Sure:
task.get_project_name()

I am struggling with configuring ssh authentication in docker mode

GentleSwallow91 Basically the agent will automatically mount the .ssh into the container , just make sure you set the following in the clearml.conf:
force_git_ssh_protocol: truehttps://github.com/allegroai/clearml-agent/blob/178af0dee84e22becb9eec8f81f343b9f2022630/docs/clearml.conf#L30

. So i'd like to use the command line argument it in the first argparse, and then hide/delete/override before running the second argparse.

Nice, hack!

task.project is the project ID (not name)
task.get_project_name() will return the project name

Why would you need to manually change the current run? you just provided the values with either default/command-line ?
what am I missing here?
ResponsiveHedgehong88 I'm not sure I state dit, but the argparser arguments and values are collected automatically from your current run and put on the Task, there is no need to manually set them if you have the argparser running on your machine. Basically it collects the current (i.e. the process running on your machine) settings, and "copies" them ...

does this mean that Task stores --args (and propagates these further through the code as CLI arguments) somewhere where i can get and manipulate them from my code?

Yes it changes the actual argparse object and pushes the new values in runtime, basically you args.parse() will return the values from the UI (backend)

@<1523701868901961728:profile|ReassuredTiger98> it works on my machine 😞

Sorry, you are correct this is where the json is created:
https://github.com/huggingface/transformers/blob/040283170cd559b59b8eb37fe9fe8e99ff7edcbc/src/transformers/feature_extraction_utils.py#L470

other links are the function calling it. make sense ?

Should be fairly easy to add no?

I think we added it somewhere in 0.14, anyhow I just checked the Logger doc, it is there now 🙂

In order to facilitate the multiple credentials one must use the Clearml SDK obviously.

Yes 🙂

👍

ohh, not really 😞 this is really low level editing the DB.
You might be able to forcefully edit the links (i.e. artifacts) on the Dataset (task)
Check if this works

from clearml.backend_api.session.client import APIClient
c = APIClient()
t = c.tasks.get_by_id("DATASET_UUID_HERE")
# you might need to loop over the artifacts
t.data.execution.artifacts[0].uri = "

"
c.tasks.edit(task=t.id, execution=t.data.execution, force=True)

Hmm so yes that is true, if you are changing the bucket values you will have to manually also adjust it in grafana. I wonder if there is a shortcut here, the data is stored in Prometheus, and I would rather try to avoid deleting old data, Wdyt?

BTW: there is a fix to the priority thing:
https://github.com/allegroai/trains-agent/blob/216b3e21790659467007957d26172698fd74e075/docs/trains.conf#L77

okay that's good, that means the agent could run it.
Now it is a matter of matching the TF with cuda (and there is no easy solution for that). Basically I htink that what you need is "nvidia/cuda:10.2-cudnn7-runtime-ubuntu16.04"

Hi TrickyRaccoon92
Are you sure plotly (the front-end module displaying the plots in the UI) supports it ?

BroadMole98

I'm still exploring what trains is for.

I guess you can think of Trains as Experiment manager + MLOps tied together.

The idea is to give a quick and easy way to move from coding/running on one machine to scaling it to multiple remote machines, with everything that comes with it.

In some ways it is like snakemake, it setups your environment and execute the code. Snakemake also allows you to setup data, which in Trains is done via code (StorageManager), pipelines are also...

Hi @<1785479228557365248:profile|BewilderedDove91>
It's all about the databases in the under the hood, so 8gb is really a must

Hi SkinnyPanda43
Yes, I think you are right the documentation might be missing it. I'll make sure they know it 🙂
In the meantime :
task.update_output_model
https://github.com/allegroai/clearml/blob/d3929033c016476c580557639ff44f900e65904a/clearml/backend_interface/task/task.py#L734

GiddyTurkey39 Hmm I'm assuming that by default it cannot access that IP range.
Are you using virtual-box for the VM?
EDIT:
Can I assume the machine running the VM (a.k.a the host) can access the trains-server ?

What about output_uri?

If you are using StorageManager directly, output_uri is not relevant

programmatically or from the UI?

btw: both should work fine

Well if we the "video" from TB is not in mp4/gif format than someone will have to encode it.
I was just pointing that for the encoding part we might need additional package

IntriguedRat44 how do I reproduce it ?
Can you confirm that marking out the Task.init(..) call will fix it ?

Hmm not sure, try the latest anyhow 🙂

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...

It looks somewhat familiar ... 😞
SuccessfulKoala55 any idea?

Hi LonelyMoth90 , where exactly are you getting the error ? Is it trains-agent running your experiment ?