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        "author": "Philipp Hanslovsky",
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        "description": "# eqip\n**E**lectron-microscopy **Q**uasi **I**sotropic **P**rediction scripts\n\nDependencies are specified as `install_requires` in `setup.py`. On top of that, you need the [CNNectome `quasi-isotropic` branch](https://github.com/hanslovsky/CNNectome/tree/quasi-isotropic) on your `PYTHONPATH`. It is likely, yet untested, that the master branch works as well.\n\n\n## Install in conda enviornment on GPU cluster:\n```sh\nconda create -n eqip -c conda-forge -c cpape python=3.6 tensorflow-gpu=1.3\nconda activate eqip\nconda install -c conda-forge -c cpape pip z5py\nconda install -c conda-forge scikit-image numpy scipy\nconda install -c conda-forge h5py requests urllib3 idna\nconda install -c conda-forge cython\nconda install -c conda-forge pymongo\npip install malis==1.0\npip install git+https://github.com/hanslovsky/eqip\n```\n\n# High Memory Usage/Unexpectedly Many Threads/Processes\n\n[Set environment, as needed](https://stackoverflow.com/a/53224849/1725687):\n```sh\nexport OMP_NUM_THREADS=1\nexport MKL_NUM_THREADS=1\nexport OPENBLAS_NUM_THREADS=1\nexport VECLIB_MAXIMUM_THREADS=1\nexport NUMEXPR_NUM_THREADS=1\n```\n \n [numpy/nump#11826](https://github.com/numpy/numpy/issues/11826)",
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