We do upload the final model manually.
wait you said upload manually, and now you are saying "saved automatically", I'm confused.
HighOtter69
By default if you are continuing an experiment it will start from the last iteration of the previous run. you can reset it with:task.set_initial_iteration(0)
@<1545216077846286336:profile|DistraughtSquirrel81> shoot an email to "support@clear.ml" and provide all the information you can on the "lost account" (i.e. the one you had the data on), this means email account that created it (or your colleagues emails), and any other information that might help to locate it.
But I have no idea what will be input of step2.
What do you mean by that? the assumption is that somehow the output of step 1 will be passed (a string reference) to step 2, what am I missing ?
It uses only one CPU core, could I use multiprocessing somehow?
Hi EcstaticMouse10
Hmm, yes it should be multi core:
https://github.com/allegroai/clearml/blob/a9774c3842ea526d222044092172980ae505e24f/clearml/datasets/dataset.py#L1175
wdyt?
LazyLeopard18 you can point the artifact directly to your azure object storage and have StorageManager download and cache it for you:
https://stackoverflow.com/questions/60860121/plotly-how-to-make-an-annotated-confusion-matrix-using-a-heatmap
MagnificentSeaurchin79 see plotly example here:
https://allegro.ai/clearml/docs/docs/examples/reporting/plotly_reporting.html
Hurray conda.
Notice it does include cudatoolkit , but conda ignores it
cudatoolkit~=11.1.1
Can you test the same one only serach and replace ~= with == ?
still it is a chatgpt interface correct ?
Actually, no. And we will change the wording on the website so it is more intuitive to understand.
The idea is you actually train your own model (not chatgpt/openai) and use that model internally, which means everything is done inside your organisation, from data through training and ending with deployment. Does that make sense ?
Hi @<1523701132025663488:profile|SlimyElephant79>
I would like to save only the last & best checkpoints and not all of them if possible.
Basically it will mimic the local file system, so if you overwrite the local files it will overwrite the remote model.
You can also disable auto logging, and manually upload the models
In Task.init pass auto_connect_frameworks False for the specific framework
see:
[None](https://clear.ml/docs/latest/docs/clearml_sdk/task_sdk/#automatic-lo...
Hi @<1523715429694967808:profile|ThickCrow29>
Is there a way to specify a callback upon an abort action from the user
You mean abort of the entire pipeline?
None
Not at all, we love ideas on improving ClearML.
I do not think there is a need to replace feast, it seems to do a lot, I'm just thinking on integrating it into the ClearML workflow. Do you have a specific use case we can start to work on? Or maybe a workflow that would make sense to implment?
Hi @<1523701066867150848:profile|JitteryCoyote63>
Thank you for bringing it! can you verify with the latest clearml-agent 1.5.3rc2 ?
 are models technicallyÂ
Task
s and can they be treated as such? If not, how to delete a model permanently (both from the server and from AWS storage)?
When you call Task.delete() it actually goes over a;; the models/artifacts and deletes them from the storage
The wheel you download from pip, for example this one torch-1.11.0-cp38-cp38-manylinux1_x86_64.whl
is actually both CPU and cuda 117
So is there any tutorial on this topic
Dude, we just invented it 🙂
Any chance you feel like writing something in a github issue, so other users know how to do this ?
Guess I’ll need to implement job schedule myself
You have a scheduler, it will pull jobs from the queue by order, then run them one after the other (one at a time)
SlipperyDove40 following on the missing section name, this seems like backwards compatibility issue. Try calling with backwards_compatibility=Falsemy_params = Task.get_parameters(backwards_compatibility=False)This should always add the section name prefix.
So there is a hack for it:CLEARML_OFFLINE_MODE=1 python3 my_main.pyWhich is the same as calling Task.set_offline
Then inside the code After the Task.init call:
` task = Task.init(...)
not sure what the if here is?!
Task.debug_simulate_remote_task(task_id="offline-1") `This will make things act as if this is running remotely , i.e. your logic Task.running_remotely() will be called.
Do notice that in remote mode, all the arguments / data is read from the clearml-server into the cod...
now realise that the ignite events callbacks seem to not be fired
So this is an ignite issue ?
I get the same "white" image in both TB & ClearML 😞
Hi MiniatureCrocodile39
I would personally recommend the ClearML show 😉
https://www.youtube.com/watch?v=XpXLMKhnV5k
https://www.youtube.com/watch?v=qz9x7fTQZZ8