Produce a collection of E4S community learning resources - initial ideas #34
Description
While E4S already has some resources for the community, we want to provide a holistic, community driven collection of resources for many types of stakeholders.
Here are some initial thoughts about what we. might do:
High-Level Plan for an E4S Learning Resources Collection
This document outlines a structured, scalable plan for creating a coherent and sustainable collection of learning resources for the E4S community. The plan is designed to align with DOE-style ecosystem stewardship, heterogeneous audiences, and the practical realities of HPC and AI software adoption.
1. Clarify Purpose and Scope
Primary Objectives
- Lower the barrier to entry for new E4S users.
- Accelerate effective use of E4S software in production and research settings.
- Build shared vocabulary and mental models across the ecosystem.
- Reinforce E4S as the curated pathway from research software to deployable capability.
Explicit Non-Goals
- Replacing package-level documentation.
- Serving as a full academic curriculum.
- Providing vendor-specific training (unless explicitly labeled).
2. Define Target Audiences and Learning Paths
Audience Personas
-
Newcomers / Explorers
- Graduate students, postdocs, new staff.
- Goal: Understand what E4S is and why it matters.
-
Application Developers
- Domain scientists and research programmers.
- Goal: Achieve performance portability, correctness, and sustainability.
-
Software Engineers / Infrastructure Experts
- CI/CD, packaging, containers, deployment specialists.
- Goal: Ensure reproducibility, integration, and scaling.
-
Facility and Program Stakeholders
- DOE programs, center leads, vendors.
- Goal: Assess ecosystem health, adoption, and return on investment.
Learning Paths
- Curated, milestone-based paths tailored to each audience.
- Focused on progression rather than time spent.
3. Organize Content into Progressive Tiers
Tier 0: Orientation (5–15 minutes)
- What is E4S?
- How the ecosystem fits together.
- Why E4S matters for modern HPC and AI.
Tier 1: Quick Starts (30–60 minutes)
- Install via Spack.
- Run a simple example.
- Use containers.
- Minimal “hello world” workflows with real tools.
Tier 2: Core Competencies (2–6 hours)
- Performance portability concepts.
- Build and dependency management.
- Debugging, profiling, and correctness.
- Reproducibility and environment management.
Tier 3: Advanced and Integrative Topics
- Mixed and low-precision techniques.
- HPC–AI workflows.
- Scaling to leadership-class systems.
- Application–facility–vendor co-design.
4. Establish a Canonical Topic Taxonomy
Learning resources should be organized around stable concepts rather than transient tools.
Example Topic Families
- Programming models
- Math libraries
- Data, I/O, and workflows
- Performance and correctness tools
- Build, packaging, and deployment
- AI-for-Science integration
- Sustainability and governance
Each resource should clearly state:
- Prerequisites
- Learning outcomes
- Placement within the taxonomy
5. Choose Resource Types Deliberately
Avoid over-reliance on a single content format.
Recommended Resource Mix
- Short written guides (Markdown, Jupyter-friendly)
- Hands-on tutorials (repository-based)
- Recorded talks with timestamps
- Conceptual explainers (architecture, tradeoffs)
- Case studies grounded in real applications and challenges
Each resource should answer:
“What problem does this help me solve?”
6. Integrate with Existing E4S Infrastructure
The learning collection should not form a parallel ecosystem.
Leverage Existing Assets
- E4S release structure and product families
- Spack environments and recipes
- Containers and CI artifacts
- Existing tutorials and documentation (curated, not duplicated)
Design principle: Learning resources should point into the ecosystem, not away from it.
7. Governance and Contribution Model
Learning content must scale socially as well as technically.
Core Principles
- Lightweight contribution process.
- Clear quality standards and editorial voice.
- Named maintainers for each topic family.
Contribution Roles
- Curators (learning-path builders)
- Content authors
- Reviewers
- Infrastructure maintainers
8. Incentives and Signals of Progress
Motivation and recognition matter.
Possible Incentives
- Completion badges tied to learning paths.
- “E4S-ready” signals for contributors and practitioners.
- Recognition in E4S release notes or community calls.
Badges should reflect demonstrated capability, not attendance.
9. Delivery Platform Strategy
Start simple while designing for future growth.
Short-Term Approach
- Markdown-first content.
- Website-hosted with GitHub-native workflows.
- Clear navigation by audience and tier.
Longer-Term Opportunities
- Interactive notebooks.
- Automated tutorial validation.
- Analytics to identify friction points in learning paths.
10. Success Metrics
Define success criteria early and measure consistently.
Quantitative Metrics
- Resource usage by tier and audience.
- Learning-path completion rates.
- Adoption signals such as downloads, citations, and reuse.
Qualitative Metrics
- User feedback and testimonials.
- Facility and project endorsements.
- Evidence of reduced onboarding friction.
11. Phased Rollout Plan
Phase 1: Pilot
- Focus on a single audience.
- Deliver one complete learning path.
- Produce 5–10 high-quality resources.
Phase 2: Expansion
- Fill gaps across tiers and topics.
- Add cross-links and case studies.
Phase 3: Ecosystem Integration
- Align learning content with E4S releases.
- Tie learning paths to community milestones.
Closing Framing
E4S Learning is not training material—it is ecosystem infrastructure.
This framing emphasizes durability, reuse, and community stewardship rather than one-off instruction.