badsyntax

# Install AVD files
yes | $ANDROID_HOME/tools/bin/sdkmanager --install 'system-images;android-29;default;x86'
yes | $ANDROID_HOME/tools/bin/sdkmanager --licenses

# Create emulator
echo "no" | $ANDROID_HOME/tools/bin/avdmanager create avd -n Pixel_API_29_AOSP -d pixel --package 'system-images;android-29;default;x86' --force

$ANDROID_HOME/emulator/emulator -list-avds

mugifly

const tf = require('@tensorflow/tfjs-node');
const Jimp = require('jimp');

// Directory path for model files (model.json, metadata.json, weights.bin)
// NOTE: It can be obtained from [Export Model] -> [Tensorflow.js] -> [Download my model]
//        on https://teachablemachine.withgoogle.com/train/image
const MODEL_DIR_PATH = `${__dirname}`;

// Path for image file to predict class
const IMAGE_FILE_PATH = `${__dirname}/example.jpg`;

kobybibas

# Having a requirements.txt file has follows
torch==1.5.0
numpy==1.18.1

# Add channels. Last added is with the highest priorety
conda config --add channels pytorch
conda config --add channels conda-forge
conda config --add channels anaconda

# Install pip for fallback

yuhanz

pip install bert-for-tf2
pip install bert-tokenizer
pip install tensorflow-hub
pip install bert-tensorflow
pip install sentencepiece


import tensorflow_hub as hub
import tensorflow as tf
import bert

nraw

""" Kedro Torch Model IO

Models need to be imported and added to the dictionary
as shown with the ExampleModel

Example of catalog entry:

modo:
  type: kedro_example.io.torch_model.TorchLocalModel
  filepath: modo.pt

pirate

#!/usr/bin/env bash

### Bash Environment Setup
# http://redsymbol.net/articles/unofficial-bash-strict-mode/
# https://www.gnu.org/software/bash/manual/html_node/The-Set-Builtin.html
# set -o xtrace
set -o errexit
set -o errtrace
set -o nounset
set -o pipefail

ZachisGit

'''	ExportModel.py - TF-Serving
#	Basically we are wrapping your pretrained model
#	in a tensorflow serving compatible format.
#	This excepts base64 encoded png images and uses
#	them as input to your model. Then we convert
#	your models output into a png encoded image and
#	it gets returned by tensorflow serving base64 encoded.
'''

import tensorflow as tf

mrk-han

Install and Create Emulators using AVDMANAGER and SDKMANAGER

TL;DR

For an emulator that mimics a Pixel 5 Device with Google APIs and ARM architecture (for an M1/M2 Macbook):

1. List All System Images Available for Download: sdkmanager --list | grep system-images

2. Download Image: sdkmanager --install "system-images;android-30;google_atd;arm64-v8a"

elowy01

// Create a Singularity image from a Docker image that is in the Docker hub
// where /tmp/ is the folder where the image will be created and ubuntu:14.04 
// is the docker image used to convert to the Singularity image
docker run \
-v /var/run/docker.sock:/var/run/docker.sock \
-v /tmp/:/output \
--privileged -t --rm \
singularityware/docker2singularity \
ubuntu:14.04
//

alexellis

Ramp up your Kubernetes development, CI-tooling or testing workflow by running multiple Kubernetes clusters on Ubuntu Linux with KVM and minikube.

In this tutorial we will combine the popular minikube tool with Linux's Kernel-based Virtual Machine (KVM) support. It is a great way to re-purpose an old machine that you found on eBay or have gathering gust under your desk. An Intel NUC would also make a great host for this tutorial if you want to buy some new hardware. Another popular angle is to use a bare metal host in the cloud and I've provided some details on that below.

We'll set up all the tooling so that you can build one or many single-node Kubernetes clusters and then deploy applications to them such as OpenFaaS using familiar tooling like helm. I'll then show you how to access the Kubernetes clusters from a remote machine such as your laptop.

Pre-reqs

• This tutorial uses Ubuntu 16.04 as a base installation, but other distributions are supported by KVM. You'll need to find out how to install