How can I add a package to an existing conda environment?
If it is a python package I can use pip install <package>, but what if pip does not work?
Is it sufficient to activate the environment and use conda install <package>?
You've answered your own question. In fact you really want to do conda install ... instead of using pip if you can.
You can install a conda package also without activating the environment. Just use conda install -n <env_name> <package> or conda install -p <path/to/env> <package>.
If you want to install a specific package inside a specific conda environment, you can use the following command.
First activate the conda environment and then do:
$ conda install --name <conda_env_name> -c <channel_name> <package_name>
For a concrete example, let's assume that you want to install chainer from the channel anaconda to an already created conda environment named chainerenv, then you can do:
$ conda install --name chainerenv -c anaconda chainer
If you want to install a package in the environment, you can use
conda install -p /path/to/env package
example:
conda install -p /users/dekstop/env-test Django
There's an alternative way to do this and I have just tested it on my own mac:
example: i want to install a non-conda package at my python2.7 environment:
go to terminal
activate the desired environment by: source activate py27
after you successfully activated the environment, you can install the package you wanted by: pip install package
The answer is yes (usually).
One example is that you can activate your conda environment and then directly do conda install pandas.tar.bz2 on the existing tar.bz2 files from /conda_envs/.pkgs (leftovers from other environments)
If you don't have a tarball package like that but you have the src with setup.py you can just do the usual install by python setup.py install (or python setup.py develop to link the src)
Related
guys:
I use conda install tensorflow-gputo install tensorflow 2.0 , and
numpy=1.20.2 would be one of the package installed, and then I use python3 -m pip install SOMEPACKAGE ,this SOMEPACKAGE needs numpy to be installed as well , but pip seems does not check or realize the package numpy has already installed...
I would like to show everything I know so far :
1.I know the packages installed via conda install would go to anaconda3/envs/YOUR_ENV/lib/site-packages
2.I use python3 -m pip install -t anaconda3/envs/YOUR_ENV/lib/site-packages to force the package would be installed to the place where conda install would be.
However,pip still tries to dwonload *.whl file and install package again,I do not want this package installation process happen again ,while it did mention that I can use --upgrade to replace the existed package...
So I would like to know
How does pip and conda install check if the target package has already existed before they actually to through install process?
I think using python3 you are not using interpreter from your current conda environment so it gets installed elsewhere
python -m pip install (or simply pip install) from your activated environment should work and ignore dependencies installed by conda if they satisfy the requirements
I have used anaconda and miniconda without problem before so I don't know why I am having doubts now about the order to use them as
create an environment with conda create
Activate the environment with conda activate
Install packages with conda install
However, lately I have read one set of instructions in which they do a different order: 1->3->2. That means installing without activating and then activating.
Is this correct? Aren't I installing libraries outside of the environment?
There are multiple ways to create and install packages using conda.
create an environment, activate, install packages
conda create --name env_name python=3.8
conda activate env_name
conda install package_name another_package
create an environment with packages
conda create -n env_name python=3.8 package_name another_package
conda activate env_name
Both methods are correct. In fact, we often use a mix here and there. For example, if I have a project with requirements.txt to be installed with pip but would like it to have pandas and scikit-learn from conda-forge. I will do:
conda create --name ml_api python=3.7 -c conda-forge scikit-learn pandas
conda activate ml_api
python -m pip install -r requirements.txt
At any point, we can add packages in conda environment with
# this will install requests to ml_api env. This can be done at any (env)
conda install --name ml_api requests
# or activate ml_api and install. This will install on activated env
conda activate ml_api
conda install requests
At the end of the day, conda is there to help you. A better flow will depend on your need.
The order you listed them is correct but you only need the conda install packagename before you run the code. So you should first create the conda environment and then activate it. Now when you get ready to run the code the first time, be sure to install your package. This enables you to import the package in your python code.
We can install tensorflow-transform using pip but how do I install it using Conda?
I have tried using conda install -c anaconda tensorflow-transform and all its variants without result.
A search of Anaconda Cloud shows that tensorflow-transform is not available through any channels on any platform. Moreover, the recommended way to install the package is to use PyPI. However, one can still install the package into a specific Conda env by first activating the env:
conda activate your_env
pip install tensorflow-transform
I use homebrew to install packages on my Mac OS High Sierra. However, I encountered a package called spdlib that is not available through homebrew. The package is available as an install through conda using the following (source):
conda install -c rios -c conda-forge spdlib
What is the preferred method of getting spdlib on my machine taking into account I do not want homebrew and conda to confict?
One way would be to install Miniconda.
Create a new environment:
conda create -n myenv python=3.6
activate it:
source activate myenv
with a new conda this should work too:
conda activate myenv
Now, you can install what you need without interfering with your homebrew packages:
(myenv) conda install -c rios -c conda-forge spdlib
I am using conda(anaconda 2). Before installing anaconda, my Ubuntu16.04 already had pip installed. I recently installed virtualenv using pip install virtualenv. I did the following steps while trying to install coala in my virtual environment.
Create a new virtual environment using virtualenv venv
Activate environment using source activate path/to/activate
Clone the coala repository.
Run pip3 install -e . while inside the repository
After all this, I get the following error from coala:
There is a conflict in the version of a dependency you have installed and the requirements of coala. This may be resolved by creating a separate virtual environment for coala or running `pip install "yapf~=0.14.0"`. Be aware that the latter solution might break other python packages that depend on the currently installed version.
I am already in a virtualenv, so I tried installing yapf as per the command: pip install "yapf~=0.14.0". After that, when I check my pip list, it still showed yapf (0.15.2), To solve this, I did:
pip uninstall yapf
pip install "yapf~=0.14.0"
Now when I check my pip list, I get the desired results. BUT, the error is still there. It still shows the same dependency error. I am unable to understand what is happening? Which pip is installing what and where, what is conda doing behind the scenes, how is virtualenv behaving with conda, and the most important, why this error and how to resolve it?
first create a virtualenv pertaining to python3.x
template
virtualenv -p {location of python3 version } {name of virtualenv folder}
like this
virtualenv -p /usr/local/bin/python3 venv
then activate it
source venv/bin/activate
then run
pip install {library you want to use}
pip install "yapf~=0.14.0"
This should install yapf into the venv folder.
try that let us know.
Answering my own question, what I found out was that conda and virtualenv do not go hand in hand. condas has the ability to create it's own virtual environment, and if using condas, we must create a conda virtual environment(See this answer).
What I did was uninstalled conda and make a virtual environment using virtualenv. What I could have done as well is uninstall virtualenv and create condas environment and work in that.