🛠️FSDL Tooling Tuesday🛠️
@DVCorg is one of the fastest growing ML experiment management tools.
The main idea of DVC is to *track ML experiments in git*
Everything is versioned -- the code, the data, the model, and the metrics created by your experiment. Pretty powerful!
@DVCorg is one of the fastest growing ML experiment management tools.
The main idea of DVC is to *track ML experiments in git*
Everything is versioned -- the code, the data, the model, and the metrics created by your experiment. Pretty powerful!
The magic of DVC is that it supports datasets and models too large to store in github.
And since every part of your experiment is versioned, you can easily roll back to an earlier run and reproduce it.
No more fiddling around to recreate that experiment from two weeks ago!
And since every part of your experiment is versioned, you can easily roll back to an earlier run and reproduce it.
No more fiddling around to recreate that experiment from two weeks ago!
What are the tradeoffs? (1/2)
*DVC does a lot*
Versioning data, experiment tracking, and running pipelines. You might prefer lighter weight tools (e.g., replicate.ai) for any one of these
*DVC does a lot*
Versioning data, experiment tracking, and running pipelines. You might prefer lighter weight tools (e.g., replicate.ai) for any one of these
What are the tradeoffs? (2/3)
*DVC imposes a workflow*
Each experiment is like a commit that you make by running your script through `dvc run`. Other tools like @weights_biases integrate into how you do things now
*DVC imposes a workflow*
Each experiment is like a commit that you make by running your script through `dvc run`. Other tools like @weights_biases integrate into how you do things now
What are the tradeoffs? (3/3)
* DVC versions your data, but the diffs are limited*
@pachyderminc focuses on versioning your entire data pipeline, and @DoltHub versions your dataset more granularly at the row level
* DVC versions your data, but the diffs are limited*
@pachyderminc focuses on versioning your entire data pipeline, and @DoltHub versions your dataset more granularly at the row level
For many these are easy tradeoffs for reproducible ML experiments out of the box!
What other tools do you like for experiment tracking, reproducibility, and data versioning?
What other tools do you like for experiment tracking, reproducibility, and data versioning?
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