if it ain't broke, don't fix it

😄

Up to you, just a few features & nicer UI.
BTW: everything is backwards compatible, there is no need to change anything all the previous trains/trains-agent packages will work without changing anything 🙂
(This even includes the configuration file, so you can keep the current ~/trains.conf and work with whatever combination you like of trains/clearml on the same machine)

Just making sure, the machine that you were running the "trains-init" on can access the API server ?

Any reason not to ?

Thanks StaleButterfly40 !

try: trains-init config

Hi @<1555362936292118528:profile|AdventurousElephant3>

hard time connecting to non-aws s3 bucket

if this is a non-s3 the output_uri should look something like:
output_uri = " None :port/backet"
Then make sure you have the correct credentials in your clearml.conf:
None

Hi GreasyPenguin14

1. Did using auto_connect_frameworks={'pytorch': False} solved the issue? ( I imagine it did )
2. Maybe we should have the option to have wildcard support so I will only auto log based on filename. Basically using auto_connect_frameworks={'pytorch': "model*.pt"} will only auto log the model files saved/logged , wdyt?

Everything seems correct...
Let's try to set it manually.
create a file ~/trains.conf , then copy paste the credentials section from the UI, it should look something like:
api { web_server: http:127.0.0.1:8080 api_server: http:127.0.0.1:8008 files_server: http:127.0.0.1:8081 credentials { "access_key" = "access" "secret_key" = "secret" } }Let's see if that works

Think multiple hyper-paremter sections that we need to reference
(under the Tasks Configuration Tab, the Hyper parameters can have multiple sections)

for example train.py & eval.py under the same repo

Once a model is saved and published, it should be downloadable right

Well that depends if you configured CLearML to autoupload it (by default it will just log the "local location").
To auto-upload add output_uri=True to Task.Init (or specify a destination with output_uri= ` s3://bucket/ )
You can also configure it as default here:
https://github.com/allegroai/clearml/blob/65f1c0baa124efb05fb7894a5386f0dd52c0536b/docs/clearml.conf#L163

No that's okay

(Not sure it actually has that information)

I get what you're saying. Only problem is in the case of AutoLogging, I don't have the model id, for the model being saved.

Task.models['output'] should return all the model objects the autologging created

Ok, so it doesn't follow the exact same rules as 

Task.init

?

Correct

I was afraid all the logs and outputs of a hyperparameter optimization task would be deleted just because no artifacts were created. (edited)

Should not happen 🙂

I have the same offset (that appear after each fail on my scalars).

Hmm, I actually would think this is the "correct" behavior, but I see your point:
Any chance you can open a GH issue ?

I had again the same problem but within a remote pipeline setup.

Are you saying the ussue is not fixed? can you verify the pipeline & pipeline components are using the at least 1.104rc0 version?

each epoch runs about 55 minutes, and that screenshot I posted earlier kind of show the logs for the rest of the info being output, if you wanted to check that out

I thought you disabled the stdout log. no?

Maybe ClearML is using

tensorboard

in ways that I can fine tune? I

You can open your TB and see, every report there is logged into clearml

Hi @<1597762318140182528:profile|EnchantingPenguin77>

, but it seems like clearml always create a virtual environmen

Yes that's correct, but the new venv inside the container inherits from the system packages (so if nothing changes it does nothing)

Is there a way that I can have the clearml-task to automatically activated a virtual environment use the activated custom virtual environment in my docker and run the scripts

Yoo can but the "correct" way to work with python and co...

Yes, or at least credentials and API...
Maybe inside your code you can later copy the model into fixed location ?
This way you have the model in the model repository and a copy in a fixed location (StorageManager can upload to a specific bucket/folder with the same credentials you already have)
Would that work?

Would love to just cap it at a fixed amount for a month for API calls.

Try the timeout configuration, I think this shoud solve all your issues, and will be fairly easy to set for everyone

Yey! 🙂

why not let the user start with an empty comparison page and add them from "Add Experiment" button as well?

Apologies, I was not clear. Yes I'm with you, this is a great idea 🙂

Different question. How can I pass PYTHONPATH env variable to a task, run by agent (so python can find classes inside m subdirectories)?

Hi HelpfulHare30
By default the working directory will be added to the python path, this means if I have under execution:
Working Dir: "." Script: "src/script.py"The root git repo will be added to the python path.
BTW: next RC you could add a flag to the agent to always add the git repo

Hi @<1523701240951738368:profile|RoundMosquito25>
Sure you can 🙂

task = Task.get_task("task_id_here")
metrics = task.get_last_scalar_metrics()
print(metrics[":monitor:gpu"])

None

VexedCat68 are you manually creating the OutputModel object?

Any chance you can open a GitHub issue so we do not forget this feature ?

https://github.com/allegroai/clearml/commit/6b8f21b30e005d50da35e2e5a5ded8782bce2f53

SweetGiraffe8 Works when I'm using plotly...
Can you please copy paste the code with the plotly, it's probably something I'm missing