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Downgrade transformers to avoid faulty release of that package #363

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merged 2 commits into from
Apr 30, 2024

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PawelPeczek-Roboflow
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Description

As reported in one of our issues (#355) and as suggested in discussions in transformers issues - we shall restrict versions of transformers we use to avoid faulty release. The problem is solved in one of newer versions, but defensively I propose to just downgrade max version of transformers a bit.

Type of change

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  • Bug fix (non-breaking change which fixes an issue)
  • New feature (non-breaking change which adds functionality)
  • This change requires a documentation update

How has this change been tested, please provide a testcase or example of how you tested the change?

  • run of CogVLM on new build yo ensure model works e2e

Any specific deployment considerations

For example, documentation changes, usability, usage/costs, secrets, etc.

Docs

  • Docs updated? What were the changes:

@grzegorz-roboflow grzegorz-roboflow self-assigned this Apr 30, 2024
@grzegorz-roboflow
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Tried below:

cd /path/to/inference
python3.8 -m venv venv
pip install -e .
pip freeze | grep transformers
# yields 4.40.1

when building image the result is the same:

cd /path/to/inference
docker build --file docker/dockerfiles/Dockerfile.onnx.cpu -t onnx_cpu .
docker run -it --rm --entrypoint /bin/bash onnx_cpu -c 'pip freeze | grep transfor'
# yields 4.40.1

@PawelPeczek-Roboflow PawelPeczek-Roboflow merged commit 30361f3 into main Apr 30, 2024
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@PawelPeczek-Roboflow PawelPeczek-Roboflow deleted the fix/fix_cogvlm branch April 30, 2024 08:45
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2 participants