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Each Coach UI deploy action (GitHub push, droplet sync, droplet provision)
expands to a list of `Step`s that must run serially. This module owns the
serial-execution loop; everything else is just a step list.
Design notes:
- We deliberately do NOT reuse `runs.spawn_subprocess_streaming` directly.
That helper assumes a single Popen per run and emits `StatusEvent('succeeded')`
+ closes subscribers as soon as the Popen exits. Chaining N steps would
prematurely close the SSE stream after step 1. Instead, this module runs
its own daemon thread, manually publishes `LogEvent`s line-by-line, and
emits exactly one terminal `StatusEvent` after all steps complete (or one
fails).
- Cancellation routes through the existing `RunRegistry.cancel()` API. We
call `registry.attach_process()` each time a new step starts, so the
registry's Popen pointer always tracks the live subprocess.
- For non-subprocess work (httpx calls to the Dev Cloud API), the provision
pipeline does not use `Step` objects — it publishes `LogEvent`s inline
and then hands off to the step runner for the SSH phases.
"""
from __future__ import annotations
import contextlib
import subprocess
import threading
from collections.abc import Callable
from pathlib import Path
from mindxtrain.deploy.amd_dev_cloud import (
AmdDevCloudClient,
AmdDevCloudConfig,
extract_public_ip,
)
from mindxtrain.deploy.cloud_init import BOOTSTRAP_SENTINEL, render
from mindxtrain.deploy.droplet import (
DropletConfig,
build_scp_plan_back,
build_ssh_probe,
build_tail_cloud_init,
build_tail_training_log,
sync_steps,
)
from mindxtrain.deploy.github_push import (
GithubConfig,
Step,
bootstrap_steps,
remote_url,
write_sha_file,
)
from mindxtrain.operator.runs import (
LogEvent,
RunRegistry,
StatusEvent,
default_registry,
parse_trainer_log_line,
)
# ---- run_pipeline --------------------------------------------------------
def _publish_log(registry: RunRegistry, run_id: str, line: str) -> None:
registry.publish_threadsafe(run_id, LogEvent(run_id=run_id, line=line))
def _open_log_file(log_path: Path) -> object:
log_path.parent.mkdir(parents=True, exist_ok=True)
return log_path.open("w", buffering=1)
def _stream_step(
*,
step: Step,
registry: RunRegistry,
run_id: str,
log_file: object,
capture: list[str],
parse_trainer: bool = False,
) -> int:
"""Run a single step's subprocess, tee stdout to log_file + events.
By default each line becomes a `LogEvent`. With `parse_trainer=True`,
lines that match the HF Trainer JSON format are upgraded to `StepEvent`
(drives Coach's loss chart and metrics table); non-matching lines still
become `LogEvent`s. This is how remote training output bridges into the
same SSE channel the in-process trainer would publish to.
Captures stdout into `capture` if `step.capture_stdout`. Returns the rc.
"""
proc = subprocess.Popen(
step.cmd,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
env=step.env or None,
text=True,
bufsize=1,
)
registry.attach_process(run_id, proc)
assert proc.stdout is not None
step_ctr = 0
for raw in proc.stdout:
line = raw.rstrip("\n")
log_file.write(raw) # type: ignore[attr-defined]
log_file.flush() # type: ignore[attr-defined]
if step.capture_stdout:
capture.append(line)
if parse_trainer:
step_ev = parse_trainer_log_line(line, fallback_step=step_ctr + 1)
if step_ev is not None:
step_ctr = step_ev.step
registry.publish_threadsafe(
run_id, step_ev.model_copy(update={"run_id": run_id}),
)
continue
_publish_log(registry, run_id, line)
return proc.wait()
def run_pipeline(
steps: list[Step],
*,
run_id: str,
out_dir: Path,
registry: RunRegistry | None = None,
on_done: Callable[[int, dict[str, str]], None] | None = None,
) -> threading.Thread:
"""Run `steps` serially in a daemon thread. Returns the thread immediately.
Predicate logic: if a step's `predicate_step` was run earlier and that
step's rc is *not* in `predicate_rc_in`, the step is skipped (with a
LogEvent) and its rc is recorded as -1 ("skipped").
Captured stdout: any step with `capture_stdout=True` has its full stdout
joined into a string and stashed in `captured[step.label]`. The `on_done`
callback receives (final_rc, captured).
Final status: emits exactly one `StatusEvent("succeeded"|"failed")` and
closes subscribers. Cancel via `registry.cancel(run_id)`.
"""
reg = registry if registry is not None else default_registry()
log_file = _open_log_file(out_dir / "pipeline.log")
def _runner() -> None:
rcs: dict[str, int] = {}
captured: dict[str, str] = {}
final_rc = 0
try:
for i, step in enumerate(steps, 1):
# Predicate: skip if the gating step's rc is not in the allowed set.
if step.predicate_step is not None:
gate_rc = rcs.get(step.predicate_step)
if gate_rc is None or gate_rc not in step.predicate_rc_in:
_publish_log(reg, run_id, f"=== skip {i}/{len(steps)}: {step.label} (predicate {step.predicate_step}={gate_rc}) ===")
rcs[step.label] = -1
continue
_publish_log(reg, run_id, f"=== step {i}/{len(steps)}: {step.label} ===")
buffer: list[str] = []
rc = _stream_step(
step=step,
registry=reg,
run_id=run_id,
log_file=log_file,
capture=buffer,
)
rcs[step.label] = rc
if step.capture_stdout:
captured[step.label] = "\n".join(buffer)
if rc != 0 and not step.allow_failure:
_publish_log(reg, run_id, f"=== step {step.label} exited rc={rc}; aborting pipeline ===")
final_rc = rc
break
if rc != 0:
_publish_log(reg, run_id, f" (probe {step.label} rc={rc} — continuing)")
status = "succeeded" if final_rc == 0 else "failed"
reg.publish_threadsafe(
run_id,
StatusEvent(run_id=run_id, status=status, message=f"rc={final_rc}"),
)
finally:
with contextlib.suppress(Exception):
log_file.close() # type: ignore[attr-defined]
reg.close_subscribers(run_id)
if on_done is not None:
with contextlib.suppress(Exception):
on_done(final_rc, captured)
t = threading.Thread(target=_runner, daemon=True, name=f"deploy-{run_id}")
t.start()
return t
# ---- GitHub push pipeline ------------------------------------------------
def github_push_pipeline(
cfg: GithubConfig,
*,
run_id: str,
out_dir: Path,
commit_message: str = "mindXtrain initial push",
force: bool = False,
registry: RunRegistry | None = None,
on_done: Callable[[int, dict[str, str]], None] | None = None,
) -> threading.Thread:
"""Drives the github_push step list. Captures HEAD sha to git_sha.txt."""
reg = registry if registry is not None else default_registry()
steps = bootstrap_steps(cfg, commit_message=commit_message, force=force)
# Wrap on_done so we can persist the captured HEAD sha + emit a clean
# guidance LogEvent if a stale remote was detected without --force.
def _on_done(rc: int, captured: dict[str, str]) -> None:
sha = captured.get("head-sha", "").strip()
existing_remote = captured.get("probe-remote", "").strip()
if sha:
target = write_sha_file(out_dir, sha)
_publish_log(reg, run_id, f" HEAD sha {sha} written to {target}")
if existing_remote and existing_remote != remote_url(cfg.repo) and not force:
_publish_log(
reg, run_id,
f" origin already points at {existing_remote!r}; "
f"re-run with force=true to switch to {remote_url(cfg.repo)!r}",
)
if on_done is not None:
with contextlib.suppress(Exception):
on_done(rc, captured)
return run_pipeline(steps, run_id=run_id, out_dir=out_dir, registry=reg, on_done=_on_done)
# ---- Existing-droplet sync pipeline -------------------------------------
def droplet_sync_pipeline(
cfg: DropletConfig,
*,
repo_root: Path,
run_id: str,
out_dir: Path,
run_bench: bool = True,
fetch_plan: bool = True,
registry: RunRegistry | None = None,
on_done: Callable[[int, dict[str, str]], None] | None = None,
) -> threading.Thread:
plan_dest = out_dir / "plan.remote.json"
steps = sync_steps(
cfg,
repo_root=repo_root,
run_bench=run_bench,
fetch_plan=fetch_plan,
plan_dest=plan_dest,
)
return run_pipeline(steps, run_id=run_id, out_dir=out_dir, registry=registry, on_done=on_done)
# ---- AMD Dev Cloud provision pipeline -----------------------------------
def droplet_provision_pipeline(
cloud_cfg: AmdDevCloudConfig,
*,
name: str,
repo: str,
branch: str,
container: str,
extras: str,
run_id: str,
out_dir: Path,
wait_for_bootstrap: bool = True,
recipe: str | None = None,
registry: RunRegistry | None = None,
client_factory: Callable[[AmdDevCloudConfig], AmdDevCloudClient] | None = None,
on_done: Callable[[int, dict[str, str]], None] | None = None,
) -> threading.Thread:
"""Create a droplet, wait for bootstrap, scp plan.json back, optionally
train + bridge training events into the run's SSE stream.
When `recipe` is set, cloud-init also runs `mindxtrain train` after
bench, and the orchestrator adds a fifth step that SSH-tails the
training log so Coach's loss chart populates live from the droplet.
Without `recipe`, behaviour is exactly as before (bench only).
"""
reg = registry if registry is not None else default_registry()
out_dir.mkdir(parents=True, exist_ok=True)
user_data = render(
repo=repo,
branch=branch,
container=container,
extras=extras,
recipe=recipe,
)
factory = client_factory or AmdDevCloudClient
def _log(line: str) -> None:
_publish_log(reg, run_id, line)
def _runner() -> None:
final_rc = 0
captured: dict[str, str] = {}
try:
with factory(cloud_cfg) as client:
_publish_log(reg, run_id, "=== step 1/4: create droplet ===")
droplet = client.create(name=name, user_data=user_data, log=_log)
droplet_id = int(droplet.get("id", 0))
captured["droplet_id"] = str(droplet_id)
(out_dir / "droplet_id.txt").write_text(str(droplet_id) + "\n")
_publish_log(reg, run_id, f"=== step 2/4: poll until active ({droplet_id}) ===")
droplet = client.poll_until_active(droplet_id, log=_log)
ip = extract_public_ip(droplet) or ""
captured["public_ip"] = ip
(out_dir / "public_ip.txt").write_text(ip + "\n")
if not ip:
_publish_log(reg, run_id, " (no public IPv4 returned; bailing out)")
final_rc = 2
return
if not wait_for_bootstrap:
_publish_log(reg, run_id, "wait_for_bootstrap=false — exiting after provision")
return
droplet_cfg = DropletConfig(
host=ip,
user="root",
ssh_key=cloud_cfg_ssh_key(cloud_cfg),
container=container,
extras=extras,
)
_publish_log(reg, run_id, "=== step 3/4: wait for ssh + tail cloud-init ===")
# Inline ssh-probe with a few retries; bench-stage cloud-init can take a while.
probe_steps = [
Step(label=f"ssh-probe-{i}", cmd=build_ssh_probe(droplet_cfg), allow_failure=True)
for i in range(60) # 60 * 5s = 5 min
]
log_file = _open_log_file(out_dir / "pipeline.log")
ready = False
try:
for ps in probe_steps:
rc = _stream_step(step=ps, registry=reg, run_id=run_id, log_file=log_file, capture=[])
if rc == 0:
ready = True
break
import time as _time
_time.sleep(5)
finally:
with contextlib.suppress(Exception):
log_file.close() # type: ignore[attr-defined]
if not ready:
_publish_log(reg, run_id, " ssh did not come up in 5 minutes; aborting")
final_rc = 3
return
tail_step = Step(label="cloud-init-tail", cmd=build_tail_cloud_init(droplet_cfg))
scp_step = Step(
label="scp-plan",
cmd=build_scp_plan_back(droplet_cfg, out_dir / "plan.remote.json"),
allow_failure=True,
)
total_steps = 5 if recipe else 4
log_file = _open_log_file(out_dir / "pipeline.log")
try:
_publish_log(reg, run_id, f" (waiting for {BOOTSTRAP_SENTINEL})")
rc = _stream_step(step=tail_step, registry=reg, run_id=run_id, log_file=log_file, capture=[])
if rc != 0:
final_rc = rc
return
_publish_log(reg, run_id, f"=== step 4/{total_steps}: scp plan.json back ===")
_stream_step(step=scp_step, registry=reg, run_id=run_id, log_file=log_file, capture=[])
if recipe:
_publish_log(
reg, run_id,
f"=== step 5/{total_steps}: bridge training log "
f"for recipe={recipe} ===",
)
train_tail = Step(
label="train-tail",
cmd=build_tail_training_log(droplet_cfg),
)
train_rc = _stream_step(
step=train_tail,
registry=reg,
run_id=run_id,
log_file=log_file,
capture=[],
parse_trainer=True,
)
if train_rc != 0:
_publish_log(
reg, run_id,
f" (remote train exit rc={train_rc})",
)
final_rc = train_rc
finally:
with contextlib.suppress(Exception):
log_file.close() # type: ignore[attr-defined]
except Exception as exc:
_publish_log(reg, run_id, f"!! {type(exc).__name__}: {exc}")
final_rc = 1
finally:
status = "succeeded" if final_rc == 0 else "failed"
reg.publish_threadsafe(
run_id,
StatusEvent(run_id=run_id, status=status, message=f"rc={final_rc}"),
)
reg.close_subscribers(run_id)
if on_done is not None:
with contextlib.suppress(Exception):
on_done(final_rc, captured)
t = threading.Thread(target=_runner, daemon=True, name=f"provision-{run_id}")
t.start()
return t
def cloud_cfg_ssh_key(cloud_cfg: AmdDevCloudConfig) -> str:
"""Default ssh key path used to SSH into a freshly-provisioned droplet.
The Dev Cloud control plane only stores the public key; the matching
private key must be on the operator host. We assume it lives at
`~/.ssh/id_ed25519` unless DROPLET_SSH_KEY is set.
"""
import os as _os
return _os.environ.get("DROPLET_SSH_KEY", "~/.ssh/id_ed25519")
__all__ = [
"droplet_provision_pipeline",
"droplet_sync_pipeline",
"github_push_pipeline",
"run_pipeline",
]
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