Polyaxon is a platform for building, training, and monitoring large scale deep learning applications. We are making a system to solve reproducibility, automation, and scalability for machine learning applications.
Polyaxon deploys into any data center, cloud provider, or can be hosted and managed by Polyaxon, and it supports all the major deep learning frameworks such as Tensorflow, MXNet, Caffe, Torch, etc.
Polyaxon makes it faster, easier, and more efficient to develop deep learning applications by managing workloads with smart container and node management. And it turns GPU servers into shared, self-service resources for your team or organization.
Gain more productivity and ship faster: Polyaxon provides an interactive workspace with a notebooks, tensorboards, visualizations,and dashboards.
User management: Collaborate with the rest of your team, share and compare experiments results.
User resources allocation: Manage your team’s resources and parallelism quotas.
Versioning and reproducibility: Reproducible results with a built-in version control for code and experiments.
Data autonomy: Maintain complete control of your data and persistence choices.
Hyperparameter search & optimization: Run group of experiments in parallel and in distributed way.
Maximizes Resource Utilization: Spin up or down, add more nodes, add more GPUs, and expand storage.
Cost Effective: Leverage commodity on-premise infrastructure or spot instances to reduce costs.
Powerful interface: Author jobs, experiments, and pipelines in Json, YAML, and Python.
Runs on any infrastructure: Deploy Polyaxon in the cloud, on-premises or in hybrid environments, including single laptop, container management platform, heroku, on docker-compose, or on Kubernetes.