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I also think that Anaconda is great. However, I hope that in the future we could install numpy, matplotlib, jupyter, etc. just using pip.

by alceufc 11y ago

drauh 11y ago
Currently, pip install of numpy (and scipy) does not pick up MKL, which you would want if you do lots of linear algebra and FFTs.
stared 11y ago
I like Anaconda (and I recommend it as the easiest installation for data sci), but on OS X it is easy to install Python and relevant numerical packages with Homebrew and pip.
zo1 11y ago
I did that earlier today: pip install jupyter pip install numpy pip install scipy pip install scikit-learn pip install matplotlib The only problem I had was with OpenCV, which requires manual make installation if you want the contrib package. The other problem was when trying to install scikit-learn, it requires manual pip installation of scipy.
- yeukhon 11y ago
  The reason you can't is because of the C libraries levaaged must be installed prior to install numpy and scipy. For example, you can't get through PyYaml unless python-dev is installed on Ubuntu. I am not sure if wheel would fix it but I don't think so.
  - takeda 10y ago
    Wheel is a binary distribution, so files would already be compiled and therefore python-dev would not be needed anymore.
    - yeukhon 10y ago
      Is wheel not default? Or play it elementary :-) how would I avoid this issue in the future?
      - takeda 10y ago
        I did not write anything worthy to send to PIP so don't know exactly but looks like it is up to the developers[1] Anyway I just noticed that PyPI only supports binary packages for Windows and Mac OS X. Although, you could still generate wheels of packages that you use by using something like this: pip wheel -r requirements.txt You can then install them with pip install <file> or (unfortunately I forgot the option, perhaps it was -i) you can use an option to point to a directory containing wheels and pip install to install the main package. It should use all dependencies in that directory as well. [1] http://pythonwheels.com/ http://pythonwheels.com/
        techdragon 10y ago
        the issue with Wheels on Linux, and this is very much a Linux problem, is that the 'pre built' nature of libraries in wheels doesnt play nice with the raw chaos of Linux's package management + distro + kernel ecosystem. I raised the question of FreeBSD and Solaris based wheels at a PyCon when I was in a face to face discussion with someone more knowledgeable, and the answer was 'in theory that should work like Windows & OSX, no one has done the hard work yet.' So yeah Linux is not the most friendly environment for Python Wheels.
- timClicks 10y ago
  I would always be worried with installing numpy/scipy via pip that I wouldn't be linking to BLAS/LAPACK correctly.
  - rhodysurf 10y ago
    What do you mean? I have always installed via pip with zero errors on all three platforms.
    - moyix 10y ago
      The worry is not that it will throw an error, it's that it will silently link against lower-performance math libraries and make your code inexplicably slow.
p4wnc6 11y ago
conda supports pip installation within the context of an environment (much more gracefully, in fact, than does virtualenv). So you're not giving anything up by using conda in this regard. I don't see the desire to have pip as the baseline. For me, the conda packaging is much more informative and placing everything you need for multiplatform support into an /info directory with a meta.yaml is a lot more effective than going through the steps of PyPI. conda also makes uploading and hosting on anaconda.org extremely easy. Normally there is the whole "gee, I don't want to learn another package manager" -- but conda / anaconda.org is extremely worth it. It really is a major engineering step forward from the existing package deployment strategies in Python. I even configure my travis.yml CI scripts to download Miniconda, create a conda environment from a requiremenets.txt, and then build and test my code via conda on the contiguous integration VM itself. The only worry is how strongly tied conda and anaconda.org are to the future of Continuum. Given how much Continuum speaks of open-source work, one would hope that these projects essentially live independently (or that forks of them would) but you never know. I do admit that is a major downside.

I also think that Anaconda is great. However, I hope that in the future we could install numpy, matplotlib, jupyter, etc. just using pip.

by alceufc 11y ago

I also think that Anaconda is great. However, I hope that in the future we could install numpy, matplotlib, jupyter, etc. just using pip.

drauh 11y ago

Currently, pip install of numpy (and scipy) does not pick up MKL, which you would want if you do lots of linear algebra and FFTs.

stared 11y ago

I like Anaconda (and I recommend it as the easiest installation for data sci), but on OS X it is easy to install Python and relevant numerical packages with Homebrew and pip.

zo1 11y ago

I did that earlier today: pip install jupyter pip install numpy pip install scipy pip install scikit-learn pip install matplotlib The only problem I had was with OpenCV, which requires manual make installation if you want the contrib package. The other problem was when trying to install scikit-learn, it requires manual pip installation of scipy.

yeukhon 11y ago
The reason you can't is because of the C libraries levaaged must be installed prior to install numpy and scipy. For example, you can't get through PyYaml unless python-dev is installed on Ubuntu. I am not sure if wheel would fix it but I don't think so.
- takeda 10y ago
  Wheel is a binary distribution, so files would already be compiled and therefore python-dev would not be needed anymore.
  - yeukhon 10y ago
    Is wheel not default? Or play it elementary :-) how would I avoid this issue in the future?
    - takeda 10y ago
      I did not write anything worthy to send to PIP so don't know exactly but looks like it is up to the developers[1] Anyway I just noticed that PyPI only supports binary packages for Windows and Mac OS X. Although, you could still generate wheels of packages that you use by using something like this: pip wheel -r requirements.txt You can then install them with pip install <file> or (unfortunately I forgot the option, perhaps it was -i) you can use an option to point to a directory containing wheels and pip install to install the main package. It should use all dependencies in that directory as well. [1] http://pythonwheels.com/ http://pythonwheels.com/
      - techdragon 10y ago
        the issue with Wheels on Linux, and this is very much a Linux problem, is that the 'pre built' nature of libraries in wheels doesnt play nice with the raw chaos of Linux's package management + distro + kernel ecosystem. I raised the question of FreeBSD and Solaris based wheels at a PyCon when I was in a face to face discussion with someone more knowledgeable, and the answer was 'in theory that should work like Windows & OSX, no one has done the hard work yet.' So yeah Linux is not the most friendly environment for Python Wheels.
timClicks 10y ago
I would always be worried with installing numpy/scipy via pip that I wouldn't be linking to BLAS/LAPACK correctly.
- rhodysurf 10y ago
  What do you mean? I have always installed via pip with zero errors on all three platforms.
  - moyix 10y ago
    The worry is not that it will throw an error, it's that it will silently link against lower-performance math libraries and make your code inexplicably slow.

p4wnc6 11y ago

conda supports pip installation within the context of an environment (much more gracefully, in fact, than does virtualenv). So you're not giving anything up by using conda in this regard. I don't see the desire to have pip as the baseline. For me, the conda packaging is much more informative and placing everything you need for multiplatform support into an /info directory with a meta.yaml is a lot more effective than going through the steps of PyPI. conda also makes uploading and hosting on anaconda.org extremely easy. Normally there is the whole "gee, I don't want to learn another package manager" -- but conda / anaconda.org is extremely worth it. It really is a major engineering step forward from the existing package deployment strategies in Python. I even configure my travis.yml CI scripts to download Miniconda, create a conda environment from a requiremenets.txt, and then build and test my code via conda on the contiguous integration VM itself. The only worry is how strongly tied conda and anaconda.org are to the future of Continuum. Given how much Continuum speaks of open-source work, one would hope that these projects essentially live independently (or that forks of them would) but you never know. I do admit that is a major downside.