Great to hear @<1523701087100473344:profile|SuccessfulKoala55> ! Is there an estimated timeline for these releases?

I didn't mention code in #340 nor did I mention data here 😄 The idea was to package non git-specific files for remote execution

That's exactly what I meant AgitatedDove14 🙂 It's just that to access that comparison page, you have to make a comparison first. It would be handy to have a link (in the side bar?) to an empty comparison

Using the PipelineController with add_function_step

TimelyPenguin76 I added pip install --update clearml-agent to the extra_vm_bash_script for the autoscaler, that should at least guarantee the latest clearml agent is used on the instance, right?

Then the username and password would be visible in the autoscaler task 😕
But it should work out of the box, as it does work like that out of the box also regardless of ClearML. The user and personal access token are used as is and it propagates down to submodules, since those are simply another git repository.
I've further checks on a different machine and it works as well 🤔

TimelyPenguin76 here's the full log (took a moment to anonynomize completely):

`
Using environment access key CLEARML_API_ACCESS_KEY=xxx
Using environment secret key CLEARML_API_SECRET_KEY=********
Current configuration (clearml_agent v1.3.0, location: /tmp/.clearml_agent.zs4e7egs.cfg):

sdk.storage.cache.default_base_dir = ~/.clearml/cache
sdk.storage.cache.size.min_free_bytes = 10GB
sdk.storage.direct_access.0.url = file://*
sdk.metrics.file_history_size = 100
sdk.m...

They are set with a .env file - it's a common practice. The .env file is, at the moment, uploaded to a temporary cache (if you remember the discussion regarding the StorageManager ), so it's also available remotely (related to issue #395)

So the config loading is not deferred until execution 😞

That could work, given that:
Could we add a preview section? One reason I don't like using the configuration section is that it makes debugging much much harder. Will the clearml-agent download and unzip the files, placing them into the same local folder as needed for execution? What if we want to include non-configuration objects? (i.e. the model case I listed)

Yes -- that's what I meant by The title is specified in the plot . I make the plots manually - title, axes labels, ticks, etc. In that sense, the figure is entirely configured. ClearML just saves it as "untitled 00/plot image"

For example, we have a complicated YAML file with built-in !include instructions, so we upload all the included files too. This then clogs up the artifacts sidebar, and it would be nice to be able to say "these are all artifacts from this one file, you can collapse it by clicking here"

I'll kill the agent and try again but with the detached mode 🤔

These issues popped when I upgraded to clearml-agent 1.5.0.

That could be a solution for the regex search; my comment on the pop-up (in the previous reply) was a bit more generic - just that it should potentially include some information on what failed while fetching experiments 😄

I also tried switching to dockerized mode now, getting the same issue 🤔

Okay this was a deep dive into clearml-agent code 😁
Took a long time to figure out that there was a specific Python version with a specific virtualenv that was old (Python 3.6.9 and Python 3.8 had latest virtualenv, but Python 3.7.5 had an old virtualenv).
Then the task requested to use Python 3.7, and that old virtualenv version was broken.

As a result -> Could the agent maybe also output the virtualenv version used with setting up the environment for the first time?

It's removed now, magic of asking for help and doing nothing 😄

First bullet point - yes, exactly

Second bullet point - all of it, really. The SDK documentation and the examples.
For example, the Task object is heavily overloaded and its documentation would benefit from being separated into logical units of work. It would also make it easier for the ClearML team to spot any formatting issues.
Any linked example to github is welcome, but some visualization/inline code with explanation is also very much welcome.

The overall flow I currently have is e.g.
Start an internal task (not ClearML Task; MLOps not initialized yet) Call some pre_init function with args so I can upload the environment file via StorageManager to S3 Call some start_run function with the configuration dictionary loaded, so I can upload the relevant CSV files and configuration file Finally initialize the MLOps (ClearML), start a task, execute remotely
I can play around with 3/4 (so e.g. upload CSVs and configuratio...

BTW AgitatedDove14 following this discussion I ended up doing the regex way myself to sync these, so our code has something like the following. We abuse the object description here to store the desired file path.

` config_path = task.connect_configuration(configuration=config_path, name=config_fname)
included_files = find_included_files_in_source(config_path)
while included_files:
file_to_include = included_files.pop()
sub_config = task.connect_configuration(
configurat...

StaleButterfly40 what use case are you looking for? I've used environment variables in the config file and then I can overwrite them in os.environ before ClearML loads the config

You mean at the container level or at clearml?

Yes, the container level (when these docker shell scripts run).
The per user ID would be nice, except I upload the .env file before the Task is created (it's only available really early in the code).

Is it CLEARML_CONFIG_FILE ? (I had to dig this from the GH code 😅 )

I see that the GUI AutoScaler is only in the paid version, wonder why the GCP driver is not open source?

Maybe create_function_task ? 🤔

@<1523701087100473344:profile|SuccessfulKoala55> could you provide some instructions?

We're using self hosted account

Thanks SuccessfulKoala55 and AgitatedDove14 ! We'll go through the hoops of setting up mongo on AWS then.
We're working to decouple the data from the helm chart, seems like a dangerous idea to store long term data on k8s in case of failure 😅

That's fine for the current use-case I believe.
Once the team is happy with the logging functionality, we'll move on to remote execution and things will update.