[英]Watson machine learning deployment takes too much time
I trained a model using watson machine learning service. 我使用Watson Machine Learning Service训练了一个模型。 The training process has completed so I ran these command lines to deploy it:
培训过程已经完成,因此我运行了以下命令行进行部署:
bx ml store training-runs model-XXXXXXX
I get the output with the model ID 我得到带有型号ID的输出
Starting to store the training-run 'model-XXXXXX' ...
OK
Model store successful. Model-ID is '93sdsdsf05-3ea4-4d9e-a751-5bcfbsdsd3391'.
Then I use the following to deploy it : 然后,我使用以下内容进行部署:
bx ml deploy 93sdsdsf05-3ea4-4d9e-a751-5bcfbsdsd3391 "my-test-model"
The problem is that I'm getting an endless message saying: 问题是我收到无尽的消息说:
Checking if content upload is complete ...
Checking if content upload is complete ...
Checking if content upload is complete ...
Checking if content upload is complete ...
Checking if content upload is complete ...
When I check in COS result bucket the model size is ~25MB so it shouldn't be that long to deploy. 当我签入COS结果存储区时,模型大小为〜25MB,因此部署时间不应该太长。 Am I missing something here ?
我在这里想念什么吗?
Deploying the same model using Python Client API
: 使用
Python Client API
部署相同的模型:
from watson_machine_learning_client import WatsonMachineLearningAPIClient
client = WatsonMachineLearningAPIClient(wml_credentials)
deployment_details = client.deployments.create( model_id, "model_name")
This showed me very quickly that there is an error with the deployment. 这很快就告诉我部署存在错误。 The strange thing is that the error doesn't pop up when deploying with
command line interface (CLI)
. 奇怪的是,使用
command line interface (CLI)
部署时,错误不会弹出。
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.