{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-wandb","slug":"pypi-wandb","name":"wandb","type":"cli","url":"https://pypi.org/project/wandb/","page_url":"https://unfragile.ai/pypi-wandb","categories":["model-training"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-wandb__cap_0","uri":"capability://automation.workflow.experiment.run.initialization.and.lifecycle.management","name":"experiment-run initialization and lifecycle management","description":"Initializes a Run object via wandb.init() that represents a single training execution, managing the complete lifecycle from creation through metrics collection to finalization. The SDK creates a unique run ID, associates it with a project, and establishes bidirectional communication with the wandb-core Go service via inter-process communication (IPC) for asynchronous metric buffering and file uploads. The Run object provides methods like log(), save(), log_artifact(), and finish() that serialize user data and queue it for transmission to the W&B backend (cloud or self-hosted).","intents":["Initialize experiment tracking for a training script with automatic project/run association","Manage run configuration and hyperparameters at startup","Gracefully finalize a run and flush pending metrics/artifacts to the backend","Track run metadata like timestamps, environment, and system information"],"best_for":["ML engineers instrumenting training pipelines","Data scientists running iterative experiments","Teams using W&B cloud or self-hosted instances"],"limitations":["Requires network connectivity to W&B backend (cloud or self-hosted) for full functionality","IPC overhead adds ~5-10ms per log() call due to message serialization and queue management","Single run per process; nested runs not supported","Offline mode has limited functionality — artifacts cannot be versioned without backend connectivity"],"requires":["Python 3.7+","wandb-core service (Go binary) auto-downloaded and spawned as subprocess","Valid W&B API key or authentication token","Network access to wandb.ai or self-hosted W&B instance"],"input_types":["configuration dictionary (hyperparameters)","project/entity names (strings)","run tags and notes (strings)"],"output_types":["Run object with methods for logging and artifact management","Unique run ID and URL for web dashboard access"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-wandb__cap_1","uri":"capability://data.processing.analysis.time.series.metrics.and.summary.statistics.logging","name":"time-series metrics and summary statistics logging","description":"Records scalar metrics, media (images, audio, video), and structured data via wandb.log() or run.log(), which serializes diverse Python objects (NumPy arrays, PyTorch tensors, PIL images, pandas DataFrames) into JSON-compatible formats and queues them for transmission. Each log() call increments a step counter, creating a time-series history. The SDK maintains two separate data structures: history (step-indexed time-series) and summary (final/best values), allowing both granular temporal analysis and efficient aggregation. Serialization is handled by custom type handlers that convert framework-specific objects into W&B's internal media types (Image, Audio, Video, Table, Histogram, etc.).","intents":["Log scalar loss/accuracy metrics at each training step","Capture images, confusion matrices, and plots during training","Record structured data like embeddings, attention weights, or model predictions","Aggregate final metrics for comparison across runs without storing full history"],"best_for":["ML practitioners tracking training dynamics across epochs","Computer vision teams logging image predictions and visualizations","NLP researchers recording token-level or sequence-level metrics","Teams needing both detailed time-series and summary statistics"],"limitations":["Media serialization (images, videos) adds 50-500ms per log() call depending on size and format","History is stored in-memory on the client until flushed; large runs (>100k steps) may consume significant RAM","Custom Python objects require explicit type handlers; arbitrary objects are converted to string representations","Step counter is global per run; no per-namespace step tracking for multi-task scenarios"],"requires":["Python 3.7+","NumPy, PyTorch, TensorFlow, or PIL for media serialization (optional but recommended)","Active wandb.init() run context"],"input_types":["scalar values (int, float)","NumPy arrays, PyTorch tensors, TensorFlow tensors","PIL Images, matplotlib figures","pandas DataFrames, dictionaries","custom objects with __dict__ or __repr__"],"output_types":["JSON-serialized metrics queued to wandb-core","Time-series data indexed by step","Media files (PNG, JPEG, MP4) uploaded to artifact storage"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-wandb__cap_10","uri":"capability://tool.use.integration.cli.commands.for.run.management.and.data.export","name":"cli commands for run management and data export","description":"Provides a command-line interface (wandb CLI) for managing runs, artifacts, and sweeps without Python code. The CLI includes commands like wandb login (authenticate), wandb sync (sync offline runs), wandb artifact (download/manage artifacts), wandb launch (submit training jobs), and wandb sweep (create/manage sweeps). The CLI also supports data export via wandb export (export run data to CSV/JSON) and wandb pull (download artifacts). The CLI is implemented in Python and uses the same SDK internals as the Python API, ensuring consistency. The CLI supports both cloud (wandb.ai) and self-hosted W&B instances via configuration.","intents":["Authenticate with W&B backend without Python code","Sync offline runs to W&B backend from command line","Download and manage artifacts without Python scripts","Submit training jobs and manage sweeps from CI/CD pipelines","Export run data for external analysis or archival"],"best_for":["DevOps engineers integrating W&B into CI/CD pipelines","ML teams managing runs and artifacts without Python","Researchers exporting data for external analysis","Organizations automating W&B workflows via shell scripts"],"limitations":["CLI commands are less flexible than Python API; complex workflows require custom scripts","Some features (e.g., custom charts, advanced filtering) are only available in Python API or web UI","CLI output is text-based; programmatic parsing requires shell scripting or jq","No built-in support for batch operations; large-scale management requires loops or custom tools"],"requires":["Python 3.7+","wandb package installed (pip install wandb)","W&B API key for authentication"],"input_types":["Command-line arguments (run ID, artifact name, etc.)","Configuration files (wandb/settings)","Environment variables (WANDB_API_KEY, WANDB_ENTITY, etc.)"],"output_types":["Authenticated session (wandb login)","Downloaded artifacts and run data","Exported CSV/JSON files","Job submission confirmations"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-wandb__cap_11","uri":"capability://search.retrieval.public.api.client.for.programmatic.run.access.and.analysis","name":"public api client for programmatic run access and analysis","description":"Provides a Python API client (wandb.Api()) for programmatic access to run data, artifacts, and projects without instrumenting training code. The API client uses the W&B GraphQL API to query runs, metrics, and artifacts, and supports filtering, sorting, and pagination. Users can fetch run data (config, metrics, summary), download artifacts, and perform bulk operations (e.g., update tags, delete runs). The API client also supports creating and managing projects, teams, and service accounts. The client is rate-limited to prevent abuse, and supports both cloud (wandb.ai) and self-hosted W&B instances.","intents":["Query run data for analysis and reporting without re-running experiments","Download artifacts and metrics for external analysis","Automate run management (tagging, deletion, archival) at scale","Build custom dashboards and reports using run data","Integrate W&B data into external ML platforms or data warehouses"],"best_for":["Data scientists analyzing experiment results post-hoc","ML engineers automating run management and cleanup","Organizations building custom dashboards and reporting tools","Teams integrating W&B with external ML platforms or data warehouses"],"limitations":["GraphQL API rate limits apply; bulk operations on large numbers of runs may require pagination and delays","API client is read-heavy; write operations (e.g., updating tags) are slower than bulk operations","No built-in support for streaming large metric datasets; pagination required for runs with >100k steps","API schema changes may break custom scripts; no versioning guarantees"],"requires":["Python 3.7+","wandb package installed","W&B API key for authentication","Network access to W&B backend (wandb.ai or self-hosted)"],"input_types":["Project/entity names (strings)","Run filters (config, metrics, tags)","Artifact names and versions","GraphQL queries (optional, for advanced use cases)"],"output_types":["Run objects with config, metrics, and metadata","Artifact objects with file lists and download URLs","Paginated results for large datasets","Bulk operation confirmations"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-wandb__cap_2","uri":"capability://memory.knowledge.artifact.versioning.and.model.registry","name":"artifact versioning and model registry","description":"Provides immutable, versioned storage for datasets, models, and files via the Artifact class and run.log_artifact() / run.use_artifact() methods. Each artifact has a type (e.g., 'dataset', 'model'), semantic version, manifest of files with SHA256 checksums, and metadata/aliases. Artifacts are stored in W&B's artifact registry (cloud or self-hosted) and can be referenced across runs and projects via entity/project/artifact-name:version syntax. The SDK implements a manifest-based system where file additions/deletions are tracked, enabling incremental uploads and deduplication. Aliases (e.g., 'latest', 'production') allow dynamic references without hardcoding versions.","intents":["Version and share trained models across team members and projects","Create immutable dataset snapshots for reproducibility","Track model lineage from training run to deployment","Implement model registry with promotion workflows (staging → production)"],"best_for":["ML teams managing model lifecycle from training to production","Data engineers versioning large datasets for reproducible experiments","Organizations requiring audit trails and immutable artifact history","Multi-team projects sharing models and datasets"],"limitations":["Artifact uploads are synchronous by default; large artifacts (>1GB) may block the training loop unless explicitly backgrounded","Manifest computation requires reading all files; slow on network filesystems or with millions of small files","No built-in deduplication across artifacts; identical files in different artifacts consume separate storage","Aliases are mutable; concurrent alias updates may race without explicit locking"],"requires":["Python 3.7+","Active wandb.init() run or wandb.Api() client","Write permissions to artifact registry","Network access to W&B backend for artifact storage"],"input_types":["local file paths (strings or Path objects)","directories (recursively added)","artifact type and name (strings)","metadata dictionaries"],"output_types":["Artifact object with version, manifest, and metadata","Artifact URI (entity/project/artifact-name:version)","File download paths for use_artifact()"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-wandb__cap_3","uri":"capability://planning.reasoning.hyperparameter.sweep.orchestration.and.optimization","name":"hyperparameter sweep orchestration and optimization","description":"Orchestrates hyperparameter search via the sweep system, which defines a search space (grid, random, Bayesian) and spawns multiple runs with different hyperparameter combinations. The sweep controller (implemented in wandb-core) manages job scheduling, early stopping, and result aggregation. Users define sweeps via YAML configuration specifying the search space (parameters, bounds, distribution), optimization metric, and stopping criteria. The SDK provides wandb.agent() to connect training scripts to the sweep controller, which injects hyperparameters via wandb.config. Supports distributed sweeps across multiple machines via a central controller that tracks run results and decides next hyperparameter suggestions.","intents":["Systematically search hyperparameter space without manual run management","Implement early stopping to terminate unpromising runs and save compute","Compare runs across different hyperparameter combinations in a single dashboard","Scale sweeps across multiple GPUs/machines with centralized orchestration"],"best_for":["ML engineers tuning model hyperparameters at scale","Teams with limited compute budgets needing efficient search","Researchers exploring large hyperparameter spaces (10+ dimensions)","Organizations running distributed training across multiple nodes"],"limitations":["Bayesian optimization requires sufficient initial runs (~10-20) to build a useful model; inefficient for very small budgets","Early stopping relies on metric monotonicity; non-monotonic metrics may cause premature termination","Sweep configuration is static; dynamic parameter injection during sweep not supported","No built-in multi-objective optimization; requires custom aggregation logic for Pareto-front analysis"],"requires":["Python 3.7+","wandb.init() and wandb.agent() in training script","Sweep configuration YAML with search space definition","W&B backend for sweep controller and result aggregation"],"input_types":["YAML sweep configuration (parameters, bounds, method)","Metric name for optimization (string)","Stopping criteria (early stopping patience, max runs)"],"output_types":["Multiple Run objects with injected hyperparameters","Sweep results dashboard with parallel coordinates plot","Best hyperparameters and corresponding metrics"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-wandb__cap_4","uri":"capability://tool.use.integration.framework.specific.integration.and.automatic.instrumentation","name":"framework-specific integration and automatic instrumentation","description":"Provides native integrations with popular ML frameworks (PyTorch, TensorFlow, Keras, JAX, Hugging Face Transformers, LightGBM, XGBoost, scikit-learn) via callback classes and monkey-patching. For PyTorch, wandb provides a WandbCallback that hooks into the training loop to log gradients, weights, and loss automatically. For TensorFlow/Keras, a WandbCallback integrates with the fit() API. Hugging Face Transformers integration uses a custom Callback that logs training/validation metrics. The SDK also patches framework-specific functions (e.g., torch.nn.Module.backward()) to capture gradients and layer activations without explicit user code. This enables zero-configuration logging for common workflows while allowing fine-grained control via explicit log() calls.","intents":["Automatically log metrics from PyTorch/TensorFlow training without modifying training code","Capture gradients, weights, and layer activations for debugging","Integrate with Hugging Face Transformers for NLP model training","Log model architecture and parameter counts automatically"],"best_for":["ML practitioners using standard frameworks (PyTorch, TensorFlow, Transformers)","Teams wanting minimal instrumentation overhead","Researchers debugging gradient flow and weight distributions","Organizations standardizing on specific frameworks"],"limitations":["Monkey-patching can conflict with other instrumentation libraries or custom training loops","Gradient logging adds 5-15% overhead per training step due to hook registration and serialization","Framework-specific integrations lag behind framework releases; newer features may not be supported","Automatic instrumentation is opinionated; custom metrics require explicit log() calls"],"requires":["Python 3.7+","Target framework installed (PyTorch 1.9+, TensorFlow 2.4+, Transformers 4.0+, etc.)","wandb.init() before framework initialization for proper hook registration"],"input_types":["PyTorch model, optimizer, and training loop","TensorFlow/Keras model and fit() call","Hugging Face Trainer object","LightGBM/XGBoost training parameters"],"output_types":["Automatically logged metrics (loss, accuracy, learning rate)","Gradient histograms and weight distributions","Model architecture visualization","Training/validation curves"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-wandb__cap_5","uri":"capability://automation.workflow.distributed.training.and.multi.gpu.synchronization","name":"distributed training and multi-gpu synchronization","description":"Supports distributed training across multiple GPUs and machines by synchronizing metrics and artifacts across worker processes. The SDK detects distributed training environments (PyTorch DDP, TensorFlow distributed strategies, Horovod) and coordinates logging to avoid duplicate metrics from multiple workers. Only the rank-0 (primary) process logs metrics by default, while other ranks can optionally log rank-specific data. The wandb-core service handles file uploads asynchronously, preventing network I/O from blocking training on any rank. For multi-node training, the SDK uses a central W&B backend to aggregate metrics from all nodes, providing a unified view of distributed training progress.","intents":["Log metrics from distributed training without duplicate entries","Track per-rank metrics for debugging distributed training issues","Synchronize artifact uploads across multiple GPUs/machines","Monitor training progress across a cluster in a single dashboard"],"best_for":["ML teams training large models on multi-GPU clusters","Researchers using PyTorch DDP, TensorFlow distributed strategies, or Horovod","Organizations with limited monitoring infrastructure for distributed jobs","Teams needing per-rank debugging and performance analysis"],"limitations":["Rank detection is framework-specific; custom distributed setups may require manual rank specification","Per-rank logging multiplies storage and dashboard load; large clusters (>100 GPUs) may cause UI slowdowns","Network bandwidth for metric aggregation can become a bottleneck on slow interconnects","No built-in support for federated learning or privacy-preserving aggregation"],"requires":["Python 3.7+","Distributed training framework (PyTorch DDP, TensorFlow distributed, Horovod, etc.)","Network connectivity between all ranks and W&B backend","Proper rank/world_size environment variables set by job launcher"],"input_types":["Distributed training configuration (framework, rank, world_size)","Per-rank metrics (optional)","Synchronized artifacts"],"output_types":["Aggregated metrics from all ranks","Per-rank metrics (optional, with rank prefix)","Unified training dashboard"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-wandb__cap_6","uri":"capability://data.processing.analysis.system.and.gpu.resource.monitoring","name":"system and gpu resource monitoring","description":"Automatically monitors system resources (CPU, memory, disk I/O) and GPU metrics (utilization, memory, temperature, power) during training via a background monitoring thread and the gpu_stats Rust module. The monitoring thread samples system metrics at configurable intervals (default 30s) and logs them to the run's history. GPU monitoring uses NVIDIA's NVML library (via gpu_stats) to capture per-GPU metrics without requiring nvidia-smi subprocess calls, reducing overhead. The SDK also captures environment metadata (Python version, CUDA version, GPU model) at run initialization. Metrics are logged with a special 'system' namespace to avoid collision with user metrics.","intents":["Track GPU utilization and memory usage during training to identify bottlenecks","Monitor CPU and system memory to detect resource contention","Correlate training performance with system resource availability","Detect hardware issues (thermal throttling, power limits) during long training runs"],"best_for":["ML engineers optimizing training efficiency on expensive hardware","Teams debugging performance issues and resource contention","Researchers studying hardware utilization patterns","Organizations monitoring cluster health and utilization"],"limitations":["GPU monitoring requires NVIDIA GPUs with NVML support; AMD/Intel GPUs not supported","Monitoring thread adds 1-3% CPU overhead; can be disabled for latency-critical applications","GPU metrics are sampled at fixed intervals; transient spikes may be missed","No support for custom hardware metrics (e.g., network bandwidth, disk I/O to specific devices)"],"requires":["Python 3.7+","NVIDIA GPU with NVML library (for GPU monitoring)","psutil library for system metrics","wandb-core service for background monitoring"],"input_types":["Monitoring interval (seconds)","GPU indices to monitor (optional)"],"output_types":["System metrics (CPU %, memory %, disk I/O)","GPU metrics (utilization %, memory %, temperature, power)","Environment metadata (GPU model, CUDA version, driver version)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-wandb__cap_7","uri":"capability://search.retrieval.experiment.comparison.and.dashboard.visualization","name":"experiment comparison and dashboard visualization","description":"Provides a web-based dashboard (wandb.ai or self-hosted) for visualizing and comparing runs across multiple dimensions. The dashboard displays metrics over time, compares hyperparameters and final metrics across runs, and provides interactive visualizations (parallel coordinates, scatter plots, histograms). The SDK logs all run data (config, metrics, artifacts, system info) to the W&B backend, which indexes and serves the data via a GraphQL API. The dashboard supports custom charts, filtering, and grouping by tags/metadata. The Python SDK also provides a local API (wandb.Api()) for programmatic access to run data, enabling custom analysis and automation.","intents":["Visually compare metrics across multiple runs to identify best hyperparameters","Create custom charts and reports for stakeholder communication","Filter and group runs by tags, project, or metadata for analysis","Export run data for external analysis or publication"],"best_for":["ML teams comparing experiment results across large hyperparameter spaces","Researchers creating publication-ready visualizations","Non-technical stakeholders reviewing experiment progress","Organizations automating analysis and reporting workflows"],"limitations":["Dashboard rendering can be slow for runs with >100k steps or >1000 concurrent runs","Custom chart creation requires manual configuration; no automatic insight generation","API rate limits apply to programmatic access; bulk exports may require pagination","Dashboard is cloud-hosted (wandb.ai) or requires self-hosted infrastructure; no local-only option"],"requires":["Python 3.7+","W&B account and API key","Network access to wandb.ai or self-hosted W&B instance","wandb.Api() client for programmatic access"],"input_types":["Run metrics, config, and metadata (logged via wandb.log())","Custom chart definitions (JSON or UI-based)","Filter and grouping criteria"],"output_types":["Interactive web dashboard with time-series plots","Comparison tables and parallel coordinates plots","Custom charts and reports","Exported data (CSV, JSON)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-wandb__cap_8","uri":"capability://memory.knowledge.configuration.management.and.hyperparameter.tracking","name":"configuration management and hyperparameter tracking","description":"Tracks and manages experiment configuration (hyperparameters, model architecture choices, data paths) via the wandb.config object, which is a special dictionary that logs all assignments to the run's metadata. Users set config values via wandb.config.key = value or wandb.config.update(dict), and the SDK automatically logs them to the run. Config is immutable after run initialization (to prevent accidental changes), and all config values are displayed in the dashboard alongside metrics. The SDK also provides a configuration system for sweep parameters, where the sweep controller injects hyperparameters into wandb.config before the training script runs. Config values are serialized as JSON and stored in the run metadata, enabling easy comparison across runs.","intents":["Record all hyperparameters and configuration choices for reproducibility","Compare configurations across runs to understand their impact on metrics","Inject sweep parameters into training scripts without code changes","Document model architecture and data preprocessing choices"],"best_for":["ML engineers ensuring reproducibility of experiments","Teams comparing configurations across large hyperparameter spaces","Researchers documenting experimental setup for publication","Organizations automating configuration management in training pipelines"],"limitations":["Config is immutable after run initialization; dynamic configuration changes require run restart","Config values must be JSON-serializable; complex objects (functions, classes) cannot be stored","No built-in config validation; invalid values are logged but not caught at initialization","Config comparison in dashboard is manual; no automatic sensitivity analysis"],"requires":["Python 3.7+","wandb.init() before config assignment","JSON-serializable config values"],"input_types":["Hyperparameter dictionaries","Model architecture choices (strings, numbers)","Data paths and preprocessing parameters"],"output_types":["Immutable config object stored in run metadata","Config comparison tables in dashboard","Config export (JSON) for external analysis"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-wandb__cap_9","uri":"capability://automation.workflow.offline.mode.and.local.first.experiment.tracking","name":"offline mode and local-first experiment tracking","description":"Supports offline mode for training environments without network connectivity, where metrics and artifacts are stored locally and synced to the W&B backend when connectivity is restored. In offline mode, wandb.init() creates a local run directory (.wandb/) with a SQLite database for metrics and a file tree for artifacts. The SDK queues all log() calls to the local database and defers uploads until sync() is called or network connectivity is detected. Offline mode is useful for training on air-gapped clusters or edge devices. The SDK also provides a 'disabled' mode where all wandb calls are no-ops, useful for development and testing.","intents":["Train models on air-gapped or offline clusters without network connectivity","Defer metric uploads to reduce network overhead during training","Develop and test training scripts without W&B backend connectivity","Sync metrics to W&B backend after training completes"],"best_for":["ML teams training on air-gapped clusters or edge devices","Researchers developing training scripts in offline environments","Organizations with intermittent network connectivity","Teams wanting to reduce network overhead during training"],"limitations":["Offline mode does not support artifact versioning or model registry features","Local SQLite database can become slow with >1M metrics; no built-in partitioning","Sync to W&B backend is manual or event-triggered; no automatic background sync","Offline mode does not support real-time dashboard updates or sweep orchestration"],"requires":["Python 3.7+","SQLite library (usually built-in)","Local disk space for metrics and artifacts (proportional to run size)"],"input_types":["Offline mode flag (wandb.init(mode='offline'))","Metrics and artifacts (same as online mode)"],"output_types":["Local .wandb/ directory with metrics and artifacts","Synced data to W&B backend after connectivity restored"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":32,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+","wandb-core service (Go binary) auto-downloaded and spawned as subprocess","Valid W&B API key or authentication token","Network access to wandb.ai or self-hosted W&B instance","NumPy, PyTorch, TensorFlow, or PIL for media serialization (optional but recommended)","Active wandb.init() run context","wandb package installed (pip install wandb)","W&B API key for authentication","wandb package installed","Network access to W&B backend (wandb.ai or self-hosted)"],"failure_modes":["Requires network connectivity to W&B backend (cloud or self-hosted) for full functionality","IPC overhead adds ~5-10ms per log() call due to message serialization and queue management","Single run per process; nested runs not supported","Offline mode has limited functionality — artifacts cannot be versioned without backend connectivity","Media serialization (images, videos) adds 50-500ms per log() call depending on size and format","History is stored in-memory on the client until flushed; large runs (>100k steps) may consume significant RAM","Custom Python objects require explicit type handlers; arbitrary objects are converted to string representations","Step counter is global per run; no per-namespace step tracking for multi-task scenarios","CLI commands are less flexible than Python API; complex workflows require custom scripts","Some features (e.g., custom charts, advanced filtering) are only available in Python API or web UI","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.34,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.9,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.061Z","last_scraped_at":"2026-05-03T15:20:23.204Z","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-wandb","compare_url":"https://unfragile.ai/compare?artifact=pypi-wandb"}},"signature":"nD4nf0zISjVh0akSM8s5IG7E6DIPadnTCu01gq0gav0h8Y/U9ZH6+na9eAvv6fGk9QUF9ccZPjVUZgwReXVHBg==","signedAt":"2026-06-15T20:54:30.985Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-wandb","artifact":"https://unfragile.ai/pypi-wandb","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-wandb","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"}}