📣 Weights & Biases - Feedback
Hey folks!I’m Morgan from Weights & Biases and we’re looking for feedback from thecommunity on our currentdocumentationfor our Hugging Face W&B integration.Any and all feedbackis a real gift(especially if there are things that are unclear or haven’t worked for you in the past)Docs to Check OutOur own W&B documentation for Hugging Facecan be found hereA briefer W&B overview from the Hugging Face docsis hereOptimize HF models with W&B integrationdemo on colabSuggested QuestionsWhat would you change or add for a Hugging Face user using W&B for thefirst-time?What would you change or add in the docs for a Hugging Face useralready using W&B?Is there anything in the docs you feel is missing or could be better explained?Are there any example descriptions or example code that would better explain the W&B integration?Since I’m here, happy to hear about any suggestions or issues with the Hugging Face W&B integration here or in@boris'sthread hereMyself and@borisare looking forward to hearing from you all!
2022-11-21T03:03:14Z
Hi@morgan,Is there any documentation on how to integrate HuggingFace Accelerate (multi-gpu on a single machine setting) with weights and biases sweep?
2022-12-05T13:14:52Z
hey@berkin, as long as your script can accept changes to the hyperparameters being set by wandb then it should work as normal. Using Accelerate you could just call the wandb tracker and get your configs from there, e.g.wandb_tracker = accelerator.get_tracker("wandb")
learning_rate = wandb_tracker.config["learning_rate"]You could also call wandb directly, assuming wandb is running on that GPUlearning_rate = wandb.config["learning_rate"]