LOL, Okay I'm not sure we can do something that one.
You should probably increase the storage on your instance 🙂
I'll make sure they get back to you
GreasyLeopard35 I think you are on to something, I think UniformParameterRange just misses a min value:
https://github.com/allegroai/clearml/blob/fcad50b6266f445424a1f1fb361f5a4bc5c7f6a3/clearml/automation/parameters.py#L168
Should be:[self.min_value + v*step_size for v in range(0, int(steps))]
Basically try with the latest RC 🙂
pip install trains 0.15.2rc0
I can add files to the data set, even after I finish the experiment?
Correct
https://clear.ml/docs/latest/docs/clearml_data#creating-a-dataset
https://clear.ml/docs/latest/docs/guides/data%20management/data_man_cifar_classification
https://github.com/allegroai/clearml/blob/master/docs/datasets.md#create-dataset-from-code
Thanks for answering, Yes, this is exactly what I wanted
Hmm should be possible, how slow is the update that we want to save the time ?
Hmm I assume it is not running from the code directory...
(I'm still amazed it worked the first time)
Are you actually using "." ?
and this?avg(100*increase(test12_model_custom:Glucose_bucket[1m])/increase(test12_model_custom:Glucose_sum[1m]))
if I use
report_image
can I get a URL to it somehow?
Let me check ...
Are hparms saved in hypeparameter section superior to hparams saved in configuration objects?
well I'm not sure about "superior" but they are structured, as opposed to configuration object, which is as generic as could be
Can you provide some further explanation, please? Sorry, I am beginner.
My bad, I was thinking out loud on improving the HPO process and allowing users to modify the configuration_object , not just the hyperparameters
TrickyRaccoon92 Thanks you so much! 😊
however setting up the interpertier on pycharm is different on mac for some reason, and the video just didnt match what I see
MiniatureCrocodile39 Are you running on a remote machine (i.e. PyCharm + remote ssh) ?
My only point is, if we have no force_git_ssh_port or force_git_ssh_user we should not touch the SSH link (i.e. less chance of us messing with the original URL if no one asked us to)
Hi CloudyHamster42
how do i have the trains-agent install my
requirements.txt
file from my repo when creating the environment?
BTW if you clear all "the installed packages", then trains-agent will user requirements.txt and update back all the packages in the UI
Hmm yes this is exactly what should not happen 🙂
Let me check it
Hi SarcasticSparrow10 , so yes it does, this is more efficient when using pytorch loaders, and in some other situations.
To disable it add to your clearml.conf:sdk.development.report_use_subprocess = false2. interesting error, maybe we can revert to "thread mode" if running under a daemon. (I have to admit, I'm not sure why python has this limitation, let me check it...)
(Do notice that even though you can spin two agents on the same GPU, the nvidia drivers cannot share allocated GPU memory, so if one Task consumes too much memory the other will not have enough free GPU memory to run)
Basically the same restriction as manually launching two processes using the same GPU
in my repo I maintain a bash script to setup a separate python env.
Hmm interesting, now I have to wonder what is the difference ? meaning why doesn't the agent build a similar one based on the requirements ?
Is it being used to ssh to the instance?
It is used for the SSH client so it "knows" the SSH server (does that make sense) ?
Hi SparklingElephant70
Anyone know how to solve?
I tired git push before,
Can you send the entire log? Could it be that the requested commit ID does not exist on the remote git (for example force push deleted it) ?
Amazing! 🎉
Let me know how we can help 🙂
JitteryCoyote63 did you add the bash script here: https://github.com/allegroai/trains-agent/blob/master/docs/trains.conf#L99
Hi JitteryCoyote63 could you provide a bit more details, is this a repo for a python module (i.e. in the installed pacakges) or a submodule ?
I found the issue, the first run it jumps over the first day (let me check if we can quickly fix that)
OutrageousGiraffe8 so basically replacing to:self.d1 = ReLU()
Could it be there is a Task.init being called Before this code snippet ?
Fixing that would make this feature great.
Hmm, I guess that is doable, this is a good point, search for the GUID is not always trivial (or maybe at least we can put in the description the project/dataset/version )
In theory it should not, in practice you could run out of space while running the experiment itself...
You can always cleanup everything from time to time (maybe worth a flag?)