Module logging 

Unified logging and observability for the rust-ai ecosystem.

Why This Module Exists

ML training and inference generate massive amounts of diagnostic data:

• Training progress (loss, learning rate, throughput)
• Memory usage and allocation patterns
• Device information and kernel timings
• Errors and warnings

Without consistent logging configuration, each crate would implement its own approach, leading to inconsistent output formats and configuration mechanisms.

This module provides:

1. Unified log initialization: Single function to configure logging for all crates
2. Structured logging helpers: Consistent field names for metrics and events
3. Progress tracking: Training loop progress bars and ETA estimation

Design Decisions

• tracing-based: Uses the tracing ecosystem for structured logging with spans
• Environment-driven: Log levels configured via RUST_LOG environment variable
• Zero-cost when disabled: All logging compiles to no-ops when level is filtered

Macros

debug

Constructs an event at the debug level.

error

Constructs an event at the error level.

info

Constructs an event at the info level.

trace

Constructs an event at the trace level.

warn

Constructs an event at the warn level.

Structs

LogConfig

Configuration for logging initialization.

Enums

LogLevel

Log level enumeration.

Functions

init_logging

Initialize logging for the rust-ai ecosystem.

log_memory_usage

Log memory usage.

log_training_step

Log a training step with standard fields.