> ## 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.

# Installation

> Install NeuralForecast with pip or conda

You can install the *released version* of `NeuralForecast` from the
[Python package index](https://pypi.org) with:

```shell theme={null}
pip install neuralforecast
```

or

```shell theme={null}
conda install -c conda-forge neuralforecast
```

> **Tip**
>
> Neural Forecasting methods profit from using GPU computation. Be sure
> to have Cuda installed.

> **Warning**
>
> We are constantly updating neuralforecast, so we suggest fixing the
> version to avoid issues. `pip install neuralforecast=="1.0.0"`

> **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).

## Extras

You can use the following extras to add optional functionality:

* distributed training with spark: `pip install neuralforecast[spark]`
* saving and loading from S3: `pip install neuralforecast[aws]`

#### Use our env (optional)

If you don’t have a Conda environment and need tools like Numba, Pandas,
NumPy, Jupyter, Tune, and Nbdev you can use ours by following these
steps:

1. Clone the NeuralForecast repo:

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

1. Create the environment using [astral’s
   uv](https://github.com/astral-sh/uv):

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

```

1. Activate the environment:

* On Linux/MacOS:

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

* On Windows:

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

1. Install NeuralForecast Dev

```bash theme={null}
$ uv pip install -e ".[dev]" --torch-backend cpu # for cpu backend
$ uv pip install -e ".[dev]" --torch-backend cu118 # for CUDA 11.8 PyTorch backend
```