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Install and configure External Secret Operator (ESO)

Install external-secret with Helm

helm repo add external-secrets https://charts.external-secrets.io

helm upgrade --install --create-namespace -n eso external-secrets \
    external-secrets/external-secrets \
    -n external-secrets \
@fsodogandji
fsodogandji / README.md
Created February 3, 2022 14:25 — forked from ajxchapman/README.md
Install Windows on Digital Ocean droplet
import { useEffect, useState } from 'react';
import { Flex, Select, Box, Text, Input, Spinner, Icon, Button } from '@chakra-ui/react';
import { useRouter } from 'next/router';
import { MdCancel } from 'react-icons/md';
import Image from 'next/image';
import { filterData, getFilterValues } from '../utils/filterData';
import { baseUrl, fetchApi } from '../utils/fetchApi';
import noresult from '../assets/images/noresult.svg';
@ashleve
ashleve / kfold_example.py
Last active April 24, 2024 10:54
Example of k-fold cross validation with PyTorch Lightning Datamodule
from pytorch_lightning import LightningDataModule
from torch_geometric.datasets import TUDataset
from torch_geometric.data import DataLoader
from sklearn.model_selection import KFold
class ProteinsKFoldDataModule(LightningDataModule):
def __init__(
self,
data_dir: str = "data/",
@kiko-datasparq
kiko-datasparq / GaussianMixture.ipynb
Created February 23, 2021 12:19
GaussianMixture_RealEstatePrices
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// Processing code by Etienne JACOB
// motion blur template by beesandbombs
// opensimplexnoise code in another tab might be necessary
// --> code here : https://gist.github.com/Bleuje/fce86ef35b66c4a2b6a469b27163591e
int[][] result;
float t, c;
float ease(float p) {
return 3*p*p - 2*p*p*p;
@ajxchapman
ajxchapman / README.md
Last active April 25, 2024 05:34
Install Windows on Digital Ocean droplet
@matt-bailey
matt-bailey / github-pages-custom-domain-gandi-dns-records.md
Last active April 17, 2024 00:26
How to set up DNS records on gandi.net to use a custom domain on Github Pages

How to set up DNS records on gandi.net to use a custom domain on Github Pages

You would think it would be easy to find this information, but none of the Github or Gandi documentation is clear so I have recorded the required steps here.

Create the following A records:

@ 1800 IN A 185.199.108.153
@ 1800 IN A 185.199.109.153
@ 1800 IN A 185.199.110.153
@vadimkantorov
vadimkantorov / compact_bilinear_pooling.py
Last active September 22, 2021 07:51
Compact Bilinear Pooling in PyTorch using the new FFT support
# References:
# [1] Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding, Fukui et al., https://arxiv.org/abs/1606.01847
# [2] Compact Bilinear Pooling, Gao et al., https://arxiv.org/abs/1511.06062
# [3] Fast and Scalable Polynomial Kernels via Explicit Feature Maps, Pham and Pagh, https://chbrown.github.io/kdd-2013-usb/kdd/p239.pdf
# [4] Fastfood — Approximating Kernel Expansions in Loglinear Time, Le et al., https://arxiv.org/abs/1408.3060
# [5] Original implementation in Caffe: https://github.com/gy20073/compact_bilinear_pooling
# TODO: migrate to use of new native complex64 types
# TODO: change strided x coo matmul to torch.matmul(): M[sparse_coo] @ M[strided] -> M[strided]
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