Research communities have developed benchmark datasets across domains to compare the performance of algorithms and techniques However, tracking the progress in these research areas is not easy, as publications appear in different venues at the same time, and many of them claim to represent the state-of-the-art. To address this, research communities often organise periodic competitions to evaluate the performance of various algorithms and techniques, thereby tracking advancements in the field. However, these competitions pose a significant operational burden. The organisers must manage and evaluate a large volume of submissions. Furthermore, participants typically develop their solutions in diverse environments, leading to compatibility issues during the evaluation of their submissions. This paper presents an online competition system that automates the submission and evaluation process for a competition. The competition system allows organisers to manage large numbers of submissions efficiently, utilising isolated environments to evaluate submissions. This system has already been used successfully for several competitions, including the Grid-Based Pathfinding Competition and the League of Robot Runners competition.

Ludii is a Java general game system with a considerable number of board games, with an API for developing new agents and a game description language to create new games. To improve versatility and ease development, we provide Python interfaces for agent programming. This allows the use of Python modules to implement general game playing agents. As a means of enabling Python for creating Ludii agents, the interfaces are implemented using different Java libraries: jpy and Py4J. The main goal of this work is to determine which version is faster. To do so, we conducted a performance analysis of two different GGP algorithms, Minimax adapted to GGP and MCTS. The analysis was performed across several combinatorial games with varying depth, branching factor, and ply time. For reproducibility, we provide tutorials and repositories. Our analysis includes predictive models using regression, which suggest that jpy is faster than Py4J, however slower than a native Java Ludii agent, as expected.

Have you ever wondered why you have to wait for DVC to pull all the files to access a single file? Maybe you have created custom scripts to work around this problem. But what if I tell you there is a better solution for this issue? Direct Data Access makes it fairly easy for you to load single or multiple files from the DagsHub DVC server. It will help you save time, as you won't be pulling the entire dataset to push a single file.

Kubeflow is a popular open-source Machine Learning platform that runs on Kubernetes. Kubeflow streamlines many valuable ML workflows i.e. it allows users to easily deploy development environments, scalable ML workflows with Kubeflow Pipelines, automated hyper-parameter tuning and neural architecture search with Katib, easy collaboration within teams and much more. With a such a large number of features also comes complexity. A full Kubeflow deployment contains many services and dependencies, making it difficult for users to customize, manage and install Kubeflow using the legacy kfctl CLI tool and a KfDef YAML file. For this reason, the upcoming Kubeflow 1.3 release has stopped using kfctl and instead is using standard Kustomize, making it easier to deploy Kubeflow with GitOps tools such as Argo CD.

Data collection and processing might be less sexy than Machine Learning but nevertheless is crucial for any progress, and it is also something that source{d} as a company was built upon and has invested a lot into. It was briefly highlighted at several conference talks (go-git, gitbase, gitbase indexes). Now is time for a full-length blog post with the details. Before we begin a small reminder: as with most of what we do at source{d}, all the tools described in this blog post are available as an Open Source software and packaged in source{d}, our end user product. Most of the recent progress on ML and Deep Learning, in particular, is attributed to the fact of having an abundance of data and plenty of computing resources to use for training large Neural Network models.

This blog post was authored by Jordan Edwards, Senior Program Manager, Microsoft Azure. At Microsoft Build 2019 we announced MLOps capabilities in Azure Machine Learning service. MLOps, also known as DevOps for machine learning, is the practice of collaboration and communication between data scientists and DevOps professionals to help manage the production of the machine learning (ML) lifecycle. Azure Machine Learning service's MLOps capabilities provide customers with asset management and orchestration services, enabling effective ML lifecycle management. With this announcement, Azure is reaffirming its commitment to help customers safely bring their machine learning models to production and solve their business's key problems faster and more accurately than ever before.

It contains packages for running our latest fully neural pipeline from the CoNLL 2018 Shared Task and for accessing the Java Stanford CoreNLP server. For detailed information please visit our official website. The PyTorch implementation of the neural pipeline in this repository is due to Peng Qi and Yuhao Zhang, with help from Tim Dozat, who is the main contributor to the Tensorflow version of the tagger and parser. If you use the CoreNLP server, please cite the software package and the respective modules as described here ("Citing Stanford CoreNLP in papers"). The CoreNLP client is mostly written by Arun Chaganty, and Jason Bolton spearheaded merging the two projects together.

Generative adversarial networks -- GANs for short -- are making waves in the world of machine learning. Yann LeCun, a legend in the deep learning community, said in a Quora post "[GANs are] the most interesting idea in the last 10 years in [machine learning]." GANs (and, more generally, neural networks) can be confusing at first. But developers have created lots of great frameworks for training pre-configured models efficiently. We'll examine a package built by Hyeonwoo Kang at Catholic University of Korea that wraps PyTorch implementations for ten different types of GANs in an easy-to-use interface.