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HP0-921 Supporting the Enterprise Modular Library (EML)

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HP0-921 exam Dumps Source : Supporting the Enterprise Modular Library (EML)

Test Code : HP0-921
Test name : Supporting the Enterprise Modular Library (EML)
Vendor name : HP
: 70 existent Questions

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HP Supporting the Enterprise Modular

Hewlett Packard commercial enterprise Unveils the world’s Most Scalable and Modular In-memory Computing Platform | killexams.com existent Questions and Pass4sure dumps

PALO ALTO – November 6, 2017 – today, Hewlett Packard industry (HPE) revealed the area’s most scalable and modular in-reminiscence computing platform, HPE Superdome Flex. The platform makes it workable for businesses of any dimension to technique and dissect broad amounts of information and turn it into actual-time industry insights.

In a world modified by using exponential statistics increase, customers countenance a fancy entangle 22 situation a course to deal with unparalleled records flows whereas conserving company continuity and agility to directly reply to company changes. one course agencies are tackling this problem is with the aid of leveraging in-memory databases, comparable to SAP HANA, Oracle Database In-reminiscence and Microsoft SQL Server. HPE is the international chief for SAP HANA as about 50 per cent of SAP HANA server deployments are on HPE programs.

to exist able to harness the full cost of in-reminiscence computing, the underlying infrastructure should exist capable of addressing gigantic facts units and manner analytics with out slowing transactions to achieve precise-time insights. HPE Superdome Flex has a unique modular design that scales seamlessly so organisations can preserve pace with evolving records calls for, whereas unmatched reliability capabilities deliver a resilient platform that safeguards vital workloads.

"shoppers wish to harness everyone of their facts to derive actionable insights in real-time to design more impactful company choices," pointed out Randy Meyer, vp and generic supervisor, Synergy & Mission faultfinding Servers, Hewlett Packard business. "With HPE Superdome Flex, purchasers can capitalize on in-reminiscence statistics analytics for his or her most vital workloads and scale seamlessly as statistics volumes grow."

HPE's newest mission significant in-memory computing platform combines the reliability of HPE Integrity Superdome X with the scalable know-how for prime efficiency facts analytics that HPE won with its acquisition of SGI. HPE Superdome Flex allows consumers to:

  • flip data into actionable insights at unparalleled scale and in precise-time: Designed with reminiscence-driven Computing ideas, HPE Superdome Flex gives a shared pool of remembrance over an ultra-speedy fabric that is in a position to scaling from 768GB to 48TB in a lone system, offering unmatched compute energy for probably the most worrying applications.
  • start little and develop seamlessly: The platform has a different modular design that scales from four to 32 sockets in four-socket increments. This equips consumer environments of everyone sizes with in-memory computing that can expand seamlessly and value-efficaciously as wants raise.
  • preserve mission-important workloads: HPE Superdome Flex presents confirmed reliability, availability and serviceability (RAS) capabilities no longer current in other regular x86 platforms to supply ninety nine.999% single-device availability.
  • Optimizing performance with HPE Pointnext

    HPE Pointnext has the capabilities and services to aid groups through their digital transformation, including:

  • Advisory features makes a speciality of constructing a enterprise strategy aligned with enterprise outcomes.
  • professional functions focuses on seamless implementation, on-funds execution and inventive configurations that net probably the most out of the HPE Superdome Flex solution. This includes HPE Pointnext's confirmed migration methodology that makes it workable for continuous industry through the total technique.
  • Operational functions offers advocate for the entire acknowledge and application ecosystem.
  • Availability

    The next technology mission essential server, HPE Superdome Flex, is now purchasable. For greater suggestions, delight consult with the Superdome Flex page here.

    About Hewlett Packard commercial enterprise

    Hewlett Packard commercial enterprise is an industry leading technology enterprise that allows for consumers to retreat extra, faster. With the trade's most comprehensive portfolio, spanning the cloud to the data middle to workplace applications, their technology and services help customers around the globe design IT more productive, more productive and more secure.


    HP modular change prepped for quicker Wi-Fi | killexams.com existent Questions and Pass4sure dumps

    HP is beefing up its modular campus switch to deal with the throughput of wireless entry facets in response to the latest 802.11ac standard.

    The industry brought at Interop this week a Wi-Fi module for the HP 5400R zl2. The industry additionally introduced a network troubleshooting software referred to as community Visualizer SDN.

    The module's foundation is HP's latest application-certain built-in circuit (ASIC). The HP modular swap wants the ASIC to system facts flows from 802.11ac entry aspects (APs). Many businesses begun swapping out slower 802.11n APs eventual 12 months. via the cessation of this year, the number of shipments of 802.11ac devices will surpass that of the older fashions, in response to IDC.

    The module ensures the 5400R zl2 is soundless advantageous in a network of next generation APs. The present unusual release of 802.11ac, called Wave 1, has more than double the throughput of 802.11n. it might furthermore handle many greater cell instruments per AP.

    Module prevents 'rip and substitute'

    HP consumers await to design employ of a swap for as many as 10 years, so the seller has to prolong the life of the hardware when unusual technology is brought in the community, stated heed Thompson, director of world product line management in HP Networking.

    "What they need to achieve is not must rip and supersede gadget in the wiring closet each time they jog through that entry point upgrade," he spoke of.

    The better to 802.11ac Wave 1 APs is painless for many businesses because the gadgets don't require a cable improve. Cat5e and Cat6 cables used via the vast majority of groups can tackle the higher throughput of Wave 1 contraptions, as lengthy as it does not exceed 1 gigabit per 2d.

    Wave 2 may well exist more frustrating. The know-how offers as much as five times the bandwidth of Wave 1 and helps as an Awful lot as four times the number of simultaneous connections per AP.

    organizations looking to near nearby to those multiples will probably need to design a pricey upgrade to Cat6a. organizations beget time to design a conclusion how to contend with Wave 2, youngsters. The number of cellular gadgets helping the habitual is expected to stay little for a couple of years.

    Multi-gig help with today's cabling

    Two supplier alliances and the IEEE are working on requirements for assisting 2.5 and 5 Gigabit Ethernet over Cat5e and Cat6. Such charges would exist extreme enough for most organizations.

    The HP module gives multi-gig help over Cat5e and Cat6. Taking expertise of the skill within the change requires the identical pilot in the AP. "If either aspect doesn’t, then it falls returned to 1 Gigabit per second Ethernet," said Mike Fratto, analyst for latest evaluation.

    Multi-gig help is expected in APs from Aruba Networks, which HP mentioned it plans to purchase with the aid of October for $2.7 billion.

    The HP 5400R zl2 sequence v3 Module is scheduled for unlock may additionally 1. Pricing will range from $3,299 to $6,799.

    network Visualizer helps diagnose and fix community bottlenecks. The troubleshooting application is constructive in correcting issues within a application-defined network (SDN), above everyone one built round HP's virtual utility Networks OpenFlow controller.

    community Visualizer is determined for unencumber in June.


    Leidos Wins $2.9B NASA IT aid shrink | killexams.com existent Questions and Pass4sure dumps

    Law360 (February 8, 2019, 7:57 PM EST) -- suggestions know-how and engineering company Leidos has won a NASA end-consumer shrink with a highest cost of $2.9 billion for the provision of IT capabilities to advocate the federal agency's Washington,...

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    Supporting the Enterprise Modular Library (EML)

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    Traditional Enterprise Reporting Platforms market analysis for 2019 just published | killexams.com existent questions and Pass4sure dumps

    The Traditional Enterprise Reporting Platforms Market upshot factors beget furthermore been discussed in the report. It further provides data on the market shares, strategies, and manufacturing cost structure along with Key Players and their profiles.

    Traditional Enterprise Reporting Platforms Market research report 2019-2024 presents a comprehensive study of the Traditional Enterprise Reporting Platforms Market in Global Industry; the study scans the progression of the market trailed over the past few years and the forecasts of growth in the coming years, primarily in terms of sales. The research report highlights the reasons liable for the disparities in the market and investigates them methodically.

    Click to Download Traditional Enterprise Reporting Platforms Market Research PDF (Sample) Copy Here: www.reportsnreports.com/contacts/r…aspx?name=1889306

    This report focuses on the Traditional Enterprise Reporting Platforms in global market, especially in North America, Europe and Asia-Pacific, South America, Middle East and Africa. This report categorizes the market based on manufacturers, regions, character and application.

    This independent 137-Page report based on analysis over 15 Major Key Players guarantees you will remain better informed than your competition. A traditional enterprise reporting platform includes capabilities to create trusted, sanctioned and highly controlled production reports and dashboards, which are then automatically distributed to large numbers of users in an enterprise and customers, or embedded in applications.

    It is most often deployed against a well-modelled data warehouse and/or data mart, including an optimization layer featuring online analytical processing (OLAP) cubes. It furthermore requires a reusable semantic layer to give content authors consistent and governed access to data sources, metrics, and other data definitions such as hierarchies and groups.

    These platforms are designed to advocate modular development of IT-produced analytics content.

    Report Covers Traditional Enterprise Reporting Platforms Market Segment by Top Manufacturers are:

  • SAP
  • Microsoft
  • IBM
  • Oracle
  • MicroStrategy
  • Information Builders
  • TIBCO Software
  • DataDeck
  • Infor
  • CXO Software
  • SAS
  • Manthan
  • Dimensional Insight
  • eQ Technologic
  • Izenda
  • Click For Direct Access of This Report: www.reportsnreports.com/purchase.aspx?name=1889306

    Traditional Enterprise Reporting Platforms Market Segmentation based on the Product Types are:

    - Cloud-based- On-premises

    Traditional Enterprise Reporting Platforms Market Segmentation based on the User Applications are:

    - BFSI- Education- Manufacturing- Telecom & IT- Others

    This report furthermore splits the Market by region:

    - Americas: United States, Canada, Mexico, Brazil

    - APAC: China, Japan, Korea, Southeast Asia, India, Australia

    - Europe: Germany, France, UK, Italy, Russia, Spain

    - Middle East & Africa: Egypt, South Africa, Israel, Turkey, GCC Countries

    Research Objectives of The Report:

  • To study and analyse the global Traditional Enterprise Reporting Platforms market size by key regions/countries, product character and application.
  • To understand the structure of Traditional Enterprise Reporting Platforms market by identifying its various sub segments.
  • Focuses on the key global Traditional Enterprise Reporting Platforms key players, to define, recount and analyse the value, market share, market competition landscape, SWOT analysis and development plans in next few years.
  • To analyse the Traditional Enterprise Reporting Platforms market with respect to individual growth trends, future prospects, and their contribution to the total market.
  • To participate circumstantial information about the key factors influencing the growth of the market (growth potential, opportunities, drivers, industry-specific challenges and risks).
  • To project the size of Traditional Enterprise Reporting Platforms submarkets, with respect to key regions (along with their respective key countries).
  • To analyse competitive developments such as expansions, agreements, unusual product launches and acquisitions in the market.
  • To strategically profile the key players and comprehensively analyse their growth strategies.
  • Table of Contents:

    1 Scope of the Report

    1.1 Market Introduction

    1.2 Research Objectives

    1.3 Years Considered

    1.4 Market Research Methodology

    1.5 Economic Indicators

    1.6 Currency Considered

    2 Executive Summary

    2.1 World Market Overview

    2.1.2 Traditional Enterprise Reporting Platforms Market CAGR by Region

    2.2 Traditional Enterprise Reporting Platforms Market Segment by Type

    2.3 Traditional Enterprise Reporting Platforms Market by Type

    2.3.2 Global Traditional Enterprise Reporting Platforms Revenue and Market participate by character (2014-2019)

    2.3.3 Global Traditional Enterprise Reporting Platforms Sale cost by character (2014-2019)

    2.4 Traditional Enterprise Reporting Platforms Market Segment by Application

    2.4.1 Supermarkets and Hypermarkets

    2.4.4 Others

    2.5 Traditional Enterprise Reporting Platforms Market by Application

    2.5.1 Global Traditional Enterprise Reporting Platforms Market participate by Application (2014-2019)

    2.5.2 Global Traditional Enterprise Reporting Platforms Value and Market participate by Application (2014-2019)

    2.5.3 Global Traditional Enterprise Reporting Platforms Sale cost by Application (2014-2019) and others…

    This email address is being protected from spambots. You need JavaScript enabled to view it.


    Lantronix to Showcase Scalable and Secure Out-of-Band Management Solutions at Cisco Live | killexams.com existent questions and Pass4sure dumps

    BARCELONA, Spain, Jan 28, 2019 (GLOBE NEWSWIRE via COMTEX) -- BARCELONA, Spain, Jan. 28, 2019 (GLOBE NEWSWIRE) -- This week at Cisco Live in Barcelona, Spain, Lantronix, Inc. LTRX, +2.77% a global provider of secure data access and management solutions for the industrial Internet of Things (IoT), will showcase its portfolio of innovative out-of-band management solutions including a number of unusual features at booth #B9 from January 28 to February 1, 2019.

    Lantronix's out-of-band management solutions, including the modular SLC(TM) 8000 advanced console manager, the SLB(TM) remote branch office manager, and ConsoleFlow(TM) centralized management software, are designed to reduce deployment and management complexity and costs while maximizing IT infrastructure uptime. The solutions provide secure out-of-band management to enable the IT personnel of any size enterprise to remotely monitor, manage and troubleshoot paraphernalia from anywhere while maintaining access to their faultfinding paraphernalia even during network outages via secondary dial-up or cellular connections.

    "IT decisionmakers in 2019 need to approach their IT management strategy from the perspective of the digital transformation. Uptrends in cloud services and the Internet of Things are significantly increasing the trial on the data center. In addition, as more and more enterprises deploy SD-WAN networks in their remote branch offices, the need to ensure network uptime and resiliency is an imperative," said Shahram Mehraban, Lantronix vice president of marketing. "Our innovative out-of-band management solutions enable both little and large enterprises to securely and reliably centralize management of their mission-critical equipment, maximizing uptime, while reducing infrastructure advocate costs."

    The latest software releases for the Lantronix SLC 8000 and SLB products that will exist exhibited at Cisco Live provide additional functionalities such as advocate for the unusual Lantronix SFP transceiver modules, enhanced LTE cellular fail-over and fail-back functionality and enhanced security. Lantronix has furthermore released unusual software for the Lantronix Spider KVM-over-IP product line which provides advocate for HTML5 as well as enhanced security.

    Not attending Cisco Live? Try the Lantronix Out-of-Band Management Products Today for Free!

  • Want to behold the SLC in action? Try their Online Test Drive - free access to a live SLC 8000, allowing you to explore its enterprise-grade features.
  • Or, try within your own environment with their 30-Day SLC 8000 or 30-Day SLB Free Trial.
  • Already beget Lantronix out-of-band management hardware? Try ConsoleFlow(TM) Cloud Edition free for 30 days.
  • Lantronix has furthermore added a unusual line of cost efficacious SFP transceiver modules to its out-of-band management product lineup, which provides seamless fiber connectivity for your networking gear. They are a cost-effective course to connect a lone network device to a wide variety of fiber cable distances and types. Lantronix SFP transceiver modules advocate common networking protocols, compatibility with Cisco SFP-based routers and switches as well as other common SFP based IT equipment. Lantronix's offering covers multi-mode and single-mode, short, mid and long range, BiDi and Duplex technologies. Samples are available today, and common availability from Lantronix authorized resellers is set for February.

    About Cisco LiveA premier education training destination for IT and computer networking professionals around the world, Cisco Live allows Cisco customers, partners and enthusiasts to participate insights on emerging technologies and solutions to unravel a variety of network and communications challenges.

    About LantronixLantronix, Inc. is a global provider of secure data access and management solutions for Internet of Things (IoT) assets. Their mission is to exist the leading supplier of IoT solutions that enable companies to dramatically simplify the creation, deployment, and management of IoT projects while providing secure access to data for applications and people.

    With more than two decades of undergo in creating robust machine to machine (M2M) technologies, Lantronix is an innovator in enabling their customers to build unusual industry models and realize the possibilities of the Internet of Things. Their connectivity solutions are deployed inside millions of machines serving a wide range of industries, including industrial, medical, security, transportation, retail, financial, environmental and government.

    Lantronix is headquartered in Irvine, California. For more information, visit www.lantronix.com.

    Learn more at the Lantronix blog, www.lantronix.com/blog, featuring industry discussion and updates. To succeed Lantronix on Twitter, delight visit www.twitter.com/Lantronix. View their video library on YouTube at www.youtube.com/user/LantronixInc or connect with us on LinkedIn at www.linkedin.com/company/lantronix.

    Lantronix Media and Analyst Contact:Shahram MehrabanVP, Marketingmedia@lantronix.cominvestors@lantronix.com949-453-7175

    Lantronix Sales:sales@lantronix.comAmericas +1 (800) 422-7055 (US and Canada) or +1 949-453-3990Europe, Middle East and Africa +31 (0)76 52 36 744Asia Pacific + 852 3428-2338China + 86 21-6237-8868Japan +81 (0) 50-1354-6201India +91 994-551-2488

    (C) 2019 Lantronix, Inc. everyone rights reserved. Lantronix is a registered trademark, and SLB and SLC are trademarks of Lantronix Inc. Other trademarks and trade names are those of their respective owners.

    (C) Copyright 2019 GlobeNewswire, Inc. everyone rights reserved.


    Machine Learning in a Box (Week 8): SAP HANA EML and TensorFlow Integration | killexams.com existent questions and Pass4sure dumps

    A Quick Recap

    Last time, they looked at how to employ Jupyter notebooks to elude kernels in Python and R and even SQL. I'll exist honest, Jupyter is now my unusual "go to" tool.

    I even contributed to the SQLAlchemy for SAP HANA GitHub repository recently to add the advocate of HDB User Store, which will allow you to connect without providing your user credentials in one of the cells.

    I hope you everyone managed to try this out, and probably some of you already decided to switch from the "good old" Eclipse IDE and its SAP HANA Tools plugin to elude your SQL.

    I know that I promised to dive into the TensorFlow integration over and over.

    All is now available and ready!!! I beget fixed the shrimp technical difficulties on my NUC and my Virtual Machines.

    I furthermore upgraded to HXE SPS03, which I highly recommend you doing so as well.

    For example, SPS03 brings the advocate of INT64 to SAP HANA EML (else you will need to adjust your models signature and cast your tensors to float).

    So, let's net started!

    You probably everyone heard about Google TensorFlow and how it can help you unravel many Machine Learning problems especially when it requires the employ of neural networks or deep Learning.

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    I won't feign that I can interpret everyone the details about the benefits of deep Learning, neural networks, or even TensorFlow, as there is plenty of content out there from people with much more undergo and credits than I have.

    So today, my goal will exist to help you net started with the SAP HANA External Machine Learning and TensorFlow integration.

    Using TensorFlow implies some understanding of the TensorFlow programming concepts but furthermore some Python coding skills. And I really cheer you to beget a sight at the TensorFlow net started and Tutorials pages.

    About TensorFlow and TensorFlow Serving (a.k.a ModelServer)

    TensorFlow ™ is an open-source software library for high-performance numerical computation. Its springy architecture allows light deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices.

    Originally developed by researchers and engineers from the Google Brain team within Google's AI organization, it comes with tough advocate for Machine Learning and deep Learning and the springy numerical computation core is used across many other scientific domains.

    For more details, you can check the TensorFlow web site.

    TensorFlow™ Serving is a flexible, high-performance serving system for Machine Learning models, designed for production environments. It makes it light to deploy unusual algorithms and experiments while keeping the selfsame server architecture and APIs. It furthermore provides out-of-the-box integration with TensorFlow models but can exist easily extended to serve other types of models and data.

    For more details, you can check the TensorFlow Serving web site.

    About SAP HANA External Machine Learning (EML)

    The integration of Google TensorFlow within SAP HANA is based on the SAP HANA Application duty Library (AFL), meaning that you beget now the skill to interact with your TensorFlow models from within SQLScript executed in SAP HANA.

    Using Google's gRPC remote procedure call, SAP HANA will access models exported in the SavedModel format from the TensorFlow Serving system.

    Here is a quick diagram explaining the interactions:

    For more details, you can check the SAP HANA External Machine Learning Library (EML) documentation.

    Installing SAP HANA EML, TensorFlow, and TensorFlow Serving

    The SAP HANA External Machine Learning (EML) library is participate of the SAP HANA, express edition downloadable package, so there is no trick here around its installation. You can simply succeed the installation guide.

    Regarding TensorFlow and TensorFlow Serving, you can install directly on your SAP HANA, express edition server or any other machine.

    But here is the "trick."

    If you conclude to install TensorFlow Serving on a SUSE Linux Enterprise or Red Hat Enterprise Linux system (which are the officially supported platforms for SAP HANA, express edition), you will need to compile it from the ground.

    If you conclude to install it on SAP HANA, express edition downloadable virtual machines, then you are furthermore using SUSE Linux Enterprise, so you will need to compile it too.

    And if you want to install it on Debian/Ubuntu distributions, the installation is pretty straightforward, it is as simple as something like:

    apt-get install tensorflow-model-server

    For circumstantial step by step setup instructions, I produced the following tutorial that guides you through the process:

    This tutorial cover Debian/Ubuntu distributions, SUSE Linux Enterprise and Red Hat Enterprise.

    The TensorFlow SavedModel Format Explained

    When exposing your TensorFlow models in TensorFlow Serving for SAP HANA consumption, you need to save them using the SavedModel format as documented in the SAP HANA EML documentation.

    You need to pay a particular attention to the model Signature definition, especially the shapes used for the input and output elements.

    The following 2 examples will highlight some of the common situations you will need to address when using content available on the TensorFlow site.

    Image Retraining

    For the Image Retraining scenario, here is the default signature definition that will exist generated when using the provided retrain script:

    The given SavedModel SignatureDef contains the following input(s): inputs['image'] tensor_info: dtype: DT_FLOAT shape: (-1, 299, 299, 3) name: Placeholder:0 The given SavedModel SignatureDef contains the following output(s): outputs['prediction'] tensor_info: dtype: DT_FLOAT shape: (-1, 5) name: final_result:0 Method name is: tensorflow/serving/predict

    You can behold that there is an input tensor ( and an output tensor (prediction).

    The input tensor has the following shape (-1, 299, 299, 3), which a rank 4 shape, a 4 dimensions input.

    In other words, you can picture the input as a vector where each vector entry is a 299 by 299 table (representing the 299 by 299 pixels of the input image) where each table cell represents a vector of 3 floats elements (representing the 3 RGB colors).

    You can check the following link for more details about Tensor dtype and shape.

    However, SAP HANA EML only advocate input tensors of rank two at most which match a table or matrix form.

    Therefore, I've added some steps in my tutorial to interpret how to add a succession of steps that process a raw image blob represented as a string and transforms it into the expected shape.

    The following signature will then exist generated:

    The given SavedModel SignatureDef contains the following input(s): inputs['inputs'] tensor_info: dtype: DT_STRING shape: (-1) name: RawJPGInput:0 The given SavedModel SignatureDef contains the following output(s): outputs['classes'] tensor_info: dtype: DT_STRING shape: (-1, 5) name: index_to_string_Lookup:0 outputs['scores'] tensor_info: dtype: DT_FLOAT shape: (-1, 5) name: TopKV2:0 Method name is: tensorflow/serving/predict

    With this signature, to process a SAP HANA EML summon for this model, you will provide:

  • one input table/view with one column that represents the image raw blob
  • one output table with ten float columns (5 for the classes and 5 for the scores)
  • Iris Classification Problem

    With the Iris classification problem, the original script doesn't actually include a save function.

    In most content available online, you will exist advised to save your models using the "parse example" API and define the serving input receiver relish this:

    def serving_input_receiver_fn(): feature_spec = tf.feature_column.make_parse_example_spec(feature_columns) recrudesce tf.estimator.export.build_parsing_serving_input_receiver_fn(feature_spec)()

    At the end, the input signature will cessation up relish this:

    inputs['inputs'] tensor_info: dtype: DT_STRING shape: (-1) name: input_example_tensor:0

    The Iris model actually uses four input floats to picture the Petal and Sepal width and length.

    Where did they retreat if now it expects only one string?

    Here is the saved model graph for the Iris model with the "parse example" API:

    When using the "parse example" API, it assumes that you will exist using tf.train.Example Protobuf objects as an input.

    A tf.train.Example Protobuf remonstrate contains a Features object, which contains a map of Feature, which is a list float (FloatList), byte (ByteList), or integer (Int64List).

    The tf.train.Example Protobuf objects are not supported by SAP HANA EML.

    Instead, you will need to save the model using the raw tensors and produce a model graph that looks relish this instead:

    You can achieve that with a piece of code relish this:

    # Define the input receiver spec feature_spec = { 'PetalLength': tf.placeholder(dtype=tf.float32, shape=[None,1], name='PetalLength'), 'PetalWidth' : tf.placeholder(dtype=tf.float32, shape=[None,1], name='PetalWidth'), 'SepalLength': tf.placeholder(dtype=tf.float32, shape=[None,1], name='SepalLength'), 'SepalWidth' : tf.placeholder(dtype=tf.float32, shape=[None,1], name='SepalWidth'), } # Define the input receiver for the raw tensors def serving_input_receiver_fn(): recrudesce tf.estimator.export.build_raw_serving_input_receiver_fn(feature_spec)()

    Which will cessation up generating the following signature:

    The given SavedModel SignatureDef contains the following input(s): inputs['PetalLength'] tensor_info: dtype: DT_FLOAT shape: (-1, 1) name: PetalLength:0 inputs['PetalWidth'] tensor_info: dtype: DT_FLOAT shape: (-1, 1) name: PetalWidth:0 inputs['SepalLength'] tensor_info: dtype: DT_FLOAT shape: (-1, 1) name: SepalLength:0 inputs['SepalWidth'] tensor_info: dtype: DT_FLOAT shape: (-1, 1) name: SepalWidth:0 The given SavedModel SignatureDef contains the following output(s): outputs['predicted_class_id'] tensor_info: dtype: DT_INT64 shape: (-1, 1) name: dnn/head/predictions/ExpandDims:0 outputs['probabilities'] tensor_info: dtype: DT_FLOAT shape: (-1, 3) name: dnn/head/predictions/probabilities:0 Method name is: tensorflow/serving/predict

    With this signature, to process a SAP HANA EML summon for this model, you will provide:

  • four distinct input tables/views with one column each that represents the Petal and Sepal width and length (it will exist using the tensor alphabetical order, as in the signature order)
  • one output table with four columns, one integer column for the predicted class id and three floats for the classes probability
  • As you can notice, each input is using a distinct table/view, whereas everyone the outputs are stored into a lone table.

    Note: DT_INT64 character is not supported with SPS02 or prior, you will need to cast it to a DT_FLOAT type

    Serve a TensorFlow Model in SAP HANA, Express Edition

    Now, let's deploy a TensorFlow model and consume it in SAP HANA, express edition!

    Actually, it's not going to exist one, but two models that I beget content available for. One that uses the well know Iris Flowers dataset and one that process Flowers images.

    The thought with the second model was to demonstrate how a blob representing an image stored in SAP HANA could exist processed thru the EML library.

    And here are the tutorial links:

    You can, of course, employ a different set of images to retrain your ImageNet model.

    Executing an EML summon is no different from other AFL call. Here is the case for Iris:

    SET SCHEMA EML_DATA; -- Define table types for iris CREATE character TT_IRIS_PARAMS AS TABLE ("Parameter" VARCHAR(100), "Value" VARCHAR(100)); CREATE character TT_IRIS_FEATURES_SEPALLENGTH AS TABLE (SEPALLENGTH FLOAT); CREATE character TT_IRIS_FEATURES_SEPALWIDTH AS TABLE (SEPALWIDTH FLOAT); CREATE character TT_IRIS_FEATURES_PETALLENGTH AS TABLE (PETALLENGTH FLOAT); CREATE character TT_IRIS_FEATURES_PETALWIDTH AS TABLE (PETALWIDTH FLOAT); CREATE character TT_IRIS_RESULTS AS TABLE ( -- when SPS02 or prior, design PREDICTED_CLASS_ID character FLOAT instead of INTEGER PREDICTED_CLASS_ID INTEGER, PROBABILITIES0 FLOAT, PROBABILITIES1 FLOAT, PROBABILITIES2 FLOAT ); -- Create description table for procedure creation CREATE COLUMN TABLE IRIS_PROC_PARAM_TABLE ( POSITION INTEGER, SCHEMA_NAME NVARCHAR(256), TYPE_NAME NVARCHAR(256), PARAMETER_TYPE VARCHAR(7) ); -- Create the result table CREATE TABLE IRIS_RESULTS relish TT_IRIS_RESULTS; -- Drop the wrapper procedure CALL SYS.AFLLANG_WRAPPER_PROCEDURE_DROP(CURRENT_SCHEMA, 'IRIS'); -- Populate the wrapper procedure parameter table INSERT INTO IRIS_PROC_PARAM_TABLE VALUES (1, CURRENT_SCHEMA, 'TT_IRIS_PARAMS' , 'in'); INSERT INTO IRIS_PROC_PARAM_TABLE VALUES (2, CURRENT_SCHEMA, 'TT_IRIS_FEATURES_PETALLENGTH' , 'in'); INSERT INTO IRIS_PROC_PARAM_TABLE VALUES (3, CURRENT_SCHEMA, 'TT_IRIS_FEATURES_PETALWIDTH' , 'in'); INSERT INTO IRIS_PROC_PARAM_TABLE VALUES (4, CURRENT_SCHEMA, 'TT_IRIS_FEATURES_SEPALLENGTH' , 'in'); INSERT INTO IRIS_PROC_PARAM_TABLE VALUES (5, CURRENT_SCHEMA, 'TT_IRIS_FEATURES_SEPALWIDTH' , 'in'); INSERT INTO IRIS_PROC_PARAM_TABLE VALUES (6, CURRENT_SCHEMA, 'TT_IRIS_RESULTS' , 'out'); -- Create the wrapper procedure CALL SYS.AFLLANG_WRAPPER_PROCEDURE_CREATE('EML', 'PREDICT', CURRENT_SCHEMA, 'IRIS', IRIS_PROC_PARAM_TABLE); -- Create and populate the parameter table CREATE TABLE IRIS_PARAMS relish TT_IRIS_PARAMS; INSERT INTO IRIS_PARAMS VALUES ('Model', 'iris'); INSERT INTO IRIS_PARAMS VALUES ('RemoteSource', 'TensorFlow'); INSERT INTO IRIS_PARAMS VALUES ('Deadline', '10000'); -- Create the input views CREATE VIEW IRIS_FEATURES_SEPALLENGTH AS SELECT SEPALLENGTH FROM TF_DATA.IRIS_DATA ORDER BY ID; CREATE VIEW IRIS_FEATURES_SEPALWIDTH AS SELECT SEPALWIDTH FROM TF_DATA.IRIS_DATA ORDER BY ID; CREATE VIEW IRIS_FEATURES_PETALLENGTH AS SELECT PETALLENGTH FROM TF_DATA.IRIS_DATA ORDER BY ID; CREATE VIEW IRIS_FEATURES_PETALWIDTH AS SELECT PETALWIDTH FROM TF_DATA.IRIS_DATA ORDER BY ID; -- summon the TensorFlow model CALL IRIS (IRIS_PARAMS, IRIS_FEATURES_PETALLENGTH, IRIS_FEATURES_PETALWIDTH, IRIS_FEATURES_SEPALLENGTH, IRIS_FEATURES_SEPALWIDTH, IRIS_RESULTS) WITH OVERVIEW; Conclusion

    Adding TensorFlow to their stack opens a huge set of capabilities including the skill to process images, videos and other character of "unstructured" content relish text.

    You can furthermore employ it for let's disclose more classic models relish with the Iris dataset.

    Again ( and sorry for repeating myself), what you really to pay attention to is the signature input and output elements where the SAP HANA EML enforce some restrictions with the shape dimensions on the both the input and output.

    A huge thank you to Burkhard Neidecker-Lutz, Former Member and Christoph Morgen from the SAP HANA engineering team for their advocate when producing this content.



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