But the artifacts and my dataset of my old experiments still use the old adress for the download ( is there a way to change that ) ?

MotionlessCoral18 the old artifacts are stored with direct links, hence the issue, as SweetBadger76 noted you might be able to replace the links directly inside the backend databases

But you can get that directly, Task.get_task(...).artifacts[name].url , no? Am I missing something?

I couldn't change the task status from draft to complete

Task.completed(ignore_errors=True)

but I have no idea what's behing 

1

, 

2

  and 

3

 compare to the first execution

This is why I would think multiple experiments, since it will store all the arguments (and I think these arguments are somehow being lost.
wdyt?

GiganticTurtle0 what's the Dataset Task status?

Okay, so I think it doesn't find the correct Task, otherwise it wouldn't print the warning,
How do you setup the HPO class ? Could you copy paste the code?

Are any files uploaded? Like artifacts etc?

cleamrl sdk (i.e. python client)
The issue is that the Task.create did not add the repo, link (again as mentioned above, you need to pass the local folder or repo link to the repo argument of the Task.create function). I "think" it could automatically deduce the repo from the script entry point, but I'm not sure. hence my question on the clearml package version

CLI? Code ?

Thanks!

Hi RobustRat47
What do you mean by "log space for hyperparameter" , what would be the difference ? (Notice that on the graph itself you can switch to log scale when viewing in the UI) ?
Or are you referring to the hyper parameter optimization, allowing you to add log space ?

It reverts back, but it cannot "delete" the last reported iteration value.
Make sense ?

It should preserve the order as the order of the update back (i.e. when executed by the agent) is the same as the order of the keys (obviously py3.7+ becuase it creates dict not Ordered Dicts)

Hi @<1535793988726951936:profile|YummyElephant76>
None
None
None

Hi StraightDog31

I am having trouble using the 

StorageManager

  to upload files to GCP bucket

Are you using the storagemanager directly ? or are you using task.upload_artifact ?
Did you provide the GS credentials in the clearml.conf file, see example here:
https://github.com/allegroai/clearml/blob/c9121debc2998ec6245fe858781eae11c62abd84/docs/clearml.conf#L110

MysteriousBee56 I would do Task.create()
you can get the full Task internal representation with task.data
Then call task._edit(script={'repo': ...}) to edit/update all the Task entries.
You can check the dull details of the task object here: https://github.com/allegroai/trains/blob/master/trains/backend_api/services/v2_8/tasks.py#L954
BTW: when you have a sample script working, consider PR-ing it, I'm sure it will be useful for others 🙂 (also a great way to get us involved with debuggin...

Fix pushed to github 🙂
pip install git+

is this a config file on your side or something I can change, if we had enterprise version?

Yes, this is one of the things you can configure

Do you think the local agent will be supported someday in the future?

We can take this ode sample and extent it. can't see any harm in that.
It will enable very easy to ran "sweeps" without any "real agent" installed.

I'm thinking roll out multiple experiments at once

You mean as multiple subprocesses, sure if you have the memory for it

Hmm that is odd. Let me take a look and ask the guys. Thank you for quickly testing the RC! I'm hoping a new RC with a fix will be there tomorrow, if we can quickly replicate

Why can we even change the pip version in the clearml.conf?

LOL mistakes learned the hard way 🙂
Basically too many times in the past pip versions were a bit broken, which is fine if they are used manually and users can reinstall a diff version, but horrible when you have an automated process like the agent, so we added a "freeze version" option, only with greater control. Make sense ?

is it there ?

Hi LovelyHamster1
That is a good point, sine the Pipeline kind of assumes the task are already in the system, it clone them (leaving you with the original Draft Task).
I think we should add a flag to that pipeline that if the Task is in draft it will use it (instead of cloning it) Since it seems your pipeline is quite straight forward, I'm not sure you actually need the pipeline controller class, you can perform the entire thing manually, see example here: https://github.com/allegroai/clea...

based on this one:
https://stackoverflow.com/questions/31436407/git-ls-remote-returns-fatal-no-remote-configured-to-list-refs-from
I think this is a specific issue of the local git repo configuration, can you verify
(btw: I tested with git 2.17.1 git ls-remote --get-url will return the remote url, without an error)

FreshParrot56 we could add this capability, but the main caveat is that f your version depends on multiple parent versions you still need to download and extract all the parent versions, which means that when you clear them you might hurt later performance. Does that make sense? What is the use-case / scenario for you?

If you have a requirements file then you can specify it:
Task.force_requirements_env_freeze(requirements_file='requirements.txt')
If you just want pip freeze output to be shown in your "Installed Packages" section then use:
Task.force_requirements_env_freeze()
Notice that in both cases you should call the function Before you call Task.init()
btw, what do you mean by "Packages will be installed from projects requirements file" ?

Yes. Though again, just highlighting the naming of

foo-mod

is arbitrary. The actual module simply has a folder structured with an implicit namespace:

Yep I think this is exactly why it fails detecting it, let me check that

And it’s failing on typing hints for functions passed in

pipe.add_function_step(…, helper_function=[…])

… I guess those aren’t being removed like the wrapped function step?

Can you provide the log? I think I'm missing what e...

It looks somewhat familiar ... 😞
SuccessfulKoala55 any idea?

Nice SoreHorse95 !
BTW: you can edit the entire omegaconf yaml externally with set/get configuration object (name = OmegaConf) , do notice you will need to change Hydra/allow_omegaconf_edit to true

Thanks BroadSeaturtle49
I think I was able to locate the issue != breaks the pytroch lookup
I will make sure we fix asap and release an RC.
BTW: how come 0.13.x have No linux x64 support? and the same for 0.12.x
https://download.pytorch.org/whl/cu111/torch_stable.html