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Upload runner.py with huggingface_hub
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runner.py
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#!/usr/bin/env python3
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# /// script
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# requires-python = ">=3.10"
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# dependencies = [
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# "inspect-ai",
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# "datasets",
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# "openai",
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# "transformers",
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# "accelerate",
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# "huggingface_hub",
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# "inspect_evals",
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# ]
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# ///
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"""Runner that downloads an eval script and executes it using inspect CLI."""
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import os
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import sys
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import subprocess
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import tempfile
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import urllib.request
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from pathlib import Path
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from huggingface_hub import HfApi
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from inspect_ai.log import bundle_log_dir
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def bundle_and_upload_to_space(log_dir: str, hf_space_id: str, hf_token: str):
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"""Bundle logs and upload to HF Space."""
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log_path = Path(log_dir)
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if not log_path.exists():
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print(f"Warning: Log directory '{log_dir}' does not exist")
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return
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with tempfile.TemporaryDirectory() as temp_bundle_dir:
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bundle_output_dir = os.path.join(temp_bundle_dir, "bundle")
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print(f"Bundling logs from {log_dir}...")
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bundle_log_dir(log_dir=log_dir, output_dir=bundle_output_dir, overwrite=True)
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api = HfApi(token=hf_token)
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print(f"Uploading to Space {hf_space_id}...")
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uploaded_count = 0
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for root, _, files in os.walk(bundle_output_dir):
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for file in files:
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local_path = os.path.join(root, file)
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rel_path = os.path.relpath(local_path, bundle_output_dir)
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path_in_repo = rel_path.replace(os.sep, "/")
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api.upload_file(
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path_or_fileobj=local_path,
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path_in_repo=path_in_repo,
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repo_id=hf_space_id,
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repo_type="space",
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)
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uploaded_count += 1
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print(f"Uploaded {uploaded_count} files")
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print(f"View at: https://huggingface.co/spaces/{hf_space_id}")
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if __name__ == "__main__":
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if len(sys.argv) < 4:
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print("Usage: eval_runner.py <eval_ref> <model> <space_id> [log_dir] [--inspect-evals] [extra_args...]")
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sys.exit(1)
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eval_ref = sys.argv[1]
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model = sys.argv[2]
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hf_space_id = sys.argv[3]
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log_dir = sys.argv[4] if len(sys.argv) > 4 and not sys.argv[4].startswith("--") else "./logs"
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# Check if this is an inspect_evals path
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is_inspect_evals = "--inspect-evals" in sys.argv
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extra_args = [arg for arg in sys.argv[5:] if arg != "--inspect-evals"]
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if is_inspect_evals:
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# Use inspect_evals path directly
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print(f"Using inspect_evals: {eval_ref}")
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eval_target = eval_ref
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cleanup_file = None
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else:
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# Download custom eval script
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print(f"Downloading eval from {eval_ref}...")
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with urllib.request.urlopen(eval_ref) as response:
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eval_code = response.read().decode('utf-8')
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eval_filename = "downloaded_eval.py"
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with open(eval_filename, 'w') as f:
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f.write(eval_code)
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eval_target = eval_filename
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cleanup_file = eval_filename
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try:
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print(f"Running inspect eval with model {model}...")
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cmd = [
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"inspect", "eval", eval_target,
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"--model", model,
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"--log-dir", log_dir,
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]
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cmd.extend(extra_args)
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print(f"Command: {' '.join(cmd)}")
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subprocess.run(cmd, check=True)
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print(f"\nUploading logs to {hf_space_id}...")
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hf_token = os.getenv("HF_TOKEN")
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if not hf_token:
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print("Warning: HF_TOKEN not set, skipping upload")
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else:
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bundle_and_upload_to_space(log_dir, hf_space_id, hf_token)
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finally:
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if cleanup_file and os.path.exists(cleanup_file):
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os.unlink(cleanup_file)
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