These project have two sub-object.
* Develop a lite edition photo manage tool, use to export photos from cellphone, memory card and other usb device. It can mange the photos by Exif information (such as date, location, and lens information).
Since eBPF was introduced into linux kernel, the eBPF verifier keeps the eBPF programs from any wrong-doing. I would like to look into the verifier and see if it's possible to extend the check to avoid reading any sensitive data in the memory.
In previous hack weeks, the first few days ended up being wasted on just getting it working. I'm pleased to share that the code quality has improved dramatically since the last hack week and there are now extensive test cases for both unit testing and testing against real vmcores, and we'll use both mypy and pylint (if installed) to perform static analysis. Packages for those are available in openSUSE or as part of the crash-python OBS repo for SLE15. It has been tested with kernels from 3.0 to 5.1.
My German reading and speaking skills suck. I've forgotten everything except "Mehr Bier, bitte". A week of intensive immersion ought to enable me to order food as well. And converse with my German team members. Especially when we go out for meals and drinks.
This should have a concrete goal, so I will write a short story in German to demonstrate my amazing new fluency*.
boot: to find logs with both kernel and user-space parts, be able to add debug flags etc. to the failing service configuration...
suspend/resume: what services are configured? Something triggered via DBus? How to find? And how to debug that?
"Make Your Own Neural Network" is a book written by Tariq Rashid for anyone who wants to understand what neural network are.
<br /> * You won’t need any special knowledge or mathematical ability beyond school maths. (The most difficult thing is gradient calculus - but even that concept will be explained so that as many readers as possible can understand it.)
over 4 years
3 hacker ♥️.
Has no hacker:
The Khadas VIM (http://khadas.com/vim/) is an arm64 DIY Set-Top-Box based on Amlogic P212 reference board that use S905X SoC.
As Odroid-C2 (based on S905 SoC) is in the mainline U-boot, it should be possible to adapt it for the Khadas VIM (of course a lot of work are needed!).
The idea is to explore the technologies and the various components to realize some AI to predict pitfalls in source code which can potentially generate run-time misbehaviours.
The potential area where this idea could have positive implications are:
It's clear that in Kubernetes world, SUSE and openSUSE chose Cilium as the main network provider, which also means choosing BPF and XDP as underlying technologies for implementing datapath and packet filtering.
That's different from what we are doing in OpenStack. SUSE OpenStack Cloud provides mostly Open vSwitch DPDK as a network solution.
io_uring is a new asynchronous I/O framework, which was merged into upstream from 5.1. During this hackweek, I want to learn about the difference between it and native aio, how it is designed and do some performace tests based on it.
Create an automated L0-support-like analytics solution for supportconfig data that is tiered across a customer's environment and SUSE environment (similar to a very modular AIOps Edge-Core approach). A pictorial overview of the ecosystem
Bug reports can be a great source of information, but usually finding the information requires extensive work in reading through all of the discussions and understanding the details about it.
Could it be that machine learning can be used to extract meaningful information out of that? That's what this project is about.
In the past year we've found ourselves in the middle of a pandemic, we merged two awesome companies together, and we have completely changed the trajectory of SUSE and Rancher. This project is intended to transfer knowledge of SUSE to Rancher and Rancher to SUSE for those who may be challenged with time and resources to try new things. This gives us a chance to explore other uses for Kubernetes all while taking advantage of older equipment (for use as workers) we may have to spare.
So you have an idea for a machine learning project for HackWeek. Have you thought about what tools you'll be using? Choosing the right set of machine learning tools and making them work together can be time consuming, not to mention the unavoidable learning curve. Perhaps you could use some help with that.