Compare commits
1 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
 Richie
|
09f7f0187f |
@@ -1,7 +1,6 @@
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from __future__ import annotations
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from dataclasses import dataclass
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from datetime import date
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from pathlib import Path
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import tomllib
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@@ -69,50 +68,10 @@ class BenchmarkConfig:
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return cls(**raw)
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@dataclass
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class BertTopicTrainConfig:
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"""BERTopic training configuration loaded from TOML."""
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sample_rate: float
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min_text_length: int
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n_topics: int
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model_save_path: str
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model_version: str | None = None
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@classmethod
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def from_toml(cls, config_path: Path) -> BertTopicTrainConfig:
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"""Load BERTopic training config from a TOML file."""
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raw = tomllib.loads(config_path.read_text())["bertopic"]["train"]
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today = date.today().isoformat()
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if raw.get("model_version") is None:
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raw["model_version"] = (
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f"{today}-{raw['sample_rate']}-{raw['min_text_length']}-{raw['n_topics']}"
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)
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return cls(**raw)
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@dataclass
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class BertTopicInferConfig:
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"""BERTopic inference configuration loaded from TOML."""
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min_text_length: int
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poc_batch_size: int
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model_version: str
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model_save_path: str
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@classmethod
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def from_toml(cls, config_path: Path) -> BertTopicInferConfig:
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"""Load BERTopic inference config from a TOML file."""
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raw = tomllib.loads(config_path.read_text())["bertopic"]["infer"]
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return cls(**raw)
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def get_config_dir() -> Path:
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"""Get the path to the config file."""
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return Path(__file__).resolve().parent.parent.parent / "config"
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def default_config_path() -> Path:
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"""Get the path to the config file."""
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return get_config_dir() / "config.toml"
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@@ -128,19 +87,3 @@ def get_benchmark_config(config_path: Path | None = None) -> BenchmarkConfig:
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if config_path is None:
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config_path = default_config_path()
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return BenchmarkConfig.from_toml(config_path)
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def get_bertopic_train_config(
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config_path: Path | None = None,
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) -> BertTopicTrainConfig:
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if config_path is None:
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config_path = default_config_path()
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return BertTopicTrainConfig.from_toml(config_path)
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def get_bertopic_infer_config(
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config_path: Path | None = None,
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) -> BertTopicInferConfig:
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if config_path is None:
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config_path = default_config_path()
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return BertTopicInferConfig.from_toml(config_path)
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@@ -1,116 +0,0 @@
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"""Nornsight — BERTopic POC Inference Script.
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Loads the trained model and labels a small batch of posts,
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writing results to main.post_topic for inspection.
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POC: processes a single batch of 1k posts to validate the pipeline end-to-end.
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"""
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from __future__ import annotations
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import logging
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import time
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from collections import Counter
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from pathlib import Path
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from bertopic import BERTopic
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from sqlalchemy import Engine, func, insert, select
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from sqlalchemy.orm import Session
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from pipelines.config import BertTopicInferConfig, get_bertopic_infer_config
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from pipelines.orm.common import get_postgres_engine
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from pipelines.orm.data_science_dev.posts import PostTopic, Posts
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from pipelines.orm.data_science_dev.posts.lang_filters import ENGLISH_LANGS
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from pipelines.pipelines.common import configure_logger
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logger = logging.getLogger(__name__)
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def main() -> None:
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"""Run BERTopic inference against a sample of posts."""
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configure_logger()
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config = get_bertopic_infer_config()
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run_inference(config)
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logger.info(
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"POC inference complete. Check main.post_topic in DBeaver to inspect results."
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)
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def run_inference(config: BertTopicInferConfig) -> None:
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model_save_path = Path(config.model_save_path)
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logger.info(f"Loading BERTopic model from {model_save_path}")
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topic_model = BERTopic.load(str(model_save_path))
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topic_info = topic_model.get_topic_info()
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label_map: dict[int, str] = dict(zip(topic_info["Topic"], topic_info["Name"]))
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logger.info(f"Model loaded with {len(label_map)} topics")
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engine = get_postgres_engine(name="DATA_SCIENCE_DEV")
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post_ids, texts = get_post_ids_and_test(engine, config)
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logger.info(f"Fetched {len(texts)} posts")
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logger.info("Running BERTopic transform")
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start = time.perf_counter()
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topics, _probabilities = topic_model.transform(texts)
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elapsed = time.perf_counter() - start
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logger.info(f"Transform complete in {elapsed:.1f}s")
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# Write results to main.post_topic
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records = [
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{
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"post_id": pid,
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"topic_id": int(topic_id),
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"topic_label": label_map.get(int(topic_id), "unknown"),
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"model_version": config.model_version,
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}
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for pid, topic_id in zip(post_ids, topics)
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]
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with Session(engine) as session:
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session.execute(insert(PostTopic), records)
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session.commit()
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count_topics(records)
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logger.info(f"Wrote {len(records)} topic labels to main.post_topic")
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def get_post_ids_and_test(
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engine: Engine,
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config: BertTopicInferConfig,
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) -> None | tuple[list[int], list[str]]:
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with Session(engine) as session:
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logger.info(f"Fetching {config.poc_batch_size} posts for inference")
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# Pull a fresh batch for inference — distinct from training sample
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# using a fixed seed offset so we're not re-labeling training posts
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stmt = select(Posts).where(
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Posts.text.is_not(None),
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Posts.langs.in_(ENGLISH_LANGS),
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func.length(Posts.text) > config.min_text_length,
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)
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if config.poc_batch_size > 0:
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stmt = stmt.limit(config.poc_batch_size)
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posts = session.scalars(stmt).all()
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if not posts:
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logger.warning("No posts were selected for inference")
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return [], []
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post_ids = [post.post_id for post in posts]
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texts = [post.text.strip() for post in posts]
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return post_ids, texts
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def count_topics(records: list[dict]) -> None:
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topic_counts = Counter(record.get("topic_label", "unknown") for record in records)
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logger.info("Topic distribution in this batch:")
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for label, count in topic_counts.most_common(10):
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logger.info(" %s: %d", label, count)
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if __name__ == "__main__":
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main()
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@@ -1,119 +0,0 @@
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"""Nornsight — BERTopic POC Training Script.
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Pulls a small stratified sample (~11.5k posts) from main.posts,
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trains BERTopic with MiniBatchKMeans on Jeeves, and saves the model locally.
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POC sample rate: random() < 0.00005 (~0.005% of 230M = ~11.5k posts)
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Full training rate will be: random() < 0.005 (~1.08M posts)
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"""
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from __future__ import annotations
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import logging
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import time
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from pathlib import Path
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from bertopic import BERTopic
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from sklearn.cluster import MiniBatchKMeans
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from sqlalchemy import func, select
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from sqlalchemy.orm import Session
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from pipelines.config import BertTopicTrainConfig, get_bertopic_train_config
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from pipelines.orm.common import get_postgres_engine
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from pipelines.orm.data_science_dev.posts import Posts
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from pipelines.orm.data_science_dev.posts.lang_filters import ENGLISH_LANGS
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from pipelines.pipelines.common import configure_logger
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logger = logging.getLogger(__name__)
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def main() -> None:
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"""Train and persist the BERTopic model."""
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configure_logger()
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config = get_bertopic_train_config()
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docs = load_sample(config)
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if not docs:
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logger.warning("No training documents were selected")
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return
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train(docs, config)
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logger.info(f"Done. Model saved as version {config.model_version}")
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logger.info("Next: run infer.py to label a sample of posts in the database")
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def load_sample(config: BertTopicTrainConfig) -> list[str]:
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logger.info("Connecting to PostgreSQL via SQLAlchemy")
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engine = get_postgres_engine(name="DATA_SCIENCE_DEV")
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logger.info(f"Pulling sample from main.posts (sample_rate={config.sample_rate})")
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start = time.perf_counter()
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with Session(engine) as session:
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texts = session.scalars(
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select(Posts.text).where(
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Posts.text.is_not(None),
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Posts.langs.in_(ENGLISH_LANGS),
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func.length(Posts.text) > config.min_text_length,
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func.random() < config.sample_rate,
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)
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).all()
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elapsed = time.perf_counter() - start
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logger.info(f"Fetched {len(texts)} rows in {elapsed:.1f}s")
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# Basic cleaning — strip whitespace and deduplicate
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docs = list({text.strip() for text in texts})
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logger.info(f"After cleaning and dedup: {len(docs)} posts")
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return docs
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def train(docs: list[str], config: BertTopicTrainConfig) -> None:
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logger.info(
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f"Initialising BERTopic with MiniBatchKMeans (n_topics={config.n_topics})"
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)
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cluster_model = MiniBatchKMeans(
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n_clusters=config.n_topics,
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random_state=42,
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batch_size=1024,
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n_init=3,
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verbose=1,
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)
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topic_model = BERTopic(
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hdbscan_model=cluster_model,
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language="english",
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calculate_probabilities=False, # saves memory
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verbose=True,
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)
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logger.info(f"Starting fit_transform on {len(docs)} posts (CPU)")
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start = time.perf_counter()
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topic_model.fit_transform(docs)
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elapsed = time.perf_counter() - start
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logger.info(f"Training complete in {elapsed:.1f}s ({elapsed / 60:.1f} min)")
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# Log topic summary for quick inspection
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topic_info = topic_model.get_topic_info()
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logger.info(f"Topics found: {len(topic_info)}")
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logger.info(f"\n{topic_info.to_string()}")
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model_save_path = Path(config.model_save_path)
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model_save_path.mkdir(parents=True, exist_ok=True)
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logger.info(f"Saving model to {model_save_path}")
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topic_model.save(
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str(model_save_path),
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serialization="safetensors",
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save_ctfidf=True,
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save_embedding_model=True,
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)
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logger.info("Model saved")
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if __name__ == "__main__":
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main()
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@@ -0,0 +1,235 @@
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"""Docker container lifecycle management for BERTopic jobs on Jeeves."""
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from __future__ import annotations
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import logging
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import os
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import subprocess
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from pathlib import Path
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from typing import Annotated, Literal
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import typer
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logger = logging.getLogger(__name__)
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JOBMode = Literal["train", "infer"]
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IMAGE_NAME = "bert-topic:latest"
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REPO_DIR = Path(__file__).resolve().parents[3]
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DEFAULT_CACHE_ROOT = Path("/zfs/storage/main/ds_thing/models/bert_topic")
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DEFAULT_POSTGRES_SOCKET_DIR = Path("/run/postgresql")
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DB_ENV_VARS = (
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"DATA_SCIENCE_DEV_DB",
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"DATA_SCIENCE_DEV_HOST",
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"DATA_SCIENCE_DEV_PORT",
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"DATA_SCIENCE_DEV_USER",
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"DATA_SCIENCE_DEV_PASSWORD",
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)
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app = typer.Typer(help="BERTopic container management.")
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def _container_name(mode: JOBMode) -> str:
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"""Return the Docker container name for the selected BERTopic job."""
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return f"bert-topic-{mode}"
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def _module_name(mode: JOBMode) -> str:
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"""Return the Python module to run inside the container."""
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return f"pipelines.bert_topic.{mode}"
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def _env_args(*, use_postgres_socket: bool) -> list[str]:
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"""Pass through database environment variables from the host shell."""
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required = [
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"DATA_SCIENCE_DEV_DB",
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"DATA_SCIENCE_DEV_PORT",
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"DATA_SCIENCE_DEV_USER",
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]
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if not use_postgres_socket:
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required.append("DATA_SCIENCE_DEV_HOST")
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missing = [name for name in required if not os.getenv(name)]
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if missing:
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message = "Missing required database environment variables: " + ", ".join(
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missing
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)
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raise RuntimeError(message)
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args: list[str] = []
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if use_postgres_socket:
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args.extend(["-e", f"DATA_SCIENCE_DEV_HOST={DEFAULT_POSTGRES_SOCKET_DIR}"])
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for name in DB_ENV_VARS:
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if use_postgres_socket and name == "DATA_SCIENCE_DEV_HOST":
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continue
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if os.getenv(name):
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args.extend(["-e", name])
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return args
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def build_image() -> None:
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"""Build the BERTopic Docker image."""
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dockerfile = REPO_DIR / "pipelines/containers/docker_files/Dockerfile.bert_topic"
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logger.info("Building BERTopic image: %s", IMAGE_NAME)
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result = subprocess.run(
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[
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"docker",
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"build",
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"--network",
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"host",
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"-f",
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str(dockerfile),
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"-t",
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IMAGE_NAME,
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str(REPO_DIR),
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],
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capture_output=True,
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text=True,
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check=False,
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)
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if result.returncode != 0:
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message = (
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"Failed to build BERTopic image. "
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f"docker build stderr:\n{result.stderr.strip()}"
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)
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raise RuntimeError(message)
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logger.info("Image built: %s", IMAGE_NAME)
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def stop_job(*, mode: JOBMode) -> None:
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"""Stop and remove the BERTopic container for the selected mode."""
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container_name = _container_name(mode)
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logger.info("Stopping BERTopic container: %s", container_name)
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subprocess.run(["docker", "stop", container_name], capture_output=True, check=False)
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subprocess.run(
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["docker", "rm", "-f", container_name], capture_output=True, check=False
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)
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def start_job(
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*,
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mode: JOBMode,
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cache_root: Path = DEFAULT_CACHE_ROOT,
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postgres_socket_dir: Path = DEFAULT_POSTGRES_SOCKET_DIR,
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detach: bool = False,
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) -> None:
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"""Run BERTopic training or inference in Docker on Jeeves."""
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cache_root = cache_root.resolve()
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cache_root.mkdir(parents=True, exist_ok=True)
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postgres_socket_dir = postgres_socket_dir.resolve()
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stop_job(mode=mode)
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use_postgres_socket = postgres_socket_dir.exists()
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command = [
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"docker",
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"run",
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"--name",
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_container_name(mode),
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"--ipc=host",
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"-v",
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f"{cache_root}:/cache",
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*_env_args(use_postgres_socket=use_postgres_socket),
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IMAGE_NAME,
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_module_name(mode),
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]
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if use_postgres_socket:
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command[7:7] = ["-v", f"{postgres_socket_dir}:{DEFAULT_POSTGRES_SOCKET_DIR}"]
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if detach:
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command.insert(2, "-d")
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logger.info("Starting BERTopic %s container", mode)
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logger.info(" Cache root: %s", cache_root)
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if use_postgres_socket:
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logger.info(" Postgres socket: %s", postgres_socket_dir)
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result = subprocess.run(command, text=True, capture_output=detach, check=False)
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if result.returncode != 0:
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detail = (
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result.stderr.strip() if result.stderr else f"exit code {result.returncode}"
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)
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raise RuntimeError(f"BERTopic container failed to start: {detail}")
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if detach:
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logger.info("Container started: %s", result.stdout.strip()[:12])
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else:
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logger.info("BERTopic %s run complete", mode)
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def logs_job(*, mode: JOBMode) -> str | None:
|
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"""Return recent logs from the BERTopic container, or None if absent."""
|
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result = subprocess.run(
|
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["docker", "logs", "--tail", "100", _container_name(mode)],
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||||
capture_output=True,
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||||
text=True,
|
||||
check=False,
|
||||
)
|
||||
if result.returncode != 0:
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return None
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return result.stdout + result.stderr
|
||||
|
||||
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||||
@app.command()
|
||||
def build(
|
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log_level: Annotated[str, typer.Option(help="Log level")] = "INFO",
|
||||
) -> None:
|
||||
"""Build the BERTopic Docker image."""
|
||||
logging.basicConfig(
|
||||
level=log_level,
|
||||
format="%(asctime)s %(levelname)s %(name)s: %(message)s",
|
||||
)
|
||||
build_image()
|
||||
|
||||
|
||||
@app.command("run")
|
||||
def run_job_command(
|
||||
mode: Annotated[JOBMode, typer.Option(help="Which BERTopic job to run")] = "train",
|
||||
cache_root: Annotated[
|
||||
Path, typer.Option(help="Host path mounted to /cache for model and HF cache")
|
||||
] = DEFAULT_CACHE_ROOT,
|
||||
postgres_socket_dir: Annotated[
|
||||
Path, typer.Option(help="Host Postgres socket directory to mount into the container")
|
||||
] = DEFAULT_POSTGRES_SOCKET_DIR,
|
||||
detach: Annotated[
|
||||
bool, typer.Option(help="Start the container in the background")
|
||||
] = False,
|
||||
log_level: Annotated[str, typer.Option(help="Log level")] = "INFO",
|
||||
) -> None:
|
||||
"""Run BERTopic training or inference inside Docker."""
|
||||
logging.basicConfig(
|
||||
level=log_level,
|
||||
format="%(asctime)s %(levelname)s %(name)s: %(message)s",
|
||||
)
|
||||
start_job(
|
||||
mode=mode,
|
||||
cache_root=cache_root,
|
||||
postgres_socket_dir=postgres_socket_dir,
|
||||
detach=detach,
|
||||
)
|
||||
|
||||
|
||||
@app.command("stop")
|
||||
def stop_job_command(
|
||||
mode: Annotated[
|
||||
JOBMode, typer.Option(help="Which BERTopic container to stop")
|
||||
] = "train",
|
||||
) -> None:
|
||||
"""Stop and remove the BERTopic container."""
|
||||
stop_job(mode=mode)
|
||||
|
||||
|
||||
@app.command("logs")
|
||||
def logs_job_command(
|
||||
mode: Annotated[
|
||||
JOBMode, typer.Option(help="Which BERTopic container logs to show")
|
||||
] = "train",
|
||||
) -> None:
|
||||
"""Show recent logs from the BERTopic container."""
|
||||
output = logs_job(mode=mode)
|
||||
if output is None:
|
||||
typer.echo(f"No BERTopic container found for mode={mode}.")
|
||||
raise typer.Exit(code=1)
|
||||
typer.echo(output)
|
||||
|
||||
|
||||
def cli() -> None:
|
||||
"""Typer entry point."""
|
||||
app()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
cli()
|
||||
@@ -0,0 +1,38 @@
|
||||
FROM python:3.12-bookworm
|
||||
|
||||
ENV DEBIAN_FRONTEND=noninteractive
|
||||
ENV PYTHONUNBUFFERED=1
|
||||
ENV PIP_NO_CACHE_DIR=1
|
||||
|
||||
RUN apt-get update && apt-get install -y \
|
||||
build-essential \
|
||||
gcc \
|
||||
g++ \
|
||||
git \
|
||||
libgomp1 \
|
||||
libpq-dev \
|
||||
&& rm -rf /var/lib/apt/lists/*
|
||||
|
||||
WORKDIR /app
|
||||
|
||||
COPY pipelines ./pipelines
|
||||
|
||||
RUN python -m pip install --upgrade pip setuptools wheel && \
|
||||
python -m pip install \
|
||||
torch \
|
||||
--index-url https://download.pytorch.org/whl/cpu && \
|
||||
python -m pip install \
|
||||
typer \
|
||||
sqlalchemy \
|
||||
bertopic \
|
||||
sentence-transformers \
|
||||
scikit-learn \
|
||||
pandas \
|
||||
numpy \
|
||||
"psycopg[binary]"
|
||||
|
||||
ENV HF_HOME=/cache/huggingface
|
||||
ENV TRANSFORMERS_CACHE=/cache/huggingface
|
||||
|
||||
ENTRYPOINT ["python", "-m"]
|
||||
CMD ["pipelines.bert_topic.train"]
|
||||
@@ -0,0 +1,11 @@
|
||||
FROM ghcr.io/unslothai/unsloth:latest
|
||||
|
||||
RUN pip install --no-cache-dir typer
|
||||
|
||||
WORKDIR /workspace
|
||||
COPY python/prompt_bench/finetune.py python/prompt_bench/finetune.py
|
||||
COPY config/prompts/summarization_prompts.toml config/prompts/summarization_prompts.toml
|
||||
COPY python/prompt_bench/__init__.py python/prompt_bench/__init__.py
|
||||
COPY python/__init__.py python/__init__.py
|
||||
|
||||
ENTRYPOINT ["python", "-m", "pipelines.prompt_bench.finetune"]
|
||||
@@ -9,7 +9,7 @@ from typing import Annotated
|
||||
|
||||
import typer
|
||||
|
||||
from pipelines.tools.containers.lib import check_gpu_free
|
||||
from pipelines.pipelines.containers.lib import check_gpu_free
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@@ -27,7 +27,7 @@ def build_image() -> None:
|
||||
"docker",
|
||||
"build",
|
||||
"-f",
|
||||
str(REPO_DIR / "python/prompt_bench/Dockerfile.finetune"),
|
||||
str(REPO_DIR / "pipelines/containers/docker_files/Dockerfile.finetune"),
|
||||
"-t",
|
||||
FINETUNE_IMAGE,
|
||||
".",
|
||||
@@ -1,25 +0,0 @@
|
||||
# Unsloth fine-tuning container for Qwen 3.5 4B on RTX 3090.
|
||||
#
|
||||
# Build:
|
||||
# docker build -f python/prompt_bench/Dockerfile.finetune -t bill-finetune .
|
||||
#
|
||||
# Run:
|
||||
# docker run --rm --device=nvidia.com/gpu=all --ipc=host \
|
||||
# -v $(pwd)/output:/workspace/output \
|
||||
# -v $(pwd)/output/finetune_dataset.jsonl:/workspace/dataset.jsonl:ro \
|
||||
# -v /zfs/models/hf:/models \
|
||||
# bill-finetune \
|
||||
# --dataset /workspace/dataset.jsonl \
|
||||
# --output-dir /workspace/output/qwen-bill-summarizer
|
||||
|
||||
FROM ghcr.io/unslothai/unsloth:latest
|
||||
|
||||
RUN pip install --no-cache-dir typer
|
||||
|
||||
WORKDIR /workspace
|
||||
COPY python/prompt_bench/finetune.py python/prompt_bench/finetune.py
|
||||
COPY config/prompts/summarization_prompts.toml config/prompts/summarization_prompts.toml
|
||||
COPY python/prompt_bench/__init__.py python/prompt_bench/__init__.py
|
||||
COPY python/__init__.py python/__init__.py
|
||||
|
||||
ENTRYPOINT ["python", "-m", "pipelines.prompt_bench.finetune"]
|
||||
Reference in New Issue
Block a user