The โcml ciโ command prepares the whole repository for CML operations after creating the cml.yaml file
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๐โโ๏ธ runner
When a workflow requires computational resources, CML can automatically allocate cloud instances using โcml runnerโ. You can spin up instances on AWS, Azure, GCP, Kubernetes or any other provider. We can start a runner using โcml runner launchโ command.
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โคด๏ธ pr
We can also commit specified files to a new branch and create a pull request using โcml pr createโ command.
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๐ฌ comment
We can effortlessly post a Markdown report as a comment on a commit or pull/merge request with โcml comment createโ command.
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๐ง๐ผโ๐ซ tensorboard
The โcml tensorboard connectโ returns a link to a tensorboard.dev page.
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๐จ๐ปโ๐ป Thanks for reading till here!
You can head straight to the docs to start integrating all these features in your projects ๐ cml.dev/doc/ref
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โข โข โข
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For their engineering final project at @Insper, Arthur Olga, Gabriel Monteiro, Guilherme Leite, and Vinicius Lima created the MLOps Guide, which provides a Complete MLOps development cycle using DVC, CML, and IBM Watson.
๐ฆ DVC is designed to improve upon past solutions to make the life of ML teams easier. Hereโs how it differentiates from other related technologies:
DVC builds upon Git by the concept of data files โ large files that should not be stored in a Git repository, but need to be tracked and versioned.
It leverages Git's features to enable managing different versions of data, data pipelines, and experiments.
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๐ฆ Git-LFS vs DVC
DVC does not require special servers like Git-LFS demands. Any cloud storage like S3, Google Cloud Storage, or even an SSH server can be used as a remote storage.
No additional databases, servers, or infrastructure are required.