{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"fastai","slug":"fastai","name":"FastAI","type":"framework","url":"https://www.fast.ai","page_url":"https://unfragile.ai/fastai","categories":["frameworks-sdks"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"fastai__cap_0","uri":"capability://code.generation.editing.transfer.learning.based.computer.vision.model.training","name":"transfer learning-based computer vision model training","description":"Enables rapid training of state-of-the-art computer vision models by leveraging pre-trained weights and fine-tuning them on custom datasets with minimal code. Uses PyTorch's autograd and optimizer abstractions under the hood, wrapping them in high-level APIs that automatically handle learning rate scheduling, data augmentation, and mixed-precision training. The framework encodes best practices like discriminative learning rates (training different layers at different rates) and progressive resizing to accelerate convergence.","intents":["Train a custom image classifier on a domain-specific dataset in under 10 lines of code","Fine-tune a pre-trained ResNet or EfficientNet model for object detection without writing boilerplate","Apply transfer learning to small datasets (< 1000 images) without overfitting"],"best_for":["Practitioners new to deep learning who want to avoid PyTorch boilerplate","Teams building computer vision MVPs with limited labeled data","Researchers prototyping vision models quickly before optimizing"],"limitations":["High-level abstractions may obscure optimization opportunities for production-scale models","Limited control over architecture modifications compared to raw PyTorch","No built-in distributed training support mentioned in source material","Abstraction layers add overhead that may impact inference latency in resource-constrained environments"],"requires":["Python 3.7+","PyTorch 1.7+ (as base dependency)","GPU with CUDA support recommended for reasonable training speed","Labeled image dataset (can be small due to transfer learning)"],"input_types":["image files (JPEG, PNG, etc.)","directory structure with class folders","pandas DataFrames with image paths and labels"],"output_types":["trained PyTorch model weights","predictions on new images","training metrics and loss curves"],"categories":["code-generation-editing","model-training","deep-learning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fastai__cap_1","uri":"capability://code.generation.editing.nlp.model.training.with.ulmfit.transfer.learning","name":"nlp model training with ulmfit transfer learning","description":"Provides pre-trained language models and transfer learning pipelines for NLP tasks using the ULMFiT (Universal Language Model Fine-tuning) approach, which enables effective fine-tuning on small text datasets. The framework handles tokenization, vocabulary building, and gradual unfreezing of model layers during training. Implements discriminative learning rates across the language model's layers to optimize convergence on downstream tasks like text classification and sentiment analysis.","intents":["Train a text classifier on a custom dataset with < 1000 labeled examples","Fine-tune a pre-trained language model for domain-specific NLP tasks","Build sentiment analysis or document classification models without training from scratch"],"best_for":["NLP practitioners working with limited labeled text data","Teams building text classification systems for domain-specific content","Researchers exploring transfer learning in NLP before the transformer era"],"limitations":["ULMFiT approach predates modern transformer models (BERT, GPT), so may be less competitive on large-scale benchmarks","No built-in support for modern tokenizers (BPE, SentencePiece) mentioned in source material","Limited to sequential models; no attention mechanism customization","Requires careful tuning of learning rates and unfreezing schedules for optimal results"],"requires":["Python 3.7+","PyTorch 1.7+","Pre-trained language model weights (included in package)","Text dataset with labels for downstream task"],"input_types":["plain text documents","CSV files with text and labels","pandas DataFrames with text columns"],"output_types":["trained language model weights","predictions on new text","attention weights and embeddings"],"categories":["code-generation-editing","model-training","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fastai__cap_10","uri":"capability://memory.knowledge.pre.trained.model.zoo.with.automatic.download.and.caching","name":"pre-trained model zoo with automatic download and caching","description":"Provides a collection of pre-trained models for computer vision and NLP tasks that are automatically downloaded and cached on first use. Models are stored in a standard location and reused across projects. Supports multiple model architectures (ResNet, EfficientNet, etc. for vision; AWD-LSTM for NLP) with weights trained on standard datasets (ImageNet for vision, Wikitext for NLP).","intents":["Use pre-trained models for transfer learning without manually downloading weights","Access multiple pre-trained architectures for different problem sizes and latency requirements","Avoid re-downloading model weights across multiple projects"],"best_for":["Practitioners building models with transfer learning who want convenient weight access","Teams with limited bandwidth who benefit from automatic caching","Researchers comparing multiple pre-trained architectures"],"limitations":["Limited to models included in the framework; custom pre-trained models require manual integration","Model weights may become outdated as better pre-trained models are released","Caching directory requires sufficient disk space (models can be several hundred MB each)","No version control for model weights; updating to new weights may break existing code"],"requires":["Python 3.7+","FastAI library installed","Internet connection for initial model download","Disk space for cached model weights (typically 100MB-1GB per model)"],"input_types":["model architecture name (string)"],"output_types":["PyTorch model with pre-trained weights loaded","model configuration metadata"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fastai__cap_11","uri":"capability://image.visual.interpretability.and.visualization.tools.for.model.understanding","name":"interpretability and visualization tools for model understanding","description":"Provides built-in visualization and interpretability tools for understanding model predictions and behavior. 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Visualizations are integrated into the Learner API and can be called with simple methods.","intents":["Visualize what features a model is using to make predictions","Debug model failures by understanding which parts of the input influenced the prediction","Explain model predictions to non-technical stakeholders through visualizations"],"best_for":["Practitioners building models that need to be explained to stakeholders","Teams debugging model failures and understanding failure modes","Researchers studying what features models learn"],"limitations":["Interpretability methods are approximate and may not fully explain model behavior","Visualizations are domain-specific and may not apply to all model types","Computational overhead for generating interpretability visualizations","Limited support for complex model architectures (e.g., vision transformers)"],"requires":["Python 3.7+","FastAI library installed","Matplotlib or other visualization library","Trained model"],"input_types":["trained model","input data (images, text, or tabular)"],"output_types":["visualizations (saliency maps, attention weights, feature importance plots)","interpretability metrics"],"categories":["image-visual","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fastai__cap_12","uri":"capability://code.generation.editing.distributed.training.across.multiple.gpus","name":"distributed training across multiple gpus","description":"Enables training models across multiple GPUs on a single machine or across multiple machines using PyTorch's distributed training primitives. 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Includes quantization support for reducing model size and inference latency. Handles model serialization and loading with automatic device placement (CPU/GPU). Supports batch inference and streaming inference patterns.","intents":["Export a trained FastAI model to ONNX format for deployment in production systems","Quantize models to reduce size and latency for edge deployment","Load and run inference on trained models without the full FastAI framework"],"best_for":["Teams deploying FastAI models to production systems","Practitioners building edge AI applications with size/latency constraints","Researchers comparing inference performance across different export formats"],"limitations":["ONNX export may not support all FastAI-specific layers or operations","Quantization may reduce model accuracy, requiring careful validation","Inference optimization is limited compared to specialized frameworks (TensorRT, CoreML)","No built-in support for model serving frameworks (TensorFlow Serving, KServe)"],"requires":["Python 3.7+","PyTorch 1.7+","ONNX library (for ONNX export)","Trained FastAI model"],"input_types":["trained PyTorch model","model configuration"],"output_types":["ONNX model file","TorchScript model file","quantized model weights","inference results"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fastai__cap_2","uri":"capability://code.generation.editing.tabular.data.model.training.with.automated.feature.engineering","name":"tabular data model training with automated feature engineering","description":"Provides high-level APIs for training gradient boosting and neural network models on tabular/structured data with minimal preprocessing. 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Supports both tree-based models (via XGBoost/LightGBM integration) and neural networks, with the framework choosing appropriate architectures and hyperparameters based on dataset characteristics.","intents":["Train a predictive model on CSV data without manual feature engineering","Build regression or classification models on tabular data with mixed categorical and numerical features","Quickly prototype baseline models for structured data problems"],"best_for":["Data scientists building tabular ML models with limited time for feature engineering","Teams working with structured business data (sales, customer, financial records)","Practitioners new to gradient boosting who want sensible defaults"],"limitations":["Automated feature engineering may miss domain-specific insights that manual engineering would capture","Limited control over model architecture compared to scikit-learn or XGBoost directly","No built-in support for time series data or sequential dependencies mentioned","Abstraction overhead may impact training speed on very large datasets (millions of rows)"],"requires":["Python 3.7+","PyTorch 1.7+","XGBoost or LightGBM (optional, for tree-based models)","Tabular dataset in CSV or pandas DataFrame format"],"input_types":["CSV files","pandas DataFrames","numpy arrays with feature matrices"],"output_types":["trained model weights","predictions on new data","feature importance scores","model performance metrics"],"categories":["code-generation-editing","model-training","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fastai__cap_3","uri":"capability://data.processing.analysis.data.loading.and.batching.with.automatic.augmentation","name":"data loading and batching with automatic augmentation","description":"Provides a DataLoaders abstraction that handles image/text/tabular data loading, batching, and augmentation with sensible defaults. Implements common augmentation techniques (random crops, rotations, color jittering for images; cutoff and masking for text) that are automatically applied during training. Uses PyTorch's DataLoader under the hood but wraps it with higher-level APIs for dataset splitting, normalization, and augmentation pipeline composition.","intents":["Load and batch image data with automatic augmentation applied during training","Split datasets into train/validation/test sets with proper stratification","Apply domain-specific data augmentation without writing custom transforms"],"best_for":["Practitioners who want augmentation without manually composing torchvision.transforms","Teams building computer vision pipelines who need consistent data handling","Researchers prototyping models quickly without custom data loading code"],"limitations":["Augmentation strategies are fixed and may not match domain-specific requirements","Limited control over batch composition (e.g., hard negative mining, stratified sampling)","No built-in support for streaming data or online learning mentioned","Augmentation parameters are not easily tunable without modifying framework code"],"requires":["Python 3.7+","PyTorch 1.7+","Pillow (for image loading)","Data in standard directory structure or pandas DataFrame"],"input_types":["image files in directory structure","CSV files with image paths","pandas DataFrames with data and labels"],"output_types":["batched tensors ready for model training","augmented images/text during training","validation batches without augmentation"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fastai__cap_4","uri":"capability://code.generation.editing.learning.rate.scheduling.and.optimization.with.discriminative.learning.rates","name":"learning rate scheduling and optimization with discriminative learning rates","description":"Provides automated learning rate scheduling and optimization strategies that apply different learning rates to different layers of a model (discriminative learning rates). Implements techniques like learning rate finder (automatically determining optimal learning rate by training briefly and observing loss), cyclical learning rates, and one-cycle policy. Wraps PyTorch optimizers (SGD, Adam) with these scheduling strategies applied automatically during training.","intents":["Find the optimal learning rate for a model without manual experimentation","Train models with different learning rates per layer to improve convergence","Apply advanced scheduling strategies (one-cycle, cyclical) without manual implementation"],"best_for":["Practitioners unfamiliar with learning rate tuning who want sensible defaults","Teams training transfer learning models where layer-specific rates improve convergence","Researchers exploring learning rate scheduling strategies quickly"],"limitations":["Learning rate finder requires a brief training run, adding overhead to the training process","Discriminative learning rates assume a specific model architecture (sequential layers); may not work well with complex architectures","Limited control over scheduling parameters compared to PyTorch's lr_scheduler directly","Automatic scheduling may not be optimal for all problem domains"],"requires":["Python 3.7+","PyTorch 1.7+","A PyTorch model to optimize"],"input_types":["PyTorch model","training data loader","loss function"],"output_types":["optimal learning rate (from learning rate finder)","trained model weights","learning rate schedule used during training"],"categories":["code-generation-editing","model-training","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fastai__cap_5","uri":"capability://code.generation.editing.mixed.precision.training.with.automatic.loss.scaling","name":"mixed-precision training with automatic loss scaling","description":"Enables training models using lower-precision floating-point numbers (float16) for faster computation and reduced memory usage, while maintaining numerical stability through automatic loss scaling. Wraps PyTorch's automatic mixed precision (AMP) with higher-level APIs that automatically enable mixed precision during training without requiring manual gradient scaling or loss scaling code.","intents":["Train larger models on GPUs with limited memory by using float16 precision","Reduce training time by leveraging float16 computation on modern GPUs","Enable mixed-precision training without manually implementing loss scaling"],"best_for":["Teams training large models on consumer GPUs with memory constraints","Practitioners who want faster training without sacrificing model quality","Researchers exploring efficiency improvements in deep learning"],"limitations":["Mixed precision may cause numerical instability in some models (requires careful tuning)","Requires GPU with float16 support (most modern NVIDIA/AMD GPUs have this)","Loss scaling parameters may need manual tuning for some models","Not all operations are optimized for float16, potentially limiting speedup"],"requires":["Python 3.7+","PyTorch 1.6+ (for native AMP support)","GPU with float16 support (NVIDIA Volta/Turing or newer, AMD RDNA or newer)","CUDA 10.2+ for NVIDIA GPUs"],"input_types":["PyTorch model","training data loader"],"output_types":["trained model weights (in float32)","training metrics with mixed precision enabled"],"categories":["code-generation-editing","model-training","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fastai__cap_6","uri":"capability://data.processing.analysis.model.evaluation.with.multiple.metrics.and.validation.strategies","name":"model evaluation with multiple metrics and validation strategies","description":"Provides built-in evaluation metrics for classification, regression, and NLP tasks, with automatic computation during training and validation. Supports custom metrics through a simple callback interface. Implements validation strategies like k-fold cross-validation and stratified splitting to ensure robust model evaluation. Metrics are computed on validation data without augmentation to provide unbiased estimates.","intents":["Track multiple metrics (accuracy, F1, precision, recall) during model training","Evaluate models using cross-validation without writing custom splitting code","Compare model performance across different architectures and hyperparameters"],"best_for":["Practitioners building classification/regression models who need standard metrics","Teams evaluating models on multiple metrics simultaneously","Researchers comparing model performance across experiments"],"limitations":["Built-in metrics cover common cases but may not include domain-specific metrics","Cross-validation implementation may be slower than scikit-learn for very large datasets","Limited support for imbalanced dataset metrics (e.g., weighted F1) mentioned in source","Metric computation adds overhead to training loop"],"requires":["Python 3.7+","PyTorch 1.7+","Validation dataset with labels"],"input_types":["model predictions","ground truth labels","custom metric functions"],"output_types":["metric values (accuracy, F1, loss, etc.)","metric history across epochs","confusion matrices and classification reports"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fastai__cap_7","uri":"capability://code.generation.editing.callback.based.training.hooks.for.custom.logic","name":"callback-based training hooks for custom logic","description":"Provides a callback system that allows injecting custom logic at various points in the training loop (before/after epoch, before/after batch, etc.). Callbacks are composable and can access/modify training state (model, optimizer, metrics). Built-in callbacks implement features like early stopping, learning rate scheduling, and checkpoint saving. Custom callbacks can be created by subclassing the Callback base class.","intents":["Implement custom training logic (e.g., custom regularization, logging) without modifying framework code","Stop training early when validation metrics stop improving","Save model checkpoints and resume training from checkpoints"],"best_for":["Practitioners who need to customize training behavior beyond built-in options","Teams implementing research ideas that require non-standard training loops","Developers building production training pipelines with custom monitoring"],"limitations":["Callback interface may have limited access to internal training state","Callback execution order and dependencies are not explicitly documented","Performance overhead from callback invocation at every batch/epoch","Debugging callback interactions can be complex in large training pipelines"],"requires":["Python 3.7+","PyTorch 1.7+","Understanding of the Learner training loop"],"input_types":["custom callback class inheriting from Callback","training state (model, optimizer, metrics)"],"output_types":["modified training state","side effects (logging, checkpointing, etc.)"],"categories":["code-generation-editing","automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fastai__cap_8","uri":"capability://data.processing.analysis.reversible.data.transformation.pipelines.with.fasttransform","name":"reversible data transformation pipelines with fasttransform","description":"Provides the fasttransform library (released Feb 2025) for building reversible data transformation pipelines using multiple dispatch. Transformations are composable and can be applied in forward and reverse directions, enabling data augmentation and inverse transformations. Uses multiple dispatch to select the appropriate transformation implementation based on input type (images, text, tabular data).","intents":["Build composable data transformation pipelines that can be applied and reversed","Apply domain-specific transformations to different data types using a unified interface","Create augmentation pipelines that maintain semantic meaning through reversibility"],"best_for":["Teams building complex data preprocessing pipelines with multiple data types","Researchers exploring reversible transformations for data augmentation","Practitioners who need to apply transformations consistently across train/inference"],"limitations":["Reversibility constraint may limit the types of transformations that can be applied","Multiple dispatch adds complexity to understanding which transformation is applied","Limited documentation available (newly released library as of Feb 2025)","Performance overhead from dispatch mechanism for each transformation"],"requires":["Python 3.7+","fasttransform library (installed separately)","Understanding of multiple dispatch concepts"],"input_types":["images","text","tabular data","custom data types with registered transformations"],"output_types":["transformed data","inverse-transformed data","transformation metadata"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fastai__cap_9","uri":"capability://text.generation.language.educational.course.integration.with.nbdev.notebooks","name":"educational course integration with nbdev notebooks","description":"Provides the nbdev tool (mentioned as separate software product) for building literate programming notebooks that serve as both documentation and executable code. Notebooks can be converted to Python modules, enabling the framework to be taught through executable examples. The 'Practical Deep Learning for Coders' course uses this approach to teach deep learning concepts through hands-on notebooks.","intents":["Learn deep learning concepts through executable notebook examples","Access the world's longest-running deep learning course with up-to-date content","Understand how to use FastAI through practical, working code examples"],"best_for":["Learners new to deep learning who prefer hands-on, example-driven learning","Practitioners who want to understand FastAI through working code rather than API documentation","Teams building educational content using literate programming approaches"],"limitations":["Notebook-based learning may be slower than reading API documentation for experienced practitioners","Course content requires time investment (full course is extensive)","Notebooks may become outdated as FastAI evolves","Limited support for IDE-based development workflows (notebooks are primarily Jupyter-based)"],"requires":["Jupyter Notebook or JupyterLab","Python 3.7+","FastAI library installed","Internet connection to access course materials"],"input_types":["Jupyter notebooks","course materials"],"output_types":["executable code examples","trained models","learning materials"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"fastai__headline","uri":"capability://data.processing.analysis.high.level.deep.learning.framework.for.computer.vision.and.nlp","name":"high-level deep learning framework for computer vision and nlp","description":"FastAI is a deep learning library built on PyTorch that simplifies the process of training state-of-the-art models for computer vision, natural language processing, and tabular data with minimal code and best practices.","intents":["best deep learning framework","deep learning for computer vision","NLP framework for beginners","easy-to-use PyTorch library","high-level API for deep learning"],"best_for":["beginners in deep learning","rapid prototyping of models"],"limitations":["not suitable for low-level custom implementations"],"requires":["basic knowledge of Python","PyTorch installed"],"input_types":["images","text","tabular data"],"output_types":["trained models","predictions"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","PyTorch 1.7+ (as base dependency)","GPU with CUDA support recommended for reasonable training speed","Labeled image dataset (can be small due to transfer learning)","PyTorch 1.7+","Pre-trained language model weights (included in package)","Text dataset with labels for downstream task","FastAI library installed","Internet connection for initial model download","Disk space for cached model weights (typically 100MB-1GB per model)"],"failure_modes":["High-level abstractions may obscure optimization opportunities for production-scale models","Limited control over architecture modifications compared to raw PyTorch","No built-in distributed training support mentioned in source material","Abstraction layers add overhead that may impact inference latency in resource-constrained environments","ULMFiT approach predates modern transformer models (BERT, GPT), so may be less competitive on large-scale benchmarks","No built-in support for modern tokenizers (BPE, SentencePiece) mentioned in source material","Limited to sequential models; 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