Hi @<1564785037834981376:profile|FrustratingBee69>

It's the previous container I've used for the task.

Notice that what you are configuring is the Default container, i.e. if the Task does not "request" a specific container, then this is what the agent will use.
On the Task itself (see Execution Tab, down below Container Image) you set the specific container for the Task. After you execute the Task on an Agent, the agent will put there the container it ended up using. This means that ...

@<1699955693882183680:profile|UpsetSeaturtle37> good progress, regrading the error, 0.15.0 is supposed to be out tomorrow, it includes a fix to that one.
BTW: can you run with --debug

@<1542316991337992192:profile|AverageMoth57> it sounds like you should use SSH authentication for the agent, just set
force_git_ssh_protocol: true
None
And make sure you have the SSH kets on the agent's machine

Should work with report surface, notice that this is not triangles, assumption is this is a fixed sampling of the surface, sample size is the numpy array matrix and the sample value (i.e. Z ) is the value on the matrix. This means that if you have a set of mesh triangles , you have to projects and sample it.
I think this is what you are after https://trimsh.org/trimesh.voxel.base.html?highlight=matrix#trimesh.voxel.base.VoxelGrid.matrix

PungentLouse55 could you test with 0.15.2rc0 see if there is any difference ?

Thanks ReassuredTiger98 , yes that makes sense.
What's the python version you are using ?

so if i plot image with matplot lib..it would not upload? i need use the logger.

Correct, if you have no "main" task , no automagic 😞

so how can i make it run with the "auto magic"

Automagic logs a single instance... unless those are subprocesses, in which case, the main task takes care of "copying" itself to the subprocess.

Again what is the use case for multiple machines?

PungentLouse55 from the screenshot I assume the experiment template you are trying to optimize is not the one from the trains/examples 🙂
In that case, and based on the screenshots, the prefix is "Args/" as this is the section name.
Regrading objective metric, again based on your screenshots:
objective_metric_title="Accuracy" objective_metric_series="Validation"Make sense ?

`

Example use case:

an_optimizer = HyperParameterOptimizer(
# This is the experiment we want to optimize
base_task_id=args['template_task_id'],
# here we define the hyper-parameters to optimize
hyper_parameters=[
UniformIntegerParameterRange('General/layer_1', min_value=128, max_value=512, step_size=128),
UniformIntegerParameterRange('General/layer_2', min_value=128, max_value=512, step_size=128),
DiscreteParameterRange('General/batch_size', values=[...

In order for the sample to work you have to run the template experiment once. Then the HP optimizer will find the best HP for it.

DepressedChimpanzee34 I cannot find cfg.py here
https://github.com/allegroai/clearml/tree/master/examples/frameworks/hydra/config_files
(or anywhere else)

That sounds like an internal tritonserver error.
https://forums.developer.nvidia.com/t/provided-ptx-was-compiled-with-an-unsupported-toolchain-error-using-cub/168292

No worries, let's assume we have:
base_params = dict( field1=dict(param1=123, param2='text'), field2=dict(param1=123, param2='text'), ... )Now let's just connect field1:
task.connect(base_params['field1'], name='field1')That's it 🙂

However, that would mean passing back the hostname to the Autoscaler class.

Sorry my bad, the agent does that automatically in real-time when it starts, no need to pass the hostname it takes it from the VM (usually they have some random number/id)

So if you set it, then all nodes will be provisioned with the same execution script.

This is okay in a way, since the actual "agent ID" is by default set based on the machine hostname, which I assume is unique ?

is that what you want to happen ?

Interesting question, should work and looks like an interesting combination, I'm curious what you come up with.
btw: grafana itself can already provide a lot of alerts for drift etc, this is basically their histogram delta feature

Okay good news, there is a fix, bad news, sync to GitHub will only be tomorrow

SmallBluewhale13
And the Task.init registers 0.17.2 , even though it prints (while running the same code from the same venv) 0.17.2 ?

(it will just create a new venv and install everything you need in the venv)

My current experience is there is only print out in the console but no training graph

Yes Nvidia TLT needs to actually use tensorboard for clearml to catch it and display it.
I think that in the latest version they added that. TimelyPenguin76 might know more

SmallBluewhale13 in your code what are you getting when you print the version:
from clearml import __version__ print(__version__)

GiddyTurkey39 what do you have in the Task itself
(i.e. git repo uncommitted changes installed packages)

I set up the alert rule on this metric by defining a threshold to trigger the alert. Did I understand correctly?

Yes exactly!

Or the new metric should...

basically combining the two, yes looks good.

there is almost zero overhead if your docker container alreadyt has everything (including the agent) preinstalled and you set it with CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL=1
it then should basically just run the code.

Hmm, Notice that it does store sym links to parent data versions (to save on multiple copies of the same file). If you call get_mutable_local_copy() you will get a standalone copy

Hi VivaciousWalrus99
Could you attach the log of the run ?
By default it will use the python it is running with.
Any chance the original experiment was executed with python2 ?

Guys, any chance you can verify the RC solves the issue?
pip install clearml==1.0.2rc0

Can you send the full log ?

CourageousKoala93 when you call Task.close() it will mark the task as completed, there is no need to do that manually. The idea with mark_completed is that you can forcefully change the state if needed, or externally stop the task and mark it completed. Make sense?