viklas

Like everyone else...well done. Great concept, easy to learn, nice results. I'll probably be a paying customer one day.

Font-resizing would be nice on the visual view. And killer feature would be two way interaction, but I can imagine that's a tricky one.

Here's a generic "SaaS subscription workflow" I made up in about 20 mins.

https://code2flow.com/NDIQIf

Love it!

We took a similar approach. Works well for our needs.

https://github.com/emergingstack/es-dev-stack (feedback/contributions welcomed)

I love this. Nice, simple implementation. Will definitely come in handy.

The first stage of the process is to take a vanilla CoreOS host and inject the CUDA drivers (one time process). After that, you can reboot the box and still retain the devices, for mapping into docker containers.

The "--device" flag allows you to map devices through to a Docker container. It runs in 'privileged' mode though, so isn't suitable for a shared host.

Nvidia make it pretty straight-forward now but we had to branch from that approach a bit for the CoreOS deployment.

https://github.com/NVIDIA/nvidia-docker (Nice pictures)

https://docs.docker.com/engine/reference/run/ (Docker documentation, search for 'privileged')

The approach is a bit different depending on your host operating system. You'll also find there are constraints when you introduce a virtualisation layer, like virtualbox or parallels on your desktop - GPUs can be mapped through, but it's painful(ish).

My money (not a billion) is on "Open, Big Learning".

Elon will probably want to build a giga-factory of neurons, then open-source some pre-trained, general model with a free API.

This is a man building electric cars, off-grid industrial-strength batteries, rockets and hyper-loops...I don't think publishing more/better research papers or winning kaggle competitions is the vision.

Awesome work, Murray (and your team). Any chance the data will be anonymised and made open? The report is great but it leaves me wanting more answers! I'm sure many folks would be happy to trawl through the dataset on their own.

Will definitely be participating next year! When do you plan to run the survey?

Agree - we crunched the numbers and came up with the same figures to do-it-yourself (~USD$9K). Although, I remember from the day this was announced (few months back) that Nvidia were loud-and-proud that they weren't going to make money from this. Each box was hand built and tested, so not deemed to be a large-scale device - they recognized that it's a niche market.

$15K is probably OK(ish) if you figure in your own time for the DIY build...probably a few days. Plus you get some vendor support, warranty on the whole package, certified working stack, future test-bed for CUDA updgrades (will work first), etc as you say.

In wild agreement. Save maybe 30% doing a custom build...so they aren't adding a huge mark-up, as they would for a gaming machine. Apparently...someone at Nvidia is looking a bit further into the future than just the short-term revenue.

And 100% on-track for awesome.

Yes, sometimes development/testing for new kubernetes features 'feels' like it's focussed-first on GCE functionality (before other platforms) and earlier on, it had some hooks that weren't great (like GCE-only external load balancers and storage). But hey, it's not even v1.0 yet - and all those things are either fixed or being worked on already.

And as a non-GCE user, you aren't a second-class citizen. It works everywhere.

We've deployed successfully in AWS, vagrant and bare-metal (in the garage), so far. All with 'one-command' automated deployment and re-use of our pod & service specs throughout.

Roadmap/Architecture-wise, it would be good to see a more 'pluggable' approach for 3rd party integration (more like an Open Stack model), but again, we're still pre-v1.0...

Also, I think the google-folk here are being very 'reasonable' in their replies. Your comment was mis-directed & ill-informed. Go do some reading or watch Kelsey Hightower's presentation from a couple of months ago:

http://chariotsolutions.com/screencast/philly-ete-2015-16-ke...

Here you go: https://www.dropbox.com/s/upjkq61j06qmjoa/Screenshot%202015-...

#theRealLukeB (although template Luke looks cooler)

Congrats guys! You know we're big fans of Tyk already (it says so right there in the testimonial)

Highly recommend this to anyone looking for a modern, light, powerful API management platform/service. We use the on-prem, open-souce version but I'm sure the SaaS version is just as good.

(no affiliation here...just lovers of great software).

SUMMARY: A kubernetes deployed 'logging container', running on every host, streams every docker-hosted application event to AWS Cloudwatch. Cheap, quick, real-time, re-usable and accessible anywhere.

I think your link is funky. Found it in the source: http://www.gifgrabber.com.

Cool tool...

The addition of angularjs is going to power some really nice dashboards. Thanks for the feature and the detailed demos (and comments on github). Looking forward to playing with this a lot!

As a new-comer to open source projects recently, I found it was hard to get a handle on the 'roles' in a project. I was used to working on corporate, in-house technology projects where there are plenty of non-technical activities to perform (PM, Business Analyst, End-User testing/training, Governance, etc) - but it isn't obvious how those apply to most open-source projects, or even if they are required at all.

I found this link pretty useful to breakdown the activities and roles: http://oss-watch.ac.uk/resources/rolesinopensource

Like everyone else will say...first involvement is a bit nerve-wracking (How will people respond? Can I make a decent contribution? Will I look like a total noob?), but you'll only feel that way once.