Hi DeliciousBluewhale87
You can achieve the same results programmatically with Task.create
https://github.com/allegroai/clearml/blob/d531b508cbe4f460fac71b4a9a1701086e7b6329/clearml/task.py#L619

Ohh ignore the YAML

I think this is due to the label map including some keys with a 

.

  in them.

Hi TenseOstrich47 what do you mean "label"

Hi PanickyMoth78
Hmm yes, I think the StorageManager (i.e. the google storage pythonclinet) also needs a json file with the credentials.
Let me check something

For that I need more info, what exactly do you need (or trying to achieve) ?

HugeArcticwolf77 oh no, I think you are correct 😞
Do you want to quickly PR a fix ?

(with matplotlib 3.2+ I get no warning, let me check with 3.1)

OddAlligator72 okay, that is possible, how would you specify the main python script entry point? (wouldn't that make more sense rather than a function call?)

How do you determine which packages to require now?

Analysis of the actual repository (i.e. it will actually look for imports 🙂 ) this way you get the exact versions you hve, but nit the clutter of the entire virtual environment

@<1523711619815706624:profile|StrangePelican34> are you saying that after the " with " block the task is marked completed? how is that possible? is this done manually ?

I did not start with python -m, as a module. I'll try that

I do not think this is the issue.
It sounds like anything you do on your specific setup will end with the same error, which might point to a problem with the git/folder ?

Yes this seems like it is stuck, could you test with the demo server ?
(basically remove the clearml.conf it will connect automatically)

i'm Jax, not Manoj! lol.

I know 😄 I just mentioned that this issue is being actively discussed

Sure. JitteryCoyote63 so what was the problem? can we fix something?

but DS in order for models to be uploaded,
you still have to set:

output_uri=True

in the

No, if you set the default_output_uri, there is no need to pass output_uri=True in the Task.init() 🙂
It is basically setting it for you, make sense ?

Hi UnevenDolphin73

I cannot initialize a task before loading the file, but the docs for

connect_configuration

Yes, that's basically the problem. you have to decide where is the main driver.
If you are executing the code "manually" (i.e. not via the agent) then there is no problem, obviously you have the local file and you can use it to load the "project name" etc, then you just call Task.connect_configuration to log the content.
If you are running the same code via the agent...

Hi StickyMonkey98

a 

very

 large number of running and pending tasks, and doing that kind of thing via the web-interface by clicking away one-by-one is not a viable solution.

Bulk operations are now supported , upgrade the clearml-server to 1.0.2 🙂

Is it possible to fetch a list of tasks via Task.get_tasks,

Sure:
Task.get_tasks(project_name='example', task_filter=dict(system_tags=['-archived']))

Hi @<1546303293918023680:profile|MiniatureRobin9> could it be the pipeline logic is created via the clrarml-task CLI? If this is the case, I think this is an edge case we should fix. Basically it creates a Task instead of pipeline, which in.essence only effects the UI. To solve it, just run the pipeline locally, notice that by default when you start it, it will actually stop the local run and relaunch itself on an agent.
Also, could you open a GitHub issue so we add a flag for it?

JitteryCoyote63

So there will be no concurrent cached files access in the cache dir?

No concurrent creation of the same entry 🙂 It is optimized...

I'm hoping we are ready to release

5 seconds will be a sleep between two consecutive pulls where there are no jobs to process, why would you increase it to a higher pull freq ?

Do we have it on the git issue ?

Yes there was a bug that it was always cached, just upgrade the clearml
pip install git+

In the UI you can see all the agents and their IDs
Then you can so

clearml-agent daemon --stop <agent id>

thanks!

in Your Additional ClearML Configuration (which is basically clearml.conf configuration)
Add the following:
environment { GOOGLE_APPLICATION_CREDENTIALS="~/gs.cred" } files { gsc { contents: "<this is your GCP storage credentials file>" path: "~/gs.cred" } }Reference:
https://github.com/allegroai/clearml-agent/blob/a5a797ec5e5e3e90b115213c0411a516cab60e83/docs/clearml.conf#L421
https://github.com/allegroai/clearml-agent/blob/a5a797ec5e5e3e90b115213c0411a...

Hmm okay let me check that, I think I understand the issue

that is because my own machine has 10.2 (not the docker, the machine the agent is on)

No that has nothing to do with it, the CUDA is inside the container. I'm referring to this image https://allegroai-trains.slack.com/archives/CTK20V944/p1593440299094400?thread_ts=1593437149.089400&cid=CTK20V944
Assuming this is the output from your code running inside the docker , it points to cuda version 10.2
Am I missing something ?

can you bump me to that thread?

https://clearml.slack.com/archives/CTK20V944/p1630610430171200

I realise I'll need to catalogue all the dataset ids created by ppl separately on a spreadsheet.

Okay this part I missed, why would you need to add additional "catalog" when you have the UI?

s there any way to see datasets uploaded to ClearML Data without downloading them using ClearML Data?

Hi VexedCat68
Currently when you create datasets with clearml-data it has to repackage your files, i.e. upload them. That said we have received numerous requests on "registering data", and we are looking into it.
Here is the main technical hurdles we are facing, and I would love to get your perspective:
If the data is not available locally, we cannot calculate the hash of the conten...

ImmensePenguin78
I think the latest RC adds it, should be released later today 🙂