# What is the official way to run a wandb sweep with hugging face (HF) transformers?

**URL:** <https://community.wandb.ai/t/what-is-the-official-way-to-run-a-wandb-sweep-with-hugging-face-hf-transformers/4668>\
**Category:** W&B Help\
**Tags:** sweeps\
**Created:** [July 1, 2023, 11:11pm UTC](https://community.wandb.ai/t/what-is-the-official-way-to-run-a-wandb-sweep-with-hugging-face-hf-transformers/4668 "2023-07-01T23:11:39Z")\
**Posts on this page:** 1\
**Showing post:** 6

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**Author:** ![uma-wandb](https://avatars.discourse-cdn.com/v4/letter/u/b77776/32.png) [@uma-wandb](https://community.wandb.ai/u/uma-wandb)\
**Post date:** [July 6, 2023, 8:04pm UTC](https://community.wandb.ai/t/what-is-the-official-way-to-run-a-wandb-sweep-with-hugging-face-hf-transformers/4668/6 "2023-07-06T20:04:17Z")

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Hey @brando, you are correct to set `report_to='wandb'` in your training args. You mentioned that you are curious to see how to load the config into the standard HFParser Arguments. Here is an example of setting up a config, training arguments, and a trainer to run a sweep:

```auto
sweep_config = {
    "method": "random",
    "name": "disaster-sweep",
    "metric": {
        "goal": "minimize",
        "name": "train/loss"
    },
    "parameters": {
        "epochs": {
            "values": [5, 10]
        },
        "batch_size": {
            "values": [8, 16, 32, 64]
        },
        "learning_rate": {
            "values": [0.005, 0.0001, 0.00005]
        },
        "weight_decay": {
            "values": [0.0001, 0.1]
        }
    }
}

# Training function with args
def train(config=None):
  with wandb.init(config=config):
    # set sweep configuration
    config = wandb.config

    # set training arguments
    training_args = TrainingArguments(
    output_dir='./results',
	report_to='wandb', # Turn on Weights & Biases logging
        num_train_epochs=config.epochs,
        learning_rate=config.learning_rate,
        weight_decay=config.weight_decay,
        per_device_train_batch_size=config.batch_size,
        per_device_eval_batch_size=config.batch_size,
        save_strategy='epoch',
        evaluation_strategy='epoch',
        logging_strategy='epoch',
        load_best_model_at_end=True,
        remove_unused_columns=False,
    )

    # define training loop
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset,
        eval_dataset=valid_dataset,
        compute_metrics=compute_metrics
    )

    # start training loop
    trainer.train()

sweep_id = wandb.sweep(sweep_config, project='fun-sweep')
wandb.agent(sweep_id, train, count=10)

```

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