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How to isolate environments for different projects in a single mlflow server?

naveen_marthala
Contributor

I am planning to deploy MLFlow server deployed in AWS ECS as a centralised repositories for my machine learning experiments and runs and to strore events and artifacts. I would like to use MLflow Tracking Server enabled with proxied artifact storage access to eliminate the need to have authentication to the underlying storage(S3 bucket) at my side(client). And I would like to have different environments or isolated environments as i keep creating projects? for example, for project titanic, i might have 10 experiments, which i would not want to see from project iris and this project will have different experiments. How do I achieve this?

Thanks.

4 REPLIES 4

Anonymous
Not applicable

I understand an experiment in the context of mlflow. i will be creating multiple experiments for a single project. and i want to not see those experiments when i start a new project but not delete them. may be some switch to show and let me usa a different page for experiments and runs, while using the same server.

by environment' i didn't mean a python or conda virtual environment. i shouldn't have used the word 'environment'. i meant to convey different (or may be a new) page or space for experiments for every new project i start, while sticking to the same mlflow server deployed in the same place(this is going to be AWS ECS)

Kaniz
Community Manager
Community Manager

Hi @Naveen Marthala​, Just a friendly follow-up. Do you still need help, or @Joseph Kambourakis​ 's response help you to find the solution? Please let us know.

yes i still need help. @josephk’s reply hasn’t helped me. I still need to know how I can isolate or have different sets of experiments in order to use a single server for different projects.

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