Late Night Tech

this the second part of dockerizing an angular 6 and spring boot app Part 1 here: http://dev.basharallabadi.com/2018/08/part-1-backend-docker-for-angular.html I followed this blog: https://mherman.org/blog/2018/02/26/dockerizing-an-angular-app/ so basically three steps: 1- create docker file 2- create docker compose 3- docker-compose build .. docker-compose up here is the files I'll be explaining to achive this: . 1- Dockerfile This docker file uses multi stage build, which means it uses multiple docker images to produce final image. why? because the image we need to build the angular app requires alot of dependencies that are not needed to run the app. in the first part of the docker file: - it starts based on a node image - prepares the app directory - copies package.json from the source code directory to the docker container directory - executes npm install which will install all node dependencies. - install angular cli globally (version 6) - copy

I have been working on an angular 4 (upgraded to 6) application backed by spring boot rest api, repositories: 1- api: https://github.com/blabadi/nutracker-api 2- angular: https://github.com/blabadi/nutritionTracker and this post is about using and adding docker to these apps to make it easier to deploy and share. Disclaimer : this is not by any means a best practice article, but was more of a hands on way to learn more about Docker work flow and challenges that one may face. Part 1: Dockerizing the backend project (nutracker-api) I was testing this process on amazon micro instance and these are the steps I followed: pre requisite: - install docker on aws: https://docs.aws.amazon.com/AmazonECS/latest/developerguide/docker-basics.html 1- add docker file (point to a built jar for now) reference: https://www.callicoder.com/spring-boot-docker-example/ this docker file is simple it: - gets a jdk image - copies our pre built jar from the build output directory - sta

I wanted to use my GPU instead of CPU in Keras, after knowing that it supports CUDA , for a simple deep learning example training, so I had to do some search on how to set it up and ended with this summary:   Pre requisites : 1- Anaconda installed 2- setup Nividia CUDA 9.0 https://developer.nvidia.com/cuda-90-download-archive 3- setup Nividia cuDNN 7 (for CUDA 9.0) https://developer.nvidia.com/rdp/cudnn-download https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#install-windows Steps: 0) run Anaconda Prompt 1) install conda packages conda create --name tf-gpu-keras-p35 python=3.5 pip activate tf-gpu-keras-p35 conda install jupyter conda install scipy conda install scikit-learn conda install pandas pip install tensorflow-gpu pip install keras The following didn't work for me: (didn't work with keras, keras installed tensorflow 1.1.0) keras keeps overriding the tensorflow-gpu you get empty tensorflow module conda install -c conda-forge keras conda install -c

In the land of Hibernate a concept of Session is important. it plays the role of tracking changes that happen to (hibernate objects) from the moment you open it. and you can't actually do much without it, it's your entry to hibernate capabilities so if you want to load an object you have to obtain a session instance first then load the object: Session s = sessionFactory.getCurrentSession(); MyObject mo = s.byId(MyObject.class).load(123); now that object (mo) is associated with that session if you do something like mo.setName('new Name'); the session will track that this object is dirty. and it does much more that you can read about. What I want to get to is that in case you have to do some background work with your object you have to be careful that: 1- the session is not thread safe (i.e. it can't be passed between threads and assume no concurrency issues will happen) 2- the objects associated with one session cannot be associated with another op