# conda: the Python environment manager

### **Installing conda:**

There are several ways to make `conda` runs on your computer. Here are two tastes, **Anaconda** is a data science toolkit all-in-one edition that makes sure you are ready to go once you have installed it. While **MiniForge** needs more configurations but it is pure and minimal, the package size is smaller, compared with Anaconda, MiniForge will only install `conda` for you.

> Anaconda (coming with essential DS tools): <https://www.anaconda.com>
>
> MiniForge (minimal edition): <https://github.com/conda-forge/miniforge>

Alternatively, you can use the command line to install conda:

**macOS:**

Needs to install macOS Package Manager `brew` from <https://brew.sh>

```bash
# Install Homebrew (It takes long time)
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

# Then
brew install miniforge
```

**Windows:**

Need to install a package manager `choco` from <https://chocolatey.org>

```powershell
# Install choco Package Manager first
Set-ExecutionPolicy Bypass -Scope Process -Force; [System.Net.ServicePointManager]::SecurityProtocol = [System.Net.ServicePointManager]::SecurityProtocol -bor 3072; iex ((New-Object System.Net.WebClient).DownloadString('https://community.chocolatey.org/install.ps1'))

# Then (in Admin Powershell)
choco install anaconda3
```

The Linux machine I have provided is already installed `conda` binary. If you are now using Linux in lanqiao.cn, the experiment environment is not connected to the Internet so I recommend you install it on your own PC.

Once you have installed the conda, please make sure you can type `conda` in the CLI. If not,

```bash
# path/to/conda refers to conda bin where you have installed
path/to/conda init
```

### Environment

The environment is a collection of Python and its utilities. It enables you to use different versions of Python on one computer. The following command is for creating an environment named `pyml`

```bash
conda create -n pyml python=3.8
```

`-n` refers to specify a name for this environment and `python=3.8` refers to this environment needs Python version at 3.8

Then, we can change our current environment to `pyml` using

```bash
conda activate pyml
```

If you are always want to use this environment, you can add this line to your shell config file (`.zshrc` or `.bashrc` ), and when you open your shell, the environment is activated automatically.

Have a look at the `python` executive.

```bash
which python
```

You can have a path returned by the last command, and it will like:

```bash
/opt/homebrew/Caskroom/miniforge/base/envs/webapi/bin/python
```

That's it, conda can change the `python` executive path whenever you activate an environment.

If you want to delete the environment, use:

```bash
conda env remove -n <env_name>
```


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://pyml.aspires.cc/chapter2/conda-the-python-version-manager.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.