> ## Documentation Index
> Fetch the complete documentation index at: https://nixtlaverse.nixtla.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Install | HierarchicalForecast

> Install HierachicalForecast with pip or conda

We recommend using `uv` as Python package manager, for which you can
find installation instructions
[here](https://docs.astral.sh/uv/getting-started/installation/).

You can then install the *released version* of `HierachicalForecast`:

```python theme={null}
uv pip install hierarchicalforecast
```

Alternatively, you can directly install from the [Python package
index](https://pypi.org) with:

```python theme={null}
pip install hierarchicalforecast
```

or within a `conda` environment:

```python theme={null}
conda install -c conda-forge hierarchicalforecast
```

> **Tip**
>
> We recommend installing your libraries inside a python virtual or
> [conda
> environment](https://docs.conda.io/projects/conda/en/latest/user-guide/install/macos.html).

#### Installing from source

We recommend using `uv` as Python package manager, for which you can
find installation instructions
[here](https://docs.astral.sh/uv/getting-started/installation/).

1. Clone the HierachicalForecast repo:

```bash theme={null}
$ git clone https://github.com/Nixtla/hierachicalforecast.git && cd hierachicalforecast
```

1. Create the environment:

```bash theme={null}
$ uv venv --python 3.10
```

1. Activate the environment:

* on MacOS / Linux:

```bash theme={null}
$ source .venv/bin/activate
```

* on Windows:

```bash theme={null}
$ .\.venv\Scripts\activate
```

1. Install the dependencies and the library

```bash theme={null}
uv pip install -r setup.py
uv pip install .
```