MLflow VS clearml

In search for a tool that needs comparable configuration I looked into mlflow and found this. https://github.com/mlflow/mlflow/blob/master/mlflow/environment_variables.py There they define a class _EnvironmentVariable and create many objects out of it, for any variable they need. The get method of this class is in principle a decorated os.getenv. Maybe that is something I can take as orientation.

I have been using DVC and MLflow since then DVC had only data tracking and MLflow only model tracking. I can say both are awesome now and maybe the only factor I would like to mention is that IMO, MLflow is a bit harder to learn while DVC is just a git practically.

Dumb question, how does this lib compare to other libs like MLFlow, https://mlflow.org/?

While Transformers already includes integration with MLflow, users still have to provide their own MLflow server, either locally or on a Cloud provider. And that can be a bit of a pain.

I have an old labmate who uses a similar setup with MLFlow and can endorse it.

MLflow - an open-source platform for managing your ML lifecycle. What’s great is that they also support popular Python libraries like TensorFlow, PyTorch, scikit-learn, and R.

even for people who don't have an ML background there's now a lot of very fully-featured model deployment environments that allow self-hosting (kubeflow has a good self-hosting option, as do mlflow and metaflow), handle most of the complicated stuff involved in just deploying an individual model, and work pretty well off the shelf.

Here, we’ll implement the experimentation workflow using DagsHub, Google Colab, MLflow, and data version control (DVC). We’ll focus on how to do this without diving deep into the technicalities of building or designing a workbench from scratch. Going that route might increase the complexity involved, especially if you are in the early stages of understanding ML workflows, just working on a small project, or trying to implement a proof of concept.

MLflow

I am seeing growing demand for MLflow (https://mlflow.org/) and I am seeing a lot of people looking at Databricks as commercial offering for MLflow. Alternatively, some popele are implementing something like Managing your Machine Learning lifecycle with MLflow. Therefore, I think this was on my wish list last year, but I really hope AWS announce a Managed MLFlow Service. I know version 2.X is too new but at least 1.X would be great start.

Hi there. ClearML is our open-source solution which is part of the PyTorch ecosystem. We would really appreciate it if you read our README and starred us if you like what you see!

I'm talking about ClearML😅 trying not to shill for open-source but ~5000 teams have already chosen 💪 https://github.com/allegroai/clearml

There are mainly two solutions that are 100% open source and free to install and use, and that may solve most of the requirements of ML practitioners: Hopsworks and ClearML. Among this two, if I had to chose one right now, it will be ClearML. Hopsworks might be much more complete, but ClearML seems to have a bigger community behind it and to be easier to install and use. So ClearML will be something to take a look at in case we go for an all-in-one package. I also like the idea of having a platform with an UI with all our projects.