Correct 🙂
btw: my_dict_with_conf_for_data can be any object, not just dict. It will list all the properties of the object (as long as they do not start with _)

You mean one machine with multiple clearml-agents ?
(worker is a unique ID of an agent, so you cannot have two agents with the exact same worker name)
Or do you mean two agents pulling from the same queue ? (that is supported)

Thanks ContemplativePuppy11 !

How would you pass data/args between one step of the pipeline to another ?
Or are you saying the pipeline class itself stores all the components ?

sorry typo client.task. should be client.tasks.

Are they expanded in the "api_server" ? (I verified on a linux machine, same error, the env in the api_server is not being resolved)

BurlyRaccoon64 by default if .ssh exists in the host user folder it should mount it to the container (actually mount a copy of it). do you have a log of two tasks from two diff machines, one failing one passes? because this is quite odd (assuming the setup itself is identical)

I don't know how I would be able to get the description and name?

Good point, how about doing that in code, then you have all the information and you can store it in jsons / pickle next to the data folder?
wdyt?

Hi ShortElephant92
You could get a local copy from the local server, then switch credentials to the hosted server and upload again, would that work?

Let's try:
` echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' > /etc/apt/apt.conf.d/docker-clean ; chown -R root /root/.cache/pip ; export DEBIAN_FRONTEND=noninteractive ; export CLEARML_APT_INSTALL="$CLEARML_APT_INSTALL libsm6 libxext6 libxrender-dev libglib2.0-0" ; [ ! -z $(which git) ] || export CLEARML_APT_INSTALL="$CLEARML_APT_INSTALL git" ; declare LOCAL_PYTHON ; for i in {10..5}; do which python3.$i && python3.$i -m pip --version && export LOCAL_PYTHON=$(which python3.$i) && b...

RipeGoose2 you are not limited to the automagic
From anywhere in your code you can always do:
from trains import Logger Logger.current_logger().report_plotly(...)So you can add any manual reporting on top of the one generated by lightning .
Sounds good?

I looked at your task log on the github issue. It seems the main issue is that your notebook is Not stored as python code. Are you running it on jupyter notebook or is it ipython that you are runnig it on? Is this reproducible? If so what's the jupyter version, python and OS versions?

Hi ItchyHippopotamus18
The iteration reporting is automatically detected if you are using tensorboard, matplotlib, or explicitly with trains.Logger
I'm assuming there were no reports, so the monitoring falls back to report every 30seconds where the iterations are seconds from start" (the thing is, this is a time series, so you have to have an X axis...)
Make sense ?

Ohh ignore the YAML

I still wonder how no one noticed ... (maybe 100 unique title/series report is relatively high threshold)

Hi PompousBeetle71 , what exactly is the scenario / problem we are trying to solve ?

Hi LittleShrimp86

just to login into your clearml app (demo or server) so I can run python files related to clearml.

I think this amounts to creating a Task and enqueueing it, am I understanding correctly ?

Hmm, I'm without, no reason why it will get stuck .
Removing all the auto loggers, this can be done with

Task.init(..., auto_connect_frameworks=False)

which would disconnect all the automatic loggers (Hydra etc) off course this is for debugging purposes

conda list | grep matplotlib ?

Exactly 🙂
If you feel like PR-ing a fix, it will be greatly appreciated 🙂

MagnificentPig49 I was not aware of jsonargparse from what I understand it's a nicer way to parse json configuration files, with argparser alike interface. Did I get that correctly?
Regrading the missing argparser, you are correct, the auto-magic is not working since jsonargparse is calling an internal ArgParser function and not the external one (hence we miss it).
The quickest fix is adding the following line before you call parse_args() :
task.connect(parent_parser)

Yey!
My pleasure 🙂

mostly out of curiosity, what is the motivation behind introducing this as an environment variable knob rather then a flag with some default in Task.init?

DepressedChimpanzee34 we will deprecate the demo server (not exactly sure when) as we have the free community one that gives better service and stores the data. It was originally set for easy on-boarding and testing, but I think that now the user experience might be better with using the community free tier.
Make sense ? btw: what ...

but is there any other way to get env vars / any value or secret from the host to the docker of a task?

if this is docker -e/--env as argument would do the same
-e VAR=somevalue

correct

sdk.storage.cache.size.cleanup_margin_percent

Hi ReassuredTiger98
This is actually future proofing the cache mechanism and allowing it be "smarter" i.e. clean based on cache folder size instead of cache folder entries, this is currently not available

sdk.storage.cache

 parameters for the agent?

For both local execution and with an agent

When are datasets deleted if I run local execution?

When you hit the cache entry limit (100 if I recall). This can a...

Let say I don’t have the data on my local machine but only S3 bucket.

You can still register it, but make sure you do not delete it from the S3 bucket because it will keep a link to it

Failed to establish a new connection: [Errno 8] nodename nor servname provided, or not known')': /

what did you put in output_uri ?

I'm not sure TB support confusion matrix regardless, from anywhere in your code you can do:from trains import Task Task.current_task().get_logger().report_confusion_matrix(...)

Nice!!!

OutrageousGrasshopper93 could you send an example of the two links from the artifacts (one local one remote) ?

Hi StaleKangaroo85 which trains version are you using ? Also which trains-server are you using?