{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"keras","slug":"keras","name":"Keras","type":"framework","url":"https://github.com/keras-team/keras","page_url":"https://unfragile.ai/keras","categories":["frameworks-sdks"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"keras__cap_0","uri":"capability://code.generation.editing.multi.backend.neural.network.compilation.with.runtime.backend.selection","name":"multi-backend neural network compilation with runtime backend selection","description":"Keras 3 compiles a single model definition into executable code for JAX, TensorFlow, PyTorch, or OpenVINO by deferring all numerical operations to a pluggable backend abstraction layer. The active backend is selected at import time via KERAS_BACKEND environment variable or ~/.keras/keras.json and cannot be changed post-import. During model construction, symbolic execution via compute_output_spec() infers shapes and dtypes without computation; during training/inference, calls dispatch to backend-specific implementations in keras/src/backend/{jax,torch,tensorflow,openvino}/. This architecture enables write-once-run-anywhere model code without backend-specific rewrites.","intents":["I want to write a model once and run it on JAX for research, PyTorch for production, and TensorFlow for mobile without rewriting code","I need to benchmark the same architecture across backends to find the fastest implementation for my hardware","I'm migrating from TensorFlow to PyTorch but want to keep my existing Keras model code working"],"best_for":["ML researchers comparing frameworks without rewriting models","teams with heterogeneous infrastructure (research on JAX, production on PyTorch)","organizations migrating between deep learning frameworks"],"limitations":["Backend cannot be switched after import — requires process restart to change backends","OpenVINO backend is inference-only; no training support","Backend-specific optimizations (e.g., PyTorch's torch.compile) require custom code outside Keras abstraction","Performance may be suboptimal on any single backend compared to native framework code due to abstraction overhead"],"requires":["Python 3.9+","One of: TensorFlow 2.16.1+, JAX 0.4.20+, PyTorch 2.1.0+, or OpenVINO 2025.3.0+","KERAS_BACKEND environment variable or ~/.keras/keras.json configuration"],"input_types":["model architecture definitions (Sequential/Functional API)","training data (NumPy arrays, tf.data.Dataset, PyTorch DataLoader)","model weights (SavedModel, ONNX, checkpoint files)"],"output_types":["trained model weights","predictions (NumPy arrays or backend-native tensors)","exported models (SavedModel, ONNX, LiteRT, OpenVINO format)"],"categories":["code-generation-editing","model-training","multi-backend-abstraction"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keras__cap_1","uri":"capability://code.generation.editing.declarative.neural.network.architecture.definition.via.sequential.and.functional.apis","name":"declarative neural network architecture definition via sequential and functional apis","description":"Keras provides two high-level APIs for composing neural networks: Sequential (linear stack of layers) and Functional (arbitrary directed acyclic graphs with multiple inputs/outputs). Both APIs accept layer instances (Dense, Conv2D, LSTM, etc.) and automatically handle tensor shape inference, weight initialization, and forward pass construction. The Functional API supports layer sharing, multi-branch architectures, and residual connections by explicitly passing tensors between layer calls. Under the hood, layers inherit from keras.layers.Layer, which implements __call__ to dispatch to backend-specific compute_output_spec (symbolic) and call (eager) methods, enabling shape validation before execution.","intents":["I want to quickly prototype a CNN or RNN without writing forward() methods or managing tensor shapes manually","I need to build a multi-input, multi-output model with skip connections and shared layers","I'm coming from TensorFlow/PyTorch and want a simpler, more declarative way to define architectures"],"best_for":["beginners learning deep learning without framework-specific boilerplate","rapid prototyping and research where iteration speed matters more than fine-grained control","teams building standard architectures (ResNets, Transformers, U-Nets) that don't require custom ops"],"limitations":["Sequential API only supports linear layer stacks; complex architectures require Functional API","Functional API requires explicit tensor passing, which can be verbose for deeply nested graphs","Custom layers with stateful logic (e.g., dynamic control flow) require subclassing Layer and implementing call() and build()","No built-in support for dynamic architectures that change structure per-batch (e.g., variable-length RNNs without padding)"],"requires":["Python 3.9+","Keras 3.0+","Understanding of layer types (Dense, Conv2D, LSTM, etc.) and their parameters"],"input_types":["layer instances (keras.layers.*)","tensor shapes (inferred from input data or explicit Input() layers)","layer parameters (units, activation, kernel_initializer, etc.)"],"output_types":["keras.Model instance (Sequential or Functional)","model summary (layer names, output shapes, parameter counts)","model graph (for visualization or export)"],"categories":["code-generation-editing","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keras__cap_10","uri":"capability://automation.workflow.model.serialization.and.deserialization.with.weight.saving.loading","name":"model serialization and deserialization with weight saving/loading","description":"Keras provides model.save() and keras.saving.load_model() for serializing and deserializing models. Models can be saved in three formats: Keras format (HDF5 or ZIP with architecture + weights), SavedModel (TensorFlow format with concrete functions), or ONNX. The Keras format stores model architecture as JSON and weights as HDF5 or NumPy files. Deserialization reconstructs the model from saved architecture and weights, and custom layers/losses/metrics can be registered via custom_objects parameter. Model checkpointing during training is handled by keras.callbacks.ModelCheckpoint, which saves the best model based on validation metrics. Weights can be saved/loaded independently via model.save_weights() and model.load_weights().","intents":["I want to save a trained model and load it later for inference without retraining","I need to save model checkpoints during training and resume from the best checkpoint","I want to share a model with others in a portable format (Keras, SavedModel, or ONNX)"],"best_for":["practitioners training models and needing to save/load checkpoints","teams sharing models across projects or with collaborators","organizations requiring reproducible model deployment"],"limitations":["Keras format (HDF5) is TensorFlow-specific; PyTorch and JAX models must be converted to SavedModel or ONNX for portability","Custom layers/losses/metrics require custom_objects parameter during loading; this adds complexity","SavedModel format is large (includes concrete functions); Keras format is more compact","Weights-only saving (model.save_weights) loses architecture; requires model definition to load"],"requires":["Python 3.9+","Keras 3.0+","h5py for HDF5 format (optional)","onnx for ONNX format (optional)"],"input_types":["trained model (keras.Model)","save format (Keras, SavedModel, ONNX)","custom_objects (dict of custom layers/losses/metrics)"],"output_types":["saved model file (.keras, .h5, SavedModel directory, .onnx)","model weights file (.h5, .weights.h5)","model architecture file (.json)"],"categories":["automation-workflow","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keras__cap_11","uri":"capability://automation.workflow.hyperparameter.optimization.and.learning.rate.scheduling","name":"hyperparameter optimization and learning rate scheduling","description":"Keras provides keras.optimizers.schedules for learning rate scheduling (ExponentialDecay, CosineDecay, PolynomialDecay, etc.) and keras.callbacks for hyperparameter tuning (LearningRateScheduler, ReduceLROnPlateau). Learning rate schedules decay the learning rate over training steps or epochs to improve convergence. Callbacks enable dynamic hyperparameter adjustment during training (e.g., reducing learning rate when validation loss plateaus). Keras also integrates with external hyperparameter optimization frameworks (Keras Tuner, Optuna, Ray Tune) via callbacks. The fit() method accepts learning rate schedules and callbacks, enabling end-to-end hyperparameter optimization without custom training loops.","intents":["I want to decay the learning rate during training to improve convergence and final accuracy","I need to reduce the learning rate when validation loss plateaus without manual intervention","I want to search over hyperparameters (learning rate, batch size, layer sizes) to find the best configuration"],"best_for":["practitioners training models and needing to tune hyperparameters","teams requiring reproducible hyperparameter optimization","researchers comparing different learning rate schedules"],"limitations":["Built-in learning rate schedules are limited to common patterns; custom schedules require subclassing","Hyperparameter search (grid search, random search) can be expensive; Bayesian optimization requires external libraries","Learning rate scheduling is optimizer-specific; some optimizers (e.g., Adam) are less sensitive to learning rate","No built-in support for multi-objective optimization (e.g., accuracy vs. latency)"],"requires":["Python 3.9+","Keras 3.0+","For hyperparameter search: Keras Tuner, Optuna, or Ray Tune"],"input_types":["learning rate schedule (keras.optimizers.schedules.*)","callback (keras.callbacks.LearningRateScheduler or ReduceLROnPlateau)","hyperparameter search space (for external frameworks)"],"output_types":["learning rate over training steps/epochs","best hyperparameters (from search)","training history with dynamic hyperparameters"],"categories":["automation-workflow","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keras__cap_12","uri":"capability://code.generation.editing.custom.layer.and.loss.function.implementation.with.automatic.differentiation","name":"custom layer and loss function implementation with automatic differentiation","description":"Keras enables custom layer implementation by subclassing keras.layers.Layer and implementing build() (weight initialization), call() (forward pass), and compute_output_spec() (shape inference). Custom loss functions can be implemented by subclassing keras.losses.Loss or as callables. Custom layers and losses automatically support automatic differentiation through the active backend (JAX, PyTorch, TensorFlow) without requiring manual gradient implementation. Custom operations can use keras.ops for backend-agnostic computation or backend-specific ops for optimization. The framework handles gradient computation, mixed-precision scaling, and distributed training for custom layers/losses without user code changes.","intents":["I want to implement a custom layer (e.g., attention, graph convolution) that works on any backend","I need to define a custom loss function (e.g., contrastive loss, focal loss) with automatic gradients","I'm implementing a research idea that requires custom operations not in the standard layer library"],"best_for":["researchers implementing novel architectures or loss functions","teams building domain-specific layers (e.g., graph neural networks, medical imaging)","practitioners extending Keras with custom components"],"limitations":["Custom layers must implement compute_output_spec() for shape inference; missing this breaks symbolic execution","Custom ops using backend-specific code (e.g., CUDA kernels) lose portability across backends","Debugging custom layers is harder because errors may originate in backend autodiff, not the custom code","Custom layers may not support all features (e.g., mixed-precision, distributed training) without explicit implementation"],"requires":["Python 3.9+","Keras 3.0+","Understanding of layer interface (build, call, compute_output_spec)","Understanding of automatic differentiation and gradient computation"],"input_types":["layer subclass (inheriting from keras.layers.Layer)","loss subclass (inheriting from keras.losses.Loss or callable)","custom operations (using keras.ops or backend-specific ops)"],"output_types":["custom layer instance (with initialized weights)","custom loss function (callable)","gradients (computed automatically via backend autodiff)"],"categories":["code-generation-editing","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keras__cap_13","uri":"capability://code.generation.editing.model.introspection.and.visualization.with.summary.and.graph.export","name":"model introspection and visualization with summary and graph export","description":"Keras provides model.summary() to print a human-readable summary of model architecture (layer names, output shapes, parameter counts, connectivity). The summary includes total trainable and non-trainable parameters, enabling quick model size estimation. Keras also supports model graph visualization via keras.utils.plot_model(), which generates a visual diagram of the model architecture (useful for Functional API models with complex connectivity). Model introspection methods (model.get_config(), model.get_weights()) enable programmatic access to architecture and weights. These tools are backend-agnostic and work identically across JAX, PyTorch, and TensorFlow.","intents":["I want to quickly check the architecture of my model (layer names, shapes, parameter counts)","I need to visualize a complex model with multiple inputs/outputs to understand connectivity","I want to programmatically inspect model architecture and weights for debugging or analysis"],"best_for":["practitioners debugging model architectures and parameter counts","teams documenting model architectures for reproducibility","researchers analyzing model complexity and connectivity"],"limitations":["model.summary() is text-based; complex models with many layers are hard to read","keras.utils.plot_model() requires graphviz; installation can be tricky on some systems","Visualization is static; dynamic architectures (with control flow) cannot be visualized","No built-in support for visualizing intermediate activations or gradients"],"requires":["Python 3.9+","Keras 3.0+","graphviz (optional, for plot_model)"],"input_types":["model (keras.Model)"],"output_types":["text summary (layer names, shapes, parameters)","visual diagram (PNG or SVG)","model configuration (JSON or dict)"],"categories":["code-generation-editing","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keras__cap_14","uri":"capability://code.generation.editing.regularization.techniques.l1.l2.dropout.batch.normalization.integrated.into.layers","name":"regularization techniques (l1/l2, dropout, batch normalization) integrated into layers","description":"Keras provides built-in regularization through layer parameters and dedicated layers: kernel_regularizer/bias_regularizer (L1/L2 weight regularization), activity_regularizer (activation regularization), Dropout layer (random unit dropping), and BatchNormalization layer (feature normalization with learnable scale/shift). Regularization is applied during training via the loss function (for weight regularization) or forward pass (for dropout, batch norm). Dropout randomly zeros activations during training and scales them during inference. BatchNormalization normalizes activations to zero mean and unit variance, reducing internal covariate shift and enabling higher learning rates. All regularization techniques are backend-agnostic and work identically across JAX, PyTorch, and TensorFlow.","intents":["I want to prevent overfitting by adding L1/L2 regularization to model weights","I need to apply dropout to reduce co-adaptation of neurons","I want to use batch normalization to stabilize training and enable higher learning rates"],"best_for":["practitioners training models on small datasets where overfitting is a concern","teams requiring standard regularization without custom implementation","researchers comparing different regularization techniques"],"limitations":["L1/L2 regularization is limited to weight regularization; activity regularization is less common","Dropout is less effective on small models or datasets with sufficient data","BatchNormalization has different semantics in distributed training; synchronization across devices is required","Regularization hyperparameters (dropout rate, L2 weight) require tuning; no automatic selection"],"requires":["Python 3.9+","Keras 3.0+","Understanding of regularization techniques and their hyperparameters"],"input_types":["regularization parameters (kernel_regularizer, dropout_rate, etc.)","training data"],"output_types":["regularized model weights","training loss (including regularization term)"],"categories":["code-generation-editing","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keras__cap_2","uri":"capability://code.generation.editing.automatic.differentiation.and.gradient.computation.across.backends","name":"automatic differentiation and gradient computation across backends","description":"Keras delegates automatic differentiation to the active backend (JAX's jax.grad, PyTorch's autograd, TensorFlow's tf.GradientTape) through a unified keras.ops interface that wraps backend-specific gradient functions. During training, the fit() method constructs a loss function, computes gradients via backend-native autodiff, and applies optimizer updates. Custom training loops can use keras.ops.grad() to compute gradients of arbitrary functions. The backend abstraction ensures that gradient computation, mixed-precision scaling, and gradient clipping work identically across JAX, PyTorch, and TensorFlow without user code changes.","intents":["I want to train a model with automatic gradient computation without manually implementing backpropagation","I need to compute gradients of a custom loss function with respect to model weights","I'm using mixed-precision training and need gradients scaled automatically for numerical stability"],"best_for":["standard supervised learning workflows where automatic differentiation is sufficient","researchers implementing custom training loops with gradient manipulation (gradient clipping, accumulation)","teams requiring consistent gradient behavior across JAX, PyTorch, and TensorFlow"],"limitations":["Gradient computation is backend-specific; custom ops may not have gradient implementations for all backends","Higher-order gradients (Hessian, Jacobian) are supported but may be slow on some backends (especially PyTorch)","Gradient checkpointing (remat) is implemented but requires explicit layer configuration","No built-in support for sparse gradients or gradient compression"],"requires":["Python 3.9+","Active backend (JAX, PyTorch, or TensorFlow) with autodiff support","Model defined using Keras layers (automatic differentiation only works for Keras ops)"],"input_types":["model (keras.Model)","loss function (keras.losses.Loss or callable)","training data (NumPy arrays or backend-native tensors)"],"output_types":["gradients (backend-native tensors)","updated model weights","training metrics (loss, accuracy, etc.)"],"categories":["code-generation-editing","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keras__cap_3","uri":"capability://code.generation.editing.built.in.layer.zoo.with.50.pre.implemented.neural.network.components","name":"built-in layer zoo with 50+ pre-implemented neural network components","description":"Keras provides a comprehensive library of pre-implemented layers (Dense, Conv1D/2D/3D, LSTM, GRU, Attention, BatchNormalization, Dropout, etc.) in keras.layers, each with configurable parameters (units, activation, regularization, initialization). Layers are backend-agnostic; their implementations in keras/src/layers/ use only keras.ops (NumPy-compatible operations) and backend-specific ops, ensuring portability across JAX, PyTorch, and TensorFlow. Each layer implements build() (weight initialization), call() (forward pass), and compute_output_spec() (shape inference). Custom layers can be created by subclassing keras.layers.Layer and implementing these methods.","intents":["I want to use standard layers (Dense, Conv2D, LSTM) without implementing them from scratch","I need layers with built-in regularization (L1/L2, dropout, batch normalization) without manual configuration","I'm building a model and want automatic weight initialization and shape validation"],"best_for":["practitioners building standard architectures (CNNs, RNNs, Transformers) without custom ops","beginners who want to focus on architecture design rather than layer implementation","teams requiring consistent layer behavior across multiple backends"],"limitations":["Custom layers with backend-specific optimizations (e.g., CUDA kernels) require subclassing and backend-specific code","Some advanced layers (e.g., sparse layers) may have limited backend support","Layer parameters are fixed at build time; dynamic parameter changes require layer recreation","No built-in support for some specialized layers (e.g., graph neural networks, capsule networks)"],"requires":["Python 3.9+","Keras 3.0+","Understanding of layer parameters and their effects on model behavior"],"input_types":["layer class (keras.layers.Dense, keras.layers.Conv2D, etc.)","layer parameters (units, activation, kernel_initializer, etc.)","input tensors (shape and dtype inferred automatically)"],"output_types":["layer instance (with initialized weights)","output tensor (shape and dtype determined by compute_output_spec)","layer configuration (for serialization)"],"categories":["code-generation-editing","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keras__cap_4","uri":"capability://automation.workflow.unified.training.loop.with.fit.method.supporting.callbacks.metrics.and.validation","name":"unified training loop with fit() method supporting callbacks, metrics, and validation","description":"Keras's fit() method provides a high-level training interface that handles gradient computation, optimizer updates, metric tracking, and validation in a single call. The method accepts a model, training data (NumPy arrays, tf.data.Dataset, or backend-native iterables), loss function, optimizer, and metrics. During training, fit() iterates over batches, computes loss and gradients via backend autodiff, applies optimizer updates, and accumulates metrics. Callbacks (keras.callbacks.Callback) hook into training events (epoch start/end, batch end) for logging, early stopping, learning rate scheduling, and checkpointing. Validation is performed at configurable intervals, and metrics are computed on both training and validation sets.","intents":["I want to train a model with automatic gradient computation, metric tracking, and validation without writing a training loop","I need to implement early stopping, learning rate scheduling, or custom logging during training","I want to save the best model checkpoint during training and resume from it"],"best_for":["practitioners training standard supervised learning models (classification, regression, segmentation)","teams requiring reproducible training with automatic metric tracking and checkpointing","researchers implementing custom training logic via callbacks without rewriting the entire training loop"],"limitations":["fit() is optimized for standard supervised learning; reinforcement learning or adversarial training require custom training loops","Callback system adds overhead (~5-10% per epoch) compared to hand-written training loops","Distributed training requires explicit configuration (keras.distribution.DataParallel or backend-specific APIs)","fit() does not support dynamic batch sizes or variable-length sequences without padding or custom data pipelines"],"requires":["Python 3.9+","Keras 3.0+","Training data in NumPy, tf.data.Dataset, or iterable format","Loss function (keras.losses.Loss or callable)","Optimizer (keras.optimizers.Optimizer)"],"input_types":["model (keras.Model)","training data (NumPy arrays, tf.data.Dataset, or iterables)","loss function (keras.losses.Loss or callable)","optimizer (keras.optimizers.Optimizer)","metrics (list of keras.metrics.Metric or callables)","callbacks (list of keras.callbacks.Callback)"],"output_types":["training history (dict of metric names to lists of values)","trained model weights","saved checkpoints (if ModelCheckpoint callback is used)"],"categories":["automation-workflow","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keras__cap_5","uri":"capability://data.processing.analysis.numpy.compatible.operations.api.keras.ops.with.backend.dispatch","name":"numpy-compatible operations api (keras.ops) with backend dispatch","description":"Keras exposes a NumPy-compatible operations API (keras.ops) that wraps backend-specific implementations (JAX, PyTorch, TensorFlow) for mathematical operations (matmul, reshape, concatenate, etc.), neural network operations (conv2d, batch_norm, etc.), and activation functions. Each operation in keras.ops has implementations in keras/src/ops/{numpy,nn,core}.py that dispatch to the active backend. This enables users to write backend-agnostic code using familiar NumPy-like syntax. Operations support automatic differentiation through backend autodiff, and the API includes both eager execution (immediate computation) and symbolic execution (shape/dtype inference via compute_output_spec).","intents":["I want to write custom layers or loss functions using NumPy-like syntax that work on any backend","I need to implement a custom operation (e.g., a specialized loss function) without backend-specific code","I'm migrating from NumPy to deep learning and want a familiar API that works across frameworks"],"best_for":["researchers implementing custom layers or loss functions that should work across backends","teams building domain-specific neural network components without framework lock-in","practitioners familiar with NumPy who want to avoid learning backend-specific APIs"],"limitations":["keras.ops covers common operations but may not include all NumPy functions; missing operations require backend-specific code","Performance may be suboptimal compared to hand-optimized backend-specific code (e.g., PyTorch's torch.compile)","Some operations have different semantics across backends (e.g., random number generation, floating-point precision)","Debugging is harder because errors may originate in backend-specific code, not keras.ops"],"requires":["Python 3.9+","Keras 3.0+","Active backend (JAX, PyTorch, or TensorFlow)","Understanding of NumPy API and tensor operations"],"input_types":["tensors (backend-native or KerasTensor)","scalar values (int, float)","operation parameters (axis, keepdims, etc.)"],"output_types":["tensors (backend-native or KerasTensor)","scalar values (int, float)","shape/dtype information (from compute_output_spec)"],"categories":["data-processing-analysis","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keras__cap_6","uri":"capability://automation.workflow.model.export.to.multiple.deployment.formats.savedmodel.onnx.litert.openvino","name":"model export to multiple deployment formats (savedmodel, onnx, litert, openvino)","description":"Keras provides model.export() and backend-specific export functions to convert trained models into deployment-ready formats: SavedModel (TensorFlow), ONNX (cross-framework), LiteRT (mobile), and OpenVINO (edge inference). Export functions in keras/src/saving/ serialize model architecture, weights, and preprocessing layers into format-specific representations. SavedModel export includes a concrete function signature for inference. ONNX export converts Keras ops to ONNX operators via a mapping layer. LiteRT and OpenVINO exports optimize models for mobile and edge devices. Exported models can be loaded and used for inference without Keras, enabling deployment on diverse hardware (mobile, edge, cloud).","intents":["I want to export a Keras model to ONNX for inference on non-Keras frameworks (e.g., ONNX Runtime, CoreML)","I need to deploy a model on mobile devices using LiteRT or on edge devices using OpenVINO","I want to save a model in SavedModel format for serving with TensorFlow Serving or other inference servers"],"best_for":["teams deploying models across heterogeneous hardware (mobile, edge, cloud, browser)","organizations requiring model portability across frameworks (PyTorch training, ONNX inference)","practitioners optimizing models for latency and memory on resource-constrained devices"],"limitations":["ONNX export may lose backend-specific optimizations (e.g., PyTorch's torch.compile); performance may degrade","LiteRT export requires TensorFlow backend; PyTorch and JAX models must be converted to TensorFlow first","OpenVINO export is inference-only; no training support","Custom layers or ops may not have export implementations for all formats, requiring manual conversion","Exported models may be significantly larger than original Keras models due to weight duplication"],"requires":["Python 3.9+","Keras 3.0+","For ONNX: onnx and onnx-simplifier packages","For LiteRT: TensorFlow backend and tf-lite-support","For OpenVINO: openvino package"],"input_types":["trained keras.Model","export format (SavedModel, ONNX, LiteRT, OpenVINO)","export options (quantization, optimization level, etc.)"],"output_types":["SavedModel directory (with saved_model.pb and variables/)","ONNX file (.onnx)","LiteRT file (.tflite)","OpenVINO files (.xml, .bin)"],"categories":["automation-workflow","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keras__cap_7","uri":"capability://automation.workflow.distributed.training.across.multiple.gpus.tpus.with.data.parallelism","name":"distributed training across multiple gpus/tpus with data parallelism","description":"Keras supports distributed training via keras.distribution.DataParallel (data parallelism) and backend-specific distributed APIs (tf.distribute.Strategy for TensorFlow, torch.nn.DataParallel for PyTorch, jax.pmap for JAX). Data parallelism splits training data across devices, computes gradients on each device, and synchronizes gradients across devices before optimizer updates. The fit() method automatically handles distributed training when a distribution strategy is configured. Gradient synchronization and optimizer updates are coordinated by the distribution backend, ensuring convergence across devices. Keras abstracts distribution details, allowing the same model code to scale from single-GPU to multi-GPU/TPU without modification.","intents":["I want to train a large model on multiple GPUs without rewriting my training code","I need to scale training across multiple machines or TPU pods for faster convergence","I'm using fit() and want automatic distributed training without manual gradient synchronization"],"best_for":["teams training large models (billions of parameters) that don't fit on a single GPU","organizations with access to multi-GPU or TPU infrastructure","practitioners requiring reproducible distributed training without framework-specific code"],"limitations":["Data parallelism scales linearly only up to ~8-16 GPUs; beyond that, model parallelism or pipeline parallelism is required","Gradient synchronization adds communication overhead (~10-30% per step depending on network bandwidth)","Distributed training requires careful batch size tuning; effective batch size = per-device batch size × number of devices","Some layers (e.g., BatchNormalization) have different semantics in distributed training; synchronization across devices is required","Debugging distributed training is harder due to asynchronous execution and device-specific errors"],"requires":["Python 3.9+","Keras 3.0+","Multiple GPUs or TPUs on the same machine or across machines","Distributed training backend (tf.distribute.Strategy, torch.nn.DataParallel, or jax.pmap)","NCCL or similar collective communication library for GPU synchronization"],"input_types":["model (keras.Model)","training data (NumPy arrays or backend-native iterables)","distribution strategy (keras.distribution.DataParallel or backend-specific)"],"output_types":["trained model weights (synchronized across devices)","training history (aggregated metrics from all devices)"],"categories":["automation-workflow","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"keras__cap_8","uri":"capability://automation.workflow.quantization.and.mixed.precision.training.for.model.compression.and.speedup","name":"quantization and mixed-precision training for model compression and speedup","description":"Keras supports quantization (reducing precision from float32 to int8/float16) and mixed-precision training (using float16 for computation, float32 for weights) to reduce memory usage and accelerate training. Quantization is implemented via keras.quantizers (post-training quantization) and quantization-aware training (QAT) layers. Mixed-precision training is enabled via keras.mixed_precision.set_global_policy(), which automatically casts operations to lower precision while maintaining numerical stability. The optimizer applies loss scaling to prevent gradient underflow in float16. 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