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As somebody who works along with Applied Scientist helping them with tasks related to model training and deployemnt; how does one get exposure to more lower lev
by cyrux004 3y ago
As somebody who works along with Applied Scientist helping them with tasks related to model training and deployemnt; how does one get exposure to more lower level engineering work like optimization, performance etc.
We have an ML infra team; but their goal is building tools around the platform, not necessarily getting workloads run optimially
- dayeye2006 3y agoI think no optimization is possible withoutprofiling. I think getting yourself familiar with the tools to understand the performance of a model might be the 1st step, e.g., https://pytorch.org/tutorials/recipes/recipes/profiler_recipe.html https://pytorch.org/tutorials/recipes/recipes/profiler_recip...
- tanelpoder 3y agoYes - understand first, then fix. And you’ll understand by measuring/profiling things. I’d also recommend the detailed pytorch optimization case studies by Paul Bridger: https://paulbridger.com/ https://paulbridger.com/
- grepLeigh 3y agoBrendan Gregg's work on system performance and profiling is a good place to start. A lot of ML perf boils down to Linux perf or what the heck is happening in an HPC scheduling system like SLURM. https://www.brendangregg.com/linuxperf.html https://www.brendangregg.com/linuxperf.html