DepressedChimpanzee34
What's the hydra version ?
I tested with 1.1.0dev3 and it worked for me

Hi PompousBeetle71 , what exactly is the scenario / problem we are trying to solve ?

Great, you can test directly from the master 🙂
pip3 install -U git+

New version will contain much more advanced search (including all the task fields)

are there any more fields in this function with partial matching? for example project? tags?

Yes they can all be filtered (basically everything you see in the UI)
notice: tags are strings (you can provide list of tags), project is an ID of the project
(Use Task.get_project_id, I think)

EnchantingWorm39 you have great timing ;)

LOL that's the spirit , making your team happy is key to success in adoption 🙂

481.2130692792125 seconds

This is very slow.
It makes no sense, it cannot be network (this is basically http post, and I'm assuming both machines on the same LAN, correct ?)
My guess is the filesystem on the clearml-server... Are you having any other performance issues ?
(I'm thinking HD degradation, which could lead to a slow write speeds, which would effect the Elastic/Mongo as well)

Hi SteadyFox10
I promised to mention here once we start working on ignite integration, you can check it here:
https://github.com/jkhenning/ignite/tree/trains-integration
Feel free to provide insights / requests 🙂

As for the model upload. The default behavior is
torch.save() calls will only be logged , nothing more. But, if you pass to the Task.init output_uri field, then all your models will be uploaded automatically. For example:
` task = Task.init('examples', 'model upload test', o...

Hi ReassuredTiger98
Basically assuming Linux, init.d will do the trick
https://unix.stackexchange.com/questions/20357/how-can-i-make-a-script-in-etc-init-d-start-at-boot

OutrageousGrasshopper93
tensorflow-gpu is not needed, it will convert tensorflow to tensorflow-gpu based on the detected cuda version (you can see it in the summary configuration when the experiment sins inside the docker)

How can i set the base python version for the newly created conda env?

You mean inside the docker ?

or even different task types

Yes there are:
https://clear.ml/docs/latest/docs/fundamentals/task#task-types
https://github.com/allegroai/clearml/blob/b3176a223b192fdedb78713dbe34ea60ccbf6dfa/clearml/backend_interface/task/task.py#L81

Right now I dun see differences, is this a deliberated design?

You mean on how to use them? I.e. best practice ?
https://clear.ml/docs/latest/docs/fundamentals/task#task-states

Noooooooooo, it is still working 🙂

I think this is the discussion you are after:
https://clearml.slack.com/archives/C01H5VAUZ8R/p1612452197004900?thread_ts=1612273112.002400&cid=C01H5VAUZ8R

ReassuredTiger98 I think it is using moviepy for the encoding... No?

Notice both needs to be str
btw, if you need the entire folder just use StorageManager.upload_folder

Well put 🙂

Optional[Sequence[Union[str, Dataset]]]None, list of string or list of Datasets objects
(each one is a parent (supporting multiple parents)

Hi WittyOwl57
That's actually how it works (original idea/design was borrowed from libclound), basically you need to create a Drive, then the storage manger will use it.
Abstract class here:
https://github.com/allegroai/clearml/blob/6c96e6017403d4b3f991f7401e68c9aa71d55aa5/clearml/storage/helper.py#L51
Is this what you had in mind ?

Yes the clearml import Must be outside if everything (so it can link with hydra), when you do it this way, by the time you import clearml, hydra is already done

let me check when a fix can be deployed for Hydra...

Ohhh I see, yes this is regexp matching, if you want the exact match:
'^{}$'.format(name)

Well if we the "video" from TB is not in mp4/gif format than someone will have to encode it.
I was just pointing that for the encoding part we might need additional package

I'm thinking of a few plots in my current in-house tooling which are slightly different than the standard charts we look at. For example a custom parallel coordinate chart that can use aggregations, categorical variables, etc.

This can be done by comparing experiments, then check the Hyper-Parameters tab, and select graph from the drop down at the top

So my question in general is pertaining to if I would need to get better at Javascript if I were to make those changes. My guess is ...

Hi SkinnyPanda43

I realized that the params are not being saved anymore

Could you test with clearml==1.0.4 ?

Hi PerplexedWalrus3
you should get something like the following on the console :
ClearML Task: created new task id=1ca59ef1f86d44bd81cb517d529d9e5a 2021-07-25 13:59:09 ClearML results page: 2021-07-25 13:59:16

Okay there now:
https://github.com/allegroai/trains/tree/0.15.1rc0

SmoothArcticwolf58 could you copy paste the entire query and what is the expected results vs reality ?

DepressedChimpanzee34
I have sad news, it is working just fine for me 😞
Python3.6 Ubuntu hydra-core==1.1.0.dev6

Hi TightElk12

would like to understand the limitations of 

Task.current_task()

Basically this will always get you an instance of the current Task. This will work from sub-processes as well as the main process. Is there a specific scenario you have in mind, or a challenge with the use case ?

The main question I have is why is the ALB not passing the request, I think you are correct it never reaches the serving server at all, which leads me to think the ALB is "thinking" the service is down or is not responding, wdyt?