"warm" as you do not need to sync it with the dataset, every time you access the dataset, clearml will make sure it is there in the cache, when you switch to a new dataset the new dataset will be cached. make sense?

Hi JuicyOtter4
Seems like you cannot access your sever ?

repeat it until they are all dead 🙂

I see... We could definitely add an argument to control it. I'll update here once there is an RC

ElegantCoyote26 what you are after is:
docker run -v ~/clearml.conf:/root/clearml.conf -p 9501:8085
Notice the internal port (i.e. inside the docker is 8080, but the external one is changed to 9501)

Thanks BroadSeaturtle49
I think I was able to locate the issue != breaks the pytroch lookup
I will make sure we fix asap and release an RC.
BTW: how come 0.13.x have No linux x64 support? and the same for 0.12.x
https://download.pytorch.org/whl/cu111/torch_stable.html

SlipperyDove40
FYI:
args = task.connect(args, name="Args")Is "kind of" reserved section for argparse. Meaning you can always use it, but argparse will also push/pull things from there. Is there any specific reason for not using a different section name?

SlipperyDove40 following on the missing section name, this seems like backwards compatibility issue. Try calling with backwards_compatibility=False
my_params = Task.get_parameters(backwards_compatibility=False)This should always add the section name prefix.

I think they (DevOps) said something about next week, internal roll-out is this week (I think)

So I assume, trains assumes I have nvidia-docker installed on the agent machine?

docker + nvidia-docker-runtime are assumed to be installed
nvidia/cuda docaker image is pulled when requested (like any other container image)

Moreover, since I'm going to use Task.execute_remotely (and not through the UI) is there any code way to specify the docker image to be used?

Sure, task.set_base_docker(docker_cmd='nvidia/cuda -v /mnt:/tmp')
Notice that you can not only pass the dock...

Yes, it should, why?

JitteryCoyote63 is this still an issue?

Hi MortifiedDove27

Looks like there is a limit of 100 images per experiment,

The limit is 100 unique combination of title/series per image.
This means that changing the title or the series name will add 100 more images (notice the 100 limit is for previous iterations)

LOL love that approach.
Basically here is what I'm thinking,
` from clearml import Task, InputModel, OutputModel

task = Task.init(...)

run this part once

if task.running_locally():
my_auxiliary_stuff = OutputModel()
my_auxiliary_stuff.system_tags = ["DATA"]
my_auxiliary_stuff.update_weights_package(weights_path="/path/to/additional/files")
input_my_auxiliary = InputModel(model_id=my_auxiliary_stuff.id)
task.connect(input_my_auxiliary, "my_auxiliary")

task.execute_remotely()
my_a...

This seems to only work for a single file (weights_path implies a single file, not multiple ones). Is that the case?See update_weights_package actually packages an entire folder as zip and will do the extraction when you get it back (check the function docstring, I think you can also specify wildcard etc if needed)

Why do you see this as preferred to the dataset method we have now?

So it answers a few requirements that you raised
It is fully visible as part of the project and se...

SubstantialElk6 on the client side?

Hmm, maybe the right way to do so is to abuse "models" which have entity, you can specify a system_tag on them, they can store a folder (and extract it if you need), they are on projects and they are cloned and can be changed.
wdyt?

What do you have under the "installed packages" ?

This one seem to work

` from clearml import Task
task = Task.init(...)
import matplotlib.pyplot as plt
import numpy as np

plt.style.use('_mpl-gallery')

make data:

np.random.seed(10)
D = np.random.normal((3, 5, 4), (0.75, 1.00, 0.75), (200, 3))

plot:

fig, ax = plt.subplots()

vp = ax.violinplot(D, [2, 4, 6], widths=2,
showmeans=False, showmedians=False, showextrema=False)

styling:

for body in vp['bodies']:
body.set_alpha(0.9)
ax.set(xlim=(0, 8), xticks=np.arang...

Yes you can 🙂 (though not on the open-source version)

BroadSeaturtle49 agent RC is out with a fix:
pip3 install clearml-agent==1.5.0rc0Let me know if it solved the issue

HandsomeCrow5 Seems like the right place would be in the artifacts, as a summary of the experiment (as opposed to on going reporting), is that the case?
If it is then in the Artifacts tab clicking on the artifact should open another tab with your summary, which sounds like what you were looking for (with the exception of the preview thumbnail 🙂

Hi HandsomeCrow5 hmm interesting use case,
we have seen html reports as artifacts, then you can press "download" and it should open in another tab, what would you expect on "debug samples" ?

FrothyShark37 any chance you can share snippet to reproduce?

Hi 

, if you don't mind having a look too,

With pleasure :)

according to the above I was expecting the config to be auto-magically updated with the new yaml config I edited in the UI, however it seems like an additional step is required.. probably connect_dict? or am I missing something

Notice the OmegaConf section description :
Full OmegaConf YAML configuration. This is a read-only section, unless 'Hydra/_allow_omegaconf_edit_' is set to TrueBy default it will alw...

Hmm do you host it somewhere? Is it pre-installed on the container?

Okay, let me check

PricklyJellyfish35
Do you mean the original OmegaConf, before the overrides ? or the configuration files used to create the OmegaConf ?

and then?

The thing is programmatically this is not easy to do as API, because at the end the "function" (i.e. LCI) never leaves, it connects to the SSH and stays

But you can query the Task it creates, the project is known, the user is known and it is of special type/tag

Hi GreasyPenguin14

However the cleanup service is also running in a docker container. How is it possible that the cleanup service has access and can remove these model checkpoints?

The easiest solution is to launch the cleanup script with a mount point from the storage directory, to inside the container ( -v <host_folder>:<container_folder> )
The other option, which clearml version 1.0 and above supports, is using the Task.delete, that now supports deleting the artifacts and mod...