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Runtime error
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init
Browse files- Dockerfile +16 -0
- chunk_config.json +10 -0
- embed_config.json +8 -0
- home.html +18 -0
- requirements.txt +8 -0
- src/__init__.py +0 -0
- src/main.py +185 -0
- src/models.py +51 -0
- style.css +28 -0
Dockerfile
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FROM python:3.9
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RUN useradd -m -u 1000 user
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USER user
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ENV HOME=/home/user \
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PATH=/home/user/.local/bin:$PATH
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WORKDIR $HOME/app
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COPY --chown=user requirements.txt requirements.txt
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RUN pip install --no-cache-dir --upgrade -r requirements.txt
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COPY --chown=user . .
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CMD ["uvicorn", "src.main:app", "--host", "0.0.0.0", "--port", "7860"]
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chunk_config.json
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{
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"input_dataset": "sergeipetrov/transformers-diffusers-docs-raw",
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"input_splits": ["train"],
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"input_text_col": "text",
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"output_dataset": "sergeipetrov/transformers-diffusers-docs-chunked",
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"strategy": "spacy",
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"split_seq": "\n\n",
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"chunk_len": 512,
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"private": "false"
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}
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embed_config.json
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{
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"input_dataset": "sergeipetrov/transformers-diffusers-docs-chunked",
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"input_splits": ["train"],
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"input_text_col": "text",
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"output_dataset": "sergeipetrov/transformers-diffusers-docs-embed",
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"private": "false",
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"semaphore_bound": 5
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}
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home.html
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<!DOCTYPE html>
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<html>
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<head>
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<meta charset="utf-8" />
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<meta name="viewport" content="width=device-width" />
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<title>Auto Re-Train</title>
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<link rel="stylesheet" href="style.css" />
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</head>
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<body>
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<div class="card">
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<h1>Auto Re-Train webhook</h1>
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<p>This is a webhook space to auto-retrain on model when a dataset changes.</p>
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<p>Check out the guide <a href="https://huggingface.co/docs/hub/webhooks-guide-auto-retrain" target="_blank">here</a>!</p>
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</div>
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</body>
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</html>
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requirements.txt
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fastapi==0.74.*
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requests==2.27.*
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huggingface_hub==0.11.*
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uvicorn[standard]==0.17.*
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numpy==1.25.*
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datasets==2.16.*
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langchain==0.0.*
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aiohttp==3.8.*
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src/__init__.py
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src/main.py
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import asyncio
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import logging
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import numpy as np
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import time
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import json
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import os
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import tempfile
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import requests
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from fastapi import FastAPI, Header, HTTPException, BackgroundTasks
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from fastapi.responses import FileResponse
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from aiohttp import ClientSession
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from langchain.text_splitter import SpacyTextSplitter
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from datasets import Dataset, load_dataset
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from tqdm import tqdm
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from tqdm.asyncio import tqdm_asyncio
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from src.models import chunk_config, embed_config, WebhookPayload
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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HF_TOKEN = os.getenv("HF_TOKEN")
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TEI_URL = os.getenv("TEI_URL")
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app = FastAPI()
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@app.get("/")
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async def home():
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return FileResponse("home.html")
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@app.post("/webhook")
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async def post_webhook(
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payload: WebhookPayload,
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task_queue: BackgroundTasks
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):
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if not (
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payload.event.action == "update"
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and payload.event.scope.startswith("repo.content")
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and (
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payload.repo.name == embed_config.input_dataset
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# or payload.repo.name == chunk_config.input_dataset
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)
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and payload.repo.type == "dataset"
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):
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# no-op
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logger.info("Update detected, no action taken")
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return {"processed": False}
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if payload.repo.name == chunk_config.input_dataset:
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task_queue.add_task(chunk_dataset)
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task_queue.add_task(embed_dataset)
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return {"processed": True}
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"""
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CHUNKING
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"""
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class Chunker:
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def __init__(self, strategy, split_seq, chunk_len):
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self.split_seq = split_seq
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self.chunk_len = chunk_len
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if strategy == "spacy":
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self.split = SpacyTextSplitter().split_text
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if strategy == "sequence":
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self.split = self.seq_splitter
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if strategy == "constant":
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self.split = self.const_splitter
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def seq_splitter(self, text):
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return text.split(self.split_seq)
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def const_splitter(self, text):
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return [
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text[i * self.chunk_len:(i + 1) * self.chunk_len]
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for i in range(int(np.ceil(len(text) / self.chunk_len)))
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]
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def chunk_generator(input_dataset, chunker):
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for i in tqdm(range(len(input_dataset))):
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chunks = chunker.split(input_dataset[i][chunk_config.input_text_col])
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for chunk in chunks:
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if chunk:
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yield {chunk_config.input_text_col: chunk}
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def chunk_dataset():
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logger.info("Update detected, chunking is scheduled")
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input_ds = load_dataset(chunk_config.input_dataset, split=chunk_config.input_splits)
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chunker = Chunker(
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strategy=chunk_config.strategy,
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split_seq=chunk_config.split_seq,
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chunk_len=chunk_config.chunk_len
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)
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dataset = Dataset.from_generator(
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chunk_generator,
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gen_kwargs={
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"input_dataset": input_ds,
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"chunker": chunker
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}
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)
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dataset.push_to_hub(
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chunk_config.output_dataset,
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private=chunk_config.private,
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token=HF_TOKEN
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)
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logger.info("Done chunking")
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return {"processed": True}
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"""
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EMBEDDING
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"""
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async def embed_sent(sentence, semaphore, tei_url, tmp_file):
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async with semaphore:
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payload = {
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"inputs": sentence,
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"truncate": True
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}
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async with ClientSession(
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headers={
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"Content-Type": "application/json",
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"Authorization": f"Bearer {HF_TOKEN}"
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}
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) as session:
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async with session.post(tei_url, json=payload) as resp:
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if resp.status != 200:
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raise RuntimeError(await resp.text())
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result = await resp.json()
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tmp_file.write(
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json.dumps({"vector": result[0], chunk_config.input_text_col: sentence}) + "\n"
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)
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async def embed(input_ds, tei_url, temp_file):
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semaphore = asyncio.BoundedSemaphore(embed_config.semaphore_bound)
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jobs = [
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asyncio.create_task(embed_sent(row[chunk_config.input_text_col], semaphore, tei_url, temp_file))
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for row in input_ds if row[chunk_config.input_text_col].strip()
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]
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logger.info(f"num chunks to embed: {len(jobs)}")
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tic = time.time()
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await tqdm_asyncio.gather(*jobs)
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logger.info(f"embed time: {time.time() - tic}")
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def wake_up_endpoint(url):
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while requests.get(
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url=url,
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headers={"Authorization": f"Bearer {HF_TOKEN}"}
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).status_code != 200:
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time.sleep(2)
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logger.info("TEI endpoint is up")
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def embed_dataset():
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logger.info("Update detected, embedding is scheduled")
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wake_up_endpoint(embed_config.tei_url)
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input_ds = load_dataset(embed_config.input_dataset, split=embed_config.input_splits)
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with tempfile.NamedTemporaryFile(mode="a", suffix=".jsonl") as temp_file:
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asyncio.run(embed(input_ds, embed_config.tei_url, temp_file))
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dataset = Dataset.from_json(temp_file.name)
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dataset.push_to_hub(
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embed_config.output_dataset,
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private=embed_config.private,
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token=HF_TOKEN
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)
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logger.info("Done embedding")
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src/models.py
ADDED
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import json
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import os
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from pydantic import BaseModel
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from typing import Literal
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class ChunkConfig(BaseModel):
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input_dataset: str
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input_splits: list[str]
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input_text_col: str
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output_dataset: str
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strategy: Literal["spacy", "sequence", "constant"]
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split_seq: str | list[str]
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chunk_len: int
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private: bool
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class EmbedConfig(BaseModel):
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input_dataset: str
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input_splits: list[str]
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input_text_col: str
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output_dataset: str
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private: bool
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semaphore_bound: int
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class WebhookPayloadEvent(BaseModel):
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action: Literal["create", "update", "delete"]
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scope: str
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class WebhookPayloadRepo(BaseModel):
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type: Literal["dataset", "model", "space"]
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name: str
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id: str
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private: bool
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headSha: str
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class WebhookPayload(BaseModel):
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event: WebhookPayloadEvent
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repo: WebhookPayloadRepo
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with open(os.path.join(os.getcwd(), "chunk_config.json")) as c:
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data = json.load(c)
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chunk_config = ChunkConfig.model_validate_json(json.dumps(data))
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with open(os.path.join(os.getcwd(), "embed_config.json")) as c:
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data = json.load(c)
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embed_config = EmbedConfig.model_validate_json(json.dumps(data))
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style.css
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@@ -0,0 +1,28 @@
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body {
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padding: 2rem;
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font-family: -apple-system, BlinkMacSystemFont, "Arial", sans-serif;
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}
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h1 {
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font-size: 16px;
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margin-top: 0;
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}
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p {
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color: rgb(107, 114, 128);
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font-size: 15px;
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margin-bottom: 10px;
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margin-top: 5px;
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}
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.card {
|
19 |
+
max-width: 620px;
|
20 |
+
margin: 0 auto;
|
21 |
+
padding: 16px;
|
22 |
+
border: 1px solid lightgray;
|
23 |
+
border-radius: 16px;
|
24 |
+
}
|
25 |
+
|
26 |
+
.card p:last-child {
|
27 |
+
margin-bottom: 0;
|
28 |
+
}
|