Fridolín Pokorný

Project Thoth is a recommendation engine that collects information about software packages, container images such as installation, assembling issues, runtime crashes or information about performance. This information is subsequently used in a recommendation engine that searches large state space of libraries and recommends the best possible combination of libraries suitable for your application using reinforcement learning. Let’s have a look at how such information is collected and how the large state space is explored to resolve the best application stack for your Python application based on different aspects.

Python ecosystem is experiencing significant growth and popularity especially with the hype machine learning, data science and AI are creating. As the ecosystem grows its many times not straightforward and easy to decide which libraries in which versions are the most suitable ones for an application. Project Thoth is a recommendation engine which aggregates various characteristics of Python packages, called "observations", and uses them to recommend the best possible software stack (a fully pinned down list of dependencies) suitable for user's runtime environment and the application purpose. In this talk, we give an overview of the project Thoth, main ideas in data aggregation and its recommendation engine based on reinforcement learning principles. We will also show how you can benefit from Thoth's recommendations.

A Linux kernel module that introduces an AFKTLS socket. AFKTLS socket can be used to transmit data over TLS 1.2 using TCP or DTLS 1.2 using UDP. Currently, there is supported only AES GCM cipher.

AFKTLS kernel module introduces an AFKTLS socket. AF_KTLS socket can be used to transmit data over TLS 1.2 using TCP or DTLS 1.2 using UDP. Currently, there is supported AES GCM cipher.

The socket does data transmission, the handshake, re-handshaking and other control messages have to be served by user space using appropriate libraries such as OpenSSL or Gnu TLS. AF_KTLS socket is suitable for CPU offloading and use cases where can be omitted user space (buffered-copy) interaction (using sendfile(2) or splice(2)).

Selinon is a project that is based on popular Python project Celery. Celery is a distributed task queue that offers one to run tasks. Selinon gives one a power to define flows and dependencies in flows, schedule tasks based on results of workers, success or any external events, handle errors, trace flow state and actions in a distributed environment. Its main goal is to split task logic (code) and persistence logic from data and time dependencies between tasks in flows that are stated in simple YAML configuration files.

Selinon is an advanced flow management above Celery project (an asynchronous distributed task queue) written in Python3, that allows you to dynamically schedule tasks based on results of previous tasks, group tasks to flows, schedule flows from other flows, store results of tasks in your storages and databases transparently, validate results against defined JSON schemas, track flow progress via the build-in tracing mechanism, complex per-task or per-flow failure handling with fallback tasks or fallback flows and run the whole system in a distributed environments orchestrated by OpenShift or Kubernetes.