Trying out a simple example using TuneSearchCV with LGBMClassifier and it fails on start.

Environment:

Python 3.8.3
tune-sklearn 0.3.0
ray 1.3.0
macos mojave 10.14.6

Code:

from ray.tune.sklearn import TuneSearchCV

from lightgbm import LGBMClassifier

lgmb_param_dists = dict(

                boosting_type=['gbdt','dart','rf'],
                num_leaves=(10,500),
 

... to make it easier to read Vespa documentation on an e-reader / offline

Vespa documentation is generated using Jekyll from .md and .html files, look into options for generating the artifact as part of site generation (there might be plugins we can use here)

For SC Operator it may be a good idea to generate CRD manifests from inside a docker container.
This should provide reproducible generation step and avoid "produces different output on my machine" issues.

Linter should also fail if generation of manifests produce diff with the commited version.

使用以下命令部署模型是，输入的thread参数不起作用，cpu使用率不超过400%：

python -m paddle_serving_server.serve --model my_model --thread 10 --port 9292 --name my_model

原因是文件：
https://github.com/PaddlePaddle/Serving/blob/develop/python/paddle_serving_server/serve.py
中第433行中processes=4，并没有更新为用户的输入参数；