Ciro Santilli @cirosantilli 35

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Merge two or more commits into one by

Ciro Santilli 35  Updated 2024-12-23  +Created 1970-01-01

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Before

7 my-feature HEAD
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5 master
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1

Oh, commit 6 was just a temporary step, should be put together with commit 7:

git rebase -i HEAD~2

Mark 6 to be squashed.

After:

67 my-feature HEAD
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5 master
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4
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2
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1

Better now, ready to push.

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AI People by

Ciro Santilli 35  Updated 2024-12-23  +Created 1970-01-01

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Video 1.

AI Game - LLM-driven NPCs that can talk by Marek Rosa (2023)

Source. Not the most amazing demo, but the idea is there. Seems to be a preview for AI People. The previous working title seems to have been AI Odyssey.

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Marek Rosa by

Ciro Santilli 35  Updated 2024-12-23  +Created 1970-01-01

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HAVING (SQL) by

Ciro Santilli 35  Updated 2024-12-23  +Created 1970-01-01

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@cirosantilli/_file/nodejs/sequelize/raw/nodejs/sequelize/raw/trigger_count.js by

Ciro Santilli 35  Updated 2024-12-23  +Created 1970-01-01

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In this example we cache track the number of posts per user on a cache column.

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@cirosantilli/_file/python/jupyter/python/jupyter/hello.ipynb by

Ciro Santilli 35  Updated 2024-12-23  +Created 1970-01-01

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JCVI-syn3B by

Ciro Santilli 35  Updated 2024-12-23  +Created 1970-01-01

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www.biorxiv.org/content/10.1101/2022.09.19.508583v1.full

CVI-syn3B strains differ from JCVI-syn3.0 by the presence of 19 additional non-essential genes that result in a more easily manipulated cell. JCVI-syn3B additionally includes a dual loxP landing pad that enables easy Cre recombinase mediated insertion of genes

It is also interesting to see how they are interested in co-culture with HeLa cells, presumably to enable infectious bacterial disease studies.

At biology.indiana.edu/news-events/news/2023/lennon-minimal-cells.html (2023) they let it re-evove to it it would regain some fitness, and it did.

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@cirosantilli/_file/js/matterjs/js/matterjs/hello.html by

Ciro Santilli 35  Updated 2024-12-23  +Created 1970-01-01

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Hello world adapted from: github.com/liabru/matter-js/wiki/Getting-started/1d138998f05766dc4de0e44ae2e35d03121bb7f2

Also asked at: stackoverflow.com/questions/28079138/how-to-make-minimal-example-of-matter-js-work/76203103#76203103

Renderer questions:

follow object on viewport: codepen.io/csims314/pen/goZQvG
draw text: github.com/liabru/matter-js/issues/321

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@cirosantilli/_file/js/matterjs/js/matterjs/examples.html by

Ciro Santilli 35  Updated 2024-12-23  +Created 1970-01-01

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A multi-scenario demo.

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Run MLperf v2.1 ResNet on Imagenette by

Ciro Santilli 35  Updated 2024-12-23  +Created 1970-01-01

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Let's run on this Imagenet10 subset, Imagenette.

First ensure that you get the dummy test data run working as per MLperf v2.1 ResNet.

Next, in the imagenette2 directory, first let's create a 224x224 scaled version of the inputs as required by the benchmark at mlcommons.org/en/inference-datacenter-21/:

#!/usr/bin/env bash
rm -rf val224x224
mkdir -p val224x224
for syndir in val/*: do
  syn="$(dirname $syndir)"
  for img in "$syndir"/*; do
    convert "$img" -resize 224x224 "val224x224/$syn/$(basename "$img")"
  done
done

and then let's create the val_map.txt file to match the format expected by MLPerf:

#!/usr/bin/env bash
wget https://gist.githubusercontent.com/aaronpolhamus/964a4411c0906315deb9f4a3723aac57/raw/aa66dd9dbf6b56649fa3fab83659b2acbf3cbfd1/map_clsloc.txt
i=0
rm -f val_map.txt
while IFS="" read -r p || [ -n "$p" ]; do
  synset="$(printf '%s\n' "$p" | cut -d ' ' -f1)"
  if [ -d "val224x224/$synset" ]; then
    for f in "val224x224/$synset/"*; do
      echo "$f $i" >> val_map.txt
    done
  fi
  i=$((i + 1))
done < <( sort map_clsloc.txt )

then back on the mlperf directory we download our model:

wget https://zenodo.org/record/4735647/files/resnet50_v1.onnx

and finally run!

DATA_DIR=/mnt/sda3/data/imagenet/imagenette2 time ./run_local.sh onnxruntime resnet50 cpu --accuracy

which gives on P51:

TestScenario.SingleStream qps=164.06, mean=0.0267, time=23.924, acc=87.134%, queries=3925, tiles=50.0:0.0264,80.0:0.0275,90.0:0.0287,95.0:0.0306,99.0:0.0401,99.9:0.0464

where qps presumably means "querries per second". And the time results:

446.78user 33.97system 2:47.51elapsed 286%CPU (0avgtext+0avgdata 964728maxresident)k

The time=23.924 is much smaller than the time executable because of some lengthy pre-loading (TODO not sure what that means) that gets done every time:

INFO:imagenet:loaded 3925 images, cache=0, took=52.6sec
INFO:main:starting TestScenario.SingleStream

Let's try on the GPU now:

DATA_DIR=/mnt/sda3/data/imagenet/imagenette2 time ./run_local.sh onnxruntime resnet50 gpu --accuracy

which gives:

TestScenario.SingleStream qps=130.91, mean=0.0287, time=29.983, acc=90.395%, queries=3925, tiles=50.0:0.0265,80.0:0.0285,90.0:0.0405,95.0:0.0425,99.0:0.0490,99.9:0.0512
455.00user 4.96system 1:59.43elapsed 385%CPU (0avgtext+0avgdata 975080maxresident)k

TODO lower qps on GPU!

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Pernosco by