This guide has moved to a GitHub repository to enable collaboration and community input via pull-requests.
https://github.com/alexellis/k8s-on-raspbian
Alex
# https://blog.hypriot.com/post/setup-simple-ci-pipeline-for-arm-images/ | |
### Choose a base image with QEMU binary | |
# FROM hypriot/rpi-alpine | |
# FROM resin/rpi-raspbian | |
# FROM resin/raspberry-pi-alpine-node:slim | |
### Register QEMU in the build agent | |
docker run --rm --privileged multiarch/qemu-user-static:register --reset |
""" | |
Create train, valid, test iterators for CIFAR-10 [1]. | |
Easily extended to MNIST, CIFAR-100 and Imagenet. | |
[1]: https://discuss.pytorch.org/t/feedback-on-pytorch-for-kaggle-competitions/2252/4 | |
""" | |
import torch | |
import numpy as np |
This guide has moved to a GitHub repository to enable collaboration and community input via pull-requests.
https://github.com/alexellis/k8s-on-raspbian
Alex
Just documenting docs, articles, and discussion related to gRPC and load balancing.
https://github.com/grpc/grpc/blob/master/doc/load-balancing.md
Seems gRPC prefers thin client-side load balancing where a client gets a list of connected clients and a load balancing policy from a "load balancer" and then performs client-side load balancing based on the information. However, this could be useful for traditional load banaling approaches in clound deployments.
https://groups.google.com/forum/#!topic/grpc-io/8s7UHY_Q1po
gRPC "works" in AWS. That is, you can run gRPC services on EC2 nodes and have them connect to other nodes, and everything is fine. If you are using AWS for easy access to hardware then all is fine. What doesn't work is ELB (aka CLB), and ALBs. Neither of these support HTTP/2 (h2c) in a way that gRPC needs.
Install Golang 1.9:
wget https://storage.googleapis.com/golang/go1.9.linux-armv6l.tar.gz
sudo tar -C /usr/local -xzf go1.9.linux-armv6l.tar.gz
export PATH=$PATH:/usr/local/go/bin # put into ~/.profile
If already installed old golang with apt-get:
# | |
# written for Amazon Linux AMI | |
# creates an AWS Lambda deployment package for pytorch deep learning models (Python 3.6.1) | |
# assumes lambda function defined in ~/main.py | |
# deployment package created at ~/waya-ai-lambda.zip | |
# | |
# | |
# install python 3.6.1 | |
# |