you will find documentation on this page : https://developers.google.com/earth-engine/python_install

I propose you to go through the native export tool of Earth Engine instead of geetools.

 import ee
 from google.colab import drive

Authenticate and mount your drive into your project

 ee.Authenticate()
 ee.Initialize() 
 drive.mount('/content/drive')

You can export images using a thread. I've never tried to export a whole collection but it seems to me that you can do it by replacing the 'image=image' attribute by 'collection=yourCollection' (not sure of that). In general, I prefer to transform my collection into an images list and then to upload images one by one.

imagesList = collection.toList(collection.size())
for i in range(0,imagesList .size().getInfo()):
  image= ee.Image(imagesList .get(i))
  #Process the export for you image into the folder of your choice into Drive 
  task = ee.batch.Export.image.toDrive(image= image,
    description='Exported from EarthEngine',
    fileNamePrefix='filename.tif',
    scale= scale,
    folder='repertoryOfYourChoice/',
    fileFormat='GeoTIFF')
 task.start()

Then you can follow the status of the last uploaded image in your list (you can upgrade this code by storing all the tasks in a list to track the status of all the images in your list) :

print(task.status())

Otherwise, about the scale it allows you to reproject an image to have a different resolution and speed up the export time. So it can be useful to use a large scale when working on very large areas. However, be careful not to scale it too large because the calculation time of the reprojection could sometimes be too greedy and exceed the maximum memory size allowed.

you will find documentation on this page : https://developers.google.com/earth-engine/python_install

I propose you to go through the native export tool of Earth Engine instead of geetools.

 import ee
 from google.colab import drive

Authenticate and mount your drive into your project

 ee.Authenticate()
 ee.Initialize() 
 drive.mount('/content/drive')

You can export images using a thread. I've never tried to export a whole collection but it seems to me that you can do it by replacing the 'image=image' attribute by 'collection=yourCollection' (not sure of that). In general, I prefer to transform my collection into an images list and then to upload images one by one.

imagesList = collection.toList(collection.size())
for i in range(0,imagesList.size().getInfo()):
  image= ee.Image(imagesList.get(i))
  #Process the export for you image into the folder of your choice into Drive 
  task = ee.batch.Export.image.toDrive(image= image,
    description='Exported from EarthEngine',
    fileNamePrefix='filename.tif',
    scale= scale,
    folder='repertoryOfYourChoice/',
    fileFormat='GeoTIFF')
 task.start()

Then you can follow the status of the last uploaded image in your list (you can upgrade this code by storing all the tasks in a list to track the status of all the images in your list) :

print(task.status())

Otherwise, about the scale it allows you to reproject an image to have a different resolution and speed up the export time. So it can be useful to use a large scale when working on very large areas. However, be careful not to scale it too large because the calculation time of the reprojection could sometimes be too greedy and exceed the maximum memory size allowed.

you will find documentation on this page : https://developers.google.com/earth-engine/python_install

I propose you to go through the native export tool of Earth Engine instead of geetools.

 import ee
 from google.colab import drive

Authenticate and mount your drive into your project

 ee.Authenticate()
 ee.Initialize() 
 drive.mount('/content/drive')

You can export images using a thread. Use this command to start the export to the folder of your choice into Drive :

 task = ee.batch.Export.image.toDrive(image= image,
    description='Exported from EarthEngine',
    fileNamePrefix='filename.tif',
    scale= scale,
    folder='repertoryOfYourChoice/',
    fileFormat='GeoTIFF')
 task.start()

I've never tried to export a whole collection but it seems to me that you can do it by replacing the 'image=image' attribute by 'collection=yourCollection'. In general, I prefer to transform my collection into an images list and then to upload images one by one. Then you can follow the status of the upload :

print(task.status())

Otherwise, about the scale it allows you to reproject an image to have a different resolution and speed up the export time. So it can be useful to use a large scale when working on very large areas. However, be careful not to scale it too large because the calculation time of the reprojection could sometimes be too greedy and exceed the maximum memory size allowed.

you will find documentation on this page : https://developers.google.com/earth-engine/python_install

I propose you to go through the native export tool of Earth Engine instead of geetools.

 import ee
 from google.colab import drive

Authenticate and mount your drive into your project

 ee.Authenticate()
 ee.Initialize() 
 drive.mount('/content/drive')

You can export images using a thread. I've never tried to export a whole collection but it seems to me that you can do it by replacing the 'image=image' attribute by 'collection=yourCollection' (not sure of that). In general, I prefer to transform my collection into an images list and then to upload images one by one.

imagesList = collection.toList(collection.size())
for i in range(0,imagesList .size().getInfo()):
  image= ee.Image(imagesList .get(i))
  #Process the export for you image into the folder of your choice into Drive 
  task = ee.batch.Export.image.toDrive(image= image,
    description='Exported from EarthEngine',
    fileNamePrefix='filename.tif',
    scale= scale,
    folder='repertoryOfYourChoice/',
    fileFormat='GeoTIFF')
 task.start()

Then you can follow the status of the last uploaded image in your list (you can upgrade this code by storing all the tasks in a list to track the status of all the images in your list) :

print(task.status())

Otherwise, about the scale it allows you to reproject an image to have a different resolution and speed up the export time. So it can be useful to use a large scale when working on very large areas. However, be careful not to scale it too large because the calculation time of the reprojection could sometimes be too greedy and exceed the maximum memory size allowed.