 Hello everyone, I'm Ching Hai-shen and we have another speaker on reaching time. We are so glad to have a list of queries in this event. And we are from NCHC Taiwan. So today I'll talk with the Gen3 Data Accountants Department for HVC Cloud. So this is our day. We have our first one is we will get some instruction on the list and the NCHC. The second one, we will start Gen3. And the last one is Gen3 deployment in NCHC Cloud resource. So NCHC, the full name is National Center for High Performance Computing. So we are one of a national-level research laboratory under Rx. We are Taiwan's only national-level supercomputing centers. We support academia and industrial research with highway and software, advanced research and patient development, including networking and professional trainings. So in the past 10 years, we built our main three computer in Taiwan. Our main are Tamiya 1, 2 and 3. And we start to serve our platform NCHC-WCC since 2019. And we also represent in Taiwan. In TCHC-WCC platform, we provide a lot of services in TCHC. For example, computing and storage on demand, big data analysis, and containerized computing environment, and so on. So based on HPEC and Cloud Platform, SPS5 team provide some several cloud services for websites, medical, culture. So we start to survey Gen3 Search Platform since 2022. So that's why we involved this kind of project. So I will give you some Gen3 introductions. Gen3 is a data platform for building data commons and data ecosystems. It consists of several open-source software services. It's about healthy data ecosystem by enabling the interaction and creating a cloud-based data resource. It's powered by CTDS from the University of Chicago. It uses a patched license version tool. So in Gen3, it has several features. It can help the health data ecosystem. For example, it has data photos, data comments, it's for data repositories. And in its platform, it provides cloud computing, large-scale processing, and some websites, for example, Jupyter Notebook, ISO Notebook. Also, in Gen3 architecture, it includes database, search engine, and the storage and the computing. So I have short videos where we will be demo-sounding the industry environment and it will be easy for you to understand. The Gen3 data commons platform allows researchers to manage, analyze, harmonize, and share large biomedical data sets to accelerate their research. In this video, we will introduce some of the technology that powers the Gen3 data commons platform. Gen3 is not just a data commons software stack. It offers unique features specially suited to the next frontier of data science, data ecosystems, comprised of multiple interoperable data commons. Fence provides authentication and authorization. It allows users to view controlled access data, typically valid data that is sorted and sorted to meet the standard level of metadata in order to create media access and for cool code, it creates a sequence of these order tools created over the Gen3 API. Okay, that's a very short demo video. You can buy it on YouTube. So I will very quickly list these features in the slide. For example, you can design your data and the data schema in the Gen3 system. So you can own your data models. And the user can use the file and the download to do the data submission into the Gen3 platform. And it provides the data dashboard and the training by data field. So it will be easier to index the data or what the user wants to do. And it also provides a workspace to analyze your data. It also provides an ADN key to support the user or less for API to use the Gen3 platform systems. So we talked about the third part. We want to talk about Gen3 deployment in NCH3 cloud resource. In this slide, I will give you some conversion between two native Gen3 deployments. The first one is a cloud deployment. So AWS only. And the second one is a double-compose version. In cloud deployment, it will be easy to scale and support fully function for Gen3. But it needs more resource requirement and complex deployment procedures. And they also highly depend on AWS service. So it did not support a new deployment report in GCP Azure. It's kind of a cloud platform. In double-compose, it will be easy to deployment. So it needs less resource requirement. So it's also available for single-order. But it's not longer up to date and it has limitation functions of the Gen3. So our goal is to provide a set controllable deployment procedure on the NCH3 cloud resource. The second one is we want to integrate all-size H8 high-ability service for example H8DB or EDX search cluster. The third one is we want to enhance double-compose version for testing, training, usage. So we'll talk about, next we'll talk about double-compose enhancement. So in this side we can see the Gen3 original micro-service dependency. So if you set it, you can see some of the micro-service have a duplicate dependency. It will cause this service to become unstable. So we want to fix it. The third point is we want to separate some micro-service from outside so we can use the powerful resource from the external. The third one we want to add some new function into Gen3 cloud system. It will be easy for us to maintain it. So for double-compose version we do some implement. For example, we update the new image version and we simplify the service dependence and come late. This will make the service style become more stable. And we also improve micro-service style procedures. And we add some new plugin for example for the manifest service for database management and the database and the launch. We also separate the database and the index service to use an external service. We also make a TWCC VCS snapshot. It will be easy to use in the NCCC cloud platform. So for current status for darker version comparison we have the NCCC testbed built in NCCC TWCC VCS VCS main virtual computing service. We integrate object storage using the TWCC cost. It's similar with S3. And we have NCCC Docker gain repository. You can see the gain repository link. So there are two ways to use the NCCC Gen3 hardware resource. The first one is you can use the native deployment from GitHub. The second one you can use the plugin for some very similar service demand by using TWCCC snapshot. So this is a hardware comparison we do for the Gen3 enhanced. The second part we will talk about Gen3 Kubernetes deployment. We will switch to Lichensai. Hello, this is Lichensai. And I will continue to introduce the Gen3 Kubernetes deployment. So the Gen3 deployment they support B3 cloud service like Amazon and Google Cloud and they also support OpenStack. But next year we will try to set up Gen3. We are only making work on database. And follow the standard following the document and the standard steps like the document. Here first one we should prepare the database service like the database and the machine and then work on other like the object storage. And then we prepare the patient of the latest is useful of the management Gen3 platform and it's got a Gen3 command and based on this command we can deploy the Kubernetes and then there is a terrible script already here so we just run it and the Kubernetes should work well and then we push the Gen3 service and then install the service and then work well with the Kubernetes. Since then we will try to move the kind of service from database to our cloud service which is based on OpenStack and the open source. Why we do that because some data provider they very care their subject data and they do not allow the kind of data in the public area like the public cloud. So we have to build a private cloud and we try to understand the architecture and moving from public to private. So we check all microservices at the related open source and the service like we have to run post-private dv in-depth research and object storage of course they kind of service all of these open source. So it's easy to migrate from database to any private cloud and we also have a making list to one-to-one to request the service and in time in our center we have cloud service high pre-killing and actually it's based on the open source, OpenStack and we unfortunately we do not have the ideas but we can create a post-private dv by ourselves and we also have some like in-depth search and we also use the key card which is a very important component to support the presentation because we should prepare the data as well and we still have some service cannot make some party we cannot find open source solution but we know the solution but we will not have time to page the code. We still have our infrastructure so we can run the G3 service in our country in my center and we have some page related to code you can see here we have we contribute to the code in the center we have so you can access that and currently in my center we have a similar provider so we use key card to change the different protocol of authentication this is the main contribution is the first contribution the same contribution is we have a change to link the data so follow the center process the user just open the container select file and the file is already in the container the user don't need to upload or download the file and keep the data in the container inside so we so the user can choose many kinds of different container service and currently some people some research can do their research well yeah this is our contribution thank you we have a boost so you have an interesting risk here thank you thank you very much