WackyRabbit7 basically starting v1.1 if you are running code without any configuration file, you will get an error (in contrast to previous versions where it defaulted to the demo-server)

Or use python:3.9 when starting the agent

This is probably the best solution 🙂

Hi @<1556450111259676672:profile|PlainSeaurchin97>

While testing the migration, we found that all of our models had their

MODEL URL

set to the IP of the old server.

Yes all the artifacts/models/debug-samples are stored "as is" , this means that if you configured your original setup with IP, it is kind of stuck there, this is why it is always preferred to use host-name ...

you apparently also need to rename

all

model URLs

Yes 😞

Hi @<1578918150261444608:profile|RoundJellyfish71>
Yes it can, there check these examples:
None
None

You will have to build your own docker image based on that docker file, and then update the docker compose

Sounds great! I really like that approach, thanks GrotesqueDog77 !

JitteryCoyote63 fix should be pushed later today 🙂
Meanwhile you can manually add the Task.init() call to the original script at the top, it is basically the same 🙂

no, at least not yet, someone definitely needs to do that though haha

Currently all the unit tests are internal (the hardest part is providing server they can run against and verify the results, hence the challange)

For example, if ClearML would offer a

TestSession

that is local and does not communicate to any backend

Offline mode? it stores everything into a folder, then zips it, you can access the target folder or the zip file and verify all the data/states

JitteryCoyote63 The release was delayed due a last minute issue, should be released later today. Anyhow the code is updated on GitHub, so you can start implementing :) let me know if I can be of help :)

Thank you! 🙂

Hi PleasantGiraffe85
Did you set git_host to only point to your host ? do you expect all the git clones to use SSH? how does the requirements.txt git link looks like ?
https://github.com/allegroai/clearml-agent/blob/bf07b7f76d3236c1118b81730c6d9718705a795a/docs/clearml.conf#L22

your account has 2FA enabled and you must use a personal access token instead of a password.I'm assuming you have created the personal access token and used it, not the pass

ImmensePenguin78 it might be... Let me check, worst case sync after the weekend 🙂
(pypi does contain 1.2.0rc4 and we are finalizing tests so that we can release a stable 1.2.0)

Sounds like something very similar, I'll try to use it,

You can set it per container with -e CLEARML_AGENT_SKIP_PIP_VENV_INSTALL=1

Or add it here:
https://github.com/allegroai/clearml-agent/blob/51eb0a713cc78bd35ca15ed9440ddc92ffe7f37c/docs/clearml.conf#L149
extra_docker_arguments: ["-e", "CLEARML_AGENT_SKIP_PIP_VENV_INSTALL=1"]

but I still clearml-agent will raise the same error

which one?

Sure thing, feel free to ping 🙂

Hi Martin, of course not,

Smart!

I was just wondering if it has been patched yet and if not what is the expected timeline for patching it

Yes, I believe the target is a patch version 1.15.1 to be released in a couple of weeks. This is not a major issue but it's always better to have have it fixed. (btw: the enterprise version never had this issue to being with, because it is of course authenticated, as well as it has additional RBAC layer on top.)

I see, that means xarray is not an actual package but a folder add to the python path.
This explains why Task.add_requirements fails, as it is supposed to add python packages to the equivalent of "requirements.txt" ...
Is the folder part of the git repository ? How would you pass it to the remote machine the cleamrl-agent is running on?

Also, how do pipelines compare here?

Pipelines are a type of Task, so like Tasks you can clone and enqueue them, or set them as the target of the trigger.

the most flexible solution would be to have some way of triggering the execution of a script in the parent task environment,

This is the exact idea of the TriggerScheduler None
What am I missing here?

Could it be the Args section of the task it clones does not have the "input_train_data" argument ?

Hi BoredSquirrel45

as of today, my required packages aren't being recognized in cloned

Are you saying you are editing the code directly in the cloned Task, then enqueue the Task an the agent does not "auto recognize" the package ?

No after, do you see the poetry lock removed in the uncommitted changes?

Hi YummyFish22
Looks like the task does not have "Task.init" call on the main script (or an import of clearml)? could that be the case?

Hi DrabCockroach54

Do we know if gpu_0_mem_usage and gpu_0_mem_used_gb, both shows current GPU usage?

the first is percentage used (memory % used at any specific moment) and the second is memory used GiB , both for the video memory

How to know from this how much GPU is reserved for the task if this task is in progress?

What do you mean by how much is reserved ? Are you running with an agent?

UnevenDolphin73 it seems this is a UI browser limit, this means we will need to move it into the server ...
See here: https://clearml.slack.com/archives/CTK20V944/p1640247879153700?thread_ts=1640135359.125200&cid=CTK20V944

I ended up using

task = Task.init(

continue_last_task

=task_id)

to reload a specific task and it seems to work well so far.

Exactly, this will initialize and auto log the current process into existing task (task_id). Without the argument continue_last_task ` it will just create a new Task and auto log everything to it 🙂

S3 access would return a different error...
Can you do:
` from clearml.storage.helper import StorageHelper
helper = StorageHelper.get("s3://<bucket>/<foo>/local/<env>/<project-name>/v0-0-1/2022-05-12-30-9-rocketclassifier.7b7c02c4dac946518bf6955e83128bc2/models/2022-05-12-30-9-rocketclassifier.pkl.gz")

print("helper", helper) `

ERROR: torch-1.12.0+cu102-cp38-cp38-linux_x86_64.whl is not a supported wheel on this platform
TartBear70 could it be you are running on a new Mac M1/2 ?

Also quick question, any chance you can test with the latest RC?
pip3 install clearml-agent==1.3.1rc6

` from clearml.automation.parameters import LogUniformParameterRange
sampler = LogUniformParameterRange(name='test', min_value=-3.0, max_value=1.0, step_size=0.5)
sampler.to_list()

Out[2]:
[{'test': 1.0},
{'test': 3.1622776601683795},
{'test': 10.0},
{'test': 31.622776601683793},
{'test': 100.0},
{'test': 316.22776601683796},
{'test': 1000.0},
{'test': 3162.2776601683795}] `

Hi GrotesqueDog77
What do you mean by share resources? Do you mean compute or storage?