cortexlabs/cortex
Cortex
Cortex is a Kubernetes-based machine learning infrastructure platform designed for deploying, scaling, and managing models and workloads. It functions as a serverless inference engine and GPU cluster orchestrator, providing the tools necessary to execute real-time, asynchronous, and batch model predictions.
The platform utilizes declarative infrastructure-as-code for provisioning model clusters and environments. It optimizes operational costs by elastically scaling CPU and GPU resources through the use of spot instances.
The system covers a broad set of operational capabilities, including workload orchestration, private cloud network isolation with integrated identity management, and observability pipelines that stream logs and performance metrics to external monitoring tools.
Features
- Production Serving Infrastructure - Deploys and serves machine learning models in production environments with scalable infrastructure and automated settings.
- Serverless Inference Engines - Provides a serverless inference engine that automatically scales real-time, asynchronous, and batch model predictions.
- GPU Resource Scaling - Dynamically adjusts GPU compute capacity using spot instances to balance performance and operational costs.
- GPU Resource Orchestrators - Elastically provisions and optimizes CPU and GPU resources using spot instances for AI workloads.
- Kubernetes ML Platforms - Provides a production platform for deploying, scaling, and managing machine learning models and workloads on Kubernetes.
- ML Infrastructure Managers - Automates the provisioning and scaling of CPU and GPU compute clusters for large-scale ML workloads.
- ML Orchestration Deployments - Orchestrates the deployment and scaling of machine learning models across production infrastructure to handle traffic loads.
- Model Inference Clusters - Provisions specialized infrastructure and environment settings specifically for serving machine learning models.
- Serverless Inference Engines - Executes real-time or batch model predictions that scale automatically based on request volume or queue length.
- Workload Orchestration - Orchestrates real-time and batch processes that scale automatically based on request volume or queue length.
- Private AI Deployments - Deploys machine learning workloads on private infrastructure to ensure data security and access control.
- Asynchronous Task Processing - Provides a queued system for executing non-real-time machine learning workloads through background workers.
- Cloud Infrastructure Cost Optimization - Reduces operational expenses through the use of spot instances and elastic compute scaling.
- Infrastructure Provisioning Tools - Automates the creation of model clusters using declarative infrastructure-as-code configurations.
- Virtual Private Clouds - Runs ML workloads within isolated virtual private clouds with integrated identity management for secure access.
- Compute Instance Scaling - Elastically scales CPU and GPU compute instances using spot instances to reduce operational expenses.
- Declarative Infrastructure Tools - Uses infrastructure-as-code and configuration templates to provision machine learning environments and clusters.
- Private Network Security - Runs workloads within isolated virtual private clouds with integrated identity management for secure access control.
- Observability Pipelines - The project tracks system behavior and errors by streaming metrics and logs to external monitoring tools or dashboards.
- General Machine Learning - Platform for deploying ML models in production.
- MLOps and Lifecycle - Deploy machine learning models.
- Deep Learning Implementations - Platform for deploying machine learning models as web services.
