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With the X amount of RAM I meant an approximate minimum of usable RAM that you need to have, in order to execute the readme example with the full dataset. I cannot test the actual RAM usage of a run with the full dataset myself since I have only 16G available.

When trying out the example at todays Hackathon (at Jina in Berlin) everyone who tested the examples was running into this issue.

With the X amount of RAM I meant an approximate minimum of usable RAM that you need to have, in order to execute the readme example with the full dataset. I cannot test the actual RAM usage of a run with the full dataset myself since I have only 16G available.

When trying out the example at todays Hackathon (at Jina in Berlin) everyone who tested the examples was running into this issue.

yes X amount is a placeholder, I have to compute this. I have tried now and I could not manage to have it working without crashing and I have 16 GB RAM.

How much memory u need to run this?

from docarray import DocumentArray

left_da = DocumentArray.pull('demo-leftda', show_progress=False)

from docarray import Document


def preproc(d: Document):
    return (
        d.load_uri_to_image_tensor()  # load
            .set_image_tensor_normalization()  # normalize color
            .set_image_tensor_channel_axis(-1, 0)
    )  # switch color axis for the PyTorch model later


left_da.apply(preproc)

import torchvision

model = torchvision.models.resnet50(pretrained=True)  # load ResNet50
left_da.embed(model, device='cpu')

right_da = (
    DocumentArray.pull('demo-rightda', show_progress=True)
        .apply(preproc)
        .embed(model, device='cpu')
)

left_da.match(right_da, limit=9)

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