Install

Prerequisites
​AWS credentials​
​Docker​
Installation
See cluster configuration to customize your installation.
# Download
curl -O https://raw.githubusercontent.com/cortexlabs/cortex/0.8/cortex.sh
​
# Change permissions
chmod +x cortex.sh
​
# Install the Cortex CLI on your machine
./cortex.sh install cli
​
# Set AWS credentials
export AWS_ACCESS_KEY_ID=***
export AWS_SECRET_ACCESS_KEY=***
​
# Configure AWS instance settings (at least 4GB memory)
export CORTEX_NODE_TYPE="m5.large"
export CORTEX_NODES_MIN="2"
export CORTEX_NODES_MAX="5"
​
# Provision infrastructure on AWS and install Cortex
./cortex.sh install
This will create resources in your AWS account which aren't included in the free teir, e.g. an EKS cluster, two Elastic Load Balancers, and EC2 instances (quantity and type as specified above). To use GPU nodes, you may need to subscribe to the EKS-optimized AMI with GPU Support and file an AWS support ticket to incease the limit for your desired instance type.
Create a deployment
# Clone the Cortex repository
git clone -b 0.8 https://github.com/cortexlabs/cortex.git
​
# Navigate to the iris classification example
cd cortex/examples/iris-classifier
​
# Deploy the model to the cluster
cortex deploy
​
# View the status of the deployment
cortex get --watch
​
# Get the API's endpoint
cortex get tensorflow
​
# Classify a sample
curl -X POST -H "Content-Type: application/json" \
     -d '{ "sepal_length": 5.2, "sepal_width": 3.6, "petal_length": 1.4, "petal_width": 0.3 }' \
     <API endpoint>
Cleanup
# Delete the deployment
cortex delete iris
See uninstall if you'd like to uninstall Cortex.
Deploy machine learning models in production
Next - Deployment docs
Deployments
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