oooh, that's awesome lol. Never thought to do it that way

Oh, here's an example, a screenshot I took of the files in my Colab instance:

Which is defined, it seems, here: https://github.com/huggingface/transformers/blob/040283170cd559b59b8eb37fe9fe8e99ff7edcbc/src/transformers/trainer_tf.py#L459

I guess I could try and edit that, somehow. Hmm

Ah, gotcha.

I've been trying to do things like "color these five experiments one color, color these other five a different color", but then once I maximize the thing the colors all change

It seems to create a folder and put things into it, I was hoping to just observe the tensorboard folder

This seems similar but not quite the thing I'm looking for: https://allegro.ai/clearml/docs/docs/tutorials/tutorial_explicit_reporting.html#step-1-setting-an-output-destination-for-model-checkpoints

https://github.com/allegroai/clearml/releases if anyone is curious!

¯_(ツ)_/¯

It's not a big deal because it happens after I'm done with everything, I can just reset the Colab runtime and start over

So, this is probably a really dumb idea, but can you add
RUN source /root.bashrcor perhaps set the entrypoint?

Yup, I just wanted to mark it completed, honestly. But then when I run it, Colab crashes.

This seems to work:

` from clearml import Logger
for test_metric in posttrain_metrics:
print(test_metric, posttrain_metrics[test_metric])

#report_scalar(title, series, value, iteration)
Logger.current_logger().report_scalar("test", test_metric, posttrain_metrics[test_metric], 0) `

I might not be able to get to that but if you create an issue I'd be happy to link or post what I came up with, wdyt?

Well they do all have different names

yup

hi!

As in, I edit Installed Packages, delete everything there, and put that particular list of packages.

Here's the actual script I'm using

very nice

And the reason is, because I have a bunch of "runs" with the same settings, and I want to compare broadly across several settings. So if I select "a bunch" with setting A I can see a general pattern when compared with setting B.

I've got 7-10 runs per setting, and about 7 or 8 settings

seconded, I'm curious about this also.

Ah, makes sense! Have you considered adding a "this is the old website! Click here to get to the new one!" banner, kinda like on docs for python2 functions? https://docs.python.org/2.7/library/string.html

Oh, that's neat! Thanks!

Gave it a try, it seems our GPU Queue doesn't have the S3 creds set up correctly. Making a separate thread about that

So for example:
` {'output_dir': 'shiba_ner_trainer', 'overwrite_output_dir': False, 'do_train': True, 'do_eval': True, 'do_predict': True, 'evaluation_strategy': 'epoch', 'prediction_loss_only': False, 'per_device_train_batch_size': 16, 'per_device_eval_batch_size': 16, 'per_gpu_train_batch_size': None, 'per_gpu_eval_batch_size': None, 'gradient_accumulation_steps': 1, 'eval_accumulation_steps': None, 'learning_rate': 0.0004, 'weight_decay': 0.0, 'adam_beta1': 0.9, 'adam_beta2': 0.999, 'adam...

OK, I guess
` training_args_dict = training_args.to_dict()

Task.current_task().set_parameters_as_dict(training_args_dict) `works, but how to change the name from "General"?

AgitatedDove14 I'm making some progress on this. I've currently got the situation that my training run saved all of these files, and Task.get_task(param['TaskA']).models['output''][-1] gets me just one of them, training_args.bin . Then -2 gets me another, rng_state.pth

If I just get Task.get_task(param['TaskA']).models['output'] , I end up getting a huge list of, like, ` [<clearml.model.Model object at 0x7fec2841c880>, <clearml.model.Model object at 0x7fec2841...

Local in the sense that my team member set it up, remote to me