jph00

#!/usr/bin/env bash
for f in $(<~/git/repos); do
  cd ~/git/$f
  git pull > /dev/null &
  cd - > /dev/null
done

wait < <(jobs -p)

for f in $(<~/git/repos); do

jph00

All of the python 3.9 standard library

(Too big for a single gist, so see first reply for the rest)

Table of contents

• abc
• aifc
• argparse
• array

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name: Quarto Publish
on:
  workflow_dispatch:
  push: { branches: master }
jobs:
  build-deploy:
    runs-on: ubuntu-latest
    permissions: {contents: write}
    steps:
      - uses: actions/checkout@v2

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# I added all the imports by hand
import torch
from torchvision.datasets import ImageFolder
from torchvision import transforms,models
from torch import nn,optim

# For all functions including this one, I wrote the name and docstring, and sometimes also the param names
def finetune(folder, model):
    """fine tune pytorch model using images from folder and report results on validation set"""
    if not os.path.exists(folder): raise ValueError(f"{folder} does not exist")

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model_name,learning_rate,pool,dataset,GPU_mem,error_rate,valid_loss,train_loss,fit_time
efficientnetv2_rw_t,0.0001,concat,pets,2.3203125,0.18335586786270142,0.5982086062431335,0.99508398771286,135.1381552380044
efficientnetv2_rw_s,0.0001,concat,pets,3.146484375,0.1779431700706482,0.5593751072883606,0.8270677328109741,145.79841202299576
efficientnetv2_rw_m,0.0001,concat,pets,5.56640625,0.15899866819381714,0.5119925141334534,0.7437679171562195,214.4970937330509
efficientnet_lite0,0.0001,concat,pets,1.51953125,0.19418132305145264,0.6388306021690369,1.144728422164917,67.13226824696176
efficientnet_es_pruned,0.0001,concat,pets,1.53515625,0.1637347936630249,0.5296803712844849,0.9239290952682496,69.60124204796739
efficientnet_es,0.0001,concat,pets,1.53515625,0.1400541067123413,0.44839274883270264,0.8084487915039062,72.84359885391314
efficientnet_b0,0.0001,concat,pets,1.666015625,0.19418132305145264,0.65826016664505,1.157211184501648,85.95306042104494
efficientnetv2_rw_t,0.0004,concat,pets,2.3203125,0.114343702793121

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dataset,model_name,GPU_mem,error_rate,valid_loss,train_loss,fit_time
pets,swinv2_cr_tiny_ns_224,3.787109375,0.0588633418083192,0.24936801195144653,0.019529560580849648,133.23359032196458
pets,swinv2_cr_tiny_ns_224,3.37109375,0.0588633418083192,0.24477967619895935,0.02033395692706108,131.0072173139779
pets,swinv2_cr_tiny_ns_224,3.330078125,0.05412721633911122,0.20794494450092316,0.021329525858163834,131.57327181001892
pets,swinv2_cr_small_ns_224,6.177734375,0.0446549654006958,0.21994665265083313,0.015108372084796429,327.2828737530508
pets,swinv2_cr_small_ns_224,6.2109375,0.05074423551559437,0.20733796060085297,0.01944943517446518,211.57673111901383
pets,swinv2_cr_small_ns_224,5.384765625,0.04668468236923218,0.2127318978309631,0.024523036554455757,344.7638177879853
pets,swinv2_cr_small_224,6.1171875,0.058186709880828746,0.2444191724061966,0.017354698851704597,345.65878029097803
pets,swinv2_cr_small_224,6.1171875,0.058186709880828746,0.2404960095882416,0.01604991964995861,213.62937671499097
pets,swinv2_cr_small_

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"Name","accuracy","User","valid_loss","GPU_mem","model parameters","model_name","num_experiments","learning_rate","fit_time","train_loss","error_rate"
"cosmic-sweep-342","0.893098771572113","jph00","0.4043380320072174","1.05859375","6263360","vit_tiny_r_s16_p8_224","3","0.008","74.63291443999333","0.08092606067657471","0.10690122097730637"
"worthy-sweep-341","0.8971583247184753","jph00","0.3881302773952484","0.9765625","6263360","vit_tiny_r_s16_p8_224","2","0.008","80.31514062899805","0.07855783402919769","0.10284167528152466"
"chocolate-sweep-340","0.8944519758224487","jph00","0.4079037010669708","0.9765625","6263360","vit_tiny_r_s16_p8_224","1","0.008","85.1170430029888","0.07688259333372116","0.10554803907871246"
"lyric-sweep-339","0.9012178778648376","jph00","0.35034531354904175","1.16015625","5643072","vit_tiny_patch16_224","3","0.008","78.18763143400429","0.0953610986471176","0.09878213703632356"
"generous-sweep-338","0.908660352230072","jph00","0.33963635563850403","1.07421875","5643072","vit_tiny_patc

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"Name","State","Created","Runtime","Learner.arch","learning_rate","model_name","num_experiments","valid_loss","train_loss","fit_time","accuracy_multi"
"blooming-sweep-315","finished","2022-06-02T02:00:29.000Z","119","vit_base_patch16_224","0.008","vit_base_patch16_224","3","0.09851958602666856","0.12880989909172058","25.836685962000047","0.9632352590560912"
"smart-sweep-314","finished","2022-06-02T01:59:02.000Z","84","vit_base_patch16_224","0.008","vit_base_patch16_224","2","0.09799090027809144","0.12930510938167572","26.209168308996595","0.9626470804214478"
"peachy-sweep-319","finished","2022-06-02T02:06:47.000Z","44","vit_base_patch16_224_sam","0.008","vit_base_patch16_224_sam","1","0.1101434975862503","0.13224607706069946","28.266185689004487","0.9608823657035828"
"sandy-sweep-328","finished","2022-06-02T02:18:27.000Z","38","vit_small_patch16_224","0.008","vit_small_patch16_224","1","0.11220161616802216","0.13845551013946533","21.900724929000717","0.9602941274642944"
"lilac-sweep-313","finished","2022-06-0

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All of the python 3.9 standard library

For a version without the collapsible details sections (so you can search the whole thing in your browser), click here.

abc

• abc.ABC
• abc.ABCMeta