EvalHub SDK

Framework Adapter SDK for EvalHub Integration

The EvalHub SDK provides a standardized way to create framework adapters that can be consumed by EvalHub, enabling a "Bring Your Own Framework" (BYOF) approach for evaluation frameworks.

Overview

The SDK creates a common API layer that allows EvalHub to communicate with ANY evaluation framework. Users only need to write minimal "glue" code to connect their framework to the standardized interface.

EvalHub → (Standard API) → Your Framework Adapter → Your Evaluation Framework

Architecture

The adapter SDK uses a job runner architecture:

graph TB
    subgraph pod["Kubernetes Job Pod"]
        subgraph adapter["Adapter Container"]
            A1["1. Read JobSpec<br/>from ConfigMap"]
            A2["2. run_benchmark_job()"]
            A3["3. Report status<br/>via callbacks"]
            A4["4. Create OCI artifacts<br/>via callbacks"]
            A5["5. Report results<br/>via callbacks"]
            A6["6. Exit"]
        end

        subgraph sidecar["Sidecar Container"]
            S1["ConfigMap mounted<br/>/meta/job.json"]
            S2["Forward status to<br/>EvalHub service (HTTP)"]
            S3["Authenticated push of<br/>OCI artifacts<br/>to OCI Registry"]
            S4["Forward results to<br/>EvalHub service (HTTP)"]
        end

        A1 -.-> S1
        A3 --> S2
        A4 --> S3
        A5 --> S4
    end

    S2 --> EvalHub["EvalHub Service"]
    S3 --> Registry["OCI Registry"]
    S4 --> EvalHub

    style pod fill:#f0f0f0,stroke:#333,stroke-width:2px
    style adapter fill:#e3f2fd,stroke:#1976d2,stroke-width:2px
    style sidecar fill:#fff3e0,stroke:#f57c00,stroke-width:2px

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Package Organization

The SDK is organized into distinct, focused packages:

Core (evalhub.models) - Shared data models

• Request/response models for API communication
• Common data structures for evaluations and benchmarks

Adapter SDK (evalhub.adapter) - Framework adapter components

• FrameworkAdapter base class with run_benchmark_job() method
• Job specification models (JobSpec, JobResults)
• Callback interface for status updates and OCI artifacts
• Example implementations

Client SDK (evalhub.client) - REST API client for EvalHub service

• HTTP client for submitting evaluations to EvalHub
• Job lifecycle: submit, status, cancel, wait, and log fetch/watch
• Resource navigation (providers, benchmarks, collections)
• See Getting Started with the CLI

Key Components

1. JobSpec - Job configuration loaded from ConfigMap at pod startup
2. FrameworkAdapter - Base class that implements run_benchmark_job() method
3. JobCallbacks - Interface for reporting status and persisting artifacts
4. JobResults - Evaluation results returned when job completes
5. EvalCardMetadata - Standardized evaluation disclosure (Dhar et al., arXiv:2511.21695): modalities, languages, capability and safety evaluations
6. EnvironmentCardMetadata - Operational context of an evaluation run: hardware, software, Kubernetes, model identity, and run provenance
7. additional_info - Supplementary key-value pairs for evaluation information beyond metrics (e.g. dataset provenance, zero-shot/alt-prompting scores)
8. Sidecar - Container that handles service communication (provided by platform)

Breaking Changes

MCP command (> 0.4.1)

The evalhub mcp command no longer starts a stdio MCP server directly. MCP hosts (Claude, etc.) that previously used evalhub mcp must switch to evalhub mcp run. The old evalhub mcp interface works only on versions <= 0.4.1.

Quick Start

1. Installation

# Install from PyPI (when available)
pip install eval-hub-sdk

# Install from source
git clone https://github.com/eval-hub/eval-hub-sdk.git
cd eval-hub-sdk
pip install -e .[dev]

Optional extras: core, adapter, client, cli, mcp, dev, server, all. The server extra bundles the eval-hub-server binary for local/embedded use and is excluded from all due to its size (~20 MB).

2. Create Your Adapter

Create a new Python file for your adapter:

# my_framework_adapter.py
from datetime import UTC, datetime
from pathlib import Path

from evalhub.adapter import (
    FrameworkAdapter,
    JobSpec,
    JobCallbacks,
    JobResults,
    JobStatus,
    JobPhase,
    JobStatusUpdate,
    EvaluationResult,
    MessageInfo,
    OCIArtifactSpec,
)

class MyFrameworkAdapter(FrameworkAdapter):
    def run_benchmark_job(
        self, config: JobSpec, callbacks: JobCallbacks
    ) -> JobResults:
        """Run a benchmark evaluation job."""

        # Report initialization
        callbacks.report_status(JobStatusUpdate(
            status=JobStatus.RUNNING,
            phase=JobPhase.INITIALIZING,
            progress=0.0,
            message=MessageInfo(
                message="Loading benchmark and model",
                message_code="initializing",
            ),
        ))

        # Load your evaluation framework and benchmark
        framework = load_your_framework()
        benchmark = framework.load_benchmark(config.benchmark_id)
        model = framework.load_model(config.model)

        # Report evaluation start
        callbacks.report_status(JobStatusUpdate(
            status=JobStatus.RUNNING,
            phase=JobPhase.RUNNING_EVALUATION,
            progress=0.3,
            message=MessageInfo(
                message=f"Evaluating on {config.num_examples} examples",
                message_code="running_evaluation",
            ),
        ))

        # Run evaluation (adapter-specific params come from parameters)
        results = framework.evaluate(
            benchmark=benchmark,
            model=model,
            num_examples=config.num_examples,
            num_few_shot=config.parameters.get("num_few_shot", 0)
        )

        # Save results to a directory and persist as OCI artifact
        results_dir = save_results(config.id, results)
        oci_artifact = None
        oci_exports = config.exports.oci if config.exports else None
        if oci_exports is not None:
            coords = oci_exports.coordinates.model_copy(deep=True)
            coords.annotations.update({
                "org.opencontainers.image.created": datetime.now(UTC).isoformat(),
                "io.github.eval-hub.benchmark": config.benchmark_id,
                "io.github.eval-hub.model": config.model.name,
                "io.github.eval-hub.job_id": config.id,
            })
            oci_artifact = callbacks.create_oci_artifact(OCIArtifactSpec(
                files_path=results_dir,
                coordinates=coords,
            ))

        # Return results
        return JobResults(
            id=config.id,
            benchmark_id=config.benchmark_id,
            benchmark_index=config.benchmark_index,
            model_name=config.model.name,
            results=[
                EvaluationResult(
                    metric_name="accuracy",
                    metric_value=results["accuracy"],
                    metric_type="float"
                )
            ],
            num_examples_evaluated=len(results),
            duration_seconds=results["duration"],
            oci_artifact=oci_artifact,
        )

3. OCI Artifact Persistence

The SDK exposes an OCI persistence API via callbacks.create_oci_artifact(...).

Using DefaultCallbacks

Use DefaultCallbacks for both production and development:

from evalhub.adapter import DefaultCallbacks

# Initialize adapter (loads settings and job spec internally)
adapter = MyFrameworkAdapter()

# Create callbacks from adapter (auto-configures sidecar, OCI proxy, etc.)
callbacks = DefaultCallbacks.from_adapter(adapter)

results = adapter.run_benchmark_job(adapter.job_spec, callbacks)

Key Points:

• Status updates: Sent to sidecar if sidecar_url is provided, otherwise logged locally. Both report_status and report_results events always include benchmark_index (and provider_id when set) so the service can associate events with the correct benchmark in multi-benchmark jobs.
• OCI artifacts: Created via SDK callbacks and pushed to the OCI registry through the sidecar-authenticated flow when mode is Kubernetes.

Saving MLflow artifacts

DefaultCallbacks.mlflow.save() saves metrics, params, and artifacts in the same run when job_spec.experiment_name is configured. Mix small in-memory artifacts with file references to stream large result files:

from pathlib import Path

from evalhub.adapter.mlflow import MlflowArtifact, MlflowFileArtifact

output_dir = Path("/tmp/lighteval-results")
artifacts: list[MlflowArtifact | MlflowFileArtifact] = [
    MlflowFileArtifact(
        path=file.name,
        local_path=file,
    )
    for file in output_dir.glob("*")
    if file.is_file()
]
artifacts.append(
    MlflowArtifact("summary.json", b'{"status": "complete"}', "application/json")
)
run_id = callbacks.mlflow.save(results, job_spec, artifacts=artifacts)
if run_id:
    results.mlflow_run_id = run_id
# Return results through the normal adapter lifecycle.

This example uploads files directly from output_dir to the MLflow run artifact root using their existing filenames. To place a file in a subdirectory, use a path such as "reports/results.json".

path is the full destination relative to the run artifact root, including the filename; it can differ from local_path. Source files must be complete, unchanged, and available until synchronous save() returns. The SDK opens and closes files but does not own or delete them. The same directory can also be used for OCI export.

The default ODH backend streams files in 64 KiB chunks and infers Content-Type from the source extension, falling back to application/octet-stream. Pass content_type on MlflowFileArtifact to override it. File uploads include Content-Length from the opened file's size while preserving bounded reads. With EVALHUB_MLFLOW_BACKEND=upstream, install mlflow or mlflow-skinny; the official API does not accept a MIME override. Upstream uploads matching filenames directly; differing destination filenames use a temporary hard link or a bounded disk copy, cleaned up even if uploading fails. Existing MlflowArtifact bytes constructors and lists remain supported. No experiment means save() returns None without opening artifact files.

Adapters in separate repositories can replace artifact file.read_bytes() calls with MlflowFileArtifact references after a release containing this API.

The E2E suite includes uploads and downloads against a local MLflow server:

make test-e2e

MLflow 3.10.* is pinned in the dev dependency group in pyproject.toml to keep the E2E test exercising its Content-Length requirement. The target uses uv run pytest, which synchronizes the dev dependencies before running the test, matching the other Make targets. The E2E fixture starts MLflow's default server on an unused localhost port with a temporary SQLite database and artifact directory, then stops it after testing. It uploads nonempty and empty files and downloads them to verify byte-for-byte equality. The MLflow test requires no preexisting server; other E2E tests need the EvalHub server binary and an OCI registry. The existing CI E2E job runs the whole suite, including the MLflow test. This checks the local MLflow artifact API; deployment-specific sidecar behavior still requires a check against the deployed ODH proxy.

4. Containerise Your Adapter

Create a Dockerfile for your adapter:

FROM registry.access.redhat.com/ubi9/python-312

WORKDIR /app

# Install dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy adapter code
COPY my_framework_adapter.py .
COPY run_adapter.py .

# Run adapter
CMD ["python", "run_adapter.py"]

Create the entrypoint script:

# run_adapter.py
from my_framework_adapter import MyFrameworkAdapter
from evalhub.adapter import DefaultCallbacks

# Initialize adapter (loads settings and job spec internally)
adapter = MyFrameworkAdapter()

# Create callbacks from adapter (auto-configures sidecar, OCI proxy, etc.)
callbacks = DefaultCallbacks.from_adapter(adapter)

# Run adapter
results = adapter.run_benchmark_job(adapter.job_spec, callbacks)

# Report final results to service via sidecar
callbacks.report_results(results)

print(f"Job completed: {results.id}")

5. Deploy to Kubernetes

The eval-hub service will create Kubernetes Jobs for your adapter:

apiVersion: batch/v1
kind: Job
metadata:
  name: eval-job-123
spec:
  template:
    spec:
      containers:
      # Your adapter container
      - name: adapter
        image: myregistry/my-adapter:latest
        volumeMounts:
        - name: job-spec
          mountPath: /meta
      # Sidecar container (provided by platform)
      - name: sidecar
        image: evalhub/sidecar:latest
        env:
        - name: EVALHUB_SERVICE_URL
          value: "http://evalhub-service:8080"
      volumes:
      - name: job-spec
        configMap:
          name: job-123-spec

For a complete working example, see examples/simple_adapter/simple_adapter.py.

Package Organization Guide

The EvalHub SDK is organized into distinct packages based on your use case:

Which Package Should I Use?

Use Case	Primary Package	Description
Building an Adapter	evalhub.adapter	Create a framework adapter for your evaluation framework
Interacting with EvalHub	evalhub.client	REST API client for submitting evaluations
Data Models	evalhub.models	Request/response models for API communication

Import Patterns

Framework Adapter Developer:

# Building your adapter
from evalhub.adapter import (
    FrameworkAdapter,
    JobSpec,
    JobCallbacks,
    JobResults,
    JobStatus,
    JobPhase,
    JobStatusUpdate,
    EvaluationResult,
    OCIArtifactSpec,
    # Card metadata (optional — auto-capture provides a baseline)
    CapabilityEvalEntry,
    EvalCardMetadata,
    EnvironmentCardMetadata,
)

EvalHub Service User:

# Interacting with EvalHub REST API
from evalhub import (
    JobLogOptions,
    SyncEvalHubClient,
    BenchmarkConfig,
    EvaluationExports,
    EvaluationExportsOCI,
    JobLogOptions,
    JobSubmissionRequest,
    ModelConfig,
    OCIConnectionConfig,
    OCICoordinates,
)

# Watch job logs while polling status until the job completes
with SyncEvalHubClient() as client:
    for update in client.jobs.watch_logs(
        "job-id",
        options=JobLogOptions(tail_lines=500),
        poll_interval=2.0,
    ):
        if update.logs:
            print(update.logs, end="")

Examples

Watch job logs

Stream workload logs while a job runs using the client API or the example script:

• Script: examples/watch_job_logs.py — runnable against a local or remote cluster; see examples/README.md
• Client API: client.jobs.get_logs() for a one-shot snapshot; client.jobs.watch_logs() to poll logs and status until the job reaches a terminal state (yields JobLogUpdate with incremental logs and current job)

Contributed Adapters

For real use-case adapter implementations, see the eval-hub-contrib repository which includes adapters for GuideLLM, LightEval, and MTEB.

Simple Adapter Example

The SDK includes a reference implementation showing all adapter patterns:

Example Adapter: examples/simple_adapter/simple_adapter.py

This example demonstrates:

• Loading JobSpec from mounted ConfigMap
• Validating configuration
• Loading benchmark data
• Running evaluation with progress reporting
• Persisting results as OCI artifacts
• Returning structured results

Using the Example

from evalhub.adapter.examples import ExampleAdapter
from evalhub.adapter import JobSpec

# Load job specification
job_spec = JobSpec(
    id="eval-123",
    provider_id="my-provider",
    benchmark_id="mmlu",
    benchmark_index=0,
    model=ModelConfig(
        url="http://vllm-service:8000",
        name="llama-2-7b"
    ),
    parameters={},
    callback_url="http://localhost:8080",
    num_examples=100
)

# Create adapter and run
adapter = ExampleAdapter()
results = adapter.run_benchmark_job(job_spec, callbacks)

Framework Adapter Interface

Your adapter must implement a single method:

from evalhub.adapter import FrameworkAdapter, JobSpec, JobCallbacks, JobResults

class MyFrameworkAdapter(FrameworkAdapter):
    def run_benchmark_job(
        self, config: JobSpec, callbacks: JobCallbacks
    ) -> JobResults:
        """Run a benchmark evaluation job.

        Args:
            config: Job specification from mounted ConfigMap
            callbacks: Callbacks for status updates and artifact persistence

        Returns:
            JobResults: Evaluation results and metadata

        Raises:
            ValueError: If configuration is invalid
            RuntimeError: If evaluation fails
        """
        # Your implementation here
        pass

Key Data Models

JobSpec - Configuration loaded from ConfigMap:

class JobSpec(BaseModel):
    # Mandatory fields
    id: str                           # Unique job identifier
    provider_id: str                   # Provider identifier
    benchmark_id: str                 # Benchmark to evaluate
    benchmark_index: int              # Index of this benchmark within the job (included in all status/result events)
    model: ModelConfig                # Model configuration (url, name)
    parameters: Dict[str, Any]  # Adapter-specific parameters
    callback_url: str                  # Base URL for callbacks (SDK appends /status, /results)

    # Optional fields
    num_examples: Optional[int]       # Number of examples to evaluate
    experiment_name: Optional[str]    # Experiment name
    tags: list[dict[str, str]]        # Custom tags (default: [])

    @classmethod
    def from_file(cls, path: Path | str) -> Self:
        """Load JobSpec from a JSON file."""

Load a job spec from file:

from evalhub.adapter import JobSpec

# Explicit path (recommended)
spec = JobSpec.from_file("/meta/job.json")

# Or use settings for the path
spec = JobSpec.from_file(settings.resolved_job_spec_path)

JobCallbacks - Interface for service communication:

class JobCallbacks(ABC):
    @abstractmethod
    def report_status(self, update: JobStatusUpdate) -> None:
        """Report status update to service"""

    @abstractmethod
    def create_oci_artifact(self, spec: OCIArtifactSpec) -> OCIArtifactResult:
        """Create and push OCI artifact"""

When using DefaultCallbacks, pass benchmark_index (and optionally provider_id) from the job spec so that status and result events sent to the service always include benchmark_index, allowing the service to associate events with the correct benchmark in multi-benchmark jobs.

JobResults - Returned when job completes:

class JobResults(BaseModel):
    id: str
    benchmark_id: str
    benchmark_index: int                       # Index within the job
    model_name: str
    results: List[EvaluationResult]           # Evaluation metrics
    overall_score: Optional[float]            # Overall score if applicable
    num_examples_evaluated: int               # Number of examples evaluated
    duration_seconds: float                   # Total evaluation time
    evaluation_metadata: Dict[str, Any]       # Framework-specific metadata
    oci_artifact: Optional[OCIArtifactResult] # OCI artifact info if persisted
    eval_card: Optional[EvalCardMetadata]     # EvalCard disclosure metadata
    env_card: Optional[EnvironmentCardMetadata] # Environment Card metadata
    additional_info: Optional[Dict[str, Any]]  # Supplementary evaluation info beyond metrics

EvalCard & Environment Card - Evaluation documentation artifacts:

EvalCards and Environment Cards are serialized into the artifacts dict on report_results() and stored by the server — no server changes required.

If a provider does not set env_card, report_results() auto-captures a best-effort Environment Card from the runtime (Python version, OS, GPU info, installed packages). The capture_completeness field (0.0–1.0) reports how many of the 26 spec fields were populated.

# Explicit capture at job start (recommended — captures hardware before eval load)
env_card = EnvironmentCardMetadata.capture(
    framework_name="lm-evaluation-harness",
    framework_version="0.4.5",
)

# EvalCard with capability and safety evaluations
eval_card = EvalCardMetadata(
    modalities_input=["text"],
    modalities_output=["text"],
    languages_count=1,
    languages=["en"],
    capability_evaluations=[
        CapabilityEvalEntry(
            ability="knowledge",
            benchmark="MMLU",
            metric="exact_match",
            alt_prompting=0.712,
            alt_prompting_description="5-Shot",
        ),
    ],
)

# Attach to results before reporting
results = JobResults(..., eval_card=eval_card, env_card=env_card)
callbacks.report_results(results)

additional_info - supplementary evaluation metadata:

additional_info is a dict[str, Any] of supplementary key-value pairs for evaluation information beyond metrics. Values can be any JSON-serializable type, including nested objects and lists. Use additional_info to supply fields such as dataset (list of dataset provenance records), zero_shot, alt_prompting, and alt_prompting_description. It is serialized as a top-level additional_info key in the benchmark_status_event payload and is available to downstream consumers such as EvalCard generation.

Override generate_additional_info() on your FrameworkAdapter subclass to centralise the derivation logic. It is called automatically by DefaultCallbacks.report_results() when results.additional_info is not already set. If a framework has no implementation the base class returns None and it becomes a no-op.

The adapter is not opinionated about where the key-value pairs come from — they can be derived from user input or from the framework's evaluation output. It is up to the implementer to decide. For example, when deriving from lm-evaluation-harness results it could look like:

from evalhub.adapter import (
    FrameworkAdapter,
    JobSpec,
    JobCallbacks,
    JobResults,
)


class LMEvalAdapter(FrameworkAdapter):

    def generate_additional_info(
        self, results: JobResults
    ) -> dict[str, Any] | None:
        """Derive supplementary EvalCard fields from lm-eval output."""
        benchmark_id = results.benchmark_id

        # Resolved n-shot (after task YAML override of the CLI value)
        n_shot = self._n_shot.get(benchmark_id, 0)

        # CoT detection — layered heuristic (no single reliable signal)
        task_config = self._task_configs.get(benchmark_id, {})
        tags = task_config.get("tag", [])
        if isinstance(tags, str):
            tags = [tags]
        doc_to_text = str(task_config.get("doc_to_text", ""))

        is_cot = (
            "chain_of_thought" in tags
            or "cot" in benchmark_id.lower().replace("-", "_").split("_")
            or "think step by step" in doc_to_text.lower()
        )

        is_zero_shot = n_shot == 0 and not is_cot
        score = results.overall_score

        # Build prompting strategy description
        alt_desc = None
        if not is_zero_shot:
            parts = []
            if n_shot > 0:
                parts.append(f"{n_shot}-Shot")
            if is_cot:
                parts.append("CoT")
            alt_desc = " ".join(parts) if parts else None

        return {
            "zero_shot": score if is_zero_shot else None,
            "alt_prompting": score if not is_zero_shot else None,
            "alt_prompting_description": alt_desc,
        }

    def run_benchmark_job(
        self, config: JobSpec, callbacks: JobCallbacks
    ) -> JobResults:
        from lm_eval import simple_evaluate

        lmeval_results = simple_evaluate(...)

        # Store framework output on self for generate_additional_info()
        self._n_shot = lmeval_results.get("n-shot", {})
        self._task_configs = lmeval_results.get("configs", {})

        return JobResults(...)

Live Endpoint Response Collection (Experimental)

The SDK includes a collector utility that adapters can call during LOADING_DATA to query a chatbot endpoint with test questions and collect responses for evaluation.

OpenAI-Compatible Endpoint

from evalhub.adapter import (
    CollectorConfig, collect_responses, is_collection_configured,
    resolve_model_credentials,
)

# In your adapter's run_benchmark_job():
if is_collection_configured(config.parameters):
    creds = resolve_model_credentials()
    collector_config = CollectorConfig.from_parameters(config.parameters)
    manifest = collect_responses(collector_config, credentials=creds)
    # manifest.output_path -> responses.jsonl for your evaluation framework

Job parameters:

{
  "live_collection": {
    "questions_path": "/test_data/questions.csv",
    "output_dir": "/tmp/collected",
    "endpoint_url": "https://my-chatbot.example/v1",
    "model": "my-chatbot-v2",
    "protocol": "openai_chat_completions"
  }
}

Generic HTTP Endpoint (MCP, Langflow, custom APIs)

{
  "live_collection": {
    "questions_path": "/test_data/questions.csv",
    "output_dir": "/tmp/collected",
    "endpoint_url": "https://mcp-chatbot.example/mcp",
    "protocol": "generic_http",
    "request_template": {
      "jsonrpc": "2.0",
      "method": "tools/call",
      "params": {"name": "chat", "arguments": {"message": "{question}"}},
      "id": "{question_id}"
    },
    "response_path": "result.content.0.text",
    "extra_response_paths": {
      "retrieved_contexts": "result.sources"
    }
  }
}

The collector uses SDK TLS auto-detection by default. In Kubernetes, the model secret contains a sidecar reference token and cannot authenticate a direct request to the live endpoint. Configure api_key_env or request_headers with the live endpoint's actual credential instead; the collector will not send the sidecar reference token directly to the endpoint.

See src/evalhub/adapter/collector.py for the complete collector configuration reference.

Deployment

Container Structure

Your adapter runs as a container in a Kubernetes Job alongside a sidecar:

FROM registry.access.redhat.com/ubi9/python-312

WORKDIR /app

# Install your framework and dependencies
RUN pip install lm-evaluation-harness==0.4.0 eval-hub-sdk

# Copy adapter implementation
COPY my_adapter.py .
COPY entrypoint.py .

CMD ["python", "entrypoint.py"]

Entrypoint Script

# entrypoint.py
from my_adapter import MyFrameworkAdapter
from evalhub.adapter import DefaultCallbacks

# Initialize adapter (loads settings and job spec internally)
adapter = MyFrameworkAdapter()

# Create callbacks from adapter (auto-configures sidecar, OCI proxy, etc.)
callbacks = DefaultCallbacks.from_adapter(adapter)

# Run adapter
results = adapter.run_benchmark_job(adapter.job_spec, callbacks)

# Report final results
callbacks.report_results(results)

print(f"Job {results.id} completed with score: {results.overall_score}")

Kubernetes Job

EvalHub creates Jobs automatically:

apiVersion: batch/v1
kind: Job
metadata:
  name: eval-job-123
spec:
  template:
    spec:
      containers:
      - name: adapter
        image: myregistry/my-framework-adapter:latest
        volumeMounts:
        - name: job-spec
          mountPath: /meta
      - name: sidecar
        image: evalhub/sidecar:latest
        env:
        - name: EVALHUB_SERVICE_URL
          value: "http://evalhub-service:8080"
      volumes:
      - name: job-spec
        configMap:
          name: job-123-spec
      restartPolicy: Never

Development

Development Setup

# Clone the repository
git clone https://github.com/eval-hub/eval-hub-sdk.git
cd eval-hub-sdk

# Install in development mode with all dependencies
pip install -e .[dev]

# Install pre-commit hooks
pre-commit install

# Run tests
pytest

# Run tests with coverage
pytest --cov=src/evalhub --cov-report=html

# Run type checking
mypy src/evalhub

# Run linting
ruff check src/ tests/
ruff format src/ tests/

Testing Your Adapter

from evalhub.adapter import AdapterSettings

def test_settings_parse(monkeypatch):
    monkeypatch.setenv("EVALHUB_MODE", "local")
    monkeypatch.setenv("OCI_INSECURE", "true")
    s = AdapterSettings.from_env()
    assert s.oci_insecure is True

Quality Assurance

Run all quality checks:

# Format code
ruff format .

# Lint and fix issues
ruff check --fix .

# Type check
mypy src/evalhub

# Run full test suite
pytest -v --cov=src/evalhub

Installing Pre-Release Versions

Pre-release development versions are published to TestPyPI. To install the latest pre-release:

pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ --pre eval-hub-sdk

The --extra-index-url flag ensures that dependencies are still resolved from the main PyPI index.

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for your changes
5. Run the test suite
6. Submit a pull request

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.