CortexFlow is an advanced ML training pipeline platform that orchestrates distributed training, automates hyperparameter optimization, and provides real-time performance monitoring. From research to production at enterprise scale.
Optimized forward and backward pass execution with automatic gradient computation, mixed precision training, and memory-efficient operations for maximum throughput.
Multi-GPU and multi-node distributed training with automatic sharding, gradient synchronization, and fault tolerance for training at massive scale.
Automated hyperparameter tuning using Bayesian optimization, grid search, and random search with early stopping and pruning strategies.
Live training metrics, resource utilization monitoring, and performance analytics with automated alerting and visualization dashboards.
Distributed data loading with automatic batching, preprocessing pipelines, and memory optimization.
Forward/backward passes with gradient computation, optimization steps, and checkpoint management.
Real-time metrics tracking, resource monitoring, and automated model deployment to production.
CortexFlow supports multiple training paradigms optimized for different model architectures and deployment scenarios.
Classification and regression with labeled datasets
Fine-tuning pre-trained models for domain adaptation
Agent training with reward-based optimization
Distributed training with privacy preservation
Train massive models across distributed GPU clusters with automatic synchronization, fault tolerance, and optimal resource utilization. Scale from single GPU to hundreds of nodes seamlessly.
Real-time distributed training across 32 NVIDIA A100 GPUs
Data parallel and model parallel training with automatic sharding and load balancing across available resources.
Automatic recovery from node failures with checkpoint restoration and dynamic resource reallocation.
Advanced optimization techniques for maximum training efficiency and resource utilization.
Automated hyperparameter tuning with advanced algorithms including Bayesian optimization, grid search, and evolutionary strategies. Find optimal configurations automatically.
Uses probabilistic models to efficiently explore hyperparameter space and find optimal configurations with minimal trials.
Adaptive resource allocation that focuses compute on promising hyperparameter configurations while pruning poor performers early.
Evolutionary approach that maintains a population of models with different hyperparameters and evolves them during training.
CortexFlow automatically generates Forward, Backward, and Predict API endpoints for your trained models with real-time monitoring and performance analytics.
Model inference endpoint for prediction requests
Gradient computation for continual learning
High-level prediction interface with preprocessing
Automatic model versioning with Model Hub integration for seamless deployment rollbacks.
Complete logging and monitoring integration with centralized analytics and alerting.
Dynamic resource allocation based on request load with cost optimization.
API authentication, rate limiting, and access control with audit trails.
Experience enterprise-grade ML training with CortexFlow. Get distributed training, hyperparameter optimization, and production-ready APIs that scale from prototype to production.