Hi GiganticTurtle0
ClearML will only list the directly imported packaged (not their requirements), meaning in your case it will only list "tf_funcs" (which you imported).
But I do not think there is a package named "tf_funcs" right ?

, it's just a custom module.

Is this your own module ? Is this a local folder we import from ?

GiganticTurtle0 is it in the same repository ?
If it is it should have detected the fact that it needs to analyze the entire repository (not just the standalone script, and then discover tensorflow)

Correct.
It starts with the initial script (entry point), if it is self contained (i.e. does not interact with the rest of the repo) it will only analyze it, otherwise it will analyze the entire repo code.

Hi JitteryCoyote63
If you want to refresh the task object, call task.reload() It will also refresh the artifacts.
The reason for not always do so when accessing the .artifacts objects is for speed optimization (It might be slow compared to dict access, and we assume users will expect it to behave the dict)

Okay, releasing a fix

: For artifacts already registered, returns simply the entry and for artifacts not existing, contact server to retrieve them

This is the current state.
Downloading the artifacts is done only when actually calling get()/get_local_copy()

Awesome ! thank you so much!
1.0.2 will be out in an hour

task.wait_for_status() task.reload() task.artifacts["output"].get()

I mean, can you install it with something like ?
pip install git+Basically the agent will install main repository, and any git submodules. But it cannot install multiple repositories, as the directory structure might be too much.
wdyt?

Ohh, hmm, that is odd, there should not be a limit there. Let me check ....

Hi VivaciousWalrus21

After restarting training huge gaps appear in iteration axis (see the screenshot).

The Task.init actually tries to understand what was the last reported interation and continue from that iteration, I'm assuming that what happens is that your code does that also, which creates a "double shift" that you see as the jump. I think the next version will try to be "smarter" about it, and detect this double gap.
In the meantime, you can do:
` task = Task.init(...)...

I was having this confusion as well. Did behavior for execute_remote change that it used to be Draft is Aborted now?

Actually it was changed, it used to reset the Task (then push into into execution queue if needed), with clearml v1.0, we now support pushing aborted Tasks back into queues, so execute_remotely aborts the Task (instead of reseting it)
(you can always manually reset it)

Any specific use case for the required "draft" mode?

Give me a minute, I'll check something

Nothing except that Draft makes sense feels like the task is being prepped and Aborted feels like something went wrong

Yes guess that if we call execute remotely, without a queue, it makes sense for you to edit it...
Is that the case TrickySheep9 ?
If it is I think we should change it to draft when it is not queued. sounds good to you guys ?

Yep that makes sense to me too. Done

Hi BrightGoat74
So merging general purpose plotly plots is very hard (i.e. putting both on the same graph)
But if you report using logger.report_scatter(...) the UI will merge the ROC curves into the dame graph, wdyt?
https://clear.ml/docs/latest/docs/guides/reporting/scatter_hist_confusion_mat_reporting#2d-scatter-plots

Sure this is basically REST query 🙂
` from clearml.backend_api.session.client import APIClient

client = APIClient()
models = client.models.get_all(name='regexp', tags=['demo'], project=['project_id'])
print(models) `

GreasyPenguin14 I think this is what you are looking for
Task.get_project_id('project_name')

BTW: get_tasks has project_name argument, I would just use it 🙂

Not really sure that's easily done ... I mean you could query the data, but I'm not sure how you would import it. Btw why would you move from pro to self hosted?

Task.get_projects() 🙂

Hi TrickySheep9
Long story short, clearml-session fully supports k8s (using k8s glue)
The --remote-gateway along side ports mode will basically allow you to setup a k8s service so that every session will register with a specific port so k8s does ingest foe you and route the SSH connection to the pod itslef, everything else is tunneled over the original SSH connection.
Make sense ?

Yes, offline got broken in 1.3.0 😞 , RC fixed it:
pip install clearml==1.3.1rc0Stable release later this week

EnviousStarfish54 Sure, see scatter2d
https://allegro.ai/docs/examples/reporting/scatter_hist_confusion_mat_reporting/#2d-scatter-plots

As long as you import clearml on the main script, it should work. Regarding the Nvidia container, it should not interfere with any running processes, the only issue is memory limit. BTW any reason not to spin an agent on a dedicated machine? What is the gpu used for in the ckearml server machine?

@<1556812486840160256:profile|SuccessfulRaven86> is the issue with flask reproducible ? if so could you open a github issue, so we do not forget to look into it?

EnviousStarfish54 generally speaking the hyper parameters are flat key/value pairs. you can have as many sections as you like, but inside each section, key/value pairs. If you pass a nested dict, it will be stored as path/to/key:value (as you witnessed).
If you need to store a more complicated configuration dict (nesting, lists etc), use the connect_configuration, it will convert your dict to text (in HOCON format) and store that.
In both cases you can edit the configuration and then when ru...

If this is a simple two level nesting:
You can use the section name:
task.connect(param['data'], name='data') task.connect(param['model'], name='model')Would that help?
The comparison reflects the way the data is stored, in the configuration context. that means section name & key value (which is what the code above does)