Real-time tracking of wildfire boundaries using satellite imagery

As global temperatures rise, wildfires around the world are becoming more frequent and more dangerous. Their effects are felt by many communities as people evacuate their homes or suffer harm even from proximity to the fire and smoke.

As part of Google’s mission to help people access trusted information in critical moments, we use satellite imagery and machine learning (ML) to track wildfires and inform affected communities. Our wildfire tracker was recently expanded. It provides updated fire boundary information every 10–15 minutes, is more accurate than similar satellite products, and improves on our previous work. These boundaries are shown for large fires in the continental US, Mexico, and most of Canada and Australia. They are displayed, with additional information from local authorities, on Google Search and Google Maps, allowing people to keep safe and stay informed about potential dangers near them, their homes or loved ones.

Real-time boundary tracking of the 2021-2022 Wrattonbully bushfire, shown as a red polygon in Google Maps.

Inputs

Wildfire boundary tracking requires balancing spatial resolution and update frequency. The most scalable method to obtain frequent boundary updates is to use geostationary satellites, i.e., satellites that orbit the earth once every 24 hours. These satellites remain at a fixed point above Earth, providing continual coverage of the area surrounding that point. Specifically, our wildfire tracker models use the GOES-16 and GOES-18 satellites to cover North America, and the Himawari-9 and GK2A satellites to cover Australia. These provide continent-scale images every 10 minutes. The spatial resolution is 2km at nadir (the point directly below the satellite), and lower as one moves away from nadir. The goal here is to provide people with warnings as soon as possible, and refer them to authoritative sources for spatially precise, on-the-ground data, as necessary.

Smoke plumes obscuring the 2018 Camp Fire in California. [Image from NASA Worldview]

Determining the precise extent of a wildfire is nontrivial, since fires emit massive smoke plumes, which can spread far from the burn area and obscure the flames. Clouds and other meteorological phenomena further obscure the underlying fire. To overcome these challenges, it is common to rely on infrared (IR) frequencies, particularly in the 3–4 μm wavelength range. This is because wildfires (and similar hot surfaces) radiate considerably at this frequency band, and these emissions diffract with relatively minor distortions through smoke and other particulates in the atmosphere. This is illustrated in the figure below, which shows a multispectral image of a wildfire in Australia. The visible channels (blue, green, and red) mostly show the triangular smoke plume, while the 3.85 μm IR channel shows the ring-shaped burn pattern of the fire itself. Even with the added information from the IR bands, however, determining the exact extent of the fire remains challenging, as the fire has variable emission strength, and multiple other phenomena emit or reflect IR radiation.

Himawari-8 hyperspectral image of a wildfire. Note the smoke plume in the visible channels (blue, green, and red), and the ring indicating the current burn area in the 3.85μm band.

Model

Prior work on fire detection from satellite imagery is typically based on physics-based algorithms for identifying hotspots from multispectral imagery. For example, the National Oceanic and Atmospheric Administration (NOAA) fire product identifies potential wildfire pixels in each of the GOES satellites, primarily by relying on the 3.9 μm and 11.2 μm frequencies (with auxiliary information from two other frequency bands).

In our wildfire tracker, the model is trained on all satellite inputs, allowing it to learn the relative importance of different frequency bands. The model receives a sequence of the three most recent images from each band so as to compensate for temporary obstructions such as cloud cover. Additionally, the model receives inputs from two geostationary satellites, achieving a super-resolution effect whereby the detection accuracy improves upon the pixel size of either satellite. In North America, we also supply the aforementioned NOAA fire product as input. Finally, we compute the relative angles of the sun and the satellites, and provide these as additional input to the model.

All inputs are resampled to a uniform 1 km–square grid and fed into a convolutional neural network (CNN). We experimented with several architectures and settled on a CNN followed by a 1x1 convolutional layer to yield separate classification heads for fire and cloud pixels (shown below). The number of layers and their sizes are hyperparameters, which are optimized separately for Australia and North America. When a pixel is identified as a cloud, we override any fire detection since heavy clouds obscure underlying fires. Even so, separating the cloud classification task improves the performance of fire detection as we incentivize the system to better identify these edge cases.

CNN architecture for the Australia model; a similar architecture was used for North America. Adding a cloud classification head improves fire classification performance.

To train the network, we used thermal anomalies data from the MODIS and VIIRS polar-orbiting satellites as labels. MODIS and VIIRS have higher spatial accuracy (750–1000 meters) than the geostationary satellites we use as inputs. However, they cover a given location only once every few hours, which occasionally causes them to miss rapidly-advancing fires. Therefore, we use MODIS and VIIRS to construct a training set, but at inference time we rely on the high-frequency imagery from geostationary satellites.

Even when limiting attention to active fires, most pixels in an image are not currently burning. To reduce the model's bias towards non-burning pixels, we upsampled fire pixels in the training set and applied focal loss to encourage improvements in the rare misclassified fire pixels.

The progressing boundary of the 2022 McKinney fire, and a smaller nearby fire.

Evaluation

High-resolution fire signals from polar-orbiting satellites are a plentiful source for training data. However, such satellites use sensors that are similar to geostationary satellites, which increases the risk of systemic labeling errors (e.g., cloud-related misdetections) being incorporated into the model. To evaluate our wildfire tracker model without such bias, we compared it against fire scars (i.e., the shape of the total burnt area) measured by local authorities. Fire scars are obtained after a fire has been contained and are more reliable than real-time fire detection techniques. We compare each fire scar to the union of all fire pixels detected in real time during the wildfire to obtain an image such as the one shown below. In this image, green represents correctly identified burn areas (true positive), yellow represents unburned areas detected as burn areas (false positive), and red represents burn areas that were not detected (false negative).

Example evaluation for a single fire. Pixel size is 1km x 1km.

We compare our models to official fire scars using the precision and recall metrics. To quantify the spatial severity of classification errors, we take the maximum distance between a false positive or false negative pixel and the nearest true positive fire pixel. We then average each metric across all fires. The results of the evaluation are summarized below. Most severe misdetections were found to be a result of errors in the official data, such as a missing scar for a nearby fire.

Test set metrics comparing our models to official fire scars.

We performed two additional experiments on wildfires in the United States (see table below). First, we evaluated an earlier model that relies only on NOAA's GOES-16 and GOES-17 fire products. Our model outperforms this approach in all metrics considered, demonstrating that the raw satellite measurements can be used to enhance the existing NOAA fire product.

Next, we collected a new test set consisting of all large fires in the United States in 2022. This test set was not available during training because the model launched before the fire season began. Evaluating the performance on this test set shows performance in line with expectations from the original test set.

Comparison between models on fires in the United States.


Conclusion

Boundary tracking is part of Google’s wider commitment to bring accurate and up-to-date information to people in critical moments. This demonstrates how we use satellite imagery and ML to track wildfires, and provide real time support to affected people in times of crisis. In the future, we plan to keep improving the quality of our wildfire boundary tracking, to expand this service to more countries and continue our work helping fire authorities access critical information in real time.


Acknowledgements

This work is a collaboration between teams from Google Research, Google Maps and Crisis Response, with support from our partnerships and policy teams. We would also like to thank the fire authorities whom we partner with around the world.



Source: Google AI Blog


Google Research, 2022 & beyond: ML & computer systems


(This is Part 3 in our series of posts covering different topical areas of research at Google. You can find other posts in the series here.)

Great machine learning (ML) research requires great systems. With the increasing sophistication of the algorithms and hardware in use today and with the scale at which they run, the complexity of the software necessary to carry out day-to-day tasks only increases. In this post, we provide an overview of the numerous advances made across Google this past year in systems for ML that enable us to support the serving and training of complex models while easing the complexity of implementation for end users. This blog post also highlights our research on leveraging ML itself to help improve and design the next generations of system stacks.




Distributed systems for ML

This year, we've made significant strides in improving our systems to better support large-scale computation in ML and scientific computing in general. The Google TPU hardware has been designed with scaling in mind since its inception, and each year we strive to push the boundaries even further. This year, we designed state-of-the-art serving techniques for large models, improved automatic partitioning of tensor programs and reworked the APIs of our libraries to make sure all of those developments are accessible to a wide audience of users.

One of our biggest efficiency improvements this year is the CollectiveEinsum strategy for evaluating the large scale matrix multiplication operations that are at the heart of neural networks. Unlike previously popular SPMD partitioning strategies that separate communication from device-local computation, this approach uses the fast TPU ICI links to overlap them, leading to up to 1.38x performance improvements. This algorithm was also a key component of our work on efficiently scaling Transformer inference, which presents a wide variety of strategies that trade off between latency and hardware utilization, reaching state-of-the-art model FLOPs utilization (MFU) of 76% in throughput-optimized configurations.

An illustration of AllGather-Einsum with 2-way intra-layer model parallelism, proposed in CollectiveEinsum strategy. Top: Illustration of non-overlapped execution. Bottom: Illustration of the CollectiveEinsum technique.

We have also integrated SPMD-style partitioning as a first class concept into both TensorFlow, with the DTensor extension, and JAX, with the redesigned array type. In both libraries, tensors that seem complete to the programmer can be transparently sharded over a number of devices just by attaching declarative layout annotations. In fact, both approaches are compatible with existing code written for single-device computations that can now scale into a multi-device program, usually without any code modifications!

Integrating SPMD partitioning into the core of our ML frameworks means that being able to infer and optimize the way array programs are mapped onto a larger set of devices is critical for performance. In the past, this motivated the development of GSPMD, an important milestone in this area. However, GSPMD relies heavily on heuristics, and it still sometimes requires non-trivial decisions to be made manually, which often results in suboptimal performance. To make partitioning inference fully automatic, we collaborated with external colleagues to develop Alpa, a fully automated system that explores strategies for both operator-level (model) parallelism and pipeline parallelism between larger sub-computations. It successfully matches hand-tuned performance on popular models such as Transformers, but is also capable of successfully scaling up other models, such as convolutional networks and mixture-of-experts models that often cause existing automated methods to struggle.

Alpa overview. The inter-operator identifies the best way to assign a subgraph to a submesh. The intra-operator pass finds the best intra-operator parallelism plan for each pipeline stage. Finally, the runtime orchestration generates a static plan that orders the computation and communication.

In a similar vein, the recently published Pathways system adds an additional layer of virtualization on top of the usual TPU runtime — accelerators are managed by long-lived processes instead of being allocated directly to users. A single end user can then connect to an arbitrary number of Pathways-controlled devices and write their program as if all the devices were attached directly to their process, even though in reality they may even span multiple data centers. Thanks to Pathways: (1) job startup time can be reduced, (2) it is easier to achieve fault tolerance, and (3) multitenancy becomes a viable option, enabling multiple jobs to be executed simultaneously for even more efficient hardware utilization. The ease with which Pathways enables computation spanning multiple TPU pods is crucial, as it lets us avoid future scaling bottlenecks.

Pathways overview. Top Left: Distributed computation expressed as a Directed Acyclic Graph. Top Right: The resource manager allocates virtual slices of accelerator meshes for each compiled function (e.g., A, B, and C). Bottom: Centralized schedulers for gang-schedule computations that are then dispatched by per-shard executors. (See paper for details.)

Another notable release is TensorStore, a new library for multi-dimensional array storage. TensorStore is particularly useful for training large language models (LLMs) with multi-controller runtimes, where every process only manages a subset of all parameters, all of which must be collated into a consistent checkpoint. TensorStore provides database-grade guarantees (ACID) for efficient and concurrent multi-dimensional array serialization into many storage backends (e.g., Google Cloud Storage, various filesystems, HTTP servers) and has been successfully used for compute-intensive workloads such as PaLM and reconstructions of the human cortex and fruit fly brain.

A fly brain reconstruction for which the underlying data can be easily accessed and manipulated using TensorStore.

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Programming languages for ML

The robustness and correctness of our technical infrastructure are vital for ML efforts, which is why we remain committed to ensuring that it is built on a sound technical and theoretical basis, backed by cutting-edge research in programming languages and compiler construction.

We continued investing in the open-source MLIR compiler infrastructure, building a more controllable, composable and modular compiler stack. In addition, much progress has been made in code generation for sparse linear algebra and it is now possible to generate both dense and sparse code from almost identical MLIR programs. Finally, we also continued the development of the IREE compiler, preparing it for use on both powerful computers located in data centers and mobile devices such as smartphones.

On the more theoretical side we explored ways to formalize and verify the code-generation techniques we use. We also published a novel approach used to implement and formalize automatic differentiation (AD) systems, which are central to ML libraries. We decomposed the reverse-mode AD algorithm into three independent program transformations, which are significantly simpler and easier to verify, highlighting the unique features of JAX's implementation.

Leveraging programming language techniques, such as abstract interpretation and program synthesis, we successfully reduced the number of resources required to perform a neural architecture search (NAS). This effort, 𝛼NAS, led to the discovery of more efficient models without degradation in accuracy.

In the past year, we published a number of new open-source libraries in the JAX ecosystem, Rax and T5X being just two examples. With the continued effort around jax2tf, JAX models can now be deployed on mobile devices using TensorFlow Lite and on the web using TensorFlow.js.

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Hardware accelerators & ML


Hardware design for ML

The use of customized hardware, such as TPUs and GPUs, has shown tremendous benefits in terms of both performance gain and energy efficiency (hence reducing the carbon footprint). In a recent MLPerf competition, we set new performance records on five benchmarks on TPUs v4, achieving speedups that are on average 1.42x higher than the next fastest submission. However, in order to keep up with recent advances, we are also developing customized hardware architectures for specific popular models.

TPUs demonstrated significant speedup in all five published benchmarks (MLPerf 2.0) over the fastest non-Google submission (NVIDIA on-premises). Taller bars are better. The numbers inside the bars represent the quantity of chips / accelerators used for each of the submissions.

However, building a new hardware accelerator incurs high initial cost and requires significant development and deployment time. To make single-workload accelerators viable, the design cycle time has to be reduced. Full-stack Search Technique (FAST) addresses this problem by introducing a hardware accelerator search framework that simultaneously optimizes data path, scheduling, and important compiler decisions. FAST introduces an approximate template capable of describing diverse types of architectures and versatile memory hierarchy resulting in accelerators that improve single-workload performance per Thermal Design Power (known to highly correlate with performance per Total Cost of Ownership) by 3.7x compared to TPU v3. This shows that single-workload accelerators could be practical for moderate-sized datacenter deployments.


ML for hardware design

To automate the chip design process as much as possible, we continue to push the capabilities of ML at various stages of the hardware design, including high-level architectural exploration, verification, and placement and routing.

We recently open-sourced a distributed RL infrastructure called Circuit Training, along with a circuit environment described in our recent Nature paper. We used this infrastructure in production to produce macro placements for the latest generation of TPU chips. Tackling architectural exploration, PRIME introduces an ML-based approach for searching hardware design space that utilizes only existing data (e.g., from traditional accelerator design efforts) without any further hardware simulation. This approach alleviates the need to run time-consuming simulations, even when the set of target applications changes. PRIME improves performance over state-of-the-art simulation-driven methods by about 1.2x–1.5x while reducing the simulation time by 93%–99%. AutoApprox automatically generates approximate low-power deep learning accelerators without any accuracy loss by mapping each neural network layer to an appropriate approximation level.

PRIME uses logged accelerator data, consisting of both feasible and infeasible accelerators, to train a conservative model, which is used to design accelerators while meeting design constraints. PRIME designs accelerators with up to 1.5x smaller latency, while reducing the required hardware simulation time by up to 99%.

Hardware-dependent model design

While NAS has shown tremendous capability in discovering state-of-the-art models in terms of accuracy and efficiency, it is still limited by lack of hardware knowledge. Platform-aware NAS addresses this gap by incorporating knowledge of the hardware architecture into the design of the NAS search space. The resulting EfficientNet-X model is 1.5x–2x faster than EfficientNet on TPU v3 and GPU v100, respectively, with similar accuracy. Both platform-aware NAS and EfficientNet-X have been deployed in production, demonstrating significant accuracy gains and up to ~40% efficiency improvement for various production vision models. NaaS goes even further by searching for neural network architectures and hardware architectures together. Using this approach on Edge TPUs, NaaS discovers vision models that are 2x more energy efficient with the same accuracy.

Overview of platform-aware NAS on TPUs/GPUs, highlighting the search space and search objectives.

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ML for navigating constrained search spaces

Apart from changing the hardware and the workload for better efficiency, we can also optimize the middle layer, including the partitioner, which maps the workload onto multiple devices, and the compiler, which translates the workload into a low-level presentation understood by the hardware. In previous years, we demonstrated how we can apply ML to find better device placement and compiler decisions. In the past year, we further explored this direction and found that many optimization search spaces are heavily constrained, where valid solutions are quite sparse.

To address this challenge, we developed several techniques to enable a learned model to effectively navigate a constrained search space. Telamalloc employs a combination of ML model plus heuristics to make a decision when multiple options are available, and leverages a constraint solver to infer further dependent decisions. Telamalloc speeds up the memory allocation pass in the Edge TPU compiler compared to a production Integer Linear Programming approach and enables important real-world models that could not otherwise be supported.

A Transferable Approach for Partitioning Machine Learning Models on Multi-Chip-Modules” proposes a slightly different approach. It applies reinforcement learning (RL) to propose the decisions in a single step, and asks the constraint solver to adjust the proposed solution to be valid. For a BERT model on an Edge TPU-based multi-chip mesh, this approach discovers a better distribution of the model across devices using a much smaller time budget compared to non-learned search strategies.

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ML for large-scale production systems

We also deployed ML to improve efficiency of various large-scale systems running in production. We recently released MLGO, the first industrial-grade general framework for integrating ML techniques systematically in the LLVM infrastructure. MLGO can replace heuristics in LLVM with an RL policy to make optimization decisions. When testing on a set of internal large-scale applications, we found that the trained policy can reduce binary size by 3%–7% when optimizing inlining decisions and can improve throughput by 0.3% ~1.5% when optimizing register allocation decisions. Within our production ML compiler, XLA, a learned cost model published a few years back, was recently deployed to guide the selection of optimal tile sizes of TPU kernels for top ML workloads, saving ~2% of the total TPU compute time in our data centers overall.We also recently replaced an existing heuristic in YouTube cache replacement algorithm with a new hybrid algorithm that combines a simple heuristic with a learned model, improving byte miss ratio at the peak by ~9%.

Illustration of MLGO during inlining. “#bbs”, “#users”, and “callsite height” are example caller-callee pair features.

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AI & sustainability

Given the global climate change crisis, there has been understandable concern about the environmental impact of ML. In a recent paper, we showed that by following best practices, ML practitioners can reduce carbon dioxide equivalent emissions (CO2e) from training by orders of magnitude. We call the practices the “4Ms”

  1. Model. The first step is to select the most efficient ML model architecture. For example, Primer runs ~4x faster on the same hardware while achieving the same quality scores than the popular Transformer developed four years earlier.
  2. Machine. The second practice is to use the most energy efficient computer available. For example, when the Transformer model was first published in 2017, a popular GPU was the Nvidia P100. Using a recent processor optimized for ML training, such as TPU v4, improves performance per Watt by ~15x.
  3. Mechanization. Computers for training needed to be housed in a data center. Large cloud data centers are typically ~1.4x more energy-efficient than the typical smaller on-premise data center.
  4. Map. The biggest surprise in our investigation was the impact on the cleanliness of the energy supply by picking the best location. Moreover, in the cloud, location is the easiest of the four factors to change. The difference between a typical location and a well chosen location can be ~9x, even within the same country.

In this example, multiplying the 4Ms together yields a 4x × 15x × 1.4x × 9x or ~750x reduction in CO2e over four years by following the best practices over the training of the original Transformer model using GPUs of 2017.

We are continuing to explore this space and in 2023 we will be releasing a further study that demonstrates how to reduce the CO2e of current model training by up to 20x by carefully selecting the machine, mechanization and location of training.

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Concluding thoughts

As the field of ML advances, we continue our investment in developing high-performance, energy-efficient, and easy-to-use systems and infrastructure to enable rapid exploration of new ideas. At the same time, we continue to explore the capability of ML to improve the performance of complex systems and automate labor-intensive tasks in system design.


Google Research, 2022 & beyond

This was the second blog post in the “Google Research, 2022 & Beyond” series. Other posts in this series are listed in the table below:


Language Models Computer Vision Multimodal Models
Generative Models Responsible AI ML & Computer Systems
Algorithms* Robotics Health
General Science & Quantum Community Engagement

* Articles will be linked as they are released.

Source: Google AI Blog


Dev Channel Update for Desktop

The dev channel has been updated to 111.0.5563.8 for Windows, Linux and Mac.


A partial list of changes is available in the log. Interested in switching release channels? Find out how. If you find a new issue, please let us know by filing a bug. The community help forum is also a great place to reach out for help or learn about common issues.

Prudhvikumar Bommana
Google Chrome

Interview with Top Kotlin Contributors – Highlighting their Contributions to the Google Dev Library

Posted by Swathi Dharshna Subbaraj, Project Coordinator, Google Dev Library

In May 2017, Google recognized the potential of Kotlin in the mobile developer community and made it an official language for Android development. As a result, talented developers in the Kotlin community used this robust programming language to build inspiring tools and open-source projects. This can be seen in the Google Dev Library, where developers have contributed extensively.

This article will showcase some of our Kotlin Google Developer Experts (GDEs) who have made significant contributions to the Google Dev Library. We hope these projects will inspire and guide your development efforts.
 

Contributors in Spotlight :


Nicola Corti

Nicola contributed Detekt to Google Dev Library, which is a static code analysis tool for Kotlin projects that helps developers detect and report on issues related to security, style, and best practices. It can be used to identify potential vulnerabilities, enforce coding standards, and improve code quality.

How did you get started in Kotlin? Is there any particular project that inspired you?

I began working with Kotlin in its early days of 2015. Though the experience was rocky, the ability to code Android apps in Kotlin rather than Java was a game-changer. At the time, it was challenging to convince my colleagues to switch due to the Java-dominant community. However, the official announcement of Kotlin support at Google I/O 2017 was a defining moment for the language. I am grateful for the ongoing support and development of such a powerful and versatile language, as well as the dedicated community that supports it daily.

I am grateful for the ongoing support and development of such a powerful and versatile language, and the dedicated community that supports it daily. Nicola Corti, GDE Kotlin 

What inspired you to inherit the Detekt project ?

Detekt, a static code analyzer tool, is not a creation of mine, but rather a project that I inherited from a friend who sought support in managing it. I have always been impressed by the capabilities of static code analyzers, particularly in terms of their ability to detect bugs and save developer time. At the time, the market for Kotlin static analyzers was relatively new, making Detekt a valuable addition to the toolkits of many Kotlin developers.

As a Kotlin GDE, what is the one piece of advice for someone who has just started as a Kotlin developer?

I highly recommend getting involved in the open-source community. My contributions to open-source projects have taught me invaluable skills and knowledge that I wouldn't have gained otherwise. Additionally, I have had the opportunity to connect with incredible contributors who have since become friends. Participating in open-source not only benefits yourself, but also the wider developer community.

John O'Reilly

John created the PeopleInSpace project, and shared it with Google Dev Library. The project utilizes the OpenNotify API to display information about people currently in space, such as their names, nationalities, and spacecraft. The focus of the project is more about demonstrating use of Kotlin Multiplatform.

How did you get started in Kotlin? Is there any particular project that inspired you?
In 2010, I began my career as an Android developer, utilizing Java as my primary programming language. As a Java backend developer for the previous decade, the transition was relatively seamless. However, it wasn't until the official announcement of Kotlin support at Google I/O 2017, that I fully realized the potential impact of this new programming language. Gradually, as my team and I started migrating to Kotlin, I came to appreciate how productive and expressive a language it was to use.

As my team and I started migrating to Kotlin, I came to appreciate how productive and expressive a language it was to use.  - John O'Reilly, GDE Kotlin

What inspired you to develop and open source the Peopleinspace project?

In 2018, I was introduced to Kotlin Multiplatform (KMP) and was immediately impressed by its practical and efficient approach to code sharing. At the time, there was still a lot of uncertainty and confusion surrounding KMP, and I saw a need for a simple, easy-to-understand sample project that could demonstrate the basics of KMP.

I had an existing open-source project, GalwayBus, which I initially used to experiment with KMP, Jetpack Compose and SwiftUI as they became available. However, this project had a significant amount of legacy code and was not ideal for showcasing the essentials of KMP.

In late 2019, I came across an article by Ken Kousen that included sample code using retrofit to retrieve a list of people in space. I realized that this could be the perfect foundation for the minimal project I had been envisioning. So, I created PeopleInSpace, a project designed to encapsulate the core elements of a KMP project, and provide a clear and concise demonstration of how the various components work together.

As a Kotlin GDE, what is the one piece of advice for someone who has just started as a Kotlin developer?

Kotlin is a powerful language that offers many advanced features; however, it is possible to be very productive when starting out without needing to use those, in many cases, there are simpler alternatives that can be used, and as you become more familiar with the language, you can gradually explore and implement the more advanced options.

Join the global community of Kotlin developers and share your open source projects or technical blogs on Dev Library. To contribute, submit your content here.

How to learn Kotlin: JetBrains, the company behind the Kotlin language, offers certificate courses and learning tools for developers and has an active user groups forum where developers get support with programming language-related issues.

Open Source Vizier: Towards reliable and flexible hyperparameter and blackbox optimization

Google Vizier is the de-facto system for blackbox optimization over objective functions and hyperparameters across Google, having serviced some of Google’s largest research efforts and optimized a wide range of products (e.g., Search, Ads, YouTube). For research, it has not only reduced language model latency for users, designed computer architectures, accelerated hardware, assisted protein discovery, and enhanced robotics, but also provided a reliable backend interface for users to search for neural architectures and evolve reinforcement learning algorithms. To operate at the scale of optimizing thousands of users’ critical systems and tuning millions of machine learning models, Google Vizier solved key design challenges in supporting diverse use cases and workflows, while remaining strongly fault-tolerant.

Today we are excited to announce Open Source (OSS) Vizier (with an accompanying systems whitepaper published at AutoML Conference 2022), a standalone Python package based on Google Vizier. OSS Vizier is designed for two main purposes: (1) managing and optimizing experiments at scale in a reliable and distributed manner for users, and (2) developing and benchmarking algorithms for automated machine learning (AutoML) researchers.


System design

OSS Vizier works by having a server provide services, namely the optimization of blackbox objectives, or functions, from multiple clients. In the main workflow, a client sends a remote procedure call (RPC) and asks for a suggestion (i.e., a proposed input for the client’s blackbox function), from which the service begins to spawn a worker to launch an algorithm (i.e., a Pythia policy) to compute the following suggestions. The suggestions are then evaluated by clients to form their corresponding objective values and measurements, which are sent back to the service. This pipeline is repeated multiple times to form an entire tuning trajectory.

The use of the ubiquitous gRPC library, which is compatible with most programming languages, such as C++ and Rust, allows maximum flexibility and customization, where the user can also write their own custom clients and even algorithms outside of the default Python interface. Since the entire process is saved to an SQL datastore, a smooth recovery is ensured after a crash, and usage patterns can be stored as valuable datasets for research into meta-learning and multitask transfer-learning methods such as the OptFormer and HyperBO.

In the distributed pipeline, multiple clients each send a “Suggest” request to the Service API, which produces Suggestions for the clients using Pythia. The clients evaluate these suggestions and return measurements. All transactions are stored to allow fault-tolerance.

Usage

Because of OSS Vizier’s emphasis as a service, in which clients can send requests to the server at any point in time, it is thus designed for a broad range of scenarios — the budget of evaluations, or trials, can range from tens to millions, and the evaluation latency can range from seconds to weeks. Evaluations can be done asynchronously (e.g., tuning an ML model) or in synchronous batches (e.g., wet lab settings involving multiple simultaneous experiments). Furthermore, evaluations may fail due to transient errors and be retried, or may fail due to persistent errors (e.g., the evaluation is impossible) and should not be retried.

This broadly supports a variety of applications, which include hyperparameter tuning deep learning models or optimizing non-computational objectives, which can be e.g., physical, chemical, biological, mechanical, or even human-evaluated, such as cookie recipes.

The OSS Vizier API allows (1) developers to integrate other packages, with PyGlove and Vertex Vizier already included, and (2) users to optimize their experiments, such as machine learning pipelines and cookie recipes.

Integrations, algorithms, and benchmarks

As Google Vizier is heavily integrated with many of Google’s internal frameworks and products, OSS Vizier will naturally be heavily integrated with many of Google’s open source and external frameworks. Most prominently, OSS Vizier will serve as a distributed backend for PyGlove to allow large-scale evolutionary searches over combinatorial primitives such as neural architectures and reinforcement learning algorithms. Furthermore, OSS Vizier shares the same client-based API with Vertex Vizier, allowing users to quickly swap between open-source and production-quality services.

For AutoML researchers, OSS Vizier is also outfitted with a useful collection of algorithms and benchmarks (i.e., objective functions) unified under common APIs for assessing the strengths and weaknesses of proposed methods. Most notably, via TensorFlow Probability, researchers can now use the JAX-based Gaussian Process Bandit algorithm, based on the default algorithm in Google Vizier that tunes internal users’ objectives.


Resources and future direction

We provide links to the codebase, documentation, and systems whitepaper. We plan to allow user contributions, especially in the form of algorithms and benchmarks, and further integrate with the open-source AutoML ecosystem. Going forward, we hope to see OSS Vizier as a core tool for expanding research and development over blackbox optimization and hyperparameter tuning.


Acknowledgements

OSS Vizier was developed by members of the Google Vizier team in collaboration with the TensorFlow Probability team: Setareh Ariafar, Lior Belenki, Emily Fertig, Daniel Golovin, Tzu-Kuo Huang, Greg Kochanski, Chansoo Lee, Sagi Perel, Adrian Reyes, Xingyou (Richard) Song, and Richard Zhang.

In addition, we thank Srinivas Vasudevan, Jacob Burnim, Brian Patton, Ben Lee, Christopher Suter, and Rif A. Saurous for further TensorFlow Probability integrations, Daiyi Peng and Yifeng Lu for PyGlove integrations, Hao Li for Vertex/Cloud integrations, Yingjie Miao for AutoRL integrations, Tom Hennigan, Varun Godbole, Pavel Sountsov, Alexey Volkov, Mihir Paradkar, Richard Belleville, Bu Su Kim, Vytenis Sakenas, Yujin Tang, Yingtao Tian, and Yutian Chen for open source and infrastructure help, and George Dahl, Aleksandra Faust, Claire Cui, and Zoubin Ghahramani for discussions.

Finally we thank Tom Small for designing the animation for this post.

Source: Google AI Blog


Chrome Dev for Android Update

Hi everyone! We've just released Chrome Dev 111 (111.0.5563.8) for Android. It's now available on Google Play.

You can see a partial list of the changes in the Git log. For details on new features, check out the Chromium blog, and for details on web platform updates, check here.

If you find a new issue, please let us know by filing a bug.

Harry Souders
Google Chrome

Early Stable Update for Desktop

The Stable channel has been updated to 110.0.5481.77 for Windows and Mac as part of our early stable release to a small percentage of users. A full list of changes in this build is available in the log.


You can find more details about early Stable releases here.

Interested in switching release channels?  Find out how here. If you find a new issue, please let us know by filing a bug. The community help forum is also a great place to reach out for help or learn about common issues.


Daniel Yip

Google Chrome