&`iUddlZddlZddlZddlZddlZddlZddlmZmZddl m Z m Z m Z m Z mZmZddlZddlmZmZmZddlmZddlmZddlmZddlmZdd lmZe r dd lm Z dd l!m"Z"ej#e$Z%da&d e'd <ej(Z)edGdde*ej+Z,dZ-dZ.dZ/dZ0dZ1dZ2eddZ3dZ4dZ5eddZ6e7ej89ddZ:ej89de,j;Z<edd Z=d!Z>d"Z?d#Z@dZAdZBe7eCej89d$d%ZDdZEed&dZFed'dZGed(dZHdZIdZJe7eCej89d)d%ZKed*dZLed+dZMe7eCej89d,d-ZNe7ed.d ZOed/dZPed0d1ZQe7ed2dZRed3dZSdZTd4ZUd5ZVd6ZWdZXdZYd7ZZed8dZ[e\ej89d9d:Z]dZ^d;Z_dZbdZced?d@ZdedAd>Zee7eCej89dBd%ZfedCdDZgedEdFZhedGdHZie\e'dI<edJdKZje\e'dL<edMdNZkeCe'dO<edPdZle7e'dQ<eeGdRdSZmd\dVZndTe,fdWZod]dYZpeeGdZd[ZqeqZrdS)^N) dataclassfield) TYPE_CHECKINGAnyDictListOptionalUnion)env_bool env_float env_integer) WORKER_MODE) update_dataset_logger_for_worker) DeveloperAPI)log_once)SchedulingStrategyTExecutionOptionsIssueDetectorsConfigurationzOptional[DataContext]_default_contextalpha) stabilityceZdZdZdZdZdZdS)ShuffleStrategyzkShuffle strategy determines shuffling algorithm employed by operations like aggregate, repartition, etcsort_shuffle_pull_basedsort_shuffle_push_based hash_shuffleN)__name__ __module__ __qualname____doc__SORT_SHUFFLE_PULL_BASEDSORT_SHUFFLE_PUSH_BASED HASH_SHUFFLEd/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/ray/data/context.pyrrs+((87!LLLr'rii@g?iiT%RAY_DATA_PANDAS_BLOCK_IGNORE_METADATAF"RAY_DATA_ITER_GET_BLOCK_BATCH_SIZE RAY_DATA_PUSH_BASED_SHUFFLE!RAY_DATA_DEFAULT_SHUFFLE_STRATEGY%RAY_DATA_MAX_HASH_SHUFFLE_AGGREGATORSSPREADDEFAULTi RAY_DATA_EAGER_FREE0 RAY_DATA_DEFAULT_MIN_PARALLELISM(RAY_DATA_ENABLE_TENSOR_EXTENSION_CASTINGRAY_DATA_USE_ARROW_TENSOR_V2RAY_DATA_TRACE_ALLOCATIONS+RAY_DATA_LOG_INTERNAL_STACK_TRACE_TO_STDOUT%RAY_DATA_RAISE_ORIGINAL_MAP_EXCEPTIONRAY_TQDM1RAY_DATA_DISABLE_PROGRESS_BARS,RAY_DATA_ENABLE_PROGRESS_BAR_NAME_TRUNCATIONRAY_DATA_PROGRESS_LOG_INTERVAL"RAY_DATA_ENABLE_RICH_PROGRESS_BARSRAY_DATA_ENFORCE_SCHEMAS)AWS Error INTERNAL_FAILUREAWS Error NETWORK_CONNECTIONAWS Error SLOW_DOWN#AWS Error UNKNOWN (HTTP status 503))rCrDrErFzAWS Error SERVICE_UNAVAILABLEl'RAY_DATA_ENABLE_OP_RESOURCE_RESERVATIONRAY_DATA_OP_RESERVATION_RATIOz0.5u⚠️ u✔️ i&RAY_DATA_DEFAULT_WAIT_FOR_MIN_ACTORS_SDRAY_DATA_ACTOR_DEFAULT_MAX_TASKS_IN_FLIGHT_TO_MAX_CONCURRENCY_FACTORRAY_DATA_PER_NODE_METRICS3RAY_DATA_MIN_HASH_SHUFFLE_AGGREGATOR_WAIT_TIME_IN_Si,:RAY_DATA_HASH_SHUFFLE_AGGREGATOR_HEALTH_WARNING_INTERVAL_S4RAY_DATA_DEFAULT_ACTOR_POOL_UTIL_UPSCALING_THRESHOLDg@+DEFAULT_ACTOR_POOL_UTIL_UPSCALING_THRESHOLD6RAY_DATA_DEFAULT_ACTOR_POOL_UTIL_DOWNSCALING_THRESHOLDg?-DEFAULT_ACTOR_POOL_UTIL_DOWNSCALING_THRESHOLD/RAY_DATA_DEFAULT_ACTOR_POOL_MAX_UPSCALING_DELTA&DEFAULT_ACTOR_POOL_MAX_UPSCALING_DELTA6RAY_DATA_ENABLE_DYNAMIC_OUTPUT_QUEUE_SIZE_BACKPRESSURE5DEFAULT_ENABLE_DYNAMIC_OUTPUT_QUEUE_SIZE_BACKPRESSUREc>eZdZUdZeZeed<eZ eed<e Z e ed<dS)AutoscalingConfigaConfiguration for autoscaling of Ray Data. Args: actor_pool_util_upscaling_threshold: Actor Pool utilization threshold for upscaling. Once Actor Pool exceeds this utilization threshold it will start adding new actors. Actor Pool utilization is defined as ratio of number of submitted tasks to the number of available concurrency-slots to run them in the current set of actors. This utilization value could exceed 100%, when the number of submitted tasks exceed available concurrency-slots to run them in the current set of actors. This is possible when `max_tasks_in_flight_per_actor` (defaults to 2 x of `max_concurrency`) > Actor's `max_concurrency` and allows to overlap task execution with the fetching of the blocks for the next task providing for ability to negotiate a trade-off between autoscaling speed and resource efficiency (i.e., making tasks wait instead of immediately triggering execution). actor_pool_util_downscaling_threshold: Actor Pool utilization threshold for downscaling. actor_pool_max_upscaling_delta: Maximum number of actors to scale up in a single scaling decision. This limits how many actors can be added at once to prevent resource contention and scheduling pressure. Defaults to 1 for conservative scaling. #actor_pool_util_upscaling_threshold%actor_pool_util_downscaling_thresholdactor_pool_max_upscaling_deltaN) rr r!r"rSr]float__annotations__rUr^rXr_intr&r'r(r\r\sf, 4( 6*5 +Q"CPPPPPr'r\returnrc"ddlm}|S)Nrr)'ray.data._internal.execution.interfacesrrs r(_execution_options_factoryrf)s#HHHHHH    r'ctr&tdtjSdtD}t |vs)Jdd|dt dt S)NzqRAY_DATA_PUSH_BASED_SHUFFLE is deprecated, please use RAY_DATA_DEFAULT_SHUFFLE_STRATEGY to set shuffling strategycg|]}|Sr&r&).0ss r( z5_deduce_default_shuffle_algorithm..9s ) ) )Aa ) ) )r'z8RAY_DATA_DEFAULT_SHUFFLE_STRATEGY has to be one of the [,z] (got ))DEFAULT_USE_PUSH_BASED_SHUFFLEloggerwarningrr$DEFAULT_SHUFFLE_STRATEGYjoin)vss r(!_deduce_default_shuffle_algorithmrt0s%( J   66 ) ) ) ) )'2--- 0sxxPR|| 0 0, 0 0 0.-- ('r'rc"ddlm}|S)Nrr)?ray.data._internal.issue_detection.issue_detector_configurationrrs r(_issue_detectors_config_factoryrwCs1 ' & ( ((r'ceZdZUdZeZeeed<e Z eed<e Z eed<e Zeed<eZeed<eZeed<eeZeed <eZeed <eZeed <d Zeed <eZeed<dZeeed<e Z!eed<e"Z#eed<dZ$eeed<dZ%e&ed<dZ'e&ed<dZ(e&ed<e)Z*e+ed<e,Z-e+ed<e.Z/eed<e0Z1eed<e2Z3eed<e4Z5eed<e6Z7eed<eZ8eed<e9Z:eed<e;Zeed!<dZ?eeed"<e@ZAeed#<eBZCeed$<eDZEeed%<eeFZGd&ed'<eHZIeed(<eJZKeed)<d ZLeed*<eMZNeed+<eOZPeed,<eQZReed-<eSZTeed.<eUZVeWeXed/<eYZZeed0<e[Z\e]eeWe^fed1<e_Z`eed2<eaZbeed3<ecZdeed4<eeZfe&ed5<egZheed6<eiZjeed7<ekZleed8<d Zmeed9<enZoeed:<epZqeed;<dZreeed<<ed=ZseWeXed><etZueed?<dZve&ed@<dAZwee&edB<dZxeeXedC<dDZyeedE<eezZ{dFedG<dZ|e&edH<dZ}eedI<e~ZeedJ<eZeedK<eZeedL<dMZdNeXdOedPdffdQ Zed_dRZed`dTZedPefdUZejdOedPdfdVZdadWeXdXedPefdYZdWeXdOedPdfdZZdWeXdPdfd[Zd_d\Zd]eXdPdfd^ZxZS)b DataContexta*Global settings for Ray Data. Configure this class to enable advanced features and tune performance. .. warning:: Apply changes before creating a :class:`~ray.data.Dataset`. Changes made after won't take effect. .. note:: This object is automatically propagated to workers. Access it from the driver and remote workers with :meth:`DataContext.get_current()`. Examples: >>> from ray.data import DataContext >>> DataContext.get_current().enable_progress_bars = False Args: target_max_block_size: The max target block size in bytes for reads and transformations. If `None`, this means the block size is infinite. target_min_block_size: Ray Data avoids creating blocks smaller than this size in bytes on read. This takes precedence over ``read_op_min_num_blocks``. streaming_read_buffer_size: Buffer size when doing streaming reads from local or remote storage. enable_pandas_block: Whether pandas block format is enabled. actor_prefetcher_enabled: Whether to use actor based block prefetcher. iter_get_block_batch_size: Maximum number of block object references to resolve in a single ``ray.get()`` call when iterating over datasets. autoscaling_config: Autoscaling configuration. use_push_based_shuffle: Whether to use push-based shuffle. pipeline_push_based_shuffle_reduce_tasks: scheduling_strategy: The global scheduling strategy. For tasks with large args, ``scheduling_strategy_large_args`` takes precedence. scheduling_strategy_large_args: Scheduling strategy for tasks with large args. large_args_threshold: Size in bytes after which point task arguments are considered large. Choose a value so that the data transfer overhead is significant in comparison to task scheduling (i.e., low tens of ms). use_polars: Whether to use Polars for tabular dataset sorts, groupbys, and aggregations. eager_free: Whether to eagerly free memory. decoding_size_estimation: Whether to estimate in-memory decoding data size for data source. min_parallelism: This setting is deprecated. Use ``read_op_min_num_blocks`` instead. read_op_min_num_blocks: Minimum number of read output blocks for a dataset. enable_tensor_extension_casting: Whether to automatically cast NumPy ndarray columns in Pandas DataFrames to tensor extension columns. use_arrow_tensor_v2: Config enabling V2 version of ArrowTensorArray supporting tensors > 2Gb in size (off by default) enable_fallback_to_arrow_object_ext_type: Enables fallback to serialize column values not suppported by Arrow natively (like user-defined custom Python classes for ex, etc) using `ArrowPythonObjectType` (simply serializing these as bytes) enable_auto_log_stats: Whether to automatically log stats after execution. If disabled, you can still manually print stats with ``Dataset.stats()``. verbose_stats_logs: Whether stats logs should be verbose. This includes fields such as `extra_metrics` in the stats output, which are excluded by default. trace_allocations: Whether to trace allocations / eager free. This adds significant performance overheads and should only be used for debugging. execution_options: The :class:`~ray.data._internal.execution.interfaces.execution_options.ExecutionOptions` to use. use_ray_tqdm: Whether to enable distributed tqdm. enable_progress_bars: Whether to enable progress bars. enable_operator_progress_bars: Whether to enable progress bars for individual operators during execution. enable_progress_bar_name_truncation: If True, the name of the progress bar (often the operator name) will be truncated if it exceeds `ProgressBar.MAX_NAME_LENGTH`. Otherwise, the full operator name is shown. enable_rich_progress_bars: Whether to use the new rich progress bars instead of the tqdm TUI. progress_bar_log_interval: The interval in seconds for logging progress bar updates in non-interactive terminals. enable_get_object_locations_for_metrics: Whether to enable ``get_object_locations`` for metrics. This is useful for tracking whether the object input of a task is local (cache hit) or not local (cache miss) to the node that task is running on. write_file_retry_on_errors: A list of substrings of error messages that should trigger a retry when writing files. This is useful for handling transient errors when writing to remote storage systems. warn_on_driver_memory_usage_bytes: If driver memory exceeds this threshold, Ray Data warns you. For now, this only applies to shuffle ops because most other ops are unlikely to use as much driver memory. actor_task_retry_on_errors: The application-level errors that actor task should retry. This follows same format as :ref:`retry_exceptions ` in Ray Core. Default to `False` to not retry on any errors. Set to `True` to retry all errors, or set to a list of errors to retry. actor_init_retry_on_errors: Whether to retry when actor initialization fails. Default to `False` to not retry on any errors. Set to `True` to retry all errors. actor_init_max_retries: Maximum number of consecutive retries for actor initialization failures. The counter resets when an actor successfully initializes. Default is 3. Set to -1 for infinite retries. op_resource_reservation_enabled: Whether to enable resource reservation for operators to prevent resource contention. op_resource_reservation_ratio: The ratio of the total resources to reserve for each operator. max_errored_blocks: Max number of blocks that are allowed to have errors, unlimited if negative. This option allows application-level exceptions in block processing tasks. These exceptions may be caused by UDFs (e.g., due to corrupted data samples) or IO errors. Data in the failed blocks are dropped. This option can be useful to prevent a long-running job from failing due to a small number of bad blocks. log_internal_stack_trace_to_stdout: Whether to include internal Ray Data/Ray Core code stack frames when logging to stdout. The full stack trace is always written to the Ray Data log file. raise_original_map_exception: Whether to raise the original exception encountered in map UDF instead of wrapping it in a `UserCodeException`. print_on_execution_start: If ``True``, print execution information when execution starts. s3_try_create_dir: If ``True``, try to create directories on S3 when a write call is made with a S3 URI. wait_for_min_actors_s: The default time to wait for minimum requested actors to start before raising a timeout, in seconds. max_tasks_in_flight_per_actor: Max number of tasks that could be submitted for execution to individual actor at the same time. Note that only up to `max_concurrency` number of these tasks will be executing concurrently while remaining ones will be waiting in the Actor's queue. Buffering tasks in the queue allows us to overlap pulling of the blocks (which are tasks arguments) with the execution of the prior tasks maximizing individual Actor's utilization retried_io_errors: A list of substrings of error messages that should trigger a retry when reading or writing files. This is useful for handling transient errors when reading from remote storage systems. default_hash_shuffle_parallelism: Default parallelism level for hash-based shuffle operations if the number of partitions is unspecifed. max_hash_shuffle_aggregators: Maximum number of aggregating actors that can be provisioned for hash-shuffle aggregations. min_hash_shuffle_aggregator_wait_time_in_s: Minimum time to wait for hash shuffle aggregators to become available, in seconds. hash_shuffle_aggregator_health_warning_interval_s: Interval for health warning checks on hash shuffle aggregators, in seconds. max_hash_shuffle_finalization_batch_size: Maximum batch size for concurrent hash-shuffle finalization tasks. If `None`, defaults to `max_hash_shuffle_aggregators`. join_operator_actor_num_cpus_per_partition_override: Override CPU allocation per partition for join operator actors. hash_shuffle_operator_actor_num_cpus_per_partition_override: Override CPU allocation per partition for hash shuffle operator actors. hash_aggregate_operator_actor_num_cpus_per_partition_override: Override CPU allocation per partition for hash aggregate operator actors. use_polars_sort: Whether to use Polars for tabular dataset sorting operations. enable_per_node_metrics: Enable per node metrics reporting for Ray Data, disabled by default. override_object_store_memory_limit_fraction: Override the fraction of object store memory limit. If `None`, uses Ray's default. memory_usage_poll_interval_s: The interval to poll the USS of map tasks. If `None`, map tasks won't record memory stats. dataset_logger_id: Optional logger ID for dataset operations. If `None`, uses default logging configuration. issue_detectors_config: Configuration for issue detection and monitoring during dataset operations. downstream_capacity_backpressure_ratio: Ratio for downstream capacity backpressure control. A higher ratio causes backpressure to kick-in later. If `None`, this type of backpressure is disabled. downstream_capacity_backpressure_max_queued_bundles: Maximum number of queued bundles before applying backpressure. If `None`, no limit is applied. enable_dynamic_output_queue_size_backpressure: Whether to cap the concurrency of an operator based on it's and downstream's queue size. enforce_schemas: Whether to enforce schema consistency across dataset operations. pandas_block_ignore_metadata: Whether to ignore pandas metadata when converting between Arrow and pandas formats for better type inference. target_max_block_sizetarget_min_block_sizestreaming_read_buffer_sizeenable_pandas_blockactor_prefetcher_enablediter_get_block_batch_size)default_factoryautoscaling_configuse_push_based_shuffle_shuffle_strategyT(pipeline_push_based_shuffle_reduce_tasks default_hash_shuffle_parallelismNmax_hash_shuffle_aggregators*min_hash_shuffle_aggregator_wait_time_in_s1hash_shuffle_aggregator_health_warning_interval_s(max_hash_shuffle_finalization_batch_size%join_operator_actor_num_cpus_override-hash_shuffle_operator_actor_num_cpus_override/hash_aggregate_operator_actor_num_cpus_overridescheduling_strategyscheduling_strategy_large_argslarge_args_threshold use_polarsuse_polars_sort eager_freedecoding_size_estimationmin_parallelismread_op_min_num_blocksenable_tensor_extension_castinguse_arrow_tensor_v2(enable_fallback_to_arrow_object_ext_typeenable_auto_log_statsverbose_stats_logstrace_allocationsrexecution_options use_ray_tqdmenable_progress_barsenable_operator_progress_bars#enable_progress_bar_name_truncationenable_rich_progress_barsprogress_bar_log_interval'enable_get_object_locations_for_metricswrite_file_retry_on_errors!warn_on_driver_memory_usage_bytesactor_task_retry_on_errorsactor_init_retry_on_errorsactor_init_max_retriesop_resource_reservation_enabledop_resource_reservation_ratiomax_errored_blocks"log_internal_stack_trace_to_stdoutraise_original_map_exceptionprint_on_execution_starts3_try_create_dirwait_for_min_actors_smax_tasks_in_flight_per_actorc*ttSN)listDEFAULT_RETRIED_IO_ERRORSr&r'r(zDataContext.hs%> ? ?r'retried_io_errorsenable_per_node_metrics+override_object_store_memory_limit_fractionrWmemory_usage_poll_interval_sdataset_logger_idF_enable_actor_pool_on_exit_hookrissue_detectors_config&downstream_capacity_backpressure_ratio3downstream_capacity_backpressure_max_queued_bundles-enable_dynamic_output_queue_size_backpressureenforce_schemaspandas_block_ignore_metadataci|_i|_|j|jj_|j|jj_t|_ tj ddu}|rZtjjjt"k}|r)t%drt&dd|_dSd|_dS)N RAY_JOB_ID3ray_data_disable_operator_progress_bars_in_ray_jobszDisabling operator-level progress bars by default in Ray Jobs. 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