Plug-and-Play Benchmarking API

Updated 25 November 2025

Plug-and-play benchmarking API is a modular evaluation framework enabling reproducible testing of algorithms using swappable benchmark components.
It employs layered architectures with containerized execution, ensuring isolated environments, strict resource management, and auditability.
The API supports dynamic plugin registration and config-driven workflows, facilitating integration across data science, optimization, and microservices domains.

A plug-and-play benchmarking API is an architectural and software pattern enabling modular, extensible, and reproducible evaluation of algorithms, systems, or models under well-defined benchmark protocols. Plug-and-play (PnP) in this context denotes that benchmarks, evaluation logic, and metrics are architected as swappable components, discoverable via registries or configuration files, with minimal friction for adding new methods or tasks. Such APIs are foundational in workflow engines like Codabench (Xu et al., 2021), modern microservices benchmark systems like NDBench (Papapanagiotou et al., 2018), extensible black-box optimization testbeds such as Bencher (Papenmeier et al., 27 May 2025), and application suites for plug-and-play evaluation in emerging domains (e.g., diffusion priors (Zheng et al., 14 Mar 2025) and VLM compression (Lv et al., 13 Aug 2025)).

1. System Architecture and Layered Design

Plug-and-play benchmarking APIs exhibit strict architectural modularity, typically via a multi-tier or client-server paradigm that enforces clear boundaries between user interfaces, orchestrators, compute resources, and the plug-in logic of benchmarks or tests.

For example, Codabench employs a three-tier architecture:

Front-end/Web Tier: Contains SPA (React/Vue) and REST API endpoints for benchmark discovery, leaderboard queries, and result inspection.
Application/Backend Tier: Orchestrator service handles registration, submission routing, scoring lifecycle, and dispatches jobs to isolated compute workers.
Compute/Storage Tier: Task execution is fully containerized (Docker/Kubernetes), with object storage for artifacts and relational/NoSQL DBs for metadata (Xu et al., 2021).

Bencher generalizes the separation with a client-server abstraction. The optimizer or algorithm ("client") queries a benchmark's objective function via a gRPC/Protobuf boundary, agnostic to benchmark implementation or runtime dependencies. Each benchmark runs in dedicated isolated Python environments, schedulable on local, cloud, or HPC resources. The API surface is minimal—typically three calls: GetMetadata, Evaluate, and BatchEvaluate, all exposed via a single well-documented port (Papenmeier et al., 27 May 2025).

NDBench for microservices evaluation adopts a plugin-oriented Java service structure split into:

Plugin layer: Client drivers for each backend system.
Core layer: Multi-threaded workload generator and orchestrator.
Web/service layer: REST/Angular UI plus service discovery for dynamic test deployment (Papapanagiotou et al., 2018).

2. Benchmark Specification and Configuration

The canonical PnP design encodes each benchmark as a manifest—frequently JSON or YAML—that declares:

Benchmark metadata (ID, title, description, docker image)
Tasks: Each with its own input data, ingestion/scoring logic, container runtime parameters (CPU, memory, GPU), and time/resource limits
Leaderboard specification: Columns, keys, aggregation/sorting semantics
Extensibility hooks: Template inheritance, runtime plugin API integration

A typical YAML schema in Codabench looks like:

benchmark:
  id: "autograph"
  title: "AutoGraph Benchmark"
  docker_image: "nehzux/kddcup2020:v2"
  tasks:
    - index: 0
      name: "Node‐Classification‐Cora"
      ingestion_program: ingestion.zip
      scoring_program: scoring.zip
      input_data: cora_train.zip
      reference_data: cora_test_gt.zip
      time_limit_ms: 60000
      resources:
        cpu: 1.0
        memory: "2Gi"
        gpu: 0
  leaderboards:
    - title: "Main Scores"
      key: main
      columns:
        - {title: "Acc",  key: "accuracy", sorting: "desc"}
        - {title: "F1",   key: "f1_score", sorting: "desc"}

(Xu et al., 2021)

LLMC+ and Bencher similarly use config-driven workflows, where registries and configuration yamls/jsons fully specify the tasks, models, compression/evaluation modules, and profiling logic (Papenmeier et al., 27 May 2025, Lv et al., 13 Aug 2025).

3. Execution and Isolation Mechanisms

Contemporary PnP benchmarking APIs provide strict task isolation and software environment reproducibility through containerization:

Container Execution: All benchmark runs execute within organizer-specified Docker images (or Singularity images for HPC), using runtime-exact software stacks and stateless execution semantics. API endpoints manage job submission and polling, typically abstracted as:

1 2	POST /api/v1/submissions GET /api/v1/submissions/{sid}/results

Compute Resource Isolation: Each task requests explicit resource minima (CPU, memory, GPU quotas); an autoscaler and fair-share scheduler ensure allocative fairness under load, often via weighted quotas (Xu et al., 2021).
Reproducibility Guarantees: Immutable image tags (never "latest"), random seed locking (e.g., seed = hash(submission_id ⊕ task_id)), and end-to-end audit/logging of job provenance enable exact reruns and experiment tracking (Xu et al., 2021, Papenmeier et al., 27 May 2025).

Bencher supports per-benchmark dependency isolation using Poetry-based Python environments per container; all user logic is accessed remotely via gRPC with serialized protobufs (thus no dependency bleeding) (Papenmeier et al., 27 May 2025).

4. Extension, Integration, and Registry Patterns

Plug-and-play APIs enable extensibility by plugin registration and dynamic discovery:

Pluggable Algorithms/Tasks: APIs expose base classes and decorators (e.g., @register_metric, @register_token_reducer in LLMC+) for transparent registration in global registries. The orchestrator never needs to be modified to add new compressors, quantizers, or evaluation metrics (Lv et al., 13 Aug 2025).
Runtime Plugin Hooks: Benchmark manifests may inherit from templates via an extends: directive, with override sections for per-task or per-method customization (Xu et al., 2021).
API and Protocol Surface: The communication surface is typically standardized via REST or gRPC. For black-box optimization (Bencher), the interface is mathematically specified:

$f_b: \mathcal{X}_b \to \mathbb{R}$

with all evaluation requests isolated across independent environments (Papenmeier et al., 27 May 2025).

Config-Driven Experimentation: Users can combine any registered component by referencing their names and parameters in YAML/JSON configs, yielding workflows such as “token reduction + quantization + mixed-task evaluation” in LLMC+, or “new prior + new algorithm” pairing in InverseBench (Zheng et al., 14 Mar 2025, Lv et al., 13 Aug 2025).

5. Metrics, Logging, and Leaderboard Computation

Flexible metric computation and rapid leaderboard updates are core features:

Built-in Metrics: Each suite defines a set of canonical metrics, parameterized by task type—classification (Accuracy, F1), regression (MSE), RL (return), compression (Compression Ratio $R=\frac{\text{orig size}}{\text{compressed size}}$ ), or dialog consistency (Lv et al., 13 Aug 2025). Custom metrics can be plugged in via decorator registration patterns.
Leaderboard Generation: Upon scoring completion, the orchestrator updates centralized leaderboards, which are queriable via API endpoints (e.g., [GET](https://www.emergentmind.com/topics/gaussian-equivalence-theory-get) /api/v1/benchmarks/{bid}/leaderboard). Sorting and aggregation semantics are schema-driven (Xu et al., 2021).
Reproducible Logging: All logs, job configs, random seeds, Docker image digests, and version information are logged and archived for auditability and result reproduction (Xu et al., 2021, Papenmeier et al., 27 May 2025).

Table: Example API Endpoints for Results

Endpoint	Description
POST /api/v1/benchmarks/{bid}/submissions	Submit algorithm/code bundle
GET /api/v1/submissions/{sid}/results	Poll for result and metrics
GET /api/v1/benchmarks/{bid}/leaderboard	Leaderboard with current scores

6. Application Domains and Notable Implementations

Plug-and-play benchmarking APIs are foundational in multiple research areas:

Data Science and Crowdsourcing: Codabench supports flexible crowdsourced benchmarking with custom ingestion/scoring pipelines, employed in GNN, medical, and RL tasks (Xu et al., 2021).
Black-box Optimization: Bencher supports >80 benchmarks, each encapsulated in isolated Python environments and accessed over a simple gRPC protocol (Papenmeier et al., 27 May 2025).
Microservices and Datastores: NDBench, used in Netflix, supports continual, dynamically reconfigurable, plugin-based traffic generation and benchmarking at production scale (Papapanagiotou et al., 2018).
Scientific Inverse Problems: InverseBench orchestrates plug-and-play evaluation of diffusion priors on complex scientific data via standardized registration and API patterns (Zheng et al., 14 Mar 2025).
Vision-LLM Compression: LLMC+ introduces modular token/model-level compression, with registry-driven extension for token reducers, quantizers, and custom evaluation metrics. Complex, multi-stage workflows are fully declarative and driven by external YAML configs (Lv et al., 13 Aug 2025).

7. Security, Authentication, and Robustness

Robust plug-and-play APIs embed security and reproducibility at the core:

Authentication/Authorization: JWT/API-key-based auth, role-based access (organizer vs. participant), and HTTPS transport (Xu et al., 2021).
Audit Logging: Every API action is logged with endpoints, payload hashes, and timestamps.
Failure Tolerance: Architectures such as NDBench and Codabench are designed to operate under dynamic conditions, including node failures and workload injection, with thread pools and plugins able to adapt without redeployment (Papapanagiotou et al., 2018, Xu et al., 2021).

Plug-and-play benchmarking APIs provide a scalable, reproducible, and extensible substrate for computational research, enabling reproducible science, rapid method integration, and fair, transparent evaluation across a range of domains (Xu et al., 2021, Papenmeier et al., 27 May 2025, Lv et al., 13 Aug 2025, Zheng et al., 14 Mar 2025, Papapanagiotou et al., 2018).