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 \
- Guide from https://glitchwitch.io/blog/2018-09/windows-10-on-digitalocean/
- Download Windows ISO from https://www.microsoft.com/en-us/software-download/windows10ISO
- Create a build droplet with 4GB RAM
- Install QEMU and run the Windows 10 installation ISO
$ apt-get update && apt-get install qemu -y
$ qemu-img create -f raw windows10.img 16G
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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'; |
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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/", |
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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; |
- Guide from https://glitchwitch.io/blog/2018-09/windows-10-on-digitalocean/
- Download Windows ISO from https://www.microsoft.com/en-us/software-download/windows10ISO
- Create a build droplet with 4GB RAM
- Install QEMU and run the Windows 10 installation ISO
$ apt-get update && apt-get install qemu -y
$ qemu-img create -f raw windows10.img 16G
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
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# 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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