System packages

Cortex looks for a file named in the top level Cortex project directory (i.e. the directory which contains cortex.yaml). For example:

├── cortex.yaml
├── ...
└── is executed with bash shell during the initialization of each replica (before installing Python packages in requirements.txt or conda-packages.txt). Typical use cases include installing required system packages to be used in your Predictor, building Python packages from source, etc. If initialization time is a concern, see Docker images for how to build and use custom Docker images.

Here is an example, which installs the tree utility:

apt-get update && apt-get install -y tree

The tree utility can now be called inside your

import subprocess

class PythonPredictor:
    def __init__(self, config):["tree"])

If you need to upgrade the Python Runtime version on your image, you can do so in your file:

# upgrade python runtime version
conda update -n base -c defaults conda
conda install -n env python=3.8.5

# re-install cortex core dependencies

Customizing Dependency Paths

Cortex allows you to specify a path for this script other than This can be useful when deploying different versions of the same API (e.g. CPU vs GPU dependencies). The path should be a relative path with respect to the API configuration file, and is specified via

For example:

# cortex.yaml

- name: my-classifier
  kind: RealtimeAPI

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