PyTorchLightning 还有这么多好用的项目

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尤其这里，它们所有的项目都值得好好的关注下。

https://github.com/PyTorchLightning

Flexible interface for high-performance research using SOTA Transformers leveraging Pytorch Lightning, Transformers, and Hydra.
基本是把各种nlp项目实现了一遍，拿来或者参考都不错。

Using Lightning-Transformers

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Task

Quick Commands

Run

Language Modeling
python train.py task=nlp/language_modeling dataset=nlp/language_modeling/wikitext trainer.gpus=1 training.batch_size=8

Multiple Choice
python train.py task=nlp/multiple_choice dataset=nlp/multiple_choice/race trainer.gpus=1

Question Answering
python train.py task=nlp/question_answering dataset=nlp/question_answering/squad trainer.gpus=1

Summarization
python train.py task=nlp/summarization dataset=nlp/summarization/xsum trainer.gpus=1

Text Classification
python train.py task=nlp/text_classification dataset=nlp/text_classification/emotion trainer.gpus=1

Token Classification
python train.py task=nlp/token_classification dataset=nlp/token_classification/conll trainer.gpus=1

Translation
python train.py task=nlp/translation dataset=nlp/translation/wmt16 trainer.gpus=1

https://github.com/PyTorchLightning/lightning-transformers

kaggle添加验证信息等

import wandb
 from kaggle_secrets import UserSecretsClient
 user_secrets = UserSecretsClient() 

personal_key_for_api = user_secrets.get_secret("wandb-key")

! wandb login $personal_key_for_api

https://github.com/ultralytics/yolov5/issues/1372