András Gyácsok's blog

 If you have relatives disappeared in komunistic millstones, and you are looking for information about them - you can visit the bessmertnybarak . Here is gathered information about thousands of victims.

Ви можете завантажити малюнок у великій роздільній здатності з Google Drive Чудовий сайт з інтерактивним колесом емоцій.

Realy good commant I've found on StackOverflow

Finished learning a Jupyter notebook "Scaling Python Processing with Ray" created by Dean Wampler. Notebook can be found on Learning O'Reilly . I like it (both, Ray and the learning material). Ray allows easy build distributed computations for stateless and stateful computing (actors), as well as it has powerful support for reinforced learning. Basically you need to know @ray.remote decorator and then call your object (function ar method) with remote method. @ray.remote def do_smt(arg1, arg2): # Some operation for distributed computing return arg1, arg2 do_smt.remote(1, 2) Second benefit is how Ray manages chains of tasks. When you call remote  Ray returns something like a Future object - id object. You can pass this objects on the next step and Ray will manage everything. There is idea of detached actors - this kind of Ray actor which won't be shutdown after Ray finishes computational tasks. You can find it by name and schedule execution. ...

Super cool module which provides Python data class to JSON or Python dict conversion -  dataclasses-json

Super cool project for audio processing  https://github.com/deezer/spleeter

Materialized PythonAnywhere

https://stackoverflow.com/questions/25706978/how-to-create-socket-in-pypy-sandbox https://stackoverflow.com/questions/13769346/interacting-with-a-sandboxed-pypy-interpreter-secondary-communicate-returns https://github.com/AdityaOli/Building-and-Breaking-a-Python-Sandbox/blob/master/OS%20Level%20Sandboxing%20using%20PyPy's%20Sandbox.md https://www.pypy.org/download.html https://www.programiz.com/python-programming/methods/built-in/exec https://stackoverflow.com/questions/3068139/how-can-i-sandbox-python-in-pure-python CodeJail

Jessica McKellar: Building and breaking a Python sandbox - PyCon 2014

Please check this link  Introduction to Federated Learning

Disclaimer: The story below is related to OpenShift Dedicated v.3. I have a project which requires event sourcing architecture. The project is running in OpenShift. My idea is to try Kafka and RabbitMQ in the broker role. Kafka installation is supported in OpenShift by Strimzi implementation called AMQ Streams . This means that, in case of OpenShift Dedicated clusters, RedHat has to enable Streams support if you are on v.3. After the Streams are enabled you can create a Kafka cluster with a simple command, like it is described in How to run Kafka on Openshift, the enterprise Kubernetes, with AMQ Streams . Just execute: $ cat << EOF | oc create -f - apiVersion: kafka.strimzi.io/v1alpha1 kind: Kafka metadata: name: my-cluster spec: kafka: replicas: 3 listeners: external: type: route storage: type: ephemeral zookeeper: replicas: 3 storage: type: ephemeral entityOperator: topicOperator: {} EOF And you will get you cluster.....

RedHat advices to use the next command: oc exec -it <broker_pod_name: e.g.="" my-cluster-kafka-0=""> -c kafka -- bin/kafka-topics.sh --zookeeper localhost:2181 --describe --topic <topic_name> RedHat portal Other interesting topics: https://access.redhat.com/documentation/en-us/red_hat_amq/7.5/html/using_amq_streams_on_openshift/uninstalling-str https://access.redhat.com/documentation/en-us/red_hat_amq/7.5/html/using_amq_streams_on_openshift/assembly-deployment-configuration-str#assembly-storage-deployment-configuration-kafka

https://github.com/jharting/openshift-rabbitmq-cluster

I guess the most valid links are:  Finite Loop Git Repo  and Kunal Repo , even previous one relates on last one. But the best way is Strimzi

«Советская колбаса» или Ложь о вкусных продуктах в СССР : Источник

Kubernetes plugin for Jenkins give us interesting ability to run multiple slaves as pods in the same time and execute commands on every of running slaves, moreover it can convert usual container into a slave. All you need is to define template like this: podContainers: [ containerTemplate( name: 'jnlp', image: "${dockerRegistry}/cd/jenkins-slave-python", workingDir: '/tmp', alwaysPullImage: true, resourceRequestMemory: '1Gi', resourceLimitMemory: '2Gi', args: '${computer.jnlpmac} ${computer.name}' ), containerTemplate( name: 'python', image: 'python:3.8-slim', alwaysPullImage: true, ttyEnabled: true, resourceRequestMemory: '1Gi', resourceLimitMemory: '2Gi', command: '', workingDir: '/tmp' ) ], Please, pay attention to the second template definition. It has, from the first look,...

I was interested in running federated model training using Horovod in the Kubernetes cluster. The first question I had was - is it possible to run Horovod on CPU, because our containers don't have GPUs. I found out on GIT repository of Horovod  , that this is possible. I have to install within Horovod non-gpu version of Tensorflow, and everything should fly. Also no need to specify GPU settings in the code, like config.gpu_options.allow_growth = True config.gpu_options.visible_device_list = str(hvd.local_rank()) Also, Fardin, advices: If you have GPUs on your machine and do not want to use them set the environment variable  CUDA_VISIBLE_DEVICES=-1 .

I found nice explanation here:  https://jovianlin.io/cat-crossentropy-vs-sparse-cat-crossentropy/