Examples: query, "exact match", wildcard*, wild?ard, wild*rd
Fuzzy search: cake~ (finds cakes, bake)
Term boost: "red velvet"^4, chocolate^2
Field grouping: tags:(+work -"fun-stuff")
Escaping: Escape characters +-&|!(){}[]^"~*?:\ with \, e.g. \+
Range search: properties.timestamp:[1587729413488 TO *] (inclusive), properties.title:{A TO Z}(excluding A and Z)
Combinations: chocolate AND vanilla, chocolate OR vanilla, (chocolate OR vanilla) NOT "vanilla pudding"
Field search: properties.title:"The Title" AND text
Answered
Hey All! Great Job On Clearml By The Way

Hey all! Great job on ClearML by the way 🙂
I’m currently exploring whether it could be a fit for me and my team and I have a few questions:
If I want to send a job from a machine with some version of the code base, my understanding is that the commit hash is picked up and any diff from the remote branch will be applied. Is that correct? Are the only two options for setting up the right environment for a Task either docker or git+pip? Do you support caching of git that evolves with the code base to speed it up? If I want to retrain the same model at a certain cadence on some streaming data (either by time passed or new data accumulated), would that something that ClearML supports or would I have to do the automation externally and trigger the agent from there. I’m sure it can be done with something like Airflow but wondering if you have something else in mind.

  
  
Posted 2 years ago
Votes Newest

Answers 7


Hi LazyTurkey38 !
Thanks for you kind words 🙏

my understanding is that the commit hash is picked up and any diff from the remote branch will be applied. Is that correct??

Correct 🙂 - do can get some control of this process or override it, if you'd like, but that's the default behavior.

Are the only two options for setting up the right environment for a Task either docker or git+pip?

You can have your ClearML Agent run the code in docker, based on some image you choose (or a default image, or even a complete standalone, prebuilt image you can build using the Agent), or you can have your ClearML Agent run the code in a virtual python environment. In both cases (unless you use a standalone image) the task's requirements and code are installed in the execution sandbox (venv inside the docker, or just the venv) and executed there.

Do you support caching of git that evolves with the code base to speed it up?

Yes 🙂 - ClearML Agent has both a venv cache and a cvs cache, so you can get a speedup rather quickly 🙂

  
  
Posted 2 years ago

In fact, if there is a good python API to list/duplicate/edit/run experiments by ID, it seems straightforward to do that from Airflow (or any other job scheduler). I’m just wondering if there is some built-in scheduler.

  
  
Posted 2 years ago

I don’t mean a serving endpoint, just the equivalent of “cloning an experiment” and running it on a different (larger) dataset.

  
  
Posted 2 years ago

If I want to retrain the same model at a certain cadence on some streaming data

Do you mean a serving endpoint?

  
  
Posted 2 years ago

You’re saying there’s a built-in scheduler? SuccessfulKoala55
If so where can I find it?

  
  
Posted 2 years ago

I meant a Python API...

  
  
Posted 2 years ago

Of course there is 🙂

  
  
Posted 2 years ago
504 Views
7 Answers
2 years ago
one year ago
Tags
Similar posts