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Learn how to decide between CPU and GPU runtime in Kaggle Notebooks, compare the options, see a ready‑to‑use implementation with device detection and OOM fallback, and verify your choice with simple checks.
Learn how to let Kubernetes automatically scale your stateless workloads with the Horizontal Pod Autoscaler. Step‑by‑step guide, example, trade‑offs, and checklist to get CPU‑based autoscaling working safely.
I am confused of the concept of millicores in Kubernetes. As per my programming knowledge, only one thread can run per core so why would I set a limit in millicores? For example, if I set a CPU limit of 600m to a container, can I use 400m for another pod or container? is that possible? I have tried installing minikube and ran on it. Will both containers or p
Goal Minimise operational cost for a sporadic speech‑recognition service by selecting the most efficient Kaldi online2 decoding path. Constraints and Uncertainty Kaldi’s online2 pipeline can run on CPU or GPU, but the documented behaviour for automatic fallback when a GPU is absent is unclear. Using a GPU for infrequent traffic may introduce scheduling overh
I have been used google cloud platform to offer the services to clients. A few days ago, I found the problem that the CPU usage of VM keeps increasing continuously. For uncovering the reason of this problem, I made the empty(or new) VMs to watch their status, and these new VMs also keeps increasing their CPU usages. I used "top" command to know which process
Goal Minimise total hosting cost while maintaining acceptable ASR accuracy for a sporadic, low‑traffic workload. Constraints & Uncertainty GPU instances carry a fixed hourly charge that is wasted when traffic is idle, whereas CPU decoding scales linearly with the number of active requests. Compact nnet3 chain models reduce CPU load but may degrade WER, e
kaggle
kubernetes
kaldi