How To Install the Anaconda Python Distribution on Debian 9
Anaconda is an open-source package manager, environment manager, and distribution of the Python and R programming languages. Designed for data science and machine learning workflows, it is commonly used for large-scale data processing, scientific computing, and predictive analytics.
Available in both free and paid enterprise versions, Anaconda offers a collection of over 1,000 data science packages. The Anaconda distribution ships with the
conda command-line utility. You can learn more about Anaconda and
conda by reading the official Anaconda Documentation.
This tutorial will guide you through installing the Python 3 version of Anaconda on a Debian 9 server.
Before you begin with this guide, you should have a non-root user with sudo privileges set up on your server.
You can achieve this prerequisite by completing our Debian 9 initial server setup guide.
The best way to install Anaconda is to download the latest Anaconda installer bash script, verify it, and then run it.
Find the latest version of Anaconda for Python 3 at the Downloads page accessible via the Anaconda home page. At the time of writing, the latest version is 5.2, but you should use a later stable version if it is available.
Next, change to the
/tmp directory on your server. This is a good directory to download ephemeral items, like the Anaconda bash script, which we won’t need after running it.
- cd /tmp
We’ll use the
curl command-line tool to download the script. Install
- sudo apt install curl
curl to download the link that you copied from the Anaconda website:
- curl -O https://repo.anaconda.com/archive/Anaconda3-5.2.0-Linux-x86_64.sh
We can now verify the data integrity of the installer with cryptographic hash verification through the SHA-256 checksum. We’ll use the
sha256sum command along with the filename of the script:
- sha256sum Anaconda3-5.2.0-Linux-x86_64.sh
You’ll receive output that looks similar to this:
You should check the output against the hashes available at the Anaconda with Python 3 on 64-bit Linux page for your appropriate Anaconda version. As long as your output matches the hash displayed in the
sha2561 row, you’re good to go.
Now we can run the script:
- bash Anaconda3-5.2.0-Linux-x86_64.sh
You’ll receive the following output:
OutputWelcome to Anaconda3 5.2.0 In order to continue the installation process, please review the license agreement. Please, press ENTER to continue >>>
ENTER to continue and then press
ENTER to read through the license. Once you’re done reading the license, you’ll be prompted to approve the license terms:
OutputDo you approve the license terms? [yes|no]
As long as you agree, type
At this point, you’ll be prompted to choose the location of the installation. You can press
ENTER to accept the default location, or specify a different location to modify it.
OutputAnaconda3 will now be installed into this location: /home/sammy/anaconda3 - Press ENTER to confirm the location - Press CTRL-C to abort the installation - Or specify a different location below [/home/sammy/anaconda3] >>>
The installation process will continue. Note that it may take some time.
Once installation is complete, you’ll receive the following output:
Output... installation finished. Do you wish the installer to prepend the Anaconda3 install location to PATH in your /home/sammy/.bashrc ? [yes|no] [no] >>>
yes so that you can use the
conda command. You’ll receive the following output next:
OutputAppending source /home/sammy/anaconda3/bin/activate to /home/sammy/.bashrc A backup will be made to: /home/sammy/.bashrc-anaconda3.bak ...
Finally, you’ll receive the following prompt regarding whether or not you would like to download Visual Studio Code (or VSCode), a free and open-source editor for code developed by Microsoft that can run on Linux. You can learn more about the editor on the official Visual Studio Code website.
At this point, you can decide whether or not to download the editor now by typing
Anaconda is partnered with Microsoft! Microsoft VSCode is a streamlined code editor with support for development operations like debugging, task running and version control. To install Visual Studio Code, you will need: - Administrator Privileges - Internet connectivity Visual Studio Code License: https://code.visualstudio.com/license Do you wish to proceed with the installation of Microsoft VSCode? [yes|no] >>>
In order to activate the installation, you should source the
- source ~/.bashrc
Once you have done that, you can verify your install by making use of the
conda command, for example with
- conda list
You’ll receive output of all the packages you have available through the Anaconda installation:
Output# packages in environment at /home/sammy/anaconda3: # # Name Version Build Channel _ipyw_jlab_nb_ext_conf 0.1.0 py36he11e457_0 alabaster 0.7.10 py36h306e16b_0 anaconda 5.2.0 py36_3 ...
Now that Anaconda is installed, we can go on to setting up Anaconda environments.
Setting Up Anaconda Environments
Anaconda virtual environments allow you to keep projects organized by Python versions and packages needed. For each Anaconda environment you set up, you can specify which version of Python to use and can keep all of your related programming files together within that directory.
First, we can check to see which versions of Python are available for us to use:
- conda search "^python$"
You’ll receive output with the different versions of Python that you can target, including both Python 3 and Python 2 versions. Since we are using the Anaconda with Python 3 in this tutorial, you will have access only to the Python 3 versions of packages.
Let’s create an environment using the most recent version of Python 3. We can achieve this by assigning version 3 to the
python argument. We’ll call the environment my_env, but you’ll likely want to use a more descriptive name for your environment especially if you are using environments to access more than one version of Python.
- conda create --name my_env python=3
We’ll receive output with information about what is downloaded and which packages will be installed, and then be prompted to proceed with
n. As long as you agree, type
conda utility will now fetch the packages for the environment and let you know when it’s complete.
You can activate your new environment by typing the following:
- source activate my_env
With your environment activated, your command prompt prefix will change:
Within the environment, you can verify that you’re using the version of Python that you had intended to use:
- python --version
OutputPython 3.7.0 :: Anaconda, Inc.
When you’re ready to deactivate your Anaconda environment, you can do so by typing:
- source deactivate
Note that you can replace the word
. to achieve the same results.
To target a more specific version of Python, you can pass a specific version to the
python argument, like
3.5, for example:
- conda create -n my_env35 python=3.5
You can update your version of Python along the same branch (as in updating Python 3.5.1 to Python 3.5.2) within a respective environment with the following command:
- conda update python
If you would like to target a more specific version of Python, you can pass that to the
python argument, as in
You can inspect all of the environments you have set up with this command:
- conda info --envs
Output# conda environments: # base * /home/sammy/anaconda3 my_env /home/sammy/anaconda3/envs/my_env my_env35 /home/sammy/anaconda3/envs/my_env35
The asterisk indicates the current active environment.
Each environment you create with
conda create will come with several default packages:
You can add additional packages, such as
numpy for example, with the following command:
- conda install --name my_env35 numpy
If you know you would like a
numpy environment upon creation, you can target it in your
conda create command:
- conda create --name my_env python=3 numpy
If you are no longer working on a specific project and have no further need for the associated environment, you can remove it. To do so, type the following:
- conda remove --name my_env35 --all
Now, when you type the
conda info --envs command, the environment that you removed will no longer be listed.
You should regularly ensure that Anaconda is up-to-date so that you are working with all the latest package releases.
To do this, you should first update the
- conda update conda
When prompted to do so, type
y to proceed with the update.
Once the update of
conda is complete, you can update the Anaconda distribution:
- conda update anaconda
Again when prompted to do so, type
y to proceed.
This will ensure that you are using the latest releases of
conda and Anaconda.
If you are no longer using Anaconda and find that you need to uninstall it, you should start with the
anaconda-clean module, which will remove configuration files for when you uninstall Anaconda.
- conda install anaconda-clean
y when prompted to do so.
Once it is installed, you can run the following command. You will be prompted to answer
y before deleting each one. If you would prefer not to be prompted, add
--yes to the end of your command:
This will also create a backup folder called
.anaconda_backup in your home directory:
OutputBackup directory: /home/sammy/.anaconda_backup/2018-09-06T183049
You can now remove your entire Anaconda directory by entering the following command:
- rm -rf ~/anaconda3
Finally, you can remove the PATH line from your
.bashrc file that Anaconda added. To do so, first open a text editor such as nano:
- nano ~/.bashrc
Then scroll down to the end of the file (if this is a recent install) or type
CTRL + W to search for Anaconda. Delete or comment out the
export PATH line:
... # added by Anaconda3 installer export PATH="/home/sammy/anaconda3/bin:$PATH"
When you’re done editing the file, type
CTRL + X to exit and
y to save changes.
Anaconda is now removed from your server.
This tutorial walked you through the installation of Anaconda, working with the
conda command-line utility, setting up environments, updating Anaconda, and deleting Anaconda if you no longer need it.
You can use Anaconda to help you manage workloads for data science, scientific computing, analytics, and large-scale data processing. From here, you can check out our tutorials on data analysis and machine learning to learn more about various tools available to use and projects that you can do.