Could not find a version that satisfies the requirement open3d==0.15.2 .. from versions: 0.10.0.0, 0.11.0, 0.11.1, 0.11.2, 0.12.0, 0.13.0)

This points to the agent installing using a different python version that you run the original code, I would guess python3.6

SpotlessFish46 unless all the code is under "uncommitted changes" section, what you have is a link to the git repo + commit id

I might gave an idea, could you test with:
` from clearml import Task
Task._report_subprocess_enabled = False

...

real code here `

MuddySquid7 I might have found something, and this is very very odd, it seems it will Not upload any new images post the history size, which is very odd considering the number of users actively using this feature...
Do you want to try a hack to see if it solved your issue ?

Ohh so even easier:
print(client.workers.get_all())

Also, the IDs as an entry in the Configuration will not be clickable in the web interface, right?

No, but on the other hand, it will be editable if you clone the Task.
Which brings me to a different scenario,
In the original one, the Main Task created the Dataset, i.e. Output Dataset (and stored it both ways).
I could think of a situation the Task is using the Dataset as input (say preprocessing or traing), then we might want to enable users to clone and change the Input dataset. wdyt?

However, regarding your recommendation of using

StorageManager

class to delete the URL, it seems that this class only contains methods for checking existence of files, downloading files and uploading files, but

no method

for actually

deleting

files based on their URL (see doc

and

).

Yes you are correct 😞 you should use a "deeper" class:

helper = StorageHelper.get(remote_url)
helper.delete(remo...

Hi GiganticTurtle0
You can keep clearml following the dictionary auto updating the UI
args = task.connect(args)

Let me see if I can reproduce something

Amazing! 🎉
Let me know how we can help 🙂

Are you saying that in the UI you do not see "confusion matrix" at all, only on the GS bucket ?

Also can you right click on the image and save it on your machine, see if it is cropped, or it is just a UI issue

Hi CrookedWalrus33
the python version is auto detected and register in "manual execution" time (i.e. when you run your code on your machine).
That said this is a suggestion for the agent, and only if it can actually find the matching Python version it will use it, otherwise it will use whatever is
available (i.e. Look through the PATH environment for a matching pythonX.Y executable)
The easiest way to support would just make sure the python binary's path is added to the PATH env.
Does...

that clearml-agent needs to be installed from system python mentioned anywhere in the docs, if not I suggest it gets added.

You are right, I will check and fix if not 🙂

Thank you so much for helping.

My pleasure

Hi @<1523701295830011904:profile|CluelessFlamingo93>
What do you mean? what's the difference between ClearML server and self hosted? both are self hosted no?

JitteryCoyote63 I think this only holds for the conda distribution.
(Actually quite interesting, I wonder what happens if you already installed cudatoolkit...)

Could you see if that makes a difference ?

Hi @<1600661428556009472:profile|HighCoyote66>

However, we need to allocate resources to ourselves manually, using an

srun

command or

sbatch

Long story short, there is a full SLURM integration, basically you push a job into the ClearML queue and it produces a slurm job that uses the agent to setup the venv/container and run your Task, but this is only part of the enterprise version 😞
You can however do the following (notice this is ...

Yeah that sounds like it, let me check if we can quickly reproduce

if i put pipe.start earlier in the code, the pipeline fails to execute the actual steps.

pipe.start should be called after the pipeline was constructed and should be the "last" call of the script.
Not sure I follow what is "before" the code?

@<1542316991337992192:profile|AverageMoth57> it sounds like you should use SSH authentication for the agent, just set
force_git_ssh_protocol: true
None
And make sure you have the SSH kets on the agent's machine

EnviousStarfish54 thanks again for the reproducible code, it seems this is a Web UI bug, I'll keep you updated.

So I checked the code, and the Pipeline constructor internally calls Task.init, that means that after you constructs the pipeline object, Task.current_task() should return a valid object....
let me know what you find out

metric=image is the name in the dropdown of the denugimages

print(requests.get(url='

print(requests.get(url='

WickedGoat98 are you running the agent with --gpus ?

How does a task specify which docker image it needs?

Either in the code itself 'task.set_base_docker' or with the CLI, or set it in the UI when you clone an experiment (everything becomes editable)

LovelyHamster1 Now I see... Interesting credentials ability. Specifically all the S3 access on trains is derived from the ~/clearml.conf credentials section :
https://github.com/allegroai/clearml/blob/ebc0733357ac9ead044d0ed32d41447763f5797e/docs/clearml.conf#L73
( or the AWS S3 environment variables )

I'm not sure how this AWS feature works, I suspect it is changing the AWS_ACCESS_KEY_ID AWS_SECRET_ACCESS_KEY variables on the ec2 instance. If this is the case, it should work out of...

BTW: how did it get there ?

the error for uploading is weird

wait, are you still getting this error?