> For the complete documentation index, see [llms.txt](https://time-series-features.gitbook.io/pyspi/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://time-series-features.gitbook.io/pyspi/installing-and-using-pyspi/installation/alternative-installation-options.md).

# Alternative Installation Options

For users encountering issues with installing *pyspi,* we recommend you first consult the troubleshooting guide. If your issue has not been addressed, we offer three alternative installation options.

## 1. Local Installation

***

To install *pyspi* using a local `pip` install, download or clone the [latest version](https://github.com/olivercliff/pyspi) from GitHub, unpack and install:

```bash
git clone https://github.com/DynamicsAndNeuralSystems/pyspi
cd pyspi
pip install .
```

***

## 2. *pyspi* Docker Image

***

> *Why should I use a **Docker** Image?*

By using a Docker image, you're not just simplifying the initial setup of *pyspi*, you're also setting the stage for a more reliable and reproducible workflow. Here are some reasons why we recommend users work with a Docker image:

1. **Ease of Setup:**

Configuration issues, operating systems compatibility problems, and dependency conflicts are a just a few of the hurdles you may encounter when trying to install any software package locally. A Docker image eliminates these hassles by providing you with a pre-configured, read-to-run container that has everything the software needs. Just download the image, and you're ready to go!

2. **Reproducibility:**

Docker images encapsulate the entire runtime environment - the software, the exact versions of all packages and the necessary configurations. This ensures that the software runs identically, no matter where or when it's executed, and that your results are reproducible each time.

### Using the *pyspi* Docker Image

To get started with using a *pyspi* docker image, follow these steps:

1. **Download the Docker Desktop Application**

   Install Docker Desktop from the [official Docker website](https://www.docker.com/products/docker-desktop/). This application runs Docker on your machine.
2. **Create a Docker Account**

   Register for an account on [Docker Hub](https://hub.docker.com) if you haven't already. This account is needed to download ('pull') Docker images.
3. **Pull and Run the pyspi Docker Image**

   With the Docker Desktop application running in the background, open the terminal or command prompt and run the following command:

   ```bash
   $ docker pull jmoo2880/pyspi:latest
   ```

   Now, to run the Docker image:

   ```bash
   $ docker run -it jmoo2880/pyspi:latest
   ```

This should start a python session with *pyspi* installed and ready to import.

***


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

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

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://time-series-features.gitbook.io/pyspi/installing-and-using-pyspi/installation/alternative-installation-options.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

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.