mirror of
https://github.com/open-thought/reasoning-gym.git
synced 2026-04-22 16:49:06 +00:00
* feat: Add initial server structure with configuration, registry, and middleware * feat: Add chain_sum dataset to experiment registry test * fix: Update test_registry to use DatasetSpec for composite config validation * refactor: Update Pydantic config to use json_schema_extra and ConfigDict * feat: Add Pydantic models for API request/response data * feat: Implement basic experiment management endpoints with tests * feat: Implement composite configuration endpoints for experiments * fix: Add missing DatasetConfigUpdate import in server.py * refactor: Update dataset config update method to properly merge config updates * fix: Correctly retrieve current dataset config in composite endpoint * feat: Add basic CLI structure with experiments and config commands * feat: Add initial CLI tool with basic experiment management commands * refactor: Reorganize CLI package structure and fix import paths * refactor: Implement initial CLI commands for experiment management * feat: Implement HTTP client for Reasoning Gym server in RGC CLI tool * fix: Move print statements inside try block to resolve SyntaxError * fix: Resolve SyntaxError in edit_config function by adding missing except block * feat: Add default app instance in server module for easier uvicorn startup * docs: Add README.md with server and RGC tool documentation * remove unused files * refactor: Remove unsupported type annotation in registry.py * refactor: Move ExperimentRegistry to coaching module and add Experiment class * fix: Add missing CompositeDataset import in test_registry.py * refactor: Implement lazy ASGI app creation for server initialization * feat: Add health check command to RGC CLI for server connection * feat: Add version tracking support to CompositeDataset * feat: Add DatasetVersionManager for tracking dataset versions * feat: Add entry_id metadata and score_answer_with_id method to CompositeDataset * feat: Add entry_id metadata combining version and index * fix: Resolve undefined variable by storing version_id before use * test: Add comprehensive unit tests for score_answer_with_id() function * test: Add comprehensive version tracking test for dataset config updates * feat: Validate dataset weights are positive in CompositeDataset initialization * feat: Add weight update and normalization methods to CompositeDataset * refactor: Centralize weight normalization in CompositeDataset and allow zero-weight datasets * feat: Add negative weight validation to CompositeDataset constructor * feat: Add duplicate dataset name check in CompositeDataset and update test * refactor: Move duplicate dataset name check inside dataset iteration loop * refactor: Update CompositeDataset weight management to use config as source of truth * refactor: Move duplicate dataset name check to CompositeConfig.validate() * test: Update composite dataset weight test assertions and validation * feat: Add methods to add and remove datasets in CompositeDataset * refactor: Remove weight normalization and use unnormalized weights directly * refactor: Remove redundant total weight check in update_dataset_weights * feat: Add batch generation and scoring endpoints to server * fix: Import BatchEntry in server.py to resolve undefined name error * refactor: Update ReasoningGymDataset to use server for batch generation and scoring * fix: Add missing List and Dict type imports * feat: Add get_batch() and score_outputs() methods to RGClient * test: Add unit tests for generate_batch and score_outputs endpoints * refactor: Add DatasetVersionManager to Experiment class and CompositeDataset constructor * feat: Add validation for base_index and batch_size in generate_batch endpoint * refactor: Remove unused BatchRequest type from imports * refactor: Convert models to use Pydantic exclusively * test: Update scoring endpoint tests to use correct request model format * refactor: Rename ScoreItem to AnswerItem and update related code * feat: Update scoring endpoint to return ordered ScoringResponse with scores and entry_ids * fix: Add missing ScoringResponse import in server.py * move verl ppo sample with server into own file * refactor: Use Pydantic models for get_batch() and score_outputs() in RGClient * refactor: Update client methods to use Pydantic models for type safety * refactor: Use Pydantic models for experiment and dataset config operations * refactor: Clean up duplicate methods and improve error handling in main.py * first bits of rg server use for verl * refactor: Optimize scoring with single HTTP request in _score_output * fix: Correct experiment creation with ExperimentCreate object * grpo tests with server
125 lines
4.2 KiB
Python
125 lines
4.2 KiB
Python
"""HTTP client for interacting with the Reasoning Gym server."""
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import os
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from typing import List, Optional
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import httpx
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from rich.console import Console
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from tools.server.models import (
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AnswerItem,
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BatchResponse,
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DatasetConfigUpdate,
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ExperimentCreate,
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ExperimentList,
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ExperimentResponse,
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ScoringRequest,
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ScoringResponse,
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)
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console = Console()
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DEFAULT_SERVER = "http://localhost:8000"
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API_KEY = os.getenv("REASONING_GYM_API_KEY", "default-key")
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class RGClient:
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"""Client for interacting with Reasoning Gym server."""
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def __init__(self, base_url: str = DEFAULT_SERVER, api_key: str = API_KEY):
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"""Initialize client with server URL and API key."""
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self.base_url = base_url.rstrip("/")
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self.headers = {"X-API-Key": api_key}
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def _url(self, path: str) -> str:
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"""Construct full URL for given path."""
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return f"{self.base_url}/{path.lstrip('/')}"
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def check_health(self) -> bool:
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"""Check server health status."""
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try:
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response = httpx.get(self._url("/health"), headers=self.headers)
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response.raise_for_status()
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return response.json()["status"] == "healthy"
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except Exception:
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return False
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def list_experiments(self) -> ExperimentList:
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"""List all registered experiments."""
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response = httpx.get(self._url("/experiments"), headers=self.headers)
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response.raise_for_status()
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return ExperimentList.model_validate(response.json())
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def create_experiment(self, name: str, config: ExperimentCreate) -> ExperimentResponse:
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"""Create a new experiment."""
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response = httpx.post(
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self._url("/experiments"),
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headers=self.headers,
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json=config.model_dump(),
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)
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response.raise_for_status()
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return ExperimentResponse.model_validate(response.json())
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def delete_experiment(self, name: str) -> None:
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"""Delete an experiment."""
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response = httpx.delete(
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self._url(f"/experiments/{name}"),
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headers=self.headers,
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)
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response.raise_for_status()
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def get_experiment_config(self, name: str) -> ExperimentResponse:
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"""Get experiment configuration."""
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response = httpx.get(
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self._url(f"/experiments/{name}/composite"),
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headers=self.headers,
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)
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response.raise_for_status()
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return ExperimentResponse.model_validate(response.json())
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def update_dataset_config(self, experiment: str, dataset: str, config: DatasetConfigUpdate) -> None:
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"""Update dataset configuration."""
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response = httpx.post(
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self._url(f"/experiments/{experiment}/composite/{dataset}"),
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headers=self.headers,
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json=config.model_dump(),
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)
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response.raise_for_status()
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def get_batch(self, experiment: str, base_index: int, batch_size: int) -> BatchResponse:
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"""Get a batch of entries from an experiment.
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Args:
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experiment: Name of the experiment
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base_index: Starting index for the batch
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batch_size: Number of entries to retrieve
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Returns:
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BatchResponse containing entries with questions and metadata
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"""
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response = httpx.get(
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self._url(f"/experiments/{experiment}/batch"),
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headers=self.headers,
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params={"base_index": base_index, "batch_size": batch_size},
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)
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response.raise_for_status()
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return BatchResponse.model_validate(response.json())
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def score_outputs(self, experiment: str, entry_answers: List[AnswerItem]) -> ScoringResponse:
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"""Score a batch of answers.
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Args:
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experiment: Name of the experiment
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entry_answers: List of AnswerItems with entry_ids and answers to score
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Returns:
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ScoringResponse containing scores and entry_ids
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"""
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request = ScoringRequest(answers=entry_answers)
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response = httpx.post(
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self._url(f"/experiments/{experiment}/score"),
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headers=self.headers,
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json=request.model_dump(),
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)
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response.raise_for_status()
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return ScoringResponse.model_validate(response.json())
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