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us just finished its 5th year of operations and i ve lately been reflective on the early days and some of the struggles we went through to get the organization started it once was just an idea in a few community minded developer s heads and now exists as an important non profit foundation for open data science democratic and reproducible discovery and a champion for technical progress through diversity when peter wang and i started continuum in early 2012 i had already started the ball rolling to create numfocus i knew that we needed to create a non profit that would create leadership and be a focus of community activity outside of any one company i strongly believe that for open source to thrive full time attention needs to be paid to it by many people this requires money with the tremendous interest in and explosion around the numpy community it was clear to me that this federation of loosely coupled people needed some kind of organization that could be community led and could be a rallying point for community activity and community led financing the potential also exists for numfocus to act as community based accountability to encourage positively re inforcing behavior in the open source communities it intersects with in late 2011 i started a new mailing list and invited anyone interested in discussing the idea of an independent community run organization to the list over 100 people responded and so i knew there was interest we debated on that list what to call the new concept for several weeks and anthony scopatz s name numfocus stuck as the best alternative over several other names as an acronym numfocus could mean numerical foundation for open code and usable science i created a new mailing list and then set about creating the legal organization called numfocus and filing necessary paperwork fernando perez john hunter in december of 2011 i coordinated with fernando perez perry greenfield john hunter and jarrod millman who had all expressed some interest in the idea and we incorporated in texas using legalzoom and became the first board of numfocus we had a very simple set of bylaws and purposes all centered around making science more accessible we decided to meet every other week we all knew we were creating something that would last a long time perry greenfield jarrod millman in early 2012 i wanted to ensure numfocus success and knew that it needed a strong full time executive director to make that happen the problem was numfocus didn t have a lot of money a few of the board members had made donations but continuum with its own limited means was funding the majority of the costs for getting numfocus started with the legal organization started i created bank accounts and setup the ability for people to donate to numfocus with help from anthony scoptatz who was the first treasurer of numfocus anthony scopatz i had met leah silen through other community interactions in austin back in 2007 i knew her to be a very capable and committed person and thought she might be available i asked her if she would come aboard and be employed by continuum but work full time for numfocus and the new board she accepted and the organization of numfocus began to improve immediately with her help we transitioned the organization from legalzoom s beginnings to register directly with the secretary of state in texas and started the application process to become a 501 c 3 she also quickly became involved in organizing the pydata conferences which continuum initially spear headed along with julie steele and edd wilder james at the time from o reilly in 2012 we had our first successful pydata conference at the googleplex in mountain view it was clear that pydata could be used as a mechanism to provide revenue for numfocus at least to support leah and other administrative help leah silen we began working under that model through 2013 and 2014 with continuum initially spending a lot of human resources and money organizing and running pydata with any proceeds going directly to numfocus there were no proceeds in those years except enough to help pay for leah s salary the rest of leah s salary and pydata expenses came from continuum which itself was still a small startup during these years of pydata growth in communities around the world james powell became a drumbeat of consistency and community engagement he has paid his own way to nearly every pydata event throughout the world he has acted as emcee volunteer extraordinaire and popular speaker with his clever implementations and explanations of the python stack james powell dontusethiscode andy terrel had been a friend of numfocus and a member of the community and active with the board from its beginning in 2014 while working at continuum he took over my board seat in that capacity he worked hard to gain financial independence for numfocus he was instrumental in moving pydata fully to numfocus management i was comfortable stepping back from the board and stepping down in my involvement around organizing and backing pydata from a financial perspective because i trusted andy s leadership and non profit management instincts he james powell leah and all the other local pydata meetups and organizations world wide have done an impressive thing in self organizing and growing the community we should all be grateful for their efforts andy terrel i am very proud of the work i did to help start numfocus and pydata i hope to remember it as one of the most useful things i ve done professionally i am very grateful for all the others who also helped to create numfocus as well as pydata so many have worked hard to ensure it can be a worldwide and community governed organization to support open data science for a long time to come i m proud of the funding and people time that continuum provided to get numfocus and pydata started as well as the on going support of numfocus that continuum and other industry partners continue to provide now as an adviser to the organization i get to hear from time to time how things are going i m very impressed at the progress being made by the dedication of the current leadership behind andy terrel as president and leah silen as executive director and the rest of the current board if you use or appreciate any of the tools in the open data science that numfocus sponsors i encourage you to join and or make a supporting donation here http www numfocus org support numfocus html help numfocus continue its mission to support the tools and communities you rely on everyday posted by travis oliphant at 9 15 pm 4 comments email this blogthis share to x share to facebook share to pinterest labels numpy open source pydata python tuesday march 29 2016 anaconda and hadoop a story of the journey and where we are now early experience with clusters my first real experience with cluster computing came in 1999 during my graduate school days at the mayo clinic these were wonderful times my advisor was dr james greenleaf he was very patient with allowing me to pester a bunch of it professionals throughout the hospital to collect their aging mac performa machines and build my own home grown cluster he also let me use a bunch of space in his ultrasound lab to host the cluster for about 6 months building my own cluster the form factor for those mac machines really made it easy to stack them i ended up with 28 machines in two stacks with 14 machines in each stack all plugged into a few power strips and a standard lab quality outlet with the recent release of yellow dog linux i wiped the standard os from all the machines and installed linux on all those macs to create a beautiful cluster of unix goodness i could really get excited about i called my system the orchard and thought it would be difficult to come up with 28 different kinds of apple varieties to name each machine after it wasn t difficult it turns out there are over 7 500 varieties of apples grown throughout the world me smiling alongside by smoothly humming orchard of interconnected macs the reason i put this cluster together was to simulate magnetic resonance elastography mre which is a technique to visualize motion using magnetic resonance imaging mri i wanted to simulate the bloch equations with a classical model for how mri images are produced the goal was to create a simulation model for the mre experiment that i could then use to both understand the data and perhaps eventually use this model to determine material properties directly from the measurements using bayesian inversion ambitiously bypassing the standard sequential steps of inverse fft and local frequency estimation now i just had to get all these machines to talk to each other and then i would be poised to do anything i read up a bit on mpi pvm and anything else i could find about getting computers to talk to each other my unfamiliarity with the field left me puzzled as i tried to learn these frameworks in addition to figuring out how to solve my immediate problem eventually i just settled down with a trusted unix book by the late w richard stevens this book explained how the internet works i learned enough about tcp ip and sockets so that i could write my own c classes representing the model these classes communicated directly with each other over raw sockets while using sockets directly was perhaps not the best approach it did work and helped me understand the internet so much better it also makes me appreciate projects like tornado and zmq that much more lessons learned i ended up with a system that worked reasonably well and i could simulate mre to some manner of fidelity with about 2 6 hours of computation this little project didn t end up being critical to my graduation path and so it was abandoned after about 6 months i still value what i learned about c how abstractions can ruin performance how to guard against that and how to get machines to communicate with each other using numeric python and my recently linked ode library early scipy i built a simpler version of the simulator that was actually faster on one machine than my cluster version was in c on 20 machines i certainly could have optimized the c code but i could have also optimized the python code the python code took me about 4 days to write the c code took me about 4 weeks this experience has markedly influenced my thinking for many years about both pre mature parallelization and pre mature use of c and other compiled languages fast forward over a decade my computer efforts until 2012 were spent on sequential array oriented programming creating scipy writing numpy solving inverse problems and watching a few parallel computing paradigms emerge while i worked on projects to provide for my family i didn t personally get to work on parallel computing problems during that time though i always dreamed of going back and implementing this mre simulator using a parallel construct with numpy and scipy directly when i needed to do the occassional parallel computing example during this intermediate period i would either use ipython parallel or multi processing parallel plans at continuum in 2012 peter wang and i started continuum created pydata and released anaconda we also worked closely with members of the community to establish numfocus as an independent organization in order to give numfocus the attention it deserved we hired the indefatigable leah silen and donated her time entirely to the non profit so she could work with the community to grow pydata and the open data science community and ecosystem it has been amazing to watch the community based organic and independent growth of numfocus it took effort and resources to jump start but now it is moving along with a diverse community driving it it is a great organization to join and contribute effort to a huge reason we started continuum was to bring the numpy stack to parallel computing for both scale up many cores and scale out many nodes we knew that we could not do this alone and it would require creating a company and rallying a community to pull it off we worked hard to establish pydata as a conference and concept and then transitioned the effort to the community through numfocus to rally the community behind the long term mission of enabling data quantitative and computational scientists with open source software to ensure everyone in the community could get the software they needed to do data science with python quickly and painlessly we also created anaconda and made it freely available in addition to important community work we knew that we would need to work alone on specific hard problems to also move things forward as part of our goals in starting continuum we wanted to significantly improve the status of python in the jvm centric hadoop world conda bokeh numba and blaze were the four technologies we started specifically related to our goals as a company beginning in 2012 each had a relationship to parallel computing including hadoop conda enables easy creation and replication of environments built around deep and complex software dependencies that often exist in the data scientist workflow this is a problem on a single node it s an even bigger problem when you want that environment easily updated and replicated across a cluster bokeh allows visualization centric applications backed by quantitative science to be built easily in the browser by non web developers with the release of bokeh 0 11 it is extremely simple to create visualization centric web applications and dashboards with simple python scripts or also r scripts thanks to rbokeh with bokeh python data scientists now have the power of both d3 and shiny all in one package one of the driving use cases of bokeh was also easy visualization of large data connecting the visualization pipeline with large scale cluster processing was always a goal of the project now with datashader this goal is now also being realized to visualize billions of points in seconds and display them in the browser our scale up computing efforts centered on the open source numba project as well as our accelerate product numba has made tremendous progress in the past couple of years and is in production use in multiple places many are taking advantage of numba vectorize to create array oriented solutions and program the gpu with ease the cuda python support in numba makes it the easiest way to program the gpu that i m aware of the cuda simulator provided in numba makes it much simpler to debug in python the logic of cuda based gpu programming the addition of parallel contexts to numba vectorize mean that any many core architecture can now be exploited in python easily early hsa support is also in numba now meaning that numba can be used to program novel hardware devices from many vendors summarizing blaze the ambitious blaze project will require another blog post to explain its history and progress well i will only try to summarize the project and where it s heading blaze came out of a combination of deep experience with...