UTC: 2024-04-25 06:08 chocolatey-community/chocolatey-packages
This file is automatically generated by the update_all.ps1 script using the Chocolatey-AU module.
Ignored | History | [Force Test](https://gist.github.com/ee5
UTC: 2024-04-25 06:08 chocolatey-community/chocolatey-packages
This file is automatically generated by the update_all.ps1 script using the Chocolatey-AU module.
Ignored | History | [Force Test](https://gist.github.com/ee5
https://stackoverflow.com/questions/48993286/is-it-possible-to-route-traffic-to-a-specific-pod?rq=1
You can guarantee session affinity with services, but not as you are describing. So, your customers 1-1000 won't use pod-1, but they will use all the pods (as a service makes a simple load balancing), but each customer, when gets back to hit your service, will be redirected to the same pod.
Note: always within time specified in (default 10800):
int main () | |
{ | |
print("hello world"); | |
return 0; | |
} |
async function check(req, res) { | |
const walletInfo = req.body.walletInfo as Wallet | |
if (!walletInfo?.connectItems?.tonProof) { | |
return res.status(httpStatus.BAD_REQUEST).send({ ok: false }) | |
} | |
const proof = walletInfo.connectItems.tonProof as TonProofItemReplySuccess | |
if (!proof) { | |
return res.status(httpStatus.BAD_REQUEST).send({ ok: false }) | |
} | |
from datasets import load_metric | |
meteor = load_metric('meteor') | |
rouge = load_metric('rouge') | |
def compute_metrics(prediction): | |
labels_ids = prediction.label_ids | |
pred_ids = prediction.predictions | |
pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True) |
Here are the simple steps needed to create a deployment from your local GIT repository to a server based on this in-depth tutorial.
You are developing in a working-copy on your local machine, lets say on the master branch. Most of the time, people would push code to a remote server like github.com or gitlab.com and pull or export it to a production server. Or you use a service like deepl.io to act upon a Web-Hook that's triggered that service.
This guide will show you how to use Intel graphics for rendering display and NVIDIA graphics for CUDA computing on Ubuntu 18.04 / 20.04 desktop.
I made this work on an ordinary gaming PC with two graphics devices, an Intel UHD Graphics 630 plus an NVIDIA GeForce GTX 1080 Ti.
Both of them can be shown via lspci | grep VGA
.
00:02.0 VGA compatible controller: Intel Corporation Device 3e92
01:00.0 VGA compatible controller: NVIDIA Corporation GP102 [GeForce GTX 1080 Ti] (rev a1)
{ | |
"workbench.iconTheme": "vscode-icons", | |
"workbench.colorTheme": "One Dark Pro Darker", | |
"editor.tabSize": 2, | |
"editor.fontFamily": "'Fira Mono', Menlo, Monaco, 'Courier New', monospace", | |
"files.exclude": { | |
"**/.git": false | |
}, | |
"editor.bracketPairColorization.enabled": true, |