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optirs_tpu/
lib.rs

1//! # OptiRS TPU - TPU Coordination and Pod Management
2//!
3//! **Version:** 0.1.0
4//! **Status:** Coming Soon (Framework Only)
5//!
6//! ⚠️ **Warning:** This crate is under active development. No functional implementation yet.
7//! Type definitions and architecture planning only.
8//!
9//! `optirs-tpu` provides TPU coordination, pod management, and XLA integration for OptiRS,
10//! built on [SciRS2](https://github.com/cool-japan/scirs)'s distributed computing abstractions.
11//!
12//! ## Dependencies
13//!
14//! - `scirs2-core` 0.1.1 - Required foundation
15//! - `optirs-core` 0.1.0 - Core optimizers
16//!
17//! ## Implementation Status (v0.1.0)
18//!
19//! - 📝 Type definitions only
20//! - 📝 Architecture planning
21//! - 📝 Module structure defined
22//! - 🚧 Implementation coming in future releases
23//! - 🚧 TPU pod coordination (planned)
24//! - 🚧 XLA integration (planned)
25//!
26//! ## Status: Coming Soon
27//!
28//! This crate is under active development for large-scale distributed training.
29//!
30//! ## Planned Features
31//!
32//! ### TPU Pod Coordination
33//! - **Pod Management** - Coordinate TPU pods (v2, v3, v4, v5)
34//! - **Synchronization** - Efficient all-reduce and parameter averaging
35//! - **Fault Tolerance** - Automatic recovery from TPU failures
36//! - **Load Balancing** - Optimal workload distribution
37//!
38//! ### XLA Integration
39//! - **XLA Compilation** - Just-in-time compilation for TPUs
40//! - **Optimization Passes** - Advanced compiler optimizations
41//! - **Kernel Fusion** - Fused operations for maximum throughput
42//! - **Memory Layout** - Optimal memory access patterns
43//!
44//! ### Distributed Training
45//! - **Data Parallelism** - Distribute data across TPU cores
46//! - **Model Parallelism** - Partition large models across TPUs
47//! - **Pipeline Parallelism** - Layer-wise parallel execution
48//! - **Hybrid Parallelism** - Combine all strategies
49//!
50//! ### Performance
51//! - **Linear Scaling** - Near-perfect scaling to thousands of cores
52//! - **Ultra-Low Latency** - Sub-millisecond synchronization
53//! - **High Throughput** - Process millions of examples per second
54//! - **Fault Tolerance** - Automatic checkpoint and resume
55//!
56//! ## Example Usage (Future)
57//!
58//! ```rust,ignore
59//! use optirs_tpu::{TpuPodCoordinator, TpuConfig};
60//! use optirs::prelude::*;
61//!
62//! // Initialize TPU pod
63//! let config = TpuConfig {
64//!     pod_size: 8,  // 8 TPU cores
65//!     use_xla: true,
66//!     fault_tolerance: true,
67//! };
68//!
69//! let mut coordinator = TpuPodCoordinator::new(config)?;
70//!
71//! // Create distributed optimizer
72//! let optimizer = Adam::new(0.001);
73//! let mut tpu_opt = coordinator.wrap_optimizer(optimizer)?;
74//!
75//! // Training automatically distributed across TPU pod
76//! let params = coordinator.distribute_parameters(&params)?;
77//! let grads = coordinator.compute_gradients(&data)?;
78//! let updated = tpu_opt.step(&params, &grads)?;
79//! ```
80//!
81//! ## Architecture
82//!
83//! Built exclusively on SciRS2:
84//! - **Distributed**: `scirs2_core::distributed::ClusterManager`
85//! - **AllReduce**: `scirs2_core::advanced_distributed_computing::AllReduce`
86//! - **Scheduler**: `scirs2_core::distributed::JobScheduler`
87//! - **JIT**: `scirs2_core::jit::JitCompiler` for XLA
88//! - **Arrays**: `scirs2_core::array_protocol::DistributedArray`
89//!
90//! ## Use Cases
91//!
92//! - **Foundation Models** - Train 100B+ parameter models
93//! - **Large-Scale RL** - Distributed reinforcement learning
94//! - **Scientific Computing** - Massive-scale simulations
95//! - **Research** - State-of-the-art model training
96//!
97//! ## Contributing
98//!
99//! TPU development follows SciRS2 integration guidelines.
100//! All distributed operations must use `scirs2_core::distributed` abstractions.
101
102pub mod coordination;
103pub mod error;
104pub mod fault_tolerance;
105pub mod monitoring;
106pub mod pod_coordination;
107pub mod synchronization;
108pub mod tpu_backend;
109pub mod xla;
110pub mod xla_compilation;
111
112// Re-export main types from mod.rs
113mod main_types;
114pub use main_types::*;
115
116pub use coordination::PodCoordinator;
117pub use tpu_backend::DeviceId;