Terminal & Shell

> The terminal is where AI engineers live. Get comfortable here.

Type: Learn

Languages: --

Prerequisites: Phase 0, Lesson 01

Time: ~35 minutes

Learning Objectives

• Use piping, redirects, and grep to filter and process training logs from the command line
• Create persistent tmux sessions with multiple panes for concurrent training and GPU monitoring
• Monitor system and GPU resources with htop, nvtop, and nvidia-smi
• Transfer files between local and remote machines using SSH, scp, and rsync

The Problem

You will spend more time in the terminal than in any editor. Training runs, GPU monitoring, log tailing, remote SSH sessions, environment management. Every AI workflow touches the shell. If you're slow here, you're slow everywhere.

This lesson covers the terminal skills that matter for AI work. No history of Unix. No deep-dive into Bash scripting. Just what you need.

The Concept

Three things running at once. One terminal. You can detach, go home, SSH back in, and reattach. The training keeps running.

Build It

Step 1: Know your shell

Check which shell you're running:

echo $SHELL

Most systems use bash or zsh. Both work fine. The commands in this course work in either.

Key things to know:

# Move around
cd ~/projects/ai-engineering-from-scratch
pwd
ls -la

# History search (most useful shortcut you'll learn)
# Ctrl+R then type part of a previous command
# Press Ctrl+R again to cycle through matches

# Clear terminal
clear   # or Ctrl+L

# Cancel a running command
# Ctrl+C

# Suspend a running command (resume with fg)
# Ctrl+Z

Step 2: Piping and redirects

Piping connects commands together. This is how you process logs, filter output, and chain tools. You will use this constantly.

# Count how many times "loss" appears in a log
cat train.log | grep "loss" | wc -l

# Extract just the loss values from training output
grep "loss:" train.log | awk '{print $NF}' > losses.txt

# Watch a log file update in real time, filtering for errors
tail -f train.log | grep --line-buffered "ERROR"

# Sort experiments by final accuracy
grep "final_accuracy" results/*.log | sort -t= -k2 -n -r

# Redirect stdout and stderr to separate files
python train.py > output.log 2> errors.log

# Redirect both to the same file
python train.py > train_full.log 2>&1

The three redirects you need:

Step 3: Background processes

Training runs take hours. You don't want to keep your terminal open the whole time.

# Run in background (output still goes to terminal)
python train.py &

# Run in background, immune to hangup (closing terminal won't kill it)
nohup python train.py > train.log 2>&1 &

# Check what's running in background
jobs
ps aux | grep train.py

# Bring a background job to foreground
fg %1

# Kill a background process
kill %1
# or find its PID and kill that
kill $(pgrep -f "train.py")

The difference between &, nohup, and screen/tmux:

For anything longer than a few minutes, use tmux.

Step 4: tmux

tmux lets you create persistent terminal sessions with multiple panes. This is the single most useful tool for managing training runs.

# Install
# macOS
brew install tmux
# Ubuntu
sudo apt install tmux

# Start a named session
tmux new -s training

# Split horizontally
# Ctrl+B then "

# Split vertically
# Ctrl+B then %

# Navigate between panes
# Ctrl+B then arrow keys

# Detach (session keeps running)
# Ctrl+B then d

# Reattach
tmux attach -t training

# List sessions
tmux ls

# Kill a session
tmux kill-session -t training

A typical AI workflow session:

tmux new -s train

# Pane 1: start training
python train.py --epochs 100 --lr 1e-4

# Ctrl+B, " to split, then run GPU monitor
watch -n1 nvidia-smi

# Ctrl+B, % to split vertically, tail the logs
tail -f logs/experiment.log

# Now detach with Ctrl+B, d
# SSH out, go get coffee, come back
# tmux attach -t train

Step 5: Monitoring with htop and nvtop

# System processes (better than top)
htop

# GPU processes (if you have NVIDIA GPU)
# Install: sudo apt install nvtop (Ubuntu) or brew install nvtop (macOS)
nvtop

# Quick GPU check without nvtop
nvidia-smi

# Watch GPU usage update every second
watch -n1 nvidia-smi

# See which processes are using the GPU
nvidia-smi --query-compute-apps=pid,name,used_memory --format=csv

htop keybindings you'll use:

• F6 or > to sort by column (sort by memory to find memory leaks)
• F5 to toggle tree view (see child processes)
• F9 to kill a process
• / to search for a process name

Step 6: SSH for remote GPU boxes

When you rent a cloud GPU (Lambda, RunPod, Vast.ai), you connect via SSH.

# Basic connection
ssh user@gpu-box-ip

# With a specific key
ssh -i ~/.ssh/my_gpu_key user@gpu-box-ip

# Copy files to remote
scp model.pt user@gpu-box-ip:~/models/

# Copy files from remote
scp user@gpu-box-ip:~/results/metrics.json ./

# Sync a whole directory (faster for many files)
rsync -avz ./data/ user@gpu-box-ip:~/data/

# Port forward (access remote Jupyter/TensorBoard locally)
ssh -L 8888:localhost:8888 user@gpu-box-ip
# Now open localhost:8888 in your browser

# SSH config for convenience
# Add to ~/.ssh/config:
# Host gpu
#     HostName 192.168.1.100
#     User ubuntu
#     IdentityFile ~/.ssh/gpu_key
#
# Then just:
# ssh gpu

Step 7: Useful aliases for AI work

Add these to your ~/.bashrc or ~/.zshrc:

source phases/00-setup-and-tooling/10-terminal-and-shell/code/shell_aliases.sh

Or copy the ones you want. The key aliases:

# GPU status at a glance
alias gpu='nvidia-smi --query-gpu=index,name,utilization.gpu,memory.used,memory.total,temperature.gpu --format=csv,noheader'

# Kill all Python training processes
alias killtraining='pkill -f "python.*train"'

# Quick virtual environment activate
alias ae='source .venv/bin/activate'

# Watch training loss
alias watchloss='tail -f logs/*.log | grep --line-buffered "loss"'

See code/shell_aliases.sh for the full set.

Step 8: Common AI terminal patterns

These come up repeatedly in practice:

# Run training, log everything, notify when done
python train.py 2>&1 | tee train.log; echo "DONE" | mail -s "Training complete" you@email.com

# Compare two experiment logs side by side
diff <(grep "accuracy" exp1.log) <(grep "accuracy" exp2.log)

# Find the largest model files (clean up disk space)
find . -name "*.pt" -o -name "*.safetensors" | xargs du -h | sort -rh | head -20

# Download a model from Hugging Face
wget https://huggingface.co/model/resolve/main/model.safetensors

# Untar a dataset
tar xzf dataset.tar.gz -C ./data/

# Count lines in all Python files (see how big your project is)
find . -name "*.py" | xargs wc -l | tail -1

# Check disk space (training data fills disks fast)
df -h
du -sh ./data/*

# Environment variable check before training
env | grep -i cuda
env | grep -i torch

Use It

Here's when each tool comes into play during this course:

Exercises

1. Install tmux, create a session with three panes, and run htop in one, watch -n1 date in another, and a Python script in the third. Detach and reattach.
2. Add the aliases from code/shell_aliases.sh to your shell config and reload with source ~/.zshrc (or ~/.bashrc).
3. Create a fake training log with for i in $(seq 1 100); do echo "epoch $i loss: $(echo "scale=4; 1/$i" | bc)"; sleep 0.1; done > fake_train.log and then use grep, tail, and awk to extract just the loss values.
4. Set up an SSH config entry for a server you have access to (or use localhost to practice the syntax).

Key Terms