Predictor

Which Predictor you use depends on how your model is exported:

  • TensorFlow Predictor if your model is exported as a TensorFlow SavedModel

  • Python Predictor for all other cases: PyTorch, ONNX, scikit-learn, XGBoost, TensorFlow (if not using SavedModels), etc.

Project files

Cortex makes all files in the project directory (i.e. the directory which contains cortex.yaml) available for use in your Predictor 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 predictor. 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
├── predictor.py
├── ...
└── requirements.txt

You can access values.json in your Predictor like this:

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

Python Predictor

Interface

# initialization code and variables can be declared here in global scope
class PythonPredictor:
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 predict(self, payload, request_id):
"""(Required) Called once per request. Preprocesses the request payload
(if necessary), runs inference, and postprocesses the inference 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:
Prediction or a batch of predictions.
"""
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 Predictor'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 predict method.

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

TensorFlow Predictor

Uses TensorFlow version 2.3.0 by default

Interface

class TensorFlowPredictor:
def __init__(self, config, tensorflow_client, metrics_client):
"""(Required) Called once before the API becomes available. Performs
setup such as downloading/initializing a vocabulary.
Args:
config (required): Dictionary passed from API configuration (if
specified).
tensorflow_client (required): TensorFlow client which is used to
make predictions. This should be saved for use in predict().
metrics_client (optional): The cortex metrics client, which allows
you to push custom metrics in order to build custom dashboards
in grafana.
"""
self.client = tensorflow_client
# Additional initialization may be done here
def predict(self, payload, request_id):
"""(Required) Called once per request. Preprocesses the request payload
(if necessary), runs inference (e.g. by calling
self.client.predict(model_input)), and postprocesses the inference
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:
Prediction or a batch of predictions.
"""
pass

Cortex provides a tensorflow_client to your Predictor's constructor. tensorflow_client is an instance of TensorFlowClient that manages a connection to a TensorFlow Serving container to make predictions using your model. It should be saved as an instance variable in your Predictor, and your predict() function should call tensorflow_client.predict() to make an inference with your exported TensorFlow model. Preprocessing of the JSON payload and postprocessing of predictions can be implemented in your predict() function as well.

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 Predictor'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 predict method.

At this moment, the AsyncAPI predict 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 predict(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 PythonPredictor:
def __init__(self, config):
pass
def predict(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 PythonPredictor:
def __init__(self, config):
pass
def predict(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 PythonPredictor:
def __init__(self, config):
pass
def predict(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 PythonPredictor:
def __init__(self, config):
pass
def predict(self, payload):
print(payload) # prints "hello world"

API responses

Currently, AsyncAPI responses of your predict() 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 predictor at http://api-<api_name>:8888/predict, 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 PythonPredictor:
def predict(self, payload):
response = requests.post("http://api-text-generator:8888/predict", json={"text": "machine learning is"})
# ...

Structured logging

You can use Cortex's logger in your predictor 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 PythonPredictor:
def predict(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.