What is up with downgrading numpy?

[Chris Barker]

Hey all,

lately, I have been finding that conda really wants to downgrade numpy (and scipy). Even when the package I'm installing has no dependencies on numpy or even any at all!

I thought it had something to do with "features", and may be because I'm running some environments that I built up a while a go, and have been kinda willy-nilly upgrading various bits.

But today I just created a brand new environment, with numpy, scipy, and a whol ebunch of other stuff (including ipython).

Then I tried in install jupyter, and got:

The following packages will be DOWNGRADED:

    blas:               1.1-openblas                          conda-forge --> 1.0-mkl          
    numpy:              1.15.0-py27_blas_openblashd3ea46f_200 conda-forge [blas_openblas] --> 1.13.1-py27_0    
    scipy:              1.1.0-py27_blas_openblash7943236_201  conda-forge [blas_openblas] --> 0.19.1-np113py27_0

I really don't want to go back to numpy 1.13....

and why from openblas to mkl???

huh??

I've been getting around this by installing stuff --no-deps, but upyter DOES need a bunch of other stuff, so that's not a great option here.

what's the deal? how can I get myself a stable environment that I can add stuff to without downgrading for no good reason.

-CHB

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[Chris Barker]

a hint:

trying to install zeromq by itself triggers the blas/numpy/scipy downgrade....

$ conda install zeromq

Solving environment: done

## Package Plan ##

  environment location: /Users/chris.barker/miniconda2/envs/gnome2

  added / updated specs: 

    - zeromq

The following NEW packages will be INSTALLED:

    intel-openmp: 2018.0.3-0                                       

    mkl:          2017.0.4-h1fae6ae_0                              

The following packages will be DOWNGRADED:

    blas:         1.1-openblas                          conda-forge --> 1.0-mkl           

    numpy:        1.15.0-py27_blas_openblashd3ea46f_200 conda-forge [blas_openblas] --> 1.13.1-py27_0     

    scipy:        1.1.0-py27_blas_openblash7943236_201  conda-forge [blas_openblas] --> 0.19.1-np113py27_0

-CHB

[Chris Barker]

Done.

But any idea what a solution I can use now???

Is the issue that defaults has a newer JUpyter Or zeromq) than conda-forge, so I'm getting a default jupyter, and then that wants a defaults numpy, etc?

-CHB

On Thu, Aug 16, 2018 at 3:08 PM, Filipe Pires Alvarenga Fernandes <oce...@gmail.com> wrote:

Chris, add your vote here to solve that problem for good:

https://github.com/conda/conda/issues/7656

Kale Franz, from AnacondaInc, said that if we can get ~50 +1 ones there he can prioritize that.

-F

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[Chris Barker]

On Thu, Aug 16, 2018 at 3:08 PM, Filipe Pires Alvarenga Fernandes <oce...@gmail.com> wrote:

Chris, add your vote here to solve that problem for good:

https://github.com/conda/conda/issues/7656

I'm not sure that IS the problem - pretty much all the packages I have are already conda-forge, and what Im trying ot istall is there.

but when I try to intall zeromq, with no deps, I get:

$ conda install --no-deps zeromq

Solving environment: done

# All requested packages already installed.

so its already there.

but If I try with deps, I get:

$ conda install zeromq
Solving environment: done

## Package Plan ##

  environment location: /Users/chris.barker/miniconda2/envs/gnome2

  added / updated specs: 
    - zeromq


The following NEW packages will be INSTALLED:

    intel-openmp: 2018.0.3-0                                       
    mkl:          2017.0.4-h1fae6ae_0                              

The following packages will be DOWNGRADED:

    blas:         1.1-openblas                          conda-forge --> 1.0-mkl           
    numpy:        1.15.0-py27_blas_openblashd3ea46f_200 conda-forge [blas_openblas] --> 1.13.1-py27_0     
    scipy:        1.1.0-py27_blas_openblash7943236_201  conda-forge [blas_openblas] --> 0.19.1-np113py27_0

Proceed ([y]/n)? n

what exactly does "added / updated specs mean? But anyway -- all this is doing is switching to mkl -- but why? zeromq doesn't depend on numpy or scipy, or ...

It does depend on -- libcxx, which depends on clangdev -- maybe that's dragging things in????