Journals & Replay

OmniLoop journals record the entire lifecycle of a training session. In v2, journals are not just a stack of telemetry frames; they interleave typed event records into the same stream, providing a unified timeline of data and control.

Journal Records

Every record in the journal carries the tick of the telemetry frame it belongs to.

Kind	Payload (JSON)	Written when
`telemetry`	the state frame	every published tick
`mutation`	`{"name", "old", "new", "source"}`	a parameter changes
`control`	`{“event”: “freeze_on_exception”	“watch_trip”
`lifecycle`	`{“event”: “session_start”	“session_end”, …}`
`hash`	8-byte LE FNV-1a 64	every `hash_every` ticks

TrainingLoop writes all of this automatically when initialized with a journal= path.

Reading Back Data

You can load and inspect journal data offline using the OmniLoop API:

from omniloop import load_events, JournalPlayer

# Iterate over events
for e in load_events("run.omni"):
    print(e["tick"], e["kind"], e["event"])

# Sequential playback
player = JournalPlayer("run.omni")
rec = player.read_next_record()
# Example output:
# {"kind": "telemetry", "tick": 0, "mono_ns": ..., "wall_ns": ..., "payload": b"..."}

Exporting Tuned Configurations

You can export the final mutated state of your parameters (a “tuned config”) either live or from a recorded journal:

# Live export during a run
loop.export_tuned_config("tuned.yaml")

# Offline export from a journal
from omniloop import export_tuned_config_from_journal
export_tuned_config_from_journal("run.omni", "tuned.yaml")