Re: [discuss] Software Carpentry style lesson for Conda

[Reid , Andrew C . E . via discuss]

  David, thanks so much for this -- your bullets below crystallize a 
number of issues I've been wrestling with at NIST, where we also have a
new HPC resource which is intended to benefit a broad user-base with
machine-learning expertise but who are not traditional HPC users,
and we're also making fairly heavy use of Conda, which is 
actually provided by the vendor for this system.

  We're simultaneously participating on a broader complementary effort, 
namely to introduce users to traditional HPC methods and workflows 
through "HPC Carpentry", which might also be useful to you (or others 
on this thread) -- like the conda effort, it's in an "alpha" state, 
possibly useful to you, and definitely open for contributions.

  The repo is at https://github.com/hpc-carpentry/hpc-intro, and
you can view the materials at https://hpc-carpentry.github.io/hpc-intro/.

-- A.



On Thu, Jun 13, 2019 at 04:29:41PM +0300, David Pugh wrote:
> Maxime,
> 
> Thanks for continuing to engage on this topic as it gets to one of my primary
> motivations for developing this lesson. Some background, at KAUST we have 
> three
> university managed clusters: two heterogeneous commodity clusters and one Cray
> XC 40 HPC.  These clusters are all managed using traditional HPC tools (i.e.,
> module system, etc) and our systems team has also provided a comprehensive 
> list
> of pre-compiled python wheels optimized for the various compute nodes.  Conda
> is also already available on all three of our clusters via the module system. 
> This traditional HPC setup does a good job of supporting traditional HPC users
> and use cases. Our traditional HPC users are very happy at present.
> 
> However, the majority of our users are not traditional HPC users; they have
> data science and machine learning workflows that are not well supported by
> traditional HPC managed system.  While these users are able to do the majority
> of their work on their laptops/workstations, they would benefit from being 
> able
> to scale their workflows on our cluster. 
> 
> * In order for these non-traditional HPC users to use our HPC managed clusters
> they must basically maintain two workflows and software environments: one for
> their laptops/workstation and one for our clusters.
> * I put high value on maintaining the portability of my data science and
> machine learning workflows: I want to be able to develop on my laptop/
> workstation and then move my workflow to our clusters  or the public cloud 
> only
> when necessary.
> * Many (most?) of our users will never encounter traditional HPC managed
> hardware after they leave KAUST; as such I would prefer to train our users on
> tools that will set them up for success in public (or similarly managed
> private) cloud environments.
> 
> I view Conda, eventually combined with Docker (for which I am also developed a
> lesson), as at least a partial solution to these concerns.
> 
> Instead of a section on "when not to use Conda" I am thinking of crafting an
> episode on "when and how to build bespoke conda packages." Although do think
> that I should make it clear that if you have a traditional HPC workflow, then
> you should "consult your local experts" as conda might not be the best
> solution.
> 
> Anyway, hopefully this provides a bit of context and motivation for some of 
> the
> lesson design choices that I have made thus far.
> 
> Thanks again for the feedback it is much appreciated!
> 
> D
> 
> 
> 
> 
> On Thu, Jun 13, 2019 at 3:48 PM Maxime Boissonneault <
> maxime.boissonnea...@calculquebec.ca> wrote:
> 
> On 2019-06-12 10:56 AM, Michael Sarahan wrote:
> 
> That's a good point, but rather than say "don't use conda at all" -
> that's more reason to have custom channels where conda is set up to
> comply with those needs.  Conda need not be mutually exclusive with
> these things, but it does take some setup to get them working 
> together.
> 
> That's not what I'm saying. What I'm saying is consult with your local
> experts.
> 
> On our clusters, *we* tell users don't use conda.
> 
> We provide a comprehensive list of precompiled python wheels. There is
> absolutely no need for conda in 99% of the cases.
> 
> I don't see why we would support custom conda channels when we can just as
> well support python wheels that don't require conda.
> 
> 
> Maxime
> 
> 
> 
> Saying "don't use conda at all" is ignoring the work that has to 
> happen
> either way.  Either you have to reproduce what conda is providing
> somehow, or you have to make conda use the part on the system side. 
> That's definitely a case-by-case scenario for everyone, and we need to
> document both paths.
> 
> For your example of MPI, conda packages are setup to explicitly 
> require
> some MPI implementation where necessary.  That package can come from 
> an
> actual conda MPICH package

Re: [discuss] Software Carpentry style lesson for Conda

[David Pugh]

Maxime,

Thanks for continuing to engage on this topic as it gets to one of my
primary motivations for developing this lesson. Some background, at KAUST
we have three university managed clusters: two heterogeneous commodity
clusters and one Cray XC 40 HPC.  These clusters are all managed using
traditional HPC tools (i.e., module system, etc) and our systems team has
also provided a comprehensive list of pre-compiled python wheels optimized
for the various compute nodes.  Conda is also already available on all
three of our clusters via the module system.  This traditional HPC setup
does a good job of supporting traditional HPC users and use cases. Our
traditional HPC users are very happy at present.

However, the majority of our users are not traditional HPC users; they have
data science and machine learning workflows that are not well supported by
traditional HPC managed system.  While these users are able to do the
majority of their work on their laptops/workstations, they would benefit
from being able to scale their workflows on our cluster.

* In order for these non-traditional HPC users to use our HPC managed
clusters they must basically maintain two workflows and software
environments: one for their laptops/workstation and one for our clusters.
* I put high value on maintaining the portability of my data science and
machine learning workflows: I want to be able to develop on my
laptop/workstation and then move my workflow to our clusters  or the public
cloud only when necessary.
* Many (most?) of our users will never encounter traditional HPC managed
hardware after they leave KAUST; as such I would prefer to train our users
on tools that will set them up for success in public (or similarly managed
private) cloud environments.

I view Conda, eventually combined with Docker (for which I am also
developed a lesson), as at least a partial solution to these concerns.

Instead of a section on "when not to use Conda" I am thinking of crafting
an episode on "when and how to build bespoke conda packages." Although do
think that I should make it clear that if you have a traditional HPC
workflow, then you should "consult your local experts" as conda might not
be the best solution.

Anyway, hopefully this provides a bit of context and motivation for some of
the lesson design choices that I have made thus far.

Thanks again for the feedback it is much appreciated!

D




On Thu, Jun 13, 2019 at 3:48 PM Maxime Boissonneault <
maxime.boissonnea...@calculquebec.ca> wrote:

> On 2019-06-12 10:56 AM, Michael Sarahan wrote:
>
> That's a good point, but rather than say "don't use conda at all" - that's
> more reason to have custom channels where conda is set up to comply with
> those needs.  Conda need not be mutually exclusive with these things, but
> it does take some setup to get them working together.
>
> That's not what I'm saying. What I'm saying is consult with your local
> experts.
>
> On our clusters, *we* tell users don't use conda.
>
> We provide a comprehensive list of precompiled python wheels. There is
> absolutely no need for conda in 99% of the cases.
>
> I don't see why we would support custom conda channels when we can just as
> well support python wheels that don't require conda.
>
>
> Maxime
>
>
>
> Saying "don't use conda at all" is ignoring the work that has to happen
> either way.  Either you have to reproduce what conda is providing somehow,
> or you have to make conda use the part on the system side.  That's
> definitely a case-by-case scenario for everyone, and we need to document
> both paths.
>
> For your example of MPI, conda packages are setup to explicitly require
> some MPI implementation where necessary.  That package can come from an
> actual conda MPICH package, or it can come from a known binary compatible
> system installation that has a conda package setup to reference it.  Conda
> is not dogmatic about being hermetic (unlike, say, bazel).  Binary
> compatibility with external libraries can be pretty tricky, though.
>
> On Wed, Jun 12, 2019 at 9:48 AM Maxime Boissonneault <
> maxime.boissonnea...@calculquebec.ca> wrote:
>
>> Hi,
>> How about including a part about when *not* to use Conda ?
>>
>> In particular, if they are going to be computing on a supercomputer, they
>> should consult with your cluster specialists first.
>> Conda works well on somebody's desktop, but it creates a lot of problems
>> on supercomputers, because it does crazy stuff like installing MPI by
>> itself instead of relying on staff-installed modules and software packages.
>>
>> Cheers,
>>
>> Maxime
>>
>>
>> On 2019-06-12 9:49 AM, David Pugh wrote:
>>
>> All,
>>
>> I have developed a Software Carpentry style lesson for Conda and would be
>> keen to get feedback from the community!
>>
>> Website:
>>
>> https://kaust-vislab.github.io/introduction-to-conda-for-data-scientists/
>>
>> Repo:
>>
>> https://github.com/kaust-vislab/introduction-to-conda-for-data-scientists
>>
>> Thanks and look forward to hearing from y

Re: [discuss] Software Carpentry style lesson for Conda

[Michael Sarahan]

Yep, that's fine. If your wheel collection is comprehensive, you've done
the requisite work to properly support your users. If people are going to
PyPI vs going to conda, though, then it's less clear to me.

On Thu, Jun 13, 2019, 07:48 Maxime Boissonneault <
maxime.boissonnea...@calculquebec.ca> wrote:

> On 2019-06-12 10:56 AM, Michael Sarahan wrote:
>
> That's a good point, but rather than say "don't use conda at all" - that's
> more reason to have custom channels where conda is set up to comply with
> those needs.  Conda need not be mutually exclusive with these things, but
> it does take some setup to get them working together.
>
> That's not what I'm saying. What I'm saying is consult with your local
> experts.
>
> On our clusters, *we* tell users don't use conda.
>
> We provide a comprehensive list of precompiled python wheels. There is
> absolutely no need for conda in 99% of the cases.
>
> I don't see why we would support custom conda channels when we can just as
> well support python wheels that don't require conda.
>
>
> Maxime
>
>
>
> Saying "don't use conda at all" is ignoring the work that has to happen
> either way.  Either you have to reproduce what conda is providing somehow,
> or you have to make conda use the part on the system side.  That's
> definitely a case-by-case scenario for everyone, and we need to document
> both paths.
>
> For your example of MPI, conda packages are setup to explicitly require
> some MPI implementation where necessary.  That package can come from an
> actual conda MPICH package, or it can come from a known binary compatible
> system installation that has a conda package setup to reference it.  Conda
> is not dogmatic about being hermetic (unlike, say, bazel).  Binary
> compatibility with external libraries can be pretty tricky, though.
>
> On Wed, Jun 12, 2019 at 9:48 AM Maxime Boissonneault <
> maxime.boissonnea...@calculquebec.ca> wrote:
>
>> Hi,
>> How about including a part about when *not* to use Conda ?
>>
>> In particular, if they are going to be computing on a supercomputer, they
>> should consult with your cluster specialists first.
>> Conda works well on somebody's desktop, but it creates a lot of problems
>> on supercomputers, because it does crazy stuff like installing MPI by
>> itself instead of relying on staff-installed modules and software packages.
>>
>> Cheers,
>>
>> Maxime
>>
>>
>> On 2019-06-12 9:49 AM, David Pugh wrote:
>>
>> All,
>>
>> I have developed a Software Carpentry style lesson for Conda and would be
>> keen to get feedback from the community!
>>
>> Website:
>>
>> https://kaust-vislab.github.io/introduction-to-conda-for-data-scientists/
>>
>> Repo:
>>
>> https://github.com/kaust-vislab/introduction-to-conda-for-data-scientists
>>
>> Thanks and look forward to hearing from you!
>>
>> David
>>
>>
>> *The Carpentries * / discuss /
> see discussions  +
> participants  + 
> delivery
> options 
> Permalink
> 
>
>
> --
> -
> Maxime Boissonneault
> Analyste de calcul - Calcul Québec, Université Laval
> Président - Comité de coordination du soutien à la recherche de Calcul Québec
> Team lead - Research Support National Team, Compute Canada
> Instructeur Software Carpentry
> Ph. D. en physique
>
>

--
The Carpentries: discuss
Permalink: 
https://carpentries.topicbox.com/groups/discuss/T32ac38bed70dbd29-M1bba3f5b070787aa2455838f
Delivery options: https://carpentries.topicbox.com/groups/discuss/subscription

Re: [discuss] Software Carpentry style lesson for Conda

[Maxime Boissonneault]

On 2019-06-12 10:56 AM, Michael Sarahan wrote:
That's a good point, but rather than say "don't use conda at all" - 
that's more reason to have custom channels where conda is set up to 
comply with those needs.  Conda need not be mutually exclusive with 
these things, but it does take some setup to get them working together.


That's not what I'm saying. What I'm saying is consult with your local 
experts.


On our clusters, *we* tell users don't use conda.

We provide a comprehensive list of precompiled python wheels. There is 
absolutely no need for conda in 99% of the cases.


I don't see why we would support custom conda channels when we can just 
as well support python wheels that don't require conda.



Maxime




Saying "don't use conda at all" is ignoring the work that has to 
happen either way.  Either you have to reproduce what conda is 
providing somehow, or you have to make conda use the part on the 
system side.  That's definitely a case-by-case scenario for everyone, 
and we need to document both paths.


For your example of MPI, conda packages are setup to explicitly 
require some MPI implementation where necessary. That package can come 
from an actual conda MPICH package, or it can come from a known binary 
compatible system installation that has a conda package setup to 
reference it.  Conda is not dogmatic about being hermetic (unlike, 
say, bazel).  Binary compatibility with external libraries can be 
pretty tricky, though.


On Wed, Jun 12, 2019 at 9:48 AM Maxime Boissonneault 
> wrote:


Hi,
How about including a part about when *not* to use Conda ?

In particular, if they are going to be computing on a
supercomputer, they should consult with your cluster specialists
first.
Conda works well on somebody's desktop, but it creates a lot of
problems on supercomputers, because it does crazy stuff like
installing MPI by itself instead of relying on staff-installed
modules and software packages.

Cheers,

Maxime


On 2019-06-12 9:49 AM, David Pugh wrote:

All,

I have developed a Software Carpentry style lesson for Conda and
would be keen to get feedback from the community!

Website:

https://kaust-vislab.github.io/introduction-to-conda-for-data-scientists/

Repo:

https://github.com/kaust-vislab/introduction-to-conda-for-data-scientists

Thanks and look forward to hearing from you!

David


*The Carpentries * / discuss 
/ see discussions  + 
participants  
+ delivery options 
 
Permalink 
 




--
-
Maxime Boissonneault
Analyste de calcul - Calcul Québec, Université Laval
Président - Comité de coordination du soutien à la recherche de Calcul Québec
Team lead - Research Support National Team, Compute Canada
Instructeur Software Carpentry
Ph. D. en physique


--
The Carpentries: discuss
Permalink: 
https://carpentries.topicbox.com/groups/discuss/Tb12fc97e5ee621f2-M594b7e821f7b475e303c2ceb
Delivery options: https://carpentries.topicbox.com/groups/discuss/subscription

Re: [discuss] Software Carpentry style lesson for Conda

[David Pugh]

Aleksandra,

Can you confirm that issue you encountered with using conda and jupyter is
the issue discussed in this blog post?

http://jakevdp.github.io/blog/2017/12/05/installing-python-packages-from-jupyter/index.html

Thanks!

David
On Wed, Jun 12, 2019 at 7:04 PM David Pugh  wrote:

> David,
>
> Thanks for passing this along! I had not seen it before and it looks
> highly relevant.
>
> D
>
> Sent from my iPhone
>
> On Jun 12, 2019, at 18:56, David Nicholson  wrote:
>
> There''s a good Jake Vanderplas blog post on this if you haven't seen it
> already:
>
> http://jakevdp.github.io/blog/2017/12/05/installing-python-packages-from-jupyter/index.html
>
> Tutorial looks pretty good at first glance, can't wait to teach this
>
> David Nicholson, Ph.D.
> https://nicholdav.info/
> https://github.com/NickleDave
> Prinz lab , Emory
> University, Atlanta, GA, USA
>
>
> On Wed, Jun 12, 2019 at 11:39 AM David Pugh 
> wrote:
>
>> Aleksandra,
>>
>> I have never encountered issues using conda and Jupyter notebooks of the
>> kind you describe.  But this long thread confirms that you are not alone.
>>
>> https://github.com/jupyter/notebook/issues/2359
>>
>> I will try to add a call out box with a best practice for installing
>> jupyter notebook and juypterlab etc.
>>
>> Thanks for clarifying!
>>
>>
>>
>> Sent from my iPhone
>>
>> On Jun 12, 2019, at 18:20, Giuseppe Profiti 
>> wrote:
>>
>> First, good job David.
>> Aleksandra: there are few things to consider when using conda and
>> jupyter. Just recently we managed to deploy a jupyterhub on a computing
>> cluster, along with several different conda environments.
>> Long story short: you should register the environment kernel in the
>> jupyter instance. I hope my boss let me write a blog post about it soon.
>>
>> Best,
>> Giuseppe
>>
>> Il giorno mer 12 giu 2019 alle ore 17:02 Aleksandra Taranov <
>> atara...@ucdavis.edu> ha scritto:
>>
>>> David, to answer your question, the reason I stopped using conda and
>>> switched to pip installs was that I'd conda install jupyter and conda
>>> install packages, but then when I tried to run them, jupyter notebooks
>>> couldn't find the package. I'm probably making some very basic error here,
>>> but I'm also likely not the only one confused about this.
>>>
>>> Thanks again for making this great resource.
>>>
>>> On Wed, Jun 12, 2019, 7:58 AM Michael Sarahan 
>>> wrote:
>>>
 That's a good point, but rather than say "don't use conda at all" -
 that's more reason to have custom channels where conda is set up to comply
 with those needs.  Conda need not be mutually exclusive with these things,
 but it does take some setup to get them working together.

 Saying "don't use conda at all" is ignoring the work that has to happen
 either way.  Either you have to reproduce what conda is providing somehow,
 or you have to make conda use the part on the system side.  That's
 definitely a case-by-case scenario for everyone, and we need to document
 both paths.

 For your example of MPI, conda packages are setup to explicitly require
 some MPI implementation where necessary.  That package can come from an
 actual conda MPICH package, or it can come from a known binary compatible
 system installation that has a conda package setup to reference it.  Conda
 is not dogmatic about being hermetic (unlike, say, bazel).  Binary
 compatibility with external libraries can be pretty tricky, though.

 On Wed, Jun 12, 2019 at 9:48 AM Maxime Boissonneault <
 maxime.boissonnea...@calculquebec.ca> wrote:

> Hi,
> How about including a part about when *not* to use Conda ?
>
> In particular, if they are going to be computing on a supercomputer,
> they should consult with your cluster specialists first.
> Conda works well on somebody's desktop, but it creates a lot of
> problems on supercomputers, because it does crazy stuff like installing 
> MPI
> by itself instead of relying on staff-installed modules and software
> packages.
>
> Cheers,
>
> Maxime
>
>
> On 2019-06-12 9:49 AM, David Pugh wrote:
>
> All,
>
> I have developed a Software Carpentry style lesson for Conda and would
> be keen to get feedback from the community!
>
> Website:
>
>
> https://kaust-vislab.github.io/introduction-to-conda-for-data-scientists/
>
> Repo:
>
>
> https://github.com/kaust-vislab/introduction-to-conda-for-data-scientists
>
> Thanks and look forward to hearing from you!
>
> David
>
>
> *The Carpentries * / discuss
> / see discussions  +
> participants  + 
> delivery
> options 
> Permalink
> 

Re: [discuss] Software Carpentry style lesson for Conda

[David Pugh]

David,

Thanks for passing this along! I had not seen it before and it looks highly 
relevant.

D

Sent from my iPhone

> On Jun 12, 2019, at 18:56, David Nicholson  wrote:
> 
> There''s a good Jake Vanderplas blog post on this if you haven't seen it 
> already:
> http://jakevdp.github.io/blog/2017/12/05/installing-python-packages-from-jupyter/index.html
> 
> Tutorial looks pretty good at first glance, can't wait to teach this
> 
> David Nicholson, Ph.D.
> https://nicholdav.info/
> https://github.com/NickleDave
> Prinz lab, Emory University, Atlanta, GA, USA
> 
> 
>> On Wed, Jun 12, 2019 at 11:39 AM David Pugh  wrote:
>> Aleksandra,
>> 
>> I have never encountered issues using conda and Jupyter notebooks of the 
>> kind you describe.  But this long thread confirms that you are not alone.
>> 
>> https://github.com/jupyter/notebook/issues/2359
>> 
>> I will try to add a call out box with a best practice for installing jupyter 
>> notebook and juypterlab etc.
>> 
>> Thanks for clarifying!
>> 
>> 
>> 
>> Sent from my iPhone
>> 
>>> On Jun 12, 2019, at 18:20, Giuseppe Profiti  
>>> wrote:
>>> 
>>> First, good job David.
>>> Aleksandra: there are few things to consider when using conda and jupyter. 
>>> Just recently we managed to deploy a jupyterhub on a computing cluster, 
>>> along with several different conda environments.
>>> Long story short: you should register the environment kernel in the jupyter 
>>> instance. I hope my boss let me write a blog post about it soon.
>>> 
>>> Best,
>>> Giuseppe
>>> 
 Il giorno mer 12 giu 2019 alle ore 17:02 Aleksandra Taranov 
  ha scritto:
 David, to answer your question, the reason I stopped using conda and 
 switched to pip installs was that I'd conda install jupyter and conda 
 install packages, but then when I tried to run them, jupyter notebooks 
 couldn't find the package. I'm probably making some very basic error here, 
 but I'm also likely not the only one confused about this.
 
 Thanks again for making this great resource.
 
> On Wed, Jun 12, 2019, 7:58 AM Michael Sarahan  wrote:
> That's a good point, but rather than say "don't use conda at all" - 
> that's more reason to have custom channels where conda is set up to 
> comply with those needs.  Conda need not be mutually exclusive with these 
> things, but it does take some setup to get them working together.
> 
> Saying "don't use conda at all" is ignoring the work that has to happen 
> either way.  Either you have to reproduce what conda is providing 
> somehow, or you have to make conda use the part on the system side.  
> That's definitely a case-by-case scenario for everyone, and we need to 
> document both paths.
> 
> For your example of MPI, conda packages are setup to explicitly require 
> some MPI implementation where necessary.  That package can come from an 
> actual conda MPICH package, or it can come from a known binary compatible 
> system installation that has a conda package setup to reference it.  
> Conda is not dogmatic about being hermetic (unlike, say, bazel).  Binary 
> compatibility with external libraries can be pretty tricky, though.
> 
>> On Wed, Jun 12, 2019 at 9:48 AM Maxime Boissonneault 
>>  wrote:
>> Hi,
>> How about including a part about when *not* to use Conda ?
>> 
>> In particular, if they are going to be computing on a supercomputer, 
>> they should consult with your cluster specialists first. 
>> Conda works well on somebody's desktop, but it creates a lot of problems 
>> on supercomputers, because it does crazy stuff like installing MPI by 
>> itself instead of relying on staff-installed modules and software 
>> packages.
>> 
>> Cheers,
>> 
>> Maxime
>> 
>> 
>>> On 2019-06-12 9:49 AM, David Pugh wrote:
>>> All,
>>> 
>>> I have developed a Software Carpentry style lesson for Conda and would 
>>> be keen to get feedback from the community!
>>> 
>>> Website:
>>> 
>>> https://kaust-vislab.github.io/introduction-to-conda-for-data-scientists/
>>> 
>>> Repo:
>>> 
>>> https://github.com/kaust-vislab/introduction-to-conda-for-data-scientists
>>> 
>>> Thanks and look forward to hearing from you!
>>> 
>>> David
> 
> The Carpentries / discuss / see discussions + participants + delivery options 
> Permalink

--
The Carpentries: discuss
Permalink: 
https://carpentries.topicbox.com/groups/discuss/Tb12fc97e5ee621f2-M233340b4d75fc92350f95f37
Delivery options: https://carpentries.topicbox.com/groups/discuss/subscription

Re: [discuss] Software Carpentry style lesson for Conda

[David Nicholson]

There''s a good Jake Vanderplas blog post on this if you haven't seen it
already:
http://jakevdp.github.io/blog/2017/12/05/installing-python-packages-from-jupyter/index.html

Tutorial looks pretty good at first glance, can't wait to teach this

David Nicholson, Ph.D.
https://nicholdav.info/
https://github.com/NickleDave
Prinz lab , Emory University,
Atlanta, GA, USA


On Wed, Jun 12, 2019 at 11:39 AM David Pugh  wrote:

> Aleksandra,
>
> I have never encountered issues using conda and Jupyter notebooks of the
> kind you describe.  But this long thread confirms that you are not alone.
>
> https://github.com/jupyter/notebook/issues/2359
>
> I will try to add a call out box with a best practice for installing
> jupyter notebook and juypterlab etc.
>
> Thanks for clarifying!
>
>
>
> Sent from my iPhone
>
> On Jun 12, 2019, at 18:20, Giuseppe Profiti 
> wrote:
>
> First, good job David.
> Aleksandra: there are few things to consider when using conda and jupyter.
> Just recently we managed to deploy a jupyterhub on a computing cluster,
> along with several different conda environments.
> Long story short: you should register the environment kernel in the
> jupyter instance. I hope my boss let me write a blog post about it soon.
>
> Best,
> Giuseppe
>
> Il giorno mer 12 giu 2019 alle ore 17:02 Aleksandra Taranov <
> atara...@ucdavis.edu> ha scritto:
>
>> David, to answer your question, the reason I stopped using conda and
>> switched to pip installs was that I'd conda install jupyter and conda
>> install packages, but then when I tried to run them, jupyter notebooks
>> couldn't find the package. I'm probably making some very basic error here,
>> but I'm also likely not the only one confused about this.
>>
>> Thanks again for making this great resource.
>>
>> On Wed, Jun 12, 2019, 7:58 AM Michael Sarahan  wrote:
>>
>>> That's a good point, but rather than say "don't use conda at all" -
>>> that's more reason to have custom channels where conda is set up to comply
>>> with those needs.  Conda need not be mutually exclusive with these things,
>>> but it does take some setup to get them working together.
>>>
>>> Saying "don't use conda at all" is ignoring the work that has to happen
>>> either way.  Either you have to reproduce what conda is providing somehow,
>>> or you have to make conda use the part on the system side.  That's
>>> definitely a case-by-case scenario for everyone, and we need to document
>>> both paths.
>>>
>>> For your example of MPI, conda packages are setup to explicitly require
>>> some MPI implementation where necessary.  That package can come from an
>>> actual conda MPICH package, or it can come from a known binary compatible
>>> system installation that has a conda package setup to reference it.  Conda
>>> is not dogmatic about being hermetic (unlike, say, bazel).  Binary
>>> compatibility with external libraries can be pretty tricky, though.
>>>
>>> On Wed, Jun 12, 2019 at 9:48 AM Maxime Boissonneault <
>>> maxime.boissonnea...@calculquebec.ca> wrote:
>>>
 Hi,
 How about including a part about when *not* to use Conda ?

 In particular, if they are going to be computing on a supercomputer,
 they should consult with your cluster specialists first.
 Conda works well on somebody's desktop, but it creates a lot of
 problems on supercomputers, because it does crazy stuff like installing MPI
 by itself instead of relying on staff-installed modules and software
 packages.

 Cheers,

 Maxime


 On 2019-06-12 9:49 AM, David Pugh wrote:

 All,

 I have developed a Software Carpentry style lesson for Conda and would
 be keen to get feedback from the community!

 Website:


 https://kaust-vislab.github.io/introduction-to-conda-for-data-scientists/

 Repo:


 https://github.com/kaust-vislab/introduction-to-conda-for-data-scientists

 Thanks and look forward to hearing from you!

 David


 *The Carpentries * / discuss
> / see discussions  +
> participants  + 
> delivery
> options 
> Permalink
> 
>

--
The Carpentries: discuss
Permalink: 
https://carpentries.topicbox.com/groups/discuss/Tb12fc97e5ee621f2-Ma06cd57462eb2853e6366455
Delivery options: https://carpentries.topicbox.com/groups/discuss/subscription

Re: [discuss] Software Carpentry style lesson for Conda

[David Pugh]

Aleksandra,

I have never encountered issues using conda and Jupyter notebooks of the kind 
you describe.  But this long thread confirms that you are not alone.

https://github.com/jupyter/notebook/issues/2359

I will try to add a call out box with a best practice for installing jupyter 
notebook and juypterlab etc.

Thanks for clarifying!



Sent from my iPhone

> On Jun 12, 2019, at 18:20, Giuseppe Profiti  
> wrote:
> 
> First, good job David.
> Aleksandra: there are few things to consider when using conda and jupyter. 
> Just recently we managed to deploy a jupyterhub on a computing cluster, along 
> with several different conda environments.
> Long story short: you should register the environment kernel in the jupyter 
> instance. I hope my boss let me write a blog post about it soon.
> 
> Best,
> Giuseppe
> 
>> Il giorno mer 12 giu 2019 alle ore 17:02 Aleksandra Taranov 
>>  ha scritto:
>> David, to answer your question, the reason I stopped using conda and 
>> switched to pip installs was that I'd conda install jupyter and conda 
>> install packages, but then when I tried to run them, jupyter notebooks 
>> couldn't find the package. I'm probably making some very basic error here, 
>> but I'm also likely not the only one confused about this.
>> 
>> Thanks again for making this great resource.
>> 
>>> On Wed, Jun 12, 2019, 7:58 AM Michael Sarahan  wrote:
>>> That's a good point, but rather than say "don't use conda at all" - that's 
>>> more reason to have custom channels where conda is set up to comply with 
>>> those needs.  Conda need not be mutually exclusive with these things, but 
>>> it does take some setup to get them working together.
>>> 
>>> Saying "don't use conda at all" is ignoring the work that has to happen 
>>> either way.  Either you have to reproduce what conda is providing somehow, 
>>> or you have to make conda use the part on the system side.  That's 
>>> definitely a case-by-case scenario for everyone, and we need to document 
>>> both paths.
>>> 
>>> For your example of MPI, conda packages are setup to explicitly require 
>>> some MPI implementation where necessary.  That package can come from an 
>>> actual conda MPICH package, or it can come from a known binary compatible 
>>> system installation that has a conda package setup to reference it.  Conda 
>>> is not dogmatic about being hermetic (unlike, say, bazel).  Binary 
>>> compatibility with external libraries can be pretty tricky, though.
>>> 
 On Wed, Jun 12, 2019 at 9:48 AM Maxime Boissonneault 
  wrote:
 Hi,
 How about including a part about when *not* to use Conda ?
 
 In particular, if they are going to be computing on a supercomputer, they 
 should consult with your cluster specialists first. 
 Conda works well on somebody's desktop, but it creates a lot of problems 
 on supercomputers, because it does crazy stuff like installing MPI by 
 itself instead of relying on staff-installed modules and software packages.
 
 Cheers,
 
 Maxime
 
 
> On 2019-06-12 9:49 AM, David Pugh wrote:
> All,
> 
> I have developed a Software Carpentry style lesson for Conda and would be 
> keen to get feedback from the community!
> 
> Website:
> 
> https://kaust-vislab.github.io/introduction-to-conda-for-data-scientists/
> 
> Repo:
> 
> https://github.com/kaust-vislab/introduction-to-conda-for-data-scientists
> 
> Thanks and look forward to hearing from you!
> 
> David
> 
> The Carpentries / discuss / see discussions + participants + delivery options 
> Permalink

--
The Carpentries: discuss
Permalink: 
https://carpentries.topicbox.com/groups/discuss/Tb12fc97e5ee621f2-M83665a3db57a07f7da5acb8d
Delivery options: https://carpentries.topicbox.com/groups/discuss/subscription

Re: [discuss] Software Carpentry style lesson for Conda

[Giuseppe Profiti]

First, good job David.
Aleksandra: there are few things to consider when using conda and jupyter.
Just recently we managed to deploy a jupyterhub on a computing cluster,
along with several different conda environments.
Long story short: you should register the environment kernel in the jupyter
instance. I hope my boss let me write a blog post about it soon.

Best,
Giuseppe

Il giorno mer 12 giu 2019 alle ore 17:02 Aleksandra Taranov <
atara...@ucdavis.edu> ha scritto:

> David, to answer your question, the reason I stopped using conda and
> switched to pip installs was that I'd conda install jupyter and conda
> install packages, but then when I tried to run them, jupyter notebooks
> couldn't find the package. I'm probably making some very basic error here,
> but I'm also likely not the only one confused about this.
>
> Thanks again for making this great resource.
>
> On Wed, Jun 12, 2019, 7:58 AM Michael Sarahan  wrote:
>
>> That's a good point, but rather than say "don't use conda at all" -
>> that's more reason to have custom channels where conda is set up to comply
>> with those needs.  Conda need not be mutually exclusive with these things,
>> but it does take some setup to get them working together.
>>
>> Saying "don't use conda at all" is ignoring the work that has to happen
>> either way.  Either you have to reproduce what conda is providing somehow,
>> or you have to make conda use the part on the system side.  That's
>> definitely a case-by-case scenario for everyone, and we need to document
>> both paths.
>>
>> For your example of MPI, conda packages are setup to explicitly require
>> some MPI implementation where necessary.  That package can come from an
>> actual conda MPICH package, or it can come from a known binary compatible
>> system installation that has a conda package setup to reference it.  Conda
>> is not dogmatic about being hermetic (unlike, say, bazel).  Binary
>> compatibility with external libraries can be pretty tricky, though.
>>
>> On Wed, Jun 12, 2019 at 9:48 AM Maxime Boissonneault <
>> maxime.boissonnea...@calculquebec.ca> wrote:
>>
>>> Hi,
>>> How about including a part about when *not* to use Conda ?
>>>
>>> In particular, if they are going to be computing on a supercomputer,
>>> they should consult with your cluster specialists first.
>>> Conda works well on somebody's desktop, but it creates a lot of problems
>>> on supercomputers, because it does crazy stuff like installing MPI by
>>> itself instead of relying on staff-installed modules and software packages.
>>>
>>> Cheers,
>>>
>>> Maxime
>>>
>>>
>>> On 2019-06-12 9:49 AM, David Pugh wrote:
>>>
>>> All,
>>>
>>> I have developed a Software Carpentry style lesson for Conda and would
>>> be keen to get feedback from the community!
>>>
>>> Website:
>>>
>>> https://kaust-vislab.github.io/introduction-to-conda-for-data-scientists/
>>>
>>> Repo:
>>>
>>> https://github.com/kaust-vislab/introduction-to-conda-for-data-scientists
>>>
>>> Thanks and look forward to hearing from you!
>>>
>>> David
>>>
>>>
>>> *The Carpentries * / discuss /
> see discussions  +
> participants  + 
> delivery
> options 
> Permalink
> 
>

--
The Carpentries: discuss
Permalink: 
https://carpentries.topicbox.com/groups/discuss/Tb12fc97e5ee621f2-Mf68644241f9dc866a4ba828f
Delivery options: https://carpentries.topicbox.com/groups/discuss/subscription

Re: [discuss] Software Carpentry style lesson for Conda

[Aleksandra Taranov]

David, to answer your question, the reason I stopped using conda and
switched to pip installs was that I'd conda install jupyter and conda
install packages, but then when I tried to run them, jupyter notebooks
couldn't find the package. I'm probably making some very basic error here,
but I'm also likely not the only one confused about this.

Thanks again for making this great resource.

On Wed, Jun 12, 2019, 7:58 AM Michael Sarahan  wrote:

> That's a good point, but rather than say "don't use conda at all" - that's
> more reason to have custom channels where conda is set up to comply with
> those needs.  Conda need not be mutually exclusive with these things, but
> it does take some setup to get them working together.
>
> Saying "don't use conda at all" is ignoring the work that has to happen
> either way.  Either you have to reproduce what conda is providing somehow,
> or you have to make conda use the part on the system side.  That's
> definitely a case-by-case scenario for everyone, and we need to document
> both paths.
>
> For your example of MPI, conda packages are setup to explicitly require
> some MPI implementation where necessary.  That package can come from an
> actual conda MPICH package, or it can come from a known binary compatible
> system installation that has a conda package setup to reference it.  Conda
> is not dogmatic about being hermetic (unlike, say, bazel).  Binary
> compatibility with external libraries can be pretty tricky, though.
>
> On Wed, Jun 12, 2019 at 9:48 AM Maxime Boissonneault <
> maxime.boissonnea...@calculquebec.ca> wrote:
>
>> Hi,
>> How about including a part about when *not* to use Conda ?
>>
>> In particular, if they are going to be computing on a supercomputer, they
>> should consult with your cluster specialists first.
>> Conda works well on somebody's desktop, but it creates a lot of problems
>> on supercomputers, because it does crazy stuff like installing MPI by
>> itself instead of relying on staff-installed modules and software packages.
>>
>> Cheers,
>>
>> Maxime
>>
>>
>> On 2019-06-12 9:49 AM, David Pugh wrote:
>>
>> All,
>>
>> I have developed a Software Carpentry style lesson for Conda and would be
>> keen to get feedback from the community!
>>
>> Website:
>>
>> https://kaust-vislab.github.io/introduction-to-conda-for-data-scientists/
>>
>> Repo:
>>
>> https://github.com/kaust-vislab/introduction-to-conda-for-data-scientists
>>
>> Thanks and look forward to hearing from you!
>>
>> David
>>
>>
>> *The Carpentries * / discuss /
> see discussions  +
> participants  + 
> delivery
> options 
> Permalink
> 
>

--
The Carpentries: discuss
Permalink: 
https://carpentries.topicbox.com/groups/discuss/Tb12fc97e5ee621f2-M857cd4c971bd1e12469ce183
Delivery options: https://carpentries.topicbox.com/groups/discuss/subscription

Re: [discuss] Software Carpentry style lesson for Conda

[Michael Sarahan]

That's a good point, but rather than say "don't use conda at all" - that's
more reason to have custom channels where conda is set up to comply with
those needs.  Conda need not be mutually exclusive with these things, but
it does take some setup to get them working together.

Saying "don't use conda at all" is ignoring the work that has to happen
either way.  Either you have to reproduce what conda is providing somehow,
or you have to make conda use the part on the system side.  That's
definitely a case-by-case scenario for everyone, and we need to document
both paths.

For your example of MPI, conda packages are setup to explicitly require
some MPI implementation where necessary.  That package can come from an
actual conda MPICH package, or it can come from a known binary compatible
system installation that has a conda package setup to reference it.  Conda
is not dogmatic about being hermetic (unlike, say, bazel).  Binary
compatibility with external libraries can be pretty tricky, though.

On Wed, Jun 12, 2019 at 9:48 AM Maxime Boissonneault <
maxime.boissonnea...@calculquebec.ca> wrote:

> Hi,
> How about including a part about when *not* to use Conda ?
>
> In particular, if they are going to be computing on a supercomputer, they
> should consult with your cluster specialists first.
> Conda works well on somebody's desktop, but it creates a lot of problems
> on supercomputers, because it does crazy stuff like installing MPI by
> itself instead of relying on staff-installed modules and software packages.
>
> Cheers,
>
> Maxime
>
>
> On 2019-06-12 9:49 AM, David Pugh wrote:
>
> All,
>
> I have developed a Software Carpentry style lesson for Conda and would be
> keen to get feedback from the community!
>
> Website:
>
> https://kaust-vislab.github.io/introduction-to-conda-for-data-scientists/
>
> Repo:
>
> https://github.com/kaust-vislab/introduction-to-conda-for-data-scientists
>
> Thanks and look forward to hearing from you!
>
> David
>
>
> *The Carpentries * / discuss /
> see discussions  +
> participants  + 
> delivery
> options 
> Permalink
> 
>

--
The Carpentries: discuss
Permalink: 
https://carpentries.topicbox.com/groups/discuss/Tb12fc97e5ee621f2-M1342d710fc4e431e3998ff41
Delivery options: https://carpentries.topicbox.com/groups/discuss/subscription

Re: [discuss] Software Carpentry style lesson for Conda

[Maxime Boissonneault]

Hi,
How about including a part about when *not* to use Conda ?

In particular, if they are going to be computing on a supercomputer, 
they should consult with your cluster specialists first.
Conda works well on somebody's desktop, but it creates a lot of problems 
on supercomputers, because it does crazy stuff like installing MPI by 
itself instead of relying on staff-installed modules and software packages.


Cheers,

Maxime


On 2019-06-12 9:49 AM, David Pugh wrote:

All,

I have developed a Software Carpentry style lesson for Conda and would 
be keen to get feedback from the community!


Website:

https://kaust-vislab.github.io/introduction-to-conda-for-data-scientists/

Repo:

https://github.com/kaust-vislab/introduction-to-conda-for-data-scientists

Thanks and look forward to hearing from you!

David
*The Carpentries * / discuss 
/ see discussions  + 
participants  
+ delivery options 
 
Permalink 
 



--
The Carpentries: discuss
Permalink: 
https://carpentries.topicbox.com/groups/discuss/Tb12fc97e5ee621f2-M3bf5ece417a2cb1b32686e63
Delivery options: https://carpentries.topicbox.com/groups/discuss/subscription

Re: [discuss] Software Carpentry style lesson for Conda

[Michael Sarahan]

Nice job.  A long time ago, I created the swc channel on anaconda.org, in
the hopes that it would make it easier for people to create custom
installers (with constructor) on a per-workshop basis and simplify the
whole setup process.  I'm happy to turn over ownership of that channel if
anyone wants it.

The repo for a workshop that I gave with these installers is at
https://github.com/msarahan/2015-11-05-ND

If there are docs changes that we can/should make on the Anaconda side to
help clarify anything, please let me know.

Best,
Michael

On Wed, Jun 12, 2019 at 9:24 AM David Pugh  wrote:

> Great comments! Please open issues or PRs on GitHub as you prefer.
>
> On Wed, Jun 12, 2019 at 5:21 PM Filipe Pires Alvarenga Fernandes <
> ocef...@gmail.com> wrote:
>
>> Cool stuff! As a conda user and conda advocate for workshops
>> installations I really liked it.
>> Just a few minor comments though (and I can do those on GH as
>> issue/PRs if you prefer):
>>
>> - The original post on "stop installing TensorFlow with pip" is
>> https://www.anaconda.com/tensorflow-in-anaconda
>>
>> - I would install git with conda and avoid the different git install
>> instruction for the different platforms. "conda install git" works for
>> macOS, Windows, and Linux.
>>
>> - Conda has an idiosyncrasy with the environment creations: "conda env
>> create" vs "conda create" and it would be nice to have a
>> disambiguation note there.
>>
>> - Loved the channel tutorial! But there are a few caveats with mixing
>> packages that should be mentioned and channel priority does not solve
>> it. It is recommend to use the "strict" option. See
>> https://conda-forge.org/docs/user/tipsandtricks.html#how-to-fix-it
>>
>> Again, amazing stuff and if you agree with the points I raised above I
>> will be happy to help with PRs.
>>
>> Best,
>>
>> -F
>>
>> On Wed, Jun 12, 2019 at 10:07 AM Aleksandra Taranov
>>  wrote:
>> >
>> > I went through the tutorial and really like it. It's clear and easy to
>> understand. It was educational for me since I use pip installs. The only
>> thing that I wish it touched on is how to use jupyter notebooks with conda.
>> >
>> > Aleks
>> >
>> > On Wed, Jun 12, 2019, 6:51 AM David Pugh 
>> wrote:
>> >>
>> >> All,
>> >>
>> >> I have developed a Software Carpentry style lesson for Conda and would
>> be keen to get feedback from the community!
>> >>
>> >> Website:
>> >>
>> >>
>> https://kaust-vislab.github.io/introduction-to-conda-for-data-scientists/
>> >>
>> >> Repo:
>> >>
>> >>
>> https://github.com/kaust-vislab/introduction-to-conda-for-data-scientists
>> >>
>> >> Thanks and look forward to hearing from you!
>> >>
>> >> David
>> >
>> > The Carpentries / discuss / see discussions + participants + delivery
>> options Permalink
>>
>> --
>> The Carpentries: discuss
>> Permalink:
>> https://carpentries.topicbox.com/groups/discuss/Tb12fc97e5ee621f2-M156b30fcfe922a812e458e8c
>> Delivery options:
>> https://carpentries.topicbox.com/groups/discuss/subscription
>>
> *The Carpentries * / discuss /
> see discussions  +
> participants  + 
> delivery
> options 
> Permalink
> 
>

--
The Carpentries: discuss
Permalink: 
https://carpentries.topicbox.com/groups/discuss/Tb12fc97e5ee621f2-M5bc0ab4424f8d89079bfdac5
Delivery options: https://carpentries.topicbox.com/groups/discuss/subscription

Re: [discuss] Software Carpentry style lesson for Conda

[David Pugh]

Great comments! Please open issues or PRs on GitHub as you prefer.

On Wed, Jun 12, 2019 at 5:21 PM Filipe Pires Alvarenga Fernandes <
ocef...@gmail.com> wrote:

> Cool stuff! As a conda user and conda advocate for workshops
> installations I really liked it.
> Just a few minor comments though (and I can do those on GH as
> issue/PRs if you prefer):
>
> - The original post on "stop installing TensorFlow with pip" is
> https://www.anaconda.com/tensorflow-in-anaconda
>
> - I would install git with conda and avoid the different git install
> instruction for the different platforms. "conda install git" works for
> macOS, Windows, and Linux.
>
> - Conda has an idiosyncrasy with the environment creations: "conda env
> create" vs "conda create" and it would be nice to have a
> disambiguation note there.
>
> - Loved the channel tutorial! But there are a few caveats with mixing
> packages that should be mentioned and channel priority does not solve
> it. It is recommend to use the "strict" option. See
> https://conda-forge.org/docs/user/tipsandtricks.html#how-to-fix-it
>
> Again, amazing stuff and if you agree with the points I raised above I
> will be happy to help with PRs.
>
> Best,
>
> -F
>
> On Wed, Jun 12, 2019 at 10:07 AM Aleksandra Taranov
>  wrote:
> >
> > I went through the tutorial and really like it. It's clear and easy to
> understand. It was educational for me since I use pip installs. The only
> thing that I wish it touched on is how to use jupyter notebooks with conda.
> >
> > Aleks
> >
> > On Wed, Jun 12, 2019, 6:51 AM David Pugh  wrote:
> >>
> >> All,
> >>
> >> I have developed a Software Carpentry style lesson for Conda and would
> be keen to get feedback from the community!
> >>
> >> Website:
> >>
> >>
> https://kaust-vislab.github.io/introduction-to-conda-for-data-scientists/
> >>
> >> Repo:
> >>
> >>
> https://github.com/kaust-vislab/introduction-to-conda-for-data-scientists
> >>
> >> Thanks and look forward to hearing from you!
> >>
> >> David
> >
> > The Carpentries / discuss / see discussions + participants + delivery
> options Permalink
>
> --
> The Carpentries: discuss
> Permalink:
> https://carpentries.topicbox.com/groups/discuss/Tb12fc97e5ee621f2-M156b30fcfe922a812e458e8c
> Delivery options:
> https://carpentries.topicbox.com/groups/discuss/subscription
>

--
The Carpentries: discuss
Permalink: 
https://carpentries.topicbox.com/groups/discuss/Tb12fc97e5ee621f2-M8183a4b34cf5d6b3de060c60
Delivery options: https://carpentries.topicbox.com/groups/discuss/subscription

Re: [discuss] Software Carpentry style lesson for Conda

[Filipe Pires Alvarenga Fernandes]

Cool stuff! As a conda user and conda advocate for workshops
installations I really liked it.
Just a few minor comments though (and I can do those on GH as
issue/PRs if you prefer):

- The original post on "stop installing TensorFlow with pip" is
https://www.anaconda.com/tensorflow-in-anaconda

- I would install git with conda and avoid the different git install
instruction for the different platforms. "conda install git" works for
macOS, Windows, and Linux.

- Conda has an idiosyncrasy with the environment creations: "conda env
create" vs "conda create" and it would be nice to have a
disambiguation note there.

- Loved the channel tutorial! But there are a few caveats with mixing
packages that should be mentioned and channel priority does not solve
it. It is recommend to use the "strict" option. See
https://conda-forge.org/docs/user/tipsandtricks.html#how-to-fix-it

Again, amazing stuff and if you agree with the points I raised above I
will be happy to help with PRs.

Best,

-F

On Wed, Jun 12, 2019 at 10:07 AM Aleksandra Taranov
 wrote:
>
> I went through the tutorial and really like it. It's clear and easy to 
> understand. It was educational for me since I use pip installs. The only 
> thing that I wish it touched on is how to use jupyter notebooks with conda.
>
> Aleks
>
> On Wed, Jun 12, 2019, 6:51 AM David Pugh  wrote:
>>
>> All,
>>
>> I have developed a Software Carpentry style lesson for Conda and would be 
>> keen to get feedback from the community!
>>
>> Website:
>>
>> https://kaust-vislab.github.io/introduction-to-conda-for-data-scientists/
>>
>> Repo:
>>
>> https://github.com/kaust-vislab/introduction-to-conda-for-data-scientists
>>
>> Thanks and look forward to hearing from you!
>>
>> David
>
> The Carpentries / discuss / see discussions + participants + delivery options 
> Permalink

--
The Carpentries: discuss
Permalink: 
https://carpentries.topicbox.com/groups/discuss/Tb12fc97e5ee621f2-M156b30fcfe922a812e458e8c
Delivery options: https://carpentries.topicbox.com/groups/discuss/subscription

Re: [discuss] Software Carpentry style lesson for Conda

[David Pugh]

Thanks! Your comment reminds me that I need to add some examples and
discussion of using pip together with conda. Can you elaborate a bit more
about using jupyter notebooks with conda?  I have some examples and
exercises showing how to install jupyterlab with conda. I could add an
example that shows how to install the Jupyter notebook.

conda install --name some-existing-env notebook=5.7.*

Is this what you are looking for? Or did you have something else in mind?

On Wed, Jun 12, 2019 at 5:07 PM Aleksandra Taranov 
wrote:

> I went through the tutorial and really like it. It's clear and easy to
> understand. It was educational for me since I use pip installs. The only
> thing that I wish it touched on is how to use jupyter notebooks with conda.
>
> Aleks
>
> On Wed, Jun 12, 2019, 6:51 AM David Pugh  wrote:
>
>> All,
>>
>> I have developed a Software Carpentry style lesson for Conda and would be
>> keen to get feedback from the community!
>>
>> Website:
>>
>> https://kaust-vislab.github.io/introduction-to-conda-for-data-scientists/
>>
>> Repo:
>>
>> https://github.com/kaust-vislab/introduction-to-conda-for-data-scientists
>>
>> Thanks and look forward to hearing from you!
>>
>> David
>>
> *The Carpentries * / discuss /
> see discussions  +
> participants  + 
> delivery
> options 
> Permalink
> 
>

--
The Carpentries: discuss
Permalink: 
https://carpentries.topicbox.com/groups/discuss/Tb12fc97e5ee621f2-M250c6fdd09e76f36ce0dbac8
Delivery options: https://carpentries.topicbox.com/groups/discuss/subscription

Re: [discuss] Software Carpentry style lesson for Conda

[Aleksandra Taranov]

I went through the tutorial and really like it. It's clear and easy to
understand. It was educational for me since I use pip installs. The only
thing that I wish it touched on is how to use jupyter notebooks with conda.

Aleks

On Wed, Jun 12, 2019, 6:51 AM David Pugh  wrote:

> All,
>
> I have developed a Software Carpentry style lesson for Conda and would be
> keen to get feedback from the community!
>
> Website:
>
> https://kaust-vislab.github.io/introduction-to-conda-for-data-scientists/
>
> Repo:
>
> https://github.com/kaust-vislab/introduction-to-conda-for-data-scientists
>
> Thanks and look forward to hearing from you!
>
> David
> *The Carpentries * / discuss /
> see discussions  +
> participants  + 
> delivery
> options 
> Permalink
> 
>

--
The Carpentries: discuss
Permalink: 
https://carpentries.topicbox.com/groups/discuss/Tb12fc97e5ee621f2-M3e31648832c54cafe4f72d73
Delivery options: https://carpentries.topicbox.com/groups/discuss/subscription

[discuss] Software Carpentry style lesson for Conda

[David Pugh]

All,

I have developed a Software Carpentry style lesson for Conda and would be
keen to get feedback from the community!

Website:

https://kaust-vislab.github.io/introduction-to-conda-for-data-scientists/

Repo:

https://github.com/kaust-vislab/introduction-to-conda-for-data-scientists

Thanks and look forward to hearing from you!

David

--
The Carpentries: discuss
Permalink: 
https://carpentries.topicbox.com/groups/discuss/Tb12fc97e5ee621f2-M20dc109f558f2ae0d5d64687
Delivery options: https://carpentries.topicbox.com/groups/discuss/subscription