{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pytorch-lightning","slug":"pytorch-lightning","name":"PyTorch Lightning","type":"framework","url":"https://github.com/Lightning-AI/pytorch-lightning","page_url":"https://unfragile.ai/pytorch-lightning","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pytorch-lightning__cap_0","uri":"capability://automation.workflow.automated.training.loop.abstraction.with.lightning.module","name":"automated-training-loop-abstraction-with-lightning-module","description":"Encapsulates PyTorch training logic into a LightningModule class that defines train_step(), validation_step(), test_step() hooks, which the Trainer orchestrates automatically. The Trainer class manages the outer loop (epochs, batches, device placement) while developers focus only on per-batch logic, eliminating boilerplate training code. Uses a callback-based hook system to inject custom logic at 50+ lifecycle points (on_train_start, on_batch_end, etc.) without modifying core training flow.","intents":["I want to write a PyTorch model without manually managing training loops, device transfers, and epoch iteration","I need to add custom logic at specific training phases (e.g., log metrics after validation) without rewriting the entire training loop","I want to switch between CPU, GPU, and multi-GPU training with a single config change, not code changes"],"best_for":["researchers prototyping supervised learning models rapidly","teams building standard classification/regression pipelines","developers migrating from raw PyTorch who want structure without losing flexibility"],"limitations":["Abstraction overhead: ~5-10% slower than hand-optimized raw PyTorch loops due to hook dispatch and state management","Custom training logic (e.g., RL, GANs with alternating discriminator/generator steps) requires dropping to Lightning Fabric or raw loops","LightningModule inheritance is mandatory; composition-based approaches not natively supported"],"requires":["Python 3.8+","PyTorch 1.12+","Subclass of LightningModule with implemented training_step() method"],"input_types":["PyTorch model (nn.Module)","DataLoader or LightningDataModule","Optimizer and loss function"],"output_types":["trained model checkpoint (state_dict)","training metrics (loss, accuracy, custom scalars)","validation/test results"],"categories":["automation-workflow","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pytorch-lightning__cap_1","uri":"capability://automation.workflow.multi.strategy.distributed.training.with.automatic.device.mapping","name":"multi-strategy-distributed-training-with-automatic-device-mapping","description":"Abstracts distributed training via a pluggable Strategy pattern that supports DDP (Distributed Data Parallel), FSDP (Fully Sharded Data Parallel), DeepSpeed, and single-GPU/CPU training through a unified interface. The Trainer detects hardware (GPUs, TPUs, CPUs) and automatically selects the optimal strategy; developers specify only `trainer = Trainer(devices='auto', strategy='ddp')` and the framework handles gradient synchronization, device placement, and communication collectives. Strategies are composable with Accelerators (GPU/TPU/CPU) and Precision plugins (FP32, FP16, BF16) for fine-grained control.","intents":["I want to scale my model from 1 GPU to 8 GPUs without rewriting training code","I need to use FSDP for memory-efficient training of large models but don't want to manually manage sharding","I want to experiment with different distributed strategies (DDP vs DeepSpeed) by changing a config parameter"],"best_for":["ML teams scaling models across multi-GPU clusters","researchers training large language models or vision transformers with memory constraints","engineers building production training pipelines that must work on heterogeneous hardware"],"limitations":["Strategy selection is automatic but not always optimal; manual tuning of batch size, gradient accumulation, and communication backend may be required for peak performance","DeepSpeed integration requires separate DeepSpeed installation and configuration file; not all DeepSpeed features are exposed through Lightning's API","Cross-strategy checkpoints are not always compatible; switching strategies mid-training may require checkpoint conversion","TPU support is limited compared to GPU; some strategies (e.g., DeepSpeed) don't support TPU"],"requires":["PyTorch 1.12+","For DDP: torch.distributed backend (NCCL for GPU, Gloo for CPU)","For FSDP: PyTorch 1.13+ (native FSDP support)","For DeepSpeed: deepspeed package and configuration file","For multi-GPU: CUDA 11.0+ and compatible GPUs"],"input_types":["LightningModule","DataLoader (must support distributed sampling via DistributedSampler)","strategy name (string) or Strategy object"],"output_types":["distributed checkpoint (aggregated across all ranks)","synchronized metrics (averaged across all processes)","trained model (gathered on rank 0)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pytorch-lightning__cap_10","uri":"capability://planning.reasoning.model.summary.and.training.debugging.utilities","name":"model-summary-and-training-debugging-utilities","description":"Provides utilities to inspect model architecture (parameter counts, layer shapes, FLOPs) via ModelSummary, and debugging tools (gradient flow visualization, activation statistics) via callbacks. The Trainer can print a model summary before training; developers can inspect gradients, weights, and activations at any training phase via callbacks or manual inspection. Supports profiling (PyTorch Profiler integration) to identify performance bottlenecks.","intents":["I want to inspect my model architecture (number of parameters, layer shapes) before training","I need to debug training issues (vanishing/exploding gradients, dead neurons) by inspecting activations and gradients","I want to profile my training loop to identify performance bottlenecks (GPU utilization, memory usage)"],"best_for":["researchers debugging model architectures and training dynamics","engineers optimizing training performance and identifying bottlenecks","teams troubleshooting training failures (NaN loss, divergence)"],"limitations":["Model summary requires a sample input tensor; dynamic models with variable input shapes may not summarize correctly","Gradient inspection and profiling add significant overhead (~10-50% slowdown); should only be used for debugging, not production training","Profiler output can be verbose and difficult to interpret; requires domain knowledge to identify bottlenecks","Activation statistics (mean, std, histogram) require additional memory and computation; not suitable for very large models"],"requires":["PyTorch 1.12+","For profiling: torch.profiler module (PyTorch 1.8+)","Sample input tensor matching model input shape"],"input_types":["LightningModule","sample input tensor","profiler configuration (optional)"],"output_types":["model summary (parameter counts, layer shapes, FLOPs)","gradient statistics (mean, std, histogram)","activation statistics (mean, std, histogram)","profiler report (execution time, memory usage per operation)"],"categories":["planning-reasoning","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pytorch-lightning__cap_11","uri":"capability://automation.workflow.reproducibility.and.deterministic.training.configuration","name":"reproducibility-and-deterministic-training-configuration","description":"Provides utilities to ensure reproducible training by setting random seeds (PyTorch, NumPy, Python), disabling non-deterministic operations, and logging training configuration. The Trainer can set seeds automatically via the seed_everything() function; developers can configure deterministic mode to disable CUDA non-deterministic algorithms. Checkpoints include random seed state, allowing exact reproduction of training from any checkpoint.","intents":["I want to ensure my training results are reproducible across different runs and machines","I need to disable non-deterministic CUDA operations for exact reproducibility, even if it impacts performance","I want to resume training from a checkpoint and continue with the same random state"],"best_for":["researchers publishing results and needing reproducible training","teams running ablation studies and needing consistent baselines","engineers building production systems requiring deterministic behavior"],"limitations":["Deterministic mode disables CUDA non-deterministic algorithms, which can reduce performance by 10-50% depending on the model","Some operations (e.g., scatter, gather) don't have deterministic implementations; these operations will raise errors in deterministic mode","Reproducibility across different PyTorch versions is not guaranteed; version pinning is required","Distributed training with deterministic mode requires careful synchronization; some operations may deadlock if not synchronized correctly"],"requires":["PyTorch 1.12+","Python 3.8+","For deterministic mode: CUDA 11.0+ (some operations may not be deterministic on older CUDA versions)"],"input_types":["seed value (integer)","deterministic mode flag (boolean)"],"output_types":["reproducible training results","deterministic random state (saved in checkpoints)"],"categories":["automation-workflow","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pytorch-lightning__cap_12","uri":"capability://automation.workflow.gradient.accumulation.and.effective.batch.size.scaling","name":"gradient-accumulation-and-effective-batch-size-scaling","description":"Provides automatic gradient accumulation via the accumulate_grad_batches parameter, which accumulates gradients over multiple batches before updating weights. This allows training with larger effective batch sizes without increasing GPU memory usage. The Trainer handles gradient accumulation transparently; developers specify accumulate_grad_batches and the Trainer skips optimizer.step() for intermediate batches.","intents":["I want to train with a larger effective batch size (e.g., 512) but my GPU only fits batch size 64","I need to use gradient accumulation to simulate distributed training on a single GPU","I want to experiment with different effective batch sizes without changing the DataLoader batch size"],"best_for":["researchers training large models on memory-constrained GPUs","teams simulating distributed training on single-GPU machines","engineers optimizing training efficiency without hardware upgrades"],"limitations":["Gradient accumulation increases training time proportionally (e.g., 8x accumulation = 8x longer training for same number of weight updates)","Batch normalization statistics are computed on the micro-batch (per-GPU batch), not the effective batch; this can impact model accuracy","Learning rate scheduling is based on the number of optimizer steps, not the number of batches; developers must adjust learning rate schedules accordingly","Gradient accumulation with distributed training requires careful synchronization; gradients must be synchronized after each accumulation step"],"requires":["PyTorch 1.12+","accumulate_grad_batches parameter in Trainer"],"input_types":["accumulate_grad_batches (integer or schedule)","DataLoader with micro-batch size"],"output_types":["trained model with effective batch size = micro-batch size * accumulate_grad_batches","training metrics (loss, accuracy)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pytorch-lightning__cap_13","uri":"capability://automation.workflow.learning.rate.scheduling.and.warmup.strategies","name":"learning-rate-scheduling-and-warmup-strategies","description":"Provides integration with PyTorch's learning rate schedulers (StepLR, CosineAnnealingLR, ReduceLROnPlateau, etc.) and built-in warmup strategies (linear, exponential). The Trainer automatically steps the scheduler at the right intervals (per batch or per epoch); developers configure the scheduler in the LightningModule's configure_optimizers() method. Supports custom schedulers via a simple interface.","intents":["I want to use a learning rate schedule (e.g., cosine annealing) without manually stepping the scheduler in the training loop","I need to implement a warmup phase (gradually increase learning rate) before the main training schedule","I want to reduce learning rate when validation metric plateaus (ReduceLROnPlateau) without manual monitoring"],"best_for":["researchers using standard learning rate schedules (cosine annealing, step decay)","teams implementing warmup strategies for stable training","engineers building production training systems with adaptive learning rates"],"limitations":["Learning rate scheduling is tightly coupled to the number of optimizer steps; changing batch size or accumulation requires recalculating the schedule","Some schedulers (e.g., ReduceLROnPlateau) require monitoring a validation metric; this adds complexity and requires careful metric selection","Custom schedulers require implementing the PyTorch scheduler interface; not all scheduling strategies are easy to express as schedulers","Warmup strategies are limited to simple approaches (linear, exponential); more complex warmup (e.g., polynomial) requires custom implementation"],"requires":["PyTorch 1.12+","torch.optim.lr_scheduler module"],"input_types":["optimizer","scheduler class (e.g., StepLR, CosineAnnealingLR)","scheduler configuration (step_size, T_max, etc.)","warmup strategy (optional)"],"output_types":["learning rate schedule (applied automatically during training)","training metrics (loss, accuracy with adaptive learning rate)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pytorch-lightning__cap_14","uri":"capability://data.processing.analysis.distributed.data.loading.with.automatic.sampler.configuration","name":"distributed-data-loading-with-automatic-sampler-configuration","description":"Automatically configures distributed data samplers (DistributedSampler, RandomSampler, SequentialSampler) based on the training strategy and number of devices, ensuring each process loads a unique subset of data without duplication or gaps. The Trainer wraps DataLoaders with the appropriate sampler and handles shuffle/seed management across distributed processes. Supports automatic batch size scaling and num_workers tuning.","intents":["I want to load data in parallel across multiple GPUs without manually configuring DistributedSampler","I need to ensure each GPU loads a unique subset of data without duplication","I want to automatically scale batch size and num_workers across different numbers of GPUs"],"best_for":["teams training on multi-GPU setups without manual sampler configuration","researchers scaling data loading to multi-node clusters","engineers optimizing data loading performance by tuning num_workers"],"limitations":["Automatic sampler configuration requires DataLoaders to be created in train_dataloader(), val_dataloader(), etc.; custom DataLoader creation is not supported","Batch size scaling requires recomputing optimal batch size; no automatic tuning","num_workers tuning is not automatic; requires manual experimentation or separate profiling tools","Shuffling across distributed processes requires careful seed management; incorrect seeding can lead to data leakage"],"requires":["PyTorch Lightning 1.5+","DataLoaders created in LightningDataModule or LightningModule"],"input_types":["DataLoader","Number of devices (automatically detected)"],"output_types":["Wrapped DataLoader with distributed sampler","Unique data subsets per process"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pytorch-lightning__cap_2","uri":"capability://automation.workflow.automatic.mixed.precision.training.with.precision.plugins","name":"automatic-mixed-precision-training-with-precision-plugins","description":"Provides pluggable Precision plugins (FP32, FP16, BF16, mixed precision) that automatically cast operations to lower precision during forward passes and upcast to FP32 for loss computation and backward passes. The Trainer applies precision casting transparently via PyTorch's autocast context manager and custom scaler logic, eliminating manual precision management. Supports both native PyTorch AMP and NVIDIA Apex for legacy compatibility.","intents":["I want to reduce memory usage and training time by using FP16 without manually managing loss scaling","I need to use BF16 (bfloat16) for stable training on newer GPUs but want automatic handling of precision edge cases","I want to experiment with different precisions (FP32 vs FP16 vs BF16) by changing a single Trainer parameter"],"best_for":["teams training large models on memory-constrained GPUs","researchers optimizing training speed without sacrificing model accuracy","engineers deploying models on hardware with native BF16 support (A100, H100)"],"limitations":["FP16 training can cause numerical instability (loss spikes, NaN gradients) with certain architectures; requires careful tuning of loss scaling","BF16 has lower precision than FP16 but better numerical stability; not all operations benefit equally from BF16","Precision casting adds ~2-5% overhead per step due to autocast context manager and dtype conversions","Some custom CUDA kernels may not support FP16/BF16; fallback to FP32 required, negating memory savings"],"requires":["PyTorch 1.12+ (for native AMP support)","For FP16: NVIDIA GPU with compute capability 7.0+ (Volta or newer)","For BF16: NVIDIA GPU with compute capability 8.0+ (Ampere or newer) or CPU with AVX-512","CUDA 11.0+ for optimal performance"],"input_types":["LightningModule with standard PyTorch operations","precision string ('32', '16', 'bf16', 'mixed')"],"output_types":["trained model with mixed-precision weights","training metrics (loss, accuracy)","memory usage statistics"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pytorch-lightning__cap_3","uri":"capability://automation.workflow.checkpoint.management.with.automatic.saving.and.resumption","name":"checkpoint-management-with-automatic-saving-and-resumption","description":"Implements a checkpoint system that automatically saves model weights, optimizer state, learning rate scheduler state, and training metadata (epoch, global step, metrics) at configurable intervals (every N epochs, every N steps, on best validation metric). Checkpoints are saved as PyTorch state dicts with Lightning-specific metadata; the Trainer can resume training from any checkpoint, restoring all state including epoch counter and optimizer momentum. Supports distributed checkpointing (aggregating state from all ranks) and cloud storage backends (S3, GCS, Azure).","intents":["I want to save model checkpoints automatically during training and resume from the best checkpoint without manual state management","I need to recover from training interruptions (hardware failure, timeout) by resuming from the last checkpoint","I want to keep only the top-K best checkpoints (by validation metric) to save disk space"],"best_for":["teams training models for hours/days and needing fault tolerance","researchers experimenting with hyperparameters and needing to resume from checkpoints","production systems requiring reproducible training with checkpoint versioning"],"limitations":["Checkpoint size equals model size + optimizer state (typically 2-3x model size); can be prohibitive for very large models without gradient checkpointing","Resuming training requires exact reproduction of the training environment (same PyTorch version, same hardware); checkpoints are not always portable across versions","Cloud storage backends (S3, GCS) add latency (~1-5 seconds per checkpoint) compared to local disk","Distributed checkpointing requires all ranks to save simultaneously; no built-in support for asynchronous checkpointing"],"requires":["PyTorch 1.12+","Disk space: at least 2-3x the model size for optimizer state","For cloud storage: boto3 (S3), google-cloud-storage (GCS), or azure-storage-blob (Azure)"],"input_types":["LightningModule","checkpoint directory path or cloud URI","checkpoint configuration (save_top_k, every_n_epochs, monitor metric)"],"output_types":["checkpoint file (.ckpt) containing model weights, optimizer state, and metadata","best checkpoint symlink (points to highest-scoring checkpoint)","checkpoint metadata (epoch, global step, metrics)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pytorch-lightning__cap_4","uri":"capability://data.processing.analysis.lightning.datamodule.abstraction.for.reproducible.data.pipelines","name":"lightning-datamodule-abstraction-for-reproducible-data-pipelines","description":"Provides a LightningDataModule base class that encapsulates data loading logic (download, preprocessing, train/val/test split) into setup(), train_dataloader(), val_dataloader(), test_dataloader() methods. The Trainer automatically calls these methods at the appropriate lifecycle phases, ensuring data is prepared consistently across training runs. Supports automatic distributed sampling (DistributedSampler) and combined loaders for multi-task learning, with built-in integration for common datasets (MNIST, CIFAR, ImageNet).","intents":["I want to define data loading logic once and reuse it across multiple experiments without duplicating code","I need to ensure train/val/test splits are reproducible and consistent across distributed training","I want to automatically handle distributed sampling (different batches on each GPU) without manual DistributedSampler setup"],"best_for":["teams running multiple experiments with the same dataset","researchers publishing code and needing reproducible data pipelines","engineers building production training systems with standardized data handling"],"limitations":["LightningDataModule is optional; developers can use raw DataLoaders with Trainer, but lose automatic distributed sampling","setup() is called once per training run; dynamic data augmentation or online preprocessing must be implemented in the DataLoader itself","CombinedLoader (for multi-task learning) adds complexity; not all distributed strategies handle combined loaders efficiently","No built-in support for streaming datasets or online learning; assumes data fits in memory or can be loaded from disk"],"requires":["PyTorch 1.12+","torch.utils.data.DataLoader","For distributed sampling: torch.utils.data.distributed.DistributedSampler"],"input_types":["raw data files (images, CSVs, etc.)","dataset class (torch.utils.data.Dataset)","DataLoader configuration (batch_size, num_workers, shuffle)"],"output_types":["train DataLoader","validation DataLoader","test DataLoader","metadata (num_classes, input_shape, etc.)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pytorch-lightning__cap_5","uri":"capability://automation.workflow.lightning.cli.for.configuration.driven.training","name":"lightning-cli-for-configuration-driven-training","description":"Provides a command-line interface (LightningCLI) that automatically generates CLI arguments from LightningModule, LightningDataModule, and Trainer configuration. Developers define hyperparameters as class attributes or Pydantic models, and LightningCLI exposes them as CLI flags (e.g., `python train.py --model.learning_rate=0.001 --trainer.max_epochs=100`). Supports YAML configuration files, automatic help generation, and config validation via Pydantic.","intents":["I want to run experiments with different hyperparameters without modifying code or creating separate scripts","I need to version control training configurations (YAML files) separately from code","I want to generate reproducible training commands that can be shared with collaborators"],"best_for":["research teams running hyperparameter sweeps and ablation studies","engineers building reproducible training pipelines with version-controlled configs","developers who prefer declarative configuration over programmatic setup"],"limitations":["LightningCLI adds ~100-200ms startup overhead due to argument parsing and Pydantic validation","Complex nested configurations can become unwieldy in YAML; no built-in support for config inheritance or templating","Automatic CLI generation works best with simple types (int, float, str, bool); custom types require manual argument parsing","No built-in support for distributed hyperparameter sweeps; requires external tools (Ray Tune, Optuna) for automated tuning"],"requires":["PyTorch Lightning 1.5+","Pydantic 1.8+ (for config validation)","Python 3.8+"],"input_types":["LightningModule class with hyperparameters","LightningDataModule class with data configuration","Trainer configuration","YAML configuration file (optional)"],"output_types":["CLI arguments (parsed from command line or YAML)","instantiated LightningModule, LightningDataModule, and Trainer","training logs and checkpoints"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pytorch-lightning__cap_6","uri":"capability://automation.workflow.callback.based.hook.system.for.training.customization","name":"callback-based-hook-system-for-training-customization","description":"Implements a callback system with 50+ lifecycle hooks (on_train_start, on_batch_end, on_validation_epoch_end, etc.) that allow injecting custom logic at any training phase without modifying the Trainer or LightningModule. Callbacks are registered with the Trainer and executed in order; each callback receives the Trainer and LightningModule as arguments, allowing read/write access to training state. Built-in callbacks include ModelCheckpoint, EarlyStopping, LearningRateMonitor, and custom callbacks can be defined by subclassing Callback.","intents":["I want to log custom metrics (e.g., gradient norms, weight distributions) at specific training phases","I need to implement early stopping based on validation metrics without modifying the Trainer","I want to adjust learning rate dynamically based on training progress (e.g., warmup, decay)"],"best_for":["researchers implementing custom training logic (gradient clipping, metric logging, learning rate scheduling)","teams building monitoring and logging systems on top of Lightning","developers extending Lightning without forking the codebase"],"limitations":["Callback execution order matters; callbacks are executed in registration order, and there's no built-in dependency resolution","Callbacks have read/write access to Trainer state, which can lead to subtle bugs if callbacks modify state unexpectedly","Callback overhead: each hook invocation adds ~1-5ms per batch due to callback dispatch and state access","No built-in support for conditional callback execution; developers must implement conditional logic inside callbacks"],"requires":["PyTorch Lightning 1.0+","Subclass of pytorch_lightning.callbacks.Callback"],"input_types":["Trainer object (provides access to training state)","LightningModule object (provides access to model and metrics)"],"output_types":["custom metrics (logged to logger)","modified training state (e.g., learning rate adjustment)","side effects (e.g., checkpoint saving, early stopping)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pytorch-lightning__cap_7","uri":"capability://automation.workflow.lightning.fabric.low.level.distributed.training.primitives","name":"lightning-fabric-low-level-distributed-training-primitives","description":"Provides Lightning Fabric, a lightweight wrapper around PyTorch's distributed training primitives (torch.distributed, torch.nn.parallel) that handles device placement, gradient synchronization, and mixed precision without enforcing a training loop structure. Developers write custom training loops and call fabric.backward(), fabric.all_reduce(), fabric.launch() to manage distributed training. Fabric shares the same Strategy and Accelerator plugins as PyTorch Lightning but requires manual loop implementation.","intents":["I want to use distributed training (DDP, FSDP) without the overhead of the Trainer abstraction","I need to implement custom training logic (RL, GANs, multi-task learning) that doesn't fit the standard supervised learning paradigm","I want to gradually migrate from raw PyTorch to Lightning without rewriting my entire training loop"],"best_for":["researchers implementing non-standard training algorithms (RL, GANs, meta-learning)","engineers building custom training systems that need distributed training support","teams with existing PyTorch training loops who want to add distributed training without restructuring code"],"limitations":["Fabric provides no training loop abstraction; developers must implement epoch loops, batch iteration, and checkpointing manually","No built-in callbacks or hooks; custom logic must be implemented inline in the training loop","Fabric requires explicit fabric.launch() calls and rank management; more boilerplate than Trainer","Debugging distributed training with Fabric is harder because there's no centralized training loop to inspect"],"requires":["PyTorch 1.12+","Python 3.8+","For distributed training: torch.distributed backend (NCCL, Gloo, etc.)"],"input_types":["PyTorch model (nn.Module)","optimizer","DataLoader","custom training loop code"],"output_types":["trained model","training metrics","checkpoints (manual saving required)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pytorch-lightning__cap_8","uri":"capability://automation.workflow.model.export.and.inference.optimization","name":"model-export-and-inference-optimization","description":"Provides utilities to export trained LightningModule models to standard formats (ONNX, TorchScript, SavedModel) for deployment and inference optimization. The Trainer can export models automatically at the end of training; exported models can be loaded and used for inference without the Lightning framework. Supports quantization (INT8, FP16) and pruning via integration with PyTorch's quantization and pruning APIs.","intents":["I want to export my trained model to ONNX format for deployment on non-PyTorch platforms (TensorFlow, CoreML, TensorRT)","I need to optimize my model for inference (reduce size, latency) via quantization or pruning","I want to serve my model using standard inference frameworks (TensorFlow Serving, TorchServe) without Lightning dependencies"],"best_for":["teams deploying models to production inference systems","engineers optimizing models for edge devices (mobile, embedded)","researchers sharing models in standard formats for reproducibility"],"limitations":["ONNX export requires tracing or scripting the model; dynamic control flow (if statements, loops) may not export correctly","TorchScript export has limitations with certain PyTorch operations; custom CUDA kernels won't export","Quantization and pruning require retraining or fine-tuning; post-training quantization often results in accuracy loss","Exported models lose Lightning-specific metadata (hyperparameters, training config); must be managed separately"],"requires":["PyTorch 1.12+","For ONNX: onnx package","For TensorFlow export: tensorflow package","For quantization: torch.quantization module (PyTorch 1.8+)"],"input_types":["trained LightningModule","sample input tensor (for tracing)","export format (ONNX, TorchScript, SavedModel)"],"output_types":["exported model file (.onnx, .pt, .pb)","quantized model (INT8, FP16)","pruned model (reduced size)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pytorch-lightning__cap_9","uri":"capability://memory.knowledge.integrated.logging.and.experiment.tracking.with.multiple.backends","name":"integrated-logging-and-experiment-tracking-with-multiple-backends","description":"Provides a unified logging interface that integrates with multiple experiment tracking backends (TensorBoard, Weights & Biases, MLflow, Neptune, Comet, etc.) through a Logger abstraction. The Trainer automatically logs metrics (loss, accuracy, learning rate) to all registered loggers; developers call self.log() in LightningModule to log custom metrics. Loggers handle metric aggregation across distributed training and upload to remote servers automatically.","intents":["I want to log training metrics to TensorBoard or Weights & Biases without writing custom logging code","I need to compare multiple experiments (different hyperparameters, architectures) in a centralized dashboard","I want to automatically upload training logs to a remote server for monitoring and collaboration"],"best_for":["research teams running multiple experiments and needing centralized tracking","engineers monitoring training jobs in production","teams collaborating on model development and needing shared experiment dashboards"],"limitations":["Logger overhead: uploading metrics to remote servers adds ~10-50ms per logging step depending on network latency","Metric aggregation across distributed training requires synchronization; can add ~5-10% overhead in distributed settings","Some loggers (e.g., Weights & Biases) require internet connectivity; offline training requires local buffering","Logger configuration is verbose; 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