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Developer tools · #kaggle
Learn how to install the Kaggle API, download competition data, and submit predictions from the command line with a worked example and common pitfalls.
Learn how to choose between CPU, GPU, and TPU in Kaggle Notebooks to optimize training speed and manage your weekly hardware quotas effectively.
Learn how to leverage Kaggle’s GPU acceleration, mount large datasets, checkpoint long training jobs, and persist model artifacts—all while staying within session limits and avoiding common pitfalls.
Learn how to correctly enable and implement GPU acceleration in Kaggle Notebooks, including PyTorch device mapping and managing VRAM quotas.
Kaggle Notebooks enforce hard execution limits—typically 12 hours for CPU and 9 hours for GPU instances. When these limits are reached, or a session is manually cancelled, the kernel process terminates immediately, resulting in the loss of all volatile memory state not explicitly committed to the /kaggle/working directory. Because the environment may not rel
pytorch
python