def common_args()

in src/autotrain/cli/utils.py [0:0]

• 132 lines of code
• 1 McCabe index (conditional complexity)

def common_args():
    args = [
        {
            "arg": "--train",
            "help": "Command to train the model",
            "required": False,
            "action": "store_true",
        },
        {
            "arg": "--deploy",
            "help": "Command to deploy the model (limited availability)",
            "required": False,
            "action": "store_true",
        },
        {
            "arg": "--inference",
            "help": "Command to run inference (limited availability)",
            "required": False,
            "action": "store_true",
        },
        {
            "arg": "--username",
            "help": "Hugging Face Hub Username",
            "required": False,
            "type": str,
        },
        {
            "arg": "--backend",
            "help": "Backend to use: default or spaces. Spaces backend requires push_to_hub & username. Advanced users only.",
            "required": False,
            "type": str,
            "default": "local",
            "choices": AVAILABLE_HARDWARE.keys(),
        },
        {
            "arg": "--token",
            "help": "Your Hugging Face API token. Token must have write access to the model hub.",
            "required": False,
            "type": str,
        },
        {
            "arg": "--push-to-hub",
            "help": "Push to hub after training will push the trained model to the Hugging Face model hub.",
            "required": False,
            "action": "store_true",
        },
        {
            "arg": "--model",
            "help": "Base model to use for training",
            "required": True,
            "type": str,
        },
        {
            "arg": "--project-name",
            "help": "Output directory / repo id for trained model (must be unique on hub)",
            "required": True,
            "type": str,
        },
        {
            "arg": "--data-path",
            "help": "Train dataset to use. When using cli, this should be a directory path containing training and validation data in appropriate formats",
            "required": False,
            "type": str,
        },
        {
            "arg": "--train-split",
            "help": "Train dataset split to use",
            "required": False,
            "type": str,
            "default": "train",
        },
        {
            "arg": "--valid-split",
            "help": "Validation dataset split to use",
            "required": False,
            "type": str,
            "default": None,
        },
        {
            "arg": "--batch-size",
            "help": "Training batch size to use",
            "required": False,
            "type": int,
            "default": 2,
            "alias": ["--train-batch-size"],
        },
        {
            "arg": "--seed",
            "help": "Random seed for reproducibility",
            "required": False,
            "default": 42,
            "type": int,
        },
        {
            "arg": "--epochs",
            "help": "Number of training epochs",
            "required": False,
            "default": 1,
            "type": int,
        },
        {
            "arg": "--gradient-accumulation",
            "help": "Gradient accumulation steps",
            "required": False,
            "default": 1,
            "type": int,
            "alias": ["--gradient-accumulation"],
        },
        {
            "arg": "--disable-gradient-checkpointing",
            "help": "Disable gradient checkpointing",
            "required": False,
            "action": "store_true",
            "alias": ["--disable-gradient-checkpointing", "--disable-gc"],
        },
        {
            "arg": "--lr",
            "help": "Learning rate",
            "required": False,
            "default": 5e-4,
            "type": float,
        },
        {
            "arg": "--log",
            "help": "Use experiment tracking",
            "required": False,
            "type": str,
            "default": "none",
            "choices": ["none", "wandb", "tensorboard"],
        },
    ]
    return args