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25 × Eureka!Hi GracefulDog98
The agent will map the ~/.ssh folder automatically into the docker's /root/.ssh
It will also convert http links to ssh pull if you set force_git_ssh_protocol
in your clearml.conf :
https://github.com/allegroai/clearml-agent/blob/351f0657c3dcf707659875d7e0a52fa387709978/docs/clearml.conf#L25
How can I add additional information, e.g. debug samples, or scalar to the data to be shown in the UI?Β Logger.current_logger() is not working
Yes π
dataset.get_logger() to the rescue
This means it will Always authenticate with SSH force_git_ssh_protocol
...
But it seems you need mixed behavior ?
Are you using github as git provider ?
These instructions should create the exact chart:
None
What am I missing ?
Hmm SuccessfulKoala55 any chance the nginx http was pushed to v1.1 on the latest cloud helm chart?
dataset catalogue as advertised.
Creating the Dataset on ClearML, is the catalog, you can move datasets around, put in sub-folders add tags add meta-data, search etc. I think this qualifies as a dataset catalog , no?
The "Optimizer task" will continue to run as long as there are sub-Tasks it launched.
Is anything else running/pending ?
Ssh is used to access the actual container, all other communication is tunneled on top of it. What exactly is the reason to bind to 0.0.0.0 ? Maybe it could be a flag that you, but I'm not sure in what's the scenario and what are we solving, thoughts?
Hmm what do you mean? Isn't it under installed packages?
Also, on the ClearML dashboard, I can see theΒ
clearml-agent
Β log:
Is the clearml-agent running in docker mode ?
Which means there will be atleast multiple published models entries of same model over time?
Only the specific one will be published (not all the Models the Task created)
Hmm, Notice that it does store sym links to parent data versions (to save on multiple copies of the same file). If you call get_mutable_local_copy() you will get a standalone copy
Which would also mean that the system knows which datasets are used in which pipelines etc
Like input
artifacts per Task ?
What do you have under the "installed packages" ?
LudicrousParrot69 we are working on adding nested project which should help with the humongous mass the HPO can create. This is a more generic solution for the nesting issue. (since nesting inside a table is probably not the best UX solution π )
Hmm that makes sense, I "think" the enterprise offering has a solution for that as well (i.e. full separation over static cluster), but probably the best way to constituent this avenue is talk to Sales (I'm assuming they'll setup a call to discuss the details)
Going back to the open source, I think that adding the credentials as part of the source code might allow to have "credentials" auto populate as part of the remote execution, wdyt?
yes, looks like. Is it possible?
Sounds odd...
Whats the exact project/task name?
And what is the output_uri?
cuda 10.1, I guess this is because no wheel exists for torch==1.3.1 and cuda 11.0
Correct
how can I enforce a specific wheel to be installed?
You mean like specific CUDA wheel ?
you can simple put the http link to the wheel in the "installed packages", it should work
Hi StoutElephant16
You mean like cron Job?
(Unfortunately if this is the case, then currently no CLI for that, but it is a great idea, maybe open a github issue to make sure we do not forget to add it π )
You cannot change the user once you have mount the shared folder with wither CIFS or NFS
Is there no await/synchronize method to wait for task update?
Yes, but then we will have to relaunch it (not unthinkable), but I'm still looking for the intimidate value of doing all that work, wdyt?
Hi EagerOtter28
The agent knows how to do the http->ssh conversion on the fly, in your cleaml.conf (on the agent's machine) set force_git_ssh_protocol: true
https://github.com/allegroai/clearml-agent/blob/42606d9247afbbd510dc93eeee966ddf34bb0312/docs/clearml.conf#L25
I can't see any reason it should not work π
In our case this is not possible due to client security (e.g. training data from clients can potentially be 'reverse engineered' from trained models in future).
Hmm I see, wouldn't it make more sense to separate clients like a multi-tenant SAAS solution ?
That seems like the k8s routing, can you try the web server curl?