InternshipTools and first-week setup
The tools a Inference Engineering intern uses and how to set up your workspace in week one.
These are the main tools for Inference Engineering tasks. You do not need all of them on day one; the Level 1 tasks tell you when to install each one.
- Python
- PyTorch
- Hugging Face
- ONNX Runtime
- llama.cpp / Ollama
- vLLM
Set up your workspace
- Create a free GitHub account if you do not have one, with a professional username, and set up your LinkedIn profile: every task is submitted with a LinkedIn post.
- Create one repository for the internship, for example inference-engineering-internship, with a README that says who you are and what the repo holds.
- Keep one folder per task, named with the task number and title, so mentors find your work quickly.
- Commit small changes every day with clear messages. Your daily work report uses these commits as proof.
- Never commit passwords, API keys or personal data. Put secrets in a .env file and add it to .gitignore.
Your first tasks
| Task | What to do |
|---|---|
| What inference engineering is | Explain training vs inference, and why latency, throughput and cost matter when serving models. |
| Python and PyTorch setup | Set up Python with PyTorch (CPU is fine) and run inference with a small pre-trained model. |
| Hugging Face pipelines | Run text classification, summarisation and embeddings with Hugging Face pipelines and time each one. |
| Tokens and tokenisers | Tokenise 5 sentences in English and an Indian language and explain why token counts differ. |