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Updated the image in TFMA component #1106

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Ark-kun
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@Ark-kun Ark-kun commented Apr 8, 2019

Incorporates the fix in #1094.

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@gaoning777
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Could you elaborate this change in the image?

@Ark-kun
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Ark-kun commented Apr 8, 2019

Could you elaborate this change in the image?

It's the bug fix we released on Friday: #1094

@gaoning777
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Why not update all the component image tags such that it looks more consistent

@Ark-kun
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Ark-kun commented Apr 8, 2019

Why not update all the component image tags such that it looks more consistent

I'm not sure it's a good idea to update all component images and component definitions every time someone make a change to a single component.

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Agreed that we need not build new component images but since we are releasing the components all at one time using the release script, why not keep the image tag consistent?
BTW, is it blocking the release? I saw 0.1.16 is released.

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Ark-kun commented Apr 9, 2019

Since we are releasing the components all at one time using the release script, why not keep the image tag consistent?

I did not want any more big somewhat untested changes right before NEXT. The reality is that the images being released are not passing through testing (we release the images that are build by CloudBuild and do not go through testing). So, I was trying not to make any unnecessary sweeping changes.
During the next release we can run the release script again and update all images to the same SHA.

BTW, is it blocking the release? I saw 0.1.16 is released.
It's not blocking the release. The 0.1.16 release is using the rolled-back TFMA image (v.0.1.14).

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Ark-kun commented Apr 17, 2019

The image was updated the next week using the normal release process.

@Ark-kun Ark-kun closed this Apr 17, 2019
@Ark-kun Ark-kun deleted the Updated-the-image-in-TFMA-component branch April 17, 2019 00:46
magdalenakuhn17 pushed a commit to magdalenakuhn17/pipelines that referenced this pull request Oct 22, 2023
KFServer uses the number of cores on the machine to set the number of process in its tornado service which can delay the initialization time and increase the memory usage a lot.

A local testing suggests that KFServer loaded 64 processes and used more than 500Mb for a simple SKLearn model. Once I set the worker number to 1, the memory usage was reduced to around 100Mb. 
```
[I 200927 18:26:06 kfserver:88] Registering model: mlguild-iris-svm
[I 200927 18:26:06 kfserver:77] Listening on port 8080
[I 200927 18:26:06 kfserver:79] Will fork 0 workers
[I 200927 18:26:06 process:126] Starting 64 processes
```
HumairAK pushed a commit to red-hat-data-services/data-science-pipelines that referenced this pull request Mar 11, 2024
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4 participants