Hello everyone,
I have recently started using Lambda Labs hardware for training deep learning models and am impressed by its performance. However…, I am curious about optimizing my workflow to fully utilize their GPUs.
Best Practices for Distributed Training: For a setup with multiple GPUs, what strategies or frameworks work best with Lambda hardware to ensure scalability and efficiency: ??
Custom Dataset Handling: Any tips for pre-processing large datasets to avoid bottlenecks during training: ??
Hyperparameter Tuning: Are there tools or techniques that integrate seamlessly with Lambda environments to automate and streamline this process; ??
Cloud vs. On-Premises: If you have used both Lambda Cloud and on-prem hardware, how do they compare in terms of performance and cost for large-scale experiments: ??
I have also searched on the forum for the solution related to my query and got this thread https://deeptalk.lambdalabs.com/t/best-practices-common-tactics-for-server-setups-data-storage-react-native-certification but couldn’t get any solution. I would love to hear your experiences and any resources or insights you can share. Thanks in advance for your help !!
Looking forward to the community’s input. Let’s optimize together !!
With Regards,
Marcelo Salas