{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"ray","slug":"ray","name":"Ray","type":"framework","url":"https://github.com/ray-project/ray","page_url":"https://unfragile.ai/ray","categories":["model-training","deployment-infra"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"ray__cap_0","uri":"capability://automation.workflow.distributed.task.execution.with.actor.model.and.compiled.dags","name":"distributed task execution with actor model and compiled dags","description":"Ray Core executes Python functions and classes as distributed tasks across a cluster using an actor model with optional compiled DAG acceleration. Tasks are submitted to Raylets (per-node schedulers) which manage local execution, while the Global Control Store (GCS) coordinates cluster state. Compiled DAGs bypass the task submission overhead by pre-planning execution graphs, enabling near-native performance for complex workflows without serialization delays.","intents":["I need to parallelize CPU-bound Python functions across multiple machines without managing distributed infrastructure","I want to define stateful services (actors) that persist across multiple requests with guaranteed ordering","I need to optimize a known computation graph to reduce scheduling overhead and improve throughput"],"best_for":["ML engineers scaling training and inference pipelines","data engineers building ETL workflows with complex dependencies","teams migrating from single-machine parallelization (multiprocessing) to cluster-scale"],"limitations":["Serialization overhead for large objects (mitigated by object store but adds latency)","Compiled DAGs require static graph definition — dynamic control flow requires fallback to standard task submission","Actor state is in-memory only — no built-in persistence across node failures without external checkpointing"],"requires":["Python 3.8+","Ray cluster initialized via ray.init() or Kubernetes/cloud deployment","Network connectivity between cluster nodes for object store communication"],"input_types":["Python functions (decorated with @ray.remote)","Python classes (decorated with @ray.remote for actors)","DAG definitions (via ray.dag.InputNode and method chaining)"],"output_types":["ObjectRef (futures pointing to distributed objects)","Materialized Python objects (via ray.get())","Streaming results (via ray.wait() for progressive retrieval)"],"categories":["automation-workflow","distributed-computing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ray__cap_1","uri":"capability://data.processing.analysis.distributed.data.processing.with.streaming.execution.and.resource.aware.scheduling","name":"distributed data processing with streaming execution and resource-aware scheduling","description":"Ray Data provides a distributed DataFrame-like API (Dataset) that executes transformations (map, filter, groupby, aggregate) in streaming fashion across cluster nodes. Unlike batch systems, Ray Data schedules tasks based on available resources and data locality, pulling data through the object store in chunks. Supports multiple data sources (Parquet, CSV, S3, Delta Lake) and sinks, with automatic partitioning and lazy evaluation until .materialize() or action calls trigger execution.","intents":["I need to process multi-terabyte datasets with map/filter/groupby operations without loading everything into memory","I want to apply ML preprocessing (tokenization, image resizing) to large datasets in parallel with automatic batching","I need to read from cloud storage (S3, GCS) and write results back with minimal data movement"],"best_for":["ML practitioners preparing training data at scale","data engineers building feature engineering pipelines","teams processing LLM training corpora (Ray Data has specialized LLM loaders)"],"limitations":["Streaming execution means no global sort or shuffle without explicit repartition (adds network overhead)","Lazy evaluation can hide performance issues until materialization — requires explicit profiling","Limited SQL support compared to Spark — complex joins require custom Python logic"],"requires":["Python 3.8+","Ray cluster with sufficient object store memory (configurable via object_store_memory)","For cloud sources: AWS/GCP credentials or connection strings"],"input_types":["Parquet files","CSV/JSON files","S3/GCS/HDFS paths","Delta Lake tables","Python iterables (via from_items())","NumPy arrays or Pandas DataFrames"],"output_types":["Parquet files (partitioned)","CSV files","S3/cloud storage paths","Python lists (via to_pandas() or to_numpy())","Pandas DataFrames (per-partition)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ray__cap_10","uri":"capability://data.processing.analysis.batch.inference.with.ray.data.and.model.serving.integration","name":"batch inference with ray data and model serving integration","description":"Ray Data enables large-scale batch inference by applying a model to a distributed dataset. Users define a UDF (user-defined function) that loads a model and applies it to batches of data, then use Ray Data's map() to parallelize across partitions. Integrates with Ray Serve for serving the same model as an HTTP endpoint, enabling code reuse between batch and online inference. Supports automatic batching, GPU allocation per task, and result writing to cloud storage.","intents":["I want to run inference on a 1TB dataset using my trained model without loading everything into memory","I need to apply preprocessing, inference, and postprocessing to a large dataset in a single pipeline","I want to reuse the same model code for both batch inference and online serving"],"best_for":["ML engineers generating predictions on large datasets (e.g., daily batch scoring)","teams building feature engineering pipelines that include model inference","organizations needing cost-efficient batch inference with automatic GPU allocation"],"limitations":["Batch inference latency is higher than online serving due to data loading and batching overhead","Model loading per task can be expensive — requires caching or actor-based serving for efficiency","Limited support for complex inference patterns (e.g., beam search, dynamic batching) — requires custom UDFs"],"requires":["Python 3.8+","Ray cluster with GPU nodes (for GPU inference) or CPU nodes","Ray Data for distributed dataset handling","Model serializable and loadable in UDF"],"input_types":["Ray Data (distributed dataset)","Model (PyTorch, TensorFlow, scikit-learn, etc.)","Inference UDF (Python function that loads model and applies it)"],"output_types":["Predictions (written to Parquet, CSV, or cloud storage)","Batch inference metrics (throughput, latency per batch)","Materialized predictions (via to_pandas())"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ray__cap_11","uri":"capability://automation.workflow.job.submission.and.scheduling.with.resource.isolation.and.priority.queues","name":"job submission and scheduling with resource isolation and priority queues","description":"Ray Jobs API allows submitting Python scripts or functions as isolated jobs to a Ray cluster, with automatic resource allocation and priority-based scheduling. Each job runs in its own namespace with isolated actor/task state, preventing interference between concurrent jobs. Jobs can be submitted via CLI (ray job submit) or Python API, with support for dependency specification (runtime environments) and result retrieval. Integrates with Ray's autoscaler for automatic cluster scaling based on job resource requirements.","intents":["I want to submit multiple training jobs to a shared Ray cluster without them interfering with each other","I need to prioritize urgent jobs over background jobs in a shared cluster","I want to submit a job with custom dependencies and retrieve results after completion"],"best_for":["teams running multiple ML experiments on shared Ray clusters","organizations needing job isolation for multi-tenant scenarios","ML practitioners submitting jobs from CI/CD pipelines or notebooks"],"limitations":["Job isolation is logical (namespace-based) not physical — resource contention can still occur","Priority queues are FIFO with priority levels — no preemption of running jobs","Job results must fit in memory or be written to external storage — no built-in result persistence"],"requires":["Python 3.8+","Ray cluster with job submission enabled","Ray CLI or Python client library","Network access to Ray head node"],"input_types":["Python script (submitted via ray job submit)","Python function (submitted via ray.job_submission.submit_job)","Runtime environment (dependencies, working directory)","Resource requirements (num_cpus, num_gpus, memory)"],"output_types":["Job ID (for tracking and result retrieval)","Job status (PENDING, RUNNING, SUCCEEDED, FAILED)","Job logs (stdout/stderr)","Job results (return value of submitted function)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ray__cap_12","uri":"capability://automation.workflow.global.control.store.gcs.for.cluster.state.management.and.coordination","name":"global control store (gcs) for cluster state management and coordination","description":"Ray's Global Control Store (GCS) is a distributed metadata service (built on Redis) that maintains cluster state: node membership, task/actor metadata, object locations, and job status. All Ray components (head node, Raylets, workers) query GCS for cluster topology and coordinate via GCS. Enables features like task scheduling (Raylets query GCS for available nodes), object location tracking (workers find objects via GCS), and fault recovery (GCS detects node failures and triggers task re-submission).","intents":["I want to understand how Ray coordinates distributed execution across cluster nodes","I need to debug task scheduling decisions and object location tracking","I want to implement custom scheduling logic that queries cluster state"],"best_for":["Ray developers and contributors understanding Ray's internals","DevOps engineers debugging cluster coordination issues","researchers implementing custom scheduling algorithms on Ray"],"limitations":["GCS is an internal component — not intended for direct user access","GCS queries add latency to task scheduling and object location lookups","GCS is a single point of failure (mitigated by Redis replication, but still a bottleneck)"],"requires":["Ray cluster running with GCS enabled (default)","Redis server (for GCS backend)","Network connectivity between Ray components and GCS"],"input_types":["Cluster state queries (node membership, task metadata, object locations)","State updates (task submission, actor creation, node failure)"],"output_types":["Cluster topology (nodes, resources, node labels)","Task/actor metadata (status, resource requirements, owner)","Object location information (which node stores each object)"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ray__cap_13","uri":"capability://automation.workflow.kubernetes.integration.via.kuberay.for.native.cluster.deployment","name":"kubernetes integration via kuberay for native cluster deployment","description":"KubeRay is a Kubernetes operator that manages Ray clusters as Kubernetes custom resources (RayCluster). Enables declarative Ray cluster definition via YAML, automatic node scaling via Kubernetes HPA, and integration with Kubernetes networking and storage. KubeRay handles Ray head node and worker pod lifecycle, including health checks, rolling updates, and resource requests/limits. Supports Ray Jobs API for job submission to KubeRay-managed clusters.","intents":["I want to deploy Ray clusters on Kubernetes without manual pod management","I need to scale Ray clusters based on Kubernetes metrics (CPU, memory, custom metrics)","I want to integrate Ray with Kubernetes networking, storage, and RBAC"],"best_for":["organizations running Kubernetes clusters and wanting native Ray integration","teams needing GitOps-style cluster management via Kubernetes manifests","enterprises requiring Kubernetes security and multi-tenancy features"],"limitations":["KubeRay adds Kubernetes complexity — requires understanding of Kubernetes concepts (pods, services, PVCs)","Kubernetes HPA scaling is slower than Ray's native autoscaler (~30-60 seconds vs ~10-30 seconds)","Limited support for heterogeneous hardware (GPUs) compared to cloud-native autoscaling"],"requires":["Kubernetes cluster (1.20+)","KubeRay operator installed (via Helm or manual deployment)","kubectl CLI for cluster management","Kubernetes API access for RayCluster resource creation"],"input_types":["RayCluster YAML manifest (cluster configuration)","Ray Jobs API for job submission","Kubernetes HPA configuration (scaling metrics)"],"output_types":["Kubernetes pods (Ray head and worker pods)","Kubernetes services (Ray head service for client access)","Cluster status (via kubectl get raycluster)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ray__cap_2","uri":"capability://automation.workflow.distributed.model.training.with.framework.integration.and.fault.tolerance","name":"distributed model training with framework integration and fault tolerance","description":"Ray Train (v2) abstracts distributed training across PyTorch, TensorFlow, and HuggingFace Transformers using a controller-worker architecture. The controller coordinates training state and checkpointing, while workers execute training loops with automatic distributed data loading. Supports multi-node distributed training (DDP, DeepSpeed), automatic fault recovery via checkpointing, and integration with Ray Tune for hyperparameter search. Handles dependency installation via runtime environments and GPU/CPU resource allocation.","intents":["I want to train a PyTorch model on multiple GPUs/nodes without manually managing distributed setup code","I need to fine-tune a HuggingFace model at scale with automatic checkpointing and recovery from failures","I want to combine distributed training with hyperparameter tuning and automated resource allocation"],"best_for":["ML engineers training large language models or vision models","teams using HuggingFace Transformers who need distributed training without Accelerate boilerplate","organizations running training on heterogeneous hardware (mix of GPU types/counts)"],"limitations":["Requires refactoring training code to use Ray Train's ScalingConfig and Trainer API — not drop-in compatible with raw PyTorch","Checkpointing overhead can be significant for large models — requires tuning checkpoint frequency","Limited support for custom distributed algorithms (e.g., federated learning) — designed for standard DDP/DeepSpeed patterns"],"requires":["Python 3.8+","PyTorch 1.12+, TensorFlow 2.10+, or Transformers 4.20+","Ray cluster with GPU nodes (for GPU training) or CPU nodes (for CPU training)","Sufficient disk space for checkpoints (typically 2-3x model size)"],"input_types":["PyTorch Dataset or DataLoader","HuggingFace datasets.Dataset","Ray Data (distributed datasets)","Custom training functions (decorated with @ray.train.checkpoint)"],"output_types":["Trained model checkpoints (saved to local/cloud storage)","Training metrics (loss, accuracy) logged to Ray Tune","Serialized model weights (PyTorch .pt, HuggingFace safetensors)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ray__cap_3","uri":"capability://planning.reasoning.hyperparameter.tuning.with.search.algorithms.and.trial.scheduling","name":"hyperparameter tuning with search algorithms and trial scheduling","description":"Ray Tune executes hyperparameter search by spawning multiple training trials (each a Ray actor) and scheduling them based on available resources. Supports multiple search algorithms (grid, random, Bayesian optimization via Optuna, population-based training) and early stopping schedulers (ASHA, median stopping rule). Each trial reports metrics back to Tune's trial manager, which decides whether to continue, pause, or terminate based on scheduler logic. Integrates with Ray Train for distributed training trials and Ray Serve for model evaluation.","intents":["I need to find optimal hyperparameters for my model without manual grid search","I want to run 100s of training trials in parallel and stop unpromising ones early to save compute","I need to integrate hyperparameter tuning with distributed training and model serving for end-to-end AutoML"],"best_for":["ML practitioners optimizing model performance on limited compute budgets","teams building AutoML pipelines with Ray Train integration","researchers exploring algorithm hyperparameter sensitivity at scale"],"limitations":["Early stopping requires metric reporting every N iterations — incompatible with training loops that checkpoint infrequently","Population-based training (PBT) requires careful tuning of exploit/explore ratios — can waste compute on bad mutations","Bayesian optimization scales poorly beyond ~50 hyperparameters — curse of dimensionality limits effectiveness"],"requires":["Python 3.8+","Ray cluster with sufficient resources to run multiple trials concurrently","Training code instrumented with tune.report() calls to report metrics","Optional: Optuna (for Bayesian optimization), Ax (for multi-objective optimization)"],"input_types":["Search space definition (via ray.tune.choice, ray.tune.uniform, etc.)","Training function that accepts hyperparameters and calls tune.report()","Scheduler configuration (ASHA, PBT, median stopping)","Search algorithm (GridSearcher, RandomSearcher, OptunaSearcher)"],"output_types":["Best trial configuration (dict of hyperparameters)","Trial results (metrics per trial, convergence history)","Checkpoint of best model (if training function saves checkpoints)","Analysis object with trial data for post-hoc analysis"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ray__cap_4","uri":"capability://automation.workflow.model.serving.with.request.batching.and.dynamic.scaling","name":"model serving with request batching and dynamic scaling","description":"Ray Serve deploys models as HTTP endpoints by wrapping them in Deployment classes that handle request routing, batching, and scaling. Each deployment runs as a Ray actor pool, with a router component that batches incoming requests and forwards them to available replicas. Supports dynamic scaling based on queue depth or custom metrics, automatic model reloading without downtime, and composition of multiple deployments (e.g., preprocessing → model → postprocessing). Integrates with Ray Data for batch inference and Ray Train for serving trained models.","intents":["I want to serve a trained model as an HTTP API with automatic batching to improve throughput","I need to scale model replicas up/down based on request volume without manual intervention","I want to compose multiple models (ensemble, multi-stage pipeline) and route requests intelligently"],"best_for":["ML engineers deploying models to production with high throughput requirements","teams building multi-model serving systems (e.g., ensemble classifiers)","organizations needing cost-efficient serving with dynamic scaling"],"limitations":["Request batching adds latency (configurable via max_batch_size and timeout) — not suitable for ultra-low-latency requirements (<10ms)","No built-in A/B testing or canary deployment — requires manual traffic splitting logic","Stateless deployments only — complex stateful serving (e.g., session management) requires custom implementation"],"requires":["Python 3.8+","Ray cluster (local or cloud-deployed)","Model serializable to pickle or custom serialization","HTTP client library (requests, curl, etc.) for inference calls"],"input_types":["Python class (decorated with @serve.deployment) with __call__ method","Pre-trained model (PyTorch, TensorFlow, scikit-learn, etc.)","HTTP requests (JSON, binary, multipart)","Ray Data for batch inference"],"output_types":["HTTP responses (JSON, binary, streaming)","Batch predictions (via batch inference API)","Metrics (latency, throughput, error rate) to Ray dashboard"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ray__cap_5","uri":"capability://automation.workflow.cluster.autoscaling.with.resource.aware.scheduling.and.node.management","name":"cluster autoscaling with resource-aware scheduling and node management","description":"Ray's autoscaler monitors cluster resource utilization and automatically launches/terminates nodes based on pending task demand. The autoscaler reads resource requirements from tasks (CPU, GPU, memory, custom resources) and compares against available capacity, launching new nodes if demand exceeds supply. Supports multiple cloud providers (AWS, GCP, Azure) via cloud-specific launch templates, and Kubernetes via KubeRay. Includes node failure detection and automatic recovery by re-submitting failed tasks to healthy nodes.","intents":["I want my Ray cluster to automatically scale up when I submit large workloads and scale down when idle to save costs","I need to run tasks with specific resource requirements (e.g., 8 GPUs) and have the cluster provision appropriate nodes","I want automatic recovery from node failures without manual intervention"],"best_for":["teams running variable workloads on cloud infrastructure (AWS, GCP, Azure)","organizations using Kubernetes who want native Ray scaling via KubeRay","ML teams needing cost optimization through dynamic resource allocation"],"limitations":["Autoscaler has ~30-60 second latency for node launch — not suitable for real-time workloads requiring immediate scaling","Custom resource tracking requires manual definition in cluster config — no automatic GPU/CPU detection","Node termination can interrupt long-running tasks — requires checkpointing for fault tolerance"],"requires":["Ray cluster config file (YAML) with cloud provider credentials","Cloud provider account (AWS, GCP, Azure) or Kubernetes cluster","IAM permissions to launch/terminate instances","Network connectivity from Ray head node to cloud provider APIs"],"input_types":["Cluster config (YAML with provider, node_config, autoscaling_config)","Task resource requirements (num_cpus, num_gpus, memory, custom_resources)","Node failure events (detected by Ray health check)"],"output_types":["Scaled cluster (new nodes launched or terminated)","Autoscaler logs (scaling decisions, node launch/termination events)","Metrics (cluster utilization, pending tasks, node count) to Ray dashboard"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ray__cap_6","uri":"capability://automation.workflow.runtime.environment.and.dependency.management.across.cluster.nodes","name":"runtime environment and dependency management across cluster nodes","description":"Ray's runtime environment system automatically installs Python dependencies (pip packages, conda environments, local code) on worker nodes at task submission time. Dependencies are specified per-task or per-job, packaged into a runtime environment, and distributed to nodes via the object store. Supports pip requirements files, conda YAML specs, and local Python packages. Enables reproducible distributed execution without pre-baking dependencies into container images.","intents":["I want to submit a Ray job with custom Python packages without pre-installing them on all nodes","I need to test different dependency versions across trials without rebuilding cluster images","I want to ensure reproducible training runs by pinning exact package versions in runtime environments"],"best_for":["teams using Ray on shared clusters where pre-installing all dependencies is impractical","ML practitioners experimenting with different package versions (e.g., PyTorch nightly)","organizations needing reproducible distributed jobs without container image management"],"limitations":["Runtime environment installation adds ~5-30 seconds per task submission — not suitable for ultra-low-latency workloads","Large dependency sets (>500MB) can saturate object store — requires tuning object_store_memory","No support for system-level dependencies (C libraries, CUDA) — requires pre-installed base images"],"requires":["Python 3.8+","Ray cluster with object store configured","pip or conda available on worker nodes","Network access to PyPI or private package repository"],"input_types":["pip requirements.txt file","conda environment.yml file","Local Python packages (via py_modules)","Working directory with code (via working_dir)"],"output_types":["Installed packages on worker nodes","Runtime environment metadata (stored in GCS)","Task execution with dependencies available"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ray__cap_7","uri":"capability://safety.moderation.observability.via.dashboard.metrics.api.and.state.api","name":"observability via dashboard, metrics api, and state api","description":"Ray provides observability through three channels: (1) web dashboard showing cluster topology, task/actor status, and resource utilization in real-time; (2) metrics API exposing Prometheus-compatible metrics (task latency, throughput, object store usage) for external monitoring; (3) state API querying cluster state (tasks, actors, nodes, jobs) programmatically. All three integrate with Ray's internal state management (GCS) to provide consistent views of cluster health.","intents":["I want to visualize my Ray cluster's resource usage and task execution in real-time","I need to export Ray metrics to Prometheus/Grafana for production monitoring","I want to programmatically query cluster state (e.g., list all running actors) for debugging"],"best_for":["ML engineers debugging distributed training and inference pipelines","DevOps teams monitoring Ray clusters in production","researchers analyzing task scheduling and resource utilization patterns"],"limitations":["Dashboard has ~1-5 second latency for metric updates — not suitable for sub-second monitoring","State API queries are eventually consistent (reads from GCS) — may lag behind actual cluster state by seconds","Metrics retention is in-memory only — requires external time-series database for historical analysis"],"requires":["Ray cluster running with dashboard enabled (default)","Network access to dashboard port (8265 by default)","For metrics export: Prometheus scraper or compatible monitoring system"],"input_types":["Ray cluster state (tasks, actors, nodes, jobs)","Internal metrics (task latency, object store usage, CPU/GPU utilization)","Custom metrics (via ray.tune.report() or custom instrumentation)"],"output_types":["Web dashboard (HTML/JavaScript UI)","Prometheus metrics (text format, Prometheus scrape endpoint)","State API responses (JSON, Python dicts)","Logs (Ray component logs, task output)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ray__cap_8","uri":"capability://memory.knowledge.object.store.with.apache.arrow.serialization.and.zero.copy.data.passing","name":"object store with apache arrow serialization and zero-copy data passing","description":"Ray's object store is a distributed in-memory cache (one per node) that stores serialized objects using Apache Arrow format for zero-copy access. When a task returns a value, it's serialized to Arrow format and stored locally; other tasks access it via ObjectRef (a pointer to the object's location and ID). Arrow's columnar format enables zero-copy deserialization for NumPy arrays and Pandas DataFrames, reducing memory overhead. Object store is managed by the Raylet (per-node scheduler) and coordinates with GCS for object location tracking.","intents":["I want to pass large NumPy arrays between tasks without serialization overhead","I need to cache intermediate results in distributed memory for reuse across multiple tasks","I want to understand memory usage and object store pressure in my Ray cluster"],"best_for":["ML engineers working with large arrays (images, embeddings, tensors)","data engineers processing multi-gigabyte datasets in memory","teams optimizing distributed pipeline performance by reducing serialization overhead"],"limitations":["Object store is in-memory only — objects are lost on node failure without external checkpointing","Zero-copy only works for Arrow-compatible types (NumPy, Pandas) — custom objects still require serialization","Object store size is fixed at cluster launch — cannot dynamically adjust without restarting"],"requires":["Ray cluster with object_store_memory configured (default: 30% of node memory)","NumPy/Pandas for zero-copy benefits (optional, but recommended)","Sufficient node memory to hold intermediate results"],"input_types":["Python objects (serialized via pickle or custom serializers)","NumPy arrays (zero-copy via Arrow)","Pandas DataFrames (zero-copy via Arrow)","Custom objects (requires custom serializer)"],"output_types":["ObjectRef (pointer to distributed object)","Materialized objects (via ray.get())","Object store metrics (usage, eviction rate)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ray__cap_9","uri":"capability://planning.reasoning.reinforcement.learning.training.with.rllib.framework","name":"reinforcement learning training with rllib framework","description":"Ray RLlib provides distributed RL training via a modular architecture supporting multiple algorithms (PPO, DQN, A3C, SAC, etc.) and environments (OpenAI Gym, custom). Training uses Ray's distributed execution to parallelize environment rollouts (data collection) and model updates across workers. Supports off-policy and on-policy algorithms, multi-agent RL, and curriculum learning. Integrates with Ray Tune for hyperparameter search and Ray Serve for policy serving.","intents":["I want to train an RL agent on a complex environment using distributed rollouts","I need to implement multi-agent RL where multiple agents learn cooperatively or competitively","I want to search over RL hyperparameters and training algorithms efficiently"],"best_for":["RL researchers training agents on complex environments (robotics, games, simulations)","teams building multi-agent systems with Ray RLlib's native multi-agent support","organizations using Ray Tune to automate RL hyperparameter tuning"],"limitations":["RLlib algorithms are optimized for specific environment types — custom environments require careful reward shaping","Sample efficiency varies widely by algorithm — some algorithms (e.g., DQN) require millions of environment steps","Multi-agent training is complex — coordination and credit assignment require domain-specific tuning"],"requires":["Python 3.8+","Ray cluster with sufficient CPU/GPU for parallel rollouts","OpenAI Gym-compatible environment or custom environment implementation","Optional: PyTorch or TensorFlow for custom policy networks"],"input_types":["RL algorithm configuration (algorithm name, hyperparameters)","Environment (OpenAI Gym interface or custom)","Policy network architecture (optional, defaults provided)","Reward function (defined in environment)"],"output_types":["Trained policy (serialized model weights)","Training metrics (episode reward, policy loss, sample efficiency)","Checkpoints for resuming training"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"ray__headline","uri":"capability://data.processing.analysis.distributed.computing.framework.for.ai.ml.workloads","name":"distributed computing framework for ai/ml workloads","description":"Ray is a powerful distributed computing framework designed to scale AI and machine learning workloads efficiently, featuring tools for training, serving, and data processing.","intents":["best distributed computing framework","distributed computing for AI workloads","Ray alternatives for machine learning","how to scale ML workloads with Ray","Ray framework for model serving"],"best_for":["scaling AI/ML tasks","distributed training","model serving"],"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 3.8+","Ray cluster initialized via ray.init() or Kubernetes/cloud deployment","Network connectivity between cluster nodes for object store communication","Ray cluster with sufficient object store memory (configurable via object_store_memory)","For cloud sources: AWS/GCP credentials or connection strings","Ray cluster with GPU nodes (for GPU inference) or CPU nodes","Ray Data for distributed dataset handling","Model serializable and loadable in UDF","Ray cluster with job submission enabled","Ray CLI or Python client library"],"failure_modes":["Serialization overhead for large objects (mitigated by object store but adds latency)","Compiled DAGs require static graph definition — dynamic control flow requires fallback to standard task submission","Actor state is in-memory only — no built-in persistence across node failures without external checkpointing","Streaming execution means no global sort or shuffle without explicit repartition (adds network overhead)","Lazy evaluation can hide performance issues until materialization — requires explicit profiling","Limited SQL support compared to Spark — complex joins require custom Python logic","Batch inference latency is higher than online serving due to data loading and batching overhead","Model loading per task can be expensive — requires caching or actor-based serving for efficiency","Limited support for complex inference patterns (e.g., beam search, dynamic batching) — requires custom UDFs","Job isolation is logical (namespace-based) not physical — resource contention can still occur","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.49999999999999994,"match_graph":0.25,"freshness":0.52,"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":"2026-06-17T09:51:05.296Z","last_scraped_at":null,"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=ray","compare_url":"https://unfragile.ai/compare?artifact=ray"}},"signature":"Ryy5tzt5NwhSyCvTpXVzgfojgOG15XRCBYpT0lxjV71/eXjPSPiNchcLTNlUEoi/FC8GvVjZ41PhB94BwSi3Bw==","signedAt":"2026-06-21T00:11:39.902Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/ray","artifact":"https://unfragile.ai/ray","verify":"https://unfragile.ai/api/v1/verify?slug=ray","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"}}