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USGS-R / rahmani_erl_data_release

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populate build-environment #7

Open aappling-usgs opened 4 years ago

aappling-usgs commented 4 years ago

Jordan has used the R build environment (the one used to create the data). I think this is fine, especially given that I've got some conda environment YAMLs going for the model code. I also think meddle offers a way to populate the build-environment with your current environment automatically?

jordansread commented 4 years ago

see ?environment_metadata()

aappling-usgs commented 4 years ago

I think we can get some useful info from Farshid/Chaopeng about their compute machine/cluster, and I already have info I can add about the python package environment on that cluster. Also want to add a reference to the environment.ymls.

aappling-usgs commented 4 years ago

Just need to stick this formatted python module list somewhere temporarily:

appdirs, v1.4.4; argon2-cffi, v20.1.0; attrs, v20.2.0; backcall, v0.2.0; basemap, v1.2.1; blas, v1.0; bleach, v3.2.1; ca-certificates, v2020.7.22; certifi, v2020.6.20; cffi, v1.14.3; colorama, v0.4.3; cudatoolkit, v10.0.130; cycler, v0.10.0; dataretrieval, v0.4; decorator, v4.4.2; defusedxml, v0.6.0; distlib, v0.3.1; entrypoints, v0.3; filelock, v3.0.12; freetype, v2.10.2; icc_rt, v2019.0.0; icu, v58.2; importlib-metadata, v1.7.0; importlib_metadata, v1.7.0; intel-openmp, v2020.2; ipykernel, v5.3.4; ipython, v7.18.1; ipython_genutils, v0.2.0; jedi, v0.17.2; jinja2, v2.11.2; joblib, v0.17.0; jpeg, v9b; jsonschema, v3.2.0; jupyter_client, v6.1.6; jupyter_core, v4.6.3; kiwisolver, v1.2.0; libpng, v1.6.37; libsodium, v1.0.18; libtiff, v4.1.0; lz4-c, v1.9.2; m2w64-gcc-libgfortran, v5.3.0; m2w64-gcc-libs, v5.3.0; m2w64-gcc-libs-core, v5.3.0; m2w64-gmp, v6.1.0; m2w64-libwinpthread-git, v5.0.0.4634.697f757; markupsafe, v1.1.1; matplotlib, v3.2.2; mistune, v0.8.4; mkl, v2020.2; mkl-service, v2.3.0; mkl_fft, v1.2.0; mkl_random, v1.1.1; msys2-conda-epoch, v20160418; nbconvert, v5.6.1; nbformat, v5.0.7; ninja, v1.10.1; notebook, v6.1.1; numpy, v1.19.1; numpy-base, v1.19.1; olefile, v0.46; openssl, v1.1.1h; packaging, v20.4; pandas, v1.1.1; pandoc, v2.10.1; pandocfilters, v1.4.2; parso, v0.7.0; patsy, v0.5.1; permutationimportance, v1.2.1.8; pickleshare, v0.7.5; pillow, v7.2.0; pip, v20.2.3; pipenv, v2020.8.13; prometheus_client, v0.8.0; prompt-toolkit, v3.0.7; pyarrow, v1.0.1; pycparser, v2.20; pygments, v2.7.1; pyparsing, v2.4.7; pyproj, v2.6.1.post1; pyqt, v5.9.2; pyrsistent, v0.17.3; pyshp, v2.1.2; python, v3.7.9; python-dateutil, v2.8.1; pytorch, v1.2.0; pytz, v2020.1; pywin32, v227; pywinpty, v0.5.7; pyzmq, v19.0.2; qt, v5.9.7; requests, v2.7.0; scikit-learn, v0.23.2; scipy, v1.1.0; send2trash, v1.5.0; setuptools, v49.6.0; sip, v4.19.8; six, v1.15.0; sqlite, v3.33.0; statsmodels, v0.11.1; terminado, v0.8.3; testpath, v0.4.4; threadpoolctl, v2.1.0; tk, v8.6.10; torchvision, v0.4.0; tornado, v6.0.4; tqdm, v4.50.2; traitlets, v5.0.4; vc, v14.1; virtualenv, v20.0.35; virtualenv-clone, v0.5.4; vs2015_runtime, v14.16.27012; wcwidth, v0.2.5; webencodings, v0.5.1; wheel, v0.35.1; wincertstore, v0.2; winpty, v0.4.3; xz, v5.2.5; zeromq, v4.3.2; zipp, v3.1.0; zlib, v1.2.11; zstd, v1.4.5

from 

readr::read_lines('pyenv.R') %>% strsplit(split=' +') %>% purrr::map(.f=function(strs) sprintf('%s, v%s', strs[1], strs[2])) %>% unlist %>% paste(collapse='; ')