Releases

Quarto 1.8

Quarto 1.8 improves brand support, introduces brand extensions, adds HTML accessibility checks, and gives access to execution context

Charlotte Wickham

quarto R package v1.5.0: Streamlined Workflows for R Users

The quarto R package 1.5.0 brings powerful new features for passing R values to Quarto metadata, inserting Markdown in HTML tables, working with R scripts, building paths from Quarto projects, and automating Quarto CLI from R

Christophe Dervieux

Quarto 1.7

Quarto 1.7 brings big improvements to dark mode along with updates to Typst, Pandoc, a new `version` shortcode, and improvements to the `julia` engine

Charlotte Wickham

Quarto 1.6

Quarto 1.6 supports unified branding across formats, updates to RevealJS, a new shortcode to reorder content, a landscape page block, and more. There are also a couple of breaking changes that will affect a small number of users

Charlotte Wickham

Version 0.14.0

Quarto 1.5

Quarto 1.5 improves Typst support, has some website enhancements like draft handling and announcement bars, adds a native Julia engine, and adds a couple of shortcodes for generating placeholder content

Charlotte Wickham

News from the sparkly-verse

Highlights to the most recent updates to `sparklyr` and friends

Edgar Ruiz

Chat with AI in RStudio

Interact with Github Copilot and OpenAI's GPT (ChatGPT) models directly in RStudio. The `chattr` Shiny add-in makes it easy for you to interact with these and other Large Language Models (LLMs)

Edgar Ruiz

Quarto 1.4

Quarto 1.4 brings new formats for dashboards and Typst, a new manuscript project type, a cross-reference overhaul, Shiny for Python support, and a ton of other updates

Charlotte Wickham

Hugging Face Integrations

Hugging Face rapidly became a very popular platform to build, share and collaborate on deep learning applications. We have worked on integrating the torch for R ecosystem with Hugging Face tools, allowing users to load and execute language models from their platform

Daniel Falbel

safetensors 0.1.0

Announcing safetensors, a new R package allowing for reading and writing files in the safetensors format

Daniel Falbel

torch 0.11.0

torch v0.11.0 is now on CRAN. This release features much-enhanced support for executing JIT operations. We also amended loading of model parameters, and added a few quality-of-life improvements, like support for temporarily modifying the default torch device, support for specifying data types as strings, and many more

Daniel Falbel

Quarto 1.3

Quarto 1.3 brings new features, improvements, and fixes

Charlotte Wickham

luz 0.4.0

luz v0.4.0 is now on CRAN. This release adds support for training models on ARM Mac GPUs, reduces the overhead of using luz, and makes it easier to checkpoint and resume failed runs

Daniel Falbel

torch 0.10.0

torch v0.10.0 is now on CRAN. This version upgraded the underlying LibTorch to 1.13.1, and added support for Automatic Mixed Precision. As an experimental feature, we now also support pre-built binaries, so you can install torch without having to deal with the CUDA installation

Daniel Falbel

Deep Learning and Scientific Computing with R torch: the book

Please allow us to introduce Deep Learning and Scientific Computing with R torch. Released in e-book format today, and available freely online, this book starts out by introducing torch basics. From there, it moves on to various deep-learning use cases. Finally, it shows how to use torch for more general topics, such as matrix computations and the Fourier Transform

AO, NAO, ENSO: A wavelet analysis example

El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), and Arctic Oscillation (AO) are atmospheric phenomena of global impact that strongly affect people's lives. ENSO, first and foremost, brings with it floods, droughts, and ensuing poverty, in developing countries in the Southern Hemisphere. Here, we use the new torchwavelets package to comparatively inspect patterns in the three series

torch 0.9.0

torch v0.9.0 is now on CRAN. This version adds support for ARM systems running macOS, and brings significant performance improvements

Daniel Falbel

luz 0.3.0

luz version 0.3.0 is now on CRAN. luz is a high-level interface for torch

Daniel Falbel

TensorFlow and Keras 2.9

New TensorFlow and Keras releases bring improvements big and small

Tomasz Kalinowski

Community spotlight: Fun with torchopt

Today, we want to call attention to a highly useful package in the torch ecosystem: torchopt. It extends torch by providing a set of popular optimization algorithms not available in the base library. As this post will show, it is also fun to use!

Revisiting Keras for R

It's been a while since this blog featured content about Keras for R, so you might've thought that the project was dormant. It's not! In fact, Keras for R is better than ever, with two recent releases adding powerful capabilities that considerably lighten previously tedious tasks. This post provides a high-level overview. Future posts will go into more detail on some of the most helpful new features, as well as dive into the powerful low-level enhancements that make the former possible

Sigrid Keydana, Tomasz Kalinowski

sparklyr.sedona: A sparklyr extension for analyzing geospatial data

We are excited to announce the availability of sparklyr.sedona, a sparklyr extension making geospatial functionalities of the Apache Sedona library easily accessible from R

sparklyr 1.7: New data sources and spark_apply() capabilities, better interfaces for sparklyr extensions, and more!

Sparklyr 1.7 delivers much-anticipated improvements, including R interfaces for image and binary data sources, several new spark_apply() capabilities, and better integration with sparklyr extensions

Que haja luz: More light for torch!

Today, we're introducing luz, a high-level interface to torch that lets you train neural networks in a concise, declarative style. In some sense, it is to torch what Keras is to TensorFlow: It provides both a streamlined workflow and powerful ways for customization

sparklyr 1.6: weighted quantile summaries, power iteration clustering, spark_write_rds(), and more

The sparklyr 1.6 release introduces weighted quantile summaries, an R interface to power iteration clustering, spark_write_rds(), as well as a number of dplyr-related improvements

torch 0.2.0 - Initial JIT support and many bug fixes

The torch 0.2.0 release includes many bug fixes and some nice new features like initial JIT support, multi-worker dataloaders, new optimizers and a new print method for nn_modules

Daniel Falbel

sparklyr 1.5: better dplyr interface, more sdf_* functions, and RDS-based serialization routines

Unlike all three previous sparklyr releases, the recent release of sparklyr 1.5 placed much more emphasis on enhancing existing sparklyr features rather than creating new ones. As a result, many valuable suggestions from sparklyr users were taken into account and were successfully addressed in a long list of bug fixes and improvements

sparklyr.flint 0.2: ASOF Joins, OLS Regression, and additional summarizers

We are excited to announce a number of powerful, new functionalities and improvements which are now part of sparklyr.flint 0.2!

sparklyr 1.4: Weighted Sampling, Tidyr Verbs, Robust Scaler, RAPIDS, and more

Sparklyr 1.4 is now available! This release comes with delightful new features such as weighted sampling and tidyr verbs support for Spark dataframes, robust scaler for standardizing data based on median and interquartile range, spark_connect interface for RAPIDS GPU acceleration plugin, as well as a number of dplyr-related improvements

Please allow me to introduce myself: Torch for R

Today, we are excited to introduce torch, an R package that allows you to use PyTorch-like functionality natively from R. No Python installation is required: torch is built directly on top of libtorch, a C++ library that provides the tensor-computation and automatic-differentiation capabilities essential to building neural networks

sparklyr 1.3: Higher-order Functions, Avro and Custom Serializers

Sparklyr 1.3 is now available, featuring exciting new functionalities such as integration of Spark higher-order functions and data import/export in Avro and in user-defined serialization formats

sparklyr 1.2: Foreach, Spark 3.0 and Databricks Connect

A new sparklyr release is now available. This sparklyr 1.2 release features new functionalities such as support for Databricks Connect, a Spark backend for the 'foreach' package, inter-op improvements for working with Spark 3.0 preview, as well as a number of bug fixes and improvements addressing user-visible pain points

pins 0.4: Versioning

A new release of pins is available on CRAN today. This release adds support to time travel across dataset versions, which improves collaboration and protects your code from breaking when remote resources change unexpectedly

tfhub: R interface to TensorFlow Hub

TensorFlow Hub is a library for the publication, discovery, and consumption of reusable parts of machine learning models. A module is a self-contained piece of a TensorFlow graph, along with its weights and assets, that can be reused across different tasks in a process known as transfer learning

Daniel Falbel

Getting started with Keras from R - the 2020 edition

Looking for materials to get started with deep learning from R? This post presents useful tutorials, guides, and background documentation on the new TensorFlow for R website. Advanced users will find pointers to applications of new release 2.0 (or upcoming 2.1!) features alluded to in the recent TensorFlow 2.0 post

tfprobability 0.8 on CRAN: Now how can you use it?

Part of the r-tensorflow ecosystem, tfprobability is an R wrapper to TensorFlow Probability, the Python probabilistic programming framework developed by Google. We take the occasion of tfprobability's acceptance on CRAN to give a high-level introduction, highlighting interesting use cases and applications

Innocent unicorns considered harmful? How to experiment with GPT-2 from R

Is society ready to deal with challenges brought about by artificially-generated information - fake images, fake videos, fake text? While this post won't answer that question, it should help form an opinion on the threat exerted by fake text as of this writing, autumn 2019. We introduce gpt2, an R package that wraps OpenAI's public implementation of GPT-2, the language model that early this year surprised the NLP community with the unprecedented quality of its creations

TensorFlow 2.0 is here - what changes for R users?

TensorFlow 2.0 was finally released last week. As R users we have two kinds of questions. First, will my keras code still run? And second, what is it that changes? In this post, we answer both and, then, give a tour of exciting new developments in the r-tensorflow ecosystem

Auto-Keras: Tuning-free deep learning from R

Sometimes in deep learning, architecture design and hyperparameter tuning pose substantial challenges. Using Auto-Keras, none of these is needed: We start a search procedure and extract the best-performing model. This post presents Auto-Keras in action on the well-known MNIST dataset

lime v0.4: The Kitten Picture Edition

A new major release of lime has landed on CRAN. lime is an R port of the Python library of the same name by Marco Ribeiro that allows the user to pry open black box machine learning models and explain their outcomes on a per-observation basis

Thomas Lin Pedersen

R Interface to Google CloudML

We are excited to announce the availability of the cloudml package, which provides an R interface to Google Cloud Machine Learning Engine. CloudML provides a number of services including on-demand access to training on GPUs and hyperparameter tuning to optimize key attributes of model architectures

tfruns: Tools for TensorFlow Training Runs

The tfruns package provides a suite of tools for tracking, visualizing, and managing TensorFlow training runs and experiments from R

Keras for R

We are excited to announce that the keras package is now available on CRAN. The package provides an R interface to Keras, a high-level neural networks API developed with a focus on enabling fast experimentation

TensorFlow Estimators

The tfestimators package is an R interface to TensorFlow Estimators, a high-level API that provides implementations of many different model types including linear models and deep neural networks

TensorFlow v1.3 Released

The final release of TensorFlow v1.3 is now available. This release marks the initial availability of several canned estimators including DNNClassifier and DNNRegressor