This is great! Thanks for the example Martin, much appreciated!
For reporting the console logs you can use :logger.report_text("my log line here", print_console=False)https://github.com/allegroai/clearml/blob/b4942321340563724bc16f60ea5dd78c9161778d/clearml/logger.py#L120
If I have access to the logs, python env and git commits, is there an API to log those to the experiments too?
Sure:task.update_task see here:
https://clear.ml/docs/latest/docs/references/sdk/task#update_task
example:task.update_task(task_data={'script': {'branch': 'new_branch', 'repository': 'new_repo'}})The easiest way to get all the different sections (they should be relatively self explanatory) is calling task.export_task() which returns a dict with all the fields you can manually configure.
https://clear.ml/docs/latest/docs/references/sdk/task#export_task
This is great! Thanks!
If I have access to the logs, python env and git commits, is there an API to log those to the experiments too?
I guess this is doable:
You can get the entire set of scalars like as pandas DF: https://www.tensorflow.org/tensorboard/dataframe_api
(another example: https://stackoverflow.com/a/45899735 )
Then iterate over the different runs and create + report scalars)
` from clearml import Task
for run in runs:
task = Task.create_task(...)
logger = task.get_logger()
not real code, just example:
w_times, step_nums, vals = zip(*event_acc.Scalars('Accuracy'))
for step, val in zip(step_nums, vals):
logger.report_scalar(title='Accuracy', series='Accuracy', iteraiton=step, value=val)
we are done
task.close() `Obviously this is missing the link to the code repository + python packages + console logs etc. But it will get you the scalars 🙂
So basically take data from tensorboard read it, and report it to the cloud ?
We have run experiments in the past (before I put ClearML into my code) which has logged scalars, plots etc. to local tensorboard. Is there any way to import this data to ClearML cloud for tracking, visualization and comparison?
Hi JumpyPig73
import data from old experiments into the dashboard.
what do you mean by "old experiments" ?