PloyD SDK Architecture

Unified API for 15+ frameworks with automatic detection, intelligent routing, and multi-cloud orchestration

PloyD SDK provides a comprehensive abstraction layer over model serving infrastructure, enabling you to deploy any model with any framework to any cloud - all through a single, unified API. The SDK handles framework detection, resource optimization, auto-scaling, and intelligent routing automatically.

PloyD SDK Core

ModelServing

Deploy & Scale

AIGateway

Intelligent Routing

ModelRegistry

Lifecycle Mgmt

Security

RBAC & Auth

Framework Adapters (15+)

vLLM

High-perf LLMs

TensorRT-LLM

NVIDIA

PyTorch

Research & Prod

TensorFlow

Enterprise ML

AI-Dynamo

Dynamic

+10 More

Triton, ONNX...

Multi-Cloud Infrastructure

AWS

EKS + SageMaker

Azure

AKS + ML

GCP

GKE + Vertex

Nebius

GPU Cloud

CoreWeave

Specialized GPU

Orchestration Layer

Kubernetes

Container Orchestration

GitOps

ArgoCD/Flux

Monitoring

Prometheus/Grafana

SDK Core Components

Essential SDK modules that power intelligent model deployment and management

FrameworkRegistry
Automatic framework detection and adapter selection for 15+ frameworks including vLLM, TensorRT-LLM, SGLang, AI-Dynamo, PyTorch, TensorFlow, and more. Handles framework-specific optimizations automatically.

ModelRegistry
Complete model lifecycle management with versioning, staging (dev/staging/prod), metadata tracking, and deployment history. Supports A/B testing and canary deployments.

AIGateway
Intelligent routing with multiple strategies: latency-based, cost-optimized, weighted round-robin. Includes circuit breakers, rate limiting, and automatic failover.

Load Balancing
Intelligent traffic distribution across model replicas with support for weighted routing, health checks, and failover mechanisms.

Performance Monitoring
Real-time metrics collection for latency, throughput, error rates, and resource utilization with integrated alerting and dashboards.

Security & Compliance
End-to-end encryption, authentication, authorization, and audit logging. SOC2 and GDPR compliant infrastructure.

Framework Adapters

PloyD supports 15+ ML frameworks through intelligent adapters that handle framework-specific optimizations automatically

Framework adapters are the heart of PloyD's flexibility, enabling seamless deployment across different ML frameworks without code changes. Each adapter is optimized for its framework's unique characteristics.

LLM

vLLM

  • PagedAttention: Memory-efficient attention mechanism
  • Continuous batching: Dynamic request batching
  • Optimized kernels: Flash Attention integration
  • Multi-GPU: Tensor parallelism support
LLM

TensorRT-LLM

  • INT8/FP8 quantization: Reduced memory footprint
  • In-flight batching: Real-time request merging
  • Multi-GPU/Multi-node: Distributed inference
  • NVIDIA optimizations: Hardware-specific tuning
LLM

SGLang

  • RadixAttention: KV cache sharing
  • Structured generation: Guided decoding
  • Automatic parallelization: Multi-device scaling
  • Efficient scheduling: Advanced batching strategies
LLM

AI-Dynamo

  • Disaggregated serving: Separate prefill/decode
  • Dynamic scheduling: GPU resource optimization
  • KV cache offloading: Memory management
  • LLM-aware routing: Intelligent request distribution
General

PyTorch

  • TorchScript: Production optimization
  • Mixed precision: AMP support
  • Model parallelism: Large model support
  • Dynamic graphs: Flexible model serving
General

TensorFlow

  • SavedModel format: Optimized serialization
  • TensorRT integration: GPU acceleration
  • TF Serving: Production-grade serving
  • Graph optimization: XLA compiler
General

ONNX Runtime

  • Cross-platform: Universal model format
  • Hardware acceleration: CPU/GPU/NPU support
  • Graph optimization: Automatic fusion
  • Quantization: INT8/FP16 inference
General

Triton Inference Server

  • Multi-framework: Single server, multiple models
  • Dynamic batching: Request aggregation
  • Model ensemble: Pipeline support
  • HTTP/gRPC: Flexible API protocols
NLP

Hugging Face Transformers

  • Pre-trained models: Instant deployment
  • Pipeline API: High-level abstractions
  • Tokenizer support: Built-in preprocessing
  • Model hub: Direct integration
Vision

Stable Diffusion / Diffusers

  • Image generation: Text-to-image models
  • Schedulers: Multiple sampling methods
  • LoRA support: Fine-tuned variants
  • Optimization: Memory-efficient attention
Edge

OpenVINO

  • Intel optimization: CPU/iGPU acceleration
  • Model compression: Pruning and quantization
  • Edge deployment: IoT and embedded
  • Heterogeneous execution: Multi-device
Custom

Custom Frameworks

  • Plugin system: Extend PloyD easily
  • Custom adapters: Your framework support
  • API compatibility: Unified interface
  • Documentation: Integration guides

Technology Stack

Modern technologies and frameworks powering our model serving platform

ML Frameworks

  • PyTorch & TorchServe
  • TensorFlow Serving
  • ONNX Runtime
  • Hugging Face Transformers
  • Custom Framework Support

Compute Infrastructure

  • NVIDIA A100, H100 GPUs
  • AMD EPYC CPUs
  • High-bandwidth memory
  • NVMe SSD storage
  • InfiniBand networking

Container Platform

  • Kubernetes orchestration
  • Docker containerization
  • Helm chart management
  • Istio service mesh
  • NVIDIA GPU Operator

Data & Storage

  • MinIO object storage
  • PostgreSQL metadata
  • Redis caching
  • Prometheus metrics
  • Grafana visualization

Deployment Workflow

Step-by-step process to deploy and serve your ML models in production

1. Model Registration

Upload and register your trained model with metadata, dependencies, and serving configuration.

# Register a new model from PloyD import ModelRegistry registry = ModelRegistry() model_version = registry.register_model( name="sentiment-classifier", model_path="./model.pkl", framework="pytorch", metadata={ "accuracy": 0.95, "training_data": "imdb-reviews", "model_size": "1.2GB" } )

2. Deployment Configuration

Configure serving parameters including resource requirements, scaling policies, and routing rules.

# Deploy model with configuration deployment = registry.deploy_model( model_version=model_version, config={ "replicas": {"min": 2, "max": 10}, "resources": { "gpu": 1, "memory": "8Gi", "cpu": "4" }, "scaling": { "target_latency": "100ms", "requests_per_second": 1000 } } )

3. Model Serving

Access your deployed model through RESTful APIs with automatic load balancing and scaling.

# Make inference requests import requests response = requests.post( f"https://api.ployd.ai/models/{deployment.endpoint}/predict", json={ "instances": [ {"text": "This movie is amazing!"}, {"text": "Not worth watching."} ] }, headers={"Authorization": "Bearer YOUR_API_KEY"} ) predictions = response.json()["predictions"]

Performance Optimizations

Advanced techniques to maximize inference speed and resource efficiency

Inference Acceleration

  • Dynamic Batching: Automatically batch multiple requests to improve GPU utilization
  • Model Quantization: Reduce model size and increase inference speed with INT8/FP16 precision
  • TensorRT Optimization: NVIDIA TensorRT integration for maximum GPU performance
  • ONNX Conversion: Cross-framework optimization and deployment

Resource Management

  • GPU Sharing: Multi-tenancy support for cost-effective GPU utilization
  • Memory Optimization: Efficient memory management and garbage collection
  • Cold Start Mitigation: Model warming and pre-loading strategies
  • Caching: Intelligent caching for frequently requested predictions

Monitoring & Observability

Comprehensive monitoring and alerting for production ML systems

Key Metrics

  • Latency: P50, P95, P99 response times
  • Throughput: Requests per second and batch processing rates
  • Error Rates: HTTP errors and model prediction failures
  • Resource Utilization: GPU, CPU, and memory usage
  • Model Drift: Input distribution and prediction quality monitoring

Alerting & Dashboards

  • Real-time Grafana dashboards for operational visibility
  • Custom alerting rules with Slack/PagerDuty integration
  • Model performance degradation detection
  • Automated incident response and escalation

Security Architecture

Enterprise-grade security and compliance for production ML workloads

Data Protection

  • Encryption: End-to-end encryption for data in transit and at rest
  • Network Isolation: VPC and subnet isolation for model serving workloads
  • Access Control: RBAC and API key management
  • Audit Logging: Comprehensive logging of all API calls and model access

Compliance

  • SOC 2 Type II certified infrastructure
  • GDPR compliance for EU data processing
  • HIPAA-ready deployment options
  • Regular security audits and penetration testing

Getting Started

Ready to deploy your models with PloyD? Get from trained model to production API in minutes.

Quick Start

Follow our Model Serving Guide to deploy your first model in under 10 minutes.

Documentation

Explore our comprehensive API documentation and integration examples for popular ML frameworks.

Expert Support

Get help from our ML infrastructure experts with personalized consultation and technical support.