tira 0.0.38

Simple access to the TIRA API.

Export datasets

Export a dataset via the cli:

tira-run --export-dataset '<task>/<tira-dataset>' --output-directory tira-dataset

Export a dataset via the python API:

from tira.rest_api_client import Client

tira = Client()
tira.download_dataset('<task>', '<tira-dataset>')

download_dataset(self, task, dataset, truth_dataset=False):

Running Jupyter Notebooks with TIRA

docker build -t tira/submission-base-image:1.0.0 -f Dockerfile .

Testing the model locally can be done using the following command:

tira-run \
  --input-directory ${PWD}/input \
  --output-directory ${PWD}/output \
  --image tira/submission-base-image:1.0.0 \
  --command 'tira-run-notebook --input $inputDataset --output $outputDir /workspace/template-notebook.ipynb'

---

Afterwards you can push the image to TIRA

docker push tira/submission-base-image:1.0.0

and set the command:

tira-run-notebook --input $inputDataset --output $outputDir /workspace/template-notebook.ipynb

---

Finally, if the actual processing in notebook is toggled via is_running_as_inference_server() (as seen in the template notebook) and your notebook defines a function named predict in the format

def predict(input_list: List) -> List:

you can start an inference server for your model with:

PORT=8001

docker run --rm -it --init \
  -v "$PWD/logs:/workspace/logs" \
  -p $PORT:$PORT \
  tira/submission-base-image:1.0.0 \
  tira-run-inference-server --notebook /workspace/template-notebook.ipynb --port $PORT

Exemplary request for a server running on localhost:8001 are

# POST (JSON list as payload)
curl -X POST -H "application/json" \
  -d "[\"element 1\", \"element 2\", \"element 3\"]" \
  localhost:8001

and

# GET (JSON object string(s) passed to the 'payload' parameter)
curl "localhost:8001?payload=\"element+1\"&payload=\"element+2\"&payload=\"element+3\""