├── requirements.txt ├── src └── opro │ ├── schema.py │ ├── gsm8k.py │ ├── settings.py │ ├── prompts.py │ ├── prompt_generation.py │ ├── opro.py │ ├── prompt_scoring.py │ └── data_io.py ├── .github └── workflows │ └── pre-commit.yaml ├── .env.template ├── README.md ├── .pre-commit-config.yaml ├── .gitignore └── LICENSE /requirements.txt: -------------------------------------------------------------------------------- 1 | appdirs==1.4.4 2 | openai==0.28.0 3 | pre-commit==3.4.0 4 | pydantic==2.3.0 5 | python-dotenv==1.0.0 6 | zstandard==0.21.0 7 | -------------------------------------------------------------------------------- /src/opro/schema.py: -------------------------------------------------------------------------------- 1 | from pydantic import BaseModel 2 | 3 | 4 | class PromptExample(BaseModel): 5 | prompt: str 6 | score: float 7 | 8 | 9 | class ProblemExample(BaseModel): 10 | question: str 11 | answer: str 12 | -------------------------------------------------------------------------------- /.github/workflows/pre-commit.yaml: -------------------------------------------------------------------------------- 1 | name: pre-commit 2 | 3 | on: 4 | pull_request: 5 | push: 6 | branches: [main] 7 | 8 | jobs: 9 | pre-commit: 10 | runs-on: ubuntu-latest 11 | steps: 12 | - uses: actions/checkout@v3 13 | - uses: actions/setup-python@v3 14 | - uses: pre-commit/action@v3.0.0 15 | -------------------------------------------------------------------------------- /.env.template: -------------------------------------------------------------------------------- 1 | # Security Warning! Do not commit this file to any VCS! 2 | # This is a local file to speed up development process, 3 | # so you don't have to change your environment variables. 4 | # 5 | # This is not applied to `.env.template`! 6 | # Template files must be committed to the VCS, but must not contain 7 | # any secret values. 8 | 9 | 10 | # === General === 11 | OPENAI_API_KEY=your-openai-api-key 12 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # OPRO Prompt Optimization 2 | Implementing the GSM8k prompt optimization from the paper "Large Language Models as Optimizers". 3 | 4 | # Prerequisites 5 | - Python 3.9+ 6 | - OpenAI API key 7 | 8 | # Installation 9 | Run `pip install -r requirements.txt` to install the required packages. 10 | 11 | # Usage 12 | Run `python main.py` to run the GSM8k prompt optimization. 13 | 14 | # Citations 15 | @misc{yang2023large, 16 | title={Large Language Models as Optimizers}, 17 | author={Chengrun Yang and Xuezhi Wang and Yifeng Lu and Hanxiao Liu and Quoc V. Le and Denny Zhou and Xinyun Chen}, 18 | year={2023}, 19 | eprint={2309.03409}, 20 | archivePrefix={arXiv}, 21 | primaryClass={cs.LG} 22 | } 23 | -------------------------------------------------------------------------------- /.pre-commit-config.yaml: -------------------------------------------------------------------------------- 1 | # See https://pre-commit.com for more information 2 | # See https://pre-commit.com/hooks.html for more hooks 3 | repos: 4 | - repo: https://github.com/pre-commit/pre-commit-hooks 5 | rev: v3.2.0 6 | hooks: 7 | - id: check-ast 8 | - id: trailing-whitespace 9 | - id: end-of-file-fixer 10 | - id: check-yaml 11 | - id: check-added-large-files 12 | - id: requirements-txt-fixer 13 | - repo: https://github.com/pycqa/flake8 14 | rev: 6.1.0 15 | hooks: 16 | - id: flake8 17 | args: [--max-line-length=120] 18 | - repo: https://github.com/asottile/reorder_python_imports 19 | rev: v3.9.0 20 | hooks: 21 | - id: reorder-python-imports 22 | - repo: https://github.com/PyCQA/autoflake 23 | rev: v2.2.1 24 | hooks: 25 | - id: autoflake 26 | -------------------------------------------------------------------------------- /src/opro/gsm8k.py: -------------------------------------------------------------------------------- 1 | import json 2 | import os 3 | from typing import List 4 | 5 | from data_io import download_file 6 | from data_io import get_cache_dir 7 | from schema import ProblemExample 8 | 9 | from src.opro.settings import FINAL_ANSWER_SEP 10 | 11 | source_uri_format = \ 12 | "https://raw.githubusercontent.com/openai/grade-school-math/master/grade_school_math/data/{split}.jsonl" 13 | 14 | 15 | def get_dataset(split: str = None) -> List[ProblemExample]: 16 | assert split in ['test', 'train'] 17 | 18 | target_path = os.path.join(get_cache_dir(), f'gsm8k_{split}.jsonl') 19 | download_file(source_uri=source_uri_format.format(split=split), target_path=target_path) 20 | with open(target_path, 'r') as fin: 21 | raw_data = [json.loads(x) for x in fin] 22 | 23 | dataset = [ProblemExample(question=x['question'], answer=x['answer'] 24 | .replace('####', FINAL_ANSWER_SEP)) for x in raw_data] 25 | 26 | return dataset 27 | -------------------------------------------------------------------------------- /src/opro/settings.py: -------------------------------------------------------------------------------- 1 | # Quote from paper on hyperparams 2 | # =============================== 3 | # Implementation details. We set the temperature to be 0 when evaluating the performance of 4 | # generated instructions, in which case the scorer LLM greedily decodes. Unless otherwise specified, we 5 | # set the default temperature to be 1.0 for optimizer LLMs to generate diverse and creative instructions. 6 | # At each optimization step, we prompt the optimizer LLM with the meta-prompt 8 times to generate 8 7 | # instructions, then we add these instructions with their training scores to the optimization trajectory 8 | # in the meta-prompt. Our meta-prompt at each step contains the best 20 instructions so far and 3 9 | # randomly picked exemplars from the training set. We study the effect of different hyperparameters in 10 | # ablation studies (Section 5.3). Appendix C.2 presents the full meta-prompts for different optimizer 11 | # LLMs. 12 | # TODO: move to config class 13 | MODEL_NAME = "gpt-3.5-turbo" 14 | MAX_TRAIN_EXAMPLES = 5 15 | MAX_TEST_EXAMPLES = 15 16 | MAX_ITER = 5 17 | MAX_RESPONSE_TOKENS = 1024 18 | MAX_PROMPT_CANDIDATES = 20 19 | CANDIDATES_PER_STEP = 8 20 | THREADS = 1 21 | 22 | FINAL_ANSWER_SEP = 'Final Answer: ' 23 | -------------------------------------------------------------------------------- /src/opro/prompts.py: -------------------------------------------------------------------------------- 1 | from typing import List 2 | 3 | from src.opro.schema import ProblemExample 4 | 5 | opt_prompt_template = """ 6 | Your task is to generate the instruction . Below are some previous instructions with their scores. 7 | The score ranges from 0 to 100. 8 | {prompt_examples} 9 | Below are some problems. 10 | Problem: 11 | {problem_examples} 12 | Generate an instruction that is different from all the instructions above, and has a higher score 13 | than all the instructions above. The instruction should begin with and end with . 14 | The instruction should be concise, effective, and generally applicable to all problems above 15 | """ 16 | 17 | prompt_example_template = """ 18 | text: 19 | {prompt} 20 | score: 21 | {score} 22 | 23 | """ 24 | 25 | problem_example_template = """ 26 | Q: {question} 27 | A: 28 | Ground truth answer: 29 | {answer} 30 | """ 31 | 32 | qna_prompt_format = """ 33 | Q: {question} 34 | A: {instruction} 35 | {answer} 36 | """ 37 | 38 | 39 | def build_problem_prompt(new_problem: ProblemExample, demo_examples: List[ProblemExample], prompt_candidate: str): 40 | formatted_prompt_examples = '\n'.join([qna_prompt_format.format(question=p.question, 41 | instruction=prompt_candidate, 42 | answer=p.answer) 43 | for p in demo_examples]) 44 | new_problem = qna_prompt_format.format(question=new_problem.question, 45 | instruction=prompt_candidate, 46 | answer="") 47 | meta_instruction = "Solve the problem in the same format as in the following examples." 48 | return f"{meta_instruction}\nExamples:\n {formatted_prompt_examples}\n Problem:\n {new_problem}" 49 | 50 | 51 | def format_openai_chat_prompt(prompt_text: str): 52 | return [{'role': 'user', 'content': prompt_text}] 53 | -------------------------------------------------------------------------------- /src/opro/prompt_generation.py: -------------------------------------------------------------------------------- 1 | import logging 2 | from typing import List 3 | 4 | import openai 5 | from prompts import format_openai_chat_prompt 6 | from prompts import opt_prompt_template 7 | from prompts import problem_example_template 8 | from prompts import prompt_example_template 9 | from schema import ProblemExample 10 | from schema import PromptExample 11 | 12 | from src.opro.settings import CANDIDATES_PER_STEP 13 | from src.opro.settings import MAX_RESPONSE_TOKENS 14 | from src.opro.settings import MODEL_NAME 15 | 16 | LOGGER = logging.getLogger(__name__) 17 | 18 | 19 | def generate_opt_prompt(prompt_examples: List[PromptExample], problem_examples: List[ProblemExample]): 20 | formatted_prompt_examples = [ 21 | prompt_example_template.format(prompt=prompt_example.prompt, score=prompt_example.score) 22 | for prompt_example in prompt_examples] 23 | 24 | formatted_problem_examples = [ 25 | problem_example_template.format(question=problem_example.question, answer=problem_example.answer) 26 | for problem_example in problem_examples] 27 | 28 | formatted_opt_prompt = opt_prompt_template.format(prompt_examples='\n'.join(formatted_prompt_examples), 29 | problem_examples='\n'.join(formatted_problem_examples)) 30 | 31 | return formatted_opt_prompt 32 | 33 | 34 | def generate_prompt_candidates(prompt_examples: List[PromptExample], problem_examples: List[ProblemExample]): 35 | LOGGER.info('Generating prompt candidates') 36 | opt_prompt = generate_opt_prompt(prompt_examples, problem_examples) 37 | 38 | response = openai.ChatCompletion.create( 39 | model=MODEL_NAME, 40 | messages=format_openai_chat_prompt(opt_prompt), 41 | temperature=1.0, 42 | max_tokens=MAX_RESPONSE_TOKENS, 43 | n=CANDIDATES_PER_STEP 44 | ) 45 | response_texts = [r.message['content'] for r in response.choices] 46 | return [r.split('')[1].split('')[0] for r in response_texts] 47 | -------------------------------------------------------------------------------- /src/opro/opro.py: -------------------------------------------------------------------------------- 1 | import logging 2 | import os 3 | from typing import List 4 | 5 | import openai 6 | from dotenv import load_dotenv 7 | from gsm8k import get_dataset 8 | from schema import ProblemExample 9 | from schema import PromptExample 10 | from tqdm import tqdm 11 | 12 | from src.opro.prompt_generation import generate_prompt_candidates 13 | from src.opro.prompt_scoring import score_prompt_candidates 14 | from src.opro.settings import MAX_ITER 15 | from src.opro.settings import MAX_PROMPT_CANDIDATES 16 | from src.opro.settings import MAX_TEST_EXAMPLES 17 | from src.opro.settings import MAX_TRAIN_EXAMPLES 18 | 19 | logging.basicConfig( 20 | format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", 21 | datefmt="%m/%d/%Y %H:%M:%S", 22 | level=logging.INFO) 23 | 24 | LOGGER = logging.getLogger(__name__) 25 | 26 | 27 | def seed_prompt_examples(demo_examples: List[ProblemExample], test_examples: List[ProblemExample]) \ 28 | -> List[PromptExample]: 29 | default_prompt_examples = ["Let’s solve the problem."] 30 | return score_prompt_candidates(default_prompt_examples, demo_examples, test_examples) 31 | 32 | 33 | def update_prompt_examples(previous_prompt_examples: List[PromptExample], 34 | scored_prompt_candidates: List[PromptExample]) -> List[PromptExample]: 35 | all_candidates = previous_prompt_examples + scored_prompt_candidates 36 | return sorted(all_candidates, key=lambda x: x.score, reverse=True)[:MAX_PROMPT_CANDIDATES] 37 | 38 | 39 | def main(): 40 | load_dotenv() 41 | openai.api_key = os.getenv("OPENAI_API_KEY") 42 | 43 | train_examples = get_dataset('train')[:MAX_TRAIN_EXAMPLES] 44 | test_examples = get_dataset('test')[:MAX_TEST_EXAMPLES] 45 | 46 | prompt_examples = seed_prompt_examples(train_examples[:1], test_examples) 47 | 48 | for _ in tqdm(range(MAX_ITER), desc='Optimization iteration'): 49 | prompt_candidates = generate_prompt_candidates(prompt_examples, train_examples) 50 | scored_prompt_candidates = score_prompt_candidates(prompt_candidates, train_examples, test_examples) 51 | prompt_examples = update_prompt_examples(prompt_examples, scored_prompt_candidates) 52 | best_score = max([x.score for x in prompt_examples]) 53 | LOGGER.info('Current best score: %s', best_score) 54 | 55 | 56 | if __name__ == '__main__': 57 | main() 58 | -------------------------------------------------------------------------------- /src/opro/prompt_scoring.py: -------------------------------------------------------------------------------- 1 | import logging 2 | import re 3 | from multiprocessing.pool import ThreadPool 4 | from typing import List 5 | 6 | import openai 7 | from prompts import build_problem_prompt 8 | from prompts import format_openai_chat_prompt 9 | from schema import ProblemExample 10 | from schema import PromptExample 11 | from tqdm import tqdm 12 | 13 | from src.opro.settings import FINAL_ANSWER_SEP 14 | from src.opro.settings import MAX_RESPONSE_TOKENS 15 | from src.opro.settings import MODEL_NAME 16 | from src.opro.settings import THREADS 17 | 18 | LOGGER = logging.getLogger(__name__) 19 | 20 | 21 | def generate_answer_proc(candidate_problem_prompt: str): 22 | try: 23 | response = openai.ChatCompletion.create( 24 | model=MODEL_NAME, 25 | messages=format_openai_chat_prompt(candidate_problem_prompt), 26 | temperature=0.0, 27 | max_tokens=MAX_RESPONSE_TOKENS 28 | ) 29 | response_text = response.choices[0].message.content 30 | answer = response_text.split(FINAL_ANSWER_SEP)[-1] 31 | answer = re.findall(r'[\d]+[.,\d]+|[\d]*[.][\d]+|[\d]+', answer)[0].replace(',', '') 32 | return answer 33 | except Exception as e: 34 | LOGGER.error(f'Error generating answer: {e}') 35 | return None 36 | 37 | 38 | def generate_answers(demo_examples, test_examples, prompt_candidate): 39 | LOGGER.info(f'Generating answers for prompt candidate: {prompt_candidate}') 40 | pool = ThreadPool(processes=THREADS) 41 | candidate_problem_prompts = [(build_problem_prompt(p, demo_examples, prompt_candidate)) for p in test_examples] 42 | answers = [] 43 | 44 | async_results = pool.map(generate_answer_proc, candidate_problem_prompts) 45 | for answer in async_results: 46 | assert answer is not None 47 | answers.append(answer) 48 | 49 | return answers 50 | 51 | 52 | def get_prompt_candidate_score(generated_answers, problem_examples): 53 | assert len(generated_answers) == len(problem_examples) 54 | ground_truth_answers = [p.answer.split(FINAL_ANSWER_SEP)[-1].strip().replace(',', '') for p in problem_examples] 55 | return sum([1 if a == b else 0 for a, b in zip(generated_answers, ground_truth_answers)]) \ 56 | / len(ground_truth_answers) 57 | 58 | 59 | def score_prompt_candidates(prompt_candidates: List[str], 60 | demo_examples: List[ProblemExample], 61 | test_examples: List[ProblemExample]) \ 62 | -> List[PromptExample]: 63 | scored_prompt_candidates = [] 64 | for prompt_candidate in tqdm(prompt_candidates, desc='Scoring prompt candidates'): 65 | answers = generate_answers(demo_examples, test_examples, prompt_candidate) 66 | prompt_candidate_score = get_prompt_candidate_score(answers, test_examples) 67 | scored_prompt_candidates.append(PromptExample(prompt=prompt_candidate, score=prompt_candidate_score)) 68 | return scored_prompt_candidates 69 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | 81 | # IPython 82 | profile_default/ 83 | ipython_config.py 84 | 85 | # pyenv 86 | # For a library or package, you might want to ignore these files since the code is 87 | # intended to run in multiple environments; otherwise, check them in: 88 | # .python-version 89 | 90 | # pipenv 91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 94 | # install all needed dependencies. 95 | #Pipfile.lock 96 | 97 | # poetry 98 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 99 | # This is especially recommended for binary packages to ensure reproducibility, and is more 100 | # commonly ignored for libraries. 101 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 102 | #poetry.lock 103 | 104 | # pdm 105 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. 106 | #pdm.lock 107 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it 108 | # in version control. 109 | # https://pdm.fming.dev/#use-with-ide 110 | .pdm.toml 111 | 112 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm 113 | __pypackages__/ 114 | 115 | # Celery stuff 116 | celerybeat-schedule 117 | celerybeat.pid 118 | 119 | # SageMath parsed files 120 | *.sage.py 121 | 122 | # Environments 123 | .env 124 | .venv 125 | env/ 126 | venv/ 127 | ENV/ 128 | env.bak/ 129 | venv.bak/ 130 | 131 | # Spyder project settings 132 | .spyderproject 133 | .spyproject 134 | 135 | # Rope project settings 136 | .ropeproject 137 | 138 | # mkdocs documentation 139 | /site 140 | 141 | # mypy 142 | .mypy_cache/ 143 | .dmypy.json 144 | dmypy.json 145 | 146 | # Pyre type checker 147 | .pyre/ 148 | 149 | # pytype static type analyzer 150 | .pytype/ 151 | 152 | # Cython debug symbols 153 | cython_debug/ 154 | 155 | # PyCharm 156 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can 157 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 158 | # and can be added to the global gitignore or merged into this file. For a more nuclear 159 | # option (not recommended) you can uncomment the following to ignore the entire idea folder. 160 | #.idea/ 161 | -------------------------------------------------------------------------------- /src/opro/data_io.py: -------------------------------------------------------------------------------- 1 | import logging 2 | import os 3 | import shlex 4 | import subprocess 5 | from typing import List 6 | from typing import Optional 7 | 8 | import zstandard 9 | from appdirs import user_cache_dir 10 | 11 | LOGGER = logging.getLogger(__name__) 12 | 13 | 14 | def shell(args: List[str]): 15 | """Executes the shell command in `args`.""" 16 | cmd = shlex.join(args) 17 | LOGGER.info(f"Executing: {cmd}") 18 | exit_code = subprocess.call(args) 19 | if exit_code != 0: 20 | raise Exception(f"Failed with exit code {exit_code}: {cmd}") 21 | 22 | 23 | def download_file(source_uri: str, target_path: str, unpack: bool = False, 24 | unpack_type: Optional[str] = None, aws_profile: str = 'default'): 25 | """Download `source_uri` to `target_path` if it doesn't exist.""" 26 | if os.path.exists(target_path): 27 | # Assume it's all good 28 | LOGGER.info(f"Not downloading {source_uri} because {target_path} already exists") 29 | return 30 | 31 | # Download 32 | tmp_path: str = f"{target_path}.tmp" 33 | if source_uri.startswith("s3://"): 34 | shell(['aws', 's3', 'cp', source_uri, tmp_path, '--profile', aws_profile]) 35 | else: 36 | # gdown is used to download large files/zip folders from Google Drive. 37 | # It bypasses security warnings which wget cannot handle. 38 | downloader_executable: str = "gdown" if source_uri.startswith( 39 | "https://drive.google.com") else "wget" 40 | shell([downloader_executable, source_uri, "-O", tmp_path]) 41 | 42 | # Unpack (if needed) and put it in the right location 43 | if unpack: 44 | if unpack_type is None: 45 | if source_uri.endswith(".tar") or source_uri.endswith(".tar.gz"): 46 | unpack_type = "untar" 47 | elif source_uri.endswith(".zip"): 48 | unpack_type = "unzip" 49 | elif source_uri.endswith(".zst"): 50 | unpack_type = "unzstd" 51 | else: 52 | raise Exception( 53 | "Failed to infer the file format from source_uri. Please specify unpack_type.") 54 | 55 | tmp2_path = target_path + ".tmp2" 56 | os.makedirs(tmp2_path, exist_ok=True) 57 | if unpack_type == "untar": 58 | shell(["tar", "xf", tmp_path, "-C", tmp2_path]) 59 | elif unpack_type == "unzip": 60 | shell(["unzip", tmp_path, "-d", tmp2_path]) 61 | elif unpack_type == "unzstd": 62 | dctx = zstandard.ZstdDecompressor() 63 | with open(tmp_path, "rb") as ifh, open(os.path.join(tmp2_path, "data"), "wb") as ofh: 64 | dctx.copy_stream(ifh, ofh) 65 | else: 66 | raise Exception("Invalid unpack_type") 67 | files = os.listdir(tmp2_path) 68 | if len(files) == 1: 69 | # If contains one file, just get that one file 70 | shell(["mv", os.path.join(tmp2_path, files[0]), target_path]) 71 | os.rmdir(tmp2_path) 72 | else: 73 | shell(["mv", tmp2_path, target_path]) 74 | os.unlink(tmp_path) 75 | else: 76 | # Don't decompress if desired `target_path` ends with `.gz`. 77 | if source_uri.endswith(".gz") and not target_path.endswith(".gz"): 78 | gzip_path = f"{target_path}.gz" 79 | shell(["mv", tmp_path, gzip_path]) 80 | # gzip writes its output to a file named the same as the input file, omitting the .gz extension 81 | shell(["gzip", "-d", gzip_path]) 82 | else: 83 | shell(["mv", tmp_path, target_path]) 84 | LOGGER.info(f"Finished downloading {source_uri} to {target_path}") 85 | 86 | 87 | def get_cache_dir(dir_name=None, create=True): 88 | cache_root = user_cache_dir("enlighten_benchmark") 89 | if dir_name is not None: 90 | cache_dir = os.path.join(cache_root, dir_name) 91 | else: 92 | cache_dir = cache_root 93 | if create and not os.path.exists(cache_dir): 94 | LOGGER.info('Creating cache dir') 95 | os.makedirs(cache_dir) 96 | return cache_dir 97 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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