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feat: add auto-generated CI documentation pre-commit hook (#2890)

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Our CI is entirely undocumented, this commit adds a README.md file with
a table of the current CI and what is does

---------

Signed-off-by: Nathan Weinberg <nweinber@redhat.com>
This commit is contained in:
Nathan Weinberg 2025-07-25 11:57:01 -04:00 • committed by Mustafa Elbehery
parent 7f834339ba
commit b381ed6d64
93 changed files with 495 additions and 477 deletions
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40
tests/integration/post_training/test_post_training.py
View file

@ -4,7 +4,6 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import logging
import sys
import time
import uuid
@ -19,10 +18,9 @@ from llama_stack.apis.post_training import (
LoraFinetuningConfig,
TrainingConfig,
)
from llama_stack.log import get_logger
# Configure logging
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s", force=True)
logger = logging.getLogger(__name__)
log = get_logger(name=__name__, category="core")
skip_because_resource_intensive = pytest.mark.skip(
@ -71,14 +69,14 @@ class TestPostTraining:
)
@pytest.mark.timeout(360) # 6 minutes timeout
def test_supervised_fine_tune(self, llama_stack_client, purpose, source):
logger.info("Starting supervised fine-tuning test")
log.info("Starting supervised fine-tuning test")
# register dataset to train
dataset = llama_stack_client.datasets.register(
purpose=purpose,
source=source,
)
logger.info(f"Registered dataset with ID: {dataset.identifier}")
log.info(f"Registered dataset with ID: {dataset.identifier}")
algorithm_config = LoraFinetuningConfig(
type="LoRA",
@ -105,7 +103,7 @@ class TestPostTraining:
)
job_uuid = f"test-job{uuid.uuid4()}"
logger.info(f"Starting training job with UUID: {job_uuid}")
log.info(f"Starting training job with UUID: {job_uuid}")
# train with HF trl SFTTrainer as the default
_ = llama_stack_client.post_training.supervised_fine_tune(
@ -121,21 +119,21 @@ class TestPostTraining:
while True:
status = llama_stack_client.post_training.job.status(job_uuid=job_uuid)
if not status:
logger.error("Job not found")
log.error("Job not found")
break
logger.info(f"Current status: {status}")
log.info(f"Current status: {status}")
assert status.status in ["scheduled", "in_progress", "completed"]
if status.status == "completed":
break
logger.info("Waiting for job to complete...")
log.info("Waiting for job to complete...")
time.sleep(10) # Increased sleep time to reduce polling frequency
artifacts = llama_stack_client.post_training.job.artifacts(job_uuid=job_uuid)
logger.info(f"Job artifacts: {artifacts}")
log.info(f"Job artifacts: {artifacts}")
logger.info(f"Registered dataset with ID: {dataset.identifier}")
log.info(f"Registered dataset with ID: {dataset.identifier}")
# TODO: Fix these tests to properly represent the Jobs API in training
#
@ -181,17 +179,21 @@ class TestPostTraining:
)
@pytest.mark.timeout(360)
def test_preference_optimize(self, llama_stack_client, purpose, source):
logger.info("Starting DPO preference optimization test")
log.info("Starting DPO preference optimization test")
# register preference dataset to train
dataset = llama_stack_client.datasets.register(
purpose=purpose,
source=source,
)
logger.info(f"Registered preference dataset with ID: {dataset.identifier}")
log.info(f"Registered preference dataset with ID: {dataset.identifier}")
# DPO algorithm configuration
algorithm_config = DPOAlignmentConfig(
reward_scale=1.0,
reward_clip=10.0,
epsilon=1e-8,
gamma=0.99,
beta=0.1,
loss_type=DPOLossType.sigmoid, # Default loss type
)
@ -211,7 +213,7 @@ class TestPostTraining:
)
job_uuid = f"test-dpo-job-{uuid.uuid4()}"
logger.info(f"Starting DPO training job with UUID: {job_uuid}")
log.info(f"Starting DPO training job with UUID: {job_uuid}")
# train with HuggingFace DPO implementation
_ = llama_stack_client.post_training.preference_optimize(
@ -226,15 +228,15 @@ class TestPostTraining:
while True:
status = llama_stack_client.post_training.job.status(job_uuid=job_uuid)
if not status:
logger.error("DPO job not found")
log.error("DPO job not found")
break
logger.info(f"Current DPO status: {status}")
log.info(f"Current DPO status: {status}")
if status.status == "completed":
break
logger.info("Waiting for DPO job to complete...")
log.info("Waiting for DPO job to complete...")
time.sleep(10) # Increased sleep time to reduce polling frequency
artifacts = llama_stack_client.post_training.job.artifacts(job_uuid=job_uuid)
logger.info(f"DPO job artifacts: {artifacts}")
log.info(f"DPO job artifacts: {artifacts}")

9
tests/integration/vector_io/test_openai_vector_stores.py
View file

@ -4,7 +4,6 @@
# This source code is licensed under the terms described in the LICENSE file in
# the root directory of this source tree.
import logging
import time
from io import BytesIO
@ -13,8 +12,10 @@ from llama_stack_client import BadRequestError, LlamaStackClient
from openai import BadRequestError as OpenAIBadRequestError
from llama_stack.apis.vector_io import Chunk
from llama_stack.core.library_client import LlamaStackAsLibraryClient
from llama_stack.log import get_logger
logger = logging.getLogger(__name__)
log = get_logger(name=__name__, category="vector-io")
def skip_if_provider_doesnt_support_openai_vector_stores(client_with_models):
@ -99,7 +100,7 @@ def compat_client_with_empty_stores(compat_client):
compat_client.vector_stores.delete(vector_store_id=store.id)
except Exception:
# If the API is not available or fails, just continue
logger.warning("Failed to clear vector stores")
log.warning("Failed to clear vector stores")
pass
def clear_files():
@ -109,7 +110,7 @@ def compat_client_with_empty_stores(compat_client):
compat_client.files.delete(file_id=file.id)
except Exception:
# If the API is not available or fails, just continue
logger.warning("Failed to clear files")
log.warning("Failed to clear files")
pass
clear_vector_stores()