Developer Optimized ML Library lancetnic for Training on 16 GB RAM

Developer optimized ML library lancetnic for training on 16 GB RAM

The developer of the open ML library lancetnic found that when training on large text datasets it was overloading the computer's RAM: on a laptop with 16 GB RAM the model could not be trained even on 25 thousand lines of text. While investigating the problem, the author found several reasons for the critical memory overspending.

What the problem was

The lancetnic library is designed for training models on text data, but when working with large datasets the training process hit the physical limit of RAM long before completion. On a typical laptop with 16 GB RAM an attempt to train a model even on a relatively modest dataset of 25 thousand lines would end in memory overload. The author describes this as a problem they encountered personally while working on the library itself, rather than as a theoretical scenario.

What the author found

The author describes starting to investigate the causes of the crash and discovering a couple of specific sources of critical memory overspending in the library's code. This is the type of problem that any ML-tools developer faces when building tools meant to work without expensive server hardware: training should fit in the memory of an ordinary laptop, not only specialized GPU workstations.

For libraries working with text data, a typical source of memory overspending is inefficient storage of intermediate dataset representations: if the entire corpus and all its vector transformations are kept in RAM simultaneously instead of streaming processing in batches, memory consumption grows linearly with dataset size and quickly hits the physical limit even on relatively small amounts of text like 25 thousand lines.

Why this concerns more than just the author

Memory problems during training on text data are a typical bottleneck for small open-source ML libraries written by enthusiasts without access to industrial computing clusters. The causes of memory overspending found in such cases are usually universal and appear in similar form to other developers solving the same problem on their own hardware.

• Library — lancetnic, an open-source tool for training ML models on text
• Test hardware — a laptop with 16 GB of RAM
• Dataset where the crash occurred — 25 thousand lines of text
• Author found several specific causes of critical memory overspending

Why training on a laptop matters at all

Not every developer experimenting with ML libraries has access to a server with dozens of gigabytes of video memory or a cloud GPU instance. For enthusiasts, students, and small teams a laptop with 16 GB RAM is standard working equipment, and it's on this machine that the library should behave predictably. When training crashes already on a dataset of 25 thousand lines — this is not an exotic edge case, but a scenario that a significant portion of potential lancetnic users will encounter already on their first run.

What this means

The lancetnic story is a characteristic example of how development of open ML tools often proceeds through identifying bottlenecks on weak hardware: if a library doesn't work on an ordinary developer's laptop, its practical value for the community is greatly reduced regardless of the quality of the model itself. Public analysis of such a problem is useful to other authors of similar open-source ML tools — it helps lay down streaming data processing instead of loading the entire dataset into memory at once in advance.