task.connect is two way, it does everything for you:
base_params = dict(param1=123, param2='text') task.connect(base_params) print(base_params)If you run this code manually, then print is exactly what you initialized base_params with. But when the agent is running it, it will take the values from the UI (including casting to the correct type), so print will result in values/types from the UI.
Make sense ?

releasing today 🙂

Hi ImpressionableRaven99
Yes, it is 🙂
Call this one before task.init, and it will run offline (at the end of the execution, you will get a link to the local zip file of the execution)
Task.set_offline(True)Then later you can import it to the system with:
Task.import_offline_session('./my_task_aaa.zip')

The import process actually creates a new Task every import, that said if you take a look here:
https://github.com/allegroai/trains/blob/10ec4d56fb4a1f933128b35d68c727189310aae8/trains/task.py#L1733
you can pass a pre-existing Task ID to "import_task" https://github.com/allegroai/trains/blob/10ec4d56fb4a1f933128b35d68c727189310aae8/trains/task.py#L1653

Hmm DepressedChimpanzee34 my bad it seems the loading is done via YAML loader, but the dumping is straight forward str casting...
https://github.com/allegroai/clearml/blob/6e6271fb91f2aeb2aa7a13c6d07d4e635baaa670/clearml/backend_interface/task/task.py#L934
What would you expect to get (BTW "value\blah" is Not a valid string assignment in python as there is no \b escape character, it should be "value\blah" which translates into the text "value\blah")

It should preserve the order as the order of the update back (i.e. when executed by the agent) is the same as the order of the keys (obviously py3.7+ becuase it creates dict not Ordered Dicts)

It's in my local conda environment though.

Meaning this is a wheel installed manually in conda? or is it a folder inside the conda environment ?

we concluded that we don't want to run it through ClearML after all, so we ran it standalone

out of curiosity, what was the conclusion and why?

Ohh I see, is this under plots or debug images ?

(you can find it in the pipeline component page)

Hi AstonishingSwan80 , what do you mean by "ec2 API"?

No Task.create is for creating an external Task not logging your own process,
That said you can probably override the git repo with env vars:
None

JitteryCoyote63 did you add the bash script here: https://github.com/allegroai/trains-agent/blob/master/docs/trains.conf#L99

I think it is free 🙂 (registration that is)

I think you can watch it after GTC on the nvidia website, and a week after that we will be able to upload it to the youtube channel 🙂

Hi GiganticTurtle0

I have found that 

clearml

 does not automatically detect the imports specified within the function decorated

The pipeline decorator will automatically detect the imports Inside the funciton, but not outside (i.e. global), to allow better control of packages (think for example one step needs the huge torch package, and the other does not.
Make sense ?

How can I tell 

clearml

 I will use the same virtual environment in all steps...

for example, one notebook will be dedicated to explore columns, spot outliers and create transformations for specific column values.

This actually implies each notebook is a standalone "process", which makes a ton of sense. But this is where notebooks and proper SW design break, in traditional SW, the notebooks are actually python files, and then of course you can import one from another, unfortunately this does not work in notebooks...

If you are really keen on using notebooks I wou...

Hi PanickyLion56
Yep savefig also works, you can also do,
from clearml import Logger Logger.current_logger().report_matplotlib_figure(title="My Plot Title", series="My Plot Series", iteration=10, figure=plt)https://github.com/allegroai/clearml/blob/0c5d12b830987aa9bb8d44d81e92ff9198008f29/examples/frameworks/matplotlib/matplotlib_example.py#L25

I'll try to create a more classic image.

That is always better, though I remember we have some flag to allow that, you can try with:
CLEARML_AGENT_SKIP_PIP_VENV_INSTALL=1 clearml-agent ...

ohh, could it be a 32bit version of python ?

JitteryCoyote63 I think this only holds for the conda distribution.
(Actually quite interesting, I wonder what happens if you already installed cudatoolkit...)

We used subprocess for it, ...

Popen? os.system? fork?

(as i see the services worker is only in the services-queue, and not my default queue (where my other servers/workers are)

So basically the service-mode is just a flag passed to the agent, and the services queue is the name of the queue it will pull from.

If i want a normal worker also

You can just add another section to the docker-compose, or run it manually after you spin the docker-compose.

LazyFox65 wdyt ?

@<1523706266315132928:profile|DefiantHippopotamus88> seems like you are missing the ports 🙂

CLEARML_WEB_HOST="

"
CLEARML_API_HOST="

"
CLEARML_FILES_HOST="

"

It all depends how we store the meta-data on the performance. You could actually retrieve it from the say val metric and deduce the epoch based on that

Hi MotionlessSeagull22
Hmm I'm not this is possible in the UI.
You can compare multiple experiments and view the images in form of thumbnails one next to the other, But full view will be a single image...
You can however right click on the image and get a direct link, then open a new tab ... :(

So I have a task that just loads a model, but I don't see it as an artifact in the UI

You should see it under Artifacts, Input model if you are calling Keras load function (or similar)

PanickyAnt52 when the docker is loaded, it will search for the highest python version to use for the agent. Then when it is launching the Task itself, it will first try to match the python version requested by the Task. It does so by looking for "python3.7" ,
what are you getting when running "which python3.7" inside the docker ? Could it be you have a venv inside the docker with the diff python version ?

JitteryCoyote63 not yet 😞
I actually wonder how poplar https://github.com/pallets/click is ?

Hi BeefyHippopotamus73

. I checked the template task and the list of “Installed Packages” indeed does not have one of my required packages in the list.

Basically the "installed packages" is auto populated based on the directly imported packages n your code base.
Could it be you do not have import snowflake-connector-python and this is a derivative package (i.e. required from a different package)

BTW: when you clone your Task in the UI you can edit and add the missing packages,...