Easy_Torch Config
Data Parameters
name (str)– Name of the dataset to use. Example: MNIST.source (str)– Data source identifier. Options include [torchvision, uci, tfds, custom]. Default is torchvision.merge_before_split (bool)– Whether to merge training/validation/test sets before splitting. Default to False.split_keys (dict)– Keys defining how to split the data. Example: {“train_x”: [“train_x”, “val_x”], “train_y”: [“train_y”, “val_y”]}.train_sizes (list of int)– Number of samples or percentage to use for training. Default to [100, 100].test_sizes (list of float or int)– Size of the test set. Interpreted as a proportion or absolute number. Default to [0.2].split_random_state (int)– Random seed used to ensure reproducibility of splits. Default to 21094.one_hot_encode (bool)– Whether to apply one-hot encoding to target labels. Default to True.scaling (str or None)– Method to scale input features. Options in [None, MinMax, Standard]. Default to MinMax.
Loader Parameters
batch_size (int)– Number of samples per batch during training or evaluation. Default to 256.num_workers (int)– Number of subprocesses to use for data loading. Default to 1.dtypes (str)– Data type for tensors. Default to float32.
Trainer Parameters
accelerator (str)– Type of accelerator to use. Options in [cpu, gpu, mps]. Default to cpu.enable_checkpointing (bool)– Whether to save checkpoints during training. Default to True.max_epochs (int)– Maximum number of training epochs. Default to 1.callbacks (list of dict)– List of training callbacks.EarlyStopping-monitor (str)– Metric to monitor for early stopping. Example: val_loss. -mode (str)– Direction of improvement. Options: [min, max]. Default: min. -patience (int)– Number of epochs without improvement before stopping. Default: 1.ModelCheckpoint-dirpath (str)– Directory to save model checkpoints. Example: ${__exp__.project_folder}/out/models/${__exp__.name}/. -filename (str)– Name format for saved checkpoint files. Default: best. -save_top_k (int)– Number of best models to retain. Default: 1. -save_last (bool)– Whether to save the last model checkpoint. Default: True. -monitor (str)– Metric to evaluate for saving. Example: val_loss. -mode (str)– Direction to optimize. Options: [min, max]. Default: min.
logger (dict)– Logging configuration. -name (str)– Logger class name. Example: CSVLogger. -params.save_dir (str)– Directory to save logs. Example: ${__exp__.project_folder}/out/log/${__exp__.name}/.
Neural Network Parameters
num_neurons (list of int)– List of neuron counts to sweep for hidden layers. Default to [1, 2, 4, 8, 16, 32, 64, 128].num_layers (list of int)– Number of layers to sweep over. Default to [1, 2, 3, 4, 5].lr (list of float)– Learning rate values to sweep over. Default to [1.0e-2, 1.0e-3, 1.0e-4].activation_function (list of str)– List of activation functions to use. Options include [Tanh, LeakyReLU].
ResNet Parameters
name (str)– Name of the ResNet architecture. Example: resnet18.torchvision_params.weights (str or None)– Pretrained weights to use. Default: Null (no pretraining).optimizer.name (str)– Name of the optimizer. Example: Adam.optimizer.params.lr (float)– Learning rate. Default: 0.1.optimizer.params.weight_decay (float)– Weight decay for optimizer. Default: 0.0005.loss (str)– Loss function used for training. Example: CrossEntropyLoss.log_params.on_epoch (bool)– Whether to log metrics at the end of each epoch. Default: True.log_params.on_step (bool)– Whether to log metrics at every step. Default: False.
Experiment Metadata
__exp__.name (str)– Name of the experiment. Example: prova.__exp__.__imports__ (list of modules)– List of modules to import before parsing the config. Example: [torchvision].
+loader_params and +trainer_params are shorthand inclusion directives for loading shared configurations, typically defined elsewhere in modular configuration files.
+model indicates the model definition to be used (e.g., resnet, nn).
seed may be defined globally via ${exp.seed} to ensure reproducibility across runs.