mehmetoguzderin (u/mehmetoguzderin)

mehmetoguzderin commented on GPT-5 for Developers openai.com/index/introduc... · Posted by u/6thbit

petercooper · 4 months ago

I read it as "... from other companies, like Google, and OpenAI have been doing a great job on this front"

I'm not sure if it's due to experience with the aforementioned APIs, but I also read the same, “issues with APIs like ..., and (in contrast) OpenAI have been doing a great job”

mehmetoguzderin commented on GPT-5 for Developers openai.com/index/introduc... · Posted by u/6thbit

mehmetoguzderin · 4 months ago

Context-free grammar and regex support are exciting. I wonder what, or whether, there are differences from the Lark-like CFG of llguidance, which powers the JSON schema of the OpenAI API [^1].

[^1]: https://github.com/guidance-ai/llguidance/blob/f4592cc0c783a...

mehmetoguzderin commented on GPU-Friendly Stroke Expansion arxiv.org/abs/2405.00127... · Posted by u/raphlinus

raphlinus · a year ago

Good catch! We took the mmark CPU numbers out (in response to review feedback) because they made the graphs hard to read, but it scales very much the same way as the Nehab timings dataset. The raw CPU numbers for mmark are in the repo[1] in the "timings" file.

[1]: https://github.com/linebender/gpu-stroke-expansion-paper

mehmetoguzderin · a year ago

Impressive! Thank you for the pointer

mehmetoguzderin commented on GPU-Friendly Stroke Expansion arxiv.org/abs/2405.00127... · Posted by u/raphlinus

mehmetoguzderin · a year ago

Fascinating, to say the least, especially considering the execution time in comparison to the best CPU result. I might be skimming too fast, but are there also CPU timings for stress test scenes in the paper, including mmark, etc.?

mehmetoguzderin commented on Apple M1 Ultra apple.com/newsroom/2022/0... · Posted by u/davidbarker

apohn · 4 years ago

Thanks. At least when I ran the benchmarks with Tensorflow, using mixed precision resulted in the CPU being used for training instead of the GPU on the M1 Pro. So if the hardware is there for fp16 and they will implement the software support for DL frameworks, that will be great.

mehmetoguzderin · 4 years ago

Yes, unfortunately, the software is to blame for the time being, and I also ran into issues myself. :\ Hope they catch up to what the hardware delivers well, including both the GPU and the Neural Engine.

mehmetoguzderin commented on Apple M1 Ultra apple.com/newsroom/2022/0... · Posted by u/davidbarker

apohn · 4 years ago

The 3090 also can do fp16 and the M1 series only supports fp32, so the M1 series of chips basically needs more RAM for the same batch sizes. So it isn't an Oranges to Oranges comparison.

Back when that M1 MAX vs 3090 blog post was released, I ran those same tests on the M1 Pro (16GB), Google Colab Pro, and free GPUs (RTX4000, RTX5000) on the Paperspace Pro plan.

To make a long story short, I don't think buying any M1 chip make senses if your primary purpose is Deep Learning. If you are just learning or playing around with DL, Colab Pro and the M1 Max provide similar performance. But Colab Pro is ~$10/month, and upgrading any laptop to M1-Max is at least $600.

The "free" RTX5000 on Paperspace Pro (~$8 month) is much faster (especially with fp16 and XLA) than M1 Max and Colab Pro, albeit the RTX5000 isn't always available. The free RTX4000 is also a faster than M1 Max, albeit you need to use smaller batch sizes due to 8GB of VRAM.

If you assume that M1-Ultra doubles the performance of M1-Max in similar fashion to how the M1-Max seems to double the gpu performance of the M1-Pro, it still doesn't make sense from a cost perspective. If you are a serious DL practitioner, putting that money towards cloud resources or a 3090 makes a lot more sense than buying the M1-Ultra.

mehmetoguzderin · 4 years ago

> The 3090 also can do fp16 and the M1 series only supports fp32

Apple Silicon (including base M1) actually has great FP16 support at the hardware level, including conversions. So it is wrong to say it only supports FP32.

mehmetoguzderin commented on NixOS on Framework Laptop kvark.github.io/linux/fra... · Posted by u/kvark

mehmetoguzderin · 4 years ago

This setup sounds like a near-dream with that 3:2 display. The aspect ratio is one of my main drivers to use iPad 12.9 for doing things. But of course, it is always a double-edged sword with how well the split-screen looks with various apps on crammed display, etc., which makes this setup extra interesting because the freedom to adjust through software is much more here.