{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-ray","slug":"pypi-ray","name":"ray","type":"framework","url":"https://github.com/ray-project/ray","page_url":"https://unfragile.ai/pypi-ray","categories":["frameworks-sdks"],"tags":["ray","distributed","parallel","machine-learning","hyperparameter-tuningreinforcement-learning","deep-learning","serving","python"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-ray__cap_0","uri":"capability://automation.workflow.distributed.task.execution.with.automatic.scheduling.and.load.balancing","name":"distributed task execution with automatic scheduling and load balancing","description":"Ray executes Python functions and methods as distributed tasks across a cluster using a centralized scheduler (Raylet) that assigns work to worker processes based on resource availability and data locality. Tasks are serialized, transmitted to remote workers, executed in isolated processes, and results are stored in a distributed object store (Apache Arrow-based) for efficient retrieval. The scheduler uses a two-level hierarchy: global GCS (Global Control Store) for cluster-wide state and per-node Raylets for local task scheduling and resource management.","intents":["I want to parallelize my Python functions across multiple machines without writing MPI or socket code","I need automatic load balancing so slow workers don't become bottlenecks","I want to run tasks with specific resource requirements (GPU, CPU, memory) and have Ray enforce them"],"best_for":["data scientists scaling batch processing from laptop to cluster","ML engineers building distributed training pipelines","teams migrating from Spark to Python-native distributed computing"],"limitations":["Task serialization overhead (~1-5ms per task) makes fine-grained parallelism inefficient; best for tasks >100ms","No built-in fault tolerance for task state — requires external checkpointing for long-running jobs","GCS becomes a bottleneck at >10k tasks/second; requires tuning for high-throughput workloads","Python GIL limits CPU parallelism within a single worker process; requires multiple worker processes"],"requires":["Python 3.8+","Ray cluster initialized with ray.init() or ray.init(address='auto')","Network connectivity between all cluster nodes","Sufficient disk space for object store (default 30% of available memory)"],"input_types":["Python functions (decorated with @ray.remote)","Function arguments (any picklable Python objects)","Resource specifications (num_cpus, num_gpus, memory, custom resources)"],"output_types":["ObjectRef (futures to remote results)","Actual return values via ray.get()","Structured task metadata via Ray dashboard"],"categories":["automation-workflow","distributed-computing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-ray__cap_1","uri":"capability://automation.workflow.actor.based.stateful.distributed.services.with.method.invocation","name":"actor-based stateful distributed services with method invocation","description":"Ray Actors are long-lived, stateful objects that run on remote workers and expose methods callable from the driver or other actors. Each actor maintains mutable state across method calls, uses a message queue for serialized method invocations, and executes methods sequentially (by default) or with concurrency control. Actors are created with @ray.remote decorator, instantiated on a specific worker, and method calls return ObjectRefs that can be chained or awaited. This pattern enables building distributed services like parameter servers, model replicas, or stateful microservices without manual socket/RPC management.","intents":["I need to maintain mutable state (like a model or cache) on a remote machine and call methods on it repeatedly","I want to build a distributed parameter server for federated learning without implementing gRPC","I need to create multiple independent replicas of a service and route requests to them"],"best_for":["distributed ML systems requiring parameter servers or model replicas","teams building stateful microservices without Kubernetes expertise","reinforcement learning systems with centralized replay buffers or value functions"],"limitations":["Sequential method execution by default creates a bottleneck; requires max_concurrency parameter for parallelism","No built-in persistence — actor state is lost on worker failure unless explicitly checkpointed","Method calls are serialized through a single queue per actor; high-frequency updates (>1000/sec) may saturate the queue","Debugging actor state is difficult; requires custom logging or dashboard inspection"],"requires":["Python 3.8+","Ray cluster with sufficient worker processes","Picklable actor class and all method arguments","Understanding of async/await patterns for concurrent method execution"],"input_types":["Python class (decorated with @ray.remote)","Constructor arguments (any picklable objects)","Method arguments (any picklable objects)","max_concurrency and other actor options"],"output_types":["ObjectRef to method return values","Actor handle for remote method invocation","Structured actor metadata via State API"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-ray__cap_10","uri":"capability://safety.moderation.observability.and.monitoring.with.real.time.dashboard.metrics.and.state.api","name":"observability and monitoring with real-time dashboard, metrics, and state api","description":"Ray provides comprehensive observability through a web-based dashboard, Prometheus-compatible metrics, and a State API for querying cluster state. The dashboard displays real-time cluster status (nodes, workers, tasks), task execution timelines, actor state, and resource utilization. Metrics are exported in Prometheus format for integration with monitoring systems. The State API allows programmatic queries of cluster state (tasks, actors, nodes, jobs) via REST or Python SDK, enabling custom monitoring and debugging. Logs are aggregated from all workers and accessible via the dashboard or API.","intents":["I want to visualize what's happening on my Ray cluster in real-time (which tasks are running, which nodes are busy)","I need to export metrics to Prometheus/Grafana for monitoring and alerting","I want to programmatically query cluster state to debug performance issues or build custom monitoring"],"best_for":["operators managing Ray clusters in production","practitioners debugging distributed application performance","teams integrating Ray with existing monitoring infrastructure"],"limitations":["Dashboard can be slow with >1000 tasks; requires filtering or aggregation for large workloads","Metrics collection adds overhead (~1-5% CPU); can impact performance on resource-constrained clusters","State API queries are eventually consistent; may not reflect very recent changes","Log aggregation can consume significant disk space; requires log rotation or external storage"],"requires":["Python 3.8+","Ray cluster with dashboard enabled (default)","Web browser for dashboard access","Prometheus/Grafana (optional, for metrics integration)"],"input_types":["Cluster state queries (task IDs, actor IDs, node IDs, job IDs)","Metrics configuration (which metrics to export)","Log filters (task name, actor name, etc.)"],"output_types":["Dashboard HTML (real-time visualization)","Prometheus metrics (text format)","State API responses (JSON)","Aggregated logs (text)"],"categories":["safety-moderation","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-ray__cap_11","uri":"capability://memory.knowledge.object.store.with.zero.copy.data.sharing.and.distributed.memory.management","name":"object store with zero-copy data sharing and distributed memory management","description":"Ray's object store is a distributed in-memory storage system (based on Apache Arrow) that stores task results and intermediate data across worker nodes. Objects are stored in a shared memory region on each node, enabling zero-copy access for tasks on the same node and efficient serialization for remote access. The object store uses a least-recently-used (LRU) eviction policy to manage memory, spilling to disk when necessary. Object references (ObjectRefs) are lightweight pointers that can be passed between tasks without copying the underlying data, enabling efficient data sharing in distributed pipelines.","intents":["I want to pass large data structures between tasks without serializing/deserializing them repeatedly","I need efficient data sharing across tasks on the same node (zero-copy access)","I want to store intermediate results from one task for consumption by multiple downstream tasks"],"best_for":["distributed ML pipelines with large intermediate data","teams processing large datasets across multiple stages","practitioners building complex DAGs with data sharing"],"limitations":["Object store memory is limited by node RAM; large objects can cause OOM or eviction","Eviction to disk is slow; can cause performance degradation if working set exceeds memory","No built-in compression; large objects consume significant memory","Garbage collection is automatic but can cause unpredictable pauses if many objects are evicted"],"requires":["Python 3.8+","Ray cluster with sufficient memory for object store","Serializable objects (picklable or Arrow-compatible)","Understanding of ObjectRef semantics"],"input_types":["Python objects (any picklable type)","NumPy arrays, Pandas DataFrames, PyTorch tensors","Large data structures (images, text, etc.)"],"output_types":["ObjectRef (lightweight reference to stored object)","Materialized objects via ray.get()","Object store statistics (size, eviction rate, etc.)"],"categories":["memory-knowledge","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-ray__cap_12","uri":"capability://automation.workflow.job.submission.and.lifecycle.management.with.scheduling.and.resource.allocation","name":"job submission and lifecycle management with scheduling and resource allocation","description":"Ray Jobs API allows submitting, monitoring, and managing long-running jobs on a Ray cluster. Jobs are submitted via ray job submit command or Python API, executed with isolated namespaces and resource allocation, and tracked via job IDs. The Jobs API handles job scheduling (respecting resource requirements), execution monitoring (logs, status), and cleanup (automatic termination on completion or timeout). Jobs support dependencies (pip packages, local files) and can be submitted to specific node groups or with specific resource constraints. Job status is queryable via API or dashboard.","intents":["I want to submit a long-running training job to a Ray cluster and monitor its progress","I need to schedule multiple jobs with different resource requirements and have Ray allocate resources fairly","I want to submit a job with specific dependencies and have Ray install them automatically"],"best_for":["teams running batch jobs on shared clusters","practitioners submitting training jobs from CI/CD pipelines","organizations needing fair resource allocation across multiple jobs"],"limitations":["Job scheduling is FIFO by default; no priority queuing without custom configuration","Resource allocation is static per job; cannot dynamically adjust resources during execution","Job isolation is namespace-based; not true process isolation (security implications)","Long-running jobs can accumulate logs; requires log rotation or external storage"],"requires":["Python 3.8+","Ray cluster with job submission enabled","Job submission script (Python or shell)","Resource specifications (num_cpus, num_gpus, memory)"],"input_types":["Job submission script (Python or shell command)","Resource requirements (num_cpus, num_gpus, memory)","Dependencies (pip packages, local files)","Environment variables","Timeout and retry policies"],"output_types":["Job ID (unique identifier)","Job status (PENDING, RUNNING, SUCCEEDED, FAILED)","Job logs (stdout, stderr)","Job metadata (submission time, completion time, etc.)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-ray__cap_2","uri":"capability://automation.workflow.compiled.dag.execution.with.accelerated.performance.for.static.computation.graphs","name":"compiled dag execution with accelerated performance for static computation graphs","description":"Ray's Compiled DAG feature allows developers to define a static directed acyclic graph (DAG) of tasks and actors, compile it into an optimized execution plan, and execute it with minimal scheduling overhead. The compilation step analyzes data dependencies, removes redundant serialization, and generates a C++ execution engine that bypasses the Python scheduler for each step. This is particularly effective for inference pipelines or iterative algorithms where the computation graph is fixed but executed many times. DAGs are defined using ray.dag API and compiled with dag.experimental_compile().","intents":["I have a fixed computation pipeline (e.g., preprocess → model → postprocess) that runs thousands of times; I want to minimize scheduling overhead","I need to execute a multi-stage inference pipeline with sub-millisecond latency","I want to avoid Python scheduler overhead for deterministic, repeatable workloads"],"best_for":["inference serving systems with fixed computation graphs","iterative algorithms (e.g., gradient descent) with static dependency structure","high-throughput batch processing with consistent pipeline topology"],"limitations":["DAGs must be static — cannot add/remove tasks or change dependencies at runtime","Compilation adds ~100-500ms overhead; only worthwhile if DAG is executed >100 times","Limited to Python 3.8+ and requires experimental API (subject to breaking changes)","Debugging compiled DAGs is harder; error messages may not map clearly to source code"],"requires":["Python 3.8+","Ray cluster initialized","Static computation graph (no dynamic branching or loops)","Understanding of ray.dag API and compilation semantics"],"input_types":["DAG definition using ray.dag.InputNode, task/actor method calls","Input data matching DAG input schema","Compilation options (num_returns, max_queue_size)"],"output_types":["Compiled DAG object (dag.experimental_compile())","Execution results matching DAG output schema","Performance metrics via Ray dashboard"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-ray__cap_3","uri":"capability://data.processing.analysis.distributed.dataset.processing.with.lazy.evaluation.and.streaming.execution","name":"distributed dataset processing with lazy evaluation and streaming execution","description":"Ray Data provides a distributed DataFrame-like API for processing large datasets across a cluster using lazy evaluation and streaming execution. Datasets are partitioned across workers, transformations (map, filter, groupby, join) are defined lazily and executed only when materialized (via .take(), .write(), or .iter_batches()), and execution uses a streaming model where partitions flow through the pipeline without materializing intermediate results. Ray Data integrates with popular formats (Parquet, CSV, JSON, images) and frameworks (Pandas, NumPy, PyTorch, TensorFlow) for seamless data loading and transformation.","intents":["I have a 100GB dataset and want to apply transformations (filter, map, groupby) without loading it all into memory","I need to prepare data for ML training by reading from cloud storage, transforming, and writing back","I want to process images or text at scale without writing custom distributed code"],"best_for":["data engineers building ETL pipelines","ML engineers preparing training data at scale","teams migrating from Pandas/Spark to distributed Python"],"limitations":["Lazy evaluation can make debugging harder; errors only surface during execution","Groupby and join operations require shuffling data across workers, causing network overhead","Streaming execution requires careful memory management; large partitions can cause OOM","No built-in support for complex SQL operations; requires custom Python code for advanced queries"],"requires":["Python 3.8+","Ray cluster with sufficient memory and disk for dataset partitions","Data source accessible from all workers (S3, GCS, local filesystem, etc.)","Appropriate libraries for data format (pandas, pyarrow, PIL, etc.)"],"input_types":["Data sources (Parquet, CSV, JSON, images, custom formats)","Transformation functions (map, filter, groupby, join, etc.)","Batch size and partition specifications","Resource hints (num_cpus, num_gpus per task)"],"output_types":["Ray Dataset object (lazy, not materialized)","Materialized data via .take(), .to_pandas(), .write_parquet()","Batches via .iter_batches() for streaming consumption","Execution statistics and performance metrics"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-ray__cap_4","uri":"capability://planning.reasoning.hyperparameter.tuning.with.population.based.training.and.advanced.search.algorithms","name":"hyperparameter tuning with population-based training and advanced search algorithms","description":"Ray Tune is a distributed hyperparameter optimization framework that supports multiple search algorithms (grid search, random search, Bayesian optimization via Optuna, population-based training, CMA-ES) and scheduling strategies (FIFO, ASHA, PBT, HyperBand). Tune manages trial execution across workers, tracks metrics in real-time, implements early stopping based on performance, and supports multi-objective optimization. Trials are executed as Ray actors or tasks, metrics are reported via callbacks, and the framework automatically scales trials based on available resources. Integration with popular ML frameworks (PyTorch Lightning, TensorFlow, Hugging Face) is built-in.","intents":["I need to find optimal hyperparameters for my ML model across a cluster without manual trial management","I want to use population-based training to evolve hyperparameters during training, not just before","I need to optimize multiple metrics simultaneously (accuracy vs latency) with Pareto frontier discovery"],"best_for":["ML researchers tuning models at scale","teams running AutoML pipelines","practitioners using population-based training for neural architecture search"],"limitations":["Search space explosion with >10 hyperparameters; requires careful space definition or Bayesian methods","Early stopping requires metric reporting at regular intervals; incompatible with long-running training without checkpoints","Population-based training requires careful population size tuning; too small = poor exploration, too large = wasted compute","Multi-objective optimization requires Pareto frontier analysis; no single 'best' trial"],"requires":["Python 3.8+","Ray cluster with sufficient workers for parallel trials","Training script that reports metrics via tune.report() or callbacks","Hyperparameter search space definition (using ray.tune.choice, ray.tune.uniform, etc.)"],"input_types":["Training function (trainable) that accepts hyperparameters and reports metrics","Search space definition (dict of hyperparameter distributions)","Search algorithm (grid, random, Bayesian, PBT, etc.)","Scheduling strategy (FIFO, ASHA, HyperBand, PBT)","Stopping criteria (max_iterations, early stopping rules)"],"output_types":["Trial results with best hyperparameters and metrics","Execution history and trial logs","Checkpoints of best models","Real-time metrics dashboard"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-ray__cap_5","uri":"capability://planning.reasoning.distributed.reinforcement.learning.with.policy.training.and.environment.simulation","name":"distributed reinforcement learning with policy training and environment simulation","description":"Ray RLlib is a distributed reinforcement learning library that trains policies (neural networks) using algorithms like PPO, DQN, A3C, and IMPALA. It parallelizes environment simulation across workers (using ray.remote), collects experience trajectories, trains policies on batches of experience, and implements off-policy and on-policy learning. RLlib uses a centralized policy server (Ray actor) that workers query for actions, and a learner process that updates the policy based on collected experience. The framework abstracts away distributed training complexity, handling synchronization, gradient aggregation, and checkpointing automatically.","intents":["I want to train an RL policy on a complex environment using distributed simulation and training","I need to scale environment rollouts across multiple workers while keeping policy updates synchronized","I want to use advanced RL algorithms (PPO, IMPALA) without implementing distributed training from scratch"],"best_for":["RL researchers training policies at scale","robotics teams using simulation for policy development","game AI and multi-agent systems"],"limitations":["Requires careful tuning of worker count, batch size, and learning rate; poor tuning leads to instability","Environment simulation must be fast enough to keep learner busy; slow environments waste compute","Off-policy algorithms (DQN) require large replay buffers; memory usage scales with buffer size","Multi-agent training requires custom environment wrappers; not all environments are supported out-of-the-box"],"requires":["Python 3.8+","Ray cluster with sufficient workers for parallel environment simulation","RL environment (OpenAI Gym compatible or custom)","PyTorch or TensorFlow for policy networks","Understanding of RL concepts (policy, value function, experience replay)"],"input_types":["RL environment (Gym-compatible)","Algorithm configuration (PPO, DQN, A3C, etc.)","Policy network architecture (neural network definition)","Training hyperparameters (learning rate, batch size, num_workers)"],"output_types":["Trained policy (neural network weights)","Training metrics (episode reward, loss, etc.)","Checkpoints for policy resumption","Evaluation results on test environments"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-ray__cap_6","uri":"capability://automation.workflow.distributed.model.training.with.framework.integration.and.automatic.fault.tolerance","name":"distributed model training with framework integration and automatic fault tolerance","description":"Ray Train provides a distributed training framework that abstracts away cluster management for PyTorch, TensorFlow, Hugging Face, and other frameworks. It launches distributed training jobs across workers, handles gradient synchronization and communication backends (NCCL, Gloo), manages checkpointing and fault recovery, and provides a simple API for single-machine code to scale to multi-machine training. Ray Train v2 uses a controller-worker architecture where the controller orchestrates training and workers execute training loops, with automatic recovery from worker failures via checkpoint restoration.","intents":["I have a single-machine PyTorch training script and want to scale it to multiple GPUs/machines without rewriting it","I need automatic fault tolerance so training resumes from checkpoints if a worker fails","I want to use distributed training strategies (DDP, FSDP) without manually configuring communication backends"],"best_for":["ML engineers scaling training from laptop to cluster","teams training large models (LLMs, vision models) on multi-GPU clusters","practitioners using Hugging Face Transformers or PyTorch Lightning"],"limitations":["Requires compatible training framework (PyTorch, TensorFlow, etc.); custom training loops need adaptation","Communication overhead (gradient synchronization) can dominate for small models or slow networks","Fault tolerance requires periodic checkpointing; checkpoint I/O can be a bottleneck for large models","Debugging distributed training is harder; errors may occur on different workers asynchronously"],"requires":["Python 3.8+","Ray cluster with sufficient GPUs/CPUs for training","Compatible ML framework (PyTorch 1.12+, TensorFlow 2.8+, etc.)","Training script using standard framework APIs (torch.nn.Module, tf.keras.Model, etc.)"],"input_types":["Training function or class (trainable)","Training configuration (num_workers, num_gpus_per_worker, backend)","Data loaders or datasets","Model and optimizer definitions"],"output_types":["Trained model weights","Training metrics (loss, accuracy, etc.)","Checkpoints for resumption","Distributed training logs and profiling data"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-ray__cap_7","uri":"capability://automation.workflow.model.serving.with.request.batching.auto.scaling.and.multi.model.composition","name":"model serving with request batching, auto-scaling, and multi-model composition","description":"Ray Serve is a distributed serving framework that deploys ML models as HTTP endpoints with automatic request batching, dynamic scaling based on load, and support for multi-model pipelines. Models are wrapped in Serve deployments (Ray actors), requests are routed to deployments via a load balancer, batching is applied to improve throughput, and scaling is controlled by metrics (queue depth, latency) or custom policies. Serve supports model composition (chaining deployments) and traffic splitting for A/B testing. Integration with popular frameworks (PyTorch, TensorFlow, Hugging Face, scikit-learn) is built-in.","intents":["I have a trained model and want to serve it as an HTTP API with automatic batching and scaling","I need to deploy multiple models and route requests to them based on input features or A/B test groups","I want to compose multiple models (e.g., ensemble) and serve the ensemble as a single endpoint"],"best_for":["ML engineers deploying models to production","teams building real-time inference services","practitioners using model ensembles or multi-stage pipelines"],"limitations":["Request batching adds latency (typically 10-100ms); not suitable for ultra-low-latency requirements (<1ms)","Auto-scaling based on queue depth can cause cascading failures if scaling is too aggressive","Multi-model composition requires careful orchestration; complex pipelines can be hard to debug","No built-in authentication or rate limiting; requires external API gateway for production security"],"requires":["Python 3.8+","Ray cluster with sufficient GPUs/CPUs for model replicas","Trained model in a supported format (PyTorch, TensorFlow, ONNX, etc.)","HTTP client for sending requests (curl, Python requests, etc.)"],"input_types":["Model definition (PyTorch module, TensorFlow model, etc.)","Deployment configuration (num_replicas, max_batch_size, batch_wait_timeout_s)","HTTP requests (JSON, form data, etc.)","Scaling policies (target_num_ongoing_requests, target_throughput, etc.)"],"output_types":["HTTP responses (JSON, binary, etc.)","Predictions from model","Serving metrics (throughput, latency, queue depth)","Deployment status and logs"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-ray__cap_8","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 automatically scales the cluster up or down based on pending tasks and resource demand. It monitors the task queue, detects when tasks cannot be scheduled due to insufficient resources, launches new nodes (via cloud provider APIs like AWS, GCP, Azure), and terminates idle nodes to save costs. The autoscaler uses a resource-aware scheduler that matches task resource requirements (CPU, GPU, memory, custom resources) to available nodes, and supports node labels for task placement constraints. Autoscaling policies are configurable via YAML and support custom scaling logic.","intents":["I want my Ray cluster to automatically scale up when I submit more tasks than available resources can handle","I need to minimize cloud costs by terminating idle nodes when demand drops","I want to place specific tasks on specific node types (e.g., GPU tasks on GPU nodes)"],"best_for":["teams running variable workloads on cloud infrastructure","practitioners wanting to minimize cloud costs","organizations with heterogeneous hardware (CPU, GPU, TPU nodes)"],"limitations":["Autoscaling has latency (typically 30-60 seconds to launch new nodes); not suitable for bursty workloads requiring immediate scaling","Cloud provider API rate limits can prevent rapid scaling; requires careful configuration","Idle node detection is based on timeout; aggressive termination can cause thrashing if workload is bursty","Custom resource tracking requires manual configuration; easy to misconfigure and cause scheduling failures"],"requires":["Ray cluster on a cloud provider (AWS, GCP, Azure) or Kubernetes","Cloud provider credentials and permissions to launch/terminate instances","Autoscaler configuration (YAML) with node types and scaling policies","Task resource specifications (num_cpus, num_gpus, memory, custom_resources)"],"input_types":["Autoscaler configuration (YAML with node types, min/max nodes, scaling policies)","Task resource requirements (num_cpus, num_gpus, memory, custom resources)","Node labels and placement constraints"],"output_types":["Cluster size (number of nodes)","Autoscaling events (node launches, terminations)","Resource utilization metrics","Cost estimates and savings"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-ray__cap_9","uri":"capability://automation.workflow.runtime.environment.management.with.dependency.isolation.and.reproducibility","name":"runtime environment management with dependency isolation and reproducibility","description":"Ray's runtime environment feature allows specifying Python dependencies, environment variables, and working directories that are automatically installed and configured on remote workers before task execution. Dependencies can be specified as pip packages, conda environments, or local Python files, and Ray handles downloading, installing, and activating them on each worker. This enables reproducible execution across heterogeneous clusters and simplifies dependency management without requiring pre-built Docker images. Runtime environments are specified per-job or per-task, and Ray caches installed environments to avoid redundant installation.","intents":["I want to run tasks with different Python dependencies without pre-building Docker images for each combination","I need to ensure reproducible execution across workers with different base environments","I want to use local Python files or packages without manually uploading them to each worker"],"best_for":["teams avoiding Docker complexity","practitioners with dynamic dependency requirements","organizations with heterogeneous worker environments"],"limitations":["Installation overhead (typically 10-30 seconds per environment) on first use; requires caching to amortize","Conda environment installation is slower than pip; large environments can take minutes","No built-in version pinning; requires explicit version specifications to ensure reproducibility","Environment conflicts can cause silent failures; requires careful testing across worker types"],"requires":["Python 3.8+","Ray cluster with internet access for downloading packages","pip or conda available on workers","Explicit dependency specifications (requirements.txt, environment.yml, etc.)"],"input_types":["pip packages (list of package names with versions)","conda environment (YAML specification)","local Python files or directories","environment variables","working directory"],"output_types":["Installed dependencies on remote workers","Environment activation and configuration","Execution logs showing installation progress"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":29,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","Ray cluster initialized with ray.init() or ray.init(address='auto')","Network connectivity between all cluster nodes","Sufficient disk space for object store (default 30% of available memory)","Ray cluster with sufficient worker processes","Picklable actor class and all method arguments","Understanding of async/await patterns for concurrent method execution","Ray cluster with dashboard enabled (default)","Web browser for dashboard access","Prometheus/Grafana (optional, for metrics integration)"],"failure_modes":["Task serialization overhead (~1-5ms per task) makes fine-grained parallelism inefficient; best for tasks >100ms","No built-in fault tolerance for task state — requires external checkpointing for long-running jobs","GCS becomes a bottleneck at >10k tasks/second; requires tuning for high-throughput workloads","Python GIL limits CPU parallelism within a single worker process; requires multiple worker processes","Sequential method execution by default creates a bottleneck; requires max_concurrency parameter for parallelism","No built-in persistence — actor state is lost on worker failure unless explicitly checkpointed","Method calls are serialized through a single queue per actor; high-frequency updates (>1000/sec) may saturate the queue","Debugging actor state is difficult; requires custom logging or dashboard inspection","Dashboard can be slow with >1000 tasks; requires filtering or aggregation for large workloads","Metrics collection adds overhead (~1-5% CPU); can impact performance on resource-constrained clusters","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.6000000000000001,"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":"2026-06-17T09:51:05.295Z","last_scraped_at":"2026-05-03T15:20:19.404Z","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-ray","compare_url":"https://unfragile.ai/compare?artifact=pypi-ray"}},"signature":"58AjShtUtViI2EamZ34funKJe1hQ0U5N3A9P30FKiwIMH2uBIQI6i8OchpL4USev0p1lUjOuSjsGFsj+3XWRCg==","signedAt":"2026-06-22T00:14:46.903Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-ray","artifact":"https://unfragile.ai/pypi-ray","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-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"}}