cortex-serving-client 0.33.2

Cortex.dev ML Serving Client for Python with garbage API collection.

Cortex Serving Client

Cortex Serving Client makes Python serving automation simple. It is a Python wrapper around Cortex's command-line client that provides garbage API collection. Cortex has official Python client now (source), but this project offers advanced features (GC, temporary deployment, timeouts) not present in the vanilla.

• Automate your Cortex AWS cluster from Python.
• Prevent accidental charges by auto-removing deployments that exceeded a timeout.
• Execute operations: deploy, delete, get, get all.
• Stream remote logs into the local log with thread name set to the API name.
• Supported Cortex Version: 0.33.0 (See requirements.txt)

Here is a video about the package.

How It Works?

After implementing your predictor module in a folder (see dummy_dir in below), you can deploy it to your Cortex cluster, and execute a prediction via a POST request.

Here is a video of the demo below.

Working Example

Below is a snippet from example.py:

deployment = dict(
    name='dummy-api',
    predictor=dict(
        type='python',
        path='dummy_predictor.py',
    ),
    compute=dict(
        cpu=1,
    )
)

with cortex.deploy_temporarily(
        deployment,
        dir="dummy_dir",
        api_timeout_sec=10 * 60,
        print_logs=True,
) as get_result:
    result = post(get_result.endpoint, json={}).json()

Garbage API Collection

Garbage API collection auto-removes forgotten APIs to reduce costs.

Each deployed API has a timeout period configured during deployment after which it definitely should not exist in the cluster anymore. This timeout is stored in a Postgres database table. Cortex client periodically checks currently deployed APIs and removes expired APIs from the cluster.

Can You Rollback?

How do you deal with new model failure in production? Do you have the ability to return to your model's previous working version? There is no generic solution for everybody. But you can implement the best suiting your needs using the Python API for Cortex. Having a plan B is a good idea.

Our Use Case

We use this project to automate deployment to auto-scalable AWS instances. The deployment management is part of application-specific Flask applications, which call to Python-Cortex-Serving-Client to command environment-dedicated Cortex cluster.

In cases where multiple environments share a single cluster, a shared Cortex database Postgres instance is required.

Read more about our use case in Cortex Client release blog post. Or you can watch a video about our use case.

Get Started

This tutorial will help you to get the basic example running under 15 minutes.

Pre-requisites

• Linux OS
• Docker
• Postgres

Setup Database

Follow below instuctions to configure local database, or configure cluster database, and re-configure db in the example script.

sudo su postgres;
psql postgres postgres;
create database cortex_test;
create role cortex_test login password 'cortex_test';
grant all privileges on database cortex_test to cortex_test;

You may need to configure also

vi /etc/postgresql/11/main/pg_hba.conf
# change a matching line into following to allow localhost network access
# host    all             all             127.0.0.1/32            trust

sudo systemctl restart postgresql;

Install Cortex

Supported Cortex.dev version is a Python dependency version.

This example runs in a simulated local cluster in local docker. To execute it in an AWS cluster, follow this Cortex tutorial, and then change cortex_env from 'local' with 'aws' in the the test script. You may be required to add your user into docker group and then re-login.

Deploy Your First Model

The deployment and prediction example resides in the example script. Make sure you have created a virtual environment, and installed requirements in requirements.txt and requirements-dev.txt, before you execute it. Please be ready to wait a couple of minutes during the first run as Cortex Docker instance images need to be downloaded and cached.

Contact Us

Submit an issue or a pull request if you have any problems or need an extra feature.