>>from around the globe. It's the cube with >>Digital coverage of IBM think 2021 brought to you by IBM. Welcome back to the cubes coverage of IBM Think 2021 virtual were not yet in real life. We're doing another remote interviews with two great guests cube alumni of course, I'm john for your host of the cube. We've got Jason McGee, IBM fellow VP and CTO of IBM cloud platform and octavian Tennessee. Senior Vice president Hybrid Cloud Engineering at Netapp. Both cube alumni. It's great to see you both. Thanks for coming on. Thank >>you. Great to be here. Thanks for having us. >>So we were just talking before we came on camera that you know what it feels like. We've had this conversation, you know, a long time ago we have Hybrid cloud has been on a trajectory for both of you guys many times on the cube. So now it's mainstream, it's here in the real world, everyone gets it. It's not, there's no real debate now. Multi cloud, that's that. People are debating that. Which means that's right around the corner. So Hybrid cloud is here and now, um, Jason this is really the focus and this is also brings together Netapp in your partnership and talk about the relationship first with hybrid cloud. >>Yeah, I mean, you know, look, we've talked to a number of times together I think in the industry, uh, maybe, maybe a few years ago people were debating whether Hybrid cloud was a real thing. We don't have that conversation anymore. I think, um, you know, enterprises today, especially maybe in the face of Covid and kind of how we work differently now realize that their cloud journey is going to be a mix of on prem and off premise systems. Probably going to be a mix of multiple public cloud providers, um, and what they're looking for now is how do I do that and how do I manage that hybrid environment? How do I have a consistent platform across the different environments I want to operate in. Um, and then how do I get more and more of my work into those environments? And it's been interesting. I think the first, the first waves of cloud, we're infrastructure centric and externally application focused, they were easier things. And now we're moving into more mission critical, more state fel more data oriented workloads. And that brings with it new challenges on where applications run and and how we leverage the club >>Octavia. You guys had a great relationship with IBM over the years, uh, data centric company that it has always been great engineering team. You're on the cloud. Hybrid cloud engineering. What's the current status of the relationship? Give us an update on how the it's vectoring into the hybrid clouds this year? Senior Vice President. Hybrid cloud engineering. >>Well, so first of all, I want to recognize 20 years of a successful partnership with IBM I think uh that happened. IBM have been companies that have embraced digital transformation and technology trends to enable that digital transformation for our customers. And we've been very successful. I think there is a very strong um joint hydrochloric value proposition for customers. Netapp storage and data services complement what IBM does in terms of products and solutions, both for on premise deployments in the cloud. I think together we can build more complete solutions solutions that span data mobility to the governance for the new workloads that Jason has talked about. >>And how are some of the customer challenges that you're seeing? Obviously software defined networking, software defined storage, uh, deVOps has now turned into Deb's sec ops. So you have now that program ability requirement with four dynamic applications, application driven infrastructure, all these buzzwords point to one thing the infrastructure has to be resilient and respond to the applications. >>Yeah, I would say uh infrastructure, you know, will continue to be uh you know, top of mind for everybody whether they're building a private uh you know, cloud or whether there um you know, trying to leverage, you know, something like IBM cloud, I think people want to consume, you know, infrastructure is an A P I I think they want simplicity, you know, security, I I think they want to manage their cost, you know very well. I think we're very proud to be partnering with IBM cloud to build such capabilities. >>Jason what's how are you guys help on some of these customers as they look at new things and sometimes retrofitting and re factoring previous stuff don't transforming but also innovating at the same time as a lot of that going on. What are you guys doing to help with the Hybrid challenges? >>Yeah, I mean, you know, there's a lot of dimensions of that problem, but the one that that I think has been kind of most interesting over the last year has been how um kind of the consumption model of public cloud, you know, api driven self service capabilities operated for you, how that consumption model is starting to spread because I think one of the challenges with hybrid and one of the challenges as customers are looking at these more mission critical data centric kind of workloads was well, I can't always move that applications of public cloud data center or I need that application to live out on the network closer to my end users out where data is being generated maybe in an IOT context. And when you had those requirements, you had to kind of switch operating models, you had to kind of move away from a public cloud service consumption model to a software deployment model. And you know, we have a common platform and things like open shift that can run everywhere. But the missing piece was how do I consume everything as a service everywhere. And so recently we launched this thing called have been brought satellite, which we've been working with the T V. And his team on on how we can actually extend the public cloud experience back into the data center out to the edge and allow people to kind of mix both locational flexibility with public consumption. When you do that, you of course running a much more diverse infrastructure environment. You have to integrate with different storage environments and you wind up with multi tier applications, you know, some stuff on the edge and some stuff in the core. And so data replication and data management start to become really interesting because you're kind of distributing your workloads across this. No complex environment. >>We've seen that relationship between compute and storage change a lot over the past decade. As the evolution goes okay, I gotta ask you this is critical path for companies. They want the storage ready infrastructure. You guys have been doing that for many, many decades party with IBM for sure. But now they're all getting a hybrid cloud big time and it's not it's attributed computing is what it is. It's an operating model. When someone asked you guys what your capabilities are, how do you answer that? In today's world? Because you have storage is well known. You got a great product, people know that, but what is net apps capabilities? When I say I'm going all in and hybrid cloud, complete changeover. >>So what we have been doing is basically rewriting a lot of our software with a few design points in mind. Um the software defined has been definitely, you know, one of the key design points. The second is the um, the hybrid cloud and the internalization of our operating system so they can run both in traditional environments as well as in the cloud. I think the last thing that we wanted to do, it's enabled the speed of scale and that has been by building um, you know, intrinsically in the, in the, in the product, both support or, and also using kubernetes as an infrastructure to achieve that agility that that scale >>talk about this data fabric vision because to me that comes up all the time in my conversations with practitioners. The number one problem that there is a problem that we're solving to solve and the conversation tends to I here was a control playing kubernetes horizontally scalable. This all points to data being available. So how do you create that availability? What does data fabric mean? What does all this mean in hybrid context? >>Well, if you if you think about it data fabric, it's a hybrid cloud, you know, concept, right. This is about enabling data governance, data mobility, data security in an environment where some of the applications will run on premises or at the edge of the smart edge and many of the, you know, perhaps data lakes and analytics, um, you know, and services rich services will be in a central locations or on many or perhaps some large, you know, data centers. So you need to have, you know, the type of, you know, capabilities, data services, you know, to enable that mobility, that governments governance, that that security across this continuum that spans the edge the core and the cloud, >>Jason, you mentioned satellite before. Cloud satellite. Can you go into more detail on it? I know it's kind of a new product, uh what is that about? And tell me what's the benefits and why does it exist and what problems does it solve? >>Yeah. So so in the most simple terms, cloud satellite is the capability to extend iBMS public cloud into on prem infrastructure infrastructure at the edge or in a multi cloud context to other public cloud infrastructures. And so you can consume all the services in the public cloud that you need to to build your application of open shift as a service databases. Deb tools, aI capabilities. Instead of being limited to only being able to consume those services in IBM's cloud regions, you can now add your private data center or add your metro provider or add your AWS or Azure account and now consume those services consistently across all those environments. Um and that really allows you to kind of combine the benefits of public ill with kind of location independence, you see in hybrid and let's solve new problems like, you know, it's really interesting, we're seeing like a I and data being a primary driver. I need my application to live in a certain country or to live next to my mainframe or to live like you know in a metro because all of my, I'm doing like video analytics on a bunch of cameras and I'm not going to stream all that data back to halfway across the country to some cloud region and so lets you extend out in that way and when you do that of course you now move the cloud into a more diverse infrastructure environment. And so like we've been working with Netapp on, how do we then expose um Netapp storage into this environment when I'm running in the data center where I'm running at the edge and I need to store that data replicate the data, secure it. Well how do I kind of plug those two things together? I think john at the beginning you kind of alluded to this idea of you know, things are becoming more application centric, Right? And we're trying to run an I. T. Architecture that's more centered around the application well by combining um clouds, knowledge of kind of where everything is running with a common platform like open shift with a kubernetes aware data fabric in storage layer, you really can achieve that. You can have an application centric kind of management that spans those environments. >>Yeah, I want to come back to that whole impact on I. T. Because this has come up as a major theme here. Think that the I. T. Transformation is going to be more about cloud scale but I want to get octavian on the satellite on Netapp role and how you complement that. How do you guys fit in? He just mentioned that you guys are playing with clouds satellite, obviously this was like an operating model, How does that fit in? >>Um simply put we extend and enable the capabilities that uh IBM satellite uh you know, platform provides, I think Jason referred to the storage aspects um and you know what we are doing, it's enabling not only storage but rich data services around tearing based on temperature or you know, replicated snapshots or you know, capabilities around, you know cashing, you know, high availability encryption and and so forth. So we believe that our our technology integrates very well with red hat open shift um and uh the kubernetes aspect enable the application mobility and in that translation of really distributed computing at scale, you know from you know from the traditional data center um to the edge and uh you know to the massive hubs that IBM is building, >>you know, I gotta say but watching you guys worked together for many decades now and and covering you with the queue for the past 10 years or 11 years now um been a great partnership. I gotta say one thing that's obviously too obvious to me and our team and mainly mainly the world is now you got a new Ceo over at IBM you have a cloud focus that's on unwavering Arvin loves the cloud. We all know that um ecosystems are changing with that. You have already had a big ecosystem and partnerships now it seems to be moving to a level where you gotta have that ecosystem really thrive in the cloud. So I guess we'll use the last couple of minutes if you guys don't mind explaining how the IBM Netapp relationship in the new context of this new partnership, new ecosystem or a new kind of world helps customers and how you guys are working together. >>Yeah, I mean I could start, I mean I think you're right that that cloud is all about platforms and about kind of the overall environment, people operating in the ecosystem is really critical and I think things like satellite have given us new ways to work together. I mean I'd be a minute up, as we said, I've been working together for a long time. We rely on them a lot in our public cloud, for example in our storage tiers but with with the kind of idea of distributed cloud and the boundaries of public cubs spreading to all these new environments. Those are just new places where we can build really interesting, valuable integrations for our clients so that they can deal with day to deal with these more complex apps, you know, in all the places that they exist. So I think it's gonna actually really exciting um to kind of leverage that opportunity to find new ways to work together and and uh and deliver solutions to our clients >>Octavia, >>I would say that data is the ecosystem and we all know that there is more data right now being created outside of the traditional data center, beat in the cloud or at the edge. Um so our mission is, you know, to enable that, you know, hybrid cloud or or that uh, you know, data mobility um and enable, you know, persistence rich data, you know, storage services, whatever data is being created. I think IBM's new satellite platform um you know, comes in and broadens the aperture of people being able to consume IBM services at the edge and or or the remote office. And I think that's very exciting. >>You guys are both experts and solely seasoned executives. Devops DEP sec ops, DEV data Ops whatever you wanna call, data's here. Ecosystems guys, thanks for coming on the key. Really appreciate the insight. >>Thank you. Thank >>you. Okay. IBM think cute coverage jOHN for your host. Thanks for watching. Mhm. Mhm. Mhm.

>> (Narrator) Live from Vancouver, Canada, it's The Cube, covering OpenStack Summit North America 2018, brought to you by Red Hat, the OpenStack Foundation, and its Ecosystem partners. >> Welcome back, I'm Stu Miniman with my co-host John Troyer and you're watching The Cube, worldwide leader in tech coverage, and this is OpenStack Summit 2018 in Vancouver. Happy to welcome to the program, first-time guest Melvin Hillsman, who's the governance board member of OpenLab, which we got to hear about in the keynote on Monday. Thanks so much for joining us. >> Thanks for having me. >> Alright, so Melvin, we were given, start us off with a little bit about your background, what brought you to the OpenStack community, and we'll go from there. >> Sure, yeah, so my background is in Linux system administration and my getting involved in OpenStack was more or less seeing the writing on the wall as it relates to virtualization and wanting to get an early start in understanding how things would pan out over the course of some years. So I probably started OpenStack maybe three or four so years ago. I was probably later to the party than I wanted to be, but through that process, started working at Rackspace first and that's how I really got more involved into OpenStack in particular. >> Yeah, you made a comment, though. The writing on the wall for virtualization. Explained that for a sec. >> So for me, I was at a shared hosting company and we weren't virtualizin' anything. We were using traditional servers, dedicated servers, installing hundreds of customers on those servers. And so, at one point, what we started doing was we would take a dedicated server, we would create a virtual machine on it, but we would use most of the resources of that dedicated server, and so what allowed that shared hosting was to tear stuff down and recreate it, but it was very manual process and so, of course, the infrastructure service and orchestration around that OpenStack was becoming the de facto standard and way of doing it, and so I didn't want to try to learn manually, or fix something up internally, I wanted to go where OpenStack was being highly developed a lot and people working on it in their day to day jobs, which is why I went to Rackspace. >> Okay, one of the things we look at, this is a community here, so it takes people from lots of different backgrounds, and some of them do it on their spare time, some of them are paid by larger companies to participate, so to tell us about OpenLab, itself, and how your company participates there. >> Sure, so I started, well I'm at Huawei now, but I was at Rackspace and that's kind of how I got more involved in the community and there I started working on testing things above the OpenStack ecosystem, so things that people want to build on top of OpenStack and during that process Huawei reached out to me and was like hey you know you're doing a great job here, and I was like yeah I would love to come and explore more of how we can increase this activity in the community at large. And so Oakland Lab was essentially born out of that, which the OpenStack community, they deliver the OpenStack API's, and they kind of stop there, you know. Everything above that is, you do that on your own, more or less, and so also, as a chair of the user committee, again, just being more concerned about the people who are using stuff, OpenLab was able, was available to facilitate me having access to hardware and access to people who are using things outside OverStack in use cases, et cetera, where we want to test out more integrated tools working with OpenStack and different versions of OpenStack. And so that's essentially what OpenLab is-- >> So in OpenLab, projects come together and it's basically, it's an Interop, boy, in the networking world, they've had the Interop plug and plug fest for a long time, but, in essence, projects come together and you integrate them and start, you invite them in and they integrate and start to test them. Starting with, I mean, I see, for this release, Terraform and Kubernetes. >> Yeah, so a lot of people want to to use Kubernetes, right? And as an OpenStack operator you essentially, you don't really want to go and learn all the bits of Kubernetes, necessarily, and so, but you want to use Kubernetes and you want to work seamlessly with OpenStack and you want to use the API's that you're used to using with OpenStack and so we work very heavily on the external cloud provider for OpenStack, enabling Cinder V3 for containers that you're spinning up in Kubernetes, so that they have seamless integration, you don't have to try to attach your volumes, they are automatically attached. You don't have to figure out what your load balancing is going to to look like. You use Octavia, which is load balancing service for OpenStack, very tightly integrated and things, you know, as you spin things up, they work as you as you would expect and so then all the other legacy applications and all the things you're used to doing with OpenStack, you bring on Kubernetes and you essentially do things the way you've been doing them before, with just an additional layer. >> Yeah, now I wonder if you can talk a little bit about the providers and the users, you know how do they get engaged, to and give us a little flavor around those. >> Yeah, so you get engaged, you go to OpenLabtesting.org and there's two options. One, is you can test out your applications and tools, by clicking get started, you fill that out. And what's great about open lab is that we actually reach out and we talk with you, we consult with you, per se, because we have a lot of variation in hardware that's available to us and so we want to figure out the right hardware that you need in order to do the tests that you want, so that we can get the output as it relates to that integration that will, of course, educate and inform the community at large of whether or not it's working and been validated. And, again, so as a person who wants to support OpenLab or for a provider, for example, who wants to support OpenLab, you click on the support OpenLab link, you fill out a form and you tell us you know, do you want to provide more infrastructure, do you want to talk with us about how clouds are being architected, integrations are being architected, things that you're seeing in the open source use cases that may not be getting the testing that they need and you're willing to work with engineers from other companies around that, so individual testers and then companies who may bring a number of testers together around a particular use case. >> Now, you're starting to publish some of the results of Interop testing and things like that. How is open lab, how does it produce its results, is it eventually going to be producing white papers and things like that or dashboards or what's your vision there? >> Yeah, so we produce a very archaic dashboard right now, but we're working with the CNCF to, if you go to CNCF.CI, and they have a very nice dashboard that kind of shows you a number of projects and whether or not they work together. And so it's open source, so what we want to do is work with that team to figure out how do we change the logos and the git repos, to how to driving those red and green, success or failure icons that are there, but they're relevant to the test that we're doing in OpenLab, so yeah. So we definitely want to have a dashboard that's very easy to decipher what tests are failing in or passing. >> Looking forward, what kinds of projects are you most interested in getting involved? >> Right now, very much Kubernetes, of course. We're really focusing on multi architecture, again, as a result of our work with Kubernetes and driving full conformance and multi architecture. That's kind of the wheelhouse at this time. We're open for folks to give us a lot of different use cases, like we were starting to look at some edge stuff, how can we participate there, we're starting looking at FPGA's and GPU's, so a lot different, we don't have a full integration in a lot of different areas, just yet, but we are having those conversations. >> So, actually, I spent a bunch of years, when I worked on the vendor side, living in an Interrupt lab, and the most valuable things were not figuring out what worked, but what broke, so what kind of things, you know, as you're working through this, what learnings back do you share with the community, both the providers and users? Big stumbling blocks that you can help people, give a red flag, or say you know, avoid these type of things. >> Yeah, exactly what you just said. You know, what's good is some of our stuff is geographically dispersed, so we can start to talk about if, what's the latency look like? You may, within that few square miles that you're operating and doing things, it works great, but when I'm sending something across the water how, is your product still moving quickly, is the latency too bad that we can't, I can't create a container over here because it takes too long, so one example of looking at something fail as it relates to that is we're talking with Octavia folks to see, if I spin up a lot of containers am I going to therefore create a lot of load balancers and if I create a lot of load balancers I'm creating a lot of VM's, or am I creating a lot of containers or are things breaking apart, so we need to dig a little bit further to understand what is and is not working with the integrations we're currently working on and then again we're exploring GPV, GPUs just landed more or less, that was a part of the keynote as well, and so now we're talking about, well, let's do some of that testing. The software, the code, is there but is it usable? And so that's one area we want to start playing around with. >> Okay, one of the other things in the keynote's got mentioned was Zul, the CIDT tool, how's that fitting into the OpenLab? >> Yeah, we use Zul as our gating, so what's great about Zul is that you can interac6t with projects from different SCM's, so we have some projects that live in github, some that utilize Garrote, some that utilize gitlab, and Zul has applicability where it can talk to different, it can talk across these different SCM's, and if you have a patch that depends on a patch in another another pod, so a patch on one project in one SCM can depend on a patch in another project, in a different SCM, and so what's great about Zul is that you can say, hey I'm depending on th6at, so before this patch lands, check to make sure this stuff works over there, so if it succeeds there and it's a dependency then you basically confirm that succeeds there and then now I can run the test here, and it passes here as well, so you know that you can use both of those projects together again, in an integration. Does it makes sense? Hopefully I'm making it very clear, the power there with the cross SCM integration. >> Yeah, Melvin, you've had a busy week, here, at the show. Any, you know, interesting things you learned this week or something that you heard from a customer that you thought, oh boy we got to, you know, get this into our lab or a road map or, you know-- >> The ARM story, the multi architecture is, I feel like that's really taking off. We've had discussions with quite a few folks around that, so yeah, that for me, that's the next thing that I think we're really going to concentrate a little bit harder on is, again, figuring out if there are some problems, because mostly it's been just x86, but we need to start exploring what's breaking as we add more to multi architecture. >> Melvin, no shortage of new things to test and play with, and every customer always brings some unique spins on things, so appreciate you giving us the update on OpenLab, thanks so much for joining us. >> You're Welcome. Thanks for having me. >> From John Troyer, I'm Stu Miniman, thanks so much for watching The Cube. (electronic music)

(upbeat music) >> Announcer: Live from Vancouver, Canada. It's The Cube covering Openstack Summit, North America, 2018. Brought to you by Red Hat, The Open Stack Foundation, and it's ecosystem partners. >> Welcome back to The Cube's coverage of Openstack Summit 2018 in Vancouver. I'm Stu Miniman with cohost of the week, John Troyer. Happy to welcome back to the program Stephan Fabel, who is the Director of Ubuntu product and development at Canonical. Great to see you. >> Yeah, great to be here, thank you for having me. Alright, so, boy, there's so much going on at this show. We've been talking about doing more things and in more places, is the theme that the Open Stack Foundation put into place, and we had a great conversation with Mark Shuttleworth, and going to dig in a little bit deeper in some of the areas with you. >> Stephan: Okay, absolutely. >> So we have the Cube, and we're go into all of the Kubernetes, Kubeflow, and all those other things that we'll mispronounce how they go. >> Stephan: Yes, yes, absolutely. >> What's your impression of the show first of all? >> Well I think that it's really, you know, there's a consolidation going on, right? I mean, we really have the people who are serious about open infrastructure here, serious about OpenStack. They're serious about Kubenetes. They want to implement, and they want to implement at a speed that fits the agility of their business. They want to really move quick with the obstrain release. I think the time for enterprise hardening delays an inertia there is over. I think people are really looking at the core of OpenStack, that's mature, it's stable, it's time for us to kind of move, get going, get success early, get it soon, then grow. I think most of the enterprise, most of the customers we talk to adopt that notion. >> One of the things that sometimes helps is help us lay out the stack a little bit here because we actually commented that some of the base infrastructure pieces we're not talking as much about because they're kind of mature, but OpenStack very much at the infrastructure level, your compute, storage, and network need to understand. But then we when we start doing things like Kubernetes as well, I can either do or, or on top of, and things like that, so give us your view as to what'd you put, what Canonical's seeing, and what customers-- how you lay out that stack? >> I think you're right, I think there's a little bit of path-finding here that needs to be done on the Kubernetes side, but ultimately, I think it's going to really converge around OpenStack being operative-centric, and operative-friendly, working and operating the infrastructure, scaling that out in a meaningful manner, providing multitenancy to all the different departments. Having Kubernetes be developer-centric and really help to on-board and accelerate the workload that option of the next gen initiatives, right? So, what we see is absolutely a use case for Kubernetes and OpenStack to work perfectly well together, be an extension of each other, possibly also sit next to each other without being too incumbenent there. But I think that ultimately having something like Kubernetes contain a based developer APIs that are providing that orchestration layer are the next thing, and they run just perfectly fine on Canonical OpenStack. >> Yeah, there certainly has been a lot of talk about that here at the show. Let's see, let's go a level above that, things we run on Kubernetes, I wanted to talk a little bit about ML and AI and Kubeflow. It seems like we're, I'd almost say that we're, this is like, if we were a movie, we're in a sequel like AI-5; this time, it's real. I really do see real enterprise applications incorporating these technologies into the workflow for what otherwise might be kind of boring, you know, line of business, can you talk a little bit about where we are in this evolution? >> You mean, John, only since we've been talking about it since the mid-1800s, so yeah. >> I was just about to point that out, I mean, AI's not new, right? We've seen it since about 60 years. It's been around for quite some time. I think that there is an unprecedented amount of sponsorship of new startups in this area, in this space, and there's a reason why this is heating up. I think the reason why ultimately it's there is because we're talking about a scale that's unprecedented, right? We thought the biggest problem we had with devices was going to be the IP addresses running out, and it turns out, that's not true at all, right? At a certain scale, and at a certain distributed nature of your rollout, you're going to have to deal with just such complexity and interaction between the underlying, the under-cloud, the over-cloud, the infrastructure, the developers. How do I roll this out? If I spin up 1000 BMs over here, why am I experiencing dropped calls over there? It's those types of things that need to be self-correlated. They need to be identified, they need to be worked out, so there's a whole operator angle just to be able to cope with that whole scenario. I think there's projects that are out there that are trying to ultimately address that, for example, Acumos (mumbles) Then, there is, of course, the new applications, right? Smart cities to connect to cars, all those car manufacturers who are, right now, faced with the problem: how do I deal with mobile, distributed inference rollout on the edge while still capturing the data continually, train my model, update, then again, distribute out to the edge to get a better experience. How do I catch up to some of the market leaders here that are out there? As the established car manufacturers are going to come and catch up, put more and more miles autonomously on the asphalt, we're going to basically have to deal with a whole lot more of proctization of machine-learning applications that just have to be managed at scale. And so we believe for all certain good company in that belief that having to manage large applications at scale, that containers and Kubernetes is a great way to do that, right? They did that for web apps. They did that for the next generation applications. This is one example where with the right operators in mind, the right CRDs, the right frameworks on top of Kubernetes managed correctly, you are actually in a great position to just go to market with that. >> I wonder if you might have a customer example that might go to walk us through kind of where they are in this discussion, talk to many companies, you know, the whole IOT even pieces were early in this. So what's actually real today, how much is planning, is this years we're talking before some of these really come to fruition? >> So yeah, I can't name a customer, but I can say that every single car manufacturer we're talking to is absolutely interested in solving the operational problem of running machine-learning frameworks as a service, making sure those are up running and up to speed at any given point in time, spin them up in a multitenant fashion, make sure that the GPU enablement is actually done properly at all layers of the virtualization. These are real operational challenges that they're facing today, and they're looking to solve with us. Pick a large car manufacturer you want. >> John: Nice. We're going down to something that I can type on my own keyboard then, and go to GitHub, right? That's one of the places to go where it is run, TensorFlow of machine-learning framework on Kubernetes is Kubeflow, and that little bit yesterday on stage, you want to talk about that maybe? >> Oh, absolutely, yes. That's the core of our current strategy right now. We're looking at Kubeflow as one of the key enablers of machine-learning frameworks as a service on top of Kubernetes, and I think they're a great example because they can really show how that as a service can be implemented on top of a virtualization platform, whether that be KVM, pure KVM, on bare metal, on OpenStack, and actually provide machine-learning frameworks such as TensorFlow, Pipe Torch, Seldon Core. You have all those frameworks being supported, and then basically start mix and matching. I think ultimately it's so interesting to us because the data scientists are really not the ones that are expected to manage all this, right? Yet they are the core of having to interact with it. In the next generation of the workloads, we're talking to PHDs and data scientists that have no interest whatsoever in understanding how all of this works on the back end, right? They just want to know this is where I'm going to submit my artifact that I'm creating, this is how it works in general. Companies pay them a lot of money to do just that, and to just do the model because that's where, until the right model is found, that is exactly where the value is. >> So Stephan, does Canonical go talk to the data scientists, or is there a class of operators who are facilitating the data scientists? >> Yes, we talk to the data scientists who understand their problems, we talk to the operators to understand their problems, and then we work with partners such as Google to try and find solutions to that. >> Great, what kind of conversations are you having here at the show? I can't imagine there's too many of those, great to hear if there are, but where are they? I think everybody here knows containers, very few know Kubernetes, and how far up the stack of building new stuff are they? >> You'd be surprised, I mean, we put this out there, and so far, I want to say the majority of the customer conversations we've had took an AI turn and said, this is what we're trying to do next year, this is what we're trying to do later in the year, this is what we're currently struggling with. So glad you have an approach because otherwise, we would spend a ton of time thinking about this, a ton of time trying to solve this in our own way that then gets us stuck in some deep end that we don't want to be. So, help us understand this, help us pave the way. >> John: Nice, nice. I don't want to leave without talking also about Microcades, that's a Kubernetes snap, you code some clojure download, Can we talk a little bit about that? >> Yeah, glad to. This was an idea that we conceived that came out of this notion of alright, well if I do have, talking to a data scientist, if I do have a data scientist, where does he start? >> Stu: Does Kubernetes have a learning curve to date? >> It does, yeah, it does. So here's the thing, as a developer, you have, what options do you have right when you get started? You can either go out and get a community stood up on one of the public clouds, but what if you're in the plane, right? You don't have a connection, you want to work on your local laptop. Possibly, that laptop also has a GPU, and you're a data scientist and you want to try this out because you know you're going to submit this training job now to a (mumbles) that runs un-prem behind the firewall with a limited training set, right? This is the situation we're talking about. So ultimately, the motivation for creating Microcades was we want to make this very, very equivalent. Now you can deploy Kubeflow on top of Microcades today, and it'll run just fine. You get your TensorBoard, you have Jupyter notebook, and you can do your work, and you can do it in a fashion that will then be compatible to your on-prem and public machine-learning framework. So that was your original motivation for why we went down this road, but then we noticed you know what, this is actually a wider need. People are thinking about local Kubernetes in many different ways. There are a couple of solutions out there. They tend to be cumbersome, or more cumbersome than developers would like it. So we actually said, you know, maybe we should turn this into a more general purpose solution. So hence, Microcades. It works like a snap on your machine, you kick that off, you have Kubernetes API, and under 30 seconds or little longer if your download speed plays a factor here, you enable DNS and you're good to go. >> Stephan, I just want to give you the opportunity, is there anything in the Queens Release that your customers have been specifically waiting for or any other product announcements before we wrap? >> Sure, we're very excited about the Queens Release. We think Queens Release is one of the great examples of the maturity of the code base and really the knot towards the operator, and that, I think was the big challenge beyond the olden days of OpenStack where the operators took a long time for the operators to be heard, and to establish that conversation. We'd like to say and to see that OpenStack Queens has matured in that respect, and we like things like Octavia. We're very exciting about (mumbles) as a service, taking its own life and being treated as a first-class citizen. I think that it was a great decision of the community to get on that road. We're supporting as a part of our distribution. >> Alright, well, appreciate the update. Really fascinating to hear about all, you know, everybody's thinking about it and really starting to move on all the ML and AI stuff. Alright, for John Troyer, I'm Tru Miniman. Lots more coverage here from OpenStack Summit 2018 in Vancouver. Thanks for watching The Cube. (upbeat music)

>> Narrator: Live from Vancouver, Canada, it's theCUBE, covering OpenStack Summit North America 2018, brought to you by Red Hat, the OpenStack Foundation, and its ecosystem partners. >> The sun has come out, but we're still talking about a lot of the cloud here at the OpenStack Summit 2018 in Vancouver. I'm Stu Miniman with my co-host John Troyer. Happy to welcome to the program the 2018 Super User Award winner, George Mihaiescu, who's the senior cloud architect with the Ontario Institute for Cancer Research or OICR. First of all, congratulations. >> Thank you very much for having me. >> And thank you so much for joining us. So cancer research, obviously is, one of the things we talk about is how can technology really help us at a global standpoint, help people. So, tell us a little about the organization first, before we get into the tech of it? >> So OICR is the largest cancer research institution in Canada, and is funded by government of Ontario. Located in Toronto, we support about 1,700 researchers, trainees and clinician staff. It's focused entirely on cancer research, it's located in a hub of cancer research in downtown Toronto, with Princess Margaret Hospital, Sick Kids Hospital, Mount Sinai, very, very powerful research centers, and OICR basically interconnects all these research centers and tries to bring together and to advance cancer research in the province, in Canada and globally. >> That's fantastic George. So with that, sketch out for us a little bit your role, kind of the purview that you have, the scope of what you cover. >> So I was hired four years ago by OICR to build and design cloud environment, based on a research grant that was awarded to a number of principal investigators in Canada to build this cloud computing infrastructure that can be used by cancer researchers to do large-scale analysis. What happens with cancer, because the variety of limitations happening in cancer patients, researchers found that they cannot just analyze a few samples and draw a conclusion, because the conclusion wouldn't be actually valid. So they needed to do large-scale research, and the ICGC, which is International Cancer Genome Consortium, an organization that's made of 17 countries that are donating, collecting and analyzing data from cancer patients, okay, they decided to put together all this data and to align it uniformly using the same algorithm and then analyze it using the same workflows, in order to actually draw conclusion that's valid across multiple data sets. They are focusing on the 50 most common types of cancer that affect most people in this world, and for each type of cancer, at least two countries provide and collect data. So for brain cancer, let's say we have data sets from two countries, for melanoma, for skin, and this basically gives you better confidence that the conclusion you draw is valid, and then the more pieces of the puzzle you throw on the table, the easier to see the big picture that's this cancer. >> You know George, I mean, I'm a former academic, and you know, the more data you get right, the more infrastructure you're going to have to have. I'm just reading off the announcement, 2,600 cores, 18 terabytes of RAM, 7.3 petabytes of storage, right, that's a lot of data, and it's a lot of... accessed by a lot of different researchers. When you came in, was the decision to use OpenStack already made, or did you make that decision, and how was the cloud architected in that way? >> The decision was basically made to use open source. We wanted basically to spend the money on capacity, on hardware, on research and not on licensing and support. >> John: Good use of everybody's tax dollars. >> Exactly, so you cannot do that if you have to spend money for paying licensing, then you probably have only half of the capacity that you could. So that means less large analysis, and longer it takes, and more costly. So Ceph for storing the data sets and OpenStack for infrastructure as a service offering was a no-brainer. My specialty was in OpenStack and Ceph, I started OpenStack seven years ago, so I was hired to design and build, and I had a chance to actually do alignment, and invitation calling for some of the data sets, so I was able to monitor the kind of stress that this workflows put on the system, so when I design it, I knew what is important, and what to focus on. So it's a cloud environment, it's customized for cancer research. We have very good ratio of RAM per CPU, we have very large local discs for the VM, for the virtual machines to be able to download very large data sets. We built it so if one compute node fails, you only impact a few workflows running there, you don't impact single small points of failures. Another tuning that we applied to the system too. >> George, can walk us through a little bit of the stack? What do you use, do you build your own OpenStack, or do you get it from someone? >> So basically, we use community hardware, we just high-density chassis, currently from Super Micro, Ubuntu for the operating system, no licensing there, OpenStack from the VM packages. We focus more on stability, scalability and support costs, internal support costs, because it's just myself and I have a colleague Gerard Baker, who's a cloud engineer, and you have to support all this environment, so we try to focus on the features that are most useful to our users, as well as less strain on our time and support resources. >> I mean that's, let's talk about the scalability right? You said the team is you and a colleague. >> George: Yes. >> But mostly, right. And you know, in the olden days, right, you would be taking care of maybe a handful of machines, and maybe some disk arrays in the lab. Now you're basically servicing an entire infrastructure for all of Canada, right? At how many universities? >> Well basically, it's global, so we have 40 research projects from four continents. So we have from Australia, from Israel, from China, from Europe, US, Canada. So approved cancer researchers that can access the data open up an account with us, and they get a quota, and they start their virtual machines, they download the data sets from the extra API of Ceph to their VMS, and they do analysis and we charge them for the time used, and because the use, everything is open source, and we don't pay any licensing fees, we are able to, and we don't run for profit, we charge them just what it costs us to be able to replenish the hardware when it fails. >> Nice, nice. And these are actually the very large machines, right? Because you have to have huge, thick data sets, you've got big data sets you have to compare all at once. >> Yeah, an average bandwidth of a file that has the normal DNA of the patient, and they need also the tumor DNA from the biopsy, an average whole genome sequence is about 150 gigabytes. So they need at least 300 gigabytes, and depending on the analysis, if they find mutations, then the output is usually five, 10 gigabytes, so much smaller. For other workflows, you have to actually align the data, so you input 150 gigabytes and the output is 150 or a bit more with metadata. And so nevertheless, you need very large storage for the virtual machines, and these are virtual machines that run very hard, in terms of you cannot do CPU over subscription, you cannot do memory over subscription, when you have a workflow that runs for four days, hundred percent CPU. So is different than other web scale environments, where you have website was running at 10%, or you can do 10 to one subscription, and then you go much cheaper or different solutions. Here you have to only provide what you have physically. >> John: That's great. >> George, you've said you participated in the OpenStack community for about seven years now. >> George: Yes. >> What kind of, do you actually contribute code, what pieces are you active in the community? >> Yeah, so I'm not a developer. My background is in networking, system administration and security, but I was involved in OpenStack since the beginning, before it was a foundation. I went to the first OpenStack public conference in Boston seven years ago, at the International Intercontinental Hotel and over time I was involved in discussions from the RAC channel, mailing list support, reporting backs. Even recently we had very interesting packet affected as well. The cloud package that is supposed to resize the disk of the VM as it boots, it was not using more than two terabytes because it was a bug, okay. So we reported this, and Scott Moffat, who's the maintainer of the cloud utils package, worked on the bug, and two days later, we had a fix, and they built a package, it's in the latest cloud Ubuntu image, and that happen, everybody else is going to use the same virtual Ubuntu package, so somebody who now has larger than two terabytes VMs, when they boot, they'll be able to resize and use the entire disk. And that's just an example of how with open source we can achieve things that would take much longer in commercial distribution, where even if you pay, doesn't necessarily mean that the response... >> Sure. Also George, any lessons learned? You've been with us a long time, right, and like Ceph. One thing we noticed today in the keynote, is actually a lot of the storage networking and compute wasn't really talked, those projects were maybe down focused a bit, as they talked about all the connectivity to everything else. So, I mean any lessons, so you... My point is, the infrastructure is stable of OpenStack, but any lessons learned along the journey? >> I think the lessons are that you can definitely build very affordable and useful and scalable infrastructure, but you have to get your expectations right. We only use from the open standard project that we consider are stable enough, so we can support them confidently without spending, like if a project adds 5% value to your offering, but eats 80% of your time debugging and trying to get it working, and doesn't have packages and missing documentation and so on, that's maybe not a good fit for your environment if you don't have the manpower to. And if it's not absolutely needed. Another very important lesson is that you have to really stay up to date, like go to the conferences, read the emails from the mailing list, be active in the community, because the OpenStack meetups in Toronto for 2018, we present there, we talk to other members. In these seven years I read tens of thousands of emails, so I learn from other users experiences, I try to help where I can. You have to be involved with the developers, I know the Ceph core developers, Sage and other people. So, you can't do this just by staying on the side and looking, you have to be involved. >> Good, George what are you looking for next from this community? You talked about the stability, are there pieces that you're hoping reach that maturity threshold for yourselves, or new functionalities that you're looking for down the road? >> I think what we want to provide to our researchers, 'cause they don't run web scale applications, so their needs are a little bit different. We want to add Magnum to our environment, to allow them deploy Kubernetes cluster easily. We want to add Octavia to expose the services, even though they don't run many web services, but you have to find a way to expose them when they run them. Maybe, Trove, database as a service, we'll see if we can deploy it safely and if it's stable enough. Anything that OpenStack comes up with, we basically look, is it useful, is it stable, can you do it, and we try it. >> George, last thing. Your group is the Super User of the Year. Can you just walk us through that journey, what led to the nomination, what does it mean to your team to win? >> I think we are a bit surprised, because we are a very small team, and our scale is not as big as T-Mobile or the other members, but I think it shows that again, for a big company to be able to deploy OpenStack at scale and make it work, it's maybe not very surprising 'cause yes, they have the resources, they have a lot of manpower and a lot of... But for a small institution or organization, or small company to be able to do it, without involving a vendor, without involving extra costs, I think that's the thing that was appreciated by the community and by the OpenStack Foundation, and yeah, we are pretty excited to have won it. >> All right, George, let me give you the final word, as somebody that's been involved with the community for a while. What would you say to people if they're, you know, still maybe looking from the outside or played with it a little bit. What tips would you give? >> I think we are living proof that it can be done, and if you wait until things are perfect, then they will never be, okay. Even Google has services in beta, Amazon has services in beta. You have to install OpenStack, it's much more performant and stable than when I started with OpenStack, where there was just a few projects, but definitely they will get help from the community, and the documentation's much better. Just go and do it, you won't regret it. >> George, as we know, software will eventually work, hardware will eventually fail. >> Absolutely. >> So, George Mihaiescu, congratulations to OICR on the Super User of the Year award, for John Troyer, I'm Stu Miniman, we're getting towards the end of day one of three days of wall to wall coverage here at OpenStack Summit 2018 in Vancouver. Thanks so much for watching theCUBE.

>>Fly from Houston, Texas. It's the cube covering Grace Hopper celebration of women in computing. >>Welcome back to the cubes coverage of the Grace Hopper conference here in Houston, Texas. I am your host, Rebecca Knight. We have a great panel today. Uh, three distinguished guests, Jeanette Epps, an aerospace astronaut. Uh, an engineer at NASA, Mimi Valdez, a executive producer on the film, hidden figures and Christina DOJ who is a, I want to call her a rocket scientist. She will not let me, she's an electrical engineer at, at, at NASA. Thank you so much for joining me. Hey Jeanette. I want to start with you. Um, recently president Obama has said we're gonna put men on Mars, men and women, men and women on Mars. How realistic is that? I mean, it's exciting for the, for the rest of us, non astronauts to hear this is, is it realistic? >>It is realistic. And one of the things I love that he, he did that was that it gives a national initiative to go back to Mars. And so that means that people will get more involved in STEM careers, especially girls will get more involved. And it's kinda like, you know what JFK did back in the 60s to give us that push a goal, an end goal to do something. Great. >>And do you think that, you know, he said by 2030, it's not very long from now. I mean, is it going to happen? I mean, what's, >>well right now a witness is working on is we have the NASA Orion program and it's a, uh, a power light capsule that will be launched off of one of the largest rockets bigger than the Saturn five or as big. And so that the mission of NASA, Orion is to take us beyond low earth orbit and go deeper into space. So we're looking at NASA, Orion, potentially maybe being the ship that will go to Mars and you know, maybe we have more work to do, but all of getting the nation onboard with going to Mars will inspire a new generation to do great things that will help us to get to Mars. Even >>Obama has said he loves science as a kid. That was his favorite subject in school. And do you think that it will have this galvanizing effect of, as you said, making sure more little boys and girls are studying STEM? >>Well, I've seen it already with some of the tweets and different things, questions that kids ask me nowadays. I think every kid has in their heart this goal to go to Mars now. And um, I can't go to a conference or anywhere without, uh, some young child asking about Mars and what, what are, how are we going to get there? When are we going to get there? And you know, I think one of my jobs is to inspire them to get involved in STEM and help us to get to Mars. There's a lot of technology that we need to develop and produce so that we can get astronauts to live longer away from the earth surface. We have the propulsion system that can get them there faster and bring them back home long. Will it take, well, it depends on the propulsion system that we developed. So there's a number of things that we're working on to make sure that what president Obama has said will get us to Mars in the 2030s if it's pushed out to a little later, that's fine. We're working on it and we're, we're going forward with them. >>Mimi, I want to talk to you now about the film hidden figures. Uh, we'll be out in some theaters and Christmas wide release in January. It is the story of black women mathematicians working in the 1960s at NASA. How did you find out about this story? Well, I give credits at Donna gelato, one of the producers on the film who optioned the book when it was a book proposal. So before the book was even written, she optioned this the story, and it's just this amazing story that the world doesn't even know about. We all know about John Glenn's mission. He came back a national hero. It was a moment in history that galvanized the world. You know, America winning the space race. But we don't know the story of all these brilliant mathematicians, all these women who actually will really responsible Katherine Johnson specifically who hand calculated those numbers for his cause. >>Everybody, I mean, I think this is such an amazing thing to do. And, and again, we just don't know her story. And as well as Dorothy Vaughn and Mary Jackson, I mean, so many women that worked at NASA that were just, um, they were hidden. You just don't know their stories. So, so the film tells their story, it tells their, um, it tells what they did, how they helped John Glenn. Did John Glenn appreciate that John Glenn actually would not go into space unless Katherine double checked the numbers. Like he wanted her to actually, you know, he, he, he trusted her because this is when the IBM computers were first coming into NASA. So they had started asserted use that and he was like, Hmm, I, I need Catherine to check the numbers before I go into say they were friends, professional colleagues. I mean he just knew who, you know, obviously she, she was really responsible and sort of that whole mission. >>And, and you know, for him it was just sort of like, eh, these computers thing, this was like a new thing at NASA. He's like, I need a human to make sure that these numbers are right. Right. Yeah. So we're excited about the film. Who's in it? Taraji P Henson plays captain Johnson. Octavia Spencer, please. I'm Dorothy Vaughan and Janell Monae. Um, she's a, uh, a musician. This is sort of her first really big role and she plays Mary Jackson. Kevin Costner is the head of the space station. Um, we have really great actors, but I think what was really important to everyone who participated in the film was everyone understood the importance of the story and wanting to make sure that we got it right. And also, you know, movies are supposed to be entertaining, but when they can be also inspirational and educate in some way, the fact that some young girls somewhere may see this movie and decide to pursue a career in math or science or any of the STEM careers is really gratifying to us because that's what we would love to accomplish. >>You know, Christina, you have been at NASA for nine years. Uh, tell me about your style, how you got, how you got to NASA in the first place. So I've always loved space since I was like a young child. I was in fifth grade when I told my parents that I wanted to work for NASA. So really since that point, like that was, you know, my dream. And so I, you know, pursued the math and science. Those were some of my favorite subjects. Um, luckily I had some supportive parents who really like saw that desire in me and kind of nurtured and encouraged me to, Hey, if that's what you want to do, then you go for it. We'll do whatever we can. Um, and then I came across some NASA opportunities in high school. Um, and one of the programs was the high school aerospace scholars program. >>And in that program I kinda got a glimpse into what it was like to be an engineer, to work at NASA. I got to speak with, you know, fight directors and um, flight controllers. And there's so many people who contribute to the space program. And that experience really solidified my desire to pursue STEM, STEM. Um, so I started to electrical engineering and then from there, um, you know, did the internship at NASA and I've enjoyed my career so far. It's been a, it's been a great experience. And so you work on the jet propulsion system of spaceships. Um, I work Lena. Um, I work on the power systems power systems. So you are, what do you do? So, um, as an electrical engineer on power systems, I work on the design of the power system testing. Um, basically everything on the spacecraft is going to need power. >>So I'm responsible for how I need to provide power, how much, um, when we talk about going to Mars, that's a, that's a long duration mission and power is something that, um, you have to budget for. So we need to advance that technology to support these missions that, um, our administration has said we want to go there. How are y'all going to accomplish this? So there's a lot of um, design hands on work and it's, you know, it's a challenge. But I mean, together as a team, we can, I believe that will, you know, meet that goal and be able to deliver a power system that will take us to Mars. So this is a question for rip for really all of you. You're an astronaut, you were working at NASA, you just made a movie to encourage young women to, to, to, to get into the STEM field. Why is it NASA recruiting here at, at, at Grace Hopper and, and should it be, do not, do you want to take this? >>Well, that's a good question. It's not that I'm mass and I don't, it's not that I don't think NASA has a desire to recruit here. I think there's recruiting times where people come out and do that. But I think I'm, one of the things that we do in the astronaut Corps, we try to go out and attend conferences like that and try to inspire students to be interested in NASA to understand what NASA is doing, to understand, you know, the shuttle retired. But we're still flying. In fact, when I fly to space and hopefully in 2018 I'll fly with the Russians. But in the meantime, NASA's building two shadows that would take astronauts to the international space station and NASA, Orion, that'll take us deeper into space. So we want to try to inspire with our stories and get people interested in science, technology, engineering, mathematics, and now even the arts, the arts play a big part of this. The arts play a big part of the well, yeah, I think I'm as a scientist and looking at patterns and things like that, there's a lot of um, people who, um, begin to work in the arts, even if it's building things with their hands and making, sculpting things, painting things. And so there's a lot of artwork that comes into play in science that is >>really refreshing, exciting, count, counterintuitive. I mean, what would you say are the ways we've talked about getting them, getting them interested through, through film. Um, talking about, uh, Obama making this, this grand announcement and Treme, what are some other ways that we can get the next generation into this field? Well, representation is obviously important. I think when people get to see images in the media of these different fields and all the possibilities, I mean every kid on this planet is obsessed with their phone, maybe not realizing like the importance of these STEM careers that are making these phones even possible or even exist. And I think the more that we can expose these careers and all these possibilities, I think it will just be just more beneficial just for humanity in general. I mean, as we know, nothing in this world can exist without math. >>Nothing. So the more that we can sort of encourage young people to see what an incredible career this is in all the possibilities that go into it, I just think we'll be better off as a nation and as a, you know, just globally as a world. Jeanette, I mean, do you have any thoughts about how, what you would advise someone? I mean you started at NASA nine years ago. So I mean, as an engineer I feel like, I mean I am making some contribution, but really the way I feel like I have more impact is through mentoring and you know, participating in those outreach type of activities for, uh, younger students like K through 12. And then also, um, you know, undergraduate like where they're really like trying to figure out what are the career options and STEM. And so that's how I feel I can have an impact there. >>And these movies help because there's a surge of like, like it's inspirational for young students to see this and be like, Oh, I never knew that that was an option. And so we get outreached to NASA, um, our request to, you know, interact with, uh, local schools and communities and kind of, um, you know, do all my lessons or just teaching with them, just talking about kind of like what the career is like. So, um, I mean I hoping that I can contribute in that way for younger, younger people. Janette are, you are an impressive astronaut, but you are also known as a black woman astronaut. Yeah. D do you do, do you bristle at that or do you embrace it? What, what do you, how, what's the responsibility? >>Oh, I totally embrace it. You know, I'm young ladies always ask me, did you have a problem being a black woman and engineering? And I always tell them that, um, I don't have a problem with being a black woman. And if other people do, then that's their problem. I totally embrace it because I'm, one of the things that I didn't realize was that, um, there's still a need to have positive role models and images of yourself. You know, growing up, my mom never taught my twin sister and I that you couldn't do something because you are a female or Brown. But there are a lot of young ladies that actually do experience that. And so having a positive roam out of it, show them that, Hey look, if I can do this, you can do this too. There's no reason you shouldn't be doing this right now yourself. >>So you are a role model. And how do you then also make sure that it's active role modeling and not just sort of standing on a pedestal of. >>One of the things I like to do is like Catherine Johnson and these great ladies that, you know, without them I, you know, I wouldn't be here is you have to do well and you have to perform well. You have to do the same work that your colleagues are doing and don't do less and don't accept less either. And when it comes to the hard work, put in the time, do the work, complete the tasks and make sure you're, you're representing yourself and your group well and you don't want to be accounted as well. You know, she's the one that we've got to help and we've got to do this, but you want to be a contributing member to every group that you're a part of and completing the tasks, doing the same work if not better. I like to say do better work, but you know, you want to be a part of the. >>Yeah. But that puts so much pressure too because it is, it's, it's be a contributor, but also don't mess up because you are under a microscope to some degree because those are, >>you know, messing up isn't, isn't. Um, failure is never, um, should never be. If it's unintentional, that's okay because you always learn from your mistakes and you have to forgive yourself and keep moving forward. If you stop right there because of a failure, um, you wouldn't go anywhere. We all fell. And it's how you respond to it. That matters. >>Yeah. Every failure is an opportunity to learn. And I think, um, you know, yeah. You can't be scared. I mean, the first and foremost is just doing a good job that, cause once, if you're just dedicated and focused on that, then I think great things can happen. And then failure is really a, a buzzword in Silicon Valley too. Right now. It's a fail fast. Um, and this idea, as you were talking about that it's your response to failure that makes a difference. Yeah. And NASA, I became familiar with this famous phrase of failing forward, meaning that yes, you're going to encounter problems, but if you are learning from the, if you're making improvements, you can design something better. So we call it failing forward. And that concept has, I've embraced that comset and it's, you know, I've encountered many failures. I mean, designing new hardware. It's not gonna, you know, work right off the box. And I'm kind of embracing that idea that it's a learning experience. As long as you don't give up as if you're applying what you learned, then that is not a failure. Christina, this is great. Christina DOJ, Mimi Valdez, Jeanette Epps. Thank you so much for joining us today. This has been such a having to be here. I'm not worthy. Thank you. This has been Rebecca Knight live coverage of the Grace Hopper conference here in Houston, Texas. We'll be back after this break.