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AI & data science · #googlecolab
Stop losing your data when Colab sessions timeout. Learn how to use drive.mount() for persistent storage and how to avoid the common I/O bottlenecks associated with cloud filesystems.
Learn how to architect persistent storage in Google Colab using Google Drive mounts, including trust boundaries, failure mitigation, and when to migrate to GCS for high-performance I/O.
Learn how to properly configure, verify, and utilize GPU acceleration in Google Colab to speed up machine learning workloads and avoid common device placement errors.
Google Colab ↔ Cloud SQL: Diagnosing Intermittent Connection Pool Exhaustion In a Colab notebook that uses SQLAlchemy to query a Cloud SQL instance, the default pool_size of five often leads to “Connection pool exhausted” errors when several threads issue queries concurrently. The Cloud SQL instance has a max_connections limit that may be reached if the pool
The goal is to understand how many concurrent TCP connections a Colab free‑tier runtime can maintain before connection errors appear, and whether this cap is defined per notebook or per runtime instance. Current observations suggest that opening more than a few hundred sockets to external services triggers failures, yet Google Colab’s official documentation
Google Colab runtimes operate on a default UTC time zone. When processing datasets containing region-specific timestamps or performing date conversions, the lack of a localized system clock can lead to unexpected offsets in time-series analysis. While libraries like pytz or zoneinfo can handle conversions within a script, the underlying runtime environment r
python
pytorch
sqlalchemy