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# Prerequisites

In this section we demonstrate how to prepare an environment with PyTorch. Dataset4EO works on Linux (Windows and macOS are not officially supported).

Step 0. Download and install Miniconda from the official website.

Step 1. Create a conda environment and activate it.

    conda create --name earthnets -y
    conda activate earthnets

Step 2. Install required libraries. Core libraries: `torch`, `torchvision`, `torchdata`

    conda install pytorch torchvision cudatoolkit=11.3 -c pytorch
    pip install torchdata
    pip install mmcv-full==1.6.0
    pip install prettytable
    pip install pycocotools
    pip install wandb

# Installation

## Install Dataset4EO

    git clone git@github.com:DeepAI4EO/Dataset4EO.git
    python -m pip install -e .

## Usage

Upon you finish the installation, you can use Dataset4EO like below:

    from Dataset4EO.datasets import list_datasets, load, landslide4sense
    from torch.utils.data import DataLoader2
    from tqdm import tqdm

    #list all the supported datasets
    print(list_datasets())

    #create new dataset object by calling:
    datasets_dir = './'
    dp = landslide4sense.Landslide4Sense(datasets_dir, split='train')

    #Then the corresponding dataset will be downloaded and decompressed automatically

    #create a dataloader by calling:
    data_loader = DataLoader2(dp.shuffle(), batch_size=4, num_workers=4, shuffle=True, drop_last=True)

    #Now, iterating the dataloader for training
    for it in tqdm(data_loader):
        print(it) # this will print the filenames of each sample