Package: polyaxon
Reproduce, Automate, Scale your data scienceWelcome to Polyaxon, 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.InstallTL;DR;Install CLI# Install Polyaxon CLI$pipinstall-UpolyaxonCreate a deployment# Create a namespace$kubectlcreatenamespacepolyaxon# Add Polyaxon charts repo$helmrepoaddpolyaxonhttps://charts.polyaxon.com# Deploy Polyaxon$polyaxonadmindeploy-fconfig.yaml# Access API$polyaxonport-forwardPlease checkpolyaxon installation guideQuick startTL;DR;Start a project# Create a project$polyaxonprojectcreate--name=quick-start--description='Polyaxon quick start.'Train and track logs & resources# Upload code and start experiments$polyaxonrun-fexperiment.yaml-u-lDashboard# Start Polyaxon dashboard$polyaxondashboard

Dashboardpagewillnowopeninyourbrowser.Continue?[Y/n]:yNotebook# Start Jupyter notebook for your project$polyaxonrun--hubnotebookTensorboard# Start TensorBoard for a run's output$polyaxonrun--hubtensorboard-Puuid=UUIDPlease check ourquick start guideto start training your first experiment.Distributed jobPolyaxon supports and simplifies distributed jobs.
Depending on the framework you are using, you need to deploy the corresponding operator, adapt your code to enable the distributed training,
and update your polyaxonfile.Here are some examples of using distributed training:Distributed TensorflowDistributed PytorchDistributed MPIHorovodRayDaskHyperparameters tuningPolyaxon has a concept for suggesting hyperparameters and managing their results very similar to Google Vizier called experiment groups.
An experiment group in Polyaxon defines a search algorithm, a search space, and a model to train.Grid searchRandom searchHyperbandBayesian OptimizationHyperoptCustom Iterative OptimizationParallel executionsYou can run your processing or model training jobs in parallel, Polyaxon provides amappingabstraction to manage concurrent jobs.DAGs and workflowsPolyaxon DAGsis a tool that provides container-native engine for running machine learning pipelines.
A DAG manages multiple operations with dependencies. Each operation is defined by a component runtime.
This means that operations in a DAG can be jobs, services, distributed jobs, parallel executions, or nested DAGs.ArchitectureDocumentationCheck out ourdocumentationto learn more about Polyaxon.DashboardPolyaxon comes with a dashboard that shows the projects and experiments created by you and your team members.To start the dashboard, just run the following command in your terminal$polyaxondashboard-yProject statusPolyaxon is stable and it's running in production mode at many startups and Fortune 500 companies.ContributionsPlease follow the contribution guide line:Contribute to Polyaxon.ResearchIf you use Polyaxon in your academic research, we would be grateful if you could cite it.Feel free tocontact us, we would love to learn about your project and see how we can support your custom need.