配置文件同样在NeMo项目下): from nemo.collections.asr.inference.factory.pipeline_builder import PipelineBuilder from omegaconf import OmegaConf # Path to the cache aware config file downloaded from above link cfg_path = 'cache_aware_rnnt.yaml ' cfg = OmegaConf.load(cfg_path) # Pass the paths of all the audio files for inferencing audios = ['
开源地址: https://github.com/mingrammer/diagrams 6、Hydra and OmegaConf 在做机器学习项目的时候,需要做一大堆的环境配置工作。 Hydra也离不开OmegaConf,两者关系密不可分,OmegaConf为Hydra的分层配置系统提供了协同的API,二者协同运作可支持YAML、配置文件、对象、CLI参数等。 开源地址: https://github.com/facebookresearch/hydra https://github.com/omry/omegaconf 7、PyTorch Lightning
to/isaacsimcd isaaclabpip install -e .使用说明基础训练示例import hydrafrom hydra.utils import instantiatefrom omegaconf import OmegaConf@hydra.main(config_path="config", config_name="base")def main(config): # 初始化仿真器 humanoidverse.envs.motion_tracking.motion_tracking import LeggedRobotMotionTrackingfrom humanoidverse.agents.ppo.ppo import PPO# 初始化运动跟踪环境config = OmegaConf.load
/omegaconf") import torch import os from datetime import datetime import time import random import cv2
import hydra from omegaconf import DictConfig, OmegaConf @hydra.main(config_name="config") def my_app (cfg: DictConfig) -> None: print(OmegaConf.to_yaml(cfg)) if __name__ == "__main__": my_app() from dataclasses import dataclass from omegaconf import MISSING, OmegaConf import hydra from hydra.core.config_store ) @hydra.main(config_path="conf", config_name="config") def my_app(cfg: Config) -> None: print(OmegaConf.to_yaml
安装必要的依赖: conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia pip install omegaconf
使用pip安装Hydra: python3 -m pip install hydra-core 接下来,我们写一段代码,来读取配置文件: import os import hydra from omegaconf
pip install pytorch_lightning==1.9.4 omegaconf==2.2.3 gradio==3.39.0 xformers==0.0.20 triton==2.0.0 pygit2
cfg.user) if __name__ == "__main__": app() 运行如下: python3 test_hydra.py +user=ua +pwd=pa 输出如下: Use OmegaConf.to_yaml hydra.main(config_path="conf", config_name="config") def my_app(cfg: DictConfig) -> None: print(OmegaConf.to_yaml
4.3.0.36 - imageio==2.9.0 - imageio-ffmpeg==0.4.2 - pytorch-lightning==1.5.0 - omegaconf 1.3.0 opencv-contrib-python==4.3.0.36 imageio==2.9.0 imageio-ffmpeg==0.4.2 pytorch-lightning==1.5.0 omegaconf
Hydra 与 OmegaConf 当进行机器学习项目的研究和实验时,总是有无数的设置需要尝试。配置管理可以变得非常复杂,并且在重要的应用程序中非常快速。 option_b ├── variation│ ├── option_a.yaml│ └── option_b.yaml├── base.yaml└── train_model.py Hydra 的表亲 OmegaConf
Controller 例如 Parchcore 的模型加载可以简化为: def get_model(self, config_path, model_path): config = OmegaConf.load
diffusers pip install transformers pip install torch pip install matplotlib pip install numpy pip install omegaconf
Hydra and OmegaConf 在机器学习项目中做研究和实验时,总是有无数的设置可以尝试,在重要的应用程序中,配置管理可能会变得非常复杂,如果有一种结构化的方法来处理这些难题就好了。
for a config named tacotron2.yaml inside the conf folder# Hydra parses the yaml and returns it as a Omegaconf
• 集成OmegaConf配置支持,提升配置管理便捷度。 • 增加早停机制与思考启用参数,丰富训练及推理策略调节空间。
opencv-python pudb==2019.2 imageio==2.9.0 imageio-ffmpeg==0.4.2 pytorch-lightning==1.4.2 torchmetrics==0.6 omegaconf
.+ kornia 0.6.12 matplotlib 3.7.1 numpy 1.24.3 omegaconf 2.1.2 opencv-python-headless 4.5.5.64
from diffusers import StableDiffusionInpaintPipeline from PIL import Image import numpy as np from omegaconf import OmegaConf from transformers import pipeline, BlipProcessor, BlipForConditionalGeneration, BlipForQuestionAnswering ) return os.path.join(head, new_file_name) def create_model(config_path, device): config = OmegaConf.load (config_path) OmegaConf.update(config, "model.params.cond_stage_config.params.device", device)
torchaudio pip install rich loguru matplotlibpip install faiss-gpupip uninstall omegaconfpip install omegaconf