GiddyTurkey39

A flag would be really cool, just in case if theres any problem with the package analysis.

Trying to think if this is a system wide flag (i.e. trains.conf) or a flag in task.init.
What do you think?

It reverts back, but it cannot "delete" the last reported iteration value.
Make sense ?

"This is Not a an S3 endpoint... what is the files server you configured for it?

LudicrousParrot69 there is already
Task.add_tags
https://github.com/allegroai/clearml/blob/2d561bf4b3598b61525511a1a5f72a9dba74953e/clearml/task.py#L964

Thanks @<1523701713440083968:profile|PanickyMoth78> for pining, let me check if I can find something in the commit log, I think there was a fix there...

GrievingTurkey78 are you able to reproduce it?

BTW: if you only need the git diff you can just copy them from the UI into a txt file and do:
git apply <copied-diff.txt>

DeliciousBluewhale87 you can try:
` import sqlite3
import pandas as pd

conn = sqlite3.connect('test_database')

sql_query = pd.read_sql_query ('''
SELECT
*
FROM products
''', conn)

sql_query.to_csv(...) `

Hi IrateBee40
What do you have in your ~/clearml.conf ?
Is it pointing to your clearml-server ?

Basically it hooks into any torch.save function (monkey patching in realtime)

No worries 🙂

Hi WickedElephant66

Setting the pipeline controller with pipeline_execution_queue as None

is actually launching the pipeline controller on your "dev" machine, not sure why you have two of them?

Of course, I used "localhost"

Do not use "localhost" use your IP then it would be registered with a URL that points to the IP and then it will work

Hi, what is host?

The IP of the machine running the ClearML server

Hi PerplexedGoat65

it appears, in a practical sense, this means to mount the second drive, and then bind them in ClearML’s configuration

Yes, the entire data folder (reason is, if you loose it, you loose all the server storage / artifacts)

Also, thinking about Docker and slower access speed for Docker mounts and such,

If the host OS is linux, you have nothing to worry about, speed will be the same.

well it should fail, but I think the error message should be fixed 🙂
maybe:
ValueError: dataset 'tmp_datset' not found in projectlavi-testing' `wdyt?

are you using http?

https://github.com/allegroai/clearml/blob/master/clearml/automation/trigger.py
Example coming soon, with docs :)

This is odd , and it is marked as failed ?
Are all the Tasks marked failed, or is it just this one ?

Hi @<1523709807092043776:profile|GrittyKangaroo27>

some of my completed datasets,

This only has an effect on the dataset when it is being uploaded, if completed it is there for logging purposes only. What is exactly the use case? (just to be verify, once a Task/Dataset is completed you cannot edit it)

No by definition the agent will only execute one Task at a time, you can spin a second agent on the same GPU :)

hmm DeliciousKoala34
what are you getting if you put this at the top of your code (the one you are running in the remote docker)
import os print([(k, os.environ[k]) for k in os.environ if k.startswith("CLEARML_")])

I suppose one way to perform this is with a

that kicks

Yes, that was my thinking.

It seems more efficient to support a triggered response to task fail.

Not sure I follow this one, I mean the pipeline logic itself monitors the execution. If I'm not mistaken, try/except will catch a step that files, and a global will catch the entire pipeline. Am I missing something ?

WickedGoat98 the agent itself can be executed on bare metal, no need to setup a docker for it (although fully supported)
Specifically the docker compose has the docker running in services mode, i.e. for CPU light weight tasks such as running pipelines .
If the agent running on GPU, the easiest way to is run on bare metal

Hi GiganticTurtle0
You can keep clearml following the dictionary auto updating the UI
args = task.connect(args)

Regrading the missing packages, you might want to test with:
force_analyze_entire_repo: falsehttps://github.com/allegroai/trains/blob/c3fd3ed7c681e92e2fb2c3f6fd3493854803d781/docs/trains.conf#L162

Or if you have a full venv you like to store instead:
https://github.com/allegroai/trains/blob/c3fd3ed7c681e92e2fb2c3f6fd3493854803d781/docs/trains.conf#L169

BTW:
What is the missed package?

I understand I can change the docker image for a component in the pipeline, but for the

it isn’t possible.

you can always to Task.current_task.connect() from the pipeline function itself, to connect more configuration arguments you basically add via the function itself, all the pipeline logic function arguments become pipeline arguments, it's kind of neat 🙂 regrading docker, the idea is that you use a very basic python docker (the default for services) queue for all...

I can't think of any actual difference in flow ...
Can you try the following?
task._setup_reporter() task.set_initial_iteration(0)

Hmm not sure, try the latest anyhow 🙂

SmarmyDolphin68

Debug Samples tab and not the Plots,

Are you doing plt.imshow ?
Also make sure you have report_image=False when calling the report_matplotlib_figure
(if it is true it will upload it as an image to "debug samples")