Looks interesting.
It seems to abstract the part about the client messaging the backend, which focuses on being able to easily plug in a number of Front-end web framework to manage client state.
Uses Socket.IO, which allows the client Javascript to register callbacks when data is updated to achieve Socket.IO, e.g. their default chat app does:so that message appear immediately as they are sent.
client.service('messages').on('created', addMessage);Their standard template from But it is in itself a completely boring app with a single splash page, and no database interaction, so not a good showcase. The actual showcase app is feathersjs/feathers-chat.
feathers generate app on @feathersjs/cli@4.5.0 includes:which looks promising! They don't have a default template for a Front-end web framework however unfortunately: docs.feathersjs.com/guides/frameworks.html#the-feathers-chat lists a few chat app versions, which is their hello world:
- Front-end web framework: not built-in on generator, but there are some sample repos pointed from the documentation, and they did work out-of-box:
And there is no official example of the chat app that is immediately deployable to Heroku: FeathersJS Heroku deployment, all setups require thinking.
In this section we will use the file nodejs/bench_mem.js, tests are run on Node.js v16.14.2 from NVM, Ubuntu 21.10, on Lenovo ThinkPad P51 (2017) which has 32 GB RAM.
Related answer: stackoverflow.com/questions/12023359/what-do-the-return-values-of-node-js-process-memoryusage-stand-for/72043884#72043884
First using
topp from stackoverflow.com/questions/1221555/retrieve-cpu-usage-and-memory-usage-of-a-single-process-on-linux/40576129#40576129 let's observe the memory usage of some baseline cases.For a Node.js infinite loop nodejs/infinite_loop.jsThis gives approximately:
topp infinite_loop.js- RSS: 20 MB
- VSZ: 230 MB
Adding a single hello world to it as in nodejs/infinite_hello.js and running:leads to:We understand that Node.js preallocates VSZ wildly. No big deal, but it does mean that VSZ is a useless measure for Node.js.
topp infinite_hello.js- RSS: 26 MB
- VSZ: 580 MB
Forcing garbage collection as in nodejs/infinite_hello.js brings it down to 20 MB however:
topp node --expose-gc infinite_hello_gc.jsFinally let's see a baseline for which gives initially:but after a few seconds randomly jumps to:so we understand that
process.memoryUsage nodejs/infinite_memoryusage.js:node --expose-gc infinite_memoryusage.js{
rss: 23851008,
heapTotal: 6987776,
heapUsed: 3674696,
external: 285296,
arrayBuffers: 10422
}{
rss: 26005504,
heapTotal: 9084928,
heapUsed: 3761240,
external: 285296,
arrayBuffers: 10422
}First a baseline case with an array of length 1:This gives the same results as with:
node --expose-gc bench_mem.js n 1node --expose-gc infinite_memoryusage.js. The same result is obtained by doing:a = undefinednode --expose-gc bench_mem.js deallocIf we use we see that the memory is now, unsurprisingly, accounted for under Results for different N:We see therefore that typed arrays are much closer to what they advertise (4 bytes per element), even for smaller element counts, as expected.
Int32Array typed array buffers instead of a simple Array:node --expose-gc bench_mem.js array-buffer n NarrayBuffers, e.g. for N 1 million:{
rss: 31776768,
heapTotal: 6463488,
heapUsed: 3674520,
external: 4285296,
arrayBuffers: 4010422
}|| N
|| `arrayBuffers`
|| `rss`
|| `rss` per elem
| 1 M
| 4 MB
| 31 MB
| 5
| 10 M
| 40 MB
| 67 MB
| 4.6
| 100 M
| 40 MB
| 427 MB
| 4Now let's try one million objects of type gives:Disaster! Memory usage is up to 70 MB! Why?? We were expecting only about 24, 4 baseline + 2 * 10 for each million int?!
{ a: 1, b: -1 }:node --expose-gc bench_mem.js obj{
rss: 138969088,
heapTotal: 105246720,
heapUsed: 70103896,
external: 285296,
arrayBuffers: 10422
}And now an equivalent version using gives the same result.
class:node --expose-gc bench_mem.js classLet's try Array:is even worse at 78 MB!! OMG why.
node --expose-gc bench_mem.js arr{
rss: 164597760,
heapTotal: 129363968,
heapUsed: 78117008,
external: 285296,
arrayBuffers: 10422
}The idea is cool. It really unifies front-and back end.
But Ciro Santilli feels the approach proposed by FeathersJS of being a glue between bigger third-party Front-end web frameworks like React and backend (object-relational mapping) is more promising and flexible.
Timeline:
- 2021-08-02 arrested in the USA for extradiction
- 2023-11-06 Stepped down from monero Core Team
By the current user:
bkill 0MacKenzie Bezos' new husband after she divorced Bezos.
www.dailymail.co.uk/femail/article-9338723/Who-billionaire-Mackenzie-Scotts-new-husband-Dan-Jewett.html Who IS billionaire Mackenzie Scott's new husband Dan Jewett?
www.wholecellviz.org/viz.php awesome visualization of simtk, paper: www.ncbi.nlm.nih.gov/pmc/articles/PMC3413483/ A Whole-Cell Computational Model Predicts Phenotype from Genotype - 2013 - Jonathan R. Karr.
Followed up by the E. Coli Whole Cell Model by Covert Lab.
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.
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 285TODO 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!
www.newyorker.com/magazine/2022/03/07/a-journey-to-the-center-of-our-cells A Journey to the Center of Our Cells (2022) by James Somers comments on M. genitalium in general, and in particular on the JCVI strains.
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.
The hello world!
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