Hi @<1709015393701466112:profile|ScatteredPeacock14> ! I think you are right. We are going to look into fixing this

Hi @<1587615463670550528:profile|DepravedDolphin12> ! get() should indeed return a python object. What clearml version are you using? Also, can you share the code?

Hi @<1695969549783928832:profile|ObedientTurkey46> ! You could try increasing sdk.storage.cache.default_cache_manager_size to a very large number

There are only 2 chunks because we don't split large files into multiple chunks

Hi @<1578918167965601792:profile|DistinctBeetle43> ! This is currently not possible. A different task will be created for each instance

Hi @<1654294820488744960:profile|DrabAlligator92> ! The way chunk size works is:
the upload will try to obtain zips that are smaller than the chunk size. So it will continuously add files to the same zip until the chunk size is exceeded. If the chunk size is exceeded, a new chunk (zip) is created. The initial file in this chunk is the file that caused the previous size to be exceeded (regardless of the fact that the file itself might exceed the size).
So in your case: am empty chunk is creat...

Hi @<1719524641879363584:profile|ThankfulClams64> ! What tensorflow/keras version are you using? I noticed that in the TensorBoardImage you are using tf.Summary which no longer exists since tensorflow 2.2.3 , which I believe is too old to work with tesorboard==2.16.2.
Also, how are you stopping and starting the experiments? When starting an experiment, are you resuming training? In that case, you might want to consider setting the initial iteration to the last iteration your prog...

Hi @<1834401593374543872:profile|SmoggyLion3> ! There are a few things I can think of:

• If you need to continue a task that is marked as completed, you can do clearml.Task.get_task(ID).mark_stopped(force=True) to mark it as stopped. You can do this in the job that picks up the task and want to continue it before calling Task.init , or in a post_execute_callback in the pipeline itself, so the pipeline function marks itself as aborted. For example:

from clearml import Pipeli...

Hi @<1571308003204796416:profile|HollowPeacock58> ! The changes should be reflected. Do you have a small example that could help us reproduce the issue?

Hi PetiteRabbit11 . This snippet works for me:
` from clearml import Task
from pathlib2 import Path

t = Task.init()
config = t.connect_configuration(Path("config.yml"))
print(open(config).read()) Note the you need to use the return value of connect_configuration ` when you open the configuration file

Yes it should word with ClearML if it works with requests

it's the same file you added your s3 creds to

Hi @<1555000557775622144:profile|CharmingSealion31> ! When creating the HyperParameterOptimizer , pass the argument optuna_sampler=YOUR_SAMPLER .

You could consider downgrading to something like 1.7.1 in the meantime, it should work with that version

JitteryCoyote63 very odd, it seems to work just fine on my machine

Hello MotionlessCoral18 . I have a few questions that might help us find out why you experience this problem:
Is there any chance you are running the program in offline mode? Is there any other message being logged that might help? The error messages might include Action failed , Failed sending , Retrying, previous request failed , contains illegal schema Are you able to connect to the backend at all from the program you are trying to get the dataset?
Thank you!

Each step is a separate task, with its own separate logger. You will not be able to reuse the same logger. Instead, you should get the logger in the step you want to use it calling current_logger

You could try this in the meantime if you don't mind temporary workarounds:
dataset.add_external_files(source_url=" ", wildcard=["file1.csv"], recursive=False)

Hi HomelyShells16 How about doing things this way? does it work for you?
` class ClearmlLightningCLI(LightningCLI):
def init(self, *args, **kwargs):
Task.add_requirements("requirements.txt")
self.task = Task.init(
project_name="example",
task_name="pytorch_lightning_jsonargparse",
)
super().init(*args, **kwargs)

def instantiate_classes(self, *args, **kwargs):
    super().instantiate_classes(*args, **kwargs)
  ...

RoundMosquito25 you might need to use cast=True when you get the parameters.
See this snippet:
` from clearml import Task

t = Task.init()
params = {}
params["Function"] = {}
params["Function"]["number"] = 123
t.set_parameters_as_dict(params)
t.close()

cloned = Task.clone(t.id)
s = cloned.get_parameters_as_dict(cast=True)
s["Function"]["number"] = 321
cloned.set_parameters_as_dict(s)
print(type(cloned.get_parameters_as_dict(cast=True)["Function"]["number"])) # will print 'int' `

Hi @<1631102016807768064:profile|ZanySealion18> ! Reporting None is not possible, but you could report np.nan instead.

Hi @<1545216070686609408:profile|EnthusiasticCow4> ! Note that the Datasets section is created only if you get the dataset with an alias? are you sure that number_of_datasets_on_remote != 0 ?
If so, can you provide a short snippet that would help us reproduce? The code you posted looks fine to me, not sure what the problem could be.

Hi ApprehensiveSeahorse83 ! Looks like this is a bug. We will fix it ASAP

Hi DangerousDragonfly8 ! At the moment, this is not possible, but we do have it in plan (we had some prior requests for this feature)

this is a bug, we will fix this asap

@<1523721697604145152:profile|YummyWhale40> are you able to manually save models from SageMaker using OutputModel ? None

The config values are not yet documented, but they all default to 10 (except for max_file_size) and represent the number of images/tables/videos etc. that are reported as previews to the dataset. Setting them to 0 disables previewing

To clear the configurations, you should use something like Dataset.list_datasets to get all the dataset IDs, then something like:

from clearml import Task


id_ = "229f14fe0cb942708c9c5feb412a7ffe"
task = Task.get_task(id_)
original_status = task.s...

We will add this to the SDK soon

@<1554638160548335616:profile|AverageSealion33> Can you run the script with HYDRA_FULL_ERROR=1 . Also, what if you run the script without clearml? Do you get the same error?

Hi @<1558986821491232768:profile|FunnyAlligator17> ! There are a few things you should consider:

• Artifacts are not necessarily pickles. The objects you upload as artifacts can be serialized in a variety of ways. Our artifacts manager handles both serialization and deserialization. Because of this, you should not pickle the objects yourself, but specify artifact_object as being the object itself.
• To get the deserialized artifact, just call task.artifacts[name].get() (not get_local...