Apache Karaf has been seen for a while as an applications server. If it’s actually the case for the standard distribution, it’s very easy to create a Karaf powered standard/immutable distribution embedding your applications. In combination with other Karaf subprojects (Decanter, Cave, …), it provides a perfect applications runtime for the cloud.nThis talk will show how to start and focus on business code and then generate different distribution packages, up to a docker image ready to run on cloud.
Going back well before the ASF was formed, community has played a vital role in the development of open source software. With the formation of the ASF, we see a great example of open source community being formalized and actively facilitated. Although it can seem counterintuitive, especially to the uninitiated, placing ‘Community over code’ helps ensure the ongoing relevance of open source as a movement, inviting individuals and organizations alike to invest in ‘free’ software. The community participation statistics across the ASF are impressive and highly convincing.nThe results of this community participation include personal growth, an ever evolving and strengthening Apache project, as well as a strong foundation for commercial business development.n- Brief historyn- ASF stats overviewn- What ‘Community over code’ means to various Apache community members At the individual project level, Apache OFBiz has benefitted from hundreds of thousands of volunteer hours (over 75,000 from one application development company alone) over the past 13 years, and relies on numerous other Apache projects. The presenter will share examples of one service provider’s approach to making community contributions, including those of individuals as well as company-wide initiatives that have worked well over the years.
The fashion industry works to meet the demand for apparel and dictates thentrends for what should be worn.nThis presentation will describe why Apache OFBiz is a great platform fornbuilding an ERP for the fashion industry and how it can be enhanced tonsupport its specific requirements. The various implementation steps of anconcrete project will be reviewed:n- Development of a custom eCommerce/B2B Responsive and Seo compliantnapplication using the Color-Size matrix feature.n- Integration of third-party applications such as retail, legacy Erp,nsocial network, WMS, Business Intelligence, mailing system.n- ‘Soft migration’. Progressive replacement of the external proprietarynsystem with OfBiz. The result is an OfBiz-centric, holonic Erp system.
Cloud computing greatly simplifies provisioning, scaling, reliability and maintenance: these aspects are particularly important for Enterprise applications like Apache OFBiz. This talk will provide effective tips, based on experience and lessons learned in the field, to enable OFBiz for the cloud. Topics covered include OFBiz specific solutions for:n- deployments in AWSn- images – AMI, Dockern- continuous deploymentsn- configurations for load balancersn- session management – sticky sessions, distributed sessions (with Redis)n- database options for the cloud – RDS (AWS), Google Cloud Spanner
Apache Marvin is an open source project that focuses on empowering data science teams to deliver industrial-grade applications supported by a high-scale, low-latency, language agnostic, and standardized architecture platform created to simplify the process of exploration and modeling in Machine Learning projects. Marvin aims at abstracting the complexities in the creation process of scalable, highly available, interoperable and maintainable predictive software. In this presentation, the speaker is going to talk about the architecture behind the platform and show the cooler features of Apache Marvin.
‘Data Scientists focuses on developing ML models with applications such as TensorFlow / MXNet / Caffe / XGBoost and do not deep dive into the complexities of computing and storage to run ML/DL jobs. Today’s most of ETL processed data are stored in HDFS and cloud, and leveraging this data to design strong ML models is a big challenge for a data scientist. In Big Data ecosystem, most of the ETL and batch jobs are running on Spark and Hive which process/ingest data to the same data stores. Data scientists find this challenging in order to effectively utilize these big data workloads to develop an effective ML model. Hadoop Submarine (https://hadoop.apache.org/submarine/) helps to bring these two worlds together and provides seamless integration across. Ecosystem around Hadoop Submarine helps to design and run ML workloads from a notebook. Integrations with notebook such as Zeppelin and workflow scheduler like Azkaban, helps user to consume data from spark or hive and effectively run ML jobs with ease on any compute cluster. In this deep dive session, we will demo the simplicity of Submarine by running distributed deep learning/machine learning applications on YARN and Kubernetes as simple as running it locally. We will also showcase the community effort in developing Submarine’s ecosystem which eases integrations with Zeppelin and Azkaban. Submarine project could easily launch in the same cluster to run DL/ML jobs without any additional upgrades or complexities of having different machines.n’
AI is evolving rapidly, and much of the recent advancement is driven by Deep Learning, a machine learning technique inspired by the inner-working of the human brain. In this session, we will discuss what deep learning is, and the new capabilities it enables. We will dive into a few computer vision and natural language models that are demonstrating super-human performance, and to integrate these models into your existing Java system leveraging Apache MXNet – an open source deep learning framework – and it’s Java API. In our session, we will show a brief introduction to DL, specifically about applications using MXNet Gluon to do the training and Java to do inference with different DL models in Production systems. We would also demostrate how to run and deploy the DL models with MXNet Java API. By the end of the session, audience will learn how to leverage deep learning tasks using Apache MXNet, the various gotchas involved, and where/how to learn more.
Many data scientists are already making heavy usage of the Jupyter ecosystem for analyzing data using interactive notebooks. Apache Toree (incubating) is a Jupyter kernel designed that enables data scientists and data engineers to easily connect and leverage Apache Spark and its powerful APIs from a standard Jupyter notebook to execute their analytics workloads. In this talk, we will go over what’s new with the most recent Apache Toree release. We will cover available magics and visualizations extensions that can be integrated with Toree to enable better data exploration and data visualizations. We will also describe some high-level design of Toree and how users can extend the functionality of Apache Toree powerful plugin system. And all of these with multiple live demos that demonstrate how Toree can help with your analytics workloads in an Apache Spark environment.
There is an increasing need to bring machine learning to a wide diversity of hardware devices from the datacenter to the edge. Current frameworks rely on vendor-specific operator libraries and optimize for a narrow range of server-class GPUs. Deploying workloads to new platforms such as mobile phones, embedded devices, and accelerators (e.g., FPGAs, ASICs) requires significant manual effort. In this talk, we will talk about Apache TVM — an end to end optimizing deep compiler stack that brings deep learning models on diverse hardware back-ends that are competitive with state-of-the-art hand-tuned libraries for low-power CPU, mobile GPU, and server-class GPUs.
Good code isn’t enough for a successful open source project. First of all, only you know how to use what you’ve made. Maybe it’s time for a little UI and UX help? At the very least some documentation! Next, how is anyone else going to find what you’ve created? And that’s only the beginning. Ruth Suehle, manager of Red Hat’s Open Source and Standards community leadership team, will take you through examples of the best and the worst, from projects large and small, to help you see what you need beyond your code to build a successful open source project and community.
