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Inference Engineering at Modal or Baseten
·
Module 1
Python and Linux Foundations for Performance Work
1
Python Values and Explicit Type Conversion
Write Python programs using numeric, Boolean, and string values with explicit type conversions.
Controlling Python Programs with Conditionals and Loops
Use conditionals and loops to control a Python program.
3
Processing and Validating Benchmark Records with Lists, Dictionaries, JSON, and CSV
Read, transform, and write benchmark records using lists, dictionaries, JSON, and CSV while rejecting malformed records.
4
Building Typed Python Functions and Unit Tests
Define reusable Python functions with type hints and verify their behavior with unit tests.
5
Organizing Python Classes into Importable Modules
Organize simple Python classes into importable local modules.
6
Creating Python Virtual Environments and Installing Packages
Create an isolated Python environment and install packages from the Linux shell.
Inspecting Linux Files, Environment, Resources, and Processes
Inspect files, environment variables, system resources, and running processes with Linux command-line tools.
8
Managing Performance Experiments with Git Commits, Branches, and Merges
Use Git commits, branches, and merges to manage a small performance experiment.
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