November 2021
Intermediate to advanced
302 pages
8h 57m
Chinese
Content preview from 机器学习流水线实战
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使用
TensorFlow Serving
进行进阶模型部署
｜
149
docker run -p 8500:8500 \
-p 8501:8501 \
-e MODEL_BASE_PATH=gcp://
bucketname
/
model_path
/ \
-e MODEL_NAME=
my_model
\
-v /home/
your_username
/.credentials/:/credentials/
➊
-e GOOGLE_APPLICATION_CREDENTIALS=/
credentials
/
sa-credentials.json
\
➋
-t tensorflow/serving
➊
挂载本地证书路径。
➋
指定容器中的证书路径。
这样就可以将远程
GCP
bucket
作为存储位置了。
9.1.2
优化远程模型加载
无论是使用本地存储还是云端存储，
TensorFlow Serving
都会默认每两秒对文件系统进行一
次轮询和模型版本更新。如果使用了云端存储，那么轮询操作会通过云服务生成一个存储
桶列表视图。如果经常更新模型版本，那么存储桶里就会填满大量的文件。这会生成大量
的列表视图信息，并且随着时间的推移占用极大的流量。这些操作所产生的流量会增加使
用云服务的成本。为了避免高昂的云服务费用，建议将轮询频率降低到每两分钟一次。在
这种设置下，流量消耗会减少到原来的
1/60
，而且每小时仍然能对模型进行
30
次更新：
docker run -p 8500:8500 \
...
-t tensorflow/serving ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.