Docker

Meta Unveils Four New Generations of Custom AI Chips to Reduce Nvidia Reliance Meta has announced a significant expansion of its custom silicon efforts with four new generations of its in-house Meta Training and Inference Accelerator (MTIA) chips. The new chips—MTIA 300, 400, 450, and 500—are engineered to manage the massive AI workloads for Meta’s services, including ranking, recommendation systems, and generative AI. This strategic move aims to reduce the company’s reliance on external chip suppliers like Nvidia while improving performance and efficiency for its specific needs. The MTIA 300 is already in production, with subsequent generations planned for a rapid six-month release cadence. The more advanced MTIA 450 and 500 models are specifically optimized for generative AI inference and are slated for mass deployment in 2027. ...

When you choose to run your kubernetes cluster on AWS, there are 2 easy ways: The almost valina way using Kops The amazon way using EKS The common thing between both ways, is using Amazon ECR for storing the docker images and have a worry free push, pull to and from Amazon ECR which requires a IAM Role that allows the worker nodes pulling the images saftly. Amazon ECR Amazon Elastic Container Service is one of the cheapst ways to store docker images and safer due to the nature of Amazon IAM. To pull from ECR you first need to authenticate using you AWS credentials, or role, get a token, do docker login to your ECR with the server address, and pull from the repository the docker image. ...

This is a short post on log rotating AWS Elasticsearch indices easily using curator In the past I’ve made a simple docker container with curator and with a simple actions file for managing indices. This worked great on my own installation of Elasticsearch that managed our logs on a small scale. The actions file was: --- actions: 1: action: delete_indices description: >- Rolling indeces according to filters options: ignore_empty_list: True #disable_action: True disable_action: False filters: - filtertype: pattern kind: prefix value: filebeat- - filtertype: age source: name direction: older timestring: '%Y.%m.%d' unit: days unit_count: ${DAYS_TO_KEEP:14} And it was controlled in the docker run command, which host to work on and what old indices should be deleted, with environment variables. ...

Using AWS Lambda has become very popular. Most of the time using it is fast and just (but not always). When you start having a lot of lambda functions the complexity of managing them can be hard. When a developer can just edit the code inline, it can become the worse manage service use ever chose. So again, the most important thing to do when you start working with Lambda is to have a proper way to deliver the code from git to aws. ...

Docker has become the preferable way and the most easy way to run continuous integration, due to its reproducible nature and fast learning curve. There are multiple ways approaching CI processes using docker. Using docker build, which is the easiest way FROM node:12 ARG NODE_ENV=production COPY . /src RUN npm install WORKDIR /src CMD [ "node", "app.js" ] Using docker multi step build, this usually helps making the final image smaller and safer without all the build binaries FROM maven AS Builder COPY . /src RUN [ "mvn" , "clean" , "install" ] FROM openjdk:12-alpine WORKDIR /srv COPY --from=Builder /src/target ./srv/ CMD [ "java", "-jar", "app.jar" ] Using docker run with a pre-built build image on a volume mount this allows having cache directories and have some more functionality during the build, and will allow multiple commands docker run -v `$pwd`:/src -v `~/.m2`:/root/.m2 maven mvn clean install Using docker in docker, like gitlab, bitbucket etc… This method is more complex than the rest and require you to mount docker socket to the as a volume to the docker container, and can be preformed in 2 ways: There is an official docker in docker image by docker inc that is tagged as docker:dind ...