Add a batch runner API

We have plans to support a batch interface to Cortex APIs (#523), but until that's implemented, it is possible to implement a batch runner which receives the batch request, splits it into individual requests, and sends them to the prediction API.

Note: this is experimental. Also, this behavior can be implemented outside of Cortex, e.g. in your backend server if you have one.

This example assumes you have deployed an iris-classifier API, e.g. examples/sklearn/iris-classifier or examples/tensorflow/iris-classifier.

Create a new directory (outside of the iris example directory) with the files listed below, and run cortex deploy in that directory to deploy the batch API. Run python http://*** to submit a batch of requests to the batch api (replace *** with your actual endpoint). You can still send individual requests to the prediction API (bypassing the batch API) if you'd like.

Feel free to reach out on gitter if you have questions.

import requests
import time
import json
from concurrent.futures import ThreadPoolExecutor
class PythonPredictor:
def __init__(self, config):
self.endpoint = config["endpoint"] # endpoint is passed in from the API configuration
print(f"endpoint: {self.endpoint}")
def predict(self, payload):
# For this example, payload will be the full list of iris-classifier prediction requests.
# We will send each as it's own prediction request to the API, however it may be beneficial
# to create batches, e.g. a single request to the classifier may contain 100 individual samples.
# For example, payload could be a list of image URLs, e.g. ["url1", "url2", "url3", "url4", "url5", "url6"]
# then batches could be a list where each item is a list of the image URLs to make in a single inference request
# e.g. [["url1", "url2", "url3"], ["url4", "url5", "url6"]] would correspond to two requests to your inference API, each containing three URLs
batches = payload
# Increasing max_workers will increase how many replicas will be used for the batch request.
# Assuming default values for target_replica_concurrency, processes_per_replica, and threads_per_process
# in your prediction API, the number of replicas created will be equal to max_workers.
# If you have changed these values, the number of replicas created will be equal to max_workers / target_replica_concurrency
# (note that the default value of target_replica_concurrency is processes_per_replica * threads_per_process).
# If max_workers starts to get large, you will also want to set the inference API's max_replica_concurrency to avoid long and imbalanced queue lengths
# Here are the autoscaling docs:
with ThreadPoolExecutor(max_workers=5) as executor:
results =, batches)
# This is to wait for all the results to be completed
# make_request() receives a single element from the batches list.
# In this case it's just a single sample, but in general it should be the info necessary
# to make one request to the inference API, e.g. a list of URLs
def make_request(self, batch):
print("making a request")
num_attempts = 0
while True:
num_attempts += 1
# Make the actual inference request
response =, data=json.dumps(batch))
if response.status_code == 200:
print(f"got response code {response.status_code}, retrying...")
# Enforce a maximum number of retries / timeout here so this request can't go on forever.
# The total timeout should be at least e.g. 10 minutes if you want to allow for new instances to spin up
if num_attempts >= 60:
# you may want to handle this case differently
print("max attempts exceeded, giving up")
# This is your inference API response
print(f"done with request: {response.text}")


- name: iris-classifier-batch
type: python
config: # you can pass in your API endpoint like this (replace ***):
endpoint: http://***
max_replicas: 1 # this API may need to autoscale depending on how many batch requests, but disable it to start
threads_per_process: 1 # set this to the number of batch requests you'd like to be able to be able to work on at a time
# once that number is exceeded, they will be queued, which may be ok
# setting this too high may lead to out of memory errors
cpu: 500m # reserve some CPU (you may be able to decrease this, or you may have to increase it)
mem: 1Gi # reserve some memory (you may be able to decrease this, or you may have to increase it)

import sys
import requests
import json
if len(sys.argv) != 2:
print("usage: python BATCH_API_URL")
print("e.g. python http://***")
batch_endpoint = sys.argv[1]
# The full list of requests to make
data = [
{"sepal_length": 5.2, "sepal_width": 3.6, "petal_length": 1.5, "petal_width": 0.3},
{"sepal_length": 7.1, "sepal_width": 3.3, "petal_length": 4.8, "petal_width": 1.5},
{"sepal_length": 6.4, "sepal_width": 3.4, "petal_length": 6.1, "petal_width": 2.6},
# This timeout and try/catch is so that you can send the request and not wait for it, since
# cortex doesn't support asynchronous requests yet
response =, data=json.dumps(data), timeout=0.05)
if response.status_code != 200:
print("an error occurred:")
except requests.exceptions.ReadTimeout: