New Release! The DVC 3.0 Stack: Beyond the Command Line
dvc exp run
and dvc repro
?
This is a really good question from @v2.03.99!
When you use dvc exp run
, DVC automatically tracks each experiment run. Using
dvc repro
leaves it to the user to track each experiment.
You can learn how dvc exp run
uses custom Git refs to track experiments in
this blog post and you can see a quick
technical overview in
the docs here.
A great question here from @quarkquark!
You can debug in VSCode by following the steps below:
debugpy
package."Run and Debug" > "Remote Attach" > localhost > someport
.python -m debugpy --listen someport --wait-for-client -m dvc mycommand
This should help you debug the stages in your pipeline in the IDE and you can find more details here.
Thanks for asking @CarsonM!
You should be able to use DVC to list the directory contents of your DVC remotes without pulling the repo. Here's an example of the command you can run:
$ dvc list https://github.com/iterative/dataset-registry/ fashion-mnist/raw
This is a really interesting question from @BrownZ!
It really depends on your use case. Separated remotes might be useful if you want to have granular control over permissions for each dataset.
In general, we would suggest a single remote and setting up a data registry to handle the different datasets through DVC.
It's awesome community members like @pria want to keep up with our releases!
You can follow all of our releases via GitHub notifications. You can browse
release notes at https://github.com/iterative/cml/releases. You can also
subscribe to release updates by clicking the Watch
button in the top-right,
navigating to Custom
, and checking the Releases
option.
cml-send-comment
work for azure devops repositories?
Thanks for the question @1cybersheep1!
Currently, the supported Source Code Management tools are GitHub, GitLab, and Bitbucket. Other SCMs may be a part of the roadmap later on.
Excellent question from @luke_imm!
In GitHub, you can mount volumes to your container, but you have to declare them within the workflow YAML
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