# Installation MLStacks is a Python package that can be installed using `pip`. It is recommended that you install MLStacks in a virtual environment. You can install MLStacks using the following command: ```bash pip install mlstacks ``` ## Other requirements and dependencies MLStacks uses Terraform on the backend to manage infrastructure. You will need to have Terraform installed. Please visit the [Terraform docs](https://learn.hashicorp.com/tutorials/terraform/install-cli#install-terraform) for installation instructions. MLStacks also uses Helm to deploy Kubernetes resources. You will need to have Helm installed. Please visit the [Helm docs](https://helm.sh/docs/intro/install/#from-script) for installation instructions. If you're using a Mac, you will need to [install `jq`](https://jqlang.github.io/jq/) in order for some of the Terraform deployment commands and scripts to work. If you want to use the `mlstacks breakdown` command to get cost estimates for your MLOps stacks, you'll need to also have `infracost` installed as well as to be logged in. Please visit the [Infracost docs](https://www.infracost.io/docs/) for installation instructions. ## Cloud provider installation MLStacks currently supports the following stack providers: * AWS * GCP * K3D If you wish to deploy using these providers you'll need to have accounts (for AWS and GCP) and the relevant CLIs installed and authenticated. You will also need to have the relevant permissions to deploy, manage and destroy resources in these accounts. Please refer to the documentation for those providers for more information. --- # 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://mlstacks.zenml.io/getting-started/installation.md?ask= ``` 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.