Polyaxon
A Platform for reproducible and scalable Machine Learning and Deep Learning applications.
Learn more about the suite of features and products that underpin today's most innovative platform for managing data science workflows.
Gain more productivity and ship faster
Polyaxon provides an interactive workspace with notebooks, tensorboards, visualizations,and dashboards.
User management
Collaborate with the rest of your team, share and compare experiments and results.
User resources allocation
Manage your team's resources and parallelism, and set 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 platforms, or on Kubernetes.
Scalable
Polyaxon can easily scale horizontally by adding more nodes.
Modular
Extend Polyaxon's functionalities with custom plugins.
Logs
Stream, filter, and search logs from all operations.
SLAs
Enforce SLAs with TTL, timeout, and retries.
Artifacts and Lineage
Track artifacts and lineage information.
Custom Run Environments
Configure reusable presets or define per run environments.
Events
Send and subscribe to events mid-run or at the end of operations.
Rendering and Visualization
Log artifacts and custom visualizations.
Manual Triggers
Start, stop, resume, restart, and copy any job or service.
Projects
Organize your efforts and define directories of work.
Component Hub
Extract reusable modules and define typed inputs and outputs for your runtime.
Model Registry
Lock experiments and promote your work for production deployment.
Powerful Search
Search by name, description, regex, specific fields, metrics, or configurations.
Cache Layer
Avoid running expensive computations several times.
Affinity and Routing
Runs are assigned to queues and they are only picked up by agents with matching tags, you can manage multiple namespaces and clusters.
Sharding
Parallelize your work with operation mapping and hyperparameter optimization.
Joining
Filter, aggregate, and annotate inputs/outputs/artifacts from multiple or parallel upstream runs.
Consensus
Define the success of your workflows based on early stopping strategies.
Concurrency
Enforce global concurrency, and split the quota on per queue or workflow level.
Priority
Define executions priority and enforce rate limits.
Versioning
Every run is versioned and has a unique hash, all outputs and artifacts are tracked.
Auto API, authZ, and authN
Every run will be checked prior to execution and will have a permissioned API.
Team Management
Invite users to your team and assign roles and permissions as appropriate.
Customization
Integrate with external systems with hooks and actions.
Versatile Runtime
Native Kubernetes, Kubeflow operators, Ray/Dask/Spark operators, custom integrations, ...
Agents
Our open-source component to enable hybrid, multi-namespace, and multi-cluster deployments.
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