Context
I am trying to clone my dev conda environment to a test server. In this environment besides of many conda packages I had to install some packages via pip. I am using miniconda.
What I've tried:
conda list --explicit > spec-file.txt
then
conda create --name myclonedenv --file spec-file.txt
however this is not installing the few packages what were installed via pip in the original conda environment.
Question
How can I export a current conda environment, and clone it on an other machine, if the current conda environment contains some packages installed via pip
The conda env export command will capture both Conda- and Pip-installed packages. The YAML can then be used with conda env create to recreate the environment.
Related
I am trying to install feature-engine module on anaconda
this is the error i am getting
Package is not available from current channels
repo.anaconda win 64 , noarch etc.
Can you please help me with the problem?
Thanks,
RD
to install from anaconda:
conda install -c conda-forge feature_engine
I believe that feature-engine is not available through anaconda channels for installation with conda install. I was able to install it via pip. Here is how I did it (in Windows):
open a CMD and run conda activate <<VIRTUALENV>>. This is the environment you create for your project. If you have not created one, then use base, the default one.
cd to the location of your pip installation within that activated conda Virtual environment (mine was within my user folder in \AppData\Local\Continuum\anaconda3\envs\<<VIRTUALENV>>\Scripts).
in there, run pip install feature-engine
you should now be able to see it listed under pip freeze or pip list, but not under conda list.
Finally, go to your code location and run the code. remember to activate that same <> each time you open a new CMD to run it.
Hope it helps.
If you are using Jupyter Notebooks, it might be the case that your Jupyter Notebook is not actually running the kernel in your (activated!) Anaconda environment (via this answer), but the generic Python3 kernel that only can import packages from your global Anaconda environment.
You can check for this by importing a package that is installed in your global environment (e.g., pandas), while running a notebook:
import pandas
pandas.__file__
If you see something likes this (on Windows), you are indeed running the wrong kernel (as you would expect the packages to be loaded from the activated environments):
'C:\\Users\\<user>\\Anaconda3\\lib\\site-packages\\pandas\\__init__.py'
Therefore, in your Anaconda Prompt, you have to create a new kernel within ipykernel (assuming cenv is your environment of interest):
$ conda activate cenv # . ./cenv/bin/activate in case of virtualenv
(cenv)$ conda install ipykernel
(cenv)$ ipython kernel install --user --name=<any_name_for_kernel>
(cenv)$ jupyter notebook
Now, in the restarted Jupyter Notebook you can change the kernel via the menu: Kernel > Change kernel > <any_name_for_kernel>
Importing the same package, like pandas, should show the following file path:
'C:\\Users\\<user>\\Anaconda3\\envs\\<cenv>\\lib\\site-packages\\pandas\\__init__.py'
and you should be able to import any package installed in that Anaconda environment.
I'm a little bit confused about relations between conda environments and jupyterhub.
As jupyterhub documentation says it can be installed from conda. So it is possible to use some conda envirnment (for example environment "root") and do "conda install jupyterhub" from it.
In the same environment will "live" jupyter. And installation of nb_conda from in this environment gives ability to select kernels and other conda environments in notebooks.
My question is about software like nbextensions and ipywidgets. Where should they be? In the same environment as jupyterhub or in an environment correponds new notebook?
The relationship between conda and jupyter can be a confusing one. Think of conda as your environment, and jupyter as just any other package. A package that you can start a process with and then serve.
To answer your question, they should be installed in your conda environment. Unfortunately it is a little more complicated than that. These extensions will be available to all users. I haven't personally tested a single user having more extensions in a different environment (if it is possible, but will update answer if I do).
If it helps, this is what the docs have to say for the matter:
To install the jupyter_contrib_nbextensions notebook extensions, three steps are required. First, the Python pip package needs to be installed. Then, the notebook extensions themselves need to be copied to the Jupyter data directory. Finally, the installed notebook extensions can be enabled, either by using built-in Jupyter commands, or more conveniently by using the jupyter_nbextensions_configurator server extension, which is installed as a dependency of this repo.
Assuming you installed the extensions via conda:
conda install -c conda-forge jupyter_contrib_nbextensions
then the --sys-prefix was used, which is good. From the docs:
--sys-prefix to install into python's sys.prefix, useful for instance in virtual environments, such as with conda.
So, to add an extension, the process should look like this:
$ sudo su -
$ pip install fileupload
$ jupyter nbextension install --sys-prefix --py fileupload
$ jupyter nbextension enable fileupload --py --sys-prefix
Since the title is asking about conda environments, I'll go into that a little bit too. I've tested these methods on Ubuntu 18.04LTS.
Very often you will want to allow users to share user created environments, never having access to root privileges. You have two good options that I have seen (someone please comment if you know other methods): 1) share an environment 2) duplicate an environment from requirements files. Don't forget you'll have to add the environment as a kernel as well.
method 1 - shared environment
Create a environment in a shared location, and then have both users add it as a kernel.
conda create -p /home/envs/test --clone root
can clone root to copy the root env, or base for the base environment. /home/envs/test will create a "test" environment, in the "envs" directory. Make sure envs has all necessary permissions for users that will be using the files.
From there as another user, just add the environment as a kernel.
$ sudo su - <user-to-install-kernel-to>
$ conda activate <test>
$ python -m ipykernel install --user --name test \
--display-name "Python (test)"
Note.. I believe I had to update the kernelspec manually to get it to point to the correct python environment 🤦♂️
method 2
Alternatively, just create a copy of the environment
$ conda env export --name test > environment.yml
$ sudo su - customer
$ conda env create --name cust-env-copy --file environment.yml
$ python -m ipykernel install --user --name cust-env-copy \
--display-name "Python (test)"
I'm looking to use the ospc taxcalc package in a Google Datalab notebook. This package must be installed via conda.
Datalab doesn't have conda by default, so this method (from https://stackoverflow.com/a/33176085/1840471) fails:
%%bash
conda install -c ospc taxcalc
Installing via pip also doesn't work:
%%bash
pip install conda
conda install -c ospc taxcalc
ERROR: The install method you used for conda--probably either pip install conda or easy_install conda--is not compatible with using conda as an application. If your intention is to install conda as a standalone application, currently supported install methods include the Anaconda installer and the miniconda installer. You can download the miniconda installer from https://conda.io/miniconda.html.
Following that URL, I tried this:
%%bash
wget https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh
bash Miniconda2-latest-Linux-x86_64.sh
wget works, but the bash command to install just kept in "Running..." state for seeming perpetuity.
This seems to be due to the conda installer prompting for several Enter keystrokes to review the license, and then for a yes indicating acceptance of the license terms. So conda's silent mode installation looked promising:
%%bash
bash Miniconda2-latest-Linux-x86_64.sh -u -b -p $HOME/miniconda
This produced the following warning:
WARNING: You currently have a PYTHONPATH environment variable set. This may cause unexpected behavior when running the Python interpreter in Miniconda2. For best results, please verify that your PYTHONPATH only points to directories of packages that are compatible with the Python interpreter in Miniconda2: /content/miniconda
And doesn't make available the conda command:
%%bash
conda install -c ospc taxcalc
bash: line 1: conda: command not found
There is a pending github issue tracking this work - https://github.com/googledatalab/datalab/issues/1376
I believe we will need to install conda and use that for python, pip and all other python packages, and in the interim it may not be possible to mix the two python environments. However someone with more experience with conda might know otherwise.
As of the 2018-02-21 release, Datalab supports Conda and kernels are each in their own Conda environment.
I am trying to run the jupyterhub locally on Ubuntu 16.04; however, I cannot seem to run the jupyterhub command on the terminal, and I get the command not found error. I have installed jupyterhub by running the following commands:
sudo npm install -g configurable-http-proxy
pip3 install jupyterhub
pip3 install --upgrade notebook
All of the above packages install successfully. My PATH variable in /etc/environment has been set as follows:
PATH="/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/root/bin"
You need to start a Jupyter service, if you create environments with anaconda you can use them later inside the jupyter as kernels, so you can have a variety of library environments as you like.
There are also other functions to give resource guarantee or limits, or initial folders for your hub.
You can edit all this in your jupyterhub_config.py file.
I leave you here my github with a complete guide for the configuration and installation.
Github/Jupyter
If you can first install Anaconda.
Then install the configurable proxy.
Run commands -
conda install -c conda-forge jupyterhub # installs jupyterhub and proxy
conda install notebook # needed if running the notebook servers locally
Now try jupyterhub
I've installed anaconda2 and anaconda3 from official website
and now have 2 folders:
user/anaconda with anaconda2
user/anaconda3/anaconda with anacaonda3
the default path for python is set for anaconda 2:
MacBook-Air:anaconda zelenov$ which python
/Users/zelenov/anaconda/bin/python
but when I run jupyter notebook I can create only python 3 notebook
how can I add my python2 environment?
I tried this, but it didn't help:
python2 -m pip install ipykernel
python2 -m ipykernel install --user
Thanks in advance for any help!
Choose one Anaconda distribution, not both. I suggest uninstalling one of those distributions, e.g. Python 2. Then create an environment within your Python 3 distribution:
> conda create --name py27 python=2.7
Once the environment is created, you need to enter it, install ipykernel, then exit the environment once completed.
> source activate py27
> conda install ipykernel
> source deactivate
Note source can be omitted for Windows users. As of Anaconda 4.2, installing ipykernel is sufficient for creating a jupyter kernel automatically. Start jupyter:
> jupyter notebook
You should see a dropdown menu in the dashboard with the name of the py27 kernel installed. For more information on installing Python kernels, see Jupyter documentation.