I am asking this because my NGINX server is giving Gateway Timeouts for delete calls sometimes.

Sync ... it might make sense if you have a lot of load. it might also be that the server is preoccupied with other requests

Have a grid view (e.g. 3 plots per line instead of just one)Yes the plots are resizable move the cursor to the separating line and drag 🙂
2. Check the group by section, they can be split per series (like in TB)

Hi @<1523701083040387072:profile|UnevenDolphin73>

How can I ensure tasks in a pipeline have the same environment as the pipeline itself?
...
but the tasks (executed remotely) do not use that same environment?

Just verifying, we are talking about pipeline decorators?

We also wanted this, we preferred to create a docker image with all we need, and let the pipeline steps use that docker image

You can specify the docker on the decorator itself:
[None](https://github.com/allegroai...

Hi @<1644147961996775424:profile|HurtStarfish47>

. I see

Add image.jpg

being printed for all my data items ...

I assume you forgot to call upload ? the sync "marks" files for uploaded / deletion but the upload call actually does the work,
Kind of like git add / push , if that makes sense ?

HandsomeCrow5 if you want to edit the Task object you can just use:
internal_task_representation = task.data internal_task_representation.execution.script = ... task._edit(execution=internal_task_representation.execution)This will make sure you do not need to worry about API version etc. the Task object will take care of it.
BTW: it seems a few more people wanted this ability, maybe we should edit a proper .edit method to Task. Thoughts ?

HandsomeCrow5
So using the _edit method you have the ability to add/edit the execution.script field, without worrying about the API version (I guess the name edit is misleading, it also does add :)

Hi @<1523709807092043776:profile|GrittyKangaroo27>

some of my completed datasets,

This only has an effect on the dataset when it is being uploaded, if completed it is there for logging purposes only. What is exactly the use case? (just to be verify, once a Task/Dataset is completed you cannot edit it)

Ephemeral Dataset, I like that! Is this like splitting a dataset for example, then training/testing, when done deleting. Making sure the entire pipeline is reproducible, but without storing the data long term?

I see, so basically fix old links that are now not accessible? If this is the case you might need to manually change the document on the mongodb running in the backend

ohh, not really 😞 this is really low level editing the DB.
You might be able to forcefully edit the links (i.e. artifacts) on the Dataset (task)
Check if this works

from clearml.backend_api.session.client import APIClient
c = APIClient()
t = c.tasks.get_by_id("DATASET_UUID_HERE")
# you might need to loop over the artifacts
t.data.execution.artifacts[0].uri = "

"
c.tasks.edit(task=t.id, execution=t.data.execution, force=True)

if the first task failed - then the remaining task are not schedule for execution which is what I expect.

agreed

I'm just surprised that if the first task is

aborted

instead by the user,

How is that different from failed? The assumption is if a component depends on another one it needs its output, if it does not then they can run in parallel. What am i missing?

Hi @<1523702307240284160:profile|TeenyBeetle18>

and url of the model refers to local file, no to the remote storage.

Do you mean that in the Model tab when you look into the model details the URL points to a local location (e.g. file:///mnt/something/model) ?
And your goal is to get a copy of that model (file) from your code, is that correct ?

@<1523715429694967808:profile|ThickCrow29> this is odd... how did you create the pipeline? can you provide code sample?

Are you suggesting the default "ubuntu:18.04" is somehow contaminated ?

This is an official Ubuntu container (nothing to do with ClearML), this is Very Very odd...

Hi RobustGoldfish9 ,

I'd much rather just have trains-agent just automatically build the image defined there than have to build the image separately and make it available for all the agents to pull.

Do you mean there is no docker image in the artifactory built based on your Dockerfile ?

Yep 🙂
Basically:
` task = Task.get_task(task_id='aaaa')
while task.status not in ('completed', 'stopped',):

do something ?

sleep(15) `(Notice task.status / task.get_status() will refresh the Task status on every call)

Okay let me check if we can reproduce, definitely not the way it is supposed to work 😞

Hi TightElk12

it would raise an error if the env where execution happens is not configured to track things on our custom server to prevent logging to the public demo server ?

What do you mean by that? catching the default server instead of the configured one ?

Hi PompousParrot44
Could you send the "Installed Packages" list?
I think there is a bug in the current trains-agent (there is already a fix but the RC is still not out),
where "packeg @ git+http" packages ignore the git+http link.
You can solve it manually by just editing the "Installed packages" (when Task is in draft mode, the section becomes editable), and remove the "package @" part, and leave the "git+http" link.

PompousParrot44 the venv created in the docker always inherits form the docker system-wide packages, so in essence if you are using the same set pf python packages, nothing will get reinstalled.

You should manually remove the cudatoolkit from the installed packages section in the UI, then try to send it to the agent and see if it works. The question is how it ended there in the first place

https://github.com/allegroai/trains-web

Basically internally we use psutil to get those stats ...
https://github.com/giampaolo/psutil/issues/1011
See psutil version that fixed that, what do you see on the Task "installed packages" ?
https://github.com/giampaolo/psutil/blob/master/HISTORY.rst#591

I will take any suggestion 🙂
git remote -v could be a good start but I'm not familiar with the output structure, is there a template for parsing ?

Hi SmarmySeaurchin8

, I was wondering if I could change the commit id to the current one as well.

Actually that would be possible, but will need a bit of code to support controlling Task properties (not just configuration parameters)

How can I do that without running this Task by it's own?

Assuming you have a committed code that already supports it. You can clone the executed Task, and then change the commit ID to the "latest on branch" (see drop down when editing)

Would t...

DilapidatedDucks58 Nice!

but it would be great to see predecessors of each experiment in the chain

So maybe we should add "manual pipeline" to create the connection post execution ? is this a one time thing ?
Maybe a service creating these flow charts ?
Should we put them in the Project's readme ? Or in the Pipeline section (coming soon)

Hi ComfortableHorse5
Yes this is more of a suggestion that you should write them using the platform capabilities, the UI implementation is being worked on, as well as a few helpers classes, I thin you'll be able to see a few in the next release 🙂

BTW: the above error is a mismatch between the TF and the docker, TF is looking for cuda 10, and the docker contains cuda 11

docstring ?
Usually the preferred way is StorageManager
https://clear.ml/docs/latest/docs/references/sdk/storage
https://clear.ml/docs/latest/docs/integrations/storage

Hi TrickyRaccoon92 , TB is automatically collected and converted into data stored on the system The UI uses plotly to display the data itself (on your web browser).
You still have the original TB protobuf file, if you want to dive deeper and debug the data (it is not automatically uploaded, but some users do upload it as additional artifact on the experiment)
Make sense ?