t5
Build-Deploy-Actions Details

This commit is contained in:
jianjiang 2023-04-26 16:02:48 +08:00
parent 15aa2c37f8
commit bc1ed5a107
2 changed files with 7 additions and 2 deletions

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@ -1,10 +1,14 @@
#FROM python:3.8.13
FROM artifacts.iflytek.com/docker-private/atp/base_image_for_ailab:0.0.1
WORKDIR /app
COPY . /app
COPY requirements.txt /app
RUN pip config set global.index-url https://pypi.mirrors.ustc.edu.cn/simple
RUN pip install -r requirements.txt
COPY . /app
CMD ["python", "app.py"]

3
app.py
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@ -25,7 +25,8 @@ demo = gr.Interface(fn=sentiment_analysis,
outputs='text',
examples=[["We describe a system called Overton, whose main design goal is to support engineers in building, monitoring, and improving production machinelearning systems. Key challenges engineers face are monitoring fine-grained quality, diagnosing errors in sophisticated applications, and handling contradictory or incomplete supervision data. Overton automates the life cycle of model construction, deployment, and monitoring by providing a set of novel high-level, declarative abstractions. Overton's vision is to shift developers to these higher-level tasks instead of lower-level machine learning tasks. In fact, using Overton, engineers can build deep-learning-based applications without writing any code in frameworks like TensorFlow. For over a year, Overton has been used in production to support multiple applications in both near-real-time applications and back-of-house processing. In that time, Overton-based applications have answered billions of queries in multiple languages and processed trillions of records reducing errors 1.7-2.9 times versus production systems."]],
theme = theme,
title = "摘要"
css = "footer {visibility: hidden}",
allow_flagging = "never",
)