Runhouse

Runhouse 允许跨环境和用户进行远程计算和数据访问。请参阅 Runhouse 文档。

此示例介绍了如何使用 LangChain 和 Runhouse 与您自己的 GPU 上托管的模型或 AWS、GCP、AWS 或 Lambda 上的按需 GPU 进行交互。

注意：代码使用 SelfHosted 名称代替 Runhouse。

%pip install --upgrade --quiet  runhouse

import runhouse as rh
from langchain.chains import LLMChain
from langchain_community.llms import SelfHostedHuggingFaceLLM, SelfHostedPipeline
from langchain_core.prompts import PromptTemplate

API 参考：LLMChain | SelfHostedHuggingFaceLLM | SelfHostedPipeline | PromptTemplate

INFO | 2023-04-17 16:47:36,173 | No auth token provided, so not using RNS API to save and load configs

# For an on-demand A100 with GCP, Azure, or Lambda
gpu = rh.cluster(name="rh-a10x", instance_type="A100:1", use_spot=False)

# For an on-demand A10G with AWS (no single A100s on AWS)
# gpu = rh.cluster(name='rh-a10x', instance_type='g5.2xlarge', provider='aws')

# For an existing cluster
# gpu = rh.cluster(ips=['<ip of the cluster>'],
#                  ssh_creds={'ssh_user': '...', 'ssh_private_key':'<path_to_key>'},
#                  name='rh-a10x')

template = """Question: {question}

Answer: Let's think step by step."""

prompt = PromptTemplate.from_template(template)

llm = SelfHostedHuggingFaceLLM(
    model_id="gpt2", hardware=gpu, model_reqs=["pip:./", "transformers", "torch"]
)

llm_chain = LLMChain(prompt=prompt, llm=llm)

question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"

llm_chain.run(question)

INFO | 2023-02-17 05:42:23,537 | Running _generate_text via gRPC
INFO | 2023-02-17 05:42:24,016 | Time to send message: 0.48 seconds

"\n\nLet's say we're talking sports teams who won the Super Bowl in the year Justin Beiber"

您还可以通过 SelfHostedHuggingFaceLLM 接口加载更多自定义模型

llm = SelfHostedHuggingFaceLLM(
    model_id="google/flan-t5-small",
    task="text2text-generation",
    hardware=gpu,
)

llm("What is the capital of Germany?")

INFO | 2023-02-17 05:54:21,681 | Running _generate_text via gRPC
INFO | 2023-02-17 05:54:21,937 | Time to send message: 0.25 seconds

'berlin'

使用自定义加载函数，我们可以直接在远程硬件上加载自定义管道

def load_pipeline():
    from transformers import (
        AutoModelForCausalLM,
        AutoTokenizer,
        pipeline,
    )

    model_id = "gpt2"
    tokenizer = AutoTokenizer.from_pretrained(model_id)
    model = AutoModelForCausalLM.from_pretrained(model_id)
    pipe = pipeline(
        "text-generation", model=model, tokenizer=tokenizer, max_new_tokens=10
    )
    return pipe


def inference_fn(pipeline, prompt, stop=None):
    return pipeline(prompt)[0]["generated_text"][len(prompt) :]

llm = SelfHostedHuggingFaceLLM(
    model_load_fn=load_pipeline, hardware=gpu, inference_fn=inference_fn
)

llm("Who is the current US president?")

INFO | 2023-02-17 05:42:59,219 | Running _generate_text via gRPC
INFO | 2023-02-17 05:42:59,522 | Time to send message: 0.3 seconds

'john w. bush'

您可以将管道直接发送到网络上的模型，但这仅适用于小型模型（<2 Gb），并且速度会很慢

pipeline = load_pipeline()
llm = SelfHostedPipeline.from_pipeline(
    pipeline=pipeline, hardware=gpu, model_reqs=["pip:./", "transformers", "torch"]
)

相反，我们还可以将其发送到硬件的文件系统，这将快得多。

import pickle

rh.blob(pickle.dumps(pipeline), path="models/pipeline.pkl").save().to(
    gpu, path="models"
)

llm = SelfHostedPipeline.from_pipeline(pipeline="models/pipeline.pkl", hardware=gpu)

相关

• LLM 概念指南
• LLM 操作指南