The bare metal installation of OCP is only this installer command:
$ openshift-baremetal-install --dir ~/clusterconfigs create cluster but I’ll take time in this post to explain how to prepare your platform and how to follow the installation.
Lots of Data Scientists want to focus on model building.
Just using a local Jupyter Notebook can be a limitation if you want to:
create scalable Machine Learning systems test local private data ingestion contribute to Kubeflow tune your model serving pipeline You can build an All-in-One Kubernetes environment with NVIDIA GPU for Data Science on your local PC or one bare-metal cloud server, let’s see how CodeReady Containers works.
Open Data Hub v0.5.1 was released February 16, 2020.
Release node: https://opendatahub.io/news/2020-02-16/odh-release-0.5.1-blog.html
Open Data Hub includes many tools that are essential to a comprehensive AI/ML end-to-end platform.
The NVIDIA GPU Operator has been available as a Beta since 2020, Jan 27, it’s a technical preview release: https://github.com/NVIDIA/gpu-operator/release
The GPU Operator manages NVIDIA GPU resources in an OpenShift cluster and automates tasks related to bootstrapping GPU nodes.
OPAE is the Open Programmable Acceleration Engine, a software framework for managing and accessing programmable accelerators (FPGAs): https://01.org/opae
The OPAE SDK is open source and available in this git: https://github.
I’ve done a talk “Machine Learning benchmarking with OpenStack and Kubernetes” at Open Infrastructure Summit Shanghai 2019, November 4.
Abstract Deep Learning and Cloud Platforms are transforming the field of Machine Learning from theory to practice.
Red Hat OpenShift Container Platform 4.2 introduces the general availability of full-stack automated deployments on OpenStack. With OpenShift 4.2, containers can be managed across multiple public and private clouds, including OpenStack.
We will describe the steps to try and download NVIDIA GRID software:
Create a NVIDIA account Redeem your Product Activation Key (PAK) Download packages Prepare the VM and operating system of the license server based on RHEL 7.
Red Hat OpenStack Platform 14 is now generally available \o/
NVIDIA GRID capabilities are available as a technology preview to support NVIDIA Virtual GPU (vGPU). Multiple OpenStack instances virtual machines can have simultaneous, direct access to a single physical GPU.
Red Hat OpenStack Platform provides two ways to use NVIDIA Tesla GPU accelerators with virtual instances:
GPU PCI passthrough (only one physical GPU per instance) vGPU GRID (one physical GPU can be shared to multiple instances, Tech Preview OSP14) This blog post is intended to show how to setup GPU PCI passthrough.
Some OpenStack users users would like to attach USB devices to OpenStack instances for security or legacy applications.
For example, a security application which run inside an OpenStack instance could require access to a Java card from an USB Gemalto eToken: