Documentation | Augment AI Platform

Installation

Deploy NeuroFlow Orchestrator to start AI-native workflow orchestration with neural network optimization.

System Requirements

Python 3.9+ with TensorFlow 2.8+ or PyTorch 1.12+
Kubernetes 1.20+ for container orchestration
Minimum 8GB RAM (16GB recommended for neural processing)
GPU support recommended for neural network training
Redis or similar for distributed state management

Install via Package Manager

Installationbash

# Install via pip
pip install augment-neuroflow-orchestrator

# Install with GPU support
pip install augment-neuroflow-orchestrator[gpu]

# Install from source
git clone https://github.com/augment-ai/neuroflow-orchestrator
cd neuroflow-orchestrator
pip install -e .

# Install neural network dependencies
pip install tensorflow torch numpy scipy scikit-learn

# Verify installation
neuroflow --version

Neural Orchestration Setup

Configure neural network-powered workflow orchestration and AI-driven scheduling:

Neural Setupbash

# Set Augment API key
export AUGMENT_API_KEY=your_api_key_here

# Configure neural orchestration
export NEUROFLOW_NEURAL_MODE=enabled
export NEUROFLOW_GPU_ENABLED=true

# Configure workflow discovery
export WORKFLOW_DISCOVERY_ENABLED=true
export KUBERNETES_CONFIG_PATH=/etc/kubernetes/config

# Configure neural network training
export NEURAL_TRAINING_ENABLED=true
export TRAINING_DATA_RETENTION=30d

# Initialize neural orchestrator
neuroflow init --neural-mode --discover-workflows --train-models

# Verify neural setup
neuroflow health-check --test-neural-networks --validate-gpu

Quick Start

Get AI-native workflow orchestration running in minutes with neural network optimization and intelligent scheduling.

1. Initialize Neural Orchestration

Neural Initializationbash

# Initialize neural workflow engine
neuroflow neural init --enable-learning --gpu-acceleration

# Discover existing workflows for neural analysis
neuroflow discover --kubernetes --docker-compose --airflow --jenkins

# Train initial neural models on workflow patterns
neuroflow train --workflow-patterns --execution-history --performance-data

# Validate neural model accuracy
neuroflow validate --models workflow_optimizer,scheduler,monitor --test-data

2. Create AI-Native Workflows

Workflow Creationbash

# Create workflow with neural optimization
neuroflow create --workflow data-pipeline --neural-optimization --adaptive-scheduling

# Enable intelligent monitoring and self-healing
neuroflow monitor --workflow data-pipeline --neural-monitoring --auto-remediation

# Configure predictive scaling based on neural insights
neuroflow scale --workflow data-pipeline --predictive-scaling --resource-optimization

# Deploy workflow with neural execution engine
neuroflow deploy --workflow data-pipeline --neural-engine --distributed-execution

3. Monitor Neural Performance

Neural Monitoringbash

# Start neural performance monitoring
neuroflow monitor --neural-dashboard --real-time-metrics --anomaly-detection

# View neural optimization results
neuroflow status --neural-insights --performance-improvements --learning-progress

# Generate neural workflow analytics
neuroflow analytics --neural-patterns --optimization-recommendations --efficiency-metrics

# Retrain neural models with new data
neuroflow retrain --incremental-learning --performance-feedback --adaptive-weights

Configuration

Configure NeuroFlow Orchestrator to align with your infrastructure and neural processing requirements.

Basic Configuration

.neuroflow.yamlyaml

version: "1.0"
organization: "your-company"
environment: "production"

neural_engine:
  enabled: true
  gpu_acceleration: true
  neural_frameworks:
    - name: "tensorflow"
      version: "2.8.0"
      gpu_memory_limit: "4GB"
    - name: "pytorch"
      version: "1.12.0"
      gpu_memory_limit: "4GB"
  model_storage:
    backend: "s3"
    bucket: "neuroflow-models"
    encryption: true
  training_config:
    batch_size: 32
    learning_rate: 0.001
    epochs: 100
    validation_split: 0.2
    early_stopping: true

workflow_orchestration:
  execution_engine:
    type: "kubernetes"
    namespace: "neuroflow"
    resource_limits:
      cpu: "4"
      memory: "8Gi"
      gpu: "1"
  scheduling:
    algorithm: "neural_scheduler"
    optimization_goals: ["minimize_latency", "maximize_throughput", "optimize_cost"]
    predictive_scaling: true
    resource_prediction_window: "1h"
  fault_tolerance:
    enabled: true
    retry_strategy: "exponential_backoff"
    circuit_breaker: true
    self_healing: true

neural_models:
  workflow_optimizer:
    enabled: true
    model_type: "lstm"
    input_features: ["execution_time", "resource_usage", "dependencies", "data_size"]
    output_targets: ["optimal_schedule", "resource_allocation"]
    training_frequency: "daily"
    performance_threshold: 0.85
  scheduler:
    enabled: true
    model_type: "reinforcement_learning"
    algorithm: "deep_q_network"
    state_space: ["queue_length", "resource_availability", "priority_scores"]
    action_space: ["schedule_now", "delay", "reschedule", "scale_resources"]
    reward_function: "composite_performance"
  monitor:
    enabled: true
    model_type: "autoencoder"
    anomaly_threshold: 0.95
    features: ["cpu_usage", "memory_usage", "network_io", "execution_patterns"]
    alert_sensitivity: "medium"

data_sources:
  kubernetes:
    enabled: true
    config_path: "/etc/kubernetes/config"
    namespaces: ["default", "production", "staging"]
  prometheus:
    enabled: true
    endpoint: "http://prometheus:9090"
    metrics: ["cpu_usage", "memory_usage", "network_io", "custom_metrics"]
  elasticsearch:
    enabled: true
    endpoint: "http://elasticsearch:9200"
    indices: ["workflow-logs-*", "performance-metrics-*"]

workflow_patterns:
  data_pipeline:
    type: "batch_processing"
    neural_optimization: true
    stages: ["extract", "transform", "load", "validate"]
    dependencies: ["database", "storage", "compute"]
    sla_requirements:
      max_execution_time: "2h"
      min_success_rate: 0.95
  ml_training:
    type: "machine_learning"
    neural_optimization: true
    stages: ["data_prep", "training", "validation", "deployment"]
    resource_requirements:
      gpu: true
      memory_intensive: true
    auto_scaling: true

monitoring:
  neural_dashboard:
    enabled: true
    port: 8080
    authentication: true
  metrics_collection:
    enabled: true
    interval: "30s"
    retention: "30d"
  alerting:
    enabled: true
    channels: ["slack", "email", "pagerduty"]
    neural_anomaly_detection: true

security:
  encryption:
    enabled: true
    algorithm: "AES-256-GCM"
    key_rotation: "weekly"
  authentication:
    method: "oauth2"
    provider: "okta"
  authorization:
    rbac: true
    neural_access_control: true
  audit_logging:
    enabled: true
    neural_behavior_tracking: true

AI Orchestration

NeuroFlow Orchestrator provides comprehensive AI-native orchestration using neural networks and machine learning.

Neural Scheduling

• Deep learning-based task scheduling
• Predictive resource allocation
• Dynamic priority optimization
• Dependency-aware execution

Adaptive Execution

• Self-healing workflow recovery
• Intelligent retry strategies
• Performance-based optimization
• Real-time adaptation to failures

Neural Monitoring

• Anomaly detection with autoencoders
• Predictive failure prevention
• Performance pattern recognition
• Intelligent alerting and escalation

Learning Engine

• Continuous model improvement
• Reinforcement learning optimization
• Transfer learning across workflows
• Federated learning capabilities

Environment Variables

Configure NeuroFlow Orchestrator behavior using environment variables for different deployment scenarios.

Variable	Description	Default
AUGMENT_API_KEY	Your Augment API key	Required
NEUROFLOW_CONFIG	Path to configuration file	.neuroflow.yaml
NEUROFLOW_NEURAL_MODE	Enable neural network processing	enabled
NEUROFLOW_GPU_ENABLED	Enable GPU acceleration	false

Basic Usage

Learn the fundamental neural orchestration patterns and AI-native workflow management.

Neural Workflow Commands

Neural Orchestrationbash

# Create neural-optimized workflow
neuroflow create --workflow ml-pipeline --neural-optimization --adaptive-execution

# Enable neural monitoring with anomaly detection
neuroflow monitor --workflow ml-pipeline --neural-monitoring --predictive-alerts

# Scale workflow using neural predictions
neuroflow scale --workflow ml-pipeline --neural-scaling --resource-prediction

# Optimize workflow performance with neural insights
neuroflow optimize --workflow ml-pipeline --neural-tuning --performance-learning

CLI Commands Reference

Complete reference for all neural orchestration and AI-native workflow commands.

neural

Manage neural network models and AI-powered orchestration features

neuroflow neural [options]

Options:
  --init                   Initialize neural orchestration engine
  --train <models>         Train specific neural models
  --validate <models>      Validate model accuracy and performance
  --deploy <models>        Deploy trained models to production
  --retrain               Retrain models with new data
  --gpu-acceleration      Enable GPU acceleration for training
  --distributed-training  Enable distributed neural network training
  --model-versioning      Enable model version management

orchestrate

Execute AI-native workflow orchestration with neural optimization

neuroflow orchestrate [options]

Options:
  --workflow <name>        Target workflow for orchestration
  --neural-scheduling     Enable neural network-based scheduling
  --adaptive-execution    Enable adaptive execution based on conditions
  --predictive-scaling    Enable predictive resource scaling
  --self-healing          Enable self-healing workflow recovery
  --performance-learning  Enable performance-based learning
  --distributed-execution Enable distributed workflow execution
  --real-time-optimization Enable real-time neural optimization

Best Practices

Neural orchestration best practices to maximize AI-powered workflow performance and reliability.

AI-Native Orchestration Strategy

Start with comprehensive workflow discovery and pattern analysis
Train neural models on historical execution data for accuracy
Implement gradual neural optimization to minimize disruption
Use GPU acceleration for complex neural network processing
Enable continuous learning with performance feedback loops
Monitor neural model performance and retrain regularly

Workflow Automation

Advanced workflow automation using neural networks to optimize execution patterns and resource allocation.

Automation Techniques

Neural Automationbash

# Enable intelligent workflow automation
neuroflow automate --neural-decision-making --adaptive-workflows --self-optimization

# Configure predictive resource management
neuroflow resources --predictive-allocation --neural-scaling --cost-optimization

# Implement self-healing automation
neuroflow healing --neural-recovery --intelligent-retry --failure-prediction

# Deploy automated optimization pipelines
neuroflow optimize --continuous-improvement --performance-learning --efficiency-maximization

Neural Networks

Deep dive into the neural network architectures powering NeuroFlow Orchestrator's AI capabilities.

Neural Architecture

Neural Network Managementbash

# Configure neural network architectures
neuroflow neural configure --lstm-scheduler --autoencoder-monitor --dqn-optimizer

# Train custom neural models for specific workflows
neuroflow neural train --custom-model --workflow-specific --transfer-learning

# Deploy neural models with A/B testing
neuroflow neural deploy --model-comparison --performance-validation --gradual-rollout

# Monitor neural network performance and accuracy
neuroflow neural monitor --model-metrics --prediction-accuracy --learning-progress

API Integration

Integrate NeuroFlow Orchestrator into your development and operations workflows.

REST API

API Usagebash

# Create neural-optimized workflow via API
curl -X POST https://api.augment.cfd/v1/neuroflow/workflows \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "ml-training-pipeline",
    "neural_optimization": true,
    "adaptive_execution": true,
    "gpu_acceleration": true
  }'

Python SDK

Python Integrationpython

from augment_neuroflow import NeuroFlowOrchestrator

# Initialize neural orchestrator
orchestrator = NeuroFlowOrchestrator(api_key=os.environ['AUGMENT_API_KEY'])

# Create neural-optimized workflow
workflow = await orchestrator.create_workflow(
    name='ml-training-pipeline',
    neural_optimization=True,
    adaptive_execution=True,
    gpu_acceleration=True
)

# Enable neural monitoring
monitoring = await orchestrator.enable_monitoring(
    workflow_id=workflow.id,
    neural_monitoring=True,
    anomaly_detection=True,
    predictive_alerts=True
)

print(f"Created workflow: {workflow.name}")

# Train neural models for workflow optimization
training = await orchestrator.train_models(
    workflow_id=workflow.id,
    models=['scheduler', 'optimizer', 'monitor'],
    gpu_acceleration=True
)

# Execute workflow with neural orchestration
execution = await orchestrator.execute_workflow(
    workflow_id=workflow.id,
    neural_scheduling=True,
    adaptive_execution=True,
    real_time_optimization=True
)

API Reference

Complete API documentation for integrating neural orchestration into your applications.

Workflow Creation Endpoint

POST /v1/neuroflow/workflows

Create AI-native workflow with neural network optimization.

Request Body:

{
  "workflow_config": {
    "name": "ml-training-pipeline",
    "description": "Machine learning model training with neural optimization",
    "type": "machine_learning",
    "stages": [
      {
        "name": "data_preparation",
        "type": "data_processing",
        "neural_optimization": true,
        "resource_requirements": {
          "cpu": "4",
          "memory": "8Gi",
          "storage": "100Gi"
        }
      },
      {
        "name": "model_training",
        "type": "ml_training",
        "neural_optimization": true,
        "resource_requirements": {
          "cpu": "8",
          "memory": "16Gi",
          "gpu": "2",
          "storage": "500Gi"
        }
      }
    ]
  },
  "neural_settings": {
    "optimization_enabled": true,
    "gpu_acceleration": true,
    "neural_models": [
      {
        "name": "scheduler",
        "type": "lstm",
        "optimization_goals": ["minimize_latency", "maximize_throughput"]
      },
      {
        "name": "resource_optimizer",
        "type": "reinforcement_learning",
        "algorithm": "deep_q_network"
      }
    ],
    "learning_config": {
      "continuous_learning": true,
      "feedback_loop": true,
      "model_retraining_interval": "7d"
    }
  },
  "execution_settings": {
    "adaptive_execution": true,
    "self_healing": true,
    "predictive_scaling": true,
    "fault_tolerance": {
      "retry_strategy": "exponential_backoff",
      "circuit_breaker": true,
      "fallback_strategy": "graceful_degradation"
    }
  },
  "monitoring_settings": {
    "neural_monitoring": true,
    "anomaly_detection": true,
    "performance_tracking": true,
    "predictive_alerts": true,
    "metrics_collection": {
      "interval": "30s",
      "retention": "30d",
      "neural_features": ["execution_time", "resource_usage", "error_patterns"]
    }
  }
}

Response:

{
  "workflow_id": "wf-nf-12345",
  "status": "created",
  "name": "ml-training-pipeline",
  "neural_optimization": {
    "enabled": true,
    "models_initialized": 3,
    "gpu_acceleration": true,
    "training_status": "in_progress"
  },
  "execution_engine": {
    "type": "kubernetes",
    "namespace": "neuroflow",
    "neural_scheduler": "enabled",
    "adaptive_execution": "enabled"
  },
  "monitoring": {
    "neural_dashboard_url": "https://dashboard.augment.cfd/neuroflow/wf-nf-12345",
    "metrics_endpoint": "https://api.augment.cfd/v1/neuroflow/workflows/wf-nf-12345/metrics",
    "anomaly_detection": "enabled",
    "predictive_alerts": "configured"
  },
  "neural_models": [
    {
      "model_id": "scheduler-lstm-001",
      "type": "lstm",
      "status": "training",
      "accuracy": 0.89,
      "training_progress": 0.65
    },
    {
      "model_id": "optimizer-dqn-001",
      "type": "deep_q_network",
      "status": "training",
      "reward_score": 0.82,
      "training_progress": 0.58
    }
  ],
  "performance_predictions": {
    "estimated_execution_time": "2h 15m",
    "resource_efficiency": 0.87,
    "cost_optimization": "23% reduction expected",
    "success_probability": 0.94
  },
  "recommendations": [
    {
      "type": "resource_optimization",
      "recommendation": "Consider GPU memory optimization for training stage",
      "confidence": 0.91,
      "potential_improvement": "15% faster execution"
    }
  ]
}

Troubleshooting

Common issues and solutions when implementing AI-native workflow orchestration.

Common Issues

Neural Model Training Issues

Neural networks not converging or showing poor performance

Solutions:

Increase training data collection period for better model accuracy
Adjust neural network hyperparameters (learning rate, batch size)
Enable GPU acceleration for faster and more stable training
Use transfer learning from pre-trained models for faster convergence

GPU Resource Conflicts

GPU memory issues or resource allocation conflicts

Solutions:

Configure GPU memory limits in neural engine settings
Enable GPU memory growth to avoid pre-allocation issues
Use distributed training across multiple GPUs
Implement GPU resource scheduling and queuing

Workflow Optimization Conflicts

Neural optimization causing workflow execution issues

Solutions:

Enable gradual optimization rollout with A/B testing
Implement safety constraints in neural optimization models
Use performance validation before applying optimizations
Enable fallback to traditional scheduling when neural models fail

Neural Orchestration Documentation Complete!

You now have comprehensive knowledge to implement NeuroFlow Orchestrator in your infrastructure. From AI-native workflow orchestration to neural network optimization, you're equipped to revolutionize workflow management with cutting-edge AI technology.

Ready to revolutionize your workflows with AI? Start your free neural orchestration trial today and discover how neural networks can transform your workflow performance and reliability.

Installation

Deploy NeuroFlow Orchestrator to start AI-native workflow orchestration with neural network optimization.

System Requirements

Python 3.9+ with TensorFlow 2.8+ or PyTorch 1.12+
Kubernetes 1.20+ for container orchestration
Minimum 8GB RAM (16GB recommended for neural processing)
GPU support recommended for neural network training
Redis or similar for distributed state management

Install via Package Manager

Installationbash

# Install via pip
pip install augment-neuroflow-orchestrator

# Install with GPU support
pip install augment-neuroflow-orchestrator[gpu]

# Install from source
git clone https://github.com/augment-ai/neuroflow-orchestrator
cd neuroflow-orchestrator
pip install -e .

# Install neural network dependencies
pip install tensorflow torch numpy scipy scikit-learn

# Verify installation
neuroflow --version

Neural Orchestration Setup

Configure neural network-powered workflow orchestration and AI-driven scheduling:

Neural Setupbash

# Set Augment API key
export AUGMENT_API_KEY=your_api_key_here

# Configure neural orchestration
export NEUROFLOW_NEURAL_MODE=enabled
export NEUROFLOW_GPU_ENABLED=true

# Configure workflow discovery
export WORKFLOW_DISCOVERY_ENABLED=true
export KUBERNETES_CONFIG_PATH=/etc/kubernetes/config

# Configure neural network training
export NEURAL_TRAINING_ENABLED=true
export TRAINING_DATA_RETENTION=30d

# Initialize neural orchestrator
neuroflow init --neural-mode --discover-workflows --train-models

# Verify neural setup
neuroflow health-check --test-neural-networks --validate-gpu

Quick Start

Get AI-native workflow orchestration running in minutes with neural network optimization and intelligent scheduling.

1. Initialize Neural Orchestration

Neural Initializationbash

# Initialize neural workflow engine
neuroflow neural init --enable-learning --gpu-acceleration

# Discover existing workflows for neural analysis
neuroflow discover --kubernetes --docker-compose --airflow --jenkins

# Train initial neural models on workflow patterns
neuroflow train --workflow-patterns --execution-history --performance-data

# Validate neural model accuracy
neuroflow validate --models workflow_optimizer,scheduler,monitor --test-data

2. Create AI-Native Workflows

Workflow Creationbash

# Create workflow with neural optimization
neuroflow create --workflow data-pipeline --neural-optimization --adaptive-scheduling

# Enable intelligent monitoring and self-healing
neuroflow monitor --workflow data-pipeline --neural-monitoring --auto-remediation

# Configure predictive scaling based on neural insights
neuroflow scale --workflow data-pipeline --predictive-scaling --resource-optimization

# Deploy workflow with neural execution engine
neuroflow deploy --workflow data-pipeline --neural-engine --distributed-execution

3. Monitor Neural Performance

Neural Monitoringbash

# Start neural performance monitoring
neuroflow monitor --neural-dashboard --real-time-metrics --anomaly-detection

# View neural optimization results
neuroflow status --neural-insights --performance-improvements --learning-progress

# Generate neural workflow analytics
neuroflow analytics --neural-patterns --optimization-recommendations --efficiency-metrics

# Retrain neural models with new data
neuroflow retrain --incremental-learning --performance-feedback --adaptive-weights

Configuration

Configure NeuroFlow Orchestrator to align with your infrastructure and neural processing requirements.

Basic Configuration

.neuroflow.yamlyaml

version: "1.0"
organization: "your-company"
environment: "production"

neural_engine:
  enabled: true
  gpu_acceleration: true
  neural_frameworks:
    - name: "tensorflow"
      version: "2.8.0"
      gpu_memory_limit: "4GB"
    - name: "pytorch"
      version: "1.12.0"
      gpu_memory_limit: "4GB"
  model_storage:
    backend: "s3"
    bucket: "neuroflow-models"
    encryption: true
  training_config:
    batch_size: 32
    learning_rate: 0.001
    epochs: 100
    validation_split: 0.2
    early_stopping: true

workflow_orchestration:
  execution_engine:
    type: "kubernetes"
    namespace: "neuroflow"
    resource_limits:
      cpu: "4"
      memory: "8Gi"
      gpu: "1"
  scheduling:
    algorithm: "neural_scheduler"
    optimization_goals: ["minimize_latency", "maximize_throughput", "optimize_cost"]
    predictive_scaling: true
    resource_prediction_window: "1h"
  fault_tolerance:
    enabled: true
    retry_strategy: "exponential_backoff"
    circuit_breaker: true
    self_healing: true

neural_models:
  workflow_optimizer:
    enabled: true
    model_type: "lstm"
    input_features: ["execution_time", "resource_usage", "dependencies", "data_size"]
    output_targets: ["optimal_schedule", "resource_allocation"]
    training_frequency: "daily"
    performance_threshold: 0.85
  scheduler:
    enabled: true
    model_type: "reinforcement_learning"
    algorithm: "deep_q_network"
    state_space: ["queue_length", "resource_availability", "priority_scores"]
    action_space: ["schedule_now", "delay", "reschedule", "scale_resources"]
    reward_function: "composite_performance"
  monitor:
    enabled: true
    model_type: "autoencoder"
    anomaly_threshold: 0.95
    features: ["cpu_usage", "memory_usage", "network_io", "execution_patterns"]
    alert_sensitivity: "medium"

data_sources:
  kubernetes:
    enabled: true
    config_path: "/etc/kubernetes/config"
    namespaces: ["default", "production", "staging"]
  prometheus:
    enabled: true
    endpoint: "http://prometheus:9090"
    metrics: ["cpu_usage", "memory_usage", "network_io", "custom_metrics"]
  elasticsearch:
    enabled: true
    endpoint: "http://elasticsearch:9200"
    indices: ["workflow-logs-*", "performance-metrics-*"]

workflow_patterns:
  data_pipeline:
    type: "batch_processing"
    neural_optimization: true
    stages: ["extract", "transform", "load", "validate"]
    dependencies: ["database", "storage", "compute"]
    sla_requirements:
      max_execution_time: "2h"
      min_success_rate: 0.95
  ml_training:
    type: "machine_learning"
    neural_optimization: true
    stages: ["data_prep", "training", "validation", "deployment"]
    resource_requirements:
      gpu: true
      memory_intensive: true
    auto_scaling: true

monitoring:
  neural_dashboard:
    enabled: true
    port: 8080
    authentication: true
  metrics_collection:
    enabled: true
    interval: "30s"
    retention: "30d"
  alerting:
    enabled: true
    channels: ["slack", "email", "pagerduty"]
    neural_anomaly_detection: true

security:
  encryption:
    enabled: true
    algorithm: "AES-256-GCM"
    key_rotation: "weekly"
  authentication:
    method: "oauth2"
    provider: "okta"
  authorization:
    rbac: true
    neural_access_control: true
  audit_logging:
    enabled: true
    neural_behavior_tracking: true

AI Orchestration

NeuroFlow Orchestrator provides comprehensive AI-native orchestration using neural networks and machine learning.

Neural Scheduling

• Deep learning-based task scheduling
• Predictive resource allocation
• Dynamic priority optimization
• Dependency-aware execution

Adaptive Execution

• Self-healing workflow recovery
• Intelligent retry strategies
• Performance-based optimization
• Real-time adaptation to failures

Neural Monitoring

• Anomaly detection with autoencoders
• Predictive failure prevention
• Performance pattern recognition
• Intelligent alerting and escalation

Learning Engine

• Continuous model improvement
• Reinforcement learning optimization
• Transfer learning across workflows
• Federated learning capabilities

Environment Variables

Configure NeuroFlow Orchestrator behavior using environment variables for different deployment scenarios.

Variable	Description	Default
AUGMENT_API_KEY	Your Augment API key	Required
NEUROFLOW_CONFIG	Path to configuration file	.neuroflow.yaml
NEUROFLOW_NEURAL_MODE	Enable neural network processing	enabled
NEUROFLOW_GPU_ENABLED	Enable GPU acceleration	false

Basic Usage

Learn the fundamental neural orchestration patterns and AI-native workflow management.

Neural Workflow Commands

Neural Orchestrationbash

# Create neural-optimized workflow
neuroflow create --workflow ml-pipeline --neural-optimization --adaptive-execution

# Enable neural monitoring with anomaly detection
neuroflow monitor --workflow ml-pipeline --neural-monitoring --predictive-alerts

# Scale workflow using neural predictions
neuroflow scale --workflow ml-pipeline --neural-scaling --resource-prediction

# Optimize workflow performance with neural insights
neuroflow optimize --workflow ml-pipeline --neural-tuning --performance-learning

CLI Commands Reference

Complete reference for all neural orchestration and AI-native workflow commands.

neural

Manage neural network models and AI-powered orchestration features

neuroflow neural [options]

Options:
  --init                   Initialize neural orchestration engine
  --train <models>         Train specific neural models
  --validate <models>      Validate model accuracy and performance
  --deploy <models>        Deploy trained models to production
  --retrain               Retrain models with new data
  --gpu-acceleration      Enable GPU acceleration for training
  --distributed-training  Enable distributed neural network training
  --model-versioning      Enable model version management

orchestrate

Execute AI-native workflow orchestration with neural optimization

neuroflow orchestrate [options]

Options:
  --workflow <name>        Target workflow for orchestration
  --neural-scheduling     Enable neural network-based scheduling
  --adaptive-execution    Enable adaptive execution based on conditions
  --predictive-scaling    Enable predictive resource scaling
  --self-healing          Enable self-healing workflow recovery
  --performance-learning  Enable performance-based learning
  --distributed-execution Enable distributed workflow execution
  --real-time-optimization Enable real-time neural optimization

Best Practices

Neural orchestration best practices to maximize AI-powered workflow performance and reliability.

AI-Native Orchestration Strategy

Start with comprehensive workflow discovery and pattern analysis
Train neural models on historical execution data for accuracy
Implement gradual neural optimization to minimize disruption
Use GPU acceleration for complex neural network processing
Enable continuous learning with performance feedback loops
Monitor neural model performance and retrain regularly

Workflow Automation

Advanced workflow automation using neural networks to optimize execution patterns and resource allocation.

Automation Techniques

Neural Automationbash

# Enable intelligent workflow automation
neuroflow automate --neural-decision-making --adaptive-workflows --self-optimization

# Configure predictive resource management
neuroflow resources --predictive-allocation --neural-scaling --cost-optimization

# Implement self-healing automation
neuroflow healing --neural-recovery --intelligent-retry --failure-prediction

# Deploy automated optimization pipelines
neuroflow optimize --continuous-improvement --performance-learning --efficiency-maximization

Neural Networks

Deep dive into the neural network architectures powering NeuroFlow Orchestrator's AI capabilities.

Neural Architecture

Neural Network Managementbash

# Configure neural network architectures
neuroflow neural configure --lstm-scheduler --autoencoder-monitor --dqn-optimizer

# Train custom neural models for specific workflows
neuroflow neural train --custom-model --workflow-specific --transfer-learning

# Deploy neural models with A/B testing
neuroflow neural deploy --model-comparison --performance-validation --gradual-rollout

# Monitor neural network performance and accuracy
neuroflow neural monitor --model-metrics --prediction-accuracy --learning-progress

API Integration

Integrate NeuroFlow Orchestrator into your development and operations workflows.

REST API

API Usagebash

# Create neural-optimized workflow via API
curl -X POST https://api.augment.cfd/v1/neuroflow/workflows \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "ml-training-pipeline",
    "neural_optimization": true,
    "adaptive_execution": true,
    "gpu_acceleration": true
  }'

Python SDK

Python Integrationpython

from augment_neuroflow import NeuroFlowOrchestrator

# Initialize neural orchestrator
orchestrator = NeuroFlowOrchestrator(api_key=os.environ['AUGMENT_API_KEY'])

# Create neural-optimized workflow
workflow = await orchestrator.create_workflow(
    name='ml-training-pipeline',
    neural_optimization=True,
    adaptive_execution=True,
    gpu_acceleration=True
)

# Enable neural monitoring
monitoring = await orchestrator.enable_monitoring(
    workflow_id=workflow.id,
    neural_monitoring=True,
    anomaly_detection=True,
    predictive_alerts=True
)

print(f"Created workflow: {workflow.name}")

# Train neural models for workflow optimization
training = await orchestrator.train_models(
    workflow_id=workflow.id,
    models=['scheduler', 'optimizer', 'monitor'],
    gpu_acceleration=True
)

# Execute workflow with neural orchestration
execution = await orchestrator.execute_workflow(
    workflow_id=workflow.id,
    neural_scheduling=True,
    adaptive_execution=True,
    real_time_optimization=True
)

API Reference

Complete API documentation for integrating neural orchestration into your applications.

Workflow Creation Endpoint

POST /v1/neuroflow/workflows

Create AI-native workflow with neural network optimization.

Request Body:

{
  "workflow_config": {
    "name": "ml-training-pipeline",
    "description": "Machine learning model training with neural optimization",
    "type": "machine_learning",
    "stages": [
      {
        "name": "data_preparation",
        "type": "data_processing",
        "neural_optimization": true,
        "resource_requirements": {
          "cpu": "4",
          "memory": "8Gi",
          "storage": "100Gi"
        }
      },
      {
        "name": "model_training",
        "type": "ml_training",
        "neural_optimization": true,
        "resource_requirements": {
          "cpu": "8",
          "memory": "16Gi",
          "gpu": "2",
          "storage": "500Gi"
        }
      }
    ]
  },
  "neural_settings": {
    "optimization_enabled": true,
    "gpu_acceleration": true,
    "neural_models": [
      {
        "name": "scheduler",
        "type": "lstm",
        "optimization_goals": ["minimize_latency", "maximize_throughput"]
      },
      {
        "name": "resource_optimizer",
        "type": "reinforcement_learning",
        "algorithm": "deep_q_network"
      }
    ],
    "learning_config": {
      "continuous_learning": true,
      "feedback_loop": true,
      "model_retraining_interval": "7d"
    }
  },
  "execution_settings": {
    "adaptive_execution": true,
    "self_healing": true,
    "predictive_scaling": true,
    "fault_tolerance": {
      "retry_strategy": "exponential_backoff",
      "circuit_breaker": true,
      "fallback_strategy": "graceful_degradation"
    }
  },
  "monitoring_settings": {
    "neural_monitoring": true,
    "anomaly_detection": true,
    "performance_tracking": true,
    "predictive_alerts": true,
    "metrics_collection": {
      "interval": "30s",
      "retention": "30d",
      "neural_features": ["execution_time", "resource_usage", "error_patterns"]
    }
  }
}

Response:

{
  "workflow_id": "wf-nf-12345",
  "status": "created",
  "name": "ml-training-pipeline",
  "neural_optimization": {
    "enabled": true,
    "models_initialized": 3,
    "gpu_acceleration": true,
    "training_status": "in_progress"
  },
  "execution_engine": {
    "type": "kubernetes",
    "namespace": "neuroflow",
    "neural_scheduler": "enabled",
    "adaptive_execution": "enabled"
  },
  "monitoring": {
    "neural_dashboard_url": "https://dashboard.augment.cfd/neuroflow/wf-nf-12345",
    "metrics_endpoint": "https://api.augment.cfd/v1/neuroflow/workflows/wf-nf-12345/metrics",
    "anomaly_detection": "enabled",
    "predictive_alerts": "configured"
  },
  "neural_models": [
    {
      "model_id": "scheduler-lstm-001",
      "type": "lstm",
      "status": "training",
      "accuracy": 0.89,
      "training_progress": 0.65
    },
    {
      "model_id": "optimizer-dqn-001",
      "type": "deep_q_network",
      "status": "training",
      "reward_score": 0.82,
      "training_progress": 0.58
    }
  ],
  "performance_predictions": {
    "estimated_execution_time": "2h 15m",
    "resource_efficiency": 0.87,
    "cost_optimization": "23% reduction expected",
    "success_probability": 0.94
  },
  "recommendations": [
    {
      "type": "resource_optimization",
      "recommendation": "Consider GPU memory optimization for training stage",
      "confidence": 0.91,
      "potential_improvement": "15% faster execution"
    }
  ]
}

Troubleshooting

Common issues and solutions when implementing AI-native workflow orchestration.

Common Issues

Neural Model Training Issues

Neural networks not converging or showing poor performance

Solutions:

Increase training data collection period for better model accuracy
Adjust neural network hyperparameters (learning rate, batch size)
Enable GPU acceleration for faster and more stable training
Use transfer learning from pre-trained models for faster convergence

GPU Resource Conflicts

GPU memory issues or resource allocation conflicts

Solutions:

Configure GPU memory limits in neural engine settings
Enable GPU memory growth to avoid pre-allocation issues
Use distributed training across multiple GPUs
Implement GPU resource scheduling and queuing

Workflow Optimization Conflicts

Neural optimization causing workflow execution issues

Solutions:

Enable gradual optimization rollout with A/B testing
Implement safety constraints in neural optimization models
Use performance validation before applying optimizations
Enable fallback to traditional scheduling when neural models fail

Neural Orchestration Documentation Complete!

Ready to revolutionize your workflows with AI? Start your free neural orchestration trial today and discover how neural networks can transform your workflow performance and reliability.

NeuroFlow Orchestrator

Neural Workflow Engine

Intelligent Scheduling

Adaptive Execution

Neural Monitoring

Installation

System Requirements

Install via Package Manager

Neural Orchestration Setup

Quick Start

1. Initialize Neural Orchestration

2. Create AI-Native Workflows

3. Monitor Neural Performance

Configuration

Basic Configuration

AI Orchestration

Neural Scheduling

Adaptive Execution

Neural Monitoring

Learning Engine

Environment Variables

Basic Usage

Neural Workflow Commands

CLI Commands Reference

neural

orchestrate

Best Practices

AI-Native Orchestration Strategy

Workflow Automation

Automation Techniques

Neural Networks

Neural Architecture

API Integration

REST API

Python SDK

API Reference

Workflow Creation Endpoint

POST /v1/neuroflow/workflows

Request Body:

Response:

Troubleshooting

Common Issues

Neural Model Training Issues

GPU Resource Conflicts

Workflow Optimization Conflicts

Neural Orchestration Documentation Complete!

NeuroFlow Orchestrator

Neural Workflow Engine

Intelligent Scheduling

Adaptive Execution

Neural Monitoring

Installation

System Requirements

Install via Package Manager

Neural Orchestration Setup

Quick Start

1. Initialize Neural Orchestration

2. Create AI-Native Workflows

3. Monitor Neural Performance

Configuration

Basic Configuration

AI Orchestration

Neural Scheduling

Adaptive Execution

Neural Monitoring

Learning Engine

Environment Variables

Basic Usage

Neural Workflow Commands

CLI Commands Reference

neural

orchestrate

Best Practices

AI-Native Orchestration Strategy

Workflow Automation

Automation Techniques

Neural Networks

Neural Architecture

API Integration

REST API