From d923de38d287e00fa9254c78cebb5beeb23805ac Mon Sep 17 00:00:00 2001 From: Thomas Young <35565423+HexToString@users.noreply.github.com> Date: Fri, 5 Feb 2021 18:44:54 +0800 Subject: [PATCH] fix update_abtest fix update_abtest --- doc/ABTEST_IN_PADDLE_SERVING_CN.md | 53 +++++++++++++++--------------- 1 file changed, 27 insertions(+), 26 deletions(-) diff --git a/doc/ABTEST_IN_PADDLE_SERVING_CN.md b/doc/ABTEST_IN_PADDLE_SERVING_CN.md index 43bb702b..0d62b979 100644 --- a/doc/ABTEST_IN_PADDLE_SERVING_CN.md +++ b/doc/ABTEST_IN_PADDLE_SERVING_CN.md @@ -16,31 +16,25 @@ sh get_data.sh ``` ### 处理数据 - -下面Python代码将处理`test_data/part-0`的数据，写入`processed.data`文件中。 - -```python -from paddle_serving_app.reader import IMDBDataset -imdb_dataset = IMDBDataset() -imdb_dataset.load_resource('imdb.vocab') - -with open('test_data/part-0') as fin: - with open('processed.data', 'w') as fout: - for line in fin: - word_ids, label = imdb_dataset.get_words_and_label(line) - fout.write("{};{}\n".format(','.join([str(x) for x in word_ids]), label[0])) -``` +由于处理数据需要用到相关库，请使用pip进行安装 +`pip install paddlepaddle` +`pip install paddle-serving-app` +`pip install Shapely` +您可以直接运行 +python [abtest_get_data.py](../python/examples/imdb/abtest_get_data.py) +文件中的Python代码将处理`test_data/part-0`的数据，并将处理后的数据生成并写入`processed.data`文件中。 ### 启动Server端 -这里采用[Docker方式](https://github.com/PaddlePaddle/Serving/blob/develop/doc/RUN_IN_DOCKER_CN.md)启动Server端服务。 +这里采用[Docker方式](RUN_IN_DOCKER_CN.md)启动Server端服务。 首先启动BOW Server，该服务启用`8000`端口： ```bash -docker run -dit -v $PWD/imdb_bow_model:/model -p 8000:8000 --name bow-server hub.baidubce.com/paddlepaddle/serving:latest -docker exec -it bow-server bash +docker run -dit -v $PWD/imdb_bow_model:/model -p 8000:8000 --name bow-server hub.baidubce.com/paddlepaddle/serving:latest /bin/bash +docker exec -it bow-server /bin/bash pip install paddle-serving-server -i https://pypi.tuna.tsinghua.edu.cn/simple +pip install paddle-serving-client -i https://pypi.tuna.tsinghua.edu.cn/simple python -m paddle_serving_server.serve --model model --port 8000 >std.log 2>err.log & exit ``` @@ -48,19 +42,22 @@ exit 同理启动LSTM Server，该服务启用`9000`端口： ```bash -docker run -dit -v $PWD/imdb_lstm_model:/model -p 9000:9000 --name lstm-server hub.baidubce.com/paddlepaddle/serving:latest -docker exec -it lstm-server bash +docker run -dit -v $PWD/imdb_lstm_model:/model -p 9000:9000 --name lstm-server hub.baidubce.com/paddlepaddle/serving:latest /bin/bash +docker exec -it lstm-server /bin/bash pip install paddle-serving-server -i https://pypi.tuna.tsinghua.edu.cn/simple +pip install paddle-serving-client -i https://pypi.tuna.tsinghua.edu.cn/simple python -m paddle_serving_server.serve --model model --port 9000 >std.log 2>err.log & exit ``` ### 启动Client端 - -在宿主机运行下面Python代码启动Client端，需要确保宿主机装好`paddle-serving-client`包。 +为了模拟ABTEST工况，您可以在宿主机运行下面Python代码启动Client端，但需确保宿主机具备相关环境，您也可以在docker环境下运行，运行前使用`pip install paddle-serving-client`安装paddle-serving-client包。 +您可以直接使用下面的命令，进行ABTEST预测。 +python [abtest_client.py](../python/examples/imdb/abtest_client.py) ```python from paddle_serving_client import Client +import numpy as np client = Client() client.load_client_config('imdb_bow_client_conf/serving_client_conf.prototxt') @@ -68,28 +65,32 @@ client.add_variant("bow", ["127.0.0.1:8000"], 10) client.add_variant("lstm", ["127.0.0.1:9000"], 90) client.connect() +print('please wait for about 10s') with open('processed.data') as f: cnt = {"bow": {'acc': 0, 'total': 0}, "lstm": {'acc': 0, 'total': 0}} for line in f: word_ids, label = line.split(';') word_ids = [int(x) for x in word_ids.split(',')] - feed = {"words": word_ids} + word_len = len(word_ids) + feed = { + "words": np.array(word_ids).reshape(word_len, 1), + "words.lod": [0, word_len] + } fetch = ["acc", "cost", "prediction"] - [fetch_map, tag] = client.predict(feed=feed, fetch=fetch, need_variant_tag=True) + [fetch_map, tag] = client.predict(feed=feed, fetch=fetch, need_variant_tag=True,batch=True) if (float(fetch_map["prediction"][0][1]) - 0.5) * (float(label[0]) - 0.5) > 0: cnt[tag]['acc'] += 1 cnt[tag]['total'] += 1 for tag, data in cnt.items(): - print('[{}](total: {}) acc: {}'.format(tag, data['total'], float(data['acc']) / float(data['total']))) + print('[{}](total: {}) acc: {}'.format(tag, data['total'], float(data['acc'])/float(data['total']) )) ``` - 代码中，`client.add_variant(tag, clusters, variant_weight)`是为了添加一个标签为`tag`、流量权重为`variant_weight`的variant。在这个样例中，添加了一个标签为`bow`、流量权重为`10`的BOW variant，以及一个标签为`lstm`、流量权重为`90`的LSTM variant。Client端的流量会根据`10:90`的比例分发到两个variant。 Client端做预测时，若指定参数`need_variant_tag=True`，返回值则包含分发流量对应的variant标签。 ### 预期结果 - +由于网络情况的不同，可能每次预测的结果略有差异。 ``` bash [lstm](total: 1867) acc: 0.490091055169 [bow](total: 217) acc: 0.73732718894 -- GitLab