reproducibility

Introduction Hello, it’s me again, Faishal, a SoR project contributor for the edgebench project. For the past these two months, my mentors and I have been working on improving the performance of our system.

Over the past few weeks, our platform development has been progressing steadily, and we are excited to share the milestones we have achieved so far. As planned in our introductory blog, we have successfully laid the groundwork for the platform with the guidance and support of our mentor.

Greetings everyone, I’m Kangrui. Over the past few weeks, we’ve dedicated our efforts and have consequently made significant progress in our drift detection methods. Now, I’m excited to present to you a detailed elaboration on how we prompted and fine-tuned Llama2 to efficiently carry out the drift detection task.

Introduction Motivation The growing popularity of Deep Neural Networks has resulted in a substantial increase in demand for Graphics Processing Units (GPUs). GPUs are crucial for conducting matrix computations in DNN training and inference.

Hello again! This is Luiza, a GSoC contributor for the LabOp Project. My task is to build bridges between programming languages for Biotechnology Laboratory automation. When talking about life sciences, reproducibility is a issue amongst most research centers.

Introduction Hello again! This is Jack, a GSoC contributor for the OpenROAD Project. My task is to update and optimise the documentation to encourage user adoption and engagement. For open-source repo maintainers, readthedocs is a godsend.

Hello there! I’m Jonathan Edwin, all the way from the beautiful archipelago of Indonesia. This year, I got the exciting chance to jump on board the 2023 Summer of Reproducibility initiative.

As part of the Reproducible Analysis & Models for Predicting Genomics Workflow Execution Time my proposal under the mentorship of In Kee Kim, Martin Putra and collaborator Charis Christopher Hulu (another OSRE fellow) aims to analyze large-scale sequencing datasets in order to gain insights on how ‘input quality’ affects genomic workflows’ execution times.

This project aims to design a system that allows researchers to conveniently record and compare the environmental information, such as CPU utilization, of different iterations and versions of code during an experiment.

Hi! I’m Charis, an undergraduate student in the IT and Big Data Analytics program at the Calvin Institute of Technology. As part of the Reproducible Analysis & Models for Predicting Genomics Workflow Execution Time my proposal under the mentorship of In Kee Kim and Martin Putra aims to gain insight into features that are highly correlated with execution times of genomics workflows and build machine learning models for predicting workflow execution time.