IBM/Red Hat and Intel Leftovers
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Troubleshooting Red Hat OpenShift applications with throwaway containers
Imagine this scenario: Your cool microservice works fine from your local machine but fails when deployed into your Red Hat OpenShift cluster. You cannot see anything wrong with the code or anything wrong in your services, configuration maps, secrets, and other resources. But, you know something is not right. How do you look at things from the same perspective as your containerized application? How do you compare the runtime environment from your local application with the one from your container?
If you performed your due diligence, you wrote unit tests. There are no hard-coded configurations or hidden assumptions about the runtime environment. The cause should be related to the configuration your application receives inside OpenShift. Is it time to run your app under a step-by-step debugger or add tons of logging statements to your code?
We’ll show how two features of the OpenShift command-line client can help: the oc run and oc debug commands.
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What piece of advice had the greatest impact on your career?
I love learning the what, why, and how of new open source projects, especially when they gain popularity in the DevOps space. Classification as a "DevOps technology" tends to mean scalable, collaborative systems that go across a broad range of challenges—from message bus to monitoring and back again. There is always something new to explore, install, spin up, and explore.
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How DevOps is like auto racing
When I talk about desired outcomes or answer a question about where to get started with any part of a DevOps initiative, I like to mention NASCAR or Formula 1 racing. Crew chiefs for these race teams have a goal: finish in the best place possible with the resources available while overcoming the adversity thrown at you. If the team feels capable, the goal gets moved up a series of levels to holding a trophy at the end of the race.
To achieve their goals, race teams don’t think from start to finish; they flip the table to look at the race from the end goal to the beginning. They set a goal, a stretch goal, and then work backward from that goal to determine how to get there. Work is delegated to team members to push toward the objectives that will get the team to the desired outcome.
[...]
Race teams practice pit stops all week before the race. They do weight training and cardio programs to stay physically ready for the grueling conditions of race day. They are continually collaborating to address any issue that comes up. Software teams should also practice software releases often. If safety systems are in place and practice runs have been going well, they can release to production more frequently. Speed makes things safer in this mindset. It’s not about doing the “right” thing; it’s about addressing as many blockers to the desired outcome (goal) as possible and then collaborating and adjusting based on the real-time feedback that’s observed. Expecting anomalies and working to improve quality and minimize the impact of those anomalies is the expectation of everyone in a DevOps world.
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Deep Learning Reference Stack v4.0 Now Available
Artificial Intelligence (AI) continues to represent one of the biggest transformations underway, promising to impact everything from the devices we use to cloud technologies, and reshape infrastructure, even entire industries. Intel is committed to advancing the Deep Learning (DL) workloads that power AI by accelerating enterprise and ecosystem development.
From our extensive work developing AI solutions, Intel understands how complex it is to create and deploy applications for deep learning workloads. That?s why we developed an integrated Deep Learning Reference Stack, optimized for Intel Xeon Scalable processor and released the companion Data Analytics Reference Stack.
Today, we?re proud to announce the next Deep Learning Reference Stack release, incorporating customer feedback and delivering an enhanced user experience with support for expanded use cases.
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Clear Linux Releases Deep Learning Reference Stack 4.0 For Better AI Performance
Intel's Clear Linux team on Wednesday announced their Deep Learning Reference Stack 4.0 during the Linux Foundation's Open-Source Summit North America event taking place in San Diego.
Clear Linux's Deep Learning Reference Stack continues to be engineered for showing off the most features and maximum performance for those interested in AI / deep learning and running on Intel Xeon Scalable CPUs. This optimized stack allows developers to more easily get going with a tuned deep learning stack that should already be offering near optimal performance.
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