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Alibaba Cloud PAI EAS

Alibaba Cloud PAI (Platform for AI) is a lightweight and cost-efficient machine learning platform that uses cloud-native technologies. It provides you with an end-to-end modelling service. It accelerates model training based on tens of billions of features and hundreds of billions of samples in more than 100 scenarios.

Machine Learning Platform for AI of Alibaba Cloud is a machine learning or deep learning engineering platform intended for enterprises and developers. It provides easy-to-use, cost-effective, high-performance, and easy-to-scale plug-ins that can be applied to various industry scenarios. With over 140 built-in optimization algorithms, Machine Learning Platform for AI provides whole-process AI engineering capabilities including data labelling (PAI-iTAG), model building (PAI-Designer and PAI-DSW), model training (PAI-DLC), compilation optimization, and inference deployment (PAI-EAS).

PAI-EAS supports different types of hardware resources, including CPUs and GPUs, and features high throughput and low latency. It allows you to deploy large-scale complex models with a few clicks and perform elastic scale-ins and scale-outs in real-time. It also provides a comprehensive O&M and monitoring system.

Setup EAS Service​

Set up environment variables to init EAS service URL and token. Use this document for more information.

export EAS_SERVICE_URL=XXX
export EAS_SERVICE_TOKEN=XXX

Another option is to use this code:

import os

from langchain_community.chat_models import PaiEasChatEndpoint
from langchain_core.language_models.chat_models import HumanMessage

os.environ["EAS_SERVICE_URL"] = "Your_EAS_Service_URL"
os.environ["EAS_SERVICE_TOKEN"] = "Your_EAS_Service_Token"
chat = PaiEasChatEndpoint(
    eas_service_url=os.environ["EAS_SERVICE_URL"],
    eas_service_token=os.environ["EAS_SERVICE_TOKEN"],
)

Run Chat Model​

You can use the default settings to call EAS service as follows:

output = chat.invoke([HumanMessage(content="write a funny joke")])
print("output:", output)

Or, call EAS service with new inference params:

kwargs = {"temperature": 0.8, "top_p": 0.8, "top_k": 5}
output = chat.invoke([HumanMessage(content="write a funny joke")], **kwargs)
print("output:", output)

Or, run a stream call to get a stream response:

outputs = chat.stream([HumanMessage(content="hi")], streaming=True)
for output in outputs:
    print("stream output:", output)

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