Sounds good!

An example would be to use detect_with_conda_freeze for one project, but not for another one. These kind of configs are project-specific and not user-specific in my opinion. Similar to project specific configurations vs user-specific configurations in most IDEs.

I have my development machine where I develop for multiple projects. I want to configure clearml differently based on the project. Similar to .vscode , .git or .idea at the project level.

Related to this: How does the local cache/agent cache work? Are the sdk.storage.cache parameters for the agent? When are datasets deleted from cache? When are datasets deleted if I run local execution?

Yep, I will add this as an issue. Btw: Should I rather post the kind of questions I am asking as an issue or do they fit better here?

I mean, could my hard drive not become full at some point? Can clearml-agent currently detect this?

Thanks for the answer. So currently the cleanup is done based number of experiments that are cached? If I have a few big experiments, this could make my agents cache overflow?

Yea, is there a guarantee that the clearml-agent will not crash because it did not clean the cache in time?

Nvm. I forgot to start my agent with --docker . So here comes my follow up question: It seems like there is no way to define that a Task requires docker support from an agent, right?

Thank you. Will try that!

I guess the supported storage mediums (e.g. S3, ceph, etc...) dont have this issue, right?

I think such an option can work, but actually if I had free wishes I would say that the clearml.Task code would need some refactoring (but I am not an experienced software engineer, so I could be totally wrong). It is not clear, what and how Task.init does what it does and the very long method declaration is confusing. I think there should be two ways to initialize tasks:
Specify a lot manually, e.g. ` task = Task.create()
task.add_requirements(from_requirements_files(..))
task.add_entr...

One last question then I have everything solved: Is it possible to pass clearml the files to analyze manually? For example my setup consists of a run_this.py and this_should_be_run_A.py and this_should_be_run_B.py . I can then programmatically choose which file to import with importlib. Is there a way to tell clearml programmatically to analyze the files, so it can built up the requirements correctly?

🙂 Tell me when you find a good way!

I have an carla.egg file on my local machine and on the worker that I include with sys.path.append before I can do import carla . It is the same procedure on my local machine and on the clearml-agent worker.

Or alternatively I just saw that Task.create takes a requirements.txt as an argument. This would also be fine for me, however I am not sure whether I should use Task.create ?

Btw: I think Task.init is more confusing than Task.create and I would rather rename the former.

Thanks a lot. To summarize: To me clearml is a framework, but I would rather have it be a library.
Other than that I am very happy with clearml and it is probably my favorite machine learning related package of the last two years! 🙂 And thanks for taking so much time to talk to me!

Perfect, works! 🙂

AlertBlackbird30 Thanks for asking. Just take everything with I grain of salt I say, because I am also not sure whether I do machine learning the correct way 😄

I think you got the right idea. I actually do reinforcement learning (RL), so I have multiple RL-environments and RL-agents. However, while the code for the agents differs between the agents, the glue code is the same. So what I do is I call python run_experiment.py --agent http://myproject.agents.my ` _agent --environm...

No no, I was just wondering how much effort it is to create something like ClearML. And your answer gives me a rough estimate 🙂

No reason in particular. How many people work at http://allegro.ai ?

Thanks! I am fascinated by what you guys offer with clearml 🙂

Sounds like a good hack, but not like a good solution 😄 But thank you anyways! 🙂

Maybe a related question: Anyone every worked with datasets larger than the clearml-agent cache? Some colleague of mine has a dataset of ~ 1 tera byte...

Thanks a lot. But even for a user, I can not set a default for all projects, right?

Thanks for answering. I don't quite get your explanation. You mean if I have 100 experiments and I start up another one (experiment "101"), then experiment "0" logs will get replaced?

Thanks, that makes sense. Can you also explain what task_log_buffer_capacity does?

AgitatedDove14

But would this not have to be a server parameter instead of a clearml.conf parameter then? Maybe someone from clearml can confirm MortifiedDove27 's explaination?