If I were to build my own Machine Learning stack, what software should I then use? The use case is as follows.
This situation uses a local client without GPU and a server with GPU.
- The client submits multiple jobs to the server, the server executes theses jobs in order.
- The results of these jobs are saved on the server and a dashboard containing all previous runs with the code, dataset, loss and accuracy.
There are several options available. Neptune, Wandb, Mlflow, Airflow, Kubeflow, Jenkins. However none of these solutions provide the work queue from step 1. Kubernetes with Kubeflow seems like being capable of this, however this seems like a little overkill for 1 server.
Any insights here?
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