{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"weights-biases-api","slug":"weights-biases-api","name":"Weights & Biases API","type":"api","url":"https://docs.wandb.ai","page_url":"https://unfragile.ai/weights-biases-api","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"weights-biases-api__cap_0","uri":"capability://data.processing.analysis.experiment.tracking.with.metric.logging","name":"experiment-tracking-with-metric-logging","description":"Programmatic logging of training metrics, hyperparameters, and metadata to a centralized cloud or self-hosted backend via the Python SDK or REST API. Metrics are persisted with timestamps and run context, enabling real-time visualization dashboards and historical comparison across experiments. The system automatically captures framework-specific integrations (PyTorch, TensorFlow, scikit-learn) to reduce boilerplate logging code.","intents":["Log training loss, validation accuracy, and custom metrics from a training loop without manual dashboard setup","Compare hyperparameter sensitivity across 50+ experiment runs to identify optimal configurations","Reproduce a specific model's training conditions by querying logged hyperparameters and random seeds","Stream live metrics to a team dashboard during long-running training jobs"],"best_for":["ML teams training models iteratively and needing centralized experiment history","Researchers comparing algorithmic variants with reproducible logging","Solo practitioners prototyping models who want lightweight metric tracking without infrastructure"],"limitations":["Free tier limited to community support; no SLA on metric ingestion latency","Self-hosted Personal tier prohibits corporate use (license restriction)","No built-in data retention policies — Enterprise tier required for HIPAA compliance","Metric ingestion rate limits not documented in public tier specifications"],"requires":["Python 3.7+ with wandb SDK (pip install wandb)","API key for cloud tier or Docker deployment for self-hosted","Network connectivity to wandb.ai cloud or internal self-hosted instance"],"input_types":["numeric scalars (loss, accuracy, learning rate)","structured dicts (hyperparameter configs)","JSON-serializable Python objects"],"output_types":["time-series metric visualization in web dashboard","exportable CSV/JSON run data","queryable run metadata via Python SDK"],"categories":["data-processing-analysis","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"weights-biases-api__cap_1","uri":"capability://planning.reasoning.hyperparameter.sweep.optimization","name":"hyperparameter-sweep-optimization","description":"Automated hyperparameter search via Bayesian optimization, grid search, or random search configured through a YAML sweep specification. The system launches parallel training jobs across local or cloud compute, logs metrics for each trial, and recommends optimal hyperparameters based on a user-defined objective (e.g., maximize validation accuracy). Supports conditional parameters, nested search spaces, and early stopping to reduce wasted compute.","intents":["Run 100 parallel hyperparameter trials on cloud compute without manually launching each job","Find optimal learning rate and batch size for a model using Bayesian optimization instead of grid search","Stop underperforming trials early to save compute cost while exploring the hyperparameter space","Export sweep results and replay the best configuration for production deployment"],"best_for":["ML engineers optimizing model performance under compute budget constraints","Teams with access to cloud compute (AWS, GCP, Azure) wanting distributed hyperparameter search","Researchers exploring high-dimensional hyperparameter spaces (10+ parameters)"],"limitations":["Sweep orchestration requires W&B cloud backend; self-hosted sweeps have limited documentation","Early stopping requires custom callback implementation; no built-in stopping rules for all frameworks","Conditional parameters and nested search spaces require YAML syntax knowledge; no visual sweep builder in free tier","Parallel trial scaling depends on external compute provider (AWS, GCP) — W&B does not provision compute directly"],"requires":["Python 3.7+ with wandb SDK","YAML sweep configuration file defining search space and objective","Cloud compute credentials (AWS, GCP, Azure) or local compute for parallel execution","Wandb API key with sweep creation permissions"],"input_types":["YAML sweep specification (parameter ranges, search strategy, objective metric)","training script accepting command-line hyperparameter arguments","metric name to optimize (e.g., 'val_accuracy')"],"output_types":["ranked list of trials with hyperparameters and final metric values","sweep visualization showing parameter importance and correlation","best trial configuration exportable as JSON/YAML"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"weights-biases-api__cap_10","uri":"capability://search.retrieval.query.expression.language.for.run.data","name":"query-expression-language-for-run-data","description":"W&B provides a query expression language (documented in 'Query Expression Language' section) enabling programmatic filtering and aggregation of experiment runs, metrics, and artifacts. Queries are executed via Python SDK or REST API, returning structured results for analysis, reporting, or automation. Supports complex filters (e.g., 'accuracy > 0.9 AND learning_rate < 0.01') and aggregations (e.g., 'max accuracy per hyperparameter').","intents":["Query all runs with accuracy > 0.9 to identify high-performing models for promotion","Aggregate metrics by hyperparameter to identify which values correlate with best performance","Export run data for 100 experiments to a CSV for external analysis","Programmatically find the best model from a sweep to deploy to production"],"best_for":["ML engineers building automated workflows that query experiment results","Data analysts extracting run data for external analysis and reporting","Practitioners building custom dashboards or tools on top of W&B data"],"limitations":["Query syntax not fully documented in provided material; requires consulting API reference","Query performance depends on number of runs; no indexing or query optimization hints documented","Aggregation functions are limited; complex statistical analysis requires exporting data to external tools","No support for time-series queries (e.g., 'accuracy trend over time'); requires post-processing results"],"requires":["Python 3.7+ with wandb SDK","Wandb API key with read permissions","Knowledge of query expression syntax (documented in W&B API reference)"],"input_types":["filter expressions (e.g., 'accuracy > 0.9')","aggregation functions (e.g., 'max', 'mean', 'group_by')","field names (metric names, hyperparameter names)"],"output_types":["filtered list of runs with metadata","aggregated metrics (e.g., max accuracy per hyperparameter)","exportable results (JSON, CSV, pandas DataFrame)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"weights-biases-api__cap_11","uri":"capability://tool.use.integration.framework.agnostic.integration.and.auto.logging","name":"framework-agnostic-integration-and-auto-logging","description":"W&B SDK provides framework-agnostic integration with popular ML libraries (PyTorch, TensorFlow, scikit-learn, XGBoost, Hugging Face Transformers, etc.) via auto-logging that intercepts native logging calls and framework hooks. Users add minimal boilerplate (e.g., `wandb.init()`, `wandb.log()`) to enable automatic metric capture, model checkpointing, and hyperparameter logging without modifying training code. Supports custom integrations via decorators and callbacks.","intents":["Add W&B logging to a PyTorch training loop with 3 lines of code (init, log, finish)","Automatically capture TensorFlow training metrics without modifying the training script","Log scikit-learn model hyperparameters and cross-validation scores automatically","Integrate W&B with a custom training framework using decorators and callbacks"],"best_for":["ML practitioners wanting lightweight experiment tracking without major code refactoring","Teams using multiple frameworks (PyTorch, TensorFlow, scikit-learn) needing unified logging","Researchers building custom training loops who want minimal overhead"],"limitations":["Auto-logging coverage varies by framework; some frameworks require manual logging","Integration overhead is minimal but not zero (~10-50ms per log call depending on network latency)","Custom integrations require Python knowledge; no visual integration builder","Framework updates may break integrations; W&B must maintain compatibility with new framework versions"],"requires":["Python 3.7+ with wandb SDK","Supported ML framework (PyTorch, TensorFlow, scikit-learn, XGBoost, etc.)","Wandb API key for cloud tier"],"input_types":["metrics (scalars, arrays, images)","hyperparameters (dicts, lists)","model checkpoints (files)","custom metadata (tags, notes)"],"output_types":["logged metrics in W&B dashboard","versioned model checkpoints","hyperparameter history","exportable run data"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"weights-biases-api__cap_12","uri":"capability://tool.use.integration.multi.tenant.team.collaboration.and.access.control","name":"multi-tenant-team-collaboration-and-access-control","description":"W&B supports team-based access control with role-based permissions (admin, member, viewer) and project-level sharing. Teams can be created in cloud tier (Pro and above) or self-hosted Enterprise tier. Access control enables fine-grained sharing of experiments, models, and reports with team members or external stakeholders. Audit logs (Enterprise tier) track all data access and modifications for compliance.","intents":["Create a team project and invite 5 team members with different permission levels (admin, member, viewer)","Share a specific model with an external stakeholder via a read-only link without exposing other team data","Audit who accessed a sensitive model and when (Enterprise tier)","Revoke access to a project when a team member leaves the organization"],"best_for":["ML teams collaborating on shared projects with multiple members","Organizations with compliance requirements needing audit trails and access control","Enterprises managing multiple teams and projects with fine-grained permissions"],"limitations":["Free tier limited to personal projects; team features require Pro tier ($60/month minimum)","Role-based access control is limited to 3 roles (admin, member, viewer); no custom roles","Audit logs available only in Enterprise tier; no audit trail in Pro tier","No row-level access control; team members with access can see all data in a project"],"requires":["Wandb Pro tier or above for team features","Team members with Wandb accounts (or email invitations for external stakeholders)","Optional: Enterprise tier for audit logs and advanced access control"],"input_types":["team member email addresses","role assignments (admin, member, viewer)","project sharing settings"],"output_types":["team project with shared experiments and models","access control list (ACL) for team members","audit logs (Enterprise tier) showing access history"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"weights-biases-api__cap_13","uri":"capability://automation.workflow.self.hosted.deployment.with.docker","name":"self-hosted-deployment-with-docker","description":"W&B Personal tier (free) and Enterprise tier support self-hosted deployment via Docker, enabling on-premise installation for teams with data residency or security requirements. Self-hosted instances run independently from W&B cloud, with optional integration to W&B cloud for cross-instance features. Supports custom domain configuration, HTTPS, and integration with corporate identity providers (LDAP, SAML, OAuth).","intents":["Deploy W&B on-premise in a Docker container for a team with data residency requirements","Configure HTTPS and custom domain for a self-hosted W&B instance","Integrate self-hosted W&B with corporate LDAP for centralized user management","Backup and restore a self-hosted W&B instance for disaster recovery"],"best_for":["Organizations with data residency or security requirements prohibiting cloud deployment","Teams wanting to self-manage infrastructure and avoid vendor lock-in","Enterprises with existing on-premise infrastructure and IT policies"],"limitations":["Personal tier (free) prohibits corporate use; Enterprise tier required for commercial deployment","Self-hosted setup requires Docker and Kubernetes knowledge; no managed self-hosted service","Backup and disaster recovery are manual; no built-in replication or failover","Self-hosted instances do not automatically sync with W&B cloud; manual integration required","Support for self-hosted is limited to Enterprise tier; no community support for Personal tier"],"requires":["Docker and Docker Compose (or Kubernetes for production)","Linux server with sufficient resources (CPU, memory, storage)","Network connectivity for team members to access the instance","Optional: HTTPS certificate and custom domain for production deployment","Optional: LDAP/SAML server for identity management"],"input_types":["Docker image (provided by W&B)","configuration file (YAML) for domain, HTTPS, identity provider","persistent storage (volume) for data"],"output_types":["running W&B instance accessible via custom domain","user authentication via corporate identity provider","backup files for disaster recovery"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"weights-biases-api__cap_2","uri":"capability://memory.knowledge.model.versioning.and.registry","name":"model-versioning-and-registry","description":"Centralized model artifact storage with versioning, lineage tracking, and metadata tagging. Models are stored as W&B Artifacts (immutable, content-addressed files) linked to specific experiment runs, enabling reproducibility by pinning a model version to its training config and metrics. Supports model comparison, promotion workflows (dev → staging → production), and integration with CI/CD pipelines for automated model deployment.","intents":["Save a trained model checkpoint after each experiment and link it to the hyperparameters and metrics that produced it","Retrieve a specific model version (e.g., 'v1.2.3') and its full training context for debugging or retraining","Promote a model from 'staging' to 'production' alias with automated downstream notifications","Query all models trained with a specific dataset version to audit model lineage"],"best_for":["ML teams managing multiple model versions across development, staging, and production environments","Practitioners needing audit trails linking models to training data, hyperparameters, and performance metrics","Organizations with CI/CD pipelines requiring programmatic model promotion and deployment triggers"],"limitations":["Artifact storage limits not documented for free tier; Enterprise tier required for custom retention policies","Model lineage queries require Python SDK; no SQL-like query interface for complex lineage searches","Promotion workflows are manual or require custom webhook logic; no built-in approval gates for production promotion","Model comparison UI limited to metrics; no built-in inference-time performance profiling (latency, memory)"],"requires":["Python 3.7+ with wandb SDK","Trained model file (PyTorch .pt, TensorFlow SavedModel, ONNX, pickle, etc.)","Wandb project and run context to link artifact to experiment","Optional: CI/CD system (GitHub Actions, GitLab CI) for automated promotion workflows"],"input_types":["model files (PyTorch, TensorFlow, scikit-learn, ONNX, custom formats)","metadata tags (e.g., 'production', 'v1.2.3', 'approved')","aliases for model promotion (e.g., 'staging', 'production')"],"output_types":["versioned model artifact with immutable content hash","lineage graph showing model → training run → dataset → hyperparameters","downloadable model file with metadata for local inference"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"weights-biases-api__cap_3","uri":"capability://data.processing.analysis.ai.model.evaluation.and.scoring","name":"ai-model-evaluation-and-scoring","description":"Framework for evaluating LLM outputs against custom scoring functions and datasets. Users define evaluation logic (e.g., BLEU score, semantic similarity, custom classifiers) that runs on model predictions, generating structured evaluation reports. Integrates with W&B Weave for tracing LLM calls and with W&B Models for comparing evaluation results across model versions. Supports batch evaluation of large datasets and cost estimation for LLM API calls.","intents":["Evaluate a fine-tuned LLM against a test dataset using BLEU, ROUGE, and custom semantic similarity metrics","Compare two model versions (e.g., base vs. fine-tuned) on the same evaluation dataset to quantify improvement","Estimate total cost of evaluating 10,000 examples using GPT-4 before running the full evaluation","Create a reusable evaluation job that runs weekly on new data to monitor model drift"],"best_for":["ML teams evaluating LLM fine-tuning or prompt engineering experiments with quantitative metrics","Practitioners comparing multiple model versions (open-source vs. commercial, different sizes) on standardized benchmarks","Organizations needing cost visibility for LLM evaluation pipelines before committing to large-scale runs"],"limitations":["Custom scoring functions require Python code; no visual rule builder for non-technical stakeholders","Evaluation results are tied to W&B runs; exporting evaluation data to external systems requires manual API calls","Cost estimation is approximate and depends on LLM provider pricing; actual costs may vary with rate changes","Batch evaluation throughput limited by LLM API rate limits; no built-in retry logic for transient failures"],"requires":["Python 3.7+ with wandb SDK","Evaluation dataset (CSV, JSON, or Python list of examples)","Custom scoring function (Python callable) or use of built-in metrics (BLEU, ROUGE, etc.)","Optional: API keys for LLM providers (OpenAI, Anthropic) if using LLM-based scorers"],"input_types":["model predictions (text, structured data)","reference outputs (ground truth labels)","custom scoring functions (Python callables)","evaluation dataset (CSV, JSON, pandas DataFrame)"],"output_types":["structured evaluation report with per-example scores and aggregate metrics","cost breakdown for LLM API calls","comparison visualization across model versions","exportable evaluation results (JSON, CSV)"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"weights-biases-api__cap_4","uri":"capability://memory.knowledge.ai.model.tracing.and.debugging","name":"ai-model-tracing-and-debugging","description":"W&B Weave provides distributed tracing for LLM applications, capturing function calls, LLM API requests, and intermediate outputs in a queryable trace tree. Traces are visualized as DAGs showing data flow through the application, enabling debugging of multi-step LLM pipelines (e.g., RAG systems, agents). Integrates with OpenAI, Anthropic, and other LLM providers to auto-capture API calls without code changes. Supports cost tracking and latency profiling per trace.","intents":["Debug a RAG pipeline by viewing the full trace of query → retrieval → LLM call → output with intermediate data at each step","Identify which LLM API calls are slow or expensive in a multi-step agent workflow","Compare traces across two versions of a prompt to see how outputs differ at each step","Export traces for compliance auditing or to replay a specific user interaction"],"best_for":["LLM application developers debugging complex multi-step workflows (RAG, agents, chains)","Teams optimizing LLM API costs by identifying expensive or redundant calls","Practitioners building production LLM systems needing observability and debugging tools"],"limitations":["Auto-capture requires SDK instrumentation; not all LLM providers are auto-instrumented (custom API clients require manual tracing)","Trace storage and query limits not documented for free tier; Enterprise tier required for long-term retention","Trace visualization is read-only; no built-in tools to modify or replay traces with different inputs","Cost tracking depends on LLM provider pricing data; may be inaccurate if pricing changes or custom pricing is used"],"requires":["Python 3.7+ with wandb SDK (weave module)","LLM provider API key (OpenAI, Anthropic, etc.) for auto-instrumentation","Application code using supported LLM libraries (langchain, openai, anthropic, etc.)","Optional: Custom instrumentation for unsupported libraries using @weave.op decorator"],"input_types":["LLM API calls (auto-captured from OpenAI, Anthropic SDKs)","function calls and intermediate outputs (via @weave.op decorator)","custom metadata (tags, user IDs, session IDs)"],"output_types":["trace tree visualization (DAG of function calls and LLM requests)","cost and latency breakdown per trace step","queryable trace database for searching by metadata","exportable trace data (JSON) for compliance or replay"],"categories":["memory-knowledge","observability"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"weights-biases-api__cap_5","uri":"capability://memory.knowledge.dataset.versioning.and.lineage.tracking","name":"dataset-versioning-and-lineage-tracking","description":"W&B Artifacts system enables versioning of datasets as immutable, content-addressed files linked to experiments. Datasets are tagged with metadata (e.g., 'train-v2.3', 'test-split-1') and tracked through the ML pipeline, creating a lineage graph showing which models were trained on which dataset versions. Supports dataset comparison (schema changes, row count diffs) and integration with data processing workflows to track transformations.","intents":["Version a training dataset and link it to all models trained on that version for reproducibility","Query which models were trained on a specific dataset version to audit model lineage","Compare two dataset versions to identify schema changes or data drift","Automatically trigger model retraining when a new dataset version is published"],"best_for":["ML teams managing multiple dataset versions and needing to track which models use which data","Data engineers building reproducible data pipelines with versioning and lineage tracking","Organizations with compliance requirements needing audit trails of data → model relationships"],"limitations":["Dataset comparison is limited to metadata (schema, row count); no built-in data profiling or statistical drift detection","Lineage queries require Python SDK; no SQL interface for complex lineage searches","Dataset size limits not documented for free tier; large datasets may require Enterprise tier","No built-in data validation or schema enforcement; validation logic must be implemented externally"],"requires":["Python 3.7+ with wandb SDK","Dataset file (CSV, Parquet, JSON, or custom format)","Wandb project and run context to link dataset to experiments","Optional: Data processing framework (Pandas, Spark, DuckDB) for transformations"],"input_types":["dataset files (CSV, Parquet, JSON, HDF5, custom formats)","metadata tags (e.g., 'train-v2.3', 'test-split-1')","schema information (column names, types)"],"output_types":["versioned dataset artifact with immutable content hash","lineage graph showing dataset → experiment → model relationships","dataset comparison report (schema changes, row count diffs)","downloadable dataset file with metadata"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"weights-biases-api__cap_6","uri":"capability://automation.workflow.ci.cd.automation.and.alerts","name":"ci-cd-automation-and-alerts","description":"W&B integrates with CI/CD systems (GitHub Actions, GitLab CI, Jenkins) to trigger model training, evaluation, and deployment workflows based on code or data changes. Supports conditional execution (e.g., 'only run sweep if accuracy improved'), automated alerts (Slack, email) on metric thresholds, and promotion workflows that move models through dev → staging → production with approval gates. Webhook system enables custom automation logic.","intents":["Automatically run a hyperparameter sweep when a new commit is pushed to the main branch","Send a Slack alert if validation accuracy drops below a threshold, indicating potential model degradation","Promote a model to production only if it outperforms the current production model by 2%","Trigger a retraining job when new data is added to the training dataset"],"best_for":["ML teams with mature CI/CD pipelines wanting to integrate model training and deployment","Organizations needing automated monitoring and alerts for model performance degradation","Practitioners building MLOps workflows with approval gates and promotion workflows"],"limitations":["CI/CD integration requires custom workflow configuration (GitHub Actions YAML, etc.); no visual workflow builder","Approval gates are manual or require custom webhook logic; no built-in approval UI","Alert rules are limited to metric thresholds; no anomaly detection or statistical significance testing","Webhook system requires external compute to process events; W&B does not provide serverless execution"],"requires":["CI/CD system (GitHub Actions, GitLab CI, Jenkins, etc.)","Wandb API key with permissions to trigger runs and sweeps","Workflow configuration file (YAML) defining triggers and actions","Optional: Slack workspace or email for alerts"],"input_types":["CI/CD events (code push, pull request, schedule)","metric thresholds for alerts (e.g., 'accuracy < 0.85')","promotion criteria (e.g., 'new_model.accuracy > old_model.accuracy * 1.02')"],"output_types":["triggered training/evaluation runs","alert notifications (Slack, email)","model promotion events","webhook event payloads for custom automation"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"weights-biases-api__cap_7","uri":"capability://text.generation.language.interactive.report.generation.and.sharing","name":"interactive-report-generation-and-sharing","description":"W&B Reports enable creation of interactive dashboards combining experiment metrics, model comparisons, and custom visualizations (plots, tables, markdown). Reports are shareable via web links with fine-grained access control (view-only, edit, admin). Supports embedding reports in documentation, exporting to PDF, and version history for collaborative editing. Reports automatically update when underlying experiment data changes.","intents":["Create a report comparing 10 model variants with side-by-side metric tables and learning curves","Share a model evaluation report with stakeholders via a web link without requiring W&B account access","Embed a live dashboard in team documentation that updates automatically as new experiments run","Export a report to PDF for a client presentation showing model performance and training details"],"best_for":["ML teams communicating experiment results to non-technical stakeholders","Researchers publishing model comparisons and evaluation results","Practitioners documenting model development process for reproducibility and knowledge sharing"],"limitations":["Report customization is limited to predefined visualizations; no custom JavaScript or interactive components","Sharing is via public links; no fine-grained row-level access control for sensitive data","PDF export may lose interactive elements (plots become static images)","Report version history is limited; no branching or merge workflows for collaborative editing"],"requires":["Wandb project with experiment runs and metrics","Web browser for report creation and viewing","Optional: Markdown knowledge for custom text sections"],"input_types":["experiment metrics and metadata","model artifacts and evaluation results","custom markdown text and images"],"output_types":["interactive web dashboard (HTML)","shareable report link with access control","PDF export of report","embedded report widget for external websites"],"categories":["text-generation-language","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"weights-biases-api__cap_8","uri":"capability://tool.use.integration.openai.compatible.inference.api","name":"openai-compatible-inference-api","description":"W&B Inference provides an OpenAI-compatible API for accessing open-source foundation models (Llama, Mistral, etc.) without managing infrastructure. API supports streaming responses, token counting, and usage tracking integrated with W&B cost monitoring. Requests are routed through W&B's hosted infrastructure or can be self-hosted. Supports both chat completions and text completions endpoints compatible with OpenAI SDK.","intents":["Use Llama 2 for inference via the OpenAI API without setting up a local model server","Track total tokens and costs for all inference requests across a team","Stream responses from an open-source model to reduce latency in user-facing applications","Switch between different open-source models (Llama, Mistral) by changing a single parameter"],"best_for":["Teams wanting to use open-source LLMs without infrastructure management","Practitioners building cost-sensitive applications needing cheaper inference than commercial APIs","Organizations with data residency requirements needing self-hosted inference"],"limitations":["Model selection is limited to open-source models; no access to GPT-4, Claude, or other commercial models","Latency and throughput depend on W&B's infrastructure; no SLA for response times in free tier","Token limits and rate limits not documented for free tier; Enterprise tier required for guaranteed capacity","Self-hosted option requires Docker and Kubernetes knowledge; no managed self-hosted service"],"requires":["Wandb API key for authentication","OpenAI Python SDK (compatible with OpenAI API format)","Optional: Self-hosted infrastructure (Docker, Kubernetes) for on-premise deployment"],"input_types":["chat messages (system, user, assistant roles)","text prompts for completion","model parameters (temperature, max_tokens, top_p)"],"output_types":["text completions (streaming or non-streaming)","token usage data (prompt tokens, completion tokens)","cost estimates based on token counts"],"categories":["tool-use-integration","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"weights-biases-api__cap_9","uri":"capability://code.generation.editing.llm.post.training.and.fine.tuning","name":"llm-post-training-and-fine-tuning","description":"W&B Training (preview) enables serverless fine-tuning and post-training of open-source LLMs using reinforcement learning and supervised fine-tuning. Users provide training data and configuration; W&B handles compute provisioning, distributed training, and checkpointing. Supports multi-turn agentic task training for building task-specific models. Results are automatically versioned and integrated with W&B model registry.","intents":["Fine-tune Llama 2 on a custom dataset without managing training infrastructure","Train a model using reinforcement learning to optimize for a specific task (e.g., code generation)","Compare fine-tuned model performance against the base model on a benchmark","Deploy a fine-tuned model via W&B Inference API after training completes"],"best_for":["Teams wanting to fine-tune open-source models without infrastructure expertise","Practitioners building task-specific models using reinforcement learning","Organizations needing serverless training for cost efficiency and simplicity"],"limitations":["Feature is in preview; API and pricing may change","Limited to open-source models; no support for fine-tuning commercial models","Training configuration options not fully documented; limited customization compared to frameworks like Hugging Face Transformers","No built-in data validation or preprocessing; users must provide clean, formatted data"],"requires":["Wandb API key with training permissions","Training dataset (JSON format with examples)","Training configuration (learning rate, batch size, epochs, etc.)","Optional: Evaluation dataset for validation during training"],"input_types":["training dataset (JSON with prompt/completion pairs or multi-turn conversations)","training configuration (YAML or JSON)","base model selection (Llama, Mistral, etc.)"],"output_types":["fine-tuned model artifact (versioned in W&B registry)","training metrics (loss, validation accuracy, etc.)","deployment-ready model for W&B Inference API"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"weights-biases-api__headline","uri":"capability://data.processing.analysis.mlops.api.for.experiment.tracking.and.model.management","name":"mlops api for experiment tracking and model management","description":"Weights & Biases API is an MLOps platform that provides programmatic access to experiment tracking, model versioning, dataset management, and hyperparameter sweeps, enabling reproducible machine learning workflows.","intents":["best MLOps API","MLOps API for experiment tracking","MLOps API for model management","best API for hyperparameter tuning","MLOps solutions for dataset management"],"best_for":["data scientists","ML engineers"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["Python 3.7+ with wandb SDK (pip install wandb)","API key for cloud tier or Docker deployment for self-hosted","Network connectivity to wandb.ai cloud or internal self-hosted instance","Python 3.7+ with wandb SDK","YAML sweep configuration file defining search space and objective","Cloud compute credentials (AWS, GCP, Azure) or local compute for parallel execution","Wandb API key with sweep creation permissions","Wandb API key with read permissions","Knowledge of query expression syntax (documented in W&B API reference)","Supported ML framework (PyTorch, TensorFlow, scikit-learn, XGBoost, etc.)"],"failure_modes":["Free tier limited to community support; 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