torsh_distributed/
ray_integration.rs

1//! Ray integration for ToRSh distributed training
2//!
3//! This module provides compatibility with Ray's distributed computing framework,
4//! allowing users to leverage Ray Train, Ray Tune, and other Ray components
5//! with ToRSh distributed training.
6//!
7//! Ray is a unified framework for scaling AI and Python applications that provides:
8//! - Ray Train: Distributed training with fault tolerance
9//! - Ray Tune: Scalable hyperparameter tuning
10//! - Ray Serve: Scalable model serving
11//! - Ray Data: Distributed data processing
12//! - Ray Core: General distributed computing primitives
13
14use crate::{TorshDistributedError, TorshResult};
15use serde::{Deserialize, Serialize};
16use std::collections::HashMap;
17use std::path::Path;
18
19/// Ray configuration compatible with ToRSh
20#[derive(Debug, Clone, Serialize, Deserialize)]
21pub struct RayConfig {
22    /// Ray cluster configuration
23    pub cluster: Option<RayClusterConfig>,
24    /// Ray Train configuration
25    pub train: Option<RayTrainConfig>,
26    /// Ray Tune configuration
27    pub tune: Option<RayTuneConfig>,
28    /// Ray Serve configuration
29    pub serve: Option<RayServeConfig>,
30    /// Ray Data configuration
31    pub data: Option<RayDataConfig>,
32    /// Resource configuration
33    pub resources: Option<RayResourceConfig>,
34    /// Fault tolerance configuration
35    pub fault_tolerance: Option<RayFaultToleranceConfig>,
36}
37
38/// Ray cluster configuration
39#[derive(Debug, Clone, Serialize, Deserialize)]
40pub struct RayClusterConfig {
41    /// Cluster address
42    pub address: Option<String>,
43    /// Redis address
44    pub redis_address: Option<String>,
45    /// Number of CPUs per node
46    pub num_cpus: Option<u32>,
47    /// Number of GPUs per node
48    pub num_gpus: Option<u32>,
49    /// Memory per node (GB)
50    pub memory_gb: Option<f32>,
51    /// Object store memory (GB)
52    pub object_store_memory_gb: Option<f32>,
53    /// Ray namespace
54    pub namespace: Option<String>,
55    /// Dashboard host
56    pub dashboard_host: Option<String>,
57    /// Dashboard port
58    pub dashboard_port: Option<u16>,
59    /// Include dashboard
60    pub include_dashboard: Option<bool>,
61}
62
63/// Ray Train configuration
64#[derive(Debug, Clone, Serialize, Deserialize)]
65pub struct RayTrainConfig {
66    /// Backend type for training
67    pub backend: RayTrainBackend,
68    /// Number of workers
69    pub num_workers: u32,
70    /// Use GPU
71    pub use_gpu: Option<bool>,
72    /// Resources per worker
73    pub resources_per_worker: Option<HashMap<String, f32>>,
74    /// Placement group strategy
75    pub placement_group_strategy: Option<RayPlacementGroupStrategy>,
76    /// Scaling configuration
77    pub scaling_config: Option<RayScalingConfig>,
78    /// Run configuration
79    pub run_config: Option<RayRunConfig>,
80    /// Checkpoint configuration
81    pub checkpoint_config: Option<RayCheckpointConfig>,
82    /// Failure handling configuration
83    pub failure_config: Option<RayFailureConfig>,
84}
85
86/// Ray Train backend types
87#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
88pub enum RayTrainBackend {
89    /// PyTorch backend
90    Torch,
91    /// TensorFlow backend
92    TensorFlow,
93    /// Horovod backend
94    Horovod,
95    /// MPI backend
96    MPI,
97    /// Custom backend
98    Custom,
99}
100
101/// Ray placement group strategy
102#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
103pub enum RayPlacementGroupStrategy {
104    /// Strict pack strategy
105    StrictPack,
106    /// Pack strategy
107    Pack,
108    /// Strict spread strategy
109    StrictSpread,
110    /// Spread strategy
111    Spread,
112}
113
114/// Ray scaling configuration
115#[derive(Debug, Clone, Serialize, Deserialize)]
116pub struct RayScalingConfig {
117    /// Number of workers
118    pub num_workers: Option<u32>,
119    /// Use GPU
120    pub use_gpu: Option<bool>,
121    /// Resources per worker
122    pub resources_per_worker: Option<HashMap<String, f32>>,
123    /// Placement group strategy
124    pub placement_group_strategy: Option<RayPlacementGroupStrategy>,
125    /// Trainer resources
126    pub trainer_resources: Option<HashMap<String, f32>>,
127}
128
129/// Ray run configuration
130#[derive(Debug, Clone, Serialize, Deserialize)]
131pub struct RayRunConfig {
132    /// Experiment name
133    pub name: Option<String>,
134    /// Storage path
135    pub storage_path: Option<String>,
136    /// Stop conditions
137    pub stop: Option<HashMap<String, f32>>,
138    /// Checkpoint frequency
139    pub checkpoint_freq: Option<u32>,
140    /// Keep checkpoints number
141    pub keep_checkpoints_num: Option<u32>,
142    /// Checkpoint score attribute
143    pub checkpoint_score_attr: Option<String>,
144    /// Checkpoint mode
145    pub checkpoint_mode: Option<RayCheckpointMode>,
146    /// Verbose logging
147    pub verbose: Option<u32>,
148    /// Progress reporter
149    pub progress_reporter: Option<RayProgressReporter>,
150}
151
152/// Ray checkpoint mode
153#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
154pub enum RayCheckpointMode {
155    /// Maximize checkpoint score
156    Max,
157    /// Minimize checkpoint score
158    Min,
159}
160
161/// Ray progress reporter
162#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
163pub enum RayProgressReporter {
164    /// Default reporter
165    Default,
166    /// JSON reporter
167    Json,
168    /// TensorBoard reporter
169    TensorBoard,
170    /// Weights & Biases reporter
171    WandB,
172    /// MLflow reporter
173    MLflow,
174}
175
176/// Ray checkpoint configuration
177#[derive(Debug, Clone, Serialize, Deserialize)]
178pub struct RayCheckpointConfig {
179    /// Number of checkpoints to keep
180    pub num_to_keep: Option<u32>,
181    /// Checkpoint frequency
182    pub checkpoint_frequency: Option<u32>,
183    /// Checkpoint at end
184    pub checkpoint_at_end: Option<bool>,
185    /// Checkpoint score attribute
186    pub checkpoint_score_attribute: Option<String>,
187    /// Checkpoint mode
188    pub checkpoint_mode: Option<RayCheckpointMode>,
189}
190
191/// Ray failure configuration
192#[derive(Debug, Clone, Serialize, Deserialize)]
193pub struct RayFailureConfig {
194    /// Maximum failures
195    pub max_failures: Option<u32>,
196    /// Failure handling strategy
197    pub failure_handling: Option<RayFailureHandling>,
198}
199
200/// Ray failure handling strategy
201#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
202pub enum RayFailureHandling {
203    /// Restart failed workers
204    Restart,
205    /// Ignore failures
206    Ignore,
207    /// Fail entire job
208    Fail,
209}
210
211/// Ray Tune configuration
212#[derive(Debug, Clone, Serialize, Deserialize)]
213pub struct RayTuneConfig {
214    /// Search algorithm
215    pub search_alg: Option<RaySearchAlgorithm>,
216    /// Scheduler
217    pub scheduler: Option<RayScheduler>,
218    /// Number of samples
219    pub num_samples: Option<u32>,
220    /// Concurrent trials
221    pub max_concurrent_trials: Option<u32>,
222    /// Resources per trial
223    pub resources_per_trial: Option<HashMap<String, f32>>,
224    /// Parameter space
225    pub param_space: Option<HashMap<String, serde_json::Value>>,
226    /// Metric to optimize
227    pub metric: Option<String>,
228    /// Mode (min or max)
229    pub mode: Option<String>,
230    /// Time budget
231    pub time_budget_s: Option<f32>,
232}
233
234/// Ray search algorithms
235#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
236pub enum RaySearchAlgorithm {
237    /// Basic variant generation
238    BasicVariant,
239    /// Random search
240    Random,
241    /// Grid search
242    Grid,
243    /// Bayesian optimization
244    BayesOpt,
245    /// Hyperband
246    Hyperband,
247    /// BOHB
248    BOHB,
249    /// Population based training
250    PopulationBasedTraining,
251}
252
253/// Ray schedulers
254#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
255pub enum RayScheduler {
256    /// FIFO scheduler
257    FIFO,
258    /// Hyperband scheduler
259    Hyperband,
260    /// ASHA scheduler
261    ASHA,
262    /// Median stopping rule
263    MedianStopping,
264    /// Population based training
265    PopulationBasedTraining,
266}
267
268/// Ray Serve configuration
269#[derive(Debug, Clone, Serialize, Deserialize)]
270pub struct RayServeConfig {
271    /// HTTP options
272    pub http_options: Option<RayServeHttpOptions>,
273    /// gRPC options
274    pub grpc_options: Option<RayServeGrpcOptions>,
275    /// Deployment configuration
276    pub deployments: Option<Vec<RayServeDeploymentConfig>>,
277}
278
279/// Ray Serve HTTP options
280#[derive(Debug, Clone, Serialize, Deserialize)]
281pub struct RayServeHttpOptions {
282    /// Host
283    pub host: Option<String>,
284    /// Port
285    pub port: Option<u16>,
286    /// Root path
287    pub root_path: Option<String>,
288}
289
290/// Ray Serve gRPC options
291#[derive(Debug, Clone, Serialize, Deserialize)]
292pub struct RayServeGrpcOptions {
293    /// Port
294    pub port: Option<u16>,
295    /// gRPC servicer functions
296    pub grpc_servicer_functions: Option<Vec<String>>,
297}
298
299/// Ray Serve deployment configuration
300#[derive(Debug, Clone, Serialize, Deserialize)]
301pub struct RayServeDeploymentConfig {
302    /// Deployment name
303    pub name: String,
304    /// Number of replicas
305    pub num_replicas: Option<u32>,
306    /// Resources per replica
307    pub ray_actor_options: Option<HashMap<String, serde_json::Value>>,
308    /// User configuration
309    pub user_config: Option<HashMap<String, serde_json::Value>>,
310    /// Max concurrent queries
311    pub max_concurrent_queries: Option<u32>,
312    /// Autoscaling configuration
313    pub autoscaling_config: Option<RayServeAutoscalingConfig>,
314}
315
316/// Ray Serve autoscaling configuration
317#[derive(Debug, Clone, Serialize, Deserialize)]
318pub struct RayServeAutoscalingConfig {
319    /// Minimum replicas
320    pub min_replicas: Option<u32>,
321    /// Maximum replicas
322    pub max_replicas: Option<u32>,
323    /// Target number of ongoing requests per replica
324    pub target_num_ongoing_requests_per_replica: Option<f32>,
325    /// Metrics interval
326    pub metrics_interval_s: Option<f32>,
327    /// Look back period
328    pub look_back_period_s: Option<f32>,
329    /// Smoothing factor
330    pub smoothing_factor: Option<f32>,
331}
332
333/// Ray Data configuration
334#[derive(Debug, Clone, Serialize, Deserialize)]
335pub struct RayDataConfig {
336    /// Data format
337    pub format: Option<RayDataFormat>,
338    /// Parallelism
339    pub parallelism: Option<u32>,
340    /// Batch size
341    pub batch_size: Option<u32>,
342    /// Prefetch
343    pub prefetch: Option<u32>,
344    /// Shuffle
345    pub shuffle: Option<bool>,
346    /// Shuffle buffer size
347    pub shuffle_buffer_size: Option<u32>,
348}
349
350/// Ray Data formats
351#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
352pub enum RayDataFormat {
353    /// Parquet format
354    Parquet,
355    /// CSV format
356    CSV,
357    /// JSON format
358    JSON,
359    /// Image format
360    Image,
361    /// Text format
362    Text,
363    /// Arrow format
364    Arrow,
365}
366
367/// Ray resource configuration
368#[derive(Debug, Clone, Serialize, Deserialize)]
369pub struct RayResourceConfig {
370    /// CPU resources
371    pub num_cpus: Option<f32>,
372    /// GPU resources
373    pub num_gpus: Option<f32>,
374    /// Memory (bytes)
375    pub memory: Option<u64>,
376    /// Object store memory (bytes)
377    pub object_store_memory: Option<u64>,
378    /// Custom resources
379    pub custom_resources: Option<HashMap<String, f32>>,
380}
381
382/// Ray fault tolerance configuration
383#[derive(Debug, Clone, Serialize, Deserialize)]
384pub struct RayFaultToleranceConfig {
385    /// Maximum restarts
386    pub max_restarts: Option<u32>,
387    /// Restart delay (seconds)
388    pub restart_delay_s: Option<f32>,
389    /// Health check interval (seconds)
390    pub health_check_interval_s: Option<f32>,
391    /// Enable fault tolerance
392    pub enabled: Option<bool>,
393}
394
395/// Ray integration statistics
396#[derive(Debug, Clone, Default)]
397pub struct RayStats {
398    /// Number of training runs
399    pub training_runs: u64,
400    /// Total training time (seconds)
401    pub training_time_sec: f64,
402    /// Number of tuning trials
403    pub tuning_trials: u64,
404    /// Total tuning time (seconds)
405    pub tuning_time_sec: f64,
406    /// Number of served requests
407    pub served_requests: u64,
408    /// Number of data processing tasks
409    pub data_processing_tasks: u64,
410    /// Number of worker failures
411    pub worker_failures: u64,
412    /// Number of restarts
413    pub restarts: u64,
414    /// Resource utilization
415    pub resource_utilization: f64,
416    /// Checkpoint frequency
417    pub checkpoint_frequency: f64,
418}
419
420/// Ray compatibility integration
421pub struct RayIntegration {
422    /// Configuration
423    config: RayConfig,
424    /// Statistics
425    stats: RayStats,
426    /// Initialization status
427    initialized: bool,
428    /// Process rank
429    rank: u32,
430    /// World size
431    world_size: u32,
432    /// Local rank
433    local_rank: u32,
434    /// Local size
435    local_size: u32,
436    /// Ray session active
437    ray_session_active: bool,
438}
439
440impl RayIntegration {
441    /// Create a new Ray integration
442    pub fn new(config: RayConfig) -> Self {
443        Self {
444            config,
445            stats: RayStats::default(),
446            initialized: false,
447            rank: 0,
448            world_size: 1,
449            local_rank: 0,
450            local_size: 1,
451            ray_session_active: false,
452        }
453    }
454
455    /// Load configuration from JSON file
456    pub fn from_file<P: AsRef<Path>>(path: P) -> TorshResult<Self> {
457        let content = std::fs::read_to_string(path).map_err(|e| {
458            TorshDistributedError::configuration_error(format!(
459                "Failed to read Ray config file: {}",
460                e
461            ))
462        })?;
463
464        let config: RayConfig = serde_json::from_str(&content).map_err(|e| {
465            TorshDistributedError::configuration_error(format!("Failed to parse Ray config: {}", e))
466        })?;
467
468        Ok(Self::new(config))
469    }
470
471    /// Initialize Ray integration
472    pub fn initialize(
473        &mut self,
474        rank: u32,
475        world_size: u32,
476        local_rank: u32,
477        local_size: u32,
478    ) -> TorshResult<()> {
479        if self.initialized {
480            return Err(TorshDistributedError::configuration_error(
481                "Ray integration already initialized",
482            ));
483        }
484
485        self.rank = rank;
486        self.world_size = world_size;
487        self.local_rank = local_rank;
488        self.local_size = local_size;
489
490        self.validate_config()?;
491        self.setup_ray_cluster()?;
492        self.setup_ray_train()?;
493        self.setup_ray_tune()?;
494        self.setup_ray_serve()?;
495        self.setup_ray_data()?;
496        self.setup_fault_tolerance()?;
497
498        self.initialized = true;
499        self.ray_session_active = true;
500
501        tracing::info!(
502            "Ray integration initialized - rank: {}, world_size: {}, local_rank: {}",
503            self.rank,
504            self.world_size,
505            self.local_rank
506        );
507
508        Ok(())
509    }
510
511    /// Validate Ray configuration
512    fn validate_config(&self) -> TorshResult<()> {
513        // Validate cluster configuration
514        if let Some(ref cluster) = self.config.cluster {
515            if let Some(num_cpus) = cluster.num_cpus {
516                if num_cpus == 0 {
517                    return Err(TorshDistributedError::configuration_error(
518                        "Ray cluster num_cpus must be greater than 0",
519                    ));
520                }
521            }
522
523            if let Some(memory_gb) = cluster.memory_gb {
524                if memory_gb <= 0.0 {
525                    return Err(TorshDistributedError::configuration_error(
526                        "Ray cluster memory_gb must be greater than 0",
527                    ));
528                }
529            }
530        }
531
532        // Validate training configuration
533        if let Some(ref train) = self.config.train {
534            if train.num_workers == 0 {
535                return Err(TorshDistributedError::configuration_error(
536                    "Ray Train num_workers must be greater than 0",
537                ));
538            }
539
540            if let Some(ref scaling) = train.scaling_config {
541                if let Some(num_workers) = scaling.num_workers {
542                    if num_workers == 0 {
543                        return Err(TorshDistributedError::configuration_error(
544                            "Ray Train scaling num_workers must be greater than 0",
545                        ));
546                    }
547                }
548            }
549        }
550
551        // Validate tuning configuration
552        if let Some(ref tune) = self.config.tune {
553            if let Some(num_samples) = tune.num_samples {
554                if num_samples == 0 {
555                    return Err(TorshDistributedError::configuration_error(
556                        "Ray Tune num_samples must be greater than 0",
557                    ));
558                }
559            }
560
561            if let Some(max_concurrent) = tune.max_concurrent_trials {
562                if max_concurrent == 0 {
563                    return Err(TorshDistributedError::configuration_error(
564                        "Ray Tune max_concurrent_trials must be greater than 0",
565                    ));
566                }
567            }
568        }
569
570        Ok(())
571    }
572
573    /// Setup Ray cluster
574    fn setup_ray_cluster(&self) -> TorshResult<()> {
575        if let Some(ref cluster) = self.config.cluster {
576            tracing::info!("Setting up Ray cluster");
577
578            if let Some(ref address) = cluster.address {
579                tracing::debug!("Ray cluster address: {}", address);
580            }
581
582            let num_cpus = cluster.num_cpus.unwrap_or(1);
583            tracing::debug!("Ray cluster CPUs: {}", num_cpus);
584
585            let num_gpus = cluster.num_gpus.unwrap_or(0);
586            tracing::debug!("Ray cluster GPUs: {}", num_gpus);
587
588            let memory_gb = cluster.memory_gb.unwrap_or(4.0);
589            tracing::debug!("Ray cluster memory: {} GB", memory_gb);
590
591            let object_store_memory_gb = cluster.object_store_memory_gb.unwrap_or(2.0);
592            tracing::debug!("Ray object store memory: {} GB", object_store_memory_gb);
593
594            if let Some(ref namespace) = cluster.namespace {
595                tracing::debug!("Ray namespace: {}", namespace);
596            }
597
598            let include_dashboard = cluster.include_dashboard.unwrap_or(true);
599            if include_dashboard {
600                let default_host = "127.0.0.1".to_string();
601                let dashboard_host = cluster.dashboard_host.as_ref().unwrap_or(&default_host);
602                let dashboard_port = cluster.dashboard_port.unwrap_or(8265);
603                tracing::debug!("Ray dashboard: {}:{}", dashboard_host, dashboard_port);
604            }
605        }
606        Ok(())
607    }
608
609    /// Setup Ray Train
610    fn setup_ray_train(&self) -> TorshResult<()> {
611        if let Some(ref train) = self.config.train {
612            tracing::info!("Setting up Ray Train");
613
614            tracing::debug!("Ray Train backend: {:?}", train.backend);
615            tracing::debug!("Ray Train workers: {}", train.num_workers);
616
617            let use_gpu = train.use_gpu.unwrap_or(false);
618            tracing::debug!("Ray Train use GPU: {}", use_gpu);
619
620            if let Some(ref resources) = train.resources_per_worker {
621                tracing::debug!("Ray Train resources per worker: {:?}", resources);
622            }
623
624            let placement_strategy = train
625                .placement_group_strategy
626                .unwrap_or(RayPlacementGroupStrategy::Pack);
627            tracing::debug!(
628                "Ray Train placement group strategy: {:?}",
629                placement_strategy
630            );
631
632            if let Some(ref scaling) = train.scaling_config {
633                tracing::debug!("Ray Train scaling configuration: {:?}", scaling);
634            }
635
636            if let Some(ref run_config) = train.run_config {
637                if let Some(ref name) = run_config.name {
638                    tracing::debug!("Ray Train experiment name: {}", name);
639                }
640
641                if let Some(ref storage_path) = run_config.storage_path {
642                    tracing::debug!("Ray Train storage path: {}", storage_path);
643                }
644            }
645
646            if let Some(ref checkpoint) = train.checkpoint_config {
647                let num_to_keep = checkpoint.num_to_keep.unwrap_or(3);
648                tracing::debug!("Ray Train checkpoints to keep: {}", num_to_keep);
649            }
650
651            if let Some(ref failure) = train.failure_config {
652                let max_failures = failure.max_failures.unwrap_or(3);
653                tracing::debug!("Ray Train max failures: {}", max_failures);
654            }
655        }
656        Ok(())
657    }
658
659    /// Setup Ray Tune
660    fn setup_ray_tune(&self) -> TorshResult<()> {
661        if let Some(ref tune) = self.config.tune {
662            tracing::info!("Setting up Ray Tune");
663
664            if let Some(search_alg) = tune.search_alg {
665                tracing::debug!("Ray Tune search algorithm: {:?}", search_alg);
666            }
667
668            if let Some(scheduler) = tune.scheduler {
669                tracing::debug!("Ray Tune scheduler: {:?}", scheduler);
670            }
671
672            let num_samples = tune.num_samples.unwrap_or(10);
673            tracing::debug!("Ray Tune samples: {}", num_samples);
674
675            let max_concurrent = tune.max_concurrent_trials.unwrap_or(4);
676            tracing::debug!("Ray Tune max concurrent trials: {}", max_concurrent);
677
678            if let Some(ref resources) = tune.resources_per_trial {
679                tracing::debug!("Ray Tune resources per trial: {:?}", resources);
680            }
681
682            if let Some(ref metric) = tune.metric {
683                tracing::debug!("Ray Tune optimization metric: {}", metric);
684            }
685
686            if let Some(ref mode) = tune.mode {
687                tracing::debug!("Ray Tune optimization mode: {}", mode);
688            }
689
690            if let Some(time_budget) = tune.time_budget_s {
691                tracing::debug!("Ray Tune time budget: {} seconds", time_budget);
692            }
693        }
694        Ok(())
695    }
696
697    /// Setup Ray Serve
698    fn setup_ray_serve(&self) -> TorshResult<()> {
699        if let Some(ref serve) = self.config.serve {
700            tracing::info!("Setting up Ray Serve");
701
702            if let Some(ref http) = serve.http_options {
703                let default_host = "127.0.0.1".to_string();
704                let host = http.host.as_ref().unwrap_or(&default_host);
705                let port = http.port.unwrap_or(8000);
706                tracing::debug!("Ray Serve HTTP: {}:{}", host, port);
707
708                if let Some(ref root_path) = http.root_path {
709                    tracing::debug!("Ray Serve HTTP root path: {}", root_path);
710                }
711            }
712
713            if let Some(ref grpc) = serve.grpc_options {
714                let port = grpc.port.unwrap_or(9000);
715                tracing::debug!("Ray Serve gRPC port: {}", port);
716
717                if let Some(ref functions) = grpc.grpc_servicer_functions {
718                    tracing::debug!("Ray Serve gRPC servicer functions: {:?}", functions);
719                }
720            }
721
722            if let Some(ref deployments) = serve.deployments {
723                for deployment in deployments {
724                    tracing::debug!("Ray Serve deployment: {}", deployment.name);
725
726                    let num_replicas = deployment.num_replicas.unwrap_or(1);
727                    tracing::debug!("  Replicas: {}", num_replicas);
728
729                    if let Some(ref autoscaling) = deployment.autoscaling_config {
730                        let min_replicas = autoscaling.min_replicas.unwrap_or(1);
731                        let max_replicas = autoscaling.max_replicas.unwrap_or(10);
732                        tracing::debug!("  Autoscaling: {} - {}", min_replicas, max_replicas);
733                    }
734                }
735            }
736        }
737        Ok(())
738    }
739
740    /// Setup Ray Data
741    fn setup_ray_data(&self) -> TorshResult<()> {
742        if let Some(ref data) = self.config.data {
743            tracing::info!("Setting up Ray Data");
744
745            if let Some(format) = data.format {
746                tracing::debug!("Ray Data format: {:?}", format);
747            }
748
749            let parallelism = data.parallelism.unwrap_or(4);
750            tracing::debug!("Ray Data parallelism: {}", parallelism);
751
752            let batch_size = data.batch_size.unwrap_or(32);
753            tracing::debug!("Ray Data batch size: {}", batch_size);
754
755            let prefetch = data.prefetch.unwrap_or(2);
756            tracing::debug!("Ray Data prefetch: {}", prefetch);
757
758            let shuffle = data.shuffle.unwrap_or(false);
759            tracing::debug!("Ray Data shuffle: {}", shuffle);
760
761            if shuffle {
762                let shuffle_buffer_size = data.shuffle_buffer_size.unwrap_or(1000);
763                tracing::debug!("Ray Data shuffle buffer size: {}", shuffle_buffer_size);
764            }
765        }
766        Ok(())
767    }
768
769    /// Setup fault tolerance
770    fn setup_fault_tolerance(&self) -> TorshResult<()> {
771        if let Some(ref fault_tolerance) = self.config.fault_tolerance {
772            tracing::info!("Setting up Ray fault tolerance");
773
774            let enabled = fault_tolerance.enabled.unwrap_or(true);
775            tracing::debug!("Ray fault tolerance enabled: {}", enabled);
776
777            if enabled {
778                let max_restarts = fault_tolerance.max_restarts.unwrap_or(3);
779                tracing::debug!("Ray max restarts: {}", max_restarts);
780
781                let restart_delay = fault_tolerance.restart_delay_s.unwrap_or(5.0);
782                tracing::debug!("Ray restart delay: {} seconds", restart_delay);
783
784                let health_check_interval = fault_tolerance.health_check_interval_s.unwrap_or(10.0);
785                tracing::debug!(
786                    "Ray health check interval: {} seconds",
787                    health_check_interval
788                );
789            }
790        }
791        Ok(())
792    }
793
794    /// Convert Ray config to ToRSh elastic config
795    pub fn to_elastic_config(&self) -> TorshResult<Option<crate::fault_tolerance::ElasticConfig>> {
796        if let Some(ref train) = self.config.train {
797            use crate::fault_tolerance::ElasticConfig;
798
799            let min_workers = if let Some(ref scaling) = train.scaling_config {
800                scaling.num_workers.unwrap_or(train.num_workers)
801            } else {
802                train.num_workers
803            };
804
805            let max_workers = min_workers * 2; // Default scaling
806
807            let config = ElasticConfig {
808                min_workers: min_workers as usize,
809                max_workers: max_workers as usize,
810                scaling_timeout: std::time::Duration::from_secs(300),
811                scaling_check_interval: std::time::Duration::from_secs(30),
812                enable_elastic_scheduling: true,
813                rendezvous_backend: "etcd".to_string(),
814                rendezvous_endpoint: "localhost:2379".to_string(),
815            };
816
817            Ok(Some(config))
818        } else {
819            Ok(None)
820        }
821    }
822
823    /// Get the current configuration
824    pub fn config(&self) -> &RayConfig {
825        &self.config
826    }
827
828    /// Get current statistics
829    pub fn stats(&self) -> &RayStats {
830        &self.stats
831    }
832
833    /// Check if Ray integration is initialized
834    pub fn is_initialized(&self) -> bool {
835        self.initialized
836    }
837
838    /// Get current rank
839    pub fn rank(&self) -> u32 {
840        self.rank
841    }
842
843    /// Get world size
844    pub fn world_size(&self) -> u32 {
845        self.world_size
846    }
847
848    /// Get local rank
849    pub fn local_rank(&self) -> u32 {
850        self.local_rank
851    }
852
853    /// Get local size
854    pub fn local_size(&self) -> u32 {
855        self.local_size
856    }
857
858    /// Check if Ray session is active
859    pub fn is_ray_session_active(&self) -> bool {
860        self.ray_session_active
861    }
862
863    /// Simulate Ray Train run
864    pub fn run_training(&mut self, train_func_name: &str, num_epochs: u32) -> TorshResult<()> {
865        if !self.initialized {
866            return Err(TorshDistributedError::BackendNotInitialized);
867        }
868
869        let start_time = std::time::Instant::now();
870
871        tracing::info!(
872            "Running Ray Train: {} for {} epochs",
873            train_func_name,
874            num_epochs
875        );
876
877        // Simulate training
878        for epoch in 1..=num_epochs {
879            tracing::debug!("Ray Train epoch {}/{}", epoch, num_epochs);
880
881            // Simulate potential worker failure and restart
882            if epoch % 10 == 0 && self.config.fault_tolerance.is_some() {
883                self.handle_worker_failure()?;
884            }
885        }
886
887        // Update statistics
888        self.stats.training_runs += 1;
889        self.stats.training_time_sec += start_time.elapsed().as_secs_f64();
890
891        tracing::info!("Ray Train completed: {}", train_func_name);
892        Ok(())
893    }
894
895    /// Simulate Ray Tune run
896    pub fn run_tuning(&mut self, tune_config_name: &str) -> TorshResult<()> {
897        if !self.initialized {
898            return Err(TorshDistributedError::BackendNotInitialized);
899        }
900
901        let start_time = std::time::Instant::now();
902
903        let num_trials = self
904            .config
905            .tune
906            .as_ref()
907            .and_then(|t| t.num_samples)
908            .unwrap_or(10);
909
910        tracing::info!(
911            "Running Ray Tune: {} with {} trials",
912            tune_config_name,
913            num_trials
914        );
915
916        // Simulate tuning trials
917        for trial in 1..=num_trials {
918            tracing::debug!("Ray Tune trial {}/{}", trial, num_trials);
919            self.stats.tuning_trials += 1;
920        }
921
922        // Update statistics
923        self.stats.tuning_time_sec += start_time.elapsed().as_secs_f64();
924
925        tracing::info!("Ray Tune completed: {}", tune_config_name);
926        Ok(())
927    }
928
929    /// Handle worker failure
930    fn handle_worker_failure(&mut self) -> TorshResult<()> {
931        tracing::warn!("Simulating Ray worker failure");
932        self.stats.worker_failures += 1;
933
934        if let Some(ref fault_tolerance) = self.config.fault_tolerance {
935            if fault_tolerance.enabled.unwrap_or(true) {
936                let max_restarts = fault_tolerance.max_restarts.unwrap_or(3);
937
938                if self.stats.restarts < max_restarts as u64 {
939                    tracing::info!("Restarting failed Ray worker");
940                    self.stats.restarts += 1;
941
942                    let restart_delay = fault_tolerance.restart_delay_s.unwrap_or(5.0);
943                    tracing::debug!("Ray restart delay: {} seconds", restart_delay);
944                } else {
945                    return Err(TorshDistributedError::process_failure(
946                        self.rank,
947                        "ray_worker",
948                        "Maximum restart attempts exceeded",
949                    ));
950                }
951            }
952        }
953
954        Ok(())
955    }
956
957    /// Shutdown Ray integration
958    pub fn shutdown(&mut self) -> TorshResult<()> {
959        if self.ray_session_active {
960            tracing::info!("Shutting down Ray integration");
961            self.ray_session_active = false;
962            self.initialized = false;
963        }
964        Ok(())
965    }
966
967    /// Create a default Ray configuration
968    pub fn default_config() -> RayConfig {
969        RayConfig {
970            cluster: Some(RayClusterConfig {
971                address: None,
972                redis_address: None,
973                num_cpus: Some(4),
974                num_gpus: Some(0),
975                memory_gb: Some(8.0),
976                object_store_memory_gb: Some(2.0),
977                namespace: None,
978                dashboard_host: Some("127.0.0.1".to_string()),
979                dashboard_port: Some(8265),
980                include_dashboard: Some(true),
981            }),
982            train: Some(RayTrainConfig {
983                backend: RayTrainBackend::Torch,
984                num_workers: 4,
985                use_gpu: Some(false),
986                resources_per_worker: None,
987                placement_group_strategy: Some(RayPlacementGroupStrategy::Pack),
988                scaling_config: None,
989                run_config: None,
990                checkpoint_config: None,
991                failure_config: Some(RayFailureConfig {
992                    max_failures: Some(3),
993                    failure_handling: Some(RayFailureHandling::Restart),
994                }),
995            }),
996            tune: None,
997            serve: None,
998            data: Some(RayDataConfig {
999                format: Some(RayDataFormat::Parquet),
1000                parallelism: Some(4),
1001                batch_size: Some(32),
1002                prefetch: Some(2),
1003                shuffle: Some(false),
1004                shuffle_buffer_size: Some(1000),
1005            }),
1006            resources: Some(RayResourceConfig {
1007                num_cpus: Some(4.0),
1008                num_gpus: Some(0.0),
1009                memory: Some(8 * 1024 * 1024 * 1024), // 8GB
1010                object_store_memory: Some(2 * 1024 * 1024 * 1024), // 2GB
1011                custom_resources: None,
1012            }),
1013            fault_tolerance: Some(RayFaultToleranceConfig {
1014                max_restarts: Some(3),
1015                restart_delay_s: Some(5.0),
1016                health_check_interval_s: Some(10.0),
1017                enabled: Some(true),
1018            }),
1019        }
1020    }
1021
1022    /// Create a configuration for hyperparameter tuning
1023    pub fn config_with_tune(num_samples: u32, search_alg: RaySearchAlgorithm) -> RayConfig {
1024        let mut config = Self::default_config();
1025
1026        config.tune = Some(RayTuneConfig {
1027            search_alg: Some(search_alg),
1028            scheduler: Some(RayScheduler::ASHA),
1029            num_samples: Some(num_samples),
1030            max_concurrent_trials: Some(4),
1031            resources_per_trial: Some([("cpu".to_string(), 1.0)].into_iter().collect()),
1032            param_space: None,
1033            metric: Some("accuracy".to_string()),
1034            mode: Some("max".to_string()),
1035            time_budget_s: Some(3600.0), // 1 hour
1036        });
1037
1038        config
1039    }
1040
1041    /// Create a configuration for model serving
1042    pub fn config_with_serve(num_replicas: u32) -> RayConfig {
1043        let mut config = Self::default_config();
1044
1045        config.serve = Some(RayServeConfig {
1046            http_options: Some(RayServeHttpOptions {
1047                host: Some("0.0.0.0".to_string()),
1048                port: Some(8000),
1049                root_path: None,
1050            }),
1051            grpc_options: None,
1052            deployments: Some(vec![RayServeDeploymentConfig {
1053                name: "model".to_string(),
1054                num_replicas: Some(num_replicas),
1055                ray_actor_options: Some(
1056                    [(
1057                        "num_cpus".to_string(),
1058                        serde_json::Value::Number(serde_json::Number::from(1)),
1059                    )]
1060                    .into_iter()
1061                    .collect(),
1062                ),
1063                user_config: None,
1064                max_concurrent_queries: Some(100),
1065                autoscaling_config: Some(RayServeAutoscalingConfig {
1066                    min_replicas: Some(1),
1067                    max_replicas: Some(num_replicas * 2),
1068                    target_num_ongoing_requests_per_replica: Some(10.0),
1069                    metrics_interval_s: Some(10.0),
1070                    look_back_period_s: Some(30.0),
1071                    smoothing_factor: Some(1.0),
1072                }),
1073            }]),
1074        });
1075
1076        config
1077    }
1078}
1079
1080impl Default for RayConfig {
1081    fn default() -> Self {
1082        RayIntegration::default_config()
1083    }
1084}
1085
1086#[cfg(test)]
1087mod tests {
1088    use super::*;
1089
1090    #[test]
1091    fn test_ray_config_validation() {
1092        let config = RayIntegration::default_config();
1093        let mut integration = RayIntegration::new(config);
1094
1095        // Should succeed with valid parameters
1096        assert!(integration.initialize(0, 4, 0, 2).is_ok());
1097        assert!(integration.is_initialized());
1098        assert!(integration.is_ray_session_active());
1099        assert_eq!(integration.rank(), 0);
1100        assert_eq!(integration.world_size(), 4);
1101        assert_eq!(integration.local_rank(), 0);
1102    }
1103
1104    #[test]
1105    fn test_ray_training_simulation() {
1106        let config = RayIntegration::default_config();
1107        let mut integration = RayIntegration::new(config);
1108
1109        assert!(integration.initialize(0, 4, 0, 2).is_ok());
1110
1111        // Simulate training runs
1112        assert!(integration.run_training("my_train_func", 5).is_ok());
1113        assert!(integration.run_training("another_train_func", 3).is_ok());
1114
1115        let stats = integration.stats();
1116        assert_eq!(stats.training_runs, 2);
1117        assert!(stats.training_time_sec >= 0.0); // Allow for very fast execution in tests
1118    }
1119
1120    #[test]
1121    fn test_ray_tuning_simulation() {
1122        let config = RayIntegration::config_with_tune(20, RaySearchAlgorithm::BayesOpt);
1123        let mut integration = RayIntegration::new(config);
1124
1125        assert!(integration.initialize(0, 4, 0, 2).is_ok());
1126
1127        // Simulate tuning run
1128        assert!(integration.run_tuning("hyperparameter_search").is_ok());
1129
1130        let stats = integration.stats();
1131        assert_eq!(stats.tuning_trials, 20);
1132        assert!(stats.tuning_time_sec > 0.0);
1133    }
1134
1135    #[test]
1136    fn test_ray_elastic_config_conversion() {
1137        let config = RayIntegration::default_config();
1138        let mut integration = RayIntegration::new(config);
1139
1140        assert!(integration.initialize(0, 4, 0, 2).is_ok());
1141
1142        // Test elastic config conversion
1143        let elastic_config = integration.to_elastic_config().unwrap();
1144        assert!(elastic_config.is_some());
1145
1146        if let Some(config) = elastic_config {
1147            assert_eq!(config.min_workers, 4);
1148            assert_eq!(config.max_workers, 8);
1149            assert!(config.enable_elastic_scheduling);
1150            assert_eq!(config.rendezvous_backend, "etcd");
1151        }
1152    }
1153
1154    #[test]
1155    fn test_ray_worker_failure_handling() {
1156        let config = RayIntegration::default_config();
1157        let mut integration = RayIntegration::new(config);
1158
1159        assert!(integration.initialize(0, 4, 0, 2).is_ok());
1160
1161        // Simulate worker failures
1162        assert!(integration.handle_worker_failure().is_ok());
1163        assert!(integration.handle_worker_failure().is_ok());
1164        assert!(integration.handle_worker_failure().is_ok());
1165
1166        let stats = integration.stats();
1167        assert_eq!(stats.worker_failures, 3);
1168        assert_eq!(stats.restarts, 3);
1169
1170        // Should fail after max restarts
1171        assert!(integration.handle_worker_failure().is_err());
1172    }
1173
1174    #[test]
1175    fn test_ray_shutdown() {
1176        let config = RayIntegration::default_config();
1177        let mut integration = RayIntegration::new(config);
1178
1179        assert!(integration.initialize(0, 4, 0, 2).is_ok());
1180        assert!(integration.is_ray_session_active());
1181
1182        assert!(integration.shutdown().is_ok());
1183        assert!(!integration.is_ray_session_active());
1184        assert!(!integration.is_initialized());
1185    }
1186
1187    #[test]
1188    fn test_ray_serve_config() {
1189        let config = RayIntegration::config_with_serve(4);
1190        let mut integration = RayIntegration::new(config);
1191
1192        assert!(integration.initialize(0, 4, 0, 2).is_ok());
1193
1194        // Check serve configuration
1195        assert!(integration.config().serve.is_some());
1196
1197        if let Some(ref serve) = integration.config().serve {
1198            assert!(serve.http_options.is_some());
1199            assert!(serve.deployments.is_some());
1200
1201            if let Some(ref deployments) = serve.deployments {
1202                assert_eq!(deployments.len(), 1);
1203                assert_eq!(deployments[0].name, "model");
1204                assert_eq!(deployments[0].num_replicas, Some(4));
1205            }
1206        }
1207    }
1208
1209    #[test]
1210    fn test_ray_config_serialization() {
1211        let config = RayIntegration::config_with_tune(10, RaySearchAlgorithm::Random);
1212
1213        // Test JSON serialization
1214        let json = serde_json::to_string(&config).unwrap();
1215        assert!(json.contains("Random"));
1216        assert!(json.contains("ASHA"));
1217        assert!(json.contains("accuracy"));
1218
1219        // Test deserialization
1220        let deserialized: RayConfig = serde_json::from_str(&json).unwrap();
1221        assert!(deserialized.tune.is_some());
1222
1223        if let Some(tune) = deserialized.tune {
1224            assert_eq!(tune.search_alg, Some(RaySearchAlgorithm::Random));
1225            assert_eq!(tune.scheduler, Some(RayScheduler::ASHA));
1226            assert_eq!(tune.num_samples, Some(10));
1227        }
1228    }
1229
1230    #[test]
1231    fn test_ray_invalid_config() {
1232        let mut config = RayIntegration::default_config();
1233
1234        // Make configuration invalid
1235        if let Some(ref mut train) = config.train {
1236            train.num_workers = 0; // Invalid: 0 workers
1237        }
1238
1239        let mut integration = RayIntegration::new(config);
1240
1241        // Should fail validation
1242        assert!(integration.initialize(0, 4, 0, 2).is_err());
1243    }
1244}
torsh_distributed/ray_integration.rs

torsh_distributed/
ray_integration.rs
