Affiliated Project scikit-learn is accepted as a Sponsored Project!
scikit–learn is a Python library for machine learning, and is one of the most widely used tools for supervised and unsupervised machine learning. Scikit–learn provides an easy-to-use, consistent interface to a large collection of machine learning models, as well as tools for model evaluation and data preparation.
OpenMBEE is a community of Engineering Practitioners and Software Developers that seek to use Open Source as a means to expand the availability of Engineering Models and Software that connect technical information in a collaborative platform.
MathJax just released version 3.1.0 of MathJax. This is a feature release that includes a number of API improvements, new extensions, updates to the assistive tools, and more examples of how to use MathJax in node applications. It also includes several bug fixes. See the release notes for details.
MathJax version 2.7.9 was also released, which updates the version of the Speech-Rule Engine (SRE) that underlies MathJax’s accessibility features, and adds localizations for SRE in German and French, along with access to the Clearspeak rules via the MathJax contextual menu. See the release notes for details
MDAnalysis released its 1.0.0 release after almost 12 years in 0.x status. The 1.x releases will still support Python 2.7 because scientific projects remain that rely on a 2.7 software stack. However, 1.x will only receive major bug fixes. Primary development is taking place for the upcoming 2.0.0 release that will only support Python >= 3.6, as outlined in our Roadmap https://www.mdanalysis.org/2019/11/06/roadmap/.
All our GSoC students passed their second evaluation and are finishing up their projects successfully. Results will be announced soon (along with another mlpack release that incorporates their great work).
The PALISADE community is hosting a monthly webinar on the use of PALISADE. We have the webinar on the last Friday of each month. Information about our webinar can be found here:
The PyMC dev team is excited to announce the first PyMCon, An asynchronous-first virtual conference for the Bayesian community, tentatively scheduled for the first week of November, 2020. This announcement also serves as an RFP for talks and tutorials, as well as a call for sponsorship and volunteers. You can find out all the details at the PyMCon website: https://pymc-devs.github.io/pymcon/
We hope you can participate!
rOpensci has released its first rOpenSci Community Contributing Guide! The purpose of the guide is to welcome people to rOpenSci and help them recognize themselves as potential contributors. It will help them figure out what they might gain by giving their time, expertise, and experience; match their needs with things that will help rOpenSci’s mission; and connect them with resources to help along the way. We hope it's also a useful blueprint for other open source communities. Thank you to NumFOCUS for supporting this project with a Small Development Grant.
Want to keep up with rOpenSci's work and learn what others are doing with our tools? Read and subscribe to our newsletter! A digest of R package news, use cases, blog posts, events, curated every two weeks.
Affiliated Project Announcements
Optuna 2.0, the next major evolution of Optuna released! 2.0 introduces hyperparameter importance to see which hyperparameters matter most, Hyperband pruning, and many performance improvements!
Optuna 2.0 includes the following stable features:
and several experimental features, such as integration with TensorBoard and AllenNLP.
PyTorch-Ignite is a high-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.
We recently released our 0.4.1 version. The focus is on bug fixes, a few new features and the consolidation of our distributed helper module.
To learn more about the project and how to start using it, please, checkout our getting started section.
Since recently, we provide various docker images for vision/nlp tasks empowered with the latest PyTorch, PyTorch-Ignite, Nvidia/Apex and others task related libraries. More docker images with, for example, Horovod support or DeepSpeed will be available in future.
Trains Ignite server is open to everyone to browse our reproducible experiment logs, compare performances and restart any run on their own Trains server and associated infrastructure. Many thanks to the folks at Allegro AI who are making this possible!
The project is currently maintained by a team of volunteers and we are looking for motivated contributors to help us to move quickly forward. Please see the contribution guidelines for more information.
PyTorch-Ignite is presented to you with love by the PyTorch community! Checkout the project on Github and follow us on twitter. For any questions, support or issues, please [reach out to us. For all other questions and inquiries, please send an email to firstname.lastname@example.org
QuTiP 4.5.2 was released on PyPI and Conda Forge on the 14th of July, 2020, bringing support for SciPy 1.5.0, bug fixes in eigenvector calculations on macOS, the stochastic solvers and the Windows build process, and some minor speedups.
Three 2020 Google Summer of Code students are completing their projects: Jake has made an overhaul of the data structures used to store quantum objects the data layer, Sidhant has extended the integration of the quantum information processing sub-package, improving the execution, and Asad has crafted a plugin that makes Hamiltonians and other quantum objects auto-differantiable with JAX.
We have activated a remote mentorship opportunity of 3 months for student projects, all-year-round, in collaboration with Unitary Fund.
We have begun work on QuTiP 5.0, which will bring a whole host of improvements across the library. Several packages are being split out into their own repositories to help manage the growing codebase, while user-facing options are getting some much-needed attention. This next major version will see a total overhaul of the data structures used to store quantum objects, letting the library seamlessly mix data representations and making it much easier for users to extend the library with new operations to suit their needs. We hope to open a public beta test in the first half of 2021, while development continues on the 4.X branch.
signac has a number of updates this month:
Google Summer of Code students Hardik and Vishav shared their final blog posts about their work on aggregation in signac-flow and an improved design for buffering/caching data in signac.
Alyssa Travitz will present "signac: a simple, open-source, data management framework," at the Women+ Data Science monthly webinar, a joint series between Michigan State University and the University of Michigan, on September 24 from 4-5 pm Eastern Time.
The primary role of the Open Source Developer Advocate is to represent and support developers of NumFOCUS open source projects by serving as a link to internal and external stakeholders as well as the global user community. You will generate attention and support by applying your technical knowledge, passion for open source data science, and excellent communication skills.
The person in this role helps to ensure smooth communication between NumFOCUS staff and representatives from our 30+ open source projects by serving as the primary point of contact. In this capacity, you will coordinate, on behalf of NumFOCUS, Google Summer of Code, Google Season of Docs, and other similar programs. You will provide support to our open source projects requesting technical and infrastructure services and help the projects to implement best practices for their long-term sustainability, such as improvements to documentation, governance, etc.
Dask is an open source library for parallel computing in Python that interoperates with existing Python data science libraries like Numpy, Pandas, Scikit-Learn, and Jupyter. Dask is used today across many different scientific domains.
Recently, we’ve observed an increase in use in a few life sciences applications:
Large scale imaging in microscopy
Single cell analysis
We see early adopters in these fields use the Dask array library along with other libraries and file formats specific to their discipline.
We would like to accelerate growth of Dask in these fields by contracting someone to assist with building out functionality for these early adopter groups, and then communicate that functionality more broadly to a wide audience.
You will be tasked with bridging the gap between life science groups and the broader Dask and PyData development communities. This will require a variety of both technical and communication activities, including sourcing requirements from several different science groups, contributing to open source software, and coaching science groups to build proof-of-concept computations. Additionally, you will disseminate these results through a variety of communication channels like blogs, webinars, and on-line trainings to downstream science communities.
NumFOCUS is seeking a Scientific Software Developer to support the SunPy project. SunPy is a Python-based open source scientific software package supporting solar physics data analysis. Contract is available for U.S. residents only. This is a 1 year contract.
The successful applicant will work to improve SunPy’s functionality. There are four main tasks:
Report on the state of the SunPy codebase by analyzing output from code coverage and API inspection tools, etc, identifying areas in the existing codebase that need more coverage, can be consolidated or removed.
Provide the ability to read spectroscopic data into a spectral data object, thereby enabling its later scientific analysis.
Implement a number of heliophysical coordinate systems using the existing SunPy and Astropy-based coordinate system framework.
Create example code snippets that use SunPy and packages from the Python in Heliophysics Community; these examples will be shared via the Python in Heliophysics Community.
The successful applicant will be expected to adhere to the SunPy and Python in Heliophysics community guidelines.