A roundup of technical Q&A's from the DVC and CML community. This month: CML runners, working with data, DVC Studio, and more.
That's a really good question @mihaj!
If you want to run DVC commands in a Python script, you have a couple of options.
You can work with the
main module from the
dvc library. This is the more
CLI-like option. An example of running an experiment would look something like
from dvc.main import main main(["exp", "run"])
The other option you have is to use the
Repo API. This API is largely
undocumented at the moment, but it closely mirrors the CLI commands. One
exception is that they will return internal data structures instead of exit
Here's an example of running an experiment with the Repo API.
from dvc.repo import Repo repo = Repo() repo.experiments.run() repo.experiments.show() # etc...
Good question from @edran!
You can check which directories have been changed by running:
$ dvc status
This will give you an output similar to this in your terminal:
train: changed deps: modified: src/train.py changed outs: deleted: model.pkl evaluate: changed deps: deleted: model.pkl
We're working on adding granularity support for this command and should have a release for this in the next few months.
Thanks for asking @GuyAR! This is a common question that comes up.
You can see all of your experiments and the associated metrics and parameters in a table in the terminal by running the following command:
$ dvc exp show
This will give you a table that looks similar to this with all of this information.
──────────────────────────────────────────────────────────────────────────────────────────── neutral:**Experiment** metric:**step** metric:**acc** metric:**val_acc** metric:**loss** metric:**val_loss** param:**lr** param:**momentum** ──────────────────────────────────────────────────────────────────────────────────────────── **workspace** **3** **0.91389** **0.87** **0.20506** **0.66306** **0.001** **0.09** **data-change** **-** **-** **-** **-** **-** **0.001** **0.09** │ ╓ 9405575 [exp-54e8a] 3 0.91389 0.87 0.20506 0.66306 0.001 0.09 │ ╟ 856d80f 2 0.90215 0.87333 0.27204 0.61631 0.001 0.09 │ ╟ 23dc98f 1 0.87671 0.86 0.35964 0.61713 0.001 0.09 ├─╨ 99a3c34 0 0.71429 0.82 0.67674 0.62798 0.001 0.09 │ ╓ 3b3a2a2 [exp-23593] 3 0.86885 0.46 0.31573 3.7067 0.001 0.09 │ ╟ 93d015d 2 0.83197 0.41333 0.36851 3.4259 0.001 0.09 │ ╟ d474c42 1 0.79918 0.43333 0.46612 3.286 0.001 0.09 ├─╨ 1582b4b 0 0.52869 0.39 0.94102 2.5967 0.001 0.09 ────────────────────────────────────────────────────────────────────────────────────────────
Great question @MadsO!
This works the exact same as when you've added data with
dvc add. So to remove
data, you would run this command:
$ dvc remove
cml runner does not support CircleCI or droneCI self–hosted runners
and you would have to deploy them manually.
You can still use
cml pr, and the other CML tools with any
Thanks for this awesome question @tpietruszka!
A really good question from @flowy!
For example, if you run something like
dvc remove folder_name/file.dvc, only
.dvc file will be removed. So your updated directory would likely still
folder_name/file since that was the file being tracked.
If you wanted to remove the tracked file as well, you would need to run
dvc remove --outs. This command removes the outputs of any target.
If there is nothing else in the folder, you'll be left with an empty directory. You can remove it and stop tracking in Git with a command like:
$ git rm -r folder_name
Very good question about Studio @Sra!
Right now this only works if it's an on-premises network or a private VPC network.
We are working on bringing custom-domain GitLab as a feature very soon! You can follow this GitHub issue and leave comments for anything you'd like to see!
There is definitely a way to do this!
You can extend the max time in your CI by adding something like this:
train: timeout-minutes: 5000
If you're using GitLab, the same update would look similar to this:
train: timeout: 72 hours
Thanks for this question @evergreengt!
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