{"action":"created","issue":{"url":"https://api.github.com/repos/dhlab-epfl/dhSegment/issues/8","repository_url":"https://api.github.com/repos/dhlab-epfl/dhSegment","labels_url":"https://api.github.com/repos/dhlab-epfl/dhSegment/issues/8/labels{/name}","comments_url":"https://api.github.com/repos/dhlab-epfl/dhSegment/issues/8/comments","events_url":"https://api.github.com/repos/dhlab-epfl/dhSegment/issues/8/events","html_url":"https://github.com/dhlab-epfl/dhSegment/issues/8","id":322533141,"number":8,"title":"Can't be installed under Windows","user":{"login":"Jim-Salmons","id":546022,"avatar_url":"https://avatars3.githubusercontent.com/u/546022?v=4","gravatar_id":"","url":"https://api.github.com/users/Jim-Salmons","html_url":"https://github.com/Jim-Salmons","followers_url":"https://api.github.com/users/Jim-Salmons/followers","following_url":"https://api.github.com/users/Jim-Salmons/following{/other_user}","gists_url":"https://api.github.com/users/Jim-Salmons/gists{/gist_id}","starred_url":"https://api.github.com/users/Jim-Salmons/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/Jim-Salmons/subscriptions","organizations_url":"https://api.github.com/users/Jim-Salmons/orgs","repos_url":"https://api.github.com/users/Jim-Salmons/repos","events_url":"https://api.github.com/users/Jim-Salmons/events{/privacy}","received_events_url":"https://api.github.com/users/Jim-Salmons/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":6,"created_at":"2018-05-12T18:35:46Z","updated_at":"2018-05-15T04:16:38Z","closed_at":null,"author_association":"NONE","body":"dhSegment is AWESOME and EXACTLY what my wife and I need for our post-cancer #PayItForward Bonus Round activity doing grassroots #CitizenScience #digitalhumanities research in support of eResearch and machine-learning in the domain of digitization of serial publications, primarily modern commercial magazines. We are working on the development of the #MAGAZINEgts ground-truth storage format providing standards-based (#cidocCRM/FRBRoo/PRESSoo) integrated complex document structure and content depiction models.\r\n\r\nWhen a tweet about dhSegment surfaced through my feed, I could barely contain myself... we have detailed, multi-valued metadata -- based on a metamodel of fine-grained use of PRESSoo's Issuing Rules -- that describe the location, bounding box, size, shape, number of colors, products featured, etc. for 7,157 advertisements appearing in the 48 issues of Softalk magazine (https://archive.org/details/softalkapple). It will be trivial for me to generate the annotated label images for all these ads as we have already programmatically extracted the ad sub-images from the full pages once we used our \"Ad Ferret\" to discovery and curate the specification for every ad. \r\n\r\nOnce we have a dhSegment instance trained on the Softalk ads, there are over 1.5M pages just within the \"collection of collections\" of computer magazines at the Internet Archive, and many millions more pages of content in magazines of all types over considerable time periods of their serial publication. The #MAGAZINEgts format, together with brilliant technical achievements like dhSegment, can open new levels of scholarship and machine access to digital collections. We believe dhSegment will be a valuable component for our research platform/framework.\r\n\r\nWith great excitement I chased down and have installed and tested the prerequisite CUDA and cuDNN frameworks/platforms under Windows. I have these features now working at the 9.1 version. (This alone was tricky, but I got it working.)\r\n\r\nUnfortunately, the current implementation of the incredibly important dhSegment environment cannot be installed under Windows 10. After the stock Anaconda environment yml file died somewhat dramatically, I then took that file and attempted to search for and install each package individually. (NOTE: I am not a Python expert, so what I report here is subject to refinement by someone who knows better...) Here is what is NOT available under Windows:\r\n```\r\n# Python packages for dh_segment not available under Windows\r\n- dbus=1.12.2\r\n- fontconfig\r\n- glib=2.53.6\r\n- gmp=6.1.2\r\n- graphite2=1.3.10\r\n- gst-plugins-base\r\n- gstreamer=1.12.4\r\n- harfbuzz=1.7.4\r\n- jasper=1.900.1\r\n- libedit=3.1\r\n- libffi=3.2.1\r\n- libgcc-ng=7.2.0\r\n- libgfortran-ng=7.2.0\r\n- libopus=1.2.1\r\n- libstdcxx-ng=7.2.0\r\n- libvpx=1.6.1\r\n- ncurses=6.0\r\n- ptyprocess=0.5.2\r\n- readline=7.0\r\n- pip:\r\n  - tensorflow-gpu==1.4.1 (I did find and installed 1.8.0 instead) \r\n```\r\nAnything not on this list made it into my Windows-based Anaconda environment, the yml for which I have included here as a file attachment.\r\n\r\n[win10_dh_segment.yml.txt](https://github.com/dhlab-epfl/dhSegment/files/1997809/win10_dh_segment.yml.txt)\r\n\r\nI am so disappointed to not be able to install and use dhSegment under Windows. While a docker image would likely be possible to create, I am skeptical that it would work at the level needed for interfacing with the NVIDIA hardware and its CUDA/cuDNN frameworks, etc. Alternatively, perhaps a cloud-based dev platform would work for us (that is affordable as we are independent and unfunded #CitizenScientists). Your workaround/alternative suggestions are welcome.\r\n\r\nAt any rate, sorry for the overly long initial issue posting. But I wanted to explain my and my wife's great interest in this important technology as well as provide what I hope is useful feedback with regard to its potential use under Windows. Looking forward, I am very interested in evolving a collaborative relationship with you good folks of DHLAB. \r\n\r\nITMT, I am going to generate the labeled training images. :-) \r\n\r\nHappy-Healthy Vibes,\r\nFactMiner Jim \r\n\r\nP.S. Here is our #DATeCH2017 poster that will further explain the focus of our research. \r\n![salmonsbabitsky_factminerssoftalk_poster](https://user-images.githubusercontent.com/546022/39960360-b3fcb1e8-55de-11e8-8720-efdc2350a878.png)\r\n\r\nP.P.S. And here is a screenshot showing a typical metadata \"spec\" for an ad. The simple integer value for the AdLocation is used in concert with an embedded DSL in the fine-grained Issuing Rules of the Advertising Model. This DSL provides a resolution-independent means to describe and compute the upper-left and bounding box of an ad. For example, the four locations of a 1/4 pg sized ad on a page with a 2-column page grid are numbered 1-4, left-to-right top-to-bottom. The proportions of these page segments based on simple geometric proportional computations.\r\n![magazinegts_documentstructure_advertisements](https://user-images.githubusercontent.com/546022/39960425-248439bc-55e0-11e8-8757-53752078009b.png)\r\n\r\nAnd finally, the evolving #MAGAZINEgts for the Softalk magazine collection at the Internet Archive is available here: https://archive.org/download/softalkapple/softalkapple_publication.xml\r\n\r\n"},"comment":{"url":"https://api.github.com/repos/dhlab-epfl/dhSegment/issues/comments/389037671","html_url":"https://github.com/dhlab-epfl/dhSegment/issues/8#issuecomment-389037671","issue_url":"https://api.github.com/repos/dhlab-epfl/dhSegment/issues/8","id":389037671,"user":{"login":"Jim-Salmons","id":546022,"avatar_url":"https://avatars3.githubusercontent.com/u/546022?v=4","gravatar_id":"","url":"https://api.github.com/users/Jim-Salmons","html_url":"https://github.com/Jim-Salmons","followers_url":"https://api.github.com/users/Jim-Salmons/followers","following_url":"https://api.github.com/users/Jim-Salmons/following{/other_user}","gists_url":"https://api.github.com/users/Jim-Salmons/gists{/gist_id}","starred_url":"https://api.github.com/users/Jim-Salmons/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/Jim-Salmons/subscriptions","organizations_url":"https://api.github.com/users/Jim-Salmons/orgs","repos_url":"https://api.github.com/users/Jim-Salmons/repos","events_url":"https://api.github.com/users/Jim-Salmons/events{/privacy}","received_events_url":"https://api.github.com/users/Jim-Salmons/received_events","type":"User","site_admin":false},"created_at":"2018-05-15T04:16:38Z","updated_at":"2018-05-15T04:16:38Z","author_association":"NONE","body":"TaDAAA!!!! :-) :-) We had an extra special \"sundown chill\" moment this evening as I just finished installing and validating our dhSegment instance under Windows 10 on a consumer/home level development box when our daily reflective moment arrived! \r\n\r\nSeriously, this gives me goosebumps. Despite NVIDIA's multitude of \"do you want to tell them or should we tell them\" non-critical warning messages about the severe memory constraints that this software was being asked to perform under and that would dramatically impact performance, I can confirm that we are up and running at the level of exercising the pre-configured demo program. :-)\r\n\r\nWe have not yet attempted our own training. But the fact that we are good to go at a proof-of-capablity level is INCREDIBLY exciting.\r\n\r\nI did go ahead and submit an application for FactMiners' participation (AKA me and Timlynn) to participate in the upcoming Tensorflow Research Cloud (beta) program. Have you good folks at DHLAB already applied to this OpenScience collaborative opportunity? If so or if not, are you interested? We would love to work with you on our research agenda via mutual access to this incredibly \"juicy\" research resource. We do have a number of fellow researchers in the EuropeanaTech community who would likely be interested in such a collaborative co-development/exploration opportunity. This might be something we could talk about sometime soon.\r\n\r\nThese future potentials aside, here is a screenshot of my progress running the dhSegment demo program on an aging local non-tricked-out dev box... and yes, downgrading to CUDA 9.0 was needed but did not require uninstalling 9.1.\r\n\r\n![dhsegment_windows_factminers](https://user-images.githubusercontent.com/546022/40036266-b3c2d4d2-57c3-11e8-939e-64ccc38db863.png)\r\n"}}
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