I think you can set the cuda version in the clearml.conf , alternatively you can have the agent use a docker image with your required version of cuda instead of setting the environment directly on the machine
Hey @<1523701949617147904:profile|PricklyRaven28> , about the S3 loading issue. The path to the model in the artifact tab, is it an S3 bucket or a local path?
Is this a jupyter notebook or something ? Can you download it properly as either a .ipynb or .py file?
Hey @<1526734437587357696:profile|ShaggySquirrel23> , what version of the clearml-agent are you using? Also, if I were you I’d check how much free disk there’s on the machine running the agents
What happens if you set the new project name to f"{config.project_id}" (notice, no .pipelines )?
Hey @<1523704757024198656:profile|MysteriousWalrus11> , given your use case, did you consider passing the path to the dataset? Like an address to an S3 bucket
Hey @<1639799308809146368:profile|TritePigeon86> , given that you want to retry on connection error, wouldn't it be easier to use retry_on_failure from PipelineController / PipelineDecorator.pipeline None ?
And the quota is not cumulative , otherwise we’d run out of storage with the oldest accounts 😃
To link a dataset to a task you need to pass the alias= parameter to the Dataset.get . See here: https://clear.ml/docs/latest/docs/clearml_data/clearml_data_sdk#accessing-datasets
It won't, for that you need full support from Ultralytics
Hey @<1603198163143888896:profile|LonelyKangaroo55> If you only use the summary writer, does it report properly to both TB and ClearML?
You can create a new dataset and specify the parent datasets as all the previous ones. Is that something that would work for you ?
Hey @<1678212417663799296:profile|JitteryOwl13> , just to make sure I understand, you want to make your imports inside the pipeline step function, and you're asking whether this will work correctly?
If so, then the answer is yes, it will work fine if you move the imports inside the pipeline step function
About the first question - yes, it will use the destination URI you set.
About the second point - did you archive or properly delete the experiments?
Hey @<1554275802437128192:profile|CumbersomeBee33> , aborted usually means that someone manually stopped the pipeline or one of it's experiments. Can you provide us with the code you used to run it?
That is not specific enough. Can you show the code? And ideally also the console log of the pipeline
Hey @<1547390438648844288:profile|ScaryJellyfish75> , can you provide the whole code for the pipeline, and also mention what clearml version are you using?
Can you please attach the code for the pipeline?
Hey @<1671689458606411776:profile|StormySeaturtle98> we do support something called "Model Design" previews, basically an architecture description of the model, a la Caffe protobufs. None For example we store this info automatically with Keras
Hey @<1535069219354316800:profile|PerplexedRaccoon19> , yes it does. Take a look at this example, and let me know if there are any more questions: None
Hey @<1661904968040321024:profile|SpotlessOwl43> that's a great question!
how the metric should be saved, via report_single_value?
That's correct
what should I enter into the title and series fields in Project Dashboard?
The title should be "Summary" and series is the name of the single value you reported
Hey @<1644147961996775424:profile|HurtStarfish47> , you can use S3 for debug images specifically , see here: https://clear.ml/docs/latest/docs/references/sdk/logger/#set_default_upload_destination but the metrics (everything you report like scalars, single values, histograms, and other plots) are stored in the backend. The fact that you are almost running out of storage could be because of either t...
Hello @<1523710243865890816:profile|QuaintPelican38> , could you try Dataset.get ing an existent dataset and tell whether there are any errors or not?
Hello @<1533257278776414208:profile|SuperiorCockroach75> , thanks for asking. It’s actually unsupervised, because modern LLMs are all trained to predict next/missing words, which is an unsupervised method
This is the method you're looking for None . But make sure you have a model saved on disk before using it. And if you don't want the model to be deleted from disk after it, make sure to set auto_delete_file=False
Hey Sana, yes you can. When you open the link, check on the upper-right side the Task's menu bar, and you will notice that you can clone the shared task.
Can you try checking if you have access to the model in the shared experiment?
This sounds like a use case for the enterprise version of ClearML. In it you can set read/write permissions. Publishing is considered a "write", so you can limit who can do it. Another thing that might be useful in your scenario is to try using "Reports", and connect the "approved" experiments info to a report and then publish it. Here's a short video introducing reports .
By the way, please note that if the experiment/report/whatever is publis...