Training Pipeline with Label Studio and Pachyderm
You can create an efficient image processing and model training pipeline by using the Neu.ro platform in conjunction with Label Studio and Pachyderm in the sandbox.
To achieve this, you will need to set up a Pachyderm pipeline that will trigger model training or re-training on every new dataset update that affects image labels. In this way, every time you process images through Label Studio, your model will be automatically re-trained.
Once your sandbox environment is set up and you have a running Pachyderm cluster, you will need to create the Pachyderm pipeline:
You will then need to download the dataset to platform storage by running
Select images from the dataset and put them under Pachyderm:
You can now test the pipeline by opening Label Studio in a browser:
Once the images are processed, Label Studio will automatically close and commit a new dataset version.
This, in turn, will trigger the Pachyderm pipeline and start model training. You can follow this process in the Pachyderm pipeline logs:
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