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Lenders can better serve their borrowers with more data and better math. Zest AI has successfully built a compliant, consistent, and equitable AI-automated underwriting technology that lenders can utilize to help make their credit decisions.
While artificial intelligence AI systems have been a tool historically used by sophisticated investors to maximize their returns, newer and more advanced AI systems will be the key innovation to democratize access to financial systems in the future.
D espite privacy, ethics, and bias issues that remain to be resolved with AI systems, the good news is that as large r datasets become progressively easier to interconnect, AI and related natural language processing NLP technology innovations are increasingly able to equalize access. T he even better news is that this democratization is taking multiple forms. AI can be used to provide risk assessments necessary to bank those under-served or denied access.
AI systems can also retrieve troves of data not used in traditional credit reports, including personal cash flow, payment applications usage, on-time utility payments, and other data buried within large datasets, to create fair and more accurate risk assessments essential to obtain credit and other financial services. By expanding credit availability to historically underserved communities, AI enables them to gain credit and build wealth.
Additionally, personalized portfolio management will become available to more people with the implementation and advancement of AI. Sophisticated financial advice and routine oversight, typically reserved for traditional investors, will allow individuals, including marginalized and low-income people, to maximize the value of their financial portfolios.
Moreover, when coupled with NLP technologies, even greater democratization can result as inexperienced investors can interact with AI systems in plain English, while providing an easier interface to financial markets than existing execution tools. Open finance technology enables millions of people to use the apps and services that they rely on to manage their financial lives — from overdraft protection, to money management, investing for retirement, or building credit.
More than 8 in 10 Americans are now using digital finance tools powered by open finance. This is because consumers see something they like or want — a new choice, more options, or lower costs. What is open finance? At its core, it is about putting consumers in control of their own data and allowing them to use it to get a better deal. When people can easily switch to another company and bring their financial history with them, that presents real competition to legacy services and forces everyone to improve, with positive results for consumers.
For example, we see the impact this is having on large players being forced to drop overdraft fees or to compete to deliver products consumers want. We see the benefits of open finance first hand at Plaid, as we support thousands of companies, from the biggest fintechs, to startups, to large and small banks. All are building products that depend on one thing - consumers' ability to securely share their data to use different services.
Open finance has supported more inclusive, competitive financial systems for consumers and small businesses in the U. and across the globe — and there is room to do much more. As an example, the National Consumer Law Consumer recently put out a new report that looked at consumers providing access to their bank account data so their rent payments could inform their mortgage underwriting and help build credit.
This is part of the promise of open finance. At Plaid, we believe a consumer should have a right to their own data, and agency over that data, no matter where it sits. This will be essential to securing benefits of open finance for consumers for many years to come. As AWS preps for its annual re:Invent conference, Adam Selipsky talks product strategy, support for hybrid environments, and the value of the cloud in uncertain economic times.
Donna Goodison dgoodison is Protocol's senior reporter focusing on enterprise infrastructure technology, from the 'Big 3' cloud computing providers to data centers. She previously covered the public cloud at CRN after 15 years as a business reporter for the Boston Herald. AWS is gearing up for re:Invent, its annual cloud computing conference where announcements this year are expected to focus on its end-to-end data strategy and delivering new industry-specific services.
Both prongs of that are important. But cost-cutting is a reality for many customers given the worldwide economic turmoil, and AWS has seen an increase in customers looking to control their cloud spending. By the way, they should be doing that all the time. The motivation's just a little bit higher in the current economic situation. This interview has been edited and condensed for clarity. Besides the sheer growth of AWS, what do you think has changed the most while you were at Tableau?
Were you surprised by anything? The number of customers who are now deeply deployed on AWS, deployed in the cloud, in a way that's fundamental to their business and fundamental to their success surprised me. There was a time years ago where there were not that many enterprise CEOs who were well-versed in the cloud. It's not just about deploying technology. The conversation that I most end up having with CEOs is about organizational transformation. It is about how they can put data at the center of their decision-making in a way that most organizations have never actually done in their history.
And it's about using the cloud to innovate more quickly and to drive speed into their organizations. Those are cultural characteristics, not technology characteristics, and those have organizational implications about how they organize and what teams they need to have. It turns out that while the technology is sophisticated, deploying the technology is arguably the lesser challenge compared with how do you mold and shape the organization to best take advantage of all the benefits that the cloud is providing.
How has your experience at Tableau affected AWS and how you think about putting your stamp on AWS? I, personally, have just spent almost five years deeply immersed in the world of data and analytics and business intelligence, and hopefully I learned something during that time about those topics. I'm able to bring back a real insider's view, if you will, about where that world is heading — data, analytics, databases, machine learning, and how all those things come together, and how you really need to view what's happening with data as an end-to-end story.
It's not about having a point solution for a database or an analytic service, it's really about understanding the flow of data from when it comes into your organization all the way through the other end, where people are collaborating and sharing and making decisions based on that data. AWS has tremendous resources devoted in all these areas.
Can you talk about the intersection of data and machine learning and how you see that playing out in the next couple of years? What we're seeing is three areas really coming together: You've got databases, analytics capabilities, and machine learning, and it's sort of like a Venn diagram with a partial overlap of those three circles.
There are areas of each which are arguably still independent from each other, but there's a very large and a very powerful intersection of the three — to the point where we've actually organized inside of AWS around that and have a single leader for all of those areas to really help bring those together. There's so much data in the world, and the amount of it continues to explode. We were saying that five years ago, and it's even more true today.
The rate of growth is only accelerating. It's a huge opportunity and a huge problem. A lot of people are drowning in their data and don't know how to use it to make decisions. Other organizations have figured out how to use these very powerful technologies to really gain insights rapidly from their data.
What we're really trying to do is to look at that end-to-end journey of data and to build really compelling, powerful capabilities and services at each stop in that data journey and then…knit all that together with strong concepts like governance. By putting good governance in place about who has access to what data and where you want to be careful within those guardrails that you set up, you can then set people free to be creative and to explore all the data that's available to them.
AWS has more than services now. Have you hit the peak for that or can you sustain that growth? We're not done building yet, and I don't know when we ever will be. We continue to both release new services because customers need them and they ask us for them and, at the same time, we've put tremendous effort into adding new capabilities inside of the existing services that we've already built.
We don't just build a service and move on. Inside of each of our services — you can pick any example — we're just adding new capabilities all the time. One of our focuses now is to make sure that we're really helping customers to connect and integrate between our different services. So those kinds of capabilities — both building new services, deepening our feature set within existing services, and integrating across our services — are all really important areas that we'll continue to invest in.
Do customers still want those fundamental building blocks and to piece them together themselves, or do they just want AWS to take care of all that? There's no one-size-fits-all solution to what customers want. It is interesting, and I will say somewhat surprising to me, how much basic capabilities, such as price performance of compute, are still absolutely vital to our customers. But it's absolutely vital. Part of that is because of the size of datasets and because of the machine learning capabilities which are now being created.
They require vast amounts of compute, but nobody will be able to do that compute unless we keep dramatically improving the price performance.
We also absolutely have more and more customers who want to interact with AWS at a higher level of abstraction…more at the application layer or broader solutions, and we're putting a lot of energy, a lot of resources, into a number of higher-level solutions. One of the biggest of those … is Amazon Connect, which is our contact center solution. In minutes or hours or days, you can be up and running with a contact center in the cloud.
At the beginning of the pandemic, Barclays … sent all their agents home. In something like 10 days, they got 6, agents up and running on Amazon Connect so they could continue servicing their end customers with customer service. We've built a lot of sophisticated capabilities that are machine learning-based inside of Connect. We can do call transcription, so that supervisors can help with training agents and services that extract meaning and themes out of those calls.
We don't talk about the primitive capabilities that power that, we just talk about the capabilities to transcribe calls and to extract meaning from the calls. It's really important that we provide solutions for customers at all levels of the stack. Given the economic challenges that customers are facing, how is AWS ensuring that enterprises are getting better returns on their cloud investments?
Now's the time to lean into the cloud more than ever, precisely because of the uncertainty. We saw it during the pandemic in early , and we're seeing it again now, which is, the benefits of the cloud only magnify in times of uncertainty. For example, the one thing which many companies do in challenging economic times is to cut capital expense.
For most companies, the cloud represents operating expense, not capital expense. You're not buying servers, you're basically paying per unit of time or unit of storage.
That provides tremendous flexibility for many companies who just don't have the CapEx in their budgets to still be able to get important, innovation-driving projects done. Another huge benefit of the cloud is the flexibility that it provides — the elasticity, the ability to dramatically raise or dramatically shrink the amount of resources that are consumed. You can only imagine if a company was in their own data centers, how hard that would have been to grow that quickly.
The ability to dramatically grow or dramatically shrink your IT spend essentially is a unique feature of the cloud. These kinds of challenging times are exactly when you want to prepare yourself to be the innovators … to reinvigorate and reinvest and drive growth forward again. We've seen so many customers who have prepared themselves, are using AWS, and then when a challenge hits, are actually able to accelerate because they've got competitors who are not as prepared, or there's a new opportunity that they spot.
We see a lot of customers actually leaning into their cloud journeys during these uncertain economic times. Do you still push multi-year contracts, and when there's times like this, do customers have the ability to renegotiate?
Many are rapidly accelerating their journey to the cloud. Some customers are doing some belt-tightening. What we see a lot of is folks just being really focused on optimizing their resources, making sure that they're shutting down resources which they're not consuming. You do see some discretionary projects which are being not canceled, but pushed out. Every customer is free to make that choice.
But of course, many of our larger customers want to make longer-term commitments, want to have a deeper relationship with us, want the economics that come with that commitment. We're signing more long-term commitments than ever these days. We provide incredible value for our customers, which is what they care about.
That kind of analysis would not be feasible, you wouldn't even be able to do that for most companies, on their own premises. So some of these workloads just become better, become very powerful cost-savings mechanisms, really only possible with advanced analytics that you can run in the cloud.
In other cases, just the fact that we have things like our Graviton processors and … run such large capabilities across multiple customers, our use of resources is so much more efficient than others. We are of significant enough scale that we, of course, have good purchasing economics of things like bandwidth and energy and so forth. So, in general, there's significant cost savings by running on AWS, and that's what our customers are focused on.
The margins of our business are going to … fluctuate up and down quarter to quarter. It will depend on what capital projects we've spent on that quarter. Obviously, energy prices are high at the moment, and so there are some quarters that are puts, other quarters there are takes. The important thing for our customers is the value we provide them compared to what they're used to.
And those benefits have been dramatic for years, as evidenced by the customers' adoption of AWS and the fact that we're still growing at the rate we are given the size business that we are.
That adoption speaks louder than any other voice. Do you anticipate a higher percentage of customer workloads moving back on premises than you maybe would have three years ago? Absolutely not. We're a big enough business, if you asked me have you ever seen X, I could probably find one of anything, but the absolute dominant trend is customers dramatically accelerating their move to the cloud.
Moving internal enterprise IT workloads like SAP to the cloud, that's a big trend. Creating new analytics capabilities that many times didn't even exist before and running those in the cloud. More startups than ever are building innovative new businesses in AWS.
Our public-sector business continues to grow, serving both federal as well as state and local and educational institutions around the world. It really is still day one. The opportunity is still very much in front of us, very much in front of our customers, and they continue to see that opportunity and to move rapidly to the cloud.
In general, when we look across our worldwide customer base, we see time after time that the most innovation and the most efficient cost structure happens when customers choose one provider, when they're running predominantly on AWS. A lot of benefits of scale for our customers, including the expertise that they develop on learning one stack and really getting expert, rather than dividing up their expertise and having to go back to basics on the next parallel stack.
That being said, many customers are in a hybrid state, where they run IT in different environments. In some cases, that's by choice; in other cases, it's due to acquisitions, like buying companies and inherited technology.
We understand and embrace the fact that it's a messy world in IT, and that many of our customers for years are going to have some of their resources on premises, some on AWS.
Some may have resources that run in other clouds. We want to make that entire hybrid environment as easy and as powerful for customers as possible, so we've actually invested and continue to invest very heavily in these hybrid capabilities. A lot of customers are using containerized workloads now, and one of the big container technologies is Kubernetes.
We have a managed Kubernetes service, Elastic Kubernetes Service, and we have a … distribution of Kubernetes Amazon EKS Distro that customers can take and run on their own premises and even use to boot up resources in another public cloud and have all that be done in a consistent fashion and be able to observe and manage across all those environments. So we're very committed to providing hybrid capabilities, including running on premises, including running in other clouds, and making the world as easy and as cost-efficient as possible for customers.
Can you talk about why you brought Dilip Kumar, who was Amazon's vice president of physical retail and tech, into AWS as vice president applications and how that will play out? He's a longtime, tenured Amazonian with many, many different roles — important roles — in the company over a many-year period. Dilip has come over to AWS to report directly to me, running an applications group. We do have more and more customers who want to interact with the cloud at a higher level — higher up the stack or more on the application layer.
We talked about Connect, our contact center solution, and we've also built services specifically for the healthcare industry like a data lake for healthcare records called Amazon HealthLake. We've built a lot of industrial services like IoT services for industrial settings, for example, to monitor industrial equipment to understand when it needs preventive maintenance. We have a lot of capabilities we're building that are either for … horizontal use cases like Amazon Connect or industry verticals like automotive, healthcare, financial services.
We see more and more demand for those, and Dilip has come in to really coalesce a lot of teams' capabilities, who will be focusing on those areas. You can expect to see us invest significantly in those areas and to come out with some really exciting innovations. Would that include going into CRM or ERP or other higher-level, run-your-business applications?
I don't think we have immediate plans in those particular areas, but as we've always said, we're going to be completely guided by our customers, and we'll go where our customers tell us it's most important to go next. It's always been our north star. Correction: This story was updated Nov.
Bennett Richardson bennettrich is the president of Protocol. Prior to joining Protocol in , Bennett was executive director of global strategic partnerships at POLITICO, where he led strategic growth efforts including POLITICO's European expansion in Brussels and POLITICO's creative agency POLITICO Focus during his six years with the company.
Prior to POLITICO, Bennett was co-founder and CMO of Hinge, the mobile dating company recently acquired by Match Group. Bennett began his career in digital and social brand marketing working with major brands across tech, energy, and health care at leading marketing and communications agencies including Edelman and GMMB. Bennett is originally from Portland, Maine, and received his bachelor's degree from Colgate University. Prior to joining Protocol in , he worked on the business desk at The New York Times, where he edited the DealBook newsletter and wrote Bits, the weekly tech newsletter.
He has previously worked at MIT Technology Review, Gizmodo, and New Scientist, and has held lectureships at the University of Oxford and Imperial College London. He also holds a doctorate in engineering from the University of Oxford. We launched Protocol in February to cover the evolving power center of tech.
It is with deep sadness that just under three years later, we are winding down the publication. As of today, we will not publish any more stories. All of our newsletters, apart from our flagship, Source Code, will no longer be sent.
Source Code will be published and sent for the next few weeks, but it will also close down in December. Building this publication has not been easy; as with any small startup organization, it has often been chaotic. But it has also been hugely fulfilling for those involved. We could not be prouder of, or more grateful to, the team we have assembled here over the last three years to build the publication.
They are an inspirational group of people who have gone above and beyond, week after week. Today, we thank them deeply for all the work they have done. We also thank you, our readers, for subscribing to our newsletters and reading our stories. We hope you have enjoyed our work. As companies expand their use of AI beyond running just a few machine learning models, and as larger enterprises go from deploying hundreds of models to thousands and even millions of models, ML practitioners say that they have yet to find what they need from prepackaged MLops systems.
As companies expand their use of AI beyond running just a few machine learning models, ML practitioners say that they have yet to find what they need from prepackaged MLops systems. Kate Kaye is an award-winning multimedia reporter digging deep and telling print, digital and audio stories.
She covers AI and data for Protocol. Her reporting on AI and tech ethics issues has been published in OneZero, Fast Company, MIT Technology Review, CityLab, Ad Age and Digiday and heard on NPR. Kate is the creator of RedTailMedia. org and is the author of "Campaign ' A Turning Point for Digital Media," a book about how the presidential campaigns used digital media and data.
On any given day, Lily AI runs hundreds of machine learning models using computer vision and natural language processing that are customized for its retail and ecommerce clients to make website product recommendations, forecast demand, and plan merchandising. And he said that while some MLops systems can manage a larger number of models, they might not have desired features such as robust data visualization capabilities or the ability to work on premises rather than in cloud environments.
As companies expand their use of AI beyond running just a few ML models, and as larger enterprises go from deploying hundreds of models to thousands and even millions of models, many machine learning practitioners Protocol interviewed for this story say that they have yet to find what they need from prepackaged MLops systems. Companies hawking MLops platforms for building and managing machine learning models include tech giants like Amazon, Google, Microsoft, and IBM and lesser-known vendors such as Comet, Cloudera, DataRobot, and Domino Data Lab.
It's actually a complex problem. Intuit also has constructed its own systems for building and monitoring the immense number of ML models it has in production, including models that are customized for each of its QuickBooks software customers.
The model must recognize those distinctions. For instance, Hollman said the company built an ML feature management platform from the ground up.
For companies that have been forced to go DIY, building these platforms themselves does not always require forging parts from raw materials. Live Tv Live MGN TV BN Channel news program local news anchor. home Aktivitas Warga Sejumlah Desa di Lumajang Terhenti Pasca-Erupsi Gunung Semeru. Aktivitas Warga Sejumlah Desa di Lumajang Terhenti Pasca-Erupsi Gunung Semeru 5 December SHARE NOW. Tag gunung semeru.
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Previous release can be found at v1. Welcome to the v1. This is a pre-release of containerd. The eighth major release of containerd includes new functionality alongside many improvements. This release is intended to be the last major release of containerd 1. x before 2. Some functionality in this release may be considered experimental or unstable, but will become stable or default in 2.
This release still adheres to our backwards compability guarantees and users who do not use or enable new functionality should use this release with the same stability expectations. The previous 1. This is a beta release and includes some functionality which is not yet complete. While most APIs are finalized before merge, they are subject to change until the official release.
The sandbox API provides a new way of managing containerd's shim, providing more flexibility and functionality for multi-container environments such as Pods and VMs.
This API makes it easier to manage these groups of containers at a higher level and offers new extension points for shim implementations and clients. The transfer service provides a simple interface to transfer artifact objects between any source and destination. This allows for pull and push operations to be done in containerd whether requested from clients or plugins.
It is experimental in this release to allow for further plugin development and integration into existing plugins. See the Transfer Docs. The Node Resource Interface is a common framework for plugging extensions into OCI-compatible container runtimes. It provides basic mechanisms for plugins to track the state of containers and to make limited changes to their configuration.
This release introduces NRI v0. There are multiple places in the code today which are being targeted for refactoring to make long term support easier and to provide more extension points. The CRI plugin is the most complex containerd plugin with a wide range of functionality.
A major effort in this release and before 2. The new sandbox and distribution interfaces provide one example of this, but it also being done for image and network management. The version of ttrpc has been updated this release to support streaming, allowing existing grpc services to use ttrpc. Services are being refactored to allow ttrpc implementations, which can be served via shim and accessed using the new sandbox management capability. Remove gogoproto. customtype Remove all gogoproto extensions Migrate off from github.
ttrpc streaming ttrpc Add unpack interface for client Add collectible resources to metadata gc Existing CRI configurations will be supported until 2. Any functionality split out of CRI will have their configuration migrated to new plugins.
Deprecated configuration versions and configurations for deprecated features will be removed in 2. The 2. Features deprecated in this release include. The twelfth patch release for containerd 1. The sixteenth patch release for containerd 1. The fifteenth patch release for containerd 1.
The tenth patch release for containerd 1. The ninth patch release for containerd 1. The fourteenth patch release for containerd 1. Skip to content. Star containerd 1. dmcgowan Derek McGowan. GPG key ID: F58C5D0AACDB. This commit was created on GitHub. GPG key ID: 4AEE18F83AFDEB The thirteenth patch release for containerd 1. Notable Updates Update overlay snapshotter to check for tmpfs when evaluating usage of userxattr Update hcsschim to v0. mod: Bump hcsshim to v0.
Assets 32 containerd Source code zip. Source code tar. Show all 32 assets. This is a pre-release of containerd The eighth major release of containerd includes new functionality alongside many improvements. Highlights Sandbox API experimental The sandbox API provides a new way of managing containerd's shim, providing more flexibility and functionality for multi-container environments such as Pods and VMs.
Sandbox API CRI Sandbox API Implementation Transfer Service experimental Transfer Service The transfer service provides a simple interface to transfer artifact objects between any source and destination.
See the Transfer Docs NRI experimental Extend NRI scope nri 16 Support for updated NRI The Node Resource Interface is a common framework for plugging extensions into OCI-compatible container runtimes. See the NRI Docs Platform Support Linux containers on FreeBSD Runtime Features Add support for CDI device injection Support for cgroups blockio Add restart policy for enhanced restart manager Road to 2.
Deprecation The 2. Contributors Kazuyoshi Kato Derek McGowan Phil Estes Maksym Pavlenko Wei Fu Akihiro Suda Sebastiaan van Stijn Samuel Karp Mike Brown Krisztian Litkey Daniel Canter Ye Sijun yanggang Adam Korcz Ed Bartosh Stefan Berger Gabriel Adrian Samfira Nashwan Azhari Paul "TBBle" Hampson Luca Comellini ruiwen-zhao xin. li Austin Vazquez Brian Goff Shengjing Zhu zounengren Danny Canter Gavin Inglis Henry Wang Iceber Gu Justin Terry Swagat Bora wanglei Akhil Mohan Kevin Parsons lengrongfu Michael Crosby Paco Xu Qiutong Song Shiming Zhang James Jenkins Michael Zappa Tobias Klauser guodong pigletfly Amit Barve Antonio Ojea Cameron Sparr Craig Ingram Eng Zer Jun Eric Lin James Sturtevant Jess Jonny Stoten Juan Hoyos Kang.
Zhang Mikko Ylinen Paul Cacheux Paul S. Schweigert Vincent Batts Yasin Turan bin liu cosmoer dependabot[bot] Abirdcfly Aditi Sharma Aman Sharma Anastassios Nanos Andrew G. Morgan Andrey Klimentyev Antti Kervinen Aviral Takkar Baoshuo Benjamin Elder Chao Dai Chuanying Du Claudiu Belu Cory Snider Danielle Lancashire Dat Nguyen Davanum Srinivas Dave David Porter Dmitry Shurupov Eric Ernst Ethan Lowman Fabian Hoffman Fabian Hoffmann Fahed Dorgaa Gabriela Cervantes Gijs Peskens Hamza El-Saawy Hsing-Yu David Chen Ikko Ashimine Jeff Widman Jeff Zvier Jeremi Piotrowski Jin Dong Jordan Karaze Joseph Sheng Joyce Brum Jukka Rissanen Kate Kathryn Baldauf Kirtana Ashok Kohei Tokunaga Kyle L Frisbie LongtaoZhang Manuel Alejandro de Brito Fontes Marc Schwind Mark Rossetti Marvin Giessing Mathis Michel Merlin Ran Nabeel Rana Nguyen Phan Huy Nikita Rybak Nobel Barakat Oleg Atamanenko Oleg Zhurakivskyy Oliver Radwell Qasim Sarfraz Quan Tian Rodrigo Campos Roy Yang Serge Logvinov Shane Jennings Shaun Lawrie Shinichi Morimoto SilverSoldier Sophie Liu Su Fei Taeho Nam Takumasa Sakao Tiger Kaovilai Tom Godkin Tomoya.
Fujita Tony Fang Tõnis Tiigi Xinlin Ma Yakul Garg Zhang Tianyang Zhongming Chang Zhuchen Wang austinvazquez calvin cardy. tang dabaooline guiyong. ou huoqifeng jianfei. zhang ningmingxiao shi yixue shuaichang songjiang han wusong xiaoyang zhu yanghesong yaozhenxiu zhang he Changes commits Prepare release notes for v1. Assets All reactions. Contributors Derek McGowan Danny Canter Phil Estes Sebastiaan van Stijn Changes 5 commits Github Security Advisory GHSA-2qjpjj9 Prepare release notes for v1.
The eleventh patch release for containerd 1. Contributors Derek McGowan Wei Fu Austin Vazquez Kirtana Ashok Maksym Pavlenko Phil Estes Qasim Sarfraz Sebastiaan van Stijn cosmoer Changes 12 commits Prepare release notes for v1.
Contributors Akihiro Suda Sebastiaan van Stijn Derek McGowan Wei Fu Phil Estes Kazuyoshi Kato Hajime Tazaki Qiutong Song Austin Vazquez Samuel Karp jonyhy Akhil Mohan Gabriel Adrian Samfira Gavin Inglis Tobias Klauser Yasin Turan rongfu. leng Changes 34 commits Prepare release notes for v1. mod: update dependencies take 2 go. mod: update dependencies take 2 Revert "go. mod: update dependencies" Revert "go. mod: update dependencies" go. mod: update logrus to v1. ReadDir fs. CopyDir: support sockets and pipes fs.
CopyDir: support sockets and pipes Fix wrapping errors fs: fix wrapping nil err fmt. Unix fs: use syscall. Unix Update CI Go version to 1.
mod: bazil. Sandbox API CRI Sandbox API Implementation Transfer Service in progress Transfer Service NRI in progress Extend NRI scope nri 16 Support for updated NRI Platform Support Linux containers on FreeBSD Runtime Features Add support for CDI device injection Support for cgroups blockio Add restart policy for enhanced restart manager Road to 2. Contributors Kazuyoshi Kato Phil Estes Derek McGowan Maksym Pavlenko Wei Fu Akihiro Suda Sebastiaan van Stijn Samuel Karp Mike Brown Daniel Canter Ye Sijun Ed Bartosh Stefan Berger Paul "TBBle" Hampson Gabriel Adrian Samfira Nashwan Azhari xin.
li Shengjing Zhu Adam Korcz Henry Wang Justin Terry wanglei zounengren Iceber Gu Kevin Parsons Brian Goff Gavin Inglis Michael Crosby Qiutong Song lengrongfu ruiwen-zhao Cameron Sparr James Jenkins Luca Comellini Michael Zappa Paco Xu Tobias Klauser pigletfly Akhil Mohan Amit Barve Eng Zer Jun Eric Lin James Sturtevant Jonny Stoten Kang. Zhang Mikko Ylinen Paul S. Schweigert Shiming Zhang Swagat Bora Vincent Batts cosmoer dependabot[bot] yaoyinnan Abirdcfly Anastassios Nanos Andrew G.
Morgan Andrey Klimentyev Antonio Ojea Antti Kervinen Austin Vazquez Baoshuo Benjamin Elder Chao Dai Claudiu Belu Cory Snider Danielle Lancashire Danny Canter Dat Nguyen Davanum Srinivas David Porter Dmitry Shurupov Eric Ernst Ethan Lowman Fabian Hoffman Fabian Hoffmann Fahed Dorgaa Gabriela Cervantes Gijs Peskens Hamza El-Saawy Ikko Ashimine Jeff Widman Jeff Zvier Jeremi Piotrowski Jordan Karaze Joseph Sheng Joyce Brum Kathryn Baldauf Kohei Tokunaga Kyle L Frisbie Marc Schwind Mark Rossetti Marvin Giessing Nabeel Rana Nguyen Phan Huy Nobel Barakat Oleg Atamanenko Oleg Zhurakivskyy Oliver Radwell Quan Tian Rodrigo Campos Roy Yang Serge Logvinov Shane Jennings Shaun Lawrie Shinichi Morimoto SilverSoldier Sophie Liu Taeho Nam Takumasa Sakao Tiger Kaovilai Tom Godkin Tomoya.
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