Handler

Your handler can be used to process any asynchronous workloads. It can also be used for running ML models using a variety of frameworks such as: PyTorch, ONNX, scikit-learn, XGBoost, TensorFlow (if not using SavedModels), etc.

If you plan on deploying models with TensorFlow in SavedModel format, you can also use the TensorFlow Handler that was specifically built for this purpose.

Project files

Cortex makes all files in the project directory (i.e. the directory which contains cortex.yaml) available for use in your handler implementation. Python bytecode files (*.pyc, *.pyo, *.pyd), files or folders that start with ., and the api configuration file (e.g. cortex.yaml) are excluded.

The following files can also be added at the root of the project's directory:

  • .cortexignore file, which follows the same syntax and behavior as

    a .gitignore file.

  • .env file, which exports environment variables that can be used in the handler. Each line of this file must follow

    the VARIABLE=value format.

For example, if your directory looks like this:

./my-classifier/
├── cortex.yaml
├── values.json
├── handler.py
├── ...
└── requirements.txt

You can access values.json in your Handler class like this:

# handler.py
import json
class Handler:
def __init__(self, config):
with open('values.json', 'r') as values_file:
values = json.load(values_file)
self.values = values

Interface

# initialization code and variables can be declared here in global scope
class Handler:
def __init__(self, config, metrics_client):
"""(Required) Called once before the API becomes available. Performs
setup such as downloading/initializing the model or downloading a
vocabulary.
Args:
config (required): Dictionary passed from API configuration (if
specified). This may contain information on where to download
the model and/or metadata.
metrics_client (optional): The cortex metrics client, which allows
you to push custom metrics in order to build custom dashboards
in grafana.
"""
pass
def handle_async(self, payload, request_id):
"""(Required) Called once per request. Preprocesses the request payload
(if necessary), runs the workload, and postprocesses the resulting output
(if necessary).
Args:
payload (optional): The request payload (see below for the possible
payload types).
request_id (optional): The request id string that identifies a workload
Returns:
Workload result or batch of results.
"""
pass

For proper separation of concerns, it is recommended to use the constructor's config parameter for information such as from where to download the model and initialization files, or any configurable model parameters. You define config in your API configuration, and it is passed through to your handler's constructor.

Your API can accept requests with different types of payloads. Navigate to the API requests section to learn about how headers can be used to change the type of payload that is passed into your handle_async method.

At this moment, the AsyncAPI handle_async method can only return JSON-parseable objects. Navigate to the API responses section to learn about how to configure it.

API requests

The type of the payload parameter in handle_async(self, payload) can vary based on the content type of the request. The payload parameter is parsed according to the Content-Type header in the request. Here are the parsing rules (see below for examples):

  1. For Content-Type: application/json, payload will be the parsed JSON body.

  2. For Content-Type: text/plain, payload will be a string. utf-8 encoding is assumed, unless specified otherwise (

    e.g. via Content-Type: text/plain; charset=us-ascii)

  3. For all other Content-Type values, payload will be the raw bytes of the request body.

Here are some examples:

JSON data

Making the request

curl http://***.amazonaws.com/my-api \
-X POST -H "Content-Type: application/json" \
-d '{"key": "value"}'

Reading the payload

When sending a JSON payload, the payload parameter will be a Python object:

class Handler:
def __init__(self, config):
pass
def handle_async(self, payload):
print(payload["key"]) # prints "value"

Binary data

Making the request

curl http://***.amazonaws.com/my-api \
-X POST -H "Content-Type: application/octet-stream" \
--data-binary @object.pkl

Reading the payload

Since the Content-Type: application/octet-stream header is used, the payload parameter will be a bytes object:

import pickle
class Handler:
def __init__(self, config):
pass
def handle_async(self, payload):
obj = pickle.loads(payload)
print(obj["key"]) # prints "value"

Here's an example if the binary data is an image:

from PIL import Image
import io
class Handler:
def __init__(self, config):
pass
def handle_async(self, payload):
img = Image.open(io.BytesIO(payload)) # read the payload bytes as an image
print(img.size)

Text data

Making the request

curl http://***.amazonaws.com/my-api \
-X POST -H "Content-Type: text/plain" \
-d "hello world"

Reading the payload

Since the Content-Type: text/plain header is used, the payload parameter will be a string object:

class Handle:
def __init__(self, config):
pass
def handle_async(self, payload):
print(payload) # prints "hello world"

API responses

Currently, AsyncAPI responses of your handle_async() method have to be a JSON-serializable dictionary.

Chaining APIs

It is possible to make requests from one API to another within a Cortex cluster. All running APIs are accessible from within the handler at http://api-<api_name>:8888/, where <api_name> is the name of the API you are making a request to.

For example, if there is an api named text-generator running in the cluster, you could make a request to it from a different API by using:

import requests
class Handler:
def handle_async(self, payload):
response = requests.post("http://api-text-generator:8888/", json={"text": "machine learning is"})
# ...

Structured logging

You can use Cortex's logger in your handler implemention to log in JSON. This will enrich your logs with Cortex's metadata, and you can add custom metadata to the logs by adding key value pairs to the extra key when using the logger. For example:

...
from cortex_internal.lib.log import logger as log
class Handler:
def handle_async(self, payload):
log.info("received payload", extra={"payload": payload})

The dictionary passed in via the extra will be flattened by one level. e.g.

{"asctime": "2021-01-19 15:14:05,291", "levelname": "INFO", "message": "received payload", "process": 235, "payload": "this movie is awesome"}

To avoid overriding essential Cortex metadata, please refrain from specifying the following extra keys: asctime , levelname, message, labels, and process. Log lines greater than 5 MB in size will be ignored.