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e0265b9 1ac9744 e0265b9 1ac9744 e0265b9 1ac9744 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 | from __future__ import annotations
import logging
from datetime import datetime, timedelta, timezone
from pathlib import Path
from adam.job_manager import JobManager, JobWorker
from adam.models import ExecutionPlan, Job, JobStatus, PlanStep
from adam.training_assistant import append_preflight_summary
def test_submission_reviews_training_before_queueing(tmp_path: Path, monkeypatch) -> None:
manager = JobManager(tmp_path, None, logging.getLogger("test.jobs"))
starts = []
monkeypatch.setattr(manager, "_start_next", lambda: starts.append(True))
dataset = tmp_path / "planned_dataset"
plan = ExecutionPlan(
request="collect and train", summary="Collect and train.",
steps=[
PlanStep("dataset_collector", "Collect", "Collect", {
"output_dir": str(dataset), "image_count": 2000,
}),
PlanStep("ddpm_trainer", "Train", "Train", {
"dataset_dir": str(dataset), "epochs": 600, "batch_size": 1,
}),
],
)
job = manager.submit(plan)
assert job.status == JobStatus.AWAITING_CONFIRMATION
assert plan.orion_review["level"] == "warning"
assert "Pre-flight:" in plan.summary
assert starts == []
assert manager._queue == []
restored = JobManager(tmp_path, None, logging.getLogger("test.jobs"))
assert restored.jobs[0].plan.orion_review == plan.orion_review
assert restored.jobs[0].status == JobStatus.AWAITING_CONFIRMATION
def test_submission_preserves_an_already_reviewed_plan(tmp_path: Path) -> None:
manager = JobManager(tmp_path, None, logging.getLogger("test.jobs"))
plan = ExecutionPlan(
request="train", summary="Train.", requires_confirmation=True,
steps=[PlanStep("ddpm_trainer", "Train", "Train", {"epochs": 10})],
)
append_preflight_summary(plan, {})
summary = plan.summary
arguments = dict(plan.steps[0].arguments)
job = manager.submit(plan)
assert job.plan.summary == summary
assert job.plan.steps[0].arguments == arguments
assert summary.count("ORION —") == 1
assert summary.count("Pre-flight:") == 1
def _job(index: int, status: JobStatus = JobStatus.FINISHED) -> Job:
return Job(
id=f"JOB{index:04d}",
plan=ExecutionPlan(
request="test",
summary="Test job",
steps=[],
project_name=f"Project {index}",
),
status=status,
)
def test_job_history_is_not_silently_truncated(tmp_path: Path) -> None:
manager = JobManager(tmp_path, None, logging.getLogger("test.jobs")) # type: ignore[arg-type]
manager.jobs = [_job(index) for index in range(125)]
manager._save()
restored = JobManager(tmp_path, None, logging.getLogger("test.jobs")) # type: ignore[arg-type]
assert len(restored.jobs) == 125
def test_history_cleanup_only_removes_completed_and_failed(tmp_path: Path) -> None:
manager = JobManager(tmp_path, None, logging.getLogger("test.jobs")) # type: ignore[arg-type]
manager.jobs = [
_job(1, JobStatus.FINISHED),
_job(2, JobStatus.FAILED),
_job(3, JobStatus.CANCELLED),
_job(4, JobStatus.INTERRUPTED),
]
assert manager.remove_completed_or_failed() == 2
assert [job.status for job in manager.jobs] == [
JobStatus.CANCELLED,
JobStatus.INTERRUPTED,
]
def test_end_task_acknowledges_an_interrupted_job(tmp_path: Path) -> None:
manager = JobManager(tmp_path, None, logging.getLogger("test.jobs")) # type: ignore[arg-type]
job = _job(1, JobStatus.INTERRUPTED)
job.logs.append("[startup] Previous session ended before this job. Review it before retrying.")
manager.jobs = [job]
assert manager.end_task(job.id) is True
assert job.status == JobStatus.CANCELLED
assert not any("Previous session ended" in line for line in job.logs)
restored = JobManager(tmp_path, None, logging.getLogger("test.jobs")) # type: ignore[arg-type]
assert restored.jobs[0].status == JobStatus.CANCELLED
def test_approved_future_job_stays_scheduled_and_survives_restart(tmp_path: Path) -> None:
manager = JobManager(tmp_path, None, logging.getLogger("test.jobs")) # type: ignore[arg-type]
plan = ExecutionPlan(
request="train later", summary="Scheduled training", steps=[],
requires_confirmation=True,
)
start = (datetime.now(timezone.utc) + timedelta(hours=2)).isoformat()
job = manager.submit(plan, scheduled_for=start)
assert job.status == JobStatus.AWAITING_CONFIRMATION
manager.confirm(job.id)
assert job.status == JobStatus.SCHEDULED
restored = JobManager(tmp_path, None, logging.getLogger("test.jobs")) # type: ignore[arg-type]
assert restored.jobs[0].status == JobStatus.SCHEDULED
assert restored.jobs[0].scheduled_for == start
def test_due_schedule_queues_behind_an_active_job(tmp_path: Path) -> None:
manager = JobManager(tmp_path, None, logging.getLogger("test.jobs")) # type: ignore[arg-type]
job = _job(2, JobStatus.SCHEDULED)
job.scheduled_for = (datetime.now(timezone.utc) - timedelta(minutes=1)).isoformat()
manager.jobs = [job]
class BusyWorker:
@staticmethod
def isRunning() -> bool:
return True
manager._worker = BusyWorker() # type: ignore[assignment]
manager._release_due_scheduled()
assert job.status == JobStatus.QUEUED
assert manager._queue == [job.id]
def test_restart_preserves_queued_jobs_and_interrupts_only_active_work(tmp_path: Path) -> None:
manager = JobManager(tmp_path, None, logging.getLogger("test.jobs")) # type: ignore[arg-type]
queued = _job(1, JobStatus.QUEUED)
running = _job(2, JobStatus.RUNNING)
manager.jobs = [queued, running]
manager._save()
restored = JobManager(tmp_path, None, logging.getLogger("test.jobs")) # type: ignore[arg-type]
assert restored.jobs[0].status == JobStatus.QUEUED
assert restored.jobs[1].status == JobStatus.INTERRUPTED
assert restored._queue == [queued.id]
def test_worker_coalesces_rapid_progress_events() -> None:
plan = ExecutionPlan(
request="train",
summary="Training",
steps=[PlanStep("ddpm_trainer", "Train", "Run training")],
)
job = Job(id="FAST0001", plan=plan, status=JobStatus.RUNNING)
class NoisyExecutor:
def execute(self, _tool_id, _arguments, **kwargs):
for percent in range(1, 101):
kwargs["progress_callback"](percent, "same training burst")
return {}
worker = JobWorker(job, NoisyExecutor()) # type: ignore[arg-type]
events = []
worker.event.connect(events.append)
worker.run()
progress_events = [event for event in events if event.get("type") == "progress"]
assert 1 <= len(progress_events) <= 2
assert progress_events[-1]["overall"] == 100
def test_step_eta_uses_measured_progress_cadence() -> None:
samples: list[dict[str, float]] = []
first = JobWorker._estimate_step_eta(
{"current_step": 10, "total_steps": 110, "unit": "step"},
samples,
100.0,
)
second = JobWorker._estimate_step_eta(
{"current_step": 20, "total_steps": 110, "unit": "step"},
samples,
120.0,
)
assert "eta_seconds" not in first
assert second["eta_seconds"] == 180
assert second["progress_current"] == 20
assert second["progress_total"] == 110
assert second["progress_unit"] == "step"
assert second["progress_rate"] == 0.5
assert second["estimated_completion_at"]
def test_active_ddpm_adjustment_is_queued_on_worker(tmp_path: Path, monkeypatch) -> None:
manager = JobManager(tmp_path, None, logging.getLogger("test.jobs")) # type: ignore[arg-type]
job = Job(
plan=ExecutionPlan(
request="train", summary="Train",
steps=[PlanStep("ddpm_trainer", "Train", "Train", {
"batch_size": 8, "gradient_accumulation_steps": 1,
"training_intensity": 100, "epochs": 20,
})],
),
status=JobStatus.RUNNING,
current_step=0,
)
class Worker:
updates = None
def request_adjustment(self, updates):
self.updates = updates
worker = Worker()
manager.jobs = [job]
manager._active_job = job
manager._worker = worker # type: ignore[assignment]
monkeypatch.setattr(manager, "_save", lambda: None)
manager.request_training_adjustment(job.id, {
"batch_size": 4,
"gradient_accumulation_steps": 2,
"training_intensity": 75,
})
assert worker.updates == {
"batch_size": 4,
"gradient_accumulation_steps": 2,
"training_intensity": 75,
}
assert job.plan.steps[0].arguments["batch_size"] == 8
def test_vram_retry_uses_old_batch_to_calculate_completed_epochs(tmp_path: Path, monkeypatch) -> None:
dataset = tmp_path / "dataset"
output = tmp_path / "output"
checkpoint = output / "checkpoint-20"
(checkpoint / "unet").mkdir(parents=True)
dataset.mkdir()
for index in range(40):
(dataset / f"{index}.png").write_bytes(b"image")
(checkpoint / "unet" / "diffusion_pytorch_model.safetensors").write_bytes(b"weights")
(checkpoint / "optimizer.bin").write_bytes(b"optimizer")
(checkpoint / "scheduler.bin").write_bytes(b"scheduler")
manager = JobManager(tmp_path, None, logging.getLogger("test.jobs")) # type: ignore[arg-type]
failed = Job(
plan=ExecutionPlan(
request="train", summary="Train",
steps=[PlanStep("ddpm_trainer", "Train", "Train", {
"dataset_dir": str(dataset), "output_dir": str(output),
"epochs": 10, "batch_size": 4, "gradient_accumulation_steps": 1,
})],
),
status=JobStatus.FAILED,
current_step=0,
error="CUDA out of memory",
)
manager.jobs = [failed]
monkeypatch.setattr(manager, "_start_next", lambda: None)
retry = manager.safer_vram_retry(failed.id)
arguments = retry.plan.steps[0].arguments
assert arguments["batch_size"] == 2
assert arguments["gradient_accumulation_steps"] == 2
assert arguments["completed_epochs"] == 2
assert arguments["epochs"] == 8
assert arguments["resume_from"] == str(checkpoint)
def test_adjustment_ready_requeues_same_job_from_checkpoint(tmp_path: Path, monkeypatch) -> None:
manager = JobManager(tmp_path, None, logging.getLogger("test.jobs")) # type: ignore[arg-type]
job = Job(
plan=ExecutionPlan(
request="train", summary="Train",
steps=[PlanStep("ddpm_trainer", "Train", "Train", {
"epochs": 20, "batch_size": 8, "training_intensity": 100,
})],
),
status=JobStatus.RUNNING,
current_step=0,
)
manager.jobs = [job]
manager._active_job = job
monkeypatch.setattr(manager, "_save", lambda: None)
manager._handle_event({
"type": "adjustment_ready",
"checkpoint": str(tmp_path / "checkpoint-40"),
"completed_epochs": 4,
"updates": {"batch_size": 4, "training_intensity": 75},
})
arguments = job.plan.steps[0].arguments
assert job.status == JobStatus.QUEUED
assert manager._queue == [job.id]
assert arguments["epochs"] == 16
assert arguments["completed_epochs"] == 4
assert arguments["batch_size"] == 4
assert arguments["training_intensity"] == 75
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