Download MetaMathQA/run_with_jobs.py from peft-internal-testing/PEFT-method-comparison-embed: direct link, hf CLI and curl.
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https://huggingface.co/spaces/peft-internal-testing/PEFT-method-comparison-embed/resolve/main/MetaMathQA/run_with_jobs.py
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curl -L -o run_with_jobs.py https://huggingface.co/spaces/peft-internal-testing/PEFT-method-comparison-embed/resolve/main/MetaMathQA/run_with_jobs.py
4.54 kB
| """ | |
| Helper to run MetaMathQA experiments on a HF jobs runner. | |
| The experiment path gives the path to the folder with the adapter_config.json | |
| (and possibly train_params.json) relative to /method_comparison/MetaMathQA/experiments/ | |
| in the repository. Alternatively, if --upload is specified, it is assumed the experiment | |
| exists locally (relative to the current working directory) and is uploaded to the | |
| remote experiments folder. | |
| Common use-cases: | |
| * run_with_jobs.py --repo mygithubhandle:my_branch lora/llama-3.2-3B-rank32 | |
| run an experiment from a custom repo/branch | |
| * run_with_jobs.py --repo https://github.com/mygithubhandle/peft.git --branch my_branch lora/llama-3.2-3B-rank32 | |
| same as above with explicit URL / branch | |
| * run_with_jobs.py --repo huggingface:main --upload lora/custom-local-lora-exp | |
| run a locally modified experiment on the PEFT main branch | |
| * run_with_jobs.py --code_bucket myuser/peft lora/llama-3.2-3B-rank32 | |
| use the PEFT code from the myuser/peft bucket instead of cloning a git repo | |
| """ | |
| import os | |
| import argparse | |
| import subprocess | |
| from base64 import b64encode | |
| from huggingface_hub import run_job, Volume, cancel_job, fetch_job_logs | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--repo", type=str, default="https://github.com/githubnemo/peft.git") | |
| parser.add_argument("--branch", type=str) | |
| parser.add_argument("--code_bucket", type=str, default=None, help="Bucket to use instead of git repo") | |
| parser.add_argument("--upload", action="store_true", default=False) | |
| parser.add_argument("experiment_path", type=str) | |
| parser.add_argument("--flavor", type=str, default="a10g-large") | |
| parser.add_argument("--debug", action="store_true", default=False) | |
| parser.add_argument("--timeout", type=int, default=7200) | |
| args = parser.parse_args() | |
| token = subprocess.run( | |
| ["hf", "auth", "token"], capture_output=True, text=True, check=True | |
| ).stdout.strip() or os.environ.get("HF_TOKEN", "") | |
| if not token: | |
| print("No token, will not be able to load private or semi-private models.") | |
| if "/" not in args.experiment_path: | |
| raise ValueError("experiment path must contain /, e.g. osf/llama-3.2-rank128") | |
| volumes = [] | |
| if args.code_bucket: | |
| volumes.append(Volume(type="bucket", source=args.code_bucket, mount_path="/tmp/peft")) | |
| if "@" not in args.repo and "://" not in args.repo: | |
| repo_parts = args.repo.split(":") | |
| args.repo = f"https://github.com/{repo_parts[0]}/peft.git" | |
| args.branch = repo_parts[1] | |
| experiment_name = os.path.split(args.experiment_path)[-1] | |
| if args.upload: | |
| adapter_config_path = os.path.join(args.experiment_path, "adapter_config.json") | |
| training_params_path = os.path.join(args.experiment_path, "training_params.json") | |
| adapter_config = "" | |
| training_params = "" | |
| if not os.path.exists(adapter_config_path): | |
| raise ValueError(f"No experiment config exists in {adapter_config_path}.") | |
| with open(adapter_config_path) as f: | |
| adapter_config = f.read() | |
| if os.path.exists(training_params_path): | |
| with open(training_params_path) as f: | |
| training_params = f.read() | |
| cmd = ( | |
| "source activate peft && " | |
| + "pip uninstall mslk torchao -y -q 2>/dev/null; " # TODO remove once this issue is resolved | |
| + (f"git clone {args.repo} /tmp/peft && " if not args.code_bucket else "") | |
| + "cd /tmp/peft && " | |
| + (f"git checkout {args.branch} && " if not args.code_bucket else "") | |
| + f"mkdir -p /tmp/peft/method_comparison/MetaMathQA/experiments/jobs/{experiment_name} && " | |
| + ( | |
| f"echo '{b64encode(adapter_config)}' | base64 -d > /tmp/peft/method_comparison/MetaMathQA/experiments/{experiment_name}/adapter_config.json &&" | |
| if args.upload | |
| else "" | |
| ) | |
| + ( | |
| f"echo '{b64encode(training_params)}' | base64 -d > /tmp/peft/method_comparison/MetaMathQA/experiments/{experiment_name}/training_params.json &&" | |
| if args.upload and training_params | |
| else "" | |
| ) | |
| + "pip install -e . --no-deps && " | |
| + "cd method_comparison/MetaMathQA && " | |
| + f"python run.py -v experiments/jobs/{experiment_name} --clean &&" | |
| + "cat temporary_results/*.json" | |
| ) | |
| if args.debug: | |
| print(cmd) | |
| job = run_job( | |
| image="huggingface/peft-gpu:latest", | |
| command=["bash", "-c", cmd], | |
| flavor=args.flavor, | |
| timeout=args.timeout, | |
| volumes=volumes, | |
| secrets={"HF_TOKEN": token}, | |
| ) | |
| print(f"Job ID: {job.id}") | |
| print(f"Status: {job.status}") | |
| for log in fetch_job_logs(job_id=job.id, follow=True): | |
| print(log) | |
| print(f"stopping job {job.id}...") | |
| cancel_job(job_id=job.id) | |