CLI doesn’t care about the state of my git repo right?

it finally finished no worries

I have been using 1.6.2

didn’t help

should I nuke the .clearml/cache

Dataset.get works fine from python script, it pulls in the data into cache. Just the cli seems broken

Thanks, I guess I need to have a bucket under Cloud Storage?

thanks

got it, nice, thanks

this is great… so it looks like best to do it in a new dir

no containers for me 😁
So if I do this in my local repo, will it mess up my git state, or should I do it in a fresh directory?

Oh I think I know what missed. When I set --project … --name … they did not match the names I used when I did task.init( ) in my code

So net-net does this mean it’s behaving as expected, or is there something I need to do enable “full venv cache”? It spends nearly 2 mins starting from
created virtual environment CPython3.8.10.final.0-64 in 97ms creator CPython3Posix(dest=/home/pchalasani/.clearml/venvs-builds/3.8, clear=False, global=False)and then printing several lines lines like this
` Successfully installed pip-20.1.1
Collecting Cython
Using cached Cython-0.29.30-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86...

I have a strong attachment to a workflow based on CLI, nice zsh auto-suggestions, Hydra and the like. Hence why I moved away from dvc 🙂

I think I’m starting to “get” this 🙂

I think I am missing one part — which command do I use on my local machine, to indicate the job needs to be run remotely? I’m imagining something like
clearml-remote run python3 my_train.py

So if I want to train with a remote agent on a remote machine, I have to:
spin up clearml-agent on the remote create a dataset using clearml-data, populate with data… from my local machine use clearml-data to upload data to google gs:// bucket modify my code so it accesses data from the dataset as here https://clear.ml/docs/latest/docs/clearml_data/clearml_data_sdk#accessing-datasetsAm I understanding right?

I would also be interested in a GCP autoscaler, I did not know it was possible/available yet.

I used
task.execute_remotely(queue_name=..., clone=True)and indeed it instantly activates the venv on the remote. I assume clone=True is fine

Yes after installing , it listed the installed packages in the console , with version of each

Actually that did not help. Still same behavior

I see, so there’s no way to launch a variant of my last run (with say some config/code tweaks) via CLI, and have it re-use the cached venv?

I'm not familiar with “installed package s” list in the task

Thanks for the quick response . Will look into this later , I think I understand

will try what you suggested above

A quick note for others who may visit this… it looks like you have to do:
Task.force_requirements_env_freeze(force=True, requirements_file="requirements.txt")to ensure any changes in requirements.txt are reflected in the remote venv

This works great, thanks AgitatedDove14 👍

I use a CLI arg remote=True so depending on that it will run locally or remotely.

I mean from the requirements .txt it gets from the git repo

But “cloning” via UI runs an exact copy of the code/config, not a variant, unless I edit those via UI (which is not ideal). So it looks like the following workflow that is trivial to do locally is not possible via remote agents:
run exp tweak code/configs in IDE, or tweak configs via CLI have it re-rerun in exact same venv (with no install overhead etc)
So maybe the remote agents are more meant for enqueuing a whole collection of settings (via code) and checking back in a few hours (in which ...