LOCKEDIN / FORWARD-DEPLOYED ENGINEERING 101
Understand it.
Then build it.
You can be an experienced engineer and still be new to agents. Start with what each thing is and how it helps a person do useful work. Explore 48 original introductions across software, agents, cloud, security and delivery. Start here without a model key; each topic connects to deeper instruction and practice.
- 101UnderstandPlain language, visual walkthrough, vocabulary
- 102Build with guidanceOriginal lessons, examples and knowledge checks
- 103Practise and explainRunnable labs, failure cases and handover
SOFTWARE / EXECUTION- 01Python / framework
- 02CUDA libraries
- 03GPU kernels
A GPU computing platform and programming model
What is CUDA?
CUDA is NVIDIA's platform for programming and accelerating work on its GPUs. It includes tools, runtime support and libraries. Many AI developers use CUDA through a framework such as PyTorch, without writing a GPU kernel themselves.
A deployment engineer must diagnose whether the model is using the intended device and record a reproducible software environment.
CONNECT IT TO WHAT YOU KNOW
Start with something familiar.
Think of an application calling an optimized database driver or numerical library: several layers translate the operation into work the hardware can execute. You can use those layers before learning to implement their internals.
CUDA is not a model, an agent or a replacement for Python. The driver, runtime, framework build and hardware must be compatible; a package importing successfully does not prove that it is using a GPU.
Words worth knowing
- Driver
- Host software that provides access to the GPU hardware.
- Runtime
- Software services used when the accelerated program executes.
- Kernel launch
- Requesting that the GPU execute a parallel function.
ONE EXAMPLE / FICTIONAL CASE
Watch the work take shape.
A container starts but inference runs slowly. Inspect device visibility and the framework's device report before changing prompts or purchasing another GPU.
SOFTWARE / EXECUTION- 01Python / framework
- 02CUDA libraries
- 03GPU kernels
Inspect the host
On an authorized NVIDIA host, inspect the installed driver and visible devices. No host is required for the local planning lab.
Hardware and driver evidenceFROM RECOGNITION TO REASONING
Write a deployment environment record.
- 01
List hardware, driver, container digest and framework version as separate fields.
- 02
Write how you would verify device visibility and execution.
- 03
Mark unavailable observations as unverified rather than inventing output.
A reproducibility record and two verification steps.
Compare your reasoning when you are ready
The record should let another engineer reproduce the environment and detect fallback to CPU execution. A driver version alone cannot establish the complete runtime configuration.
Keep notes in your own working copy. Moving through these cards does not record completion.
TURN THE IDEA INTO USEFUL WORK
You can now go deeper.
Start by using supported GPU libraries; learn kernel programming when a measured bottleneck warrants it.
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