File Sensor

Once you have a Union account, install union:

pip install union

Export the following environment variable to build and push images to your own container registry:

        
# replace with your registry name
export IMAGE_SPEC_REGISTRY="<your-container-registry>"

Then run the following commands to run the workflow:

        
    
git clone https://github.com/unionai/unionai-examples
cd unionai-examples
union run --remote tutorials/sentiment_classifier/sentiment_classifier.py main --model distilbert-base-uncased

The source code for this tutorial can be found here {octicon}mark-github.

This example shows how to use the FileSensor to detect files appearing in your local or remote filesystem. First, import the required libraries.

        
from flytekit import task, workflow
from flytekit.sensor.file_sensor import FileSensor

Next, create a FileSensor task.

sensor = FileSensor(name="test_file_sensor")

To use the FileSensor created in the previous step, you must specify the path parameter. In the sandbox, you can use the S3 path.

        
@task()
def t1():
    print("SUCCEEDED")

@workflow()
def wf():
    sensor(path="s3://my-s3-bucket/file.txt") >> t1()

if __name__ == "__main__":
    wf()

You can also use the S3 or GCS file system. We have already set the minio credentials in the agent by default. If you test the sandbox example locally, you will need to set the AWS credentials in your environment variables.

        
    
export FLYTE_AWS_ENDPOINT="http://localhost:30002"
export FLYTE_AWS_ACCESS_KEY_ID="minio"
export FLYTE_AWS_SECRET_ACCESS_KEY="miniostorage"