Pipelines¶

Pipelines provide pre-configured training workflows for common dataset and model combinations, simplifying the process of training and evaluating logical reasoning models.

Overview¶

Located in logitorch.pipelines, these functions handle the complete training lifecycle including dataset loading, model training, checkpointing, and evaluation with minimal configuration.

Available Pipelines¶

QA Pipelines¶

Pre-configured pipelines for question answering tasks.

RuleTaker Pipeline¶

Train models on the RuleTaker dataset:

from logitorch.pipelines.qa_pipelines import ruletaker_pipeline
from logitorch.pl_models.ruletaker import PLRuleTaker

model = PLRuleTaker(learning_rate=1e-5, weight_decay=0.1)

ruletaker_pipeline(
    model=model,
    dataset_name="depth-5",
    saved_model_path="models/",
    saved_model_name="best_ruletaker",
    batch_size=32,
    epochs=10,
    accelerator="gpu",
    devices=1
)

ProofWriter Pipeline¶

Train models on the ProofWriter dataset:

from logitorch.pipelines.proof_qa_pipelines import proofwriter_pipeline
from logitorch.pl_models.proofwriter import PLProofWriter

model = PLProofWriter(learning_rate=1e-5, weight_decay=0.1)

proofwriter_pipeline(
    model=model,
    dataset_name="depth-5",
    saved_model_path="models/",
    saved_model_name="best_proofwriter",
    batch_size=16,
    epochs=10,
    accelerator="gpu",
    devices=1
)

FLD Pipeline¶

Train models on the FLD (Fine-tuned Language Decoder) dataset:

from logitorch.pipelines.proof_qa_pipelines import fld_pipeline
from logitorch.pl_models.fld import PLFLDAllAtOnceProver

model = PLFLDAllAtOnceProver(learning_rate=1e-5, weight_decay=0.1)

fld_pipeline(
    model=model,
    dataset_name="FLD",
    saved_model_path="models/",
    saved_model_name="best_fld",
    batch_size=16,
    epochs=10,
    accelerator="gpu",
    devices=1
)

Pipeline Parameters¶

Common Parameters¶

All pipelines accept these common parameters:

model: PyTorch Lightning model instance
saved_model_path: Directory to save checkpoints
saved_model_name: Name for the checkpoint file
batch_size: Training batch size
epochs: Number of training epochs
accelerator: Training accelerator (“gpu”, “cpu”, “tpu”)
devices: Number of devices to use

Optional Parameters¶

Additional configuration options:

learning_rate: Override model’s default learning rate
weight_decay: Override model’s default weight decay
accumulate_grad_batches: Gradient accumulation steps
gradient_clip_val: Maximum gradient norm for clipping
val_check_interval: Validation frequency
early_stopping_patience: Early stopping patience

Dataset-Specific Parameters¶

Some pipelines accept dataset-specific parameters:

# RuleTaker with different depth
ruletaker_pipeline(
    model=model,
    dataset_name="depth-3",  # or "depth-5", "depth-0", etc.
    ...
)

# ProofWriter with different splits
proofwriter_pipeline(
    model=model,
    dataset_name="OWA",  # or "CWA"
    ...
)

Usage Examples¶

Basic Training¶

Train a model with default settings:

from logitorch.pipelines.qa_pipelines import ruletaker_pipeline
from logitorch.pl_models.ruletaker import PLRuleTaker

model = PLRuleTaker()

ruletaker_pipeline(
    model=model,
    dataset_name="depth-5",
    saved_model_path="checkpoints/",
    saved_model_name="ruletaker_model",
    batch_size=32,
    epochs=10,
    accelerator="gpu",
    devices=1
)

Advanced Configuration¶

Customize training with advanced parameters:

from logitorch.pipelines.qa_pipelines import ruletaker_pipeline
from logitorch.pl_models.ruletaker import PLRuleTaker

model = PLRuleTaker(
    learning_rate=2e-5,
    weight_decay=0.01
)

ruletaker_pipeline(
    model=model,
    dataset_name="depth-5",
    saved_model_path="checkpoints/",
    saved_model_name="ruletaker_advanced",
    batch_size=16,
    epochs=20,
    accelerator="gpu",
    devices=2,  # Multi-GPU training
    accumulate_grad_batches=4,  # Effective batch size: 16 * 4 = 64
    gradient_clip_val=1.0,
    val_check_interval=0.5,  # Validate twice per epoch
    early_stopping_patience=3
)

Multi-GPU Training¶

Scale training across multiple GPUs:

ruletaker_pipeline(
    model=model,
    dataset_name="depth-5",
    saved_model_path="checkpoints/",
    saved_model_name="ruletaker_multigpu",
    batch_size=32,  # Per-device batch size
    epochs=10,
    accelerator="gpu",
    devices=4,  # Use 4 GPUs
    strategy="ddp"  # Distributed data parallel
)

Pipeline Outputs¶

Training Results¶

Pipelines save checkpoints to the specified directory:

checkpoints/
├── best_ruletaker_model.ckpt    # Best model checkpoint
├── last.ckpt                     # Last epoch checkpoint
└── epoch=XX-step=YYYY.ckpt       # Periodic checkpoints

Best Practices¶

Start small: Begin with small batch sizes and short training runs to verify setup
Monitor GPU memory: Adjust batch size based on available memory
Use gradient accumulation: Achieve larger effective batch sizes without OOM errors
Enable checkpointing: Always save model checkpoints during training
Validate regularly: Set appropriate val_check_interval for your dataset size
Use early stopping: Prevent overfitting with early stopping callbacks
Log metrics: Track training progress with tools like TensorBoard or Weights & Biases