could it be the polling on the Task (can't remember whats the interval), but it will update it's state once every X minutes/seconds

100 entries:
https://github.com/allegroai/trains/blob/838c9cb0d2a5df5c193dfc85286abe59a80217c2/trains/storage/cache.py#L15

Hi DeliciousKoala34

I am using Pycharm and i have set up the clear-ml plugin, but it still doesnt work.

Did you provide the key/secret to the plugin? I think this is a must for it to actually work

That works AND the feature works!

YEY

Quick follow up question, is there any way to abort a pipeline and all of the tasks it ran?

Hmm yes currently if you abort the pipeline is has no "time" to abort the running Tasks (the DAG itself will stop, because the pipeline controller was aborted, bit the running Tasks will continue).
In order to have better support, we need to add a previously requested feature for "abort" callback. This is actually not as straight forward as it sound...

Ephemeral Dataset, I like that! Is this like splitting a dataset for example, then training/testing, when done deleting. Making sure the entire pipeline is reproducible, but without storing the data long term?

I'm hoping we are ready to release

"sub nodes" inside pipeline, in my opinion, makes them much more useful, in sense that all the steps are visible.

Yeah I really like this idea... continuing this thread, would it also make sense to have a Task object per "sub-node" and run the sub-nodes as subprocess of the parent Node? I'm thinking this sounds like a combination of both local pipeline execution and remote pipeline execution.
wdyt?

--docker or in clearml.conf https://github.com/allegroai/clearml-agent/blob/21c4857795e6392a848b296ceb5480aca5f98e4b/docs/clearml.conf#L153

Hi @<1547028074090991616:profile|ShaggySwan64>

I'm guessing just copying the data folder with rsync is not the most robust way to do that since there can be writes into mongodb etc.

Yep

Does anyone have experience with something like that?

basically you should just backup the 3 DBs (mongo, redis, elastic) each one based on their own backup workflows. Then just rsync the files server & configuration.

I want to build a real time data streaming anomaly detection service with clearml-serving

Oh, so the way it currently works clearml-serving will push the data in real-time into Prometheus (you can control the stats/input/out), then you can build the anomaly detection in grafana (for example alerts on histograms over time is out-of-the-box, and clearml creates the histograms overtime).
Would you also need access to the stats data in Prometheus ? or are you saying you need to process it ...

Actually it hasn't changed ...

Hi @<1610083503607648256:profile|DiminutiveToad80>
Yes, it does. They are also cached by default (on the machine with the agent)
None

time-based, dataset creation, model publish (tag),
Anything you think is missing ?

I cannot test it at the moment, hence my question.
JuicyFox94 any chance you can blindly approve ?

Hi SolidSealion72

"/tmp" contained alot of artifacts from ClearML past runs (1.6T in our case).

How did you end up with 1.6TB of artifacts there? what are the workflows on that machine? at least in theory, there should not be any leftover in the tmp folder, after the process is completed.

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 🙂

Hi SmallDeer34
Is the Dataset in clearml-data ? If it is then Dataset.get().get_local_copy() will get you a cached local copy of the entire dataset.
If it is not, then you can use StorageManager.get_local_copy(url_here) to download the dataset.

2. Any Argparser is automatically logged (and later can be overridden from the UI). Specifically HfArgumentParser will be automatically logged https://github.com/huggingface/transformers/blob/e43e11260ff3c0a1b3cb0f4f39782d71a51c0191/examples/pytorc...

No, an old experiment changed, nothing was rerun

ohh, that is odd. I think the max iteration value is stored on the DB, which is odd if it changed after an update.
BTW: just making sure, could it be these Tasks were imported ? (i.e. offline execution + import)

In that case I suggest you turn on the venv cache, it will accelerate the conda environment building because it will cache the entire conda env.

Hi LudicrousParrot69
A bit of background:
A Task is a job executed in the system (sometime it is an experiment training, sometime a controller like the pipeline). Basically everything process can be a task.
Specifically the pipeline controller itself (i.e. the process running the Bayesian optimization) is Task in the system (i.e. a job running). What it does (using the HyperParameterOptimizer) is cloning previously executed Tasks (e.g. training experiments), change their parameters and moni...

Depends on what you want to do, what do you want to do ?

Hi GloriousPenguin2 , Sorry this is a bit confusing. Let me expand:
When converting into a plotly object (the default), you cannot really control the dimensions of the plot in the UI programatically, you can however drag the seperator and expand width / height If you pass to report_matplotlib_figure the argument " report_image=True, " it will create a static image from matplotlib figure (as rendered locally) and use that as the figure, this way you get exactly wysiwyg , but the...

Hi JitteryCoyote63 report_frequency_sec=30. controller how frequently monitoring events are sent to the server, default is every 30 seconds (you can change the UI display to wall-time to review). You can change it to 180 so it will only send an event every 3 minutes (for example).
sample_frequency_per_sec is the sampling frequency it uses internally, then it will average the results over the course of the report_frequency_sec time window, and send the averaged result on the repo...

I'm not sure the files-server supports "continue" from last position...

OutrageousSheep60 so if this is the case I think you need to add "external links" i.e. upload the individual files to GCS, then register the links to GCS, does that make sense ?

Sorry found the code on the Task, duh 🙂
` # get_ipython().magic('pip install clearml')
import clearml
from clearml import Task
task = Task.init(project_name='examples', task_name='test param', reuse_last_task_id=False)
param = {
'tuple_double_quotes_r': (r"value\blah", 1),
'tuple_double_quotes': ("value\blah", 1),
'tuple_single_quotes': ('value\blah', 1),
"double_quotes_r": r"value\blah",
'double_quotes': "value\blah",
'single_quotes': 'value\blah'
...

YEY!

Hi @<1523706645840924672:profile|VirtuousFish83>
could it be you have some permission issues ?

: Forbidden: updates to statefulset spec for fields other than 'replicas', 

It might be that you will need to take it down and restart it. not while it is running.
(do make sure you backup your server 🙂 )

Hi PungentLouse55
Are you referring to the example code ?

Thanks DeliciousBluewhale87 ! greatly appreciated 🙂