Still the same problem 😞

AgitatedDove14 In theory yes there is no downside, in practice running an app inside docker inside a VM might introduce slowdowns. I guess it’s on me to check whether this slowdown is negligible or not

No space, I will add and test 🙂

yes, the only thing I changed is:
install_requires=[ ... "my-dep @ git+ ]to:
install_requires=[ ... "git+ "]

yes, because it won’t install the local package which has this setup.py with the problem in its install_requires described in my previous message

Ok to be fair I get the same curve even when I remove clearml from the snippet, not sure why

correct, you could also use

Task.create

that creates a Task but does not do any automagic.

Yes, I didn't use it so far because I didn't know what to expect since the doc states:
"Create a new, non-reproducible Task (experiment). This is called a sub-task."

What I put in the clearml.conf is the following:

agent.package_manager.pip_version = "==20.2.3" agent.package_manager.extra_index_url: [" "] agent.python_binary = python3.8

it also happens without hitting F5 after some time (~hours)

The number of documents in the old and the new env are the same though 🤔 I really don’t understand where this extra space used comes from

Ho and also use the colors of the series. That would be a killer feature. Then I simply need to match the color of the series to the name to check the tags

1.1.2

So I installed docker, added user to group allowed to run docker (not to have to run with sudo, otherwise it fails), then ran these two commands and it worked

Awesome! Thanks! 🙏

Thanks!3. I don't know, I never used Highcharts 🙂

this is the last line, same a before

There is a pinned github thread on https://github.com/allegroai/clearml/issues/81 , seems to be the right place?

hoo thats cool! I could place torch==1.3.1 there

is there a command / file for that?

Yes I agree, but I get a strange error when using dataloaders:
RuntimeError: [enforce fail at context_gpu.cu:323] error == cudaSuccess. 3 vs 0. Error at: /pytorch/caffe2/core/context_gpu.cu:323: initialization error
only when I use num_workers > 0

So it is there already, but commented out, any reason why?

Is it safe to turn off replication while a reindex operation is happening? the reindexing is rather slow and I am wondering if turning of replication will speed up the process

Update: I successfully isolated one of the reason, mem leak in matplotib itself, I opened an issue on their repo here

Hi SuccessfulKoala55 , will I be able to update all references to the old s3 bucket using this command?

the deep learning AMI from nvidia (Ubuntu 18.04)

Can I simply set agent.python_binary = path/to/conda/python3.6 ?

Maybe the agent could be adapted to have a max_batch_size parameter?

agent.cuda_version = 0 agent.cudnn_version = 0

On clearml or clearml-server?

Nevertheless there might still be some value in that, because it would allow to reduce the starting time by removing the initial setup of the agent + downloading of the data to the instance - but not as much as I described initially, if instances stopped are bound to the same capacity limitations as new instances launched