import runhouse as rh
from transformers import pipeline
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1")
pipeline = pipeline(model="bert-base-uncased", task="feature-extraction")
rh.blob(pickle.dumps(pipeline),
    path="models/pipeline.pkl").save().to(gpu, path="models")
embeddings = SelfHostedHFEmbeddings.from_pipeline(
    pipeline="models/pipeline.pkl",
    hardware=gpu,
    model_reqs=["./", "torch", "transformers"],
)
Validators
raise_deprecation » all fields
set_verbose » verbose
field inference_fn: Callable = <function _embed_documents>#
Inference function to extract the embeddings on the remote hardware.
field inference_kwargs: Any = None#
Any kwargs to pass to the model’s inference function.
embed_documents(texts: List[str]) → List[List[float]][source]#
Compute doc embeddings using a HuggingFace transformer model.
Parameters
texts – The list of texts to embed.s
Returns
List of embeddings, one for each text.
embed_query(text: str) → List[float][source]#
Compute query embeddings using a HuggingFace transformer model.
Parameters
text – The text to embed.
Returns
Embeddings for the text.
pydantic model langchain.embeddings.SelfHostedHuggingFaceEmbeddings[source]#
Runs sentence_transformers embedding models on self-hosted remote hardware.
Supported hardware includes auto-launched instances on AWS, GCP, Azure,
and Lambda, as well as servers specified
by IP address and SSH credentials (such as on-prem, or another cloud
like Paperspace, Coreweave, etc.).
To use, you should have the runhouse python package installed.
Example
from langchain.embeddings import SelfHostedHuggingFaceEmbeddings
rtdocs_stable/api.python.langchain.com/en/stable/reference/modules/embeddings.html