GraphCanon updated 3w · GitHub synced 3w
Decision brief
Use Polyaxon for advanced orchestration of ML workflows with emphasis on hyperparameter tuning, CI/CD integrations, and deep support for Jupyter and TensorBoard.
Good fit when
- When you need comprehensive hyperparameter optimization.
- When integration with Kubernetes is a requirement.
Avoid when
- If preferring lightweight tools without complex setup.
- With simpler projects not requiring fine-grained orchestration.
Observed Jul 12, 2026 · Source: enrich:decision_facts
Verify the decision
Maintenance and security
Full trust report- Maintenance
- Very active (0d since push)
- As of 3w
- Provenance
- Not a fork · Organization account
- As of 3w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/polyaxon/polyaxonSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
Polyaxon is a platform for orchestrating and managing machine learning workflows, including hyperparameter optimization, model training, tracking experiments, and using Jupyter notebooks.
Capability facts
- Languages
- mdx
Source: github.language · Aug 3, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 3, 2026)
$ pip install -U polyaxonSource link
Tags
README
Install
TL;DR;
-
Install CLI
# Install Polyaxon CLI $ pip install -U polyaxon -
Create a deployment
# Create a namespace $ kubectl create namespace polyaxon # Add Polyaxon charts repo $ helm repo add polyaxon https://charts.polyaxon.com # Deploy Polyaxon $ polyaxon admin deploy -f config.yaml # Access API $ polyaxon port-forward
Please check polyaxon installation guide
Quick start
TL;DR;
-
Start a project
# Create a project $ polyaxon project create --name=quick-start --description='Polyaxon quick start.' -
Train and track logs & resources
# Upload code and start experiments $ polyaxon run -f experiment.yaml -u -l -
Dashboard
# Start Polyaxon dashboard $ polyaxon dashboard Dashboard page will now open in your browser. Continue? [Y/n]: y
- Notebook
# Start Jupyter notebook for your project $ polyaxon run --hub notebook
- Tensorboard
# Start TensorBoard for a run's output $ polyaxon run --hub tensorboard -P uuid=UUID
Please check our quick start guide to start training your first experiment.
For agents
This page has a .md twin and JSON over the API.