AgitatedDove14 Does it make any sense to chdnge system_site_packages to true if I run in clearml using Docker?

Completed task:

AgitatedDove14 Looks like that. First, I've created a toy task running in "services" queue (you didn't tell that but I guess you assumed). I haven't found how to specify the queue to run in code ( Task.equeue(task, queue_name='services') returned an error), so I ran toy.py first in "default" queue, aborted toy.py, started nntraining in "default" queue. Then I reset toy.py and enqueued it to "services" queue. Toy.py failed shortly. I've also reset both toy.py and nntraining and enqueue...

AgitatedDove14 How can the first process corrupt the second and why doesn't this occur if I run pipeline from command line? Just to be precise - I run all the processes as administrator. However, I've tested running the pipeline from command line in non-administrator mode, it works fine.

AgitatedDove14 Great, thanks! Wow, guys, your response while being helpful is too fast, I didn't use to this! 🙂

AgitatedDove14
git diff gives nothing - current local repository is up-to-date with gitlab origin.

Yes that is the git repository cache, you are correct. I wonder what happened there ?

So far my local and remote gitlab repositories are synchronized, I suspect, that Failed applying git diff, see diff above error is caused by cached repository from which clearml tries to run the process. I've cleaned the cache, but it haven't helped.

The installed packages is fully editab...

AgitatedDove14 According to the logs (up to traceback message), the only difference between those two tasks is task id name

AgitatedDove14 Yes, the difference in installed packages is large - the training stage, which runs ok has all the following:

Well, I'm pretty sure that nntraining is executed in the same queue for these two cases:

Exactly! To be more specified - the same base_task_id fails, if the pipeline is cloned and started from UI. I've checked the queues for failed and completed tasks - they are the same (default, gpu-all).

AgitatedDove14 Yes, that's what I have - for me it's weird, too.

AgitatedDove14 Ok, I'll try to do this with clearml-data. However, I've found that I don't understand the logic, where newly generated data (by pipeline) are placed. I think, it's a major issue with my code. And, also, I should understand this for using clearml-data as well.
Say, script_a.py generates file test.json in project folder. script_b.py should use this file for further processing. When I run script-by-script, test.json is generated and used Ok. However, when I run...

Thanks. Not yet, but will watch, by all means.

JitteryCoyote63 Is there an example of how the learning pipeline can be triggered (started) by changes in dataset?