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Now a separate thread is started to execute training jobs. Training requests now return job ID before the job completes. (Which fixes API timeouts for any jobs that take longer than a minute.) Note: the scheduler code is meant to be spun out in the future into a common provider service that can be reused for different APIs and providers. It is also expected to back the /jobs API proposed here: https://github.com/meta-llama/llama-stack/discussions/1238 Hence its somewhat generalized form which is expected to simplify its adoption elsewhere in the future. Note: this patch doesn't attempt to implement missing APIs (e.g. cancel or job removal). This work will belong to follow-up PRs. Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
120 lines
3.4 KiB
Python
120 lines
3.4 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import asyncio
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import pytest
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from llama_stack.providers.utils.scheduler import JobStatus, Scheduler
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@pytest.mark.asyncio
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async def test_scheduler_unknown_backend():
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with pytest.raises(ValueError):
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Scheduler(backend="unknown")
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@pytest.mark.asyncio
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async def test_scheduler_naive():
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sched = Scheduler()
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# make sure the scheduler starts empty
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with pytest.raises(ValueError):
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sched.get_job("unknown")
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assert sched.get_jobs() == []
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called = False
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# schedule a job that will exercise the handlers
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async def job_handler(on_log, on_status, on_artifact):
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nonlocal called
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called = True
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# exercise the handlers
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on_log("test log1")
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on_log("test log2")
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on_artifact({"type": "type1", "path": "path1"})
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on_artifact({"type": "type2", "path": "path2"})
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on_status(JobStatus.completed)
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job_id = "test_job_id"
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job_type = "test_job_type"
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sched.schedule(job_type, job_id, job_handler)
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# make sure the job was properly registered
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with pytest.raises(ValueError):
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sched.get_job("unknown")
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assert sched.get_job(job_id) is not None
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assert sched.get_jobs() == [sched.get_job(job_id)]
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assert sched.get_jobs("unknown") == []
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assert sched.get_jobs(job_type) == [sched.get_job(job_id)]
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# now shut the scheduler down and make sure the job ran
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await sched.shutdown()
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assert called
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job = sched.get_job(job_id)
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assert job is not None
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assert job.status == JobStatus.completed
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assert job.scheduled_at is not None
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assert job.started_at is not None
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assert job.completed_at is not None
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assert job.scheduled_at < job.started_at < job.completed_at
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assert job.artifacts == [
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{"type": "type1", "path": "path1"},
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{"type": "type2", "path": "path2"},
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]
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assert [msg[1] for msg in job.logs] == ["test log1", "test log2"]
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assert job.logs[0][0] < job.logs[1][0]
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@pytest.mark.asyncio
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async def test_scheduler_naive_handler_raises():
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sched = Scheduler()
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async def failing_job_handler(on_log, on_status, on_artifact):
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on_status(JobStatus.running)
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raise ValueError("test error")
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job_id = "test_job_id1"
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job_type = "test_job_type"
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sched.schedule(job_type, job_id, failing_job_handler)
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job = sched.get_job(job_id)
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assert job is not None
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# confirm the exception made the job transition to failed state, even
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# though it was set to `running` before the error
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for _ in range(10):
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if job.status == JobStatus.failed:
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break
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await asyncio.sleep(0.1)
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assert job.status == JobStatus.failed
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# confirm that the raised error got registered in log
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assert job.logs[0][1] == "test error"
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# even after failed job, we can schedule another one
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called = False
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async def successful_job_handler(on_log, on_status, on_artifact):
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nonlocal called
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called = True
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on_status(JobStatus.completed)
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job_id = "test_job_id2"
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sched.schedule(job_type, job_id, successful_job_handler)
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await sched.shutdown()
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assert called
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job = sched.get_job(job_id)
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assert job is not None
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assert job.status == JobStatus.completed
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