Thanks SmallDeer34 !
This is exactly what I needed

If you have a requirements file then you can specify it:
Task.force_requirements_env_freeze(requirements_file='requirements.txt')
If you just want pip freeze output to be shown in your "Installed Packages" section then use:
Task.force_requirements_env_freeze()
Notice that in both cases you should call the function Before you call Task.init()
btw, what do you mean by "Packages will be installed from projects requirements file" ?

Full markdown edit on the project so you can create your own reports and share them (you can also put links to the experiments themselves inside the markdown). Notice this is not per experiment reporting (we kind of assumed maintaining a per experiment report is not realistic)

Sure thing! this feature is all you guys, ask and shall receive 🙂

Correct 🙂

Notice that you can embed links to specific view of an experiment, by copying the full address bar when viewing it.

Hi CloudySwallow27

how can I just "define" it on my local PC, but not actually run it.

You can use the clearml-task CLI
https://clear.ml/docs/latest/docs/apps/clearml_task#how-does-clearml-task-work
Or you can add the following line in your code, that will cause the execution to stop, and to continue on a remote machine (basically creating the Task and pushing it into an execution queue, or just aborting it)
task = Task.init(...) task.execute_remotely()https://clear.ml/do...

Hi StrangePelican34 , you mean poetry as package manager of the agent? The venvs cache will only work for pip and conda, poetry handles everything internally:(

Thank you StrangePelican34 !!!

If you spin two agent on the same GPU, they are not ware of one another ... So this is expected behavior ...
Make sense ?

if the file is untracked by git, it is not saved by clearml

Yep 😞

Does clearml-agent install the repo with 

pip install -e .

It is supported, but the path to the repo cannot be absolute (as it will probably be something else in the agent env)
You can add "git+ https://github.com ...." to the "installed packages" The root path of your repository is always added to the PYTHONPATH when the agents executes it, so in theory there is no need to install it wi...

RC you can see on the main readme, (for some reason the Conda badge will show RC and the PyPi won't)
https://github.com/allegroai/clearml/

I'll check what we can do on running in a daemon subprocess

CooperativeFox72 a bit of info on how it works:
In "manual" execution (i.e. without an agent)

path = task.connect_configuration(local_path, name=name

path = local_path , and the content of local_path is stored on the Task

In "remote" execution (i.e. agent)

path = task.connect_configuration(local_path, name=name

"local_path" is ignored, path is a temp file, and the content of the temp file is the content that is stored (or edited) on the Task configuration.
Make sense ?

This one should work:
` path = task.connect_configuration(path, name=name)
if task.running_locally():
my_params = read_from_path(path)
my_params = change_parmas(my_params) # change some staff

store back the change, my_params assumed to be the content of the param file (text)

task.set_configuration_object(name=name, config_taxt=my_params) `

So if I will edit the temp file

you mean from the UI or code?

CooperativeFox72 could you expand on "not working"?
If you have a yaml file, I would do:
` # local_path = './my_config.yaml'
path = task.connect_configuration(local_path, name=name)
if task.running_locally():
with open(local_path, "r") as config_file:
my_params_dict = yaml.load(config_file, Loader=yaml.FullLoader)
my_params_dict['change_me'] = 'new value'
my_params_text = yaml.dump(my_params_dict)

store back the change, my_params assumed to be the content of the param file (tex...

Hi @<1547028052028952576:profile|ExuberantBat52>

task = Task.get_task(...)
print(task.data)

wdyt?

GentleSwallow91 how come it does not already find the correct pytorch version inside the docker ? whats the clearml-agent version you are using ?

Hi PompousBeetle71
I remember it was an issue, but it was solved a while ago. Which Trains version are you using?

hmm that is odd, it should have detected it, can you verify the issue still exists with the latest RC?
pip3 install clearml-agent==1.2.4rc3

Woot woot!
awesome, this RC is stable you can feel free to use it, the official release is probably due to be out next week :)

Hmm no, the publish is internal "publish" state, it will not just "open" your server to the world (I guess that would be weird 😉 )
SmallDeer34 so maybe rerun it on the "free hosted" server and share it there?
(I'm assuming you do not intent to just open access to your own server)

ScaryKoala63
When it fails what's the number of files you have in:
/home/developer/.clearml/cache/storage_manager/global/ ?

Yey! MysteriousBee56 kudos on keep trying!
I'll make sure we report those errors, because this debug process should have much shorter 🙂

Suppose that I have three models and these models can't be loaded simultaneously on GPU memory(

Oh!!!

For now, this is the behavior I observe: Suppose I have two models, A and B. ....

Correct

Yes this is a current limitation of the Triton backend BUT!
we are working on a new version that does Exactly what you mentioned (because it is such a common case where in some cases models are not being used that frequently)
The main caveat is the loading time, re-loading models from dist...

Hi @<1523701601770934272:profile|GiganticMole91>
Do you mean something like a git ops triggered by PR / tag etc ?

CheerfulGorilla72 my guess is the Slack token does not have credentials for the private channel, could that be ?

CourageousLizard33 VM?! I thought we are talking fresh install on ubuntu 18.04?!
Is the Ubuntu in a VM? If so, I'm pretty sure 8GB will do, maybe less, but I haven't checked.
How much did you end up giving it?

Sure thing 🙂