HPE GreenLake Machine Learning Ops Services

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HPE GREENLAKE FOR ML OPS CONTENTS Service description................................................................................................................................................................................................................................................................................................................ 2 Service components ............................................................................................................................................................................................................................................................................................................ 3 Customer responsibilities................................................................................................................................................................................................................................................................................................ 4 Service limitations.................................................................................................................................................................................................................................................................................................................. 5 Details of hardware, software, and services included ............................................................................................................................................................................................................................ 5 Details of hardware, software, and services included (continued)............................................................................................................................................................................................ 6

Solution guide

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SERVICE DESCRIPTION HPE GreenLake for ML Ops is an on-premises end-to-end solution for data science that includes hardware, software, and services. It brings the cloud to the data center providing data scientists and their Dev Ops counterparts with an enterprise-scale, data science service that keeps data on premises.

The HPE GreenLake ML Ops service makes it easier and faster to deploy AI/ML workloads on premises with a cloud experience: self-service, pay-per-use, scalable, and managed for you. The service platform integrates hardware, software, and service to enable customers to build, train and deploy ML workloads quickly and at scale. The offering supports the end-to-end data science lifecycle in a flexible design that provides the data scientist a single environment to work.

By removing the operational heavy lifting from each step of the ML process, HPE GreenLake for ML Ops provides the required ML tools, making it easier to deploy high-quality ML models and allowing customers to focus on building applications without worrying about provisioning and managing underlying Kubernetes clusters and associated infrastructure.

FIGURE 1. HPE GreenLake for ML Ops integrates various offerings in one prepackaged solution

By providing an enterprise-grade ML service offering that runs as a cloud in the customer’s data center, HPE GreenLake for ML Ops: • Allows data scientists to spend more time focused on data science by providing on-demand resources with hardware, infrastructure, and data science software as a service, and secure, self-serve provisioning and management via HPE GreenLake Central • Reduces operational risk by delivering as a fully managed service from control plane up to the infrastructure level and ML Ops-curated applications • Solves data gravity, data sovereignty, and compliance issues that are experienced with public cloud by keeping the data on premises and preinstalling open-source applications within the system • Reduces costs through flexibility to elastically scale up and down capacity usage dependent on needs and consumption-based pricing and billing • Offers a low-friction entry point with a trial experience option on prestaged hardware and software to test out the service before committing

FIGURE 2. A visual representation of the HPE GreenLake for ML Ops service solution and its components

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SERVICE COMPONENTS TABLE 1. Service component highlights Component

Delivery specifications

Service highlights

The HPE GreenLake for ML Ops service includes: • Installation and setup services • Post-install technical engagement • On-premises, fully managed hardware optimized for ML Ops, Including: – For compute: HPE Apollo 6500 Gen10 system integrated with accelerated NVIDIA® Tesla V100 GPUs and HPE ProLiant DL360 integrated with NVIDIA Tesla T4 GPUs – For storage: HPE Apollo 4200 Gen10 server includes NVMe storage in the performance configuration • Choice of two configurations – Standard configuration – Performance optimized configuration Note: Expansion options for both configurations shall be available after first release.

• Software: HPE Ezmeral ML Ops software with model registry, project repository, optional GitHub integration • Five ready-to-use Al/ML application images • HPE GreenLake Central for centralized management, provisioning, and visibility to usage and cost • HPE Datacenter Care with single point of contact relationship management • HPE GreenLake Management Services (GMS) for full management and operation of the HPE GreenLake for ML Ops service • Consumption-based billing

System technical configuration and capacity

• The HPE designed and factory-built system includes: – HPE Apollo 6500 servers integrated with accelerated NVIDIA Tesla V100 GPUs and HPE ProLiant DL360 servers integrated with NVIDIA Tesla T4 GPUs – HPE Ezmeral ML Ops software with model registry, project repository, optional GitHub integration • HPE ships the infrastructure to the customer’s data center or colocation facility.

• HPE sets up, remotely manages, and operates the full solution.

• Key differences between the two configuration options: Components

Standard Configuration

Performance Configuration

Usable Storage

228 TB

394 TB

6, 96

6, 120

V100 GPU

4

8

T4 GPU

4

4

NVMe storage

-

150 TB

CPUs, usable CPU cores

End-to-end management with HPE GMS

HPE GMS is included in the HPE GreenLake for ML Ops solution. This frees up the customer’s operational resources from the day-to-day overhead of maintaining an IT environment optimized for AI and ML. HPE GMS provides remote infrastructure and application monitoring, management, and optimization for the entire solution following HPE best-practice technology and service management principles and processes.

For a complete description of services, see the HPE GMS data sheet.

HPE GreenLake Central and ML Ops widget

Access to the ML Ops widget is from within the HPE GreenLake Central management console. The ML Ops widget allows the DevOps user in the customer’s IT organization to create clusters ready to be used in data science projects and assigns these to individual data scientists with the appropriate access rights. Data scientists can in turn access the projects assigned to them. The data scientist can also launch the HPE Ezmeral ML Ops console to work on training and deploying their data science models.

A demo of this functionality is available here.

Ready-to-use AI/ML images

HPE GreenLake for ML Ops includes five standard container images with data science tooling. The images allow the customer to train ML models, deploy trained models, ingest data, and preprocess data. The images include software such as JupyterHub, NumPy, TensorFlow, Python, and Kafka.

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TABLE 1. Service component highlights (continued) Component

Delivery specifications

Consumption billing

HPE GreenLake for ML Ops pricing is made up of a recurring price for reserved capacity, plus monthly charges for any usage over the reserved capacity.

The customer agrees to a reserved capacity at a certain price. The minimum reserved capacity for HPE GreenLake for ML Ops, for both configurations, is 50%.

If the customer consumes at or below the reserved capacity, the monthly bill does not change month to month.

Monthly charges over the reserved capacity are calculated based upon the following 4 meters: • Storage: GB average usage per hour • CPU cores: Usage by minute • V100 GPU: Usage by minute • T4 GPU: Usage by minute Pricing is based on a rate card. The higher the reserved capacity, the lower the unit price for the 4 meters.

To retain a consistent rate and consistent monthly reserve payment, every month will be set to have the same number of days, 30.477, based on the average number of days in a month over the year (365.25/12 = 30.477).

Compute is measured and billed to the nearest minute (per month).

Storage is measured and billed to the nearest hour (per month).

The 4-year contract is paid monthly.

If the customer uses more than their reserved monthly capacity, they pay for that extra consumption across the meters where they exceeded the reserve. The rate at which they pay is the unit price at the reserved amount.

How usage is metered

For compute, the HPE metering tool calls an OS level API that returns CPU ticks every minute. CPU ticks indicate the CPU is in use. If the CPU returns a usage rate of 5% during the minute, we regard that CPU as in use, and bill for that minute at the agreed hourly rate. The first 5% is intended to cover low-level system jobs not initiated by the user and not counted as HPE GreenLake usage.

For storage, HPE uses HPE Ezmeral technology to check at the beginning of the hour and the end of the hour what the storage consumption is for the particular drive or volume, and then it calculates the average. For example, if the meter shows 50% used at the beginning of the hour and 100% at the end of the hour, HPE bills on 75%. HPE does not charge for ephemeral storage; this is reported but not charged.

Consumption analytics

Via the HPE GreenLake Central portal, HPE GreenLake for ML Ops customers have access to HPE Consumption Analytics. HPE Consumption Analytics provides detailed reports, views, and analytics to help customers manage consumption and help maximize investments across public cloud and hybrid cloud infrastructure. Included are interactive charts, drag-and-drop features, data mapping, monitoring, and powerful insights.

The HPE GreenLake for ML Ops monthly charge report displays actual usage compared to reserved usage.

Ordering and pricing

• U.S. prices are published for a sample set of reserve levels on the HPE GreenLake for ML Ops webpage.

• Contact HPE sales for a quote in your country.

• HPE GreenLake for ML Ops shall be available for sale in 21 countries.

Governance

HPE GreenLake for ML Ops includes an assigned account team (AAT) that works closely with the customer to understand their business and IT objectives, with the goal to help ensure these needs are met. The AAT is the customer’s advocate and technical focal point for ongoing support of the IT environment. The AAT works with the customer to develop and routinely review a mutually agreed account support plan.

CUSTOMER RESPONSIBILITIES The customer responsibilities include, but are not limited to, the following: The customer will: • Provide an internet connection necessary for HPE GreenLake Central service • Provide a location suitable for delivery of the service, public (guest) internet access for the HPE delivery consultants, and any network connections required • Allow HPE all reasonably necessary access to all locations where delivery service is to be performed • Assign a project sponsor, project manager, and other personnel, as appropriate, to work with HPE throughout the project’s lifecycle

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SERVICE LIMITATIONS HPE GreenLake for ML Ops limitations and exclusions include, but are not limited to, the following: Standard container images.

• HPE GreenLake for ML Ops includes five standard container images with data science tooling.

• The images allow the customer to train ML models, deploy trained models, ingest data, and preprocess data.

• At first release, these images will be deployable on the HPE Ezmeral Container Platform using the HPE Container Platform orchestrator.

• HPE shall update the HPE GreenLake for ML Ops service as new capabilities become available.

• The images include software such as JupyterHub, ELK, TensorFlow, Python, and Kafka.

These standard images are supported by HPE via the software support process.

• HPE allows customizations of these container images but does not provide support for customized images.

• If support for customized images is needed, this can be purchased as a service via Advisory and Professional Services from HPE Pointnext Services.

DETAILS OF HARDWARE, SOFTWARE, AND SERVICES INCLUDED Software

• HPE Ezmeral Machine Learning Ops 4-year Select Subscription 24x7 support E-LTU x 280 • Access to HPE GreenLake Central

Services

Provision and installation of the HPE GreenLake for ML Ops systems in customer’s data center or colocation Reactive and proactive support services including relationship management and single point of contact see HPE Foundation Care data sheet and HPE Datacenter Care data sheet specifically Table 1. Core features HPE GMS: Remote infrastructure and application monitoring, management, and optimization for the entire solution. See detailed data sheet.

Post-install technical engagement service

The goal of this engagement is to take a single existing end-to-end ML OPs pipeline from the customer end user and port it to their new HPE GreenLake for ML Ops environment.

The service consists of the three work packages: Work package 1 Project planning and discovery: Information is gathered through kickoff meeting, which serves as the input to project planning Work package 2 Single ML OPs migration and deployment—perform deployment of HPE Container Platform. The solution architecture document serves as reference document and functional tests are performed as captured in the functional test document.

Work package 3 Knowledge transfer: Operational run book is used for knowledge transfer of the solution being implemented to customer operational teams.

Hardware and software components

Note: HPE may modify the hardware and software components included in the service, for example, to upgrade to latest model generations or software versions.

Hardware/common components

Both standard and performance configurations include a common networking block and a management gateway providing support for HPE GreenLake orchestration and lifecycle management.

Both standard and performance configurations include an inferencing block with two HPE ProLiant DL360 servers. This block is optimized for deployment of models. Taken together, the two HPE ProLiant DL360 servers’ specification is as follows: • 4 x 6226R (16 core, 2.9 GHz) • 48 x 16 GB DIMM/384 GB Memory • 4 x NVIDIA Tesla T4 16 GB • 4 x 960 GB SATA RI SSD • 24 x 6.4 TB SAS MU SFF • 4 x 25GbE

Solution guide

DETAILS OF HARDWARE, SOFTWARE, AND SERVICES INCLUDED (CONTINUED) Hardware within standard The standard rack configuration includes five HPE Apollo 4200 storage servers, each with the following specification: configuration • 2 x 4214R/12 core, 2.4 GHz • 12 x 16 GB DIMM/192 GB memory • 4 x 960 GB SAS MU LFF • 12 x 10 TB SAS 12G 7.2K LFF • 2 x 100GbE and 2 x 25GbE Note: The raw device sizes do not denote available capacity or billing capacity.

The standard rack configuration includes the following one HPE Apollo 6500 Gen10 system training server.

• 2 x 6226R (16 core, 2.9 GHz) • 24 x 16 GB DIMM/384 GB memory • 4 x NVIDIA Tesla V100 32 GB SXM2 • 2 x 960 GB SATA RI SSD • 6 x 6.4 TB SAS MU SFF • 2 x 100GbE and 2 x 25GbE Hardware within performance-optimized configuration

The performance-optimized configuration includes five HPE Apollo 4200 storage servers, each with the following specification: • 2 x 4214R/12 core, 2.4 GHz • 12 x 16 GB DIMM/192 GB memory • 4 x 960 GB SAS MU LFF • 18 x 10 TB SAS 12G 7.2K LFF • 6 x 6.4 TB NVMe x4 MU SFF • 2 x 100GbE and 2 x 25GbE Note: The raw device sizes do not denote available capacity or billing capacity.

The performance-optimized configuration includes one HPE Apollo 6500 training server with the following specification: • 2 x 6258R (28 core, 2.7 GHz) • 24 x 32 GB DIMM/768 GB memory • 8 x NVIDIA Tesla V100 32 GB SXM2 • 2 x 960 GB SATA RI SSD • 6 x 6.4 TB SAS MU SFF • 2 x 100GbE and 2 x 25GbE

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