Hi TenseOstrich47 whats the matplotlib version and clearml version you are using ?

Sure, it will revert to the old behavior and run in threads

it is using the wrong URL - an https:// instead of an s3:// so it can't upload the preprocess.py file

What do you see as the link in the UI for that specific model ?
What is the full model add command ?

Hangs there ? could it be that it's uploading slowly ?
Can you check the network ?

(with matplotlib 3.2+ I get no warning, let me check with 3.1)

What's the host you have in the clearml.conf ?
is it something like " http://localhost:8008 " ?

The additional edges in the graph suggest that these steps somehow contain dependencies that I do not wish them to have.

PanickyMoth78 I think I understand what you are saying, but it is hard to see if there is a "bug" here or a feature...
Can you post the full code of the pipline?

So could you re-explain assuming my piepline object is created by 

pipeline = PipelineController(...)

?

pipe.add_step(name='stage_train', parents=['stage_process', ], monitor_artifact=['my_created_artifact'], base_task_project='examples', base_task_name='pipeline step 3 train model', parameter_override={'General/dataset_task_id': '${stage_process.id}'})This will put the artifact names "my_created_artifact" from the step Tas...

Hi UnevenDolphin73
If you "remove" the lock file the agent will default to pip.
You can hack it with uncommitted changes section?

Hi SarcasticSparrow10 , so yes it does, this is more efficient when using pytorch loaders, and in some other situations.
To disable it add to your clearml.conf:sdk.development.report_use_subprocess = false2. interesting error, maybe we can revert to "thread mode" if running under a daemon. (I have to admit, I'm not sure why python has this limitation, let me check it...)

Any chance you can test with the latest RC ? 1.8.4rc2

Thanks!

but out of curiosity, whats the point on doing a hyperparam search on the value of the loss on the last epoch of the experiment

The problem is that you might end up with global min that is really nice, but it was 3 epochs ago, and you have the last checkpoint ...
BTW, global min and last min should not be very diff if the model converge, wdyt?

Hi SubstantialElk6
where exactly in the log do you see the credentials ?

/tmp/.clearml_agent.234234e24s.cfg

What's the exact setup ? (I mean are you using the glue? if that's the case I think the temp config file is only created inside the pod/docker so upon completion it will be deleted along side the pod.

Yep 🙂 but only in RC (or github)

From creating the event to actually sending it ... 30 min sounds like enough "time"...

none of my pipeline tasks are reporting these graphs, regardless of runtime. I guess this line would also fix that?

Same issue, that said, good point, maybe with pipeline we should somehow make that a default ?

Click on the "k8s_schedule" queue, then on the right hand side, you should see your Task, click on it, it will open the Task page. There click on the "Info" Tab, there look for "STATUS MESSAGE" and "STATUS REASON". What do you have there?

Oh if this is the case you can probably do
` import os
import subprocess
from clearml import Task
from clearml.backend_api.session.client import APIClient

client = APIClient()

queue_ids = client.queues.get_all(name="queue_name_here")

while True:
result = client.queues.get_next_task(queue=queue_ids[0].id)
if not result or not result.entry:
sleep(5)
continue
task_id = result.entry.task
client.tasks.started(task=task_id)
env = dict(**os.environ)
env['CLEARML_TASK_ID'] = ta...

LovelyHamster1 verified, this is a UI bug with old limitation enforced.
I will make sure they know about it, it should be fixed for the upcoming release 🙂

Hi RobustRat47

My guess is it's something from the converting PyTorch code to TorchScript. I'm getting this error when trying the

I think you are correct see here:
https://github.com/allegroai/clearml-serving/blob/d15bfcade54c7bdd8f3765408adc480d5ceb4b45/examples/pytorch/train_pytorch_mnist.py#L136
you have to convert the model to TorchScript for Triton to serve it

RobustRat47 what's the Triton container you are using ?
BTW, the Triton error is:
model_repository_manager.cc:1152] failed to load 'test_model_pytorch' version 1: Internal: unable to create stream: the provided PTX was compiled with an unsupported toolchain.https://github.com/triton-inference-server/server/issues/3877

This seems to only work for a single file (weights_path implies a single file, not multiple ones). Is that the case?See update_weights_package actually packages an entire folder as zip and will do the extraction when you get it back (check the function docstring, I think you can also specify wildcard etc if needed)

Why do you see this as preferred to the dataset method we have now?

So it answers a few requirements that you raised
It is fully visible as part of the project and se...

Basically when running remotely, the first argument to any configuration (whether object or string, or whatever) is ignored, right?

Correct 🙂

Is there a planned documentation overhaul?

you mean specifically for the connect_configuration ? or in general on the connect approach rationale ?

single task in the DAG is an entire ClearML

pipeline

.

just making sure detials are not lost, "entire ClearML pipeline ." : the pipeline logic is process A running on machine AA.
Every step of that pipeline can be (1) subprocess, but that means the exact same environement is used for everything, (2) The DEFAULT behavior, each step B is running on a different machine BB.

The non-ClearML steps would orchestrate putting messages into a queue, doing retry logic, and tr...

Hi CloudySwallow27

how can I just "define" it on my local PC, but not actually run it.

You can use the clearml-task CLI
https://clear.ml/docs/latest/docs/apps/clearml_task#how-does-clearml-task-work
Or you can add the following line in your code, that will cause the execution to stop, and to continue on a remote machine (basically creating the Task and pushing it into an execution queue, or just aborting it)
task = Task.init(...) task.execute_remotely()https://clear.ml/do...

adding the functionality to clearml-task sounds very attractive!

Hmm, what do you think?
parser.add_argument('--configuration', type=str, default=None, help='Specify local configuration file' ) parser.add_argument('--configuration-name', type=str, default=None, help='configuration section name' ) ... with open(args.configuration, 'rt') as f: create_populate.task.set_configuration_object(args.name, config_text=f.read())Add h...

1

One reason I don't like using the configuration section is that it makes debugging much much harder.

debugging ? please explain how it relates to the configuration, and presentation (i.e. preview)
2.
Yes in theory, but in your case it will not change things, unless these "configurations" are copied on any Task (which is just storage, otherwise no real harm)
3.
I was thinking "zip" file that the Task creates and uploads, and a new configuration type, say "external/zip" , and in the c...

BattyLion34 I have a theory, I think that any Task on the "default" queue qill fail if a Task is running on the "service" queue.
Could you create a toy Task that just print "." and sleeps for 5 seconds and then prints again.
Then while that Task is running, from the UI launch the Task that passed on the "default" queue. If my theory holds it should fail, then we will be getting somewhere 🙂

FierceRabbit20 it seems the Pipeline Task that was created is missing the "installed requirements" section. How are you creating the actual pipeline Task? is this from code?