IanOzsvald

5 ms·

1.
by IanOzsvald 4mo ago
I continue to build my London based monthly directed-exploration LLM days where we try to collaboratively push through a benchmark. We're doing ARC AGI 2026 again in a couple of weeks: https://playgroup.org.uk/ Recentl
2.
by IanOzsvald 5mo ago
I've spent 6 months building a network of curious LLM hackers in London who want to push the edge of what we can do. This Friday we pull GPT apart and rebuild bits by hand. A few weeks back we tackled ARC AGI 2026. Prevoiusly we did fi
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by IanOzsvald 1y ago
+1 Merlin. I also stop and do a few minutes with Duolingo in the park, then take a breath and just listen to the wind and birdsong.
4.
by IanOzsvald 2y ago
There's a profiler cell magic for Notebooks which helps identify if you run out of VRAM (it says what runs on CPU and GPU). There's an open PR to turn on low-VRAM reporting as a diagnostic. CuDF is impressive, but getting a worki
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by IanOzsvald 2y ago
Thanks :-) I use it for all my talks and finally decided I'd better start sharing it a bit. It really is useful to understand the memory cost of things like Pandas operations
6.
by IanOzsvald 2y ago
I'm the co-author of High Performance Python, Micha and I are working on the 3rd ed (for 2025). Lots of bits of the book came from my past conference talks, they're available here (and the public talks will generally be on youtube
7.
by IanOzsvald 2y ago
@munhitsu gave me a demo at the weekend (I'm on Android and it is iPhone only), it seemed pretty slick and very easy to use, though I confess not something I personally need right now
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by IanOzsvald 3y ago
I've just spent the morning uninstalling and reinstalling different versions of Nvidia driver (Linux) to get nvcc back for llama.cpp after Linux Mint did an update - I had CUDA 12.3 and 12.4 (5GB each), in conflict, with no guidance. 5
9.
by IanOzsvald 3y ago
Don't forget that a sequence of numpy operations will likely each allocate their own temporary memory. Numba can often fuse these together, so although the implementation behind numpy is compiled C you end up with fewer memory allocati
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by IanOzsvald 3y ago
Really Numba will speed up numpy and some scipy (there's partial API coverage) and math based pure python. I think it is unlikely it'd be used away from math problems. As another commenter mentioned it can be used to accelerate n
11.
by IanOzsvald 3y ago
That's my book :-) Micha and I are working on the 3rd edition right now. Cheers!
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by IanOzsvald 3y ago
How about hyperlinking each title so it can easily be opened in a new tab?
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by IanOzsvald 3y ago
You may want to look sideways to companies such as hedge funds. They have DNN teams and experiment with LLMs, you may find interesting optimisation opportunities with such teams. Charge according to opportunity that you open up, not electri
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by IanOzsvald 3y ago
In the UK I use Redber for beans, I drink decaf (Swiss Water prices) fresh ground in the afternoon and have 2-3 caf cups in the morning. Redber had a wide selection and several roast levels. No caffeine after noon. I use a 2-cup espresso B
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by IanOzsvald 3y ago
Indeed I was the one who got confused by the name! Thanks for attending the discussion Jay and I'm happy to see Daft being discussed here
16.
by IanOzsvald 4y ago
PyPy uses a modified Mark and Sweep garbage collector, CPython uses Reference Counting. C extensions such as NumPy (and so Pandas, sklearn etc) are compiled expecting Reference Counting. A translation layer is needed for memory management f
17.
by IanOzsvald 4y ago
I use a FLIR One on Android. I've charted internal leaks (where cold air blows in through cracks) and external (where heat escapes through eg old windows). Wait for a cold day (eg 0C), heat the house, investigate everything you can. M
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by IanOzsvald 4y ago
Hey Ritchie. Re legacy I'm thinking about wider teams in large organisations (eg SWEng system support teams) and IT mandating library upgrade frequency - switching to new libraries can have widespread impacts and the cost can be high.
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by IanOzsvald 4y ago
This https://docs.dask.org/en/stable/spark.html notes "However, Dask is able to easily represent far more complex algorithms and expose the creation of these algorithms to normal users [compared to spark]&quo
20.
by IanOzsvald 4y ago
I'd argue a little differently. I'm co-author of O'Reilly's High Performance Python book and I've been teaching a course around this for years, often to quants. 1. Pandas if you stay in RAM, if the team and org alre
21.
by IanOzsvald 4y ago
Can other data scientists comment? I'm 15 years in with python and scientific work. For a lot of years I liked conda but then it got crazy slow. Next I started making conda environments and installing packages with pip. Now I'm ex
22.
by IanOzsvald 4y ago
With our infant using a digital thermometer in the ear, we routinely observe that the left ear is hotter than the right. We take 3 measurements in each ear, we see a similar variance as you mention per ear. Have you observed a similar diffe
23.
by IanOzsvald 4y ago
I did some checking and found it hard to get clear numbers. It seems that the majority of cars in London will be compliant but the majority of vans will not be, so business is affected over private households. The scrappage ("upgrade&q
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by IanOzsvald 4y ago
I do some of this in data science when I'm giving strategic support to teams. Be aware that managers generally see you as a cost who doesn't bring financial benefit to the team (whilst the team are desperately asking for practical
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by IanOzsvald 4y ago
I interviewed author Ritchie Vink on my newsletter (NotANumber) some months back, he's smart and the library has a nice design. I still barely know anyone trying it, it did get a write up just recently here: https://news.yco
26.
by IanOzsvald 4y ago
Here's a benchmark for 3.8-3.11b: https://www.phoronix.com/review/python-311-benchmarks/4 The geometric mean of the 3.8 to 3.11b benchmarks was a 45% speedup.
27.
by IanOzsvald 4y ago
What's your plan for redframes? It looks really new? I'm the co-author of O'Reilly's High Performance Python so I'm always on the lookout for pandas alternatives. Are you looking at speed implications too? Bigger-th
28.
by IanOzsvald 4y ago
Numba focuses on scientific use cases speeding up most of numpy and some of scipy. Whilst it can compile some parts of pure Python (eg numeric array loops), generally you have to copy the data in to the numba side which can be slow in volum
29.
by IanOzsvald 5y ago
I author a Python data science focused newsletter https://buttondown.email/NotANumber , I've built it up over 4 or so years to a couple of thousand readers. I don't use automatic link gathering but hand-write advi
30.
by IanOzsvald 5y ago
Years back I built IPython Memory Usage[0] which shows how much RAM and time was used per cell in Jupyter Notebooks (and originally the IPython shell). This is very useful for diagnosing why some Pandas and NumPy operations use a lot of RAM

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