mirror of
https://github.com/bentoml/OpenLLM.git
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101 lines
3.4 KiB
Python
Executable File
101 lines
3.4 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2023 BentoML Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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import os
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import typing as t
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import inflection
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import tomlkit
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import openllm
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START_COMMENT = f"<!-- {os.path.basename(__file__)}: start -->\n"
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END_COMMENT = f"<!-- {os.path.basename(__file__)}: stop -->\n"
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ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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def main() -> int:
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with open(os.path.join(ROOT, "pyproject.toml"), "r") as f:
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deps = tomlkit.parse(f.read()).value["project"]["optional-dependencies"]
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with open(os.path.join(ROOT, "README.md"), "r") as f:
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readme = f.readlines()
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start_index, stop_index = readme.index(START_COMMENT), readme.index(END_COMMENT)
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formatted: dict[t.Literal["Model", "CPU", "GPU", "Installation"], list[str]] = {
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"Model": [],
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"CPU": [],
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"GPU": [],
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"Installation": [],
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}
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max_name_len_div = 0
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max_install_len_div = 0
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does_not_need_custom_installation: list[str] = []
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for name, config in openllm.CONFIG_MAPPING.items():
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dashed = inflection.dasherize(name)
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model_name = f"[{dashed}]({config.__openllm_url__})"
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if len(model_name) > max_name_len_div:
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max_name_len_div = len(model_name)
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formatted["Model"].append(model_name)
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formatted["GPU"].append("✅")
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formatted["CPU"].append("✅" if not config.__openllm_requires_gpu__ else "❌")
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instruction = "`pip install openllm`"
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if dashed in deps:
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instruction = f"""`pip install "openllm[{dashed}]"`"""
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else:
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does_not_need_custom_installation.append(model_name)
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if len(instruction) > max_install_len_div:
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max_install_len_div = len(instruction)
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formatted["Installation"].append(instruction)
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meta = ["\n"]
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# NOTE: headers
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meta += f"| Model {' ' * (max_name_len_div - 6)} | CPU | GPU | Installation {' ' * (max_install_len_div - 8)}|\n"
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# NOTE: divs
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meta += f"| {'-' * max_name_len_div}" + " | --- | --- | " + f"{'-' * max_install_len_div} |\n"
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# NOTE: rows
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for links, cpu, gpu, custom_installation in t.cast("tuple[str, str, str, str]", zip(*formatted.values())):
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meta += (
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"| "
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+ links
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+ " " * (max_name_len_div - len(links))
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+ f" | {cpu} | {gpu} | "
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+ custom_installation
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+ " "
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* (
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max_install_len_div
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- len(custom_installation)
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- (0 if links not in does_not_need_custom_installation else 1)
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)
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+ " |\n"
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)
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meta += "\n"
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readme = readme[:start_index] + [START_COMMENT] + meta + [END_COMMENT] + readme[stop_index + 1 :]
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with open(os.path.join(ROOT, "README.md"), "w") as f:
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f.writelines(readme)
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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