This example demonstrates how to use Runhouse primitives to embed a large number of websites in parallel.
We use a BGE large model from Hugging Face and load it via
the SentenceTransformer class from the huggingface library.
Optionally, set up a virtual environment:
$ conda create -n parallel-embed python=3.9.15 $ conda activate parallel-embed
Install the required dependencies:
$ pip install "runhouse[aws]" torch beautifulsoup4 tqdm
We'll be launching an AWS EC2 instance via SkyPilot, so we need to make sure our AWS credentials are set up:
$ aws configure $ sky check
We import runhouse and other utility libraries; only the ones that are needed to run the script locally.
Imports of libraries that are needed on the remote machine (in this case, the huggingface dependencies)
can happen within the functions that will be sent to the Runhouse cluster.
import time from functools import partial from multiprocessing.pool import ThreadPool from urllib.parse import urljoin, urlparse import requests import runhouse as rh import torch from bs4 import BeautifulSoup
Then, we define an extract_urls function that will extract all URLs from a given URL, recursively up to a
maximum depth. This'll be a useful helper function that we'll use to collect our list of URLs to embed.
def _extract_urls_helper(url, visited, original_url, max_depth=1, current_depth=1): """ Extracts all URLs from a given URL, recursively up to a maximum depth. """ if ( url in visited or urlparse(url).netloc != urlparse(original_url).netloc or "redirect" in url ): return [] visited.add(url) urls = [url] if current_depth <= max_depth: response = requests.get(url) soup = BeautifulSoup(response.text, "html.parser") for link in soup.find_all("a"): href = link.get("href") if href: # Ignore links within the same page if href.startswith("#"): continue if not href.startswith("http"): href = urljoin(url, href) parsed_href = urlparse(href) if bool(parsed_href.scheme) and bool(parsed_href.netloc): urls.extend( _extract_urls_helper( href, visited, original_url, max_depth, current_depth + 1 ) ) return urls def extract_urls(url, max_depth=1): visited = set() return _extract_urls_helper( url, visited, original_url=url, max_depth=max_depth, current_depth=1 )
Next, we define a class that will hold the model and the logic to extract a document from a URL and embed it.
Later, we'll instantiate this class with rh.module and send it to the Runhouse cluster. Then, we can call
the functions on this class and they'll run on the remote machine.
Learn more in the Runhouse docs on functions and modules.
class URLEmbedder: def __init__(self, **model_kwargs): from sentence_transformers import SentenceTransformer self.model = torch.compile(SentenceTransformer(**model_kwargs)) def embed_docs(self, url: str, **embed_kwargs): from langchain_community.document_loaders import WebBaseLoader from langchain_text_splitters import RecursiveCharacterTextSplitter docs = WebBaseLoader(web_paths=[url]).load() splits = RecursiveCharacterTextSplitter( chunk_size=1000, chunk_overlap=200 ).split_documents(docs) splits_as_str = [doc.page_content for doc in splits] embedding = self.model.encode(splits_as_str, **embed_kwargs) return embedding
Note
Make sure that any code in your Python file that's meant to only run locally runs within
a if __name__ == "__main__": block, as shown below. Otherwise, the script code will run
when Runhouse attempts to import your code remotely.
if __name__ == "__main__":
Now, we define the main function that will run locally when we run this script, and set up our Runhouse module on a remote cluster.
We set up some parameters for our embedding task.
num_replicas = 4 # Number of models to load side by side url_to_recursively_embed = "https://en.wikipedia.org/wiki/Poker"
We recursively extract all children URLs from the given URL.
start_time = time.time() urls = extract_urls(url_to_recursively_embed, max_depth=2) print(f"Time to extract {len(urls)} URLs: {time.time() - start_time}")
First, we create a cluster with the desired instance type and provider.
Our instance_type here is defined as A10G:1, which is the accelerator type and count we need We could
alternatively specify a specific AWS instance type, such as p3.2xlarge or g4dn.xlarge. However, we
provision num_replicas number of these instances. This gives us one Runhouse cluster that has
several separate GPU machines that it can access.
This is one major advantage of Runhouse: you can use a multinode machine as if it were one opaque cluster, and send things to it from your local machine. This is especially useful for embarrassingly parallel tasks like this one. Note that it is also far easier to provision several A10G:1 machines as spot instances than it is to provision a single A10G:4 machine, which is why we do it this way.
Note that if the cluster was
already up (e.g. if we had run this script before), the code would just bring it up instead of creating a
new one, since we have given it a unique name "rh-4xa10g".
Learn more in the Runhouse docs on clusters.
start_time = time.time() cluster = rh.cluster( f"rh-{num_replicas}xa10g", instance_type="A10G:1", num_nodes=num_replicas, spot=True, default_env=rh.env( reqs=[ "langchain", "langchain-community", "langchainhub", "bs4", "sentence_transformers", "fake_useragent", ] ), ).up_if_not()
Generally, when using Runhouse, you would initialize an env with rh.env, and send your module to
that env. Each env runs in a separate process on the cluster. In this case, we want to have 4 copies of the
embedding model in separate processes, because we have 4 GPUs. We can do this by creating 4 separate envs
and 4 separate modules, each sent to a separate env. We do this in a loop here, with a list of dependencies
that we need remotely to run the module.
In this case, each env is also on a separate machine, but we could also provision an A10G:4 instance,
and send all 4 envs to the same machine. Each env runs within a separate process on the machine, so they
won't interfere with each other.
Note that we send the URLEmbedder class to the cluster, and then can construct our modules using the
returned "remote class" instead of the normal local class. These instances are then actually constructed
on the cluster, and any methods called on these instances would run on the cluster.
RemoteURLEmbedder = rh.module(URLEmbedder).to( cluster, rh.process(name="langchain_embed_env") ) remote_url_embedder = RemoteURLEmbedder( model_name_or_path="BAAI/bge-large-en-v1.5", device="cuda", name="doc_embedder", ) embedder_pool = remote_url_embedder.distribute( "pool", replicas=num_replicas, replicas_per_node=1, max_concurrency=32 )
We'll simply use the embed_docs function on the remote module to embed all the URLs in parallel. Note that
we can call this function exactly as if it were a local module. The semaphore and asyncio logic allows us
to run all the functions in parallel, up to a maximum total concurrency.
with ThreadPool(num_replicas) as pool: embed = partial( embedder_pool.embed_docs, normalize_embeddings=True, stream_logs=False ) pool.map(embed, urls)