Hi Peter, Nature had a piece on containers recently --
https://www.nature.com/news/software-simplified-1.22059 which has Lorena Barba making exactly the same points as you about software robustness! So at least the opinions are getting out there... In my experience, however, it's difficult to get scientist to understand that they should immediately stop working with their 15-year-old stack of software and pipelines and reengineer it from scratch so as to be robust ;). So bandaids are sadly the soup du decade. Less tongue-in-cheek, for many non-technical reasons, we've settled for an incredibly fragile software infrastructure. I don't see us working our way out of that anytime soon. A few thoughts and links here: http://ivory.idyll.org/blog/2017-pof-software-archivability.html best, --titus kOn Wed, Jun 14, 2017 at 09:11:47AM +0200, Peter Steinbach wrote: > Hi everyone, > > thanks for the interesting discussion so far. From my personal point of > view, I'd fully agree with the computational burst based argument. If a > robust pipeline needs to scale for a short amount of time and local HPC > resources are blocked, the cloud is an essential resource. > > However, with projects like [1] or [2] I don't buy into the argument > that using HPC is forbidding due to reproducibility of scientific > results. I know that many HPC installations are very conservative when > it comes to containerized execution (like in the cloud) and have a long > lag of implementing modern technologies, but containerized execution for > the sake of having a fixed set of dependencies can also be considered as > a lack of software quality. For me, this in turn is as a result of our > academic system of incentives, i.e. published results are valued higher > than the tools that produced them (which makes people invest less in > infrastructure). The latter often leads to brittle build systems and the > lack of tests. It's interesting (if not paradox) to me that people tend > to take money in their hands to buy compute hours in the cloud to > actually mitigate this. > > Cheers, > Peter > > [1] http://www.nersc.gov/research-and-development/user-defined-images/ > [2] http://singularity.lbl.gov/ > > > On 06/13/2017 08:12 PM, C. Titus Brown wrote: >> Hi all, >> >> we have done varying amounts of cloud computing, but it tends not be >> price competitive when developing/debugging analysis pipelines for large >> sets of data (vertebrate GWAS, etc.) because of the disk space needs. >> >> The UCSC Genome Center folk are relying increasingly on cloud computing >> because it is so flexible and burst-scalable - also see Dockstore.org >> for something that they are doing across cancer centers. >> >> With regard to Alex Savio's comment on clinical data - I don't know where in >> the world you are, Alex, but at least in the US there are several portions of >> AWS that are HIPAA-compliant. The entire UC system can use AWS for clinical >> data now, for example. I can seek out details if anyone is interested. >> >> Personally I think HPCs are a problem for reproducibility (see >> blogs.nature.com/naturejobs/2017/06/01/techblog-c-titus-brown-predicting-the-paper-of-the-future) >> for a small bit of context and am a big fan of >> computing *like* you're in the cloud (VMs or docker or singularity) so as to >> manage dependencies. But while that is something that quite a few experienced >> computational folk seem to agree with, I'm not sure how many people I will be >> able to convince of that in the broader world ;). >> >> best, >> --titus >> >> On Tue, Jun 13, 2017 at 05:54:36PM +0000, [email protected] wrote: >>> Hi Peter, >>> >>> I wouldnt be able to use such services with clinical data. It's totally not >>> an option for me. >>> Although I've seen some talks and the performance seems quite competitive >>> since scalability is easy. It's true that uploading a big quantity of data >>> can take a considerable time and bandwith, some labs use the weekends for >>> data uploading. One problem may be to convince University fund managers to >>> pay for external computing services when they already provide HPC services. >>> >>> My five cents... >>> >>> On Tue, 13 Jun 2017, 13:38 Peter Steinbach, <[email protected]> wrote: >>> >>>> Dear both, >>>> >>>> as a side note (and my apologies for digressing), I was wondering how >>>> popular cloud computing for data processing at scale in an academic >>>> context is in the US or elsewhere? >>>> >>>> Here in Europe, many universities run their own HPC centers where people >>>> can sign up to process larger amounts of data or do larger simulations >>>> or whatnot ... mostly people here are concerned about efficiency (data >>>> connnections into the cloud are typically poor, VM overhead is >>>> considerable) and security/confidentiality when putting scientific >>>> workflows into the cloud. >>>> What is your take on this? >>>> >>>> Best, >>>> Peter >>>> >>>> >>>> PS. I love the "serverless" metaphor. Get's rid of all the problems of >>>> computers. ;) >>>> >>>> On 06/12/2017 06:02 PM, Marianne Corvellec wrote: >>>>> Hi Justin, >>>>> >>>>> Thank you so much for the quick reply! >>>>> >>>>> I'm going to give this new package a try. >>>>> >>>>> Best, >>>>> Marianne >>>>> >>>>> On Fri, Jun 9, 2017 at 11:20 AM, Justin Kitzes <[email protected]> >>>> wrote: >>>>>> Hi Marianne, >>>>>> >>>>>> PyWren by Eric Jonas sounds like it's pretty similar to what you're >>>> looking for - >>>>>> >>>>>> http://pywren.io/ >>>>>> >>>>>> It's a relatively new package that's still in active development, but >>>> Eric is very interested in expanding it (and has some support from the >>>> riselab at UC Berkeley to do so). I know that he's also actively looking >>>> for use cases, so I'd definitely suggest getting in touch with him if >>>> you're interested. >>>>>> >>>>>> Best, >>>>>> >>>>>> Justin >>>>>> >>>>>> -- >>>>>> Justin Kitzes >>>>>> Energy and Resources Group >>>>>> Berkeley Institute for Data Science >>>>>> University of California, Berkeley >>>>>> >>>>>>> On Jun 9, 2017, at 6:51 AM, Marianne Corvellec < >>>> [email protected]> wrote: >>>>>>> >>>>>>> Dear community, >>>>>>> >>>>>>> I'm curious as to whether some of you might have worked on or used >>>>>>> solutions such as AWS Lambda in the context of your scientific >>>>>>> research. >>>>>>> >>>>>>> If so, have you documented it in a blog post that you could share? >>>>>>> Thanks in advance! >>>>>>> >>>>>>> Without even considering workflows or full-fledged projects, wouldn't >>>>>>> we want to be able to make a standard API call to, say, fit a >>>>>>> polynomial to some data? Is anyone aware of any effort in this >>>>>>> direction? >>>>>>> >>>>>>> A friend of mine just drew my attention to this general issue, which >>>>>>> touches on open science and reproducible research... In the meantime, >>>>>>> I'll encourage him to join this mailing list! >>>>>>> >>>>>>> Thank you, >>>>>>> Marianne >>>>>>> _______________________________________________ >>>>>>> Discuss mailing list >>>>>>> [email protected] >>>>>>> http://lists.software-carpentry.org/listinfo/discuss >>>>>> >>>>> _______________________________________________ >>>>> Discuss mailing list >>>>> [email protected] >>>>> http://lists.software-carpentry.org/listinfo/discuss >>>>> >>>> _______________________________________________ >>>> Discuss mailing list >>>> [email protected] >>>> http://lists.software-carpentry.org/listinfo/discuss >>> >>> -- >>> >>> Sent from my phone, sorry for brevity or typos. >> >>> _______________________________________________ >>> Discuss mailing list >>> [email protected] >>> http://lists.software-carpentry.org/listinfo/discuss >> -- C. Titus Brown, [email protected] _______________________________________________ Discuss mailing list [email protected] http://lists.software-carpentry.org/listinfo/discuss
