Great, you can test directly from the master 🙂
pip3 install -U git+

Hi @<1624941407783358464:profile|GrievingTiger47>
I think you should try to contact the sales guys here: None

Hi JitteryCoyote63
Wait a few hours, there is a new fix, I'll make sure we upload it later today (scheduled to be there anyhow, I'll push it forward)

Awesome! let me know how/if it works 🙂

Are you sure you added the pytorch channel in clearml.conf ?
https://github.com/allegroai/clearml-agent/blob/822984301889327ae1a703ffdc56470ad006a951/docs/clearml.conf#L64

a bit sad that there is no working integration with one of the leading time series framework...

You mean a series darts reports ? if it does report it, where does it do so? are you suggesting we have Darts integration (which sounds like a good idea) ?

One thing though - I am running agent on behalf of a regular user.

Oh that might be credentials / docker service issue (i.e. the user might not have the ability to rn a docker with --gpus, but as you mentioned,, that seems like an arch thing 🙂 )

@<1569858449813016576:profile|JumpyRaven4> fyi clearml-serving was synced 🤞

Like, let's say I want "a 15GB GPU or better" and there's 4 queues, two of which fit the description... is there any way to set it so that ClearML will just queue it up on whichever one's available?

How do you know that? Also if you know that, what do you know about the queues ?
Generally speaking this type of granularity is k8s, but it has lots of caveats, specifically that you need to Know what you need in term of resources, that you can specify resources that do not exist, and that...

• Set hashed passwords with  pass_hashed: true
• Generate passwords using  python3 -c 'import bcrypt,base64; print(base64.b64encode(bcrypt.hashpw("password".encode(), bcrypt.gensalt())))'  (obviously, replace "password" with the actual password). The resulting b64 string should be placed in the password field for each user.
  For example, assuming your password is "123456":
• bash:

> python3 -c 'import bcrypt,base64; print(base64.b64encode(bcrypt.hashpw("123456".encode(), bcrypt.gensal...

is it displaying that it is running anything?

Thanks!

Many thanks!

Change to add_missing_installed_packages=False, here, and see if you end up with git diff
https://github.com/allegroai/clearml/blob/1f82b0c4010799be6157f5c845c7f6ac48e71c0c/clearml/backend_interface/task/populate.py#L158

VexedCat68 I think this is the issue described here:
https://github.com/allegroai/clearml/issues/491
Can you test with the latest RC:
pip install clearml==1.1.5rc1

  is the "installed packages" part editable? good to know

Of course it is, when you clone a Task everything is Editable 🙂

Isn't it a bit risky manually changing a package version?

worst case it will crash quickly, and you reset/edit/enqueue 🙂
(Should work though)

Yes please 🤩

Are you hosting your own server? Is it on http://app.clear.ml ?

LittleShrimp86 did you try to run the pipeline form the UI on remote machines (i.e. with the agents)? Did that work?

I’m not sure if this was solved, but I am encountering a similar issue.

Yep, it was solved (I think v1.7+)

With

spawn

and

forkserver

(which is used in the script above) ClearML is not able to automatically capture PyTorch scalars and artifacts.

The "trick" is to have Task.init before you spawn your code, then (since your code will not start from the same state), you should call Task.current_task(), which would basically make sure everything is...

For local testing, we have added a

ScantChimpanzee51 there is already an environment variable for that, you can just set CLEARML_OFFLINE_MODE 🙂

By the way, if we don’t wrap other calls in

is_offline()

we get errors like “DateTime object is not serializable”, but that’s a secondary issue.

I think this was fixed, can you verify with the latest RC 1.7.3rc0 ? If this still happens can you share the code

However, this results in the process getti...

feature is however available in the Enterprise Version as HyperDatasets. Am i correct?

Correct
BTW you could do:
datasets_used = dict(dataset_id="83cfb45cfcbb4a8293ed9f14a2c562c0") task.connect(datasets_used, name='datasets') from clearml import Dataset dataset_path = Dataset.get(dataset_id=datasets_used['dataset_id']).get_local_copy()This will ensure that not only you have a new section called "datasets" on the Task's configuration, buy tou will also be able to replace the datase...

WackyRabbit7 hmmm seems like non regular character inside the diff.
Let me check something

Hi RoughTiger69
Interesting question, maybe something like:

` @PipelineDecorator.component(...)
def process_sub_list(things_to_do=[0,1,2]):
r = []
for i in things_to_do:
print("doing", i)
r.append("done{}".format(i))
return r

@PipelineDecorator.pipeline(...)
def pipeline():

create some stuff to do:

results = []
for step in range(10):
r = process_sub_list(list(range(step*10, (step+1)*10)))
results.append(r)

push into one list with all result, this will ac...

Hmm this is odd, could you provide the pipeline code maybe?

so all models are part of the same experiment and has the experiment name in their name.

Oh that explains it, (1) you can use the model filename to control the model name in clearml (2) you can disable the autologging and manually upload the model, then you can control the model name
wdyt?

Nice 👍

Could you verify you have 8 subfolders named 'venv.X' in the cache folder ~/. trains ?

Hi SillySealion58

"keep N best checkpoints" logic in my training loop.

If this is the usecase, may I suggest overwriting them locally? (the same will happen on the remote storage) This is exactly how the lightning / ignite feature is implemented

You mean like for your internal support channel inside your company ?