kermt-monitor

name	kermt-monitor
description	Check progress for a detached KERMT run (pretrain, finetune, or any kermt_run_detached invocation). Reads run.json, queries docker for container state, tails the pretrain/finetune log, and parses progress lines (epoch, step, val loss).
license	Apache-2.0
compatibility	Requires docker and jq. Designed for Claude Code, Codex, and Nemotron.
metadata	{"owner":"[e-mail]","classification":"atomic-skill","risk_tier":"skill"}

kermt-monitor

Companion skill for any KERMT workflow that runs detached: the three pretrain skills (kermt-continue-pretrain, kermt-pretrain-scratch, kermt-add-cmim-pretrain) plus kermt-finetune. kermt-infer and kermt-embed run blocking by default and don't need this skill, but if a user launches them detached on purpose the monitor still works (the workflow-dispatch in step 4 handles unknown workflows by tailing the most-recent log file in the run dir). Reads the run directory's run.json, queries docker for the container's state, surfaces the latest progress, and either tails or follows the log.

Hardware requirements

None. This skill only reads disk + queries docker; no GPU compute.

Inputs

One of:

• <run-dir> — a positional argument pointing at the directory containing run.json (e.g. runs/continue-pretrain_2026-05-17T10-23Z). Preferred.
• --container <name-or-id> — direct container reference; the skill still reads run.json from the run dir referenced inside the container's inspect output if available, but works degraded-mode without it.

Optional:

• --lines N — number of trailing log lines to print (default 50).
• --follow — stream docker logs -f until ^C. Useful for "watch the loss". Without it, the skill is one-shot and exits.
• --json — emit a structured status report instead of human-readable text. Useful when the parent agent wants to take downstream action.

Workflow

Let RUN_DIR=$1 (or whatever path the user supplies).

1. Locate the manifest.

   MANIFEST=$RUN_DIR/run.json
   

   Refuse to proceed if it doesn't exist; surface a helpful message pointing the user at the run-dir convention (runs/<workflow>_<ts>/).

2. Parse the manifest (Python helper):

   workflow=$(jq -r .workflow $MANIFEST)
   container_name=...   # not directly in run.json today; the skill that
                        # launched stored it in run.json under
                        # container.name during launch (see below note).
   logs_dir=$(jq -r .logs_dir $MANIFEST)
   image_tag=$(jq -r .container.image_tag $MANIFEST)
   started_at=$(jq -r .started_at $MANIFEST)
   

3. Query docker for container state.

   docker ps --filter "name=$container_name" --format \
       '{{.ID}}\t{{.Status}}\t{{.CreatedAt}}'
   

   If absent, fall back to docker inspect $container_name --format '{{.State.Status}} (exit {{.State.ExitCode}})' to see whether the container exited (ok or failed) or was removed (--rm after exit).

4. Find the live log file.

   case "$workflow" in
     continue-pretrain|pretrain-scratch)  LOG=$logs_dir/pretrain_ddp.log ;;
     finetune)                            LOG=$logs_dir/finetune.log ;;
     *)                                   LOG=$(ls -1t $logs_dir/*.log 2>/dev/null | head -n 1) ;;
   esac
   

   The manifest's workflow field disambiguates pretrain (pretrain_ddp.log) from finetune (finetune.log). Other workflows fall back to the most-recently-modified .log in $logs_dir.

5. Show the latest progress.

   • tail -n $LINES $LOG for the raw recent output.
   • Parse the last few progress lines and surface a human-friendly summary. The format differs per workflow:
     • Pretrain: epoch / step / val_loss

       Current epoch: 12/100  step: 4523/9000  val_loss: 0.832 (best 0.821 @ step 4100)
       

     • Finetune: fold / epoch / val_ (e.g. val_mae for regression, val_auc for classification — read args_applied.metric from run.json)

       Fold 0  epoch 12/30  val_mae 0.187 (best 0.182 @ epoch 9)
       

     Wall-clock: 1h 23m since started_at; ETA ~6h remaining.
     

6. If is AND the container has exited cleanly (, ) AND exists, parse it and emit a per-task metric table:

Hard rules

• Read-only on the user's data. Never modify run.json, never touch the container's checkpoint dir. The monitor only inspects.
• Don't kill the container without explicit user instruction. If the user asks to stop, run docker stop; if they ask to abandon, leave it running and just exit.
• Don't pull or modify the kermt image. The monitor only reads.
• JSON output mode is non-interactive. Skip the "press ^C to exit" prompts and emit a single JSON document so the parent agent can pipe it.

Note on container_name plumbing

The run.json schema as currently written does not yet include the launched container name — kermt_run_detached prints it to stdout but the runner script doesn't capture it into run.json. The monitor falls back to a filesystem-based lookup: list runs/<workflow>_*/ directories and match by mtime; or accept --container <name> explicitly. Follow-up: have the launching skill record container name into run.json before exiting.

Output (text mode, default)

KERMT continue-pretrain · runs/continue-pretrain_2026-05-17T10-23Z
  Container : kermt-continue-pretrain-…  (Up 1 hour, status: running)
  Image     : kermt:latest@sha256:…
  Repo      : 2fe00f9 (clean)
  Started   : 2026-05-17T10:23:14Z (1h 23m ago)
  Workflow  : continue-pretrain, pretrain_mode=hybrid, world_size=2

  Latest log (last 50 lines from $LOG):
    [Epoch 12/100] step 4523/9000 loss 0.832 lr 1.2e-4
    [val] step 4100 val_loss 0.821 (new best)
    ...

  Progress: epoch 12/100, ~12% done. ETA ~6h.
  TensorBoard: tensorboard --logdir $RUN_DIR/logs/tb
  Replay command: $(jq -r .cmd_replay $RUN_DIR/run.json)