Cortex is an open source platform for large-scale inference workloads.

• Supports deploying TensorFlow, PyTorch, sklearn and other models as realtime or batch APIs.
• Ensures high availability with availability zones and automated instance restarts.
• Runs inference on on-demand instances or spot instances with on-demand backups.
• Autoscales to handle production workloads with support for overprovisioning.

# cluster.yaml

region: us-east-1
instance_type: g4dn.xlarge
min_instances: 10
max_instances: 100
spot: true

$ cortex cluster up --config cluster.yaml

￮ configuring autoscaling ✓
￮ configuring networking ✓
￮ configuring logging ✓

cortex is ready!

• Package dependencies, code, and configuration for reproducible deployments.
• Configure compute, autoscaling, and networking for each API.
• Integrate with your data science platform or CI/CD system.
• Deploy custom Docker images or use the pre-built defaults.

class PythonPredictor:
  def __init__(self, config):
    from transformers import pipeline

    self.model = pipeline(task="text-generation")

  def predict(self, payload):
    return self.model(payload["text"])[0]

requirements = ["tensorflow", "transformers"]

api_spec = {
  "name": "text-generator",
  "kind": "RealtimeAPI",
  "compute": {
    "gpu": 1,
    "mem": "8Gi"
  },
  "autoscaling": {
    "min_replicas": 1,
    "max_replicas": 10
  }
}

• Scale to handle production workloads with request-based autoscaling.
• Stream performance metrics and logs to any monitoring tool.
• Serve many models efficiently with multi-model caching.
• Use rolling updates to update APIs without downtime.
• Configure traffic splitting for A/B testing.

import cortex

cx = cortex.client("aws")
cx.create_api(api_spec, predictor=PythonPredictor, requirements=requirements)

# creating https://example.com/text-generator

$ curl https://example.com/text-generator -X POST -H "Content-Type: application/json" -d '{"text": "hello world"}'

• Read the docs
• Report an issue
• Join our community