This post provides some commonly used conda command.
(I use Mac and Linux OS, so the commands here assume that you use Mac and Linux OS. If you are using Windows, most of the conda commands should be the same, but some command might be slightly different. For example, the one I am aware of is that the command for activating and deactivating conda environment is a bit different, you do not need to add “source” in the command like Mac and Linux OS do).
(To use conda, you need to have either Miniconda or Anaconda installed on your machine. Check out here for installing Miniconda on Mac, and here for installing Miniconda on CentOS7/RedHat7. Check here if you would like to know the difference between Miniconda and Anaconda.)
The first part of this post introduces common conda commands, and the second part of this post provides some package installation ( e.g., OpenCV, scikit-image, jupyter notebook etc.) using conda.
Common conda commands
Check whether conda is installed / check conda version info
In your terminal, type in the following command
$ conda -V
#if you see something like below it means conda is installed, and it provides its version info.
conda 4.5.11
Check conda is up to date
In your terminal, type in
$ conda update conda
# Upadate any packages if necessary by typing y to proceed.
Create a virtual environment using conda for your project
with conda, we can create virtual environment for different versions of pythons.
To see a list of available python versions available in conda repository, type the following command with regular expression and then press enter.
$ conda search "^python$"
# you should see a list of python versions, including python2.X and python3.X
Now, let us create a virtual environment with conda
use the following command to create a virtual environment for a python version you specified, replace x.x with the Python version you would like to use.
$ conda create -n yourenvname python=x.x
# for example, the following command will create a Python 3.6 conda virtual environment called "conda-venv3_py36".
$ conda create -n conda-venv3_py36 python=3.6
# the following command will create a Python 2.7 conda virtual environment called "conda-venv_py27".
$ conda create -n conda-venv_py27 python=2.7
Press y
to proceed. This will install the Python version (and all the associated anaconda packaged libraries if you installed conda via Anaconda) at “path_to_your_anaconda_location/anaconda/envs/yourenvname” or at “path_to_your_miniconda_location/miniconda/envs/yourenvname”
Activate your virtual environment
Once we created a virtual environment using conda, before we start to using it, we need to activate it first each time we need to use the virtual environment.
To activate or switch into your virtual environment, simply type the following where yourenvname is the name you gave to your environement when creating.
$ source activate yourenvname
Activating a conda environment modifies the PATH and shell variables to point to the specific isolated Python you created. Note that the command prompt will change to indicate which conda environemnt you are currently in by prepending (yourenvname)
.
If you do not remember your virtualenv or do not want to type it, you can use the following command to see a list of all your environments,
$ conda info -e
Install (additional) Python packages to a virtual environment
To install packages only to your virtual environment (not system wide), enter the following command
$ conda install -n yourenvname package-name
# yourenvname is the name of your environment, and package-name is the name of the package you would like to install.
# Note that if not specify “-n yourenvname” will install the package to the root Python installation.
Or you can simply first activate and into the virtual environment you would like to install packages [see the command above in (4)], and then use the following command
$ conda install package-name
(For some specific packages installation, check section 2 below in this post.)
Deactivate your virtual environment.
Each time once we finish working in your virtual environment created using conda, we will need to deactivate the virtual environment to exit from it.
To end a session in the current virtual environment, enter the following command .
$ source deactivate
# Note that we do not need to specify the envname - whichever is currently active will be deactivated, and the PATH and shell variables will return to normal.
Delete a virtual environment
When we do not need a virtual environment created by conda any more, we can simply remove it by the following command.
conda remove -n yourenvname -all
# yourenvname is the name of the environment you would like to delete. # (You may instead use $ conda env remove -n myenv.)
To verify that the environment was removed, in your Terminal window or an conda Prompt, run the following
$ conda info -e
The environments list that displays should not show the removed environment.
Cloning a conda virtual environment
To make an exact copy of an environment by creating a clone of it, using the following command,
$ conda create --name myclone --clone myenv
# NOTE: replace myclone
with the name of the new environment. Replace myenv
with the name of the existing environment that you want to copy. # see the following for an example of cloning py35 and naming the new copy as py35-2 $ conda create --clone py35 --name py35-2
To verify that the copy was made:
conda info -e
In the environments list that displays, you should see both the source environment and the new copy.
Viewing a list of your environments
To see a list of all of your conda virtual environments, in your Terminal or in one of your conda virtual environment, run one of the following commands:
$ conda info -e
OR
$ conda info --envs
OR
$ conda env list
You will see a list of all your conda environments, and the active environment is shown with *.
Viewing a list of the packages in a conda environment
To see a list of all packages installed in a specific environment,
- If the environment is not activated, in your Terminal window or a conda prompt, run the following:
$ conda list -n myenv
- If the environment is activated, in your Terminal window or a conda prompt, run the following:
$ conda list
To see whether a specific package is installed in a conda environment,
- If the environment is not activated, in your Terminal window or a conda prompt, run the following:
$ conda list -n myenv package-name # for example, the following command will list the opencv versions installed in the conda environment you specified $ conda list -n myenv opencv
- If the environment is activated, in your Terminal window or a conda prompt, run the following:
$ conda list package-name # for example, the following command will list the opencv versions installed in the current active conda environment you are in $ conda list opencv
Package installation using conda
Before installing packages using conda, make sure to first create a conda virtual environment [see the command at (3) in section 1 above in this post] and then activate and into the environment you would like to install the packages into [see the command for (4) in section 1 above in this post].
Install Numpy
$ conda install numpy
Install Matplotlib
$ conda install matplotlib
Install Keras
$ conda install keras
This should also install tensorflow
Install h5py
$ conda install h5py
Install Jupyter Notebook
$ conda install jupyter
Install IPython
$ conda install ipython
Install OpenCV3 (https://opencv.org/)
$ conda install -c conda-forge opencv
The command above will install (by default) the latest version of opencv available in the conda repository. if you would like to specifiy which version of openCV to install, you can first use the following comamnd to check OpenCV versions available.
$ conda search "^openCV$"
# you should see a list of openCV versions.
Then you could use the following command to install the version of OpenCV you would like to install,
$ conda install -c conda-forge opencv=x.x
# for example, the following command will install openCV 3..4.1, instead of the current lastest version 3.4.2. Note that is opencv==3.4.1, not opencv=3.4, if opencv=3.4, it will install openCV3.4.2 (the latest version in 3.4 series).$ conda install -c conda-forge opencv==3.4.1
Install Scikit-image
$ conda install -c conda-forge scikit-image
Install Django
use the following command to search what vesion of django is available in your conda environment.
$ conda search "^django$"
use the following command to install specific version of django you would like to install into your conda environment.
$ conda install -c conda-forge django==1.11.8
use the following to test whether the django is installed successfully in your conda environment.
$ python
Python 3.6.7 |Anaconda, Inc.| (default, Oct 23 2018, 19:16:44)
[GCC 7.3.0] on linux
Type “help”, “copyright”, “credits” or “license” for more information.
>>> import django
>>> print(django.__version__)
1.11.8
>>>
Installing non-conda packages
If a package is not available from conda or Anaconda.org, you may be able to find and install the package via conda-forge or with another package manager like pip.
Pip packages do not have all the features of conda packages and we recommend first trying to install any package with conda. If the package is unavailable through conda, try finding and installing it with conda-forge.
If you still cannot install the package, you can try installing it with pip. The differences between pip and conda packages cause certain unavoidable limits in compatibility but conda works hard to be as compatible with pip as possible.
Removing packages using conda
- To remove a package such as SciPy in an environment such as myenv:
conda remove -n myenv scipy
- To remove a package such as SciPy in the current environment:
conda remove scipy
- To remove multiple packages at once, such as SciPy and cURL:
conda remove scipy curl
- To confirm that a package has been removed:
conda list the package name
References
- Create virtual environments for python with conda
- Conda cheat sheet (PDF if not retrievable)
- Command reference — Conda documentation
- Managing environments –Conda documentation (PDF if not retrievable)
- https://github.com/jrobchin/Computer-Vision-Basics-with-Python-Keras-and-OpenCV
- Managing packages — conda