Product articles

Reproducible & Portable Machine Learning packages

As we are testing release candidates for the upcoming PolyaxonV1.1. Several users have been asking about the new Polyaxonfile changes and…

Streamlining the machine Learning lifecycle

The v1 release delivers some new functionalities and features to streamline the machine learning lifecycle.

Polyaxon v0.5

Today, we are pleased to announce the v0.5 release of our MLOps platform, a stable version which brings significant improvements to Polyaxon’s deployment and management experience, model management and Machine Learning (ML) workflows automation.

Polyaxon v0.4.3: stable — Improved dashboard, setup, and documentation

Today, we are pleased to announce the v0.4.3 release of our MLOps platform, a stable version with improved functionalities and documentation.

Polyaxon v0.4.2: Kubeflow, Horovod, and MPI integrations

Polyaxon now integrates with Kubeflow, Horovod, and MPI natively.

Polyaxon v0.4: Integrations, Machine Learning CI, Better support of external repos, Better support of cloud storages...

Polyaxon v0.4 added integrations, machine learning CI, experiment tracking, cloud storage support, TPU scheduling, and workflow improvements.

Polyaxon 0.2.7: experiments comparison & model analysis

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

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.

Mounting multiple data and outputs volumes

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

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.