clearml
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allegroai
ClearML - Auto-Magical CI/CD to streamline your AI workload. Experiment Management, Data Management, Pipeline, Orchestration, Scheduling & Serving in one MLOps/LLMOps solution
We are currently product-hunting for our MLOps infrastructure and ClearML, Kedro, MLRun are on our short list. We are considering to combine ClearML with Kedro. They are similar in purpose...
performance under docker is 10% lower than on bare metal, at a fraction of the scale, this becomes a significant savings in clearml/automation/cloud_driver.py has bash_script_template and docker support is hardcoded...
Hi. I have created [an issue](https://github.com/speechbrain/speechbrain/issues/1515) in the Speechbrain repo, but I think it would be useful if I mention it here too.
Heya! Right now, ClearML automagically logs the configuration for models it recognizes, but not all parameters of interest are logged. For example, we occasionally use a linear regression model in...
Hello, I am trying to develop a pipeline with ClearML for a computer vision problem. I am using OpenVINO pre-trained models for this task. Does ClearML supports to use these...
I was looking for an easy example of training end-to-end on data whose metadata is stored in ClearML, and expected [the mnist example](https://github.com/allegroai/clearml/blob/0397f2b41e41325db2a191070e01b218251bc8b2/examples/frameworks/pytorch/pytorch_mnist.py) to be that example. Turnes out this...
Setting agent.docker_default.image has no effect on the 'Execution' view of the experiment, while the agent.docker_default.arguments are correctly parsed. Experiment queueing fails unless an image is specified.
After upgrading `clearml` to `1.4.0,` our code started producing deadlocks that had not been happening before. The part of our code that was caught in a deadlock was inside `multiprocessing.Pool()`....
Hi everyone, I'm currently facing a problem while creating a dataset. I run the following code and it cannot pass `Dataset.create`. ``` python from clearml import Task from clearml import...
Using the below code and adding tag to task from the ui the 'on_task_published' is not called : def on_task_published(*args, **kwargs): print("task_trigger called") trigger.add_task_trigger( name="run1", schedule_function=on_task_published, schedule_queue="pipelines", trigger_on_tags=["run1"], )
