{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-tensorflow","slug":"pypi-tensorflow","name":"tensorflow","type":"framework","url":"https://www.tensorflow.org/","page_url":"https://unfragile.ai/pypi-tensorflow","categories":["model-training"],"tags":["tensorflow","tensor","machine","learning"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-tensorflow__cap_0","uri":"capability://code.generation.editing.sequential.neural.network.model.definition.via.keras.api","name":"sequential neural network model definition via keras api","description":"Enables declarative composition of neural networks by stacking layers (Dense, Flatten, Dropout, Conv2D, etc.) in linear order using tf.keras.models.Sequential. The framework automatically constructs the underlying computation graph and manages tensor flow between layers without requiring explicit graph definition. Layers are instantiated with hyperparameters (units, activation functions, regularization) and composed into a model object that encapsulates the entire architecture.","intents":["I want to quickly prototype a feedforward neural network without writing custom training loops","I need to build a standard CNN or RNN architecture using pre-built layer components","I want to define a model architecture that's readable and maintainable for my team"],"best_for":["ML engineers building standard supervised learning models","Data scientists prototyping classification and regression tasks","Teams migrating from scikit-learn to deep learning"],"limitations":["Sequential API only supports linear layer stacking — cannot express branching, skip connections, or multi-input/multi-output architectures (use Functional API instead)","No built-in support for dynamic architectures where layer count depends on input data","Verbose boilerplate compared to scikit-learn for simple models"],"requires":["Python 3.7+","TensorFlow 2.x installed via pip","Basic understanding of neural network layer types and activation functions"],"input_types":["layer specifications (class instantiation with hyperparameters)"],"output_types":["compiled Keras model object with forward pass and training methods"],"categories":["code-generation-editing","neural-network-architecture"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tensorflow__cap_1","uri":"capability://code.generation.editing.functional.api.for.non.sequential.neural.network.architectures","name":"functional api for non-sequential neural network architectures","description":"Enables definition of complex neural network topologies with branching, skip connections, multi-input/multi-output paths, and shared layers by explicitly connecting layer outputs to layer inputs using a functional composition pattern. Each layer is instantiated as a callable object, and the model is constructed by chaining function calls (layer(input_tensor)) to create a directed acyclic graph (DAG) of tensor transformations. This approach decouples layer definition from model topology, allowing arbitrary connectivity patterns.","intents":["I need to build a ResNet with skip connections or an Inception module with parallel branches","I want to create a multi-input model that processes different data types (e.g., images + tabular features) separately then merges them","I need a multi-output model that predicts multiple targets from a single input"],"best_for":["ML engineers building state-of-the-art architectures (ResNet, Inception, Transformer-based models)","Researchers implementing novel network topologies from papers","Teams building production models with complex data flows"],"limitations":["More verbose than Sequential API for simple linear models","Requires explicit tensor shape management — shape mismatches produce cryptic error messages","Cannot express dynamic control flow (e.g., conditional layer execution based on input values) — use Model Subclassing for that","Debugging is harder because the DAG is implicit in function calls rather than explicit"],"requires":["Python 3.7+","TensorFlow 2.x","Understanding of tensor shapes and broadcasting rules"],"input_types":["layer specifications with explicit input tensor connections"],"output_types":["Keras Model object with arbitrary topology (DAG)"],"categories":["code-generation-editing","neural-network-architecture"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tensorflow__cap_10","uri":"capability://memory.knowledge.pre.trained.model.access.and.fine.tuning.via.tensorflow.hub","name":"pre-trained model access and fine-tuning via tensorflow hub","description":"Provides access to a repository of pre-trained models (BERT, ResNet, MobileNet, etc.) that can be loaded and fine-tuned for downstream tasks using tf.hub.load() or tf.keras.layers.Hub(). Models are distributed as SavedModel format and can be fine-tuned by adding task-specific layers on top and training with a small labeled dataset. This enables transfer learning, reducing training time and data requirements for custom tasks.","intents":["I want to use a pre-trained BERT model for text classification without training from scratch","I need to fine-tune a ResNet model for a custom image classification task with limited labeled data","I want to leverage pre-trained embeddings to reduce training time"],"best_for":["ML engineers building models with limited labeled data","Teams with limited compute resources (fine-tuning is faster than training from scratch)","Researchers using transfer learning for downstream tasks"],"limitations":["Pre-trained models may not be optimized for your specific task — fine-tuning may not recover full performance","Model size is fixed — cannot customize architecture of pre-trained backbone","Limited control over pre-training data and methodology — may have biases or limitations","Fine-tuning requires careful hyperparameter tuning (learning rate, number of epochs) to avoid overfitting","Model updates are infrequent — may miss improvements in pre-trained models"],"requires":["Python 3.7+","TensorFlow 2.x","Internet connection to download pre-trained models","Labeled data for fine-tuning"],"input_types":["pre-trained model URL from TensorFlow Hub"],"output_types":["fine-tuned model with task-specific layers"],"categories":["memory-knowledge","neural-network-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tensorflow__cap_11","uri":"capability://planning.reasoning.reinforcement.learning.agent.training.via.tensorflow.agents","name":"reinforcement learning agent training via tensorflow agents","description":"Provides a library for building and training reinforcement learning (RL) agents using TensorFlow, including implementations of standard algorithms (DQN, PPO, A3C, SAC) and utilities for environment interaction, experience replay, and policy optimization. Agents are defined as tf.keras.Model subclasses that take observations and output actions, trained using custom training loops that collect experience from environments and optimize policies using gradient descent.","intents":["I want to train an RL agent to play a game or control a robot","I need to implement a custom RL algorithm (e.g., policy gradient variant)","I want to use off-the-shelf RL algorithms (DQN, PPO) without implementing them from scratch"],"best_for":["ML researchers implementing RL algorithms","Robotics teams training control policies","Game developers building AI agents"],"limitations":["RL training is sample-inefficient — requires millions of environment interactions","Hyperparameter tuning is critical and difficult — small changes can cause training failure","Debugging is extremely hard — RL agents exhibit chaotic behavior during training","Limited support for continuous control — discrete action spaces are more stable","No automatic curriculum learning or other advanced training techniques"],"requires":["Python 3.7+","TensorFlow 2.x","TensorFlow Agents library (pip install tf-agents)","RL environment (OpenAI Gym, custom environment)"],"input_types":["RL environment (observation space, action space, reward function)","agent policy (tf.keras.Model)"],"output_types":["trained agent policy"],"categories":["planning-reasoning","neural-network-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tensorflow__cap_12","uri":"capability://code.generation.editing.graph.neural.network.modeling.via.tensorflow.gnn","name":"graph neural network modeling via tensorflow gnn","description":"Provides a library for building graph neural networks (GNNs) that operate on graph-structured data (nodes, edges, node/edge features) using message-passing algorithms. GNNs are defined as tf.keras.layers that aggregate information from neighboring nodes and update node representations iteratively. The library supports various GNN architectures (GraphConv, GraphAttention, GraphSage) and provides utilities for graph batching and sampling.","intents":["I want to build a model that predicts node properties in a graph (e.g., molecule properties, social network analysis)","I need to classify edges in a graph (e.g., link prediction)","I want to apply graph neural networks to relational data (knowledge graphs, recommendation systems)"],"best_for":["ML engineers working with graph-structured data","Chemists predicting molecular properties","Recommendation system teams"],"limitations":["GNN training is computationally expensive — requires careful batching and sampling for large graphs","Limited support for dynamic graphs — graphs must be static during training","Debugging is harder because graph operations are implicit in message-passing","No automatic graph sampling or mini-batching — requires manual implementation","Limited pre-trained models compared to CNNs/RNNs"],"requires":["Python 3.7+","TensorFlow 2.x","TensorFlow GNN library (pip install tensorflow-gnn)","Graph data in supported format (NetworkX, DGL, PyTorch Geometric)"],"input_types":["graph data (nodes, edges, node/edge features)"],"output_types":["node/edge/graph-level predictions"],"categories":["code-generation-editing","neural-network-architecture"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tensorflow__cap_13","uri":"capability://automation.workflow.production.ml.pipeline.orchestration.via.tensorflow.extended.tfx","name":"production ml pipeline orchestration via tensorflow extended (tfx)","description":"Provides a framework for building end-to-end ML pipelines that automate data validation, feature engineering, model training, evaluation, and deployment. Pipelines are defined declaratively using TFX components (ExampleGen, StatisticsGen, SchemaGen, Transform, Trainer, Evaluator, Pusher) that can be orchestrated using Apache Airflow, Kubeflow, or other workflow engines. TFX handles data versioning, model versioning, and automated retraining, enabling production-grade ML systems.","intents":["I want to automate the entire ML pipeline from data ingestion to model deployment","I need to monitor model performance in production and trigger retraining when performance degrades","I want to ensure data quality and consistency across training and serving"],"best_for":["ML teams building production systems","Organizations with large-scale ML infrastructure","Teams requiring automated retraining and monitoring"],"limitations":["Steep learning curve — requires understanding of data pipelines, orchestration, and ML infrastructure","Significant operational overhead — requires deployment of Airflow/Kubeflow and monitoring infrastructure","Limited flexibility for custom components — requires implementing custom TFX components for non-standard workflows","Debugging is harder because pipelines are distributed across multiple components","Performance overhead from pipeline orchestration — adds latency to training and serving"],"requires":["Python 3.7+","TensorFlow 2.x","TensorFlow Extended (TFX) library (pip install tfx)","Orchestration engine (Apache Airflow, Kubeflow, or other)","Data infrastructure (data warehouse, feature store)"],"input_types":["raw data (CSV, TFRecord, BigQuery)","data schema (TFDV schema)"],"output_types":["trained and validated model ready for deployment"],"categories":["automation-workflow","neural-network-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tensorflow__cap_14","uri":"capability://code.generation.editing.probabilistic.modeling.and.bayesian.inference.via.tensorflow.probability","name":"probabilistic modeling and bayesian inference via tensorflow probability","description":"Provides a library for building probabilistic models (Bayesian neural networks, variational autoencoders, mixture models) using TensorFlow, with support for automatic differentiation variational inference (ADVI) and Markov chain Monte Carlo (MCMC) sampling. Models are defined using probabilistic programming constructs (distributions, random variables) and trained using variational inference or sampling-based methods.","intents":["I want to build a Bayesian neural network that quantifies uncertainty in predictions","I need to implement a variational autoencoder (VAE) for generative modeling","I want to perform Bayesian inference on a custom probabilistic model"],"best_for":["ML researchers implementing probabilistic models","Teams requiring uncertainty quantification in predictions","Statisticians building Bayesian models"],"limitations":["Steep learning curve — requires understanding of probability theory and Bayesian inference","Training is slower than standard neural networks due to sampling overhead","Limited support for complex probabilistic models — inference can be unstable","Debugging is harder because probabilistic operations are implicit","No automatic model selection or hyperparameter tuning for probabilistic models"],"requires":["Python 3.7+","TensorFlow 2.x","TensorFlow Probability library (pip install tensorflow-probability)","Understanding of probability theory and Bayesian inference"],"input_types":["probabilistic model definition (distributions, random variables)"],"output_types":["trained probabilistic model with uncertainty estimates"],"categories":["code-generation-editing","neural-network-architecture"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tensorflow__cap_2","uri":"capability://code.generation.editing.custom.model.definition.via.model.subclassing","name":"custom model definition via model subclassing","description":"Enables creation of fully custom neural network models by subclassing tf.keras.Model and implementing forward pass logic in the call() method using imperative Python code. This approach allows arbitrary control flow (if/else, loops, dynamic layer instantiation) and custom training logic by overriding the train_step() method. The framework handles automatic differentiation and gradient computation through tf.GradientTape context managers, enabling fine-grained control over training dynamics.","intents":["I need to implement a model with dynamic architecture where the number of layers depends on input data","I want custom training logic that doesn't fit the standard compile/fit pattern (e.g., adversarial training, multi-task learning with custom loss weighting)","I need to implement a research paper that requires non-standard forward passes or loss computations"],"best_for":["ML researchers implementing novel training algorithms","Teams building production models with complex training dynamics","Engineers implementing generative models (GANs, VAEs) with custom loss functions"],"limitations":["Requires manual gradient computation using tf.GradientTape — significantly more verbose than Sequential/Functional APIs","No automatic shape inference — developer must ensure tensor shapes are compatible","Debugging is harder because control flow is implicit in Python code rather than explicit in model definition","Cannot use high-level training utilities like model.fit() with custom train_step() — must implement training loop manually","Performance overhead from Python control flow (if/else, loops) compared to graph-optimized Sequential/Functional models"],"requires":["Python 3.7+","TensorFlow 2.x with eager execution enabled","Deep understanding of automatic differentiation and gradient computation","Familiarity with tf.GradientTape API"],"input_types":["Python class definition with call() method implementation"],"output_types":["Custom Keras Model object with arbitrary forward pass logic"],"categories":["code-generation-editing","neural-network-architecture"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tensorflow__cap_3","uri":"capability://code.generation.editing.automatic.differentiation.and.gradient.computation.via.tf.gradienttape","name":"automatic differentiation and gradient computation via tf.gradienttape","description":"Provides automatic differentiation (backpropagation) by recording tensor operations within a context manager (tf.GradientTape) and computing gradients of a loss function with respect to model parameters using the chain rule. The tape tracks all operations on watched variables, enabling computation of gradients via tape.gradient(loss, variables). This enables custom training loops where developers explicitly compute gradients, apply optimization steps, and update model weights without relying on the high-level compile/fit API.","intents":["I want to implement a custom training loop with fine-grained control over gradient computation and weight updates","I need to compute gradients with respect to inputs (for adversarial examples, saliency maps) rather than just model parameters","I want to implement gradient clipping, gradient accumulation, or other custom gradient manipulation"],"best_for":["ML researchers implementing novel optimization algorithms","Teams building adversarial robustness or interpretability tools","Engineers implementing reinforcement learning agents with custom policy gradients"],"limitations":["Significantly more verbose than model.fit() — requires manual training loop implementation","Performance overhead from Python loop execution compared to graph-optimized training","Debugging is harder because gradient computation is implicit in the tape — errors produce cryptic messages about tape state","No automatic learning rate scheduling or other training utilities — must implement manually","Eager execution mode required (graph mode not supported with custom tapes)"],"requires":["Python 3.7+","TensorFlow 2.x with eager execution enabled","Deep understanding of backpropagation and the chain rule","Familiarity with optimizer APIs (tf.keras.optimizers)"],"input_types":["tensor operations within tf.GradientTape context"],"output_types":["gradient tensors (same shape as variables)"],"categories":["code-generation-editing","neural-network-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tensorflow__cap_4","uri":"capability://automation.workflow.high.level.model.training.via.compile.and.fit.api","name":"high-level model training via compile and fit api","description":"Provides a declarative training interface where developers specify an optimizer, loss function, and metrics via model.compile(), then train the model using model.fit(x_train, y_train, epochs=N, batch_size=B). The framework automatically handles batching, gradient computation, weight updates, metric evaluation, and epoch management. This abstraction hides the training loop complexity and enables integration with callbacks for checkpointing, early stopping, and learning rate scheduling.","intents":["I want to train a model with minimal boilerplate using standard supervised learning","I need to monitor training progress with metrics and validation data","I want to use callbacks for checkpointing, early stopping, or learning rate scheduling without implementing them manually"],"best_for":["ML engineers building standard supervised learning models","Data scientists prototyping models quickly","Teams with limited ML infrastructure expertise"],"limitations":["Abstracts away training loop details — harder to debug gradient computation issues","Limited customization for non-standard training dynamics (use custom train_step() for adversarial training, multi-task learning, etc.)","Batch size and epoch management are implicit — less control over training dynamics than manual loops","Callback system adds ~5-10% overhead per epoch for metric computation and logging"],"requires":["Python 3.7+","TensorFlow 2.x","Compiled Keras model (via Sequential, Functional, or Subclassing API)","Training data in NumPy arrays or tf.data.Dataset format"],"input_types":["training data (X, y) as NumPy arrays or tf.data.Dataset","optimizer (string name or tf.keras.optimizers instance)","loss function (string name or callable)","metrics (list of string names or callable objects)"],"output_types":["History object with training/validation metrics per epoch","trained model with updated weights"],"categories":["automation-workflow","neural-network-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tensorflow__cap_5","uri":"capability://data.processing.analysis.data.pipeline.construction.and.optimization.via.tf.data.api","name":"data pipeline construction and optimization via tf.data api","description":"Provides a declarative API for building efficient input pipelines that load, preprocess, batch, and shuffle training data using tf.data.Dataset. Pipelines are constructed by chaining operations (map, batch, shuffle, prefetch, cache) that are automatically optimized and parallelized by the framework. The API supports reading from multiple sources (NumPy arrays, TFRecord files, CSV, images) and applying transformations (augmentation, normalization) efficiently on CPU while GPU trains, reducing I/O bottlenecks.","intents":["I want to load and preprocess large datasets that don't fit in memory","I need to apply data augmentation (rotation, flipping, color jittering) efficiently during training","I want to optimize data loading to reduce GPU idle time by prefetching batches"],"best_for":["ML engineers building production models with large datasets","Computer vision teams applying data augmentation","Teams optimizing training throughput and GPU utilization"],"limitations":["Learning curve is steep — requires understanding of lazy evaluation and graph optimization","Debugging is harder because transformations are applied lazily — errors occur during iteration, not during pipeline definition","Limited support for stateful transformations (e.g., augmentation that depends on previous batches)","Performance gains require careful tuning of prefetch buffer size and number of parallel workers","No built-in support for distributed data loading across multiple machines (use TFX for that)"],"requires":["Python 3.7+","TensorFlow 2.x","Data in supported format (NumPy, TFRecord, CSV, image files, etc.)","Understanding of batch processing and data augmentation"],"input_types":["data sources (NumPy arrays, file paths, TFRecord files)","transformation functions (map, filter, flat_map)"],"output_types":["tf.data.Dataset object that yields batches of preprocessed data"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tensorflow__cap_6","uri":"capability://automation.workflow.distributed.training.across.multiple.gpus.and.tpus.via.distribution.strategy.api","name":"distributed training across multiple gpus and tpus via distribution strategy api","description":"Enables training on multiple devices (GPUs, TPUs, multiple machines) by automatically distributing model parameters and data across devices using distribution strategies (MirroredStrategy for single-machine multi-GPU, MultiWorkerMirroredStrategy for multi-machine, TPUStrategy for TPU pods). The framework handles gradient aggregation, synchronization, and loss scaling automatically, requiring minimal code changes — developers wrap the model creation in strategy.scope() and use the standard compile/fit API.","intents":["I want to train a model on 4 GPUs on a single machine without manual gradient synchronization","I need to scale training to multiple machines with automatic gradient aggregation","I want to train on TPU pods for large-scale models"],"best_for":["ML engineers training large models that require multi-GPU/multi-TPU setups","Teams with access to TPU infrastructure (Google Cloud)","Organizations scaling training from single GPU to multi-machine"],"limitations":["Requires careful batch size tuning — effective batch size is multiplied by number of devices, affecting convergence","Synchronization overhead adds ~10-20% latency per training step for multi-machine setups","Limited support for custom training loops with Distribution Strategy — requires using train_step() override","Debugging is harder because errors are distributed across devices — stack traces are hard to interpret","TPUStrategy requires Google Cloud infrastructure and TPU-specific code changes (e.g., XLA compilation)"],"requires":["Python 3.7+","TensorFlow 2.x","Multiple GPUs (for MirroredStrategy) or multiple machines (for MultiWorkerMirroredStrategy) or TPU access (for TPUStrategy)","NCCL library for GPU communication (installed automatically on most systems)"],"input_types":["model definition (Sequential, Functional, or Subclassing)","distribution strategy specification (MirroredStrategy, MultiWorkerMirroredStrategy, TPUStrategy)"],"output_types":["trained model with synchronized weights across all devices"],"categories":["automation-workflow","neural-network-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tensorflow__cap_7","uri":"capability://automation.workflow.model.serialization.and.deployment.via.savedmodel.format","name":"model serialization and deployment via savedmodel format","description":"Enables saving trained models in TensorFlow's SavedModel format (a directory containing model architecture, weights, and computation graph) using model.save(), which can be loaded and deployed on servers, mobile devices, browsers, or edge devices without requiring the original training code. The format supports both eager and graph execution modes, enabling deployment across heterogeneous hardware (CPUs, GPUs, TPUs, mobile processors). Models can be served via TensorFlow Serving for production inference with automatic batching and multi-model serving.","intents":["I want to save a trained model and load it in a different Python environment without retraining","I need to deploy a model to production servers for inference","I want to convert a model to mobile (Android/iOS) or browser (JavaScript) format"],"best_for":["ML engineers deploying models to production","Teams building mobile/web applications with ML inference","Organizations using TensorFlow Serving for model deployment"],"limitations":["SavedModel format is TensorFlow-specific — cannot be loaded by PyTorch or other frameworks without conversion","Model size can be large (100MB+) for large models — requires optimization (quantization, pruning) for mobile deployment","Inference latency depends on hardware — no built-in performance guarantees","Custom layers and training logic may not serialize correctly — requires custom save/load implementations","Version compatibility issues — models saved in TensorFlow 2.x may not load in older versions"],"requires":["Python 3.7+","TensorFlow 2.x","Trained Keras model (Sequential, Functional, or Subclassing)","TensorFlow Serving (for production deployment) or LiteRT (for mobile/edge)"],"input_types":["trained Keras model"],"output_types":["SavedModel directory (contains assets/, variables/, saved_model.pb files)"],"categories":["automation-workflow","neural-network-deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tensorflow__cap_8","uri":"capability://automation.workflow.mobile.and.edge.device.inference.via.litert.tensorflow.lite","name":"mobile and edge device inference via litert (tensorflow lite)","description":"Enables deployment of trained models on mobile devices (Android, iOS) and edge devices (Raspberry Pi, Edge TPU) by converting SavedModel to LiteRT format (a lightweight, optimized binary) using tf.lite.TFLiteConverter. LiteRT applies quantization (reducing model size by 4x), pruning, and other optimizations to reduce memory footprint and latency. Models run on-device without network connectivity, enabling privacy-preserving inference and offline operation.","intents":["I want to deploy a model to Android or iOS without sending data to a server","I need to run inference on a Raspberry Pi or other edge device with limited compute","I want to reduce model size from 100MB to 25MB for mobile app distribution"],"best_for":["Mobile app developers integrating ML inference","IoT teams deploying models on edge devices","Organizations with privacy requirements (on-device inference)"],"limitations":["Model size reduction via quantization reduces accuracy — requires retraining or fine-tuning to recover performance","Inference latency is device-dependent — no guaranteed performance across different mobile devices","Limited support for custom operations — some TensorFlow operations are not supported in LiteRT","Debugging is harder because quantized models behave differently than full-precision models","No automatic batching — inference is single-sample only (batch size = 1)"],"requires":["Python 3.7+","TensorFlow 2.x","SavedModel or Keras model","Android SDK (for Android deployment) or Xcode (for iOS deployment)"],"input_types":["SavedModel or Keras model"],"output_types":["LiteRT model file (.tflite)"],"categories":["automation-workflow","neural-network-deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-tensorflow__cap_9","uri":"capability://automation.workflow.browser.based.inference.via.tensorflow.js","name":"browser-based inference via tensorflow.js","description":"Enables deployment of trained models in web browsers and Node.js using TensorFlow.js, a JavaScript library that runs inference on client-side hardware (CPU, WebGL GPU, WebAssembly). Models are converted from SavedModel format to JavaScript format using tf.keras.saving.save_model() or tf.saved_model.save(), then loaded in JavaScript using tf.loadLayersModel(). Inference runs entirely in the browser, enabling privacy-preserving predictions and offline operation without server communication.","intents":["I want to add ML inference to a web application without building a backend API","I need to run inference in the browser for privacy (data never leaves the client)","I want to build interactive ML demos that run entirely in the browser"],"best_for":["Web developers building ML-powered applications","Teams with privacy requirements (on-device inference)","Researchers building interactive ML demos"],"limitations":["Inference latency is browser-dependent — varies significantly across browsers and devices","Model size is limited by browser memory — large models (>100MB) may cause out-of-memory errors","No GPU acceleration in most browsers (WebGL is limited) — inference is slower than server-side","Limited support for custom operations — only standard TensorFlow operations are supported","Debugging is harder because errors occur in JavaScript runtime — stack traces are cryptic"],"requires":["JavaScript/TypeScript knowledge","TensorFlow.js library (npm install @tensorflow/tfjs)","SavedModel or Keras model converted to JavaScript format","Modern web browser (Chrome, Firefox, Safari, Edge)"],"input_types":["SavedModel or Keras model"],"output_types":["JavaScript model files (model.json + weights.bin files)"],"categories":["automation-workflow","neural-network-deployment"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","TensorFlow 2.x installed via pip","Basic understanding of neural network layer types and activation functions","TensorFlow 2.x","Understanding of tensor shapes and broadcasting rules","Internet connection to download pre-trained models","Labeled data for fine-tuning","TensorFlow Agents library (pip install tf-agents)","RL environment (OpenAI Gym, custom environment)","TensorFlow GNN library (pip install tensorflow-gnn)"],"failure_modes":["Sequential API only supports linear layer stacking — cannot express branching, skip connections, or multi-input/multi-output architectures (use Functional API instead)","No built-in support for dynamic architectures where layer count depends on input data","Verbose boilerplate compared to scikit-learn for simple models","More verbose than Sequential API for simple linear models","Requires explicit tensor shape management — shape mismatches produce cryptic error messages","Cannot express dynamic control flow (e.g., conditional layer execution based on input values) — use Model Subclassing for that","Debugging is harder because the DAG is implicit in function calls rather than explicit","Pre-trained models may not be optimized for your specific task — fine-tuning may not recover full performance","Model size is fixed — cannot customize architecture of pre-trained backbone","Limited control over pre-training data and methodology — may have biases or limitations","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.42,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.060Z","last_scraped_at":"2026-05-03T15:20:13.888Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pypi-tensorflow","compare_url":"https://unfragile.ai/compare?artifact=pypi-tensorflow"}},"signature":"IJ11h0iDnOtBiwJPQ+aa0LBE96vLpIdEMcQr4GgQKxFL17pUaUFf6AQ4dZA5Lu+iNDPBf62Hd5iGi2yJTGubBg==","signedAt":"2026-06-22T23:10:00.979Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-tensorflow","artifact":"https://unfragile.ai/pypi-tensorflow","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-tensorflow","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}