Lesson 3 - Official Topic

Part 1 (2020)
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how can we make voila work on the paperspace?
As in, it wont really work by changing the URL of the paperspace hosted notebook.

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We will see the deployment in a minute.

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Anything about security issues using iPyWidgets / Voila on production ?

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I have, and can testify that it’s easy and quick. This article offers a walk through for a simple dashboard hosted using heroku. https://towardsdatascience.com/quickly-build-and-deploy-an-application-with-streamlit-988ca08c7e83

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Sure you could, but how many images do you think it would take to capture “not-bearness”? Interesting experiment to try. My bet would be that it would be a deal-breakingly large number…

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You will have to set up Colab differently

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This is awesome, I also didn’t know the connection with vue.js and jupyter

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Definitely. Streamlit, so far, is pretty solid.

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If I am having difficulty running one of the .nbs, should I start a new post or post to an existing thread? I previously could run fastai last year on this machine, but it says now it doesn’t have enough memory on the GPU for 01_intro this year. Basically just firefox and the docker are running. nvidia-smi shows basically nothing else running.

Running in LXD docker natively with GPU passthrough to a GTX 1080 with ubuntu 18 lts host.

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Thanks

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Yep I am not sure either…

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Use a new thread please. You should adjust your batch size as necessary if you have memory issues.

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Additional note on CPU vs. GPU for deployment:

You might want to build and deploy a desktop application in an enterprise environment. Basically a big use case believe it or not, especially for my customers. :slight_smile:

Dilbert and friends don’t have GPUs on their PCs.

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If you do need to deploy directly on a phone, you can use PyTorch Mobile as well.

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Yes, this is not easy and it’s the drawback with Classification in DL today. Hence my question.

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I don’t think you have to train a model to recognize “not-bearness”. You have to train a model to find features that indicate “bearness” and in the absence of “bearness” it would output “not bear”.

For example I can train a model to recognize my face. I don’t have to give it the face of all other people on earth in order to train it.

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Maybe to see how not sure the model is for a given image say and setting a threshold? Would that approach work?

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is Caffe2 another way of deploying PyTorch models like ONNX ?

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2 posts were merged into an existing topic: Lesson 3 - Non-beginner discussion

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Look into this maybe?:

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