Model training configuration

📘

Note

This article refers to BaseModel accessed via Docker container. Please refer to Snowflake Native App section if you are using BaseModel as SF GUI application.

training_params block allows you to set constructor parameters for PyTorch Lightning Trainer.
These settings (eg. learning rate, epochs etc.) influence the training process.

• epochs: int
  default: 1
  Number of epochs to train the model for.
  
  
• learning_rate: float
  default: 0.001
  The learning rate.
  
  
• devices : Union[List[int], str, int, None]
  default: [0]
  The devices to use. Can be set to a positive number (int or str), a sequence of device indices(list or str), the value -1 to indicate all available devices should be used, or auto for automatic selection based on the chosen accelerator.
  
  
• accelerator : Literal["cpu", "gpu"]
  default: "gpu"
  The accelerator to use: GPU or CPU.
  
  
• precision : Literal[64, 32, 16, "64", "32", "16", "bf16", "16-true", "16-mixed", "bf16-true", "bf16-mixed", "32-true", "64-true"]
  default="DEFAULT_PRECISION"
  Controls Float precision used for training; double precision (64, ‘64’ or ‘64-true’), full precision (32, ‘32’ or ‘32-true’), 16bit mixed precision (16, ‘16’, ‘16-mixed’) or bfloat16 mixed precision (‘bf16’, ‘bf16-mixed’). DEFAULT_PRECISION constant sets precision to "bf16-mixed" if CUDA is available, else "16-mixed".
  
  
• limit_train_batches : Union[int, float]
  default: None
  Limits the number of train batches per epoch (float = fraction, int = num_batches). Use eg. to speed up testing.
  
  
• limit_val_batches : Union[int, float]
  default: None
  Limits the number of validation batches per epoch (float = fraction, int = num_batches). Use eg. to speed up testing.
  
  
• loss : Callable | None
  default: None
  The loss function to use. If not provided, default loss function for a task will be used.
  
  
• metrics : [Dict[str, Metric]]
  default: None
  Metrics to use in validation. If not provided, default validation metrics function for a task will be used.
  
  
• checkpoint_dir : [Union[str, Path]]
  default: None
  If provided, points to the location where model checkpoints will be stored.
  
  
• metric_to_monitor : str
  default: None
  Determines which metric should be used to select the best model for saving.
  
  
• metric_monitoring_mode : str ["min", "max"]
  default: None
  Indicates whether the smaller or greater value of selected metric is the better.
  
  
• callbacks : list[Callback]
  default: Lightning factory default
  List of additional Pytorch Lightning callbacks to add to training.
  
  
• resume : boolean
  default: False
  Whether to resume the training. If True, training will be resumed from the last checkpoint if such exists, an error will be thrown otherwise.
  
  
• overwrite : boolean
  default: False
  Whether to overwrite the previous training results. If True, results will be overwritten. Otherwise, if resume is not set and checkpoints from previous training are present, error will be raised.
  
  
• gradient_clip_val : Union[int, float]
  default: None
  Gradient clipping value (above which the gradients are clipped) passed to PytorchLightning trainer.
  
  
• warm_start_steps : int
  default: 0
  Number of warm-start training steps used while fine-tuning a supervised model. Ignored if no pretrained model is used.
  
  
• top_k : int
  default: 12
  IGNORE AT FOUNDATION LEVEL STAGE. Only valid at downstream model stage, for a recommendation task. Number of targets to recommend. Top k targets will be included in validation metrics, it does not have impact on model training.
  
  
• targets_to_include : List[str]
  default: None
  IGNORE AT FOUNDATION LEVEL STAGE. Only valid at downstream model stage, for a recommendation task. Target names that should be used for validation metrics, it does not have impact on model training.
  
  
• checkpoint_every_n_steps : int
  default: None
  Whether intra-epoch checkpointing should be performed.
  
  

training_params:
  learning_rate: 0.00005
  epochs: 3
  checkpoint_dir: "my_fm/"
  devices: [1]

🚧

Remember

You can quickly test your configuration (eg. data sources) by limiting the epochs to 1 and your training batches per epoch to eg. 10. While the created model would not be very useful, this will drastically reduce the runtime, and let you check if all your settings are correct. For that, see the training_params below.

training_params:
  learning_rate: 0.0001
  epochs: 1
  limit_train_batches: 10
  limit_val_batches: 10