Sending File Input
You may also use files as input data to your model.
This is particularly useful if you have a model that takes in images, audio files, or other binary data.
To do this, you will need to supply at least an initial pre-processing function.
You can then use the files in your model as you would any other input data.
It is important to note that conduit.run expects the files to be passed in as binary data on the cloud, so you will need to open the files and read them in as binary data before passing them to test with conduit.run locally.
def pre_process(data, files):
import numpy as np
from PIL import Image
labelled = {d: Image.open(f) for d, f in zip(data, files)}
return labelled
from cerebrium import Conduit, model_type
conduit = Conduit('<MODEL_NAME>', '<API_KEY>', [('<MODEL_TYPE>', '<MODEL_FILE>', {"pre": pre_process"})])
conduit.load('./')
conduit.run(data, [files]) # files should be binary objects - for images it would the return of Image.open(f)
conduit.deploy()
Request and Response with Forms
You currently cannot use model_api_request with file input. You will need to
use curl instead.
curl --location --request POST '<ENDPOINT>' \
--header 'Authorization: <API_KEY>' \
--header 'Content-Type: multipart/form-data' \
-F data='[<DATA_INPUT>]' \
-F files=@<FILES_PATH>
The parameters to a form request are similar to a JSON request, however both the data and files are passed in as form data.
This is the Cerebrium API key used to authenticate your request. You can get it from your Cerebrium dashboard.
The content type of your request. Must be multipart/form-data for forms.
{
"result": [<MODEL_PREDICTION>],
"run_id": "<RUN_ID>",
"run_time_ms": <RUN_TIME_MS>
"prediction_ids": ["<PREDICTION_ID>"]
}
Response Parameters
The result of your model prediction.
The run ID associated with your model predictions.
The amount of time if took your model to run down to the millisecond. This is what we charge you based on.
The prediction IDs associated with each of your model predictions. Used to track your model predictions with monitoring tools.
curl --location --request POST '<ENDPOINT>' \
--header 'Authorization: <API_KEY>' \
--header 'Content-Type: multipart/form-data' \
-F data='[<DATA_INPUT>]' \
-F files=@<FILES_PATH>
{
"result": [<MODEL_PREDICTION>],
"run_id": "<RUN_ID>",
"run_time_ms": <RUN_TIME_MS>
"prediction_ids": ["<PREDICTION_ID>"]
}