Tools for Deep Learning

A notebook that runs on a laptop may fail as soon as the model, batch, or context grows. The next decision is not automatically to buy the accelerator with the highest advertised throughput. First determine whether the workload fits in memory, whether computation or data movement is limiting it, and what a completed experiment will cost. Training and serving then impose different constraints: distributed training incurs accelerator-communication costs, whereas serving must also meet latency and throughput targets while storing persistent model and cache state.

This part develops these decisions from a single notebook to a deployed system. Use the following table to find the section relevant to your problem.

Task	Section	Principal question
Run and inspect the book locally	30.1  Notebooks	How should code, state, and outputs be organized for reproducible iteration?
Start without a local installation	30.2  Colab and Kaggle	Which hosted environment provides the required framework and accelerator?
Rent compute	30.3  Cloud Computing	What is the total cost per completed run, including setup and data transfer?
Choose hardware	30.4  Hardware	Does the workload fit, and will compute, memory bandwidth, or interconnect limit it?
Find models, datasets, and evidence	30.5  Ecosystem	Which artifact, implementation, or benchmark can be reproduced and trusted?
Scale training	30.6  Distributed Model Training	How should parameters, activations, data, and communication be partitioned?
Serve a model	30.7  Model Serving	How do batching, latency, throughput, and cache memory interact?
Contribute to the book	30.8  Contributor Guide	How are changes tested and kept consistent across four frameworks?
Look up book utilities	30.9  Utility Functions and Classes and 30.10  The d2l API Reference	Where is a class or function defined and documented?

Readers building a system can follow the table from top to bottom. Readers with a specific operational question can consult the corresponding section directly. Hardware names, prices, cloud interfaces, and library versions change quickly, so the linked sections date such facts and emphasize measurements that remain useful when the products change. The utility and d2l API pages are searchable references rather than part of the teaching sequence.