Day 2 of F8 2019: Developer News Roundup

The second day of F8 focused on the long-term investments we’re making in AI and AR/VR. In the opening keynote, Chief Technology Officer Mike Schroepfer talked about the AI tools we’re using to address a range of challenges across our products — and why he’s optimistic about what comes next. Here are some of the top developer stories from Day 2 of F8 2019:

PyTorch Adds New Dev Tools As It Hits Production Scale

Building on the initial launch of PyTorch in 2017, Facebook partnered with the AI community to ship the stable release of PyTorch 1.0 last December. Key features of PyTorch v1.1 include:

• TensorBoard: First-class and native support for visualization and model debugging with TensorBoard, a web application suite for inspecting and understanding training runs and graphs.
• JIT compiler: Improvements to just-in-time (JIT) compilation. These include bug fixes and expanded capabilities in TorchScript, such as support for dictionaries, user classes, and attributes.
• New APIs: Support for Boolean tensors and better support for custom recurrent neural networks.
• Distributed Training: Improved performance for common models such as CNNs, and support for multi-device modules. See the latest tutorials here.

Using PyTorch across industries

Facebook is now using PyTorch 1.0 end-to-end workflows for building and deploying translation and natural language processing (NLP) services at scale. These systems provide nearly 6 billion translations a day for applications such as realtime translation in Messenger. Many leading businesses are also moving to PyTorch to accelerate development and deployment of new AI systems, including Airbnb, ATOM, Genentech, Microsoft and Toyota Research Institute (TRI).

Expanding educational resources and ecosystem

University-level classes — including Stanford NLP, UC Berkeley Computer Vision, and Caltech Robotics courses — are now being taught on PyTorch. In addition, today, we’re announcing our sponsorship of a new Udacity course that covers important concepts around building private and secure AI.

PyTorch 1.0 benefits from a growing ecosystem of tools, libraries and models to further support and accelerate AI development. Today we're expanding this ecosystem with:

• BoTorch (Bayesian Optimization for PyTorch): A package for optimizing hyper-parameters in the model, for training and inference. More details here.
• Ax (Adaptive Experimentation): A platform for iterative experimentation with different parameters.
• Google AI Platform Notebooks: Provides a higher-level platform for authoring, training and deploying models.
• Google Colab: Enables running a PyTorch notebook in Colab, making it easy to get started with PyTorch.

More details available here.

Introducing App Review Merging and Revamped Notifications

We are excited to announce the launch of one of the top developer requests — Instagram and Marketing API permissions can now be found in the main App Review tab and can be submitted along with other permissions, meaning only one submission is required. We are launching revamped jewel notifications in the app dashboard which are designed to be more organized and actionable, allowing developers to see all notifications related to personal or business-owned apps.

More details available here.

Open source at F8

At F8, we open-sourced tools and frameworks that simplify machine learning experimentation and optimization, speed up test execution times, and help solve memory-related performance issues. See the full list below, and click through to get started with our latest releases.

Adaptive Experiment (Ax)

The Ax platform makes it easier for developers to optimize their products and infrastructure by leveraging recent advances provided by BoTorch. Start using Ax.

BoTorch

BoTorch is a library for for Bayesian optimization (BO) research, built on PyTorch. BoTorch that significantly boosts developer efficiency by combining a modular design and use of Monte Carlo-based acquisition functions with PyTorch's auto-differentiation feature. Start using BoTorch.

Idb

The iOS development bridge (idb) makes it easy to build complex workflows so that automation on iOS can be distributed amongst a fleet of machines. Start using idb.

Memscout

Memscout is an analysis tool that scouts out allocator inefficiencies and provides insights into memory allocation patterns of the process, and then presents a breakdown of statistics that can be used to quickly diagnose memory-related performance issues. Start using Memscout.

Mvfst

Mvfst features include stream multiplexing as a transport feature, 0-RTT connection establishment, better loss recovery, security built in from the ground up, and flexible congestion control. Start using mvfst.

More details available here.

Advancing self-supervision, CV, NLP to keep our platforms safer

We use AI in a wide range of applications at Facebook today — and one of the most important is helping keep people safe on our platforms. In order to make all these systems more effective, we need to continue to improve our AI in two areas in particular: understanding content and working effectively with less labeled training data. Here we are highlighting how we’re improving the accuracy and efficiency of our content understanding systems and finding new ways to do more with less supervised learning.

More details available here.

Building Inclusive AI at Facebook

Teaching machines to be inclusive may sound unusual, but it's important in order to ensure that AI-powered devices work well for everyone, regardless of their skin tone and other attributes. AI models may perform differently for different people, in part because they’re trained with datasets created by people, and these datasets may contain limitations, flaws, or other issues. The inclusive AI process provides a set of working guidelines to help researchers and programmers design data sets, measure product performance, and test new systems through the lens of inclusivity.

More details available here.

Navigating Ethical Design in Tech

Responsible design means being intentional about decisions, being cognizant of the values that inform those decisions, and doing our best to anticipate the impact those decisions might have. And it means being careful to not define the potential impact of our products too narrowly, but take a broader view of the social and political contexts in which they operate. Some of the design challenges we are are addressing are combating misinformation and challenges of memorialization that works for different cultures and traditions.

More details available here.