Training Pipeline with Label Studio and Pachyderm

You can create an efficient image processing and model training pipeline by using the 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:
$ neuro-flow run create_pipeline --param mlflow_storage $MLFLOW_STORAGE --param mlflow_uri $MLFLOW_URI
You will then need to download the dataset to platform storage by running
$ neuro-flow run prepare_remote_dataset
Select images from the dataset and put them under Pachyderm:
$ neuro-flow run extend_data --param extend_dataset_by <number_of_images>
You can now test the pipeline by opening Label Studio in a browser:
$ neuro-flow run label_studio
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:
pachctl config update context default --pachd-address <Pachyderm server address>
pachctl logs -f -p train