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computer/notebooks/eval_osworld.ipynb
2025-10-06 20:27:01 -07:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ComputerAgent HUD Integration for OSWorld\n",
"\n",
"This notebook demonstrates how to use the ComputerAgent with HUD for OSWorld benchmarking.\n",
"The ComputerAgent integration provides the same interface as OperatorAgent but works with both Claude and OpenAI models."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# # Install dependencies if needed\n",
"# !uv venv \n",
"# !source .venv/bin/activate\n",
"# !uv sync"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from dotenv import load_dotenv\n",
"import os\n",
"\n",
"# Load environment variables from ../.env\n",
"load_dotenv(dotenv_path='../.env')\n",
"\n",
"# Required environment variables:\n",
"# - HUD_API_KEY (for HUD access)\n",
"# - ANTHROPIC_API_KEY (for Claude models)\n",
"# - OPENAI_API_KEY (for OpenAI models)\n",
"assert os.getenv('HUD_API_KEY') is not None\n",
"assert os.getenv('ANTHROPIC_API_KEY') is not None or os.getenv('OPENAI_API_KEY') is not None\n",
"\n",
"from pprint import pprint"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Quick single-task smoke test on OSWorld-Verified\n",
"\n",
"The ComputerAgent integration can use Claude, OpenAI, UI-TARS, or composed models just like the original ComputerAgent:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from agent.integrations.hud import run_single_task\n",
"\n",
"# Quick single-task smoke test on OSWorld-Verified\n",
"# You can swap \"hud-evals/OSWorld-Verified\" -> \"hud-evals/SheetBench-V2\" to test SheetBench.\n",
"await run_single_task(\n",
" dataset=\"hud-evals/OSWorld-Verified\",\n",
" model=\"openai/computer-use-preview+openai/gpt-5\", # or any supported model string\n",
" task_id=155 # open last tab task (easy)\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Run OSWorld-Verified in parallel"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import uuid\n",
"from agent.integrations.hud import run_full_dataset\n",
"\n",
"# Full dataset evaluation (runs via HUD's run_dataset under the hood)\n",
"job_name = f\"osworld-test-{str(uuid.uuid4())[:4]}\"\n",
"\n",
"results = await run_full_dataset(\n",
" dataset=\"hud-evals/OSWorld-Verified\", # You can also pass a Dataset or a list[dict]\n",
" job_name=job_name, # Optional; defaults to a timestamp for custom datasets\n",
" model=\"openai/computer-use-preview\", # Or any supported model string\n",
" max_concurrent=20, # Tune to your infra\n",
" max_steps=50, # Safety cap per task\n",
" split=\"train[:3]\" # Limit to just 3 tasks\n",
")\n",
"\n",
"# results is a list from hud.datasets.run_dataset; inspect/aggregate as needed\n",
"print(f\"Job: {job_name}\")\n",
"print(f\"Total results: {len(results)}\")\n",
"pprint(results[:3]) # preview"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Benchmark Composed Agents"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import uuid\n",
"from agent.integrations.hud import run_full_dataset\n",
"\n",
"models_to_test = [\n",
" \"openai/computer-use-preview+anthropic/claude-opus-4-20250514\",\n",
"]\n",
" \n",
"\n",
"for model in models_to_test:\n",
" # Full dataset evaluation (runs via HUD's run_dataset under the hood)\n",
" job_uuid = str(uuid.uuid4())[:6]\n",
" job_name = f\"osworld {job_uuid} {model}\"\n",
"\n",
" results = await run_full_dataset(\n",
" dataset=\"hud-evals/OSWorld-Verified\",\n",
" job_name=job_name, \n",
" model=model,\n",
" max_concurrent=20, \n",
" max_steps=75,\n",
" trajectory_dir=f\"trajectories/osworld_{job_uuid}\",\n",
" only_n_most_recent_images=3\n",
" )\n",
"\n",
" # results is a list from hud.datasets.run_dataset; inspect/aggregate as needed\n",
" print(f\"Job: {job_name}\")\n",
" print(f\"Total results: {len(results)}\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "cua",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.11"
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"nbformat": 4,
"nbformat_minor": 4
}