclusion in the pipeline configuration. Each pipeline unit

can decide when and whether it should be included in the pipeline configuration

considering aspects for the user software stacks, such as:



* hardware available in the runtime environment when running the

  application (e.g. CPU, GPU)



* operating system and it's version used in the runtime environment where the

  application is supposed to be run



* software provided by the operating system, such as Python interpreter

  version, CUDA version (for GPU computation) and other native dependencies

  (e.g. glibc version, Intel MKL libraries, ...) and their ABI



* user's intention with the software built - e.g. building a computational

  intensive application, an application which should be secure for production

  environments, latest-greatest software, ...



* `type of inspections for the software quality checks

  `__ - Dependency Monkey scenario



* ...



All these vectors stated above form a "context" for pipeline builder (the robot

in the animation) that creates the pipeline configuration (a set of pipeline units).



**See also:**



* `dev.to: How to beat Python’s pip: Software stack resolution pipelines `__

* `YouTube: Pipeline units in a software stack resolution process `__

* `Jupyter Notebook: Pipeline units in a software stack resolution process `__



Predictor guided resolution process

===================================



Once the pipeline configuration is constructed, it is used to resolve software

stacks meeting desired quality and purpose.



.. image:: https://github.com/thoth-station/adviser/blob/master/docs/source/_static/pipeline.gif?raw=true

   :alt: A resolution pipeline run.

   :align: center



Resolver (the fairy in the animation) can resolve and walk through the

dependency graph respecting Python packaging based on the `pre-aggregated data

from dependency solver runs `__. The

resolution process is guided by an abstraction called "Predictor" (shown as a

magician in the animation). Predictor decides which packages in the dependency

graph should be resolved by Resolver and thus be included in the resulting

software stacks. Packages that are resolved go through the software stack

resolution pipeline which scores packages (positively, negatively or completely

discard a package from a software stack resolved). The resolution pipeline can:



* inject new packages or new package versions to the dependency graph based on

  packages resolved (e.g. a package accidentally not stated as a dependency of

  a library, dependency underpinning issues, ...)



* remove a dependency in a specific version or the whole dependency from the

  dependency graph (e.g. a package accidentally stated a a dependency, missing

  ABI symbols in the runtime environment, dependency overpinning issues, ...)



* score a package occurring in the dependency graph positively - prioritize

  resolution of a specific package in the dependency graph (e.g. positive

  performance aspect of a package in a specific version/build)



* score a package in a specific version occurring in the dependency graph

  negatively - prioritize resolution of other versions (e.g. a security

  vulnerability present in a specific release)



* prevent resolving a specific package in a specific version so that resolver

  tries to find a different resolution path, if any (e.g. buggy package releases)



The pipeline units present in the pipeline configuration can take into account

"context" as stated above - pipeline units can take into account

characteristics of the runtime environment used (software and hardware

available), purpose of the application, ...



Pipeline units are of different types - Boots, Pseudonyms, Sieves, Steps,

Strides and Wraps. `Follow the online documentation for more info

`__.



Predictor can be switched and the type of predictor can help with the

desired resolution process. For recommending high quality software stacks,

reinforcement learning algorithms, such as `MCTS

`__ or `TD-learning

`__ are used.



**See also:**



* `dev.to: How to beat Python’s pip: Reinforcement learning-based dependency resolution `__

* `YouTube: Reinforcement learning-based dependency resolution `__



Resolution process as Markov Decision Process

=============================================



The whole resolution process can be modeled as a Markov Decision Process (MDP)

thus the reinforcement learning (RL) principles stated above can apply.

Recommending the best possible set of packages than corresponds to solving the

given MDP.  See `Thoth's documentation for more info

`__.



To obey terms often used in the reinforcement learning terminology, Predictor

can be seen as an agent. Resolver and Software stack resolution pipeline can be

seen as entities that interact with the environment. Names used in Thoth

intentionally do not correspond to RL terminology as RL based resolution is

just one of the possible resolutions that can be implemented (others can be

hill-climbing, random walks in the dependency graph, ...).



**See also:**



* `dev.to: How to beat Python’s pip: Reinforcement learning-based dependency resolution `__

* `YouTube: Reinforcement learning-based dependency resolution `__



Dependency Monkey

=================



See `the following article for in-depth explanation with

a video `__.



The primary use-case for Dependency Monkey is to generate software stacks that

are subsequently validated and scored in the `Amun

`__ service. Simply, when generating

all the possible software stacks, we can find the best software stack for an

application by validating it in a CI (or Amun in case of Thoth), running the

application in the specific runtime environment (e.g. Fedora 33 with installed

native packages - RPMs) on some specific hardware configuration. Generating and

scoring all the possible software stacks is, however, most often not doable in

a reasonable time. For this purpose, Dependency Monkey can create a sample of

software stacks that can be taken as representatives. These representatives are

scored and aggregated data are used for predicting the best application stack

(again, generated and run through CI/Amun to make predictions more accurate by

learning over time).



See `Dependency Monkey documentation

`_

for more info.



**See also:**



* `Resolve Python dependencies with Thoth Dependency Monkey `__

* `Developers Red Hat: AI software stack inspection with Thoth and TensorFlow `__

* `dev.to: How to beat Python’s pip: Inspecting the quality of machine learning software `__

* `YouTube: Thoth Amun API: Inspecting the quality of software `__

* `dev.to: How to beat Python’s pip: Dependency Monkey inspecting the quality of TensorFlow dependencies `__

* `YouTube: Dependency Monkey inspecting Python dependencies of TensorFlow `__



Advises and Recommendations

===========================



In Thoth's terminology, advises and recommendations are the same. Based on

the aggregated knowledge stored in the database, provide the best application

stack with reasoning on why the given software stack is used. Pipeline units

present in the pipeline configuration score packages resolved and provide such

reasoning. The reasoning is called "justification" in Thoth's terminology. See

Thoth's pages to `see some of them

`__.



Provenance Checks

=================



As Thoth aggregates information about packages available, it can verify

a user's stack against its knowledge base. See `Provenance Checks

`_

for more info.



Installation and deployment

===========================



Adviser is built using OpenShift Source-to-Image and deployed

automatically with Thoth's deployment available in the

`thoth-station/thoth-application repository

`__.



In a Thoth deployment, adviser is run based on requests coming to the `user API

`__ - each deployed adviser is run

per a user request. You can run adviser locally as well by installing it and

using its command line interface:



::



  pip3 install thoth-adviser

  thoth-adviser --help

  # Or use git repo directly for the latest code:

  # pip3 install git+https://github.com/thoth-station/adviser



Note a database needs to be available.  See `thoth-storages repository

`__ on how to run Thoth's knowledge

graph locally and example `notebooks

`__ with experiments.



When thoth-adviser is scheduled in a deployment, it is actually executed as a

CLI with arguments passed via environment variables.



Blocking a bogus pipeline unit

==============================



Adviser also considers environment variable ``THOTH_ADVISER_BLOCKED_UNITS`` that

states a comma separated list of pipeline units that should not be added to

the pipeline. This can be handy if an issue with a unit arises in a deployment

- Thoth operator can remove pipeline unit by adjusting environment variable in

the adviser deployment manifest and provide this configuration without a need

to deploy a new version of adviser.



Disabling pipeline unit validation

==================================



For prod-like deployments, you can disable pipeline unit validation. By doing

so, the pipeline unit configuration can be constructed faster. Provide

``THOTH_ADVISER_VALIDATE_UNIT_CONFIGURATION_SCHEMA=0`` environment variable to

disable pipeline unit configuration validation.



Running adviser locally

=======================



Often, it is useful to run adviser locally to experiment or verify your changes

in implementation. You can do so easily by running:



.. code-block:: console



  pipenv install --dev

  PYTHONPATH=. pipenv run ./thoth-adviser --help



This command will run adviser locally - adviser will try to connect to a local

PostgreSQL instance and compute recommendations. `Browse docs here

`__ to see how to setup a local

PostgreSQL instance. Also, follow the developer's guide to get `more

information about developer's setup

`__.