cortex

README

Deploy machine learning models in production

Cortex is an open source platform that takes machine learning models—trained with nearly any framework—and turns them into production web APIs in one command.

Key features

• Autoscaling: Cortex automatically scales APIs to handle production workloads.

• Multi framework: Cortex supports TensorFlow, PyTorch, scikit-learn, XGBoost, and more.

• CPU / GPU support: Cortex can run inference on CPU or GPU infrastructure.

• Rolling updates: Cortex updates deployed APIs without any downtime.

• Log streaming: Cortex streams logs from deployed models to your CLI.

• Prediction monitoring: Cortex monitors network metrics and tracks predictions.

• Minimal configuration: Deployments are defined in a single cortex.yaml file.

Usage

Define your API

# predictor.py

model = download_my_model()

def predict(sample, metadata):
    return model.predict(sample["text"])

Configure your deployment

# cortex.yaml

- kind: deployment
  name: sentiment

- kind: api
  name: classifier
  predictor:
    path: predictor.py
  tracker:
    model_type: classification
  compute:
    gpu: 1

Deploy to AWS

$ cortex deploy

creating classifier (http://***.amazonaws.com/sentiment/classifier)

Serve real-time predictions

$ curl http://***.amazonaws.com/sentiment/classifier \
    -X POST -H "Content-Type: application/json" \
    -d '{"text": "the movie was great!"}'

positive

Monitor your deployment

$ cortex get classifier --watch

status   up-to-date   available   requested   last update   avg latency
live     1            1           1           8s            123ms

class     count
positive  8
negative  4

How it works

The CLI sends configuration and code to the cluster every time you run cortex deploy. Each model is loaded into a Docker container, along with any Python packages and request handling code. The model is exposed as a web service using Elastic Load Balancing (ELB), Flask, TensorFlow Serving, and ONNX Runtime. The containers are orchestrated on Elastic Kubernetes Service (EKS) while logs and metrics are streamed to CloudWatch.

Examples

• Sentiment analysis in TensorFlow with BERT
• Image classification in TensorFlow with Inception
• Text generation in PyTorch with DistilGPT2
• Iris classification in XGBoost / ONNX