standalone-mode gives me "Could not freeze installed packages"

that was tough but I finally manage to make it working! thanks a lot for your help, I definitely wouldn't be able to do it without you =)

the only problem that I still encounter is that sometimes there are random errors in the beginning of the runs, especiialy when I enqueue multiple experiments at the same time (I have 4 workers for 4 GPUs).
for example, this
from torch.utils.tensorboard import SummaryWrite
writer = SummaryWriter()
sometimes randomly leads to FileNotFoundError: [Errno...

well okay, it's probably not that weird considering that worker just runs the container

after the very first click, there is a popup with credentials request. nothing happens after that

okay, I will try it with port

we often do ablation studies with more than 50 experiments, and it was very convenient to compare their dynamics at the different epochs

https://cloud.yandex.com/en-ru/docs/storage/s3/

Error 12 : Validation error (value ‘['13b46b9325954517ab99381d5f45237d’, ‘bc76c3a7f0f6431b8e064212e9bdd2c0’, ‘5d2a57cd39b94250b8c8f52303ccef92’, ‘e4731ee5b33e41d992d6d3fdb2913045’, ‘698d9231155e41fbb61f8f3faa605727’, ‘2171b190507f40d1be35e222045c58ea’, ‘55c81a5db0ad40bebf72fdcc1b3be2a4’, ‘94fbdbe26ef242d793e18d955cb3de58’, ‘7d8a6c8f2ae246478b39ae5e87def2ad’, ‘141594c146fe495886d477d9a27c465f’, ‘640f87b02dc94a4098a0aba4d855b8f5’]' length is bigger than allowed maximum ‘10’.)

yes. we upload artifacts to Yandex.Cloud S3 using ClearML. we set " s3://storage.yandexcloud.net/clearml-models " as output uri parameter and add this section to the config:
{
host: " http://storage.yandexcloud.net "
key: "KEY"
secret:"SECRET_KEY",
secure: true
}

this works like a charm. but download button in UI is not working

even without port

fantastic, everything is working perfectly
thanks guys

https://allegro.ai/clearml/docs/docs/examples/frameworks/pytorch/notebooks/table/tabular_training_pipeline.html

kind of

is it in documentation somewhere?

we're using the latest version of clearml, clearml agent and clearml server, but we've been using trains/clearml for 2.5 years, so there are some old tasks left, I guess 😃

that's right
for example, there are tasks A, B, C
we run multiple experiments for A, finetune some of them in separate tasks, then choose one or more best checkpoints, run some experiments for task B, choose the best experiment, and finally run task C

so we get a chain of tasks: A - A-ft - B- C

ClearML pipeline doesn't quite work here because we would like to analyze results of each step before starting next task

but it would be great to see predecessors of each experiment in the chain

there is no method for setting last iteration , which is used for reporting when continuing the same task. maybe I could somehow change this value for the task?

works like a charm!

agent.hide_docker_command_env_vars.extra_keys: ["DB_PASSWORD=password"]

like this? or ["DB_PASSWORD", "password"]

ah, I see, I still keep it in agent.extra_docker_arguments

it works, but it's not very helpful since everybody can see a secret in logs:
Executing: ['docker', 'run', '-t', '--gpus', '"device=0"', '-e', 'DB_PASSWORD=password']

we're using os.getenv in the script to get a value for these secrets

any suggestions on how to fix it?

0.16.1

I guess, this could overcomplicate ui, I don't see a good solution yet.

as a quick hack, we can just use separate name (eg "best_val_roc_auc") for all metric values for the current best checkpoint. then we can just add columns with the last value of this metric

self-hosted ClearML server 1.2.0
SDK version 1.1.6

this is the artifactory, this is how I install these packages in the Docker image:
pip3 install --pre torch torchvision -f https://download.pytorch.org/whl/nightly/cu101/torch_nightly.html

the files are used for training and evaluation (e.g., precomputed pycocotools meta-info). I could theoretically include them in the repo, but some of them might be quite heavy. what do you mean when you say that they get lost? I copy them from the host machine when I build the custom image, so they are i...

yes

will do, thanks

will it pass variables to the training containers?