Adding an example of a CLIP model using SageMaker #6
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Maurits-de-Groot
requested review from
aws-maens,
aws-mesharma and
rgrandhiamzn
as code owners
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Together with isamosss I've created an example notebook that showcases how to compile and deploy a pretrained CLIP model from HuggingFace Transformers, using the AWS Deep Learning Containers. We use AWS Deep Learning Containers as they offer a convenient, pre-configured environment with necessary deep learning framework and AWS Neuron dependencies.
In this notebook an example is shown how to use a
ml.trn1instance to compile the model before deploying it, making this example runnable in SageMaker Studio.