If you are using colab, you have to use python 3.9. Here are the details.

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1. Install python 3.9

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from IPython.display import clear_output

!python --version

!wget -O mini.sh https://repo.anaconda.com/miniconda/Miniconda3-py39_4.9.2-Linux-x86_64.sh

!chmod +x mini.sh

!bash ./mini.sh -b -f -p /usr/local

!conda install -q -y jupyter

!conda install -q -y google-colab -c conda-forge

!python -m ipykernel install --name "py39" --user

clear_output()

!python --version

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2. Install libs

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!pip install rasterio==1.3.0

!pip install radiant_mlhub==0.5.2

Note that these libs. are only available in the shell command

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3. Write a script with your own api key

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%%writefile my_downloader.sh

import rasterio

from radiant_mlhub import Dataset

import radiant_mlhub

print(rasterio.__version__, radiant_mlhub.__version__)

import os

import glob

Full_bands = ['B01', 'B02', 'B03', 'B04','B05', 'B06', 'B07', 'B08','B8A', 'B09', 'B11', 'B12']

selected_bands = Full_bands # Full_bands[1:4] + [Full_bands[-5]] #'B02', 'B03', 'B04', 'B08'

#define dataset collection_id , assets and necessary paths to collections

main = 'ref_agrifieldnet_competition_v1'

assets = ['field_ids','raster_labels']

source_collection = f'{main}_source'

train_label_collection = f'{main}_labels_train'

test_label_collection = f'{main}_labels_test'

api_key = "YOUR API KEY"

os.environ['MLHUB_API_KEY'] = api_key # getpass.getpass(prompt="MLHub API Key: ")

dataset = Dataset.fetch(main)

my_filter = dict(

ref_agrifieldnet_competition_v1_labels_train=assets,

ref_agrifieldnet_competition_v1_labels_test=[assets[0]],

ref_agrifieldnet_competition_v1_source=selected_bands

)

dataset.download(collection_filter=my_filter)

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4. Download data

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!python my_downloader.sh

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5. Zip and download the file

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Good luck,

M