The document discusses Cisco's Container Platform and provides the following key points: 1. Cisco's Container Platform provides a turnkey solution for production-grade Kubernetes container environments that is easy to acquire, deploy and manage on hybrid cloud infrastructures. 2. It features native Kubernetes integration that is 100% upstream compatible, integrated networking, management and security capabilities, and support for AI/ML workloads. 3. The platform architecture includes hardware from Cisco (UCS servers, Nexus switches), virtualization software (VMware, HyperFlex), and container-specific software like Kubernetes, Istio and Prometheus for orchestration, networking and monitoring of container workloads.

1. Meenakshi Kaushik
Wednesday, July 17, 2019 10:00 am
The Enhanced
Cisco Container Platform

2. © 2016 Cisco and/or its affiliates. All rights reserved. Cisco Confidential
Challenges when Deploying Kubernetes Clusters
Source CNCF
Survey
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3. © 2016 Cisco and/or its affiliates. All rights reserved. Cisco Confidential
Cisco Container Platform
Hybrid Cloud Optimized
E.g.: AWS, Google, …
Flexible Deployment Model
VMware (air gapped) | OpenStack (air gapped) | Public cloud
Integrated
Networking | Management | Security | AI/ML Support
Native Kubernetes (100% Upstream)
Direct updates and best practices from open source community
Turnkey Solution
for Production-Grade Container
Environments
Easy to acquire, deploy and manage | Open and consistent | Extensible platform | World-class advisory and support
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4. © 2016 Cisco and/or its affiliates. All rights reserved. Cisco Confidential
Control Plane Data Plane
VM VM
Control Plane Kubernetes
Automation
Orchestration
Operations
HX Connect Cluster/
Machine
Controllers
VM VM VM
Cluster 1 Kubernetes
Cluster1
Workloads
Cluster1
Ops
Pod
Pod
Pod
VM VM VM
Cluster 2 Kubernetes
Cluster2
Workloads
Cluster2
Ops
Pod
Pod
Pod
Kubernetes Fluentd Prometheus Kibana Hyperflex Contiv
Storage (HyperFlex / VMware )
Networking (Nexus 9K)
Compute Hardware (UCS)
Hypervisor Layer (HyperFlex / VMware )
Cisco Container Platform Architecture
VM VM
Istio
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5. Overview Demo

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© 2019 Cisco and/or its affiliates. All rights reserved. Cisco Public#CLUS
Cisco UCS HX/Compute
Cisco Container Platform – Enterprise Kubernetes
Hardened Container OS
vSphere v6.5 U1 (and above), Non-VMware
100% Upstream
Kubernetes
NVIDIA
GPU*
Hardware
* V100
3rd Party
Compute /
Storage
Automated Installer (Wizard-Driven, Modular, Robust, Repeatable)
Containerized Deployment (Separated Control and Data Planes) Day 0
Cisco Nexus 9000
(ACI)
Cisco Blades, Racks (B / C)
Day N
Software
Lifecycle Manager (Day 0 to N operations – Cluster Mgmt, Update/Upgrades, Patches, API)
HA (Control Plane)
Ubiquitous Security (Hardened Linux, RBAC, non-root, Secure External Registry, etc. )
Failure Recovery (Control Plane Snapshotting)
Logging & Assurance (Logging, EFK - Analytics and Visualization stack)
Integrated Tools (Visualization: Prometheus)
Unified Management System (Multi-Tenant, Single Pane of Glass, GUI, REST API)
Container Networking (Calico, ACI, Contiv)
Performance Enhancement (Fast Data with Fd.io/VPP-Via Contiv, multi-gpu node-pools)
Load Balancing (L3/L4: MetalLB, L7: nginx, Istio-Envoy, Ent Grade Integration-AVI, F5)
Hybrid Cloud Integration (AWS EKS, Istio service mesh)
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7. © 2016 Cisco and/or its affiliates. All rights reserved. Cisco Confidential
Accelerate Innovation
Developers
Freedom to access
the best platforms and
tools for increased
agility
IT Ops
One consistent
environment to
eliminate silos &
drive efficiency
Security team
Visibility and control
across one hybrid
environment without
slowing innovation
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8. SimplifyAI/ML

9. Multi-GPU as a
Service

10. © 2016 Cisco and/or its affiliates. All rights reserved. Cisco Confidential
AI / ML On-Prem
Source Kubeflow
Community
Survey
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11. © 2016 Cisco and/or its affiliates. All rights reserved. Cisco Confidential
© 2019 Cisco and/or its affiliates. All rights reserved. Cisco Public#CLUS
Use Case – Multi-GPU as a Service
• Manage AI/ML K8s workload similar to any other K8s App workload
• GPU consumption automatic; Seamless scale up/down
• Auto install, upgrade and manage dependency for CUDA drivers, device drivers and Kubernetes
• Enable high performance distributed training/serving
• MultipleGPU per worker nodes
• GPU selection with high performance links between GPUs / CPUs
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12. © 2016 Cisco and/or its affiliates. All rights reserved. Cisco Confidential
GPU Support On Cisco Container Platform
Worker VM
HYPERVISOR
Worker VM
Kubernetes Cluster 1 Kubernetes Cluster N
Worker VMWorker VMWorker VM
…
GPU GPU
UCS Server
GPU Node Pool GPU Node PoolNon-GPU Node Pool
UCS Server
HYPERVISOR
Cisco Container Platform v4.0
• Ubuntu 18.0.4 & K8s 1.13+
• Multiple GPU per worker node (Default)
• GPU passthrough (native performance), TESLA V100 PCIe, …
• Auto provisioning of Nvidia CUDA drivers, container runtime, NVIDIA device plugin: GPU workload provisioning acceleration
New
Feature
GPU
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13. Multi-GPU as a Service Demo

14. AI/ML Lifecycle
Management with
Kubeflow

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© 2019 Cisco and/or its affiliates. All rights reserved. Cisco Public#CLUS
Customers are looking for ML Lifecycle Manager
Source Kubeflow
Community
Survey
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16. © 2016 Cisco and/or its affiliates. All rights reserved. Cisco Confidential
© 2019 Cisco and/or its affiliates. All rights reserved. Cisco Public#CLUS
Make it easy for everyone to
develop, deploy and manage
portable, scalable ML
everywhere
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17. © 2016 Cisco and/or its affiliates. All rights reserved. Cisco Confidential
© 2019 Cisco and/or its affiliates. All rights reserved. Cisco Public#CLUS
Kubeflow Essentials
General Applications
Infrastructure
(Cloud/On-Prem)
Container Runtimes
ML Workloads
(Modelling, training, roll-out, serving, ...)
Infrastructure
(Cloud/On-Prem)
Container Runtimes
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18. © 2016 Cisco and/or its affiliates. All rights reserved. Cisco Confidential
© 2019 Cisco and/or its affiliates. All rights reserved. Cisco Public#CLUS
Kubeflow Architecture
Libraries and CLIs
Higher Level Services
Low Level APIs / Services
Arena kfctl kubectl
Katib Pipelines
Notebooks
Fairing
TFJob PyTorchJob
Jupyter CR
Seldon CR
Kubebench
Metadata
Orchestration
Pipelines CR
Argo
Study Job
MPIJob
Spark Job
Model DB
TFX
Developed By Kubeflow
Developed Outside Kubeflow
Adapted from Kubeflow Contributor Summit 2019
talk: Kubeflow and ML Landscape
(Not all components are shown)
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19. Kubeflow Demo

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