PyTorch Assessment

Online assessment to evaluate advanced PyTorch modeling and MLOps skills

This assessment helps employers evaluate a candidate’s expertise in building, training, and deploying models using PyTorch for large-scale production systems. It covers advanced-level topics such as distributed data parallelism, mixed-precision training, deterministic experimentation, and inference optimization. The test also examines skills in debugging, experiment tracking, and reproducible model development. Ideal for ML engineers and AI researchers, this assessment identifies professionals who can maintain stable, efficient PyTorch workflows in production. Detailed reports and automated scoring support faster, skill-based hiring decisions.

Sample Question

Your team is shipping a PyTorch recommendation model that trains nightly on billions of interactions. The new trainer frequently OOMs despite the same GPU model and memory as last quarter. Profiling shows spikes when gradients are computed and retained for some large intermediate tensors. You’re asked to stabilize training this week without materially changing the model architecture or dataset size. You must propose a change that is minimally invasive to code and safe for production. What should you do first?