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It slightly lags, but you can include pip requirements in an environment.yml file, and they install normally. I really only use conda for the non-python bits o

by wclax04 11y ago

It slightly lags, but you can include pip requirements in an environment.yml file, and they install normally. I really only use conda for the non-python bits of our stack: numpy/scipy/pandas etc - packages that are a pain to install on Heroku.

gh02t 11y ago
It's also pretty straightforward to set up your own Conda package tree. Nice for packaging your app for deployment or making sure you have very precise dependencies. http://conda.pydata.org/docs/custom-channels.html http://conda.pydata.org/docs/custom-channels.html
- sandGorgon 11y ago
  I think deployment is a solved problem with docker. Its libraries like blas,etc that are a huge pain. I'm not sure why static linked bumpy is not possible - even anaconda could not achieve it.
  - gh02t 11y ago
    If you've ever tried to dive into the NumPy build process you'd see why. It's unbelievably complicated... not that they really could do it better given that they are compiling about a billion scientific libraries and support alternatives and optimizations (like MKL).
    - sandGorgon 11y ago
      Yes - unfortunately I have and I failed miserably. These days I'm trying to see if there's a docker build that can build a great numpy (with all optimizations). Interestingly there are even docker images to call cuda APIs from python.
sandGorgon 11y ago
They are a pain to install on a desktop as well! Especially with all the blas linking. Was there anything special you had to do or was it simply conda install numpy-blas or something like that ?
- wclax04 11y ago
  Fro Heroku we created/modified a custom buildpack (https://github.com/joshowen/conda-buildpack https://github.com/joshowen/conda-buildpack), and use that with the multibuildpack. We use conda-env (https://github.com/conda/conda-env https://github.com/conda/conda-env) which makes it really simple to manage environments locally and in remote environments.

by wclax04 11y ago

gh02t 11y ago

It's also pretty straightforward to set up your own Conda package tree. Nice for packaging your app for deployment or making sure you have very precise dependencies. http://conda.pydata.org/docs/custom-channels.html http://conda.pydata.org/docs/custom-channels.html

sandGorgon 11y ago
I think deployment is a solved problem with docker. Its libraries like blas,etc that are a huge pain. I'm not sure why static linked bumpy is not possible - even anaconda could not achieve it.
- gh02t 11y ago
  If you've ever tried to dive into the NumPy build process you'd see why. It's unbelievably complicated... not that they really could do it better given that they are compiling about a billion scientific libraries and support alternatives and optimizations (like MKL).
  - sandGorgon 11y ago
    Yes - unfortunately I have and I failed miserably. These days I'm trying to see if there's a docker build that can build a great numpy (with all optimizations). Interestingly there are even docker images to call cuda APIs from python.

sandGorgon 11y ago

They are a pain to install on a desktop as well! Especially with all the blas linking. Was there anything special you had to do or was it simply conda install numpy-blas or something like that ?

wclax04 11y ago
Fro Heroku we created/modified a custom buildpack (https://github.com/joshowen/conda-buildpack https://github.com/joshowen/conda-buildpack), and use that with the multibuildpack. We use conda-env (https://github.com/conda/conda-env https://github.com/conda/conda-env) which makes it really simple to manage environments locally and in remote environments.