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c4bfd6cced
1. refine quick start and pipeline doc 2. remove tf pytorch easynlp from requirements 3. lazy import for torch and tensorflow 4. test successfully on linux and mac intel cpu 5. update api doc Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/8882373
96 lines
2.8 KiB
Markdown
96 lines
2.8 KiB
Markdown
# 快速开始
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## python环境配置
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首先，参考[文档](https://docs.anaconda.com/anaconda/install/) 安装配置Anaconda环境
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安装完成后，执行如下命令为maas library创建对应的python环境。
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```shell
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conda create -n maas python=3.6
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conda activate maas
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```
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检查python和pip命令是否切换到conda环境下。
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```shell
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which python
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# ~/workspace/anaconda3/envs/maas/bin/python
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which pip
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# ~/workspace/anaconda3/envs/maas/bin/pip
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```
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注： 本项目只支持`python3`环境，请勿使用python2环境。
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## 第三方依赖安装
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MaaS Library支持tensorflow，pytorch两大深度学习框架进行模型训练、推理， 在Python 3.6+, Pytorch 1.8+, Tensorflow 2.6上测试可运行，用户可以根据所选模型对应的计算框架进行安装，可以参考如下链接进行安装所需框架:
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* [Pytorch安装指导](https://pytorch.org/get-started/locally/)
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* [Tensorflow安装指导](https://www.tensorflow.org/install/pip)
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## MaaS library 安装
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注： 如果在安装过程中遇到错误，请前往[常见问题](faq.md)查找解决方案。
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### pip安装
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```shell
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pip install -r http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/release/maas/maas.txt
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```
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安装成功后，可以执行如下命令进行验证安装是否正确
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```shell
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python -c "from maas_lib.pipelines import pipeline;print(pipeline('image-matting',model='damo/image-matting-person')('http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/data/test/maas/image_matting/test.png'))"
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```
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### 使用源码
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适合本地开发调试使用，修改源码后可以直接执行
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```shell
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git clone git@gitlab.alibaba-inc.com:Ali-MaaS/MaaS-lib.git maaslib
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git fetch origin master
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git checkout master
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cd maaslib
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#安装依赖
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pip install -r requirements.txt
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# 设置PYTHONPATH
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export PYTHONPATH=`pwd`
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```
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安装成功后，可以执行如下命令进行验证安装是否正确
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```shell
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python -c "from maas_lib.pipelines import pipeline;print(pipeline('image-matting',model='damo/image-matting-person')('http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/data/test/maas/image_matting/test.png'))"
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```
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## 训练
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to be done
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## 评估
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to be done
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## 推理
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pipeline函数提供了简洁的推理接口，示例如下， 更多pipeline介绍和示例请参考[pipeline使用教程](tutorials/pipeline.md)
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```python
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import cv2
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import os.path as osp
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from maas_lib.pipelines import pipeline
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from maas_lib.utils.constant import Tasks
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# 根据任务名创建pipeline
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img_matting = pipeline(
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Tasks.image_matting, model='damo/image-matting-person')
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result = img_matting(
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'http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/data/test/maas/image_matting/test.png'
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)
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cv2.imwrite('result.png', result['output_png'])
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print(f'result file path is {osp.abspath("result.png")}')
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```
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