Yeah that make sense 🙂

Yeah I think using voxel for forensics makes sense. What's your use case ?

I'm hoping i can find an end to end solution that also includes experiment management

Well of course biased here, but ClearML with the hyperdatasets is probably the most complete one.
Specifically with model performance analysis I would add voxel open-source to dissect specific results. but the combination of the abstraction and query capabilities of hyperdatasets, orchestration and experiment management are really unmatched for.
(and again of course I'm biased, but really there is n...

Sometimes it is working fine, but sometimes I get this error message

@<1523704461418041344:profile|EnormousCormorant39> can I assume there is a gateway at --remote-gateway <internal-ip> ?
Could it be that this gateway has some network firewall blocking some of the traffic ?
If this is all local network, why do you need to pass --remote-gateway ?

Hi @<1523704157695905792:profile|VivaciousBadger56>
You should replace

task.mark_completed()

with:

task.close()

To your point

parameters = task.connect(parameters)

Will be retrieved with:

task.get_parameters()

fyi:
connect_configuration -> get_configuration_objects

In the documentation it warns about

.close()

"Only call Task.close if you are certain the Task is not needed."

Maybe this is not clear enough, this means you do not need to automatically Add/Log/Track things into the Task in the current process.
This does Not mean you cannot access the Task or its artifacts

Mark closed means to externally (i..e not from the process that crated the Task, maybe even from a different machine) close and mark the task as completed (this...

StraightDog31 can you elaborate? where are the parameters stored? who is trying to access them, and maybe for what purpose ?

FiercePenguin76 in the Tasks execution tab, under "script path", change to "-m filprofiler run catboost_train.py".
It should work (assuming the "catboost_train.py" is in the working directory).

and I have no way to save those as clearml artifacts

You could do (at the end of the code
task.upload_artifact('profiler', Path('./fil-result/'))wdyt?

but this will be invoked before fil-profiler starts generating them

I thought it will flush in the background 😞
You can however configure the profiler to a specific folder, then mount the folder to the host machine:
In the "base docker args" section add -v /host/folder/for/profiler:/inside/container/profile

Hi SkinnyPanda43
Do you mean the cleaml-agent or the cleaml python (a.k.a the auto package detection) ?

I pass my dataset as parameter of pipeline:

@<1523704757024198656:profile|MysteriousWalrus11> I think you were expecting the dataset_df dataframe to be automatically serialized and passed, is that correct ?
If you are using add_step, all arguments are simple types (i.e. str, int etc.)
If you want to pass complex types, your code should be able to upload it as an artifact and then you can pass the artifact url (or name) for the next step.

Another option is to use pipeline from dec...

I failed to update the "STARTED AT" and the "COMPLETED AT" attributes in the "INFO" tab.

I'm not sure this can actually be overridden...

Seems like it is working (including seaborn)

The latest TAO doesn't use python for fine tuning, rather it uses the CLI entirely

It's a good question, but I think the CLI actually just runs a python code (the CLI is their interface). Generally speaking I'm pretty sure it will not be complicated to convert the TLT integration to support TAO (Nvidia helps with that, and I think we had a similar proces with Nvidia Clara/MONAI)
BTW: how are you using Nvidia TAO ?

LazyLeopard18 you can point the artifact directly to your azure object storage and have StorageManager download and cache it for you:

Hi RipeGoose2 all PR's are welcome, feel free to submit :)

Yep 🙂

Hi @<1523701304709353472:profile|OddShrimp85>
Do you mean Dataset.get_local_copy() ?

Are you saying this component should pull a specific git repo?
PipelineDecorator.component( ..., )seems like there is no reference to a specific repo (arguments repo and repo_branch etc are missing) is that correct?

Hmm I seems to fit the code 1x784 with float32, no?

Hi SubstantialElk6
You are uploading an artifact, a good use case for numpy artifact would be a feature table.
If you want to upload an image use either report_media or report_image or upload PIL image as artifact.
What do you think?

@<1523710674990010368:profile|GreasyPenguin14> make sure it to uses https not ssh:
edit ~/clearml.conf

force_git_ssh_protocol: false

and that you have both git_user & git_pass set in your clearml.conf

JitteryCoyote63

I agree that its name is not search-engine friendly,

LOL 😄
It was an internal joke the guys decided to call it "trains" cause you know it trains...
It was unstoppable, we should probably do a line of merchandise with AI 🚆 😉
Anyhow, this one definitely backfired...

Hmm I see what you mean. It is on the roadmap (ETA the next version 0.17, 0.16 is due in a week or so) to add multiple models per Task so it is easier to see the connections in the UI. I'm assuming this will solve the problem?

I think it fails because it tries to install trains twice. Could you remove the trains package, and test? I'm also curious how do you have both installed?!

Hi CharmingShrimp37
Go to Github to your newly forked repo, you should have a green button suggesting to take your branch and making it a PR. It is that simple 🙂

Yes actually that might be it. Here is how it works,
It launch a thread in the background to do all the analysis of the repository, extracting all the packages.
If the process ends (for any reason), it will give the background thread 10 seconds to finish and then it will give up. If the repository is big, the analysis can take longer, and it will quit

We actually added a specific call to stop the local execution and continue remotely , see it here: https://github.com/allegroai/trains/blob/master/trains/task.py#L2409

I presume is via the 

project_name

  and 

task_name

 parameters.

You are correct in your assumption, it only happens when you call Task.init but two distinctions:
ArgParser arguments are overridden (with trains-agent) even before Task.init is called Task.init when running under trains-agent will totally ignore the project/task name, it receives a pre-made task id, and uses it. So the project name and experiment are meaningless if you are running the tas...