I'm very happy with pyright. Most bug reports are fixed within a week and new peps/features are added very rapidly usually before pep is accepted (under experimental flag). Enough that I ended up dropping pylint and consider pyright enough for lint purposes as well. The most valuable lints for my work require good multi-file/semantic analysis and pylint had various false positives.

Main tradeoff is this only works if your codebase/core dependencies are typed. For a while that was not true and we used pylint + pyright. Eventually most of our code is typed and we added type stubs for our main untyped dependencies.

edit: Also on pylint, it did work well mostly. tensorflow was main library that created most false positives. Other thing I found awkward was occasionally pylint produces non-deterministic lints on my codebase.

Uv is installer not a build backend. It’s similar to pip. If you install library with uv it will call backend like setuptools as needed. It is not a replacement for setuptools.

If I assume you mean LLM like models similar to chatgpt that is pretty debated in the community. Several years ago many people in ML community believed we were at plateau and that throwing more compute/money would not give significant improvements. Well then LLMs did much better than expected as they scaled up and continue to iterate now on various benchmarks.

So are we now at performance plateau? I know people at openai like places that think AGI is likely in next 3-5 years and is mostly scaling up context/performance/a few other key bets away. I know others who think that is unlikely in next few decades.

My personal view is I would expect 100x speed up to make ML used even more broadly and to allow more companies outside big players to have there own foundation models tuned for their use cases or other specialized domain models outside language modeling. Even now I still see tabular datasets (recommender systems, pricing models, etc) as most common to work in industry jobs. As for impact 100x compute will have for leading models like openai/anthropic I honestly have little confidence what will happen.

The rest of this is very speculative and not sure of, but my personal gut is we still need other algorithmic improvements like better ways to represent storing memory that models can later query/search for, but honestly part of that is just math/cs background in me not wanting everything to end up being hardware problem. Other part is I’m doubtful human like intelligence is so compute expensive and we can’t find more cost efficient ways for models to learn but maybe our nervous system is just much faster at parallel computation?

My experience working on ml at couple faang like companies is gpus actually tend to be too fast compute wise and often models are unable to come close to theoretical nvidia flops numbers. In that very frequently bottlenecks from profiling are elsewhere. It is very easy to have your data reading code be bottleneck. I have seen some models where our networking was bottleneck and could not keep up with the compute and we had adjust model architecture in ways to reduce amount of data transferred in training steps across the cluster. Or maybe you have gpu memory bandwidth as bottleneck. Key idea in flash attention work is optimizing attention kernels to lower amount of vram usage and stick to smaller/faster sram. This is valuable work, but is also kind of work that is pretty rare engineer I have worked with would have cuda kernel experience to create custom efficient kernels. Some of the models I train use a lot of sparse tensors as features and tensorflow’s sparse gpu kernel is rather bad with many operations either falling back to cpu or sometimes I have had gpu sparse kernel that was slower than cpu equivalent kernel. Several times densifying and padding tensors with large fraction of 0’s was faster than using sparse kernel.

I’m sure a few companies/models are optimized enough to fit ideal case but it’s rare.

Edit: Another aspect of this is nature of model architecture that are good today is very hardware driven. Major advantage of transformers over recurrent lstm models is training efficiency on gpu. The gap in training efficiency is much more dramatic with gpu than cpu for these two architectures. Similarly other architectures with sequential components like tree structured/recursive dynamic models tend to fit badly for gpu performance wise.

I’ve worked at companies with async training. Async training does help on fault tolerance and also can assist with training thoroughput by being less reliant on slowest machine. It does add meaningful training noise and when we did experiments against sync training we got much more stable results with sync training and some of our less stable models would even sometimes have loss explosions/divergence issues with async training but be fine with sync training.

Although even for async training generally I see dataset just sharded and if worker goes down then shard of data may be loss/skipped not some kind of smarter dynamic file assignment factoring when workers go down. Even basic things like job fails continue from last checkpoint with same dataset state for large epoch is messy when major libraries like tensorflow lack a good dataset checkpointing mechanism.