A service for managing and provisioning Bare Metal servers. Mirror of code maintained at opendev.org.

A Python agent for provisioning and deprovisioning Bare Metal servers. Mirror of code maintained at opendev.org.

Ansible roles and playbooks to enable a standalone Ironic install. Mirror of code maintained at opendev.org.

Container image to run OpenStack Ironic as part of Metal³

A python client implementing the Ironic API. Mirror of code maintained at opendev.org.

OpenStack Baremetal (Ironic) Specifications. Mirror of code maintained at opendev.org.

Ansible roles and playbook to setup an Ironic standalone server to deploy baremetal hosts

OpenStack Ironic Puppet Module. Mirror of code maintained at opendev.org.

Hardware introspection daemon for OpenStack Ironic. Mirror of code maintained at opendev.org.

a set of playbooks to deploy taco, leveraging ironic, kubespray, rook-ceph, decapod (kustomize, argo-wf, argo-cd)

Tracery grammars for building creative Twitterbots. Contains generative grammars for story-telling, metaphor generation, ironic put-downs, Trumpisms, short poems, etc.

Terraform provider for Ironic

Operator to maintain an Ironic deployment for Metal3

Ironic plugin UI for Horizon to allow users to view and manage bare metal. Mirror of code maintained at opendev.org.

Deployment of containerised OpenStack to bare metal using OpenStack kolla and bifrost

Research emulator for the ARM9 core in the Nintendo Wii

My xmonad config, the colour scheme is currently Zenburn, but a lime green and black scheme is in the testing-colour branch, "macstyle" is an ironic white black and blue color scheme

A python library of common ironic utilities. Mirror of code maintained at opendev.org.

Creating baremetal ironic images with packer

Role Ironic for OpenStack-Ansible. Mirror of code maintained at opendev.org.

Code and data used for participation in SemEval-2018 Task 3: "Irony detection in English tweets"

Scripts and tools to build ironic-python-agent. Mirror of code maintained at opendev.org.

Program created for ironic purposes.

Code and data from our ACL 2014 paper "Humans Require Context to Infer Ironic Intent (so Computers Probably do, too)"

Ironic HTTP(S) Client

MOVED: now at https://opendev.org/x/fuel-plugin-ironic

The detection of irony and sarcasm is one of the most insidious challenges in the field of Natural Language Processing. Over the years, several techniques have been studied to analyze these rhetorical figures, trying to identify the elements that discriminate, in a significant way, what is sarcastic or ironic from what is not. Within this study, some models that are state of the art are analyzed. As far as Machine Learning is concerned, the most discriminating features such as part of speech, pragmatic particles and sentiment are studied. Subsequently, these models are optimized, comparing Bayesian optimization techniques and random search. Once, the best hyperparameters are identified, ensemble methods such as Bayesian Model Averaging (BMA) are exploited. In relation to Deep Learning, two main models are analyzed: DeepMoji, developed by MIT, and a model called Transformer Based, which exploits the generalization power of Roberta Transformer. As soon as these models are compared, the main goal is to identify a new system able to better capture the two rhetorical figures. To this end, two models composed of attention mechanisms are proposed, exploiting the principle of Transfer Learning, using Bert Tweet Model and DeepMoji Model as feature extractors. After identifying the various architectures, an ensemble method is applied on the set of approaches proposed, in order to identify the best combination of algorithms that can achieve satisfactory results. Frameworks used: Pytorch, TF 2.0, Scikit Learn, Scikit-Optimize, Transformers

A python client and OpenStackClient plugin for Ironic Inspector. Mirror of code maintained at opendev.org.

Bare stack of OpenStack Ironic based environment to enable deployment of Genestack hardware

MOVED: now at https://opendev.org/x/cisco-ironic-contrib