MacKenzie Bezos went on to marry a science teacher who taught their children.
The contrast with Bezos's girlfriend is simply comical. MacKenzie married the idealistic morally upright science teacher, while Bezos went for a silly sex bomb. Ah, bruta flor, do querer!
It is a shame, but this game just doesn't feel good. The controls are just not as snappy as Mario Kart 64, the levels are too wide which limits player interaction, and the weapons feel clumsy weak and unexciting. These are all aspects that the closed source smashkarts.io gets pretty well.
It is interpreted. It actually implements a Python (-like ?) interpreter that can run on a microcontroller. See e.g.: Compile MicroPython code for Micro Bit locally.
As a result, it is both very convenient, as it does not require a C toolchain to build for, but also very slow and produces larger images.
Instructions at:
Ubuntu 22.10 setup with tiny dummy manually generated ImageNet and run on ONNX:
sudo apt install pybind11-dev
git clone https://github.com/mlcommons/inference
cd inference
git checkout v2.1
virtualenv -p python3 .venv
. .venv/bin/activate
pip install numpy==1.24.2 pycocotools==2.0.6 onnxruntime==1.14.1 opencv-python==4.7.0.72 torch==1.13.1
cd loadgen
CFLAGS="-std=c++14" python setup.py develop
cd -
cd vision/classification_and_detection
python setup.py develop
wget -q https://zenodo.org/record/3157894/files/mobilenet_v1_1.0_224.onnx
export MODEL_DIR="$(pwd)"
export EXTRA_OPS='--time 10 --max-latency 0.2'
tools/make_fake_imagenet.sh
DATA_DIR="$(pwd)/fake_imagenet" ./run_local.sh onnxruntime mobilenet cpu --accuracyLast line of output on P51, which appears to contain the benchmark resultswhere presumably
TestScenario.SingleStream qps=58.85, mean=0.0138, time=0.136, acc=62.500%, queries=8, tiles=50.0:0.0129,80.0:0.0137,90.0:0.0155,95.0:0.0171,99.0:0.0184,99.9:0.0187qps means queries per second, and is the main results we are interested in, the more the better.Running:produces a tiny ImageNet subset with 8 images under
tools/make_fake_imagenet.shfake_imagenet/.fake_imagenet/val_map.txt contains:val/800px-Porsche_991_silver_IAA.jpg 817
val/512px-Cacatua_moluccensis_-Cincinnati_Zoo-8a.jpg 89
val/800px-Sardinian_Warbler.jpg 13
val/800px-7weeks_old.JPG 207
val/800px-20180630_Tesla_Model_S_70D_2015_midnight_blue_left_front.jpg 817
val/800px-Welsh_Springer_Spaniel.jpg 156
val/800px-Jammlich_crop.jpg 233
val/782px-Pumiforme.JPG 285- 817: 'sports car, sport car',
- 89: 'sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita',
TODO prepare and test on the actual ImageNet validation set, README says:
Prepare the imagenet dataset to come.
Since that one is undocumented, let's try the COCO dataset instead, which uses COCO 2017 and is also a bit smaller. Note that his is not part of MLperf anymore since v2.1, only ImageNet and open images are used. But still:
wget https://zenodo.org/record/4735652/files/ssd_mobilenet_v1_coco_2018_01_28.onnx
DATA_DIR_BASE=/mnt/data/coco
export DATA_DIR="${DATADIR_BASE}/val2017-300"
mkdir -p "$DATA_DIR_BASE"
cd "$DATA_DIR_BASE"
wget http://images.cocodataset.org/zips/val2017.zip
wget http://images.cocodataset.org/annotations/annotations_trainval2017.zip
unzip val2017.zip
unzip annotations_trainval2017.zip
mv annotations val2017
cd -
cd "$(git-toplevel)"
python tools/upscale_coco/upscale_coco.py --inputs "$DATA_DIR_BASE" --outputs "$DATA_DIR" --size 300 300 --format png
cd -Now:fails immediately with:The more plausible looking:first takes a while to preprocess something most likely, which it does only one, and then fails:
./run_local.sh onnxruntime mobilenet cpu --accuracyNo such file or directory: '/path/to/coco/val2017-300/val_map.txt./run_local.sh onnxruntime mobilenet cpu --accuracy --dataset coco-300Traceback (most recent call last):
File "/home/ciro/git/inference/vision/classification_and_detection/python/main.py", line 596, in <module>
main()
File "/home/ciro/git/inference/vision/classification_and_detection/python/main.py", line 468, in main
ds = wanted_dataset(data_path=args.dataset_path,
File "/home/ciro/git/inference/vision/classification_and_detection/python/coco.py", line 115, in __init__
self.label_list = np.array(self.label_list)
ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 2 dimensions. The detected shape was (5000, 2) + inhomogeneous part.TODO!
A Three-Dimensional Model of the Myoglobin Molecule Obtained by X-Ray Analysis (1958) Updated 2025-01-10 +Created 1970-01-01
@cirosantilli/_file/nodejs/nodejs/read_child_process_lines.js Updated 2025-01-10 +Created 1970-01-01
This example reads lines from a child process one by one, as soon as lines become fully available. Related:
- math.stackexchange.com/questions/361422/why-isnt-np-conp "Why isn't NP = coNP?"
- stackoverflow.com/questions/17046440/whats-the-difference-between-np-and-co-np
- cs.stackexchange.com/questions/9795/is-the-open-question-np-co-np-the-same-as-p-np
- mathoverflow.net/questions/31821/problems-known-to-be-in-both-np-and-conp-but-not-known-to-be-in-p
Their status is a mess as of 2020s, with several systems ongoing. Long live the "original" collegiate university!
A person who gives financial advice, notably personal finance advice. Some of them are questinable guru-like beings, and many are on YouTube.
A more photon-specific version of the Bloch sphere.
In it, each of the six sides has a clear and simple to understand photon polarization state, either of:
- left/right
- diagonal up/diagonal down
- rotation clockwise/counterclockwise
The sphere clearly suggests for example that a rotational or diagonal polarizations are the combination of left/right with the correct phase. This is clearly explained at: Video "Quantum Mechanics 9b - Photon Spin and Schrodinger's Cat II by ViaScience (2013)".
Examples under python/sklearn
. .venv/bin/activate
pip install sklearn matplotlib seaborn @cirosantilli/_file/python/typing_cheat/python/typing_cheat/hello.py Updated 2025-01-10 +Created 1970-01-01
The hello world!
math.mit.edu/classes/18.783, Wow, good slides! Well organized site! This is a good professor! And brutal course. 25 lectures, and lecture one ends in BSD conjecture!
Some points from math.mit.edu/classes/18.783/2022/LectureSlides1.pdf:
- definition of elliptic curves
To Ciro Santilli, a key observation is:Clearly the rich will be much, much more shielded by keeping large parts of their wealth in shares... from this point of view, it is insane to print money!!! Tax the rich instead...
The rich are more easily able to avoid the harm than poor and middle-class people [...] they are more likely to have large amounts of non-cash assets to shield themselves from inflation.
There are unlisted articles, also show them or only show them.