Beginner Tutorials¶
Welcome! These tutorials are designed for people who are new to Scalable and new to distributed computing. Unlike the standard tutorials (which assume familiarity with clusters, containers, and cloud infrastructure), these beginner tutorials explain every concept from first principles.
Who Are These For?¶
These tutorials are perfect if you:
Have basic Python experience (functions, imports, loops) but haven’t used distributed computing frameworks before.
Are unfamiliar with terms like “workers,” “schedulers,” “containers,” or “declarative programming.”
Want to understand not just how to use Scalable, but why it works the way it does.
Prefer learning with extensive explanations, analogies, and context before diving into code.
If you already understand distributed computing, YAML configuration, and cloud/Kubernetes concepts, the standard tutorials will be more efficient for you.
How These Tutorials Work¶
Each beginner tutorial mirrors a standard tutorial topic but adds:
Key Concept boxes — definitions of terms you’ll encounter
Why This Approach? — design rationale and alternatives considered
Under the Hood — peeks at what Scalable is doing internally
Common Questions — FAQ-style answers to typical beginner questions
Vocabulary Summary — list of terms you mastered in each tutorial
Learning Path¶
- Beginner Tutorial 1: Your First Workflow
- Beginner Tutorial 2: Understanding the Manifest System
- Beginner Tutorial 3: How Distributed Computing Works
- Beginner Tutorial 4: Caching — Avoiding Redundant Work
- Beginner Tutorial 5: Cloud Computing Fundamentals
- Beginner Tutorial 6: Understanding What Happened
- Beginner Tutorial 7: When Things Go Wrong
- Beginner Tutorial 8: Container Orchestration with Kubernetes
- Beginner Tutorial 9: Machine Learning for Smarter Workflows
- Beginner Tutorial 10: AI-Assisted Workflow Development
# |
Tutorial |
Concepts You’ll Learn |
|---|---|---|
1 |
Workflows, Dask, CLI, virtual environments, manifests |
|
2 |
Declarative programming, YAML, schemas, overlays |
|
3 |
Distributed computing, clusters, schedulers, providers |
|
4 |
Hashing, memoization, content-addressable storage, decorators |
|
5 |
Cloud computing, object storage, serverless, IAM |
|
6 |
Observability, structured logging, event streams, metrics |
|
7 |
Fault tolerance, retries, idempotency, partial success |
|
8 |
Beginner Tutorial 8: Container Orchestration with Kubernetes |
Containers, orchestration, pods, operators, namespaces |
9 |
Machine learning, training/inference, features, adaptive scaling |
|
10 |
LLMs, heuristics, code generation, templates |
Prerequisites¶
You need:
Python 3.11 or later installed on your computer.
A text editor (VS Code, PyCharm, or even Notepad).
A terminal/command prompt.
Basic Python knowledge: you can write functions, use
import, and run.pyfiles.
You do NOT need:
Docker or container experience.
Cloud accounts (AWS, GCP).
A Kubernetes cluster.
Machine learning background.
Experience with distributed systems.
All of these are explained as you encounter them.
Graduating to Standard Tutorials¶
After completing a beginner tutorial, you can move to the corresponding standard tutorial for deeper technical content, production patterns, and advanced configuration. Each beginner tutorial ends with a “Next Steps” section that bridges you to the standard version.
Beginner Tutorial |
Standard Tutorial |
|---|---|
Beginner Tutorial 8: Container Orchestration with Kubernetes |
|