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Developer tools · #kaggle
A technical decision guide for choosing between GPU and TPU accelerators in Kaggle, covering hardware trade-offs, VRAM vs HBM, and validation commands.
Stop relying on interactive sessions for Kaggle submissions. Learn how to use the 'Save & Run All' workflow and the /kaggle/working directory to ensure your model scores consistently on the leaderboard.
Reproducibility is hard when code runs on different machines. Kaggle Kernels bundle code, data, and GPU/TPU backends into a single, shareable notebook, eliminating local environment drift. This article walks through a Titanic example, shows how to share, and discusses limits and next steps.
Learn why Kaggle notebooks die, how to check memory, idle time, lifetime caps, and quotas, and what fixes to apply for each cause.
Kaggle notebooks often work interactively but fail on commit because they rely on hidden session state. Pin dataset versions, read from /kaggle/input, and validate in commit mode.
Learn how to configure Kaggle Notebooks for GPU acceleration, manage read-only input directories, and use background commits to prevent session timeouts during model training.
Kaggle's free GPU accelerators come with weekly quotas and volatile sessions. Learn how to verify your accelerator in code, checkpoint around timeouts, and treat GPU hours as a scarce resource.
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.
Kaggle's free GPU comes with a 9-hour session cap, a shared weekly quota, and an environment that resets on disconnect. Here's how to decide when the GPU is worth it and how to design notebooks that survive the limits.
pytorch