Mlops Weights And Biases 集成

# Framework Integrations Guide Complete guide to integrating W&B with popular ML frameworks. ## Table of Contents - HuggingFace Transformers - PyTorch Lightning - Keras/TensorFlow - Fast.ai - XGBoost/LightGBM - PyTorch Native - Custom Integrations ## HuggingFace Transformers ### Automatic Integration python from transformers import Trainer, TrainingArguments import wandb # Initialize W&B wandb.init(project="hf-transformers", name="bert-finetuning") # Training arguments with W&B training_args = TrainingArguments( output_dir="./results", report_to="wandb", # Enable W&B logging run_name="bert-base-finetuning", # Training params num_train_epochs=3, per_device_train_batch_size=16, per_device_eval_batch_size=64, learning_rate=2e-5, # Logging logging_dir="./logs", logging_steps=100, logging_first_step=True, # Evaluation evaluation_strategy="steps", eval_steps=500, save_steps=500, # Other load_best_model_at_end=True, metric_for_best_model="eval_accuracy" ) # Trainer automatically logs to W&B trainer = Trainer( model=model, args=training_args, train_dataset=train_dataset, eval_dataset=eval_dataset, compute_metrics=compute_metrics ) # Train (metrics logged automatically) trainer.train() # Finish W&B run wandb.finish() ### Custom Logging python from transformers import Trainer, TrainingArguments from transformers.integrations import WandbCallback import wandb class CustomWandbCallback(WandbCallback): def on_evaluate(self, args, state, control, metrics=None, **kwargs): super().on_evaluate(args, state, control, metrics, **kwargs) # Log custom metrics wandb.log({ "custom/eval_score": metrics["eval_accuracy"] * 100, "custom/epoch": state.epoch }) # Use custom callback trainer = Trainer( model=model, args=training_args, train_dataset=train_dataset, eval_dataset=eval_dataset, callbacks=[CustomWandbCallback()] ) ### Log Model to Registry python from transformers import Trainer, TrainingArguments training_args = TrainingArguments( output_dir="./results", report_to="wandb", load_best_model_at_end=True ) trainer = Trainer( model=model, args=training_args, train_dataset=train_dataset, eval_dataset=eval_dataset ) trainer.train() # Save final model as artifact model_artifact = wandb.Artifact( 'hf-bert-model', type='model', de