Course on High Performance Data Analytics with Ophidia

This online course set up within ESiWACE2 illustrates the main features of the Ophidia HPDA framework applied to climate data analysis and provides a practical tutorial on how to use the framework in examples of real-world analysis. Check it out here: https://www.oercommons.org/courseware/lesson/86887

ECAS/Ophidia training @ EGU 2019 General Assembly

A short course session on ECAS/Ophidia called “Data Analysis made easy with the ENES Climate Analytics Service (ECAS)“, co-organized by DKRZ and CMCC, is going to be held on the 10th of April, 2019 in the context of the EGU General Assembly 2019. The short course will consist of a teaching part, with some presentations […] Read More

PyOphidia v1.8.0 is available

PyOphidia, the Ophidia python bindings, v1.8.0 is now available! You can install the package from PyPI (https://pypi.python.org/pypi/PyOphidia/), conda-forge repository (https://anaconda.org/conda-forge/pyophidia) or get the source code from github (http://github.com/OphidiaBigData/PyOphidia/). This new version is fully compatibile with the latest Ophidia release v1.5.0.

The official release of Ophidia v1.5.0 is available!

The official Ophidia release v1.5.0 is now available on github (http://github.com/OphidiaBigData)! This release fixes various bugs and provides several new features, such as: -full compatibility with Ubuntu 18 and CentOS7; -improved support for dynamic cluster deployment, now providing also cluster information; -support for mixed single-user / multi-user mode; -re-engineered management of host and DBMS instances […] Read More

Ophidia/ECAS talk and training @ SOSC 2018 School Open Science Cloud

A talk titled “The Ophidia project: towards a High Performance Data Analytics and Machine Learning framework for climate change” and a training session on Ophidia/ECAS will be held in the context of the SOSC 2018 Second International PhD School Open Science Cloud (Perugia, Italy) on September 19, 2018. The training relates to the execution of […] Read More

Ophidia/ECAS talk and training @ 3rd ENES Workshop on Workflows

A talk titled “Analytics workflows with Ophidia/ECAS” and a training session on Ophidia/ECAS will be held in the context of the 3rd ENES Workshop on Workflows funded by ESiWACE (Brussels) on September 13, 2018. The training relates to the execution of python notebooks that exploit the PyOphidia package for scientific analyses on climate data in […] Read More

PyOphidia v1.7.0 is available

PyOphidia, the Ophidia python bindings, v1.7.0 is now available! You can install the package from PyPI (https://pypi.python.org/pypi/PyOphidia/), conda-forge repository (https://anaconda.org/conda-forge/pyophidia) or get the source code from github (http://github.com/OphidiaBigData/PyOphidia/). This new version is fully compatibile with the latest Ophidia release v1.4.0.

The official release of Ophidia v1.4.0 is available!

The official Ophidia release v1.4.0 is now available on github (http://github.com/OphidiaBigData)! This release fixes various bugs and provides several new features, such as: -new operator to upload data to B2DROP; -improved support for reserved host partition management; -new version of WPS integrating all Ophidia operators; -support for multi-thread execution in several operators; -improved version of […] Read More

PyOphidia v1.6.0 is available

PyOphidia, the Ophidia python bindings, v1.6.0 is now available! You can install the package from PyPI (https://pypi.python.org/pypi/PyOphidia/), conda-forge repository (https://anaconda.org/conda-forge/pyophidia) or get the source code from github (http://github.com/OphidiaBigData/PyOphidia/). This new version is fully compatibile with the latest Ophidia release v1.3.0.

Ophidia @ PASC18 Conference

A talk titled “Integrating Machine Learning Algorithms and HPDA Frameworks to Run Predictive Analytics on Large-Scale Climate and Weather Datasets” will be presented at the PASC18 Conference held in Basel (July 02-04, 2018). The presentation will focus on a couple of case studies integrating machine learning capabilities into Ophidia taking advantage of a HPDA approach […] Read More