Blog

Polyaxon 0.2.7: experiments comparison & model analysis

Today’s release is the first step towards experiments comparison on Polyaxon and overall model analysis.

Install Polyaxon on MicroK8s

This is a guide to assist you through the process of setting up a Polyaxon deployment using MicroK8s.

Install Polyaxon using kubeadm on Kubernetes

This is a guide to assist you through the process of setting up a Polyaxon deployment using kubeadm and Kubernetes.

Install Polyaxon on Minikube

This is a guide to assist you through the process of setting up a Polyaxon deployment using Minikube.

Polyaxon 0.2.3: Experiment stability, better tracking, better visualization

We’re pleased to announce the new release of Polyaxon 0.2.3. Today’s release continues to focus on internal improvements and stability, and make the best experience to run and scale machine learning on Kubernetes.

Polyaxon v0.2

The new version of Polyaxon comes with a lot of new features, bug fixes, and performance enhancement.

CartPole game by Reinforcement Learning, a journey from training to inference

This project is intended to play with CartPole game using Reinforcement Learning and to know how we may train a different model experiments with enough observability (metrics/monitoring). The model is divided basically in three parts: Neural network model, QLearning algorithm and application runner

Mounting multiple data and outputs volumes

For some advanced uses cases, users might need to mount more than one data and/or outputs volumes.

Autoscaling of preemptible GPU resources

Discussion about how to use a pool of preemptible GPUs that are automatically used whenever experiments or a hyperparameter search is created in Polyaxon.

Advanced scheduling of experiments and jobs on Polyaxon

For some advanced use cases, users might need to have more control over where specific Polyaxon jobs should be deployed on their clusters.

Saving, resuming, and restarting experiments with Polyaxon

In this post we will introduce a new feature on Polyaxon, checkpointing, resuming, and restarting experiments.

Hyperparameters tuning on Kubernetes

hyperparameters tuning is very important concept in order to choose the optimal hyperparameters for a given algorithm!

Distributed Deep Learning on Kubernetes

In this short tutorial, we will be going over a new feature in Polyaxon, distributed training.

Jupyter notebooks and tensorboards on Polyaxon

We’re introducing a new feature at Polyaxon to help you iterate on your projects and share your work; Jupyter Notebook and Tensorboard.

A Deep Learning stack on Kubernetes

This post tries to answer a couple of practical questions about the project and the intent behind it’s creation.