Evaggelos Balaskas - System Engineer

🚀 Curious about trying out a Large Language Model (LLM) like Mistral directly on your own macbook?

Here’s a simple step-by-step guide I used on my MacBook M1 Pro. No advanced technical skills required, but some techinal command-line skills are needed. Just follow the commands and you’ll be chatting with an AI model in no time.

🧰 What We’ll Need

• LLM: A CLI utility and Python library for interacting with Large Language Models → a command-line tool and Python library that makes it easy to install and run language models.
• Mistral → a modern open-source language model you can run locally.
• Python virtual environment → a safe “sandbox” where we install the tools without messing with the rest of the system.
• MacBook → All Apple Silicon MacBooks (M1, M2, M3, M4 chips) feature an integrated GPU on the same chip as the CPU.

🧑‍🔬 About Mistral 7B

Mistral 7B is a 7-billion parameter large language model, trained to be fast, efficient, and good at following instructions.

Technical requirements (approximate):

• Full precision model (FP16) → ~13–14 GB of RAM (fits best on a server or high-end GPU).
• Quantized model (4-bit, like the one we use here) → ~4 GB of RAM, which makes it practical for a MacBook or laptop.
• Disk storage → the 4-bit model download is around 4–5 GB.
• CPU/GPU → runs on Apple Silicon (M1/M2/M3) CPUs and GPUs thanks to the MLX library. It can also run on Intel Macs, though it may be slower.

👉 In short:
With the 4-bit quantized version, you can run Mistral smoothly on a modern MacBook with 8 GB RAM or more. The more memory and cores you have, the faster it runs.

⚙️ Step 1: Create a Virtual Environment

We’ll create a clean workspace just for this project.

python3 -m venv ~/.venvs/llm

source ~/.venvs/llm/bin/activate

👉 What happens here:

• python3 -m venv creates a new isolated environment named llm.
• source .../activate switches you into that environment, so all installs stay inside it.

📦 Step 2: Install the LLM Tool

Now, let’s install LLM.

pip install -U llm

👉 This gives us the llm command we’ll use to talk to models.

🛠️ Step 3: Install Extra Dependencies

Mistral needs a few extra packages:

pip install mlx

pip install sentencepiece

👉 mlx is Apple’s library that helps models run efficiently on Mac.
👉 sentencepiece helps the model break down text into tokens (words/pieces).

🔌 Step 4: Install the Mistral Plugin

We now connect LLM with Mistral:

llm install llm-mlx

👉 This installs the llm-mlx plugin, which allows LLM to use Mistral models via Apple’s MLX framework.

Verify the plugin with this

llm plugins

result should look like that:

[
  {
    "name": "llm-mlx",
    "hooks": [
      "register_commands",
      "register_models"
    ],
    "version": "0.4"
  }
]

⬇️ Step 5: Download the Model

Now for the fun part — downloading Mistral 7B.

llm mlx download-model mlx-community/Mistral-7B-Instruct-v0.3-4bit

👉 This pulls down the model from the community in a compressed, 4-bit version (smaller and faster to run on laptops).

Verify the model is on your system:

llm models | grep -i mistral

output should be something similar with this:

MlxModel: mlx-community/Mistral-7B-Instruct-v0.3-4bit (aliases: m7)

🏷️ Step 6: Set a Shortcut (Alias)

Typing the full model name is long and annoying. Let’s create a shortcut:

llm aliases set m7 mlx-community/Mistral-7B-Instruct-v0.3-4bit

👉 From now on, we can just use -m m7 instead of the full model name.

💡 Step 7: One last thing

if you are using Homebrew then most probably you already have OpenSSL on your system, if you do not know what we are talking about, then you are using LibreSSL and you need to make a small change:

pip install "urllib3<2"

only if you are using brew run:

brew install openssl@3

💬 Step 8: Ask Your First Question

Time to chat with Mistral!

llm -m m7 'Capital of Greece ?'

👉 Expected result:
The model should respond with:

Athens

🎉 Congratulations — you’ve just run a powerful AI model locally on your Mac!

👨‍💻 A More Technical Example

Mistral isn’t only for trivia — it can help with real command-line tasks too.

For example, let’s ask it something more advanced:

llm -m m7 'On Arch Linux, give only the bash command using find
 that lists files in the current directory larger than 1 GB,
 do not cross filesystem boundaries. Output file sizes in
 human-readable format with GB units along with the file paths.
 Return only the command.'

👉 Mistral responds with:

find . -type f -size +1G -exec du -sh {} +

💡 What this does:

• find . -type f -size +1G → finds files bigger than 1 GB in the current folder.
• -exec ls -lhS {} ; → runs ls on each file to display the size in human-readable format (GB).

This is the kind of real-world productivity boost you get by running models locally.

Full text example output:

This command will find all files (-type f) larger than 1 GB (-size +1G) in the current directory (.) and execute the du -sh command on each file to display the file size in a human-readable format with GB units (-h). The + after -exec tells find to execute the command once for each set of found files, instead of once for each file.

🌟 Why This Is Cool

• 🔒 No internet needed once the model is downloaded.
• 🕵️ Privacy: your text never leaves your laptop.
• 🧪 Flexible: you can try different open-source models, not just Mistral.

though it won’t be as fast as running it in the cloud.

That’s it !

PS. These are my personal notes from my home lab; AI was used to structure and format the final version of this blog post.

🖥️ I’ve been playing around with the python cli LLM and Perplexity, trying to get a setup that works nicely from the command line. Below are my notes, with what worked, what I stumbled on, and how you can replicate it.

📌 Background & Why

I like working with tools that let me automate or assist me with shell commands, especially when exploring files, searching, or scripting stuff. LLM + Perplexity give me that power: AI suggestions + execution.

If you’re new to this, it helps you avoid googling every little thing, but still keeps you in control.

Also, I have a Perplexity Pro account, and I want to learn how to use it from my Linux command line.

⚙️ Setup: Step by Step

1️⃣ Prepare a Python virtual environment

I prefer isolating things so I don’t mess up my global Python. Here’s how I did it by creating a new python virtual environment and activate it:

PROJECT="llm"

python3 -m venv ~/.venvs/${PROJECT}
source ~/.venvs/${PROJECT}/bin/activate

# Install llm project
pip install -U ${PROJECT}

This gives you a clean llm install.

2️⃣ Get Perplexity API key 🔑

You’ll need an API key from Perplexity to use their model via LLM.

• Go to Perplexity.ai 🌐

• Sign in / register

• Go to your API keys page: https://www.perplexity.ai/account/api/keys

• Copy your key

  Be careful, in order to get the API, you need to type your Bank Card details. In my account, I have a free tier of 5 USD. You can review your tokens via the Usage metrics in Api Billing section.

3️⃣ Install plugins for LLM 🧩

I used two plugins:

• ⚡ llm-cmd — for LLM to suggest/run shell commands

• 🔍 llm-perplexity — so LLM can use Perplexity as a model provider

Commands:

llm install llm-cmd

llm install llm-perplexity

Check what’s installed:

llm plugins

Sample output:

[
  {
    "name": "llm-cmd",
    "hooks": [
      "register_commands"
    ],
    "version": "0.2a0"
  },
  {
    "name": "llm-perplexity",
    "hooks": [
      "register_models"
    ],
    "version": "2025.6.0"
  }
]

4️⃣ Configure your Perplexity key inside LLM 🔐

Tell LLM your Perplexity key so it can use it:

❯ llm keys set perplexity
# then paste your API key when prompted

Verify:

❯ llm keys
perplexity

You should just see “perplexity” listed (or the key name), meaning it is stored.

Available models inside LLM 🔐

Verify and view what are the available models to use:

llm models

the result on my setup, with perplexity enabled is:

OpenAI Chat: gpt-4o (aliases: 4o)
OpenAI Chat: chatgpt-4o-latest (aliases: chatgpt-4o)
OpenAI Chat: gpt-4o-mini (aliases: 4o-mini)
OpenAI Chat: gpt-4o-audio-preview
OpenAI Chat: gpt-4o-audio-preview-2024-12-17
OpenAI Chat: gpt-4o-audio-preview-2024-10-01
OpenAI Chat: gpt-4o-mini-audio-preview
OpenAI Chat: gpt-4o-mini-audio-preview-2024-12-17
OpenAI Chat: gpt-4.1 (aliases: 4.1)
OpenAI Chat: gpt-4.1-mini (aliases: 4.1-mini)
OpenAI Chat: gpt-4.1-nano (aliases: 4.1-nano)
OpenAI Chat: gpt-3.5-turbo (aliases: 3.5, chatgpt)
OpenAI Chat: gpt-3.5-turbo-16k (aliases: chatgpt-16k, 3.5-16k)
OpenAI Chat: gpt-4 (aliases: 4, gpt4)
OpenAI Chat: gpt-4-32k (aliases: 4-32k)
OpenAI Chat: gpt-4-1106-preview
OpenAI Chat: gpt-4-0125-preview
OpenAI Chat: gpt-4-turbo-2024-04-09
OpenAI Chat: gpt-4-turbo (aliases: gpt-4-turbo-preview, 4-turbo, 4t)
OpenAI Chat: gpt-4.5-preview-2025-02-27
OpenAI Chat: gpt-4.5-preview (aliases: gpt-4.5)
OpenAI Chat: o1
OpenAI Chat: o1-2024-12-17
OpenAI Chat: o1-preview
OpenAI Chat: o1-mini
OpenAI Chat: o3-mini
OpenAI Chat: o3
OpenAI Chat: o4-mini
OpenAI Chat: gpt-5
OpenAI Chat: gpt-5-mini
OpenAI Chat: gpt-5-nano
OpenAI Chat: gpt-5-2025-08-07
OpenAI Chat: gpt-5-mini-2025-08-07
OpenAI Chat: gpt-5-nano-2025-08-07
OpenAI Completion: gpt-3.5-turbo-instruct (aliases: 3.5-instruct, chatgpt-instruct)
Perplexity: sonar-deep-research
Perplexity: sonar-reasoning-pro
Perplexity: sonar-reasoning
Perplexity: sonar-pro
Perplexity: sonar
Perplexity: r1-1776
Default: gpt-4o-mini

as of this blog post date written.

🚀 First Use: Asking LLM to Suggest a Shell Command

okay, here is where things get fun.

I started with something simply, identify all files that are larger than 1GB and I tried this prompt:

llm -m sonar-pro cmd "find all files in this local directory that are larger than 1GB"

It responded with something like:

Multiline command - Meta-Enter or Esc Enter to execute
> find . -type f -size +1G -exec ls -lh {} ;

  ## Citations:
  [1] https://tecadmin.net/find-all-files-larger-than-1gb-size-in-linux/
  [2] https://chemicloud.com/kb/article/find-and-list-files-bigger-or-smaller-than-in-linux/
  [3] https://manage.accuwebhosting.com/knowledgebase/3647/How-to-Find-All-Files-Larger-than-1GB-in-Linux.html
  [4] https://hcsonline.com/support/resources/blog/find-files-larger-than-1gb-command-line

Aborted!

I did not want to execute this, so I interrupted the process.

💡 Tip: Always review AI-suggested commands before running them — especially if they involve find /, rm -rf, or anything destructive.

📂 Example: Running the command manually

If you decide to run manually, you might do:

find . -xdev -type f -size +1G -exec ls -lh {} ;

My output was like:

-rw-r--r-- 1 ebal ebal 3.5G Jun  9 11:20 ./.cache/colima/caches/9efdd392c203dc39a21e37036e2405fbf5b0c3093c55f49c713ba829c2b1f5b5.raw
-rw-r--r-- 1 ebal ebal 13G Jun  9 11:58 ./.local/share/rancher-desktop/lima/0/diffdisk

Cool way to find big files, especially if disk is filling up 💾.

🤔 Things I Learned / Caveats

• ⚠️ AI-suggested commands are helpful, but sometimes they assume things (permissions, paths) that I didn’t expect.

• 🐍 Using a virtual env helps avoid version mismatches.

• 🔄 The plugins sometimes need updates; keep track of version changes.

• 🔑 Be careful with your API key — don’t commit it anywhere.

✅ Summary & What’s Next

So, after doing this:

• 🛠️ Got llm working with Perplexity

• 📜 Asked for shell commands

• 👀 Reviewed + tested output manually

Next, I would like to run Ollama in my home lab. I don’t have a GPU yet, so I’ll have to settle for Docker on an old CPU, which means things will be slow and require some patience. I also want to play around with mixing an LLM and tools like Agno framework to set up a self-hosted agentic solution for everyday use.

That’s it !

PS. These are my personal notes from my home lab; AI was used to structure and format the final version of this blog post.

Managing SSL/TLS certificates for your domains can be effortless with the right tools. In this post, I’ll walk you through using acme.sh and LuaDNS to issue wildcard certificates for your domain.

Let’s dive into the step-by-step process of setting up DNS-based validation using the LuaDNS API.

📋 Prerequisites

• You own a domain and manage its DNS records with LuaDNS.
• You have acme.sh installed.
• You’ve generated an API token from your LuaDNS account.

🧼 Step 1: Clean Up Old Certificates (Optional)

If you’ve previously issued a certificate for your domain and want to start fresh, you can remove it with:

acme.sh --remove -d ebalaskas.gr

This will remove the certificate metadata from acme.sh, but not delete the actual files. You’ll find those under:

/root/.acme.sh/ebalaskas.gr

Feel free to delete them manually if needed.

🔑 Step 2: Set Your LuaDNS API Credentials

Log into your LuaDNS account and generate your API token from:

👉 https://api.luadns.com/settings

Then export your credentials in your shell session:

export LUA_Email="youremail@example.com"
export LUA_Key="your_luadns_api_key"

Example:

export LUA_Email="api.luadns@example.org"
export LUA_Key="a86ee24d7087ad83dc51dadbd35b31e4"

📜 Step 3: Issue the Wildcard Certificate

Now you can issue a certificate using DNS-01 validation via the LuaDNS API:

acme.sh --issue --dns dns_lua -d ebalaskas.gr -d *.ebalaskas.gr --server letsencrypt

This command will:

• Use Let’s Encrypt as the Certificate Authority.
• Add two DNS TXT records (_acme-challenge.ebalaskas.gr) using LuaDNS API.
• Perform domain validation.
• Remove the TXT records after verification.
• Issue and store the certificate.

Sample output will include steps like:

Adding txt value: ... for domain: _acme-challenge.ebalaskas.gr
The txt record is added: Success.
Verifying: ebalaskas.gr
Verifying: *.ebalaskas.gr
Success
Removing DNS records.
Cert success.

You’ll find the certificate and key files in:

/root/.acme.sh/ebalaskas.gr/

File paths:

• Certificate: ebalaskas.gr.cer
• Private Key: ebalaskas.gr.key
• CA Chain: ca.cer
• Full Chain: fullchain.cer

✅ Step 4: Verify the Certificate

You can check your currently managed certificates with:

acme.sh --cron --list

Output should look like:

Main_Domain        KeyLength  SAN_Domains            CA                    Created                       Renew
ebalaskas.gr       ""         *.ebalaskas.gr         LetsEncrypt.org       Thu Apr 17 14:39:24 UTC 2025  Mon Jun 16 14:39:24 UTC 2025

🎉 Done!

That’s it! You’ve successfully issued and installed a wildcard SSL certificate using acme.sh with LuaDNS.

You can now automate renewals via cron, and integrate the certificate into your web server or load balancer.

🔁 Bonus Tip: Enable Auto-Renewal

acme.sh is cron-friendly. Just make sure your environment has access to the LUA_Key and LUA_Email variables, either by exporting them in a script or storing them in a config file.

Let me know if you’d like this blog post exported or published to a static site generator (like Hugo, Jekyll, or Hexo) or posted somewhere specific!

That’s it !

This blog post was made with chatgpt

original post on github

a blog post series to my homelab

check here for Introduction to Traefik - Part Two

Part Three

In this blog post series, I will connect several docker containers and a virtual machine behind the Traefik reverse proxy on my homelab, and set up Let’s Encrypt for TLS.

In this article, I will try to connect a virtual machine to the Traefik reverse proxy. In Linux, Docker containers and virtual machines (VMs) run on different networks due to the way their networking is set up. To enable communication between Docker containers and VMs, we need to configure somekind of network bridging, port forwarding, or use a common network interface that allows them to communicate with each other. To simplify the setup, I will try to put Traefik docker container to a common network with the virtual machine.

Disclaimer: This homelab is intended for testing and learning purposes, as are the services we’ll explore. Make sure it fits your needs; I’m not suggesting you copy/paste everything here as-is. In the end, I may decide to delete this homelab and create a new one! But for now, let’s enjoy this journey together. Thank you!

I’ve also made a short video to accompany this blog post:

Virtual Machine

I use Qemu/KVM (kernel virtual machine) in my home lab. I also use terraform with libvirtd to automate my entire setup. That said, this post is not about that !

For the purpose of this article, I created an ubuntu 24.04 LTS running transmission service. It’s IP is: 192.168.122.79 and listens to TCP Port: 9091. Transmission also has configured with a Basic Auth authentication mechanism which username and password are the defaults: transmission:transmission.

Setup diagram

something similar to the below scheme

                       ┌────┐
                       │    │
                       │    │   192.168.122.x:9091
                       │    │
    ┌────┐             │    │        ┌───────┐
    │    │             │    │        │       │
    │    │  ─────────► │    ├───────►│       │
  ┌─└────┘─┐           │    │        │       │
  └────────┘           │    │        └───────┘
  192.168.1.3          │    │            VM
                       │    │
                       │    │        ┌──┐┌──┐
                       │    ├───────►│  ││  │whoami
                       └────┘        └──┘└──┘   172.19.0.x
                                     ┌──┐┌──┐
                       Traefik       │  ││  │
                                     └──┘└──┘
                                      docker
                                     containers

Traefik Network Mode

By default the network mode in docker compose is bridge mode which isolates the docker containers from the host. In the bridge mode our docker containers can communicate with each other directly.

But we need to either bridge our VM network (192.168.122.x/24) to the 172.19.0.x/24 network, or—what seems easier to me—change Traefik’s network mode from bridge to host.

In docker compose yaml file, in traefik service we need to make two changes:

first add the host network mode:

    # Very important in order to access the VM
    network_mode: host

and by using host we can now remove any port declaration

remove:

    ports:
      # The Web UI (enabled by --api.insecure=true)
      - 8080:8080
      # The HTTP port
      - 80:80

so our docker-compose.yml now looks like:

---
services:
  traefik:
    image: traefik:v3.3
    container_name: traefik
    hostname: traefik
    env_file:
      - path: ./.env
        required: true
    restart: unless-stopped
    volumes:
      - ./traefik:/etc/traefik
      - /var/run/docker.sock:/var/run/docker.sock:ro
    # Add health check
    healthcheck:
      test: curl -s --fail http://127.0.0.1:8080/ping
      interval: 30s
      retries: 3
      timeout: 10s
      start_period: 10s
    # Very important in order to access the VM
    network_mode: host

  # A container that exposes an API to show its IP address
  whoami:
    image: traefik/whoami
    container_name: whoami
    hostname: whoami
    depends_on:
      - traefik
    labels:
        - "traefik.enable=true"                                       # To enable whoami to Traefik
        - "traefik.http.routers.whoami.rule=Host(`whoami.localhost`)" # Declare the host rule for this service
        - "traefik.http.routers.whoami.entrypoints=web"               # Declare the EntryPoint

Start services:

docker compose up -d

Test whoami

let us test now if whoami service works

curl -H Host:whoami.localhost http://127.0.0.1

output:

Hostname: whoami
IP: 127.0.0.1
IP: ::1
IP: 172.19.0.2
RemoteAddr: 172.19.0.1:43968
GET / HTTP/1.1
Host: whoami.localhost
User-Agent: curl/8.12.1
Accept: */*
Accept-Encoding: gzip
X-Forwarded-For: 127.0.0.1
X-Forwarded-Host: whoami.localhost
X-Forwarded-Port: 80
X-Forwarded-Proto: http
X-Forwarded-Server: traefik
X-Real-Ip: 127.0.0.1

okay, seems it works are before.

Transmission

as mentioned above, our transmission application runs on the virtual machine. It is protected by a Basic Authentication setup and listens on a TCP port.

Let’s test the connection:

curl 192.168.122.79:9091

result is:

<h1>401: Unauthorized</h1>

To add Basic Auth to curl is simple:

curl -u transmission:transmission 192.168.122.79:9091

now the output is:

<h1>301: Moved Permanently</h1>

we can add -v to see more details:

curl -v -u transmission:transmission 192.168.122.79:9091

full output:

*   Trying 192.168.122.79:9091...
* Connected to 192.168.122.79 (192.168.122.79) port 9091
* using HTTP/1.x
* Server auth using Basic with user 'transmission'
> GET / HTTP/1.1
> Host: 192.168.122.79:9091
> Authorization: Basic dHJhbnNtaXNzaW9uOnRyYW5zbWlzc2lvbg==
> User-Agent: curl/8.12.1
> Accept: */*
>
* Request completely sent off
< HTTP/1.1 301 Moved Permanently
< Server: Transmission
< Access-Control-Allow-Origin: *
< Location: /transmission/web/
< Date: Thu, 27 Feb 2025 15:47:02 GMT
< Content-Length: 31
< Content-Type: text/html; charset=ISO-8859-1
<
* Connection #0 to host 192.168.122.79 left intact
<h1>301: Moved Permanently</h1>

The Location section is interesting: Location: /transmission/web/

Authorization header

by looking very careful the above output, we see that curl uses the Authorization header for Basic Auth.
This is interesting and we can use this.

Let’s try the command:

curl -H "Authorization: Basic dHJhbnNtaXNzaW9uOnRyYW5zbWlzc2lvbg==" http://192.168.122.79:9091/

output:

<h1>301: Moved Permanently</h1>

okay !
So, another way to access transmission is via Authorization header. Curl sends the credentials through base64 encoding, which can be reproduced by

echo -n "transmission:transmission" | base64

and we verify the output:

dHJhbnNtaXNzaW9uOnRyYW5zbWlzc2lvbg==

Traefik file provider

For the purpose of this lab, we want to access the application on the VM from localhost without providing any credentials, with Traefik handling everything.

                                    ┌─────────┐
http://localhost/transmission/ ---> | Traefik | --> VM (IP:PORT + Basic Auth)/transmision/
                                    └─────────┘

To do that, we need to introduce a PathPrefix Rule to Traefik so it redirects every request for /transmission to the VM. And what a better place to introduce the file provider on our static Traefik configuration

    # Enable file provider
    file:
        directory: /etc/traefik/dynamic/
        watch: true

under our docker provider.

so the entire traefik/traefik.yml should look like:

# The /ping health-check URL
ping: {

}

# API and dashboard configuration
api:
    insecure: true

# Debug log
log:
    filePath: /etc/traefik/traefik.log
    level: DEBUG

# Enable EntryPoints
entryPoints:
    web:
        address: ":80"
        reusePort: true

# Providers
providers:
    # Enable docker provider
    docker:
        exposedByDefault: false

    # Enable file provider
    file:
        directory: /etc/traefik/dynamic/
        watch: true

Dynamic Directory

I am sure you have already noticed that we also have introduced a new directory under our Traefik folder and instructed Traefik to watch it. This is extremely useful because Traefik will automatically reload any configuration in that folder without the need to restart (stop/start or down/up) the Traefik service.

As this change is on static configuration, we need to stop/start the services:

docker compose down

Create the dynamic directory:

mkdir -pv ./traefik/dynamic/

ls -la ./traefik/dynamic/

and

docker compose up -d

Traefik Design

To help you understand how traefik works,

                    HTTP        HTTP                  

┌───────────┐      ┌──────┐   ┌──────────┐   ┌───────┐
│           │      │      │   │          │   │       │
│EntryPoints│ ───► │Routes│──►│Middleware│──►│Service│
│           │      │      │   │          │   │       │
└───────────┘      └──────┘   └──────────┘   └───────┘

We have alredy explained EntryPoints on our previous article, so we are going to focus on routers, middlewares and services.

Traefik Services

To avoid any complicated explanation, the Traefik Service is the final destination of an HTTP request.

For our example, should look like the below config. Please be aware, the <service name> is a placeholder for later.

http:
  services:
    <service-name>:
      loadBalancer:
        servers:
          - url: 'http://192.168.122.79:9091'

Traefik Middlewares

As we already mentioned above, we need to send login credentials to transmission. Be aware <middleware name> is a place holder for later.

http:
  middlewares:
    <middlewar-name>:
      headers:
        customRequestHeaders:
          Authorization: "Basic dHJhbnNtaXNzaW9uOnRyYW5zbWlzc2lvbg=="

Traefik Routes

Traefik HTTP Routes are part of Traefik’s dynamic configuration and define how HTTP requests are handled and routed to the correct services.

Which means the routers is the component that connects everything (EntryPoint, Middleware, and Service) together. This is also where we add our PathPrefix rule for the Transmission location.

Be aware <router|service|middleware name> is a place holder for later.

eg.

http:
  routers:
    <router-name>>:
      entryPoints: web
      service: <service-name>
      rule: PathPrefix(`/transmission`)
      middlewares:
        - <middleware-name>>

Traefik dynamic configuration

We are ready to pull things together.

Create a file named transmission yaml under the dynamic configuration directory:

./traefik/dynamic/transmission.yml

http:

  middlewares:
    middleware-transmission:
      headers:
        customRequestHeaders:
          Authorization: "Basic dHJhbnNtaXNzaW9uOnRyYW5zbWlzc2lvbg=="

  routers:
    router-transmission:
      entryPoints: web
      service: service-transmission
      rule: PathPrefix(`/transmission`)
      middlewares:
        - middleware-transmission

  services:
    service-transmission:
      loadBalancer:
        servers:
          - url: 'http://192.168.122.79:9091'

NO need to restart our services with dynamic configuration!

Test Traefik new transmission route

from command line

curl localhost/transmission/

output:

<h1>301: Moved Permanently</h1>

from dashboard

from browser

That’s It !!

docker compose down

original post on github

a blog post series to my homelab

check here for Introduction to Traefik - Part One

Part Two

In this blog post series, I will connect several docker containers and a virtual machine behind the Traefik reverse proxy on my homelab, and set up Let’s Encrypt for TLS. In this post, I will connect our first docker container to the Traefik reverse proxy for testing and to learn how to do this.

I’ve also made a short video to accompany this blog post:

WhoAmI?

Traefik, whoami is often used as a simple test service to demonstrate how Traefik handles routing, especially when using dynamic routing and reverse proxy setups.

  # A container that exposes an API to show its IP address
  whoami:
    image: traefik/whoami
    container_name: whoami
    hostname: whoami

our updated docker compose file should look like:

docker-compose.yml

---
services:
  traefik:
    image: traefik:v3.3
    container_name: traefik
    hostname: traefik
    env_file:
      - path: ./.env
        required: true
    restart: unless-stopped
    ports:
      # The Web UI (enabled by --api.insecure=true)
      - 8080:8080
      # The HTTP port
      - 80:80
    volumes:
      - ./traefik:/etc/traefik
      - /var/run/docker.sock:/var/run/docker.sock:ro

  # A container that exposes an API to show its IP address
  whoami:
    image: traefik/whoami
    container_name: whoami
    hostname: whoami

Start all the services

docker compose up -d

output:

$ docker compose up -d
[+] Running 3/3
 ✔ Network homelab_default  Created    0.3s
 ✔ Container whoami         Started    2.2s
 ✔ Container traefik        Started    2.3s

Test WhoAmI - the wrong way

We can test our traefik reverse proxy with

curl -H Host:whoami.localhost http://127.0.0.1

but the result should be:

404 page not found

Why ?

We have not yet changed our traefik configuration file to enable an EntryPoint. EntryPoints are the network entry points into Traefik.

So let’s go back one step.

docker compose down

Traefik Configuration

The simplest approach is to update our configuration file and add a new EntryPoint. However, I’d like to take this opportunity to expand our configuration a little.

EntryPoints are Static Configuration, so they must be included on traefik.yml file.

## Static configuration
entryPoints:
  web:
   address: ":80"

so traefik/traefik.yml should look like:

# API and dashboard configuration
api:
  insecure: true

## Static configuration
entryPoints:
  web:
   address: ":80"

That should work, but given the opportunity, let’s enhance our Traefik configuration by including:

Ping

# The /ping health-check URL
ping: {

}

It would be useful to add a health check in the Docker Compose file later on.

Logs

This is how to enable the DEBUG (or INFO - just replace the verb in level)

# Debug log
log:
  filePath: /etc/traefik/traefik.log
  level: DEBUG

Docker provider

I want to explicitly include the Docker provider with a caveat: I don’t want to automatically expose all my docker containers behind Traefik. Instead, I prefer to manually add each docker container that I want to expose to the internet, rather than exposing all of them by default.

providers:
  # Enable docker provider
  docker: {
    exposedByDefault: false
  }

Traefik Configuration file updated

and now traefik/traefik.yml looks like:

# The /ping health-check URL
ping: {

}

# API and dashboard configuration
api:
    insecure: true

# Debug log
log:
    filePath: /etc/traefik/traefik.log
    level: DEBUG

# Enable EntryPoints
entryPoints:
    web:
        address: ":80"
        reusePort: true

# Providers
providers:
    # Enable docker provider
    docker: {
        exposedByDefault: false
    }

by running

docker compose up traefik -d

we can review Traefik dashboard with the new web EntryPoint and ping

WhoAmI - the correct way

okay, we now have our EntryPoint in Traefik but we need to explicit expose our whoami docker container and in order to do that, we need to add some labels!

  # A container that exposes an API to show its IP address
  whoami:
    image: traefik/whoami
    container_name: whoami
    hostname: whoami
    labels:
        - "traefik.enable=true" # To enable whoami to Traefik
        - "traefik.http.routers.whoami.rule=Host(`whoami.localhost`)" # Declare the host rule for this service
        - "traefik.http.routers.whoami.entrypoints=web" # Declare the EntryPoint

let’s put everything together:

---
services:
  traefik:
    image: traefik:v3.3
    container_name: traefik
    hostname: traefik
    env_file:
      - path: ./.env
        required: true
    restart: unless-stopped
    ports:
      # The Web UI (enabled by --api.insecure=true)
      - 8080:8080
      # The HTTP port
      - 80:80
    volumes:
      - ./traefik:/etc/traefik
      - /var/run/docker.sock:/var/run/docker.sock:ro

  # A container that exposes an API to show its IP address
  whoami:
    image: traefik/whoami
    container_name: whoami
    hostname: whoami
    labels:
        - "traefik.enable=true" # To enable whoami to Traefik
        - "traefik.http.routers.whoami.rule=Host(`whoami.localhost`)" # Declare the host rule for this service
        - "traefik.http.routers.whoami.entrypoints=web" # Declare the EntryPoint

docker compose up -d

Test Who Am I

curl -H Host:whoami.localhost http://127.0.0.1

output:

Hostname: whoami
IP: 127.0.0.1
IP: ::1
IP: 172.19.0.3
RemoteAddr: 172.19.0.2:41276
GET / HTTP/1.1
Host: whoami.localhost
User-Agent: curl/8.12.1
Accept: */*
Accept-Encoding: gzip
X-Forwarded-For: 172.19.0.1
X-Forwarded-Host: whoami.localhost
X-Forwarded-Port: 80
X-Forwarded-Proto: http
X-Forwarded-Server: traefik
X-Real-Ip: 172.19.0.1

Health Checks and Depends

before finishing this article, I would like to include two more things.

Traefik Health Check

We added above the ping section on Traefik configuration, it is time to use it. On our docker compose configuration file, we can add a health check section for Traefik service.

We can test this from our command line

curl -s --fail http://127.0.0.1:8080/ping

the result should be an OK !

and we can extend the Traefik service to include this

    healthcheck:
      test: curl -s --fail http://127.0.0.1:8080/ping
      interval: 30s
      retries: 3
      timeout: 10s
      start_period: 10s

Depends On

The above health check option can be used to specify service dependencies in docker compose, so we can ensure that the whoami docker service starts after Traefik.

    depends_on:
      - traefik

that means our docker compose yaml file should look like:

---
services:
  traefik:
    image: traefik:v3.3
    container_name: traefik
    hostname: traefik
    env_file:
      - path: ./.env
        required: true
    restart: unless-stopped
    ports:
      # The Web UI (enabled by --api.insecure=true)
      - 8080:8080
      # The HTTP port
      - 80:80
    volumes:
      - ./traefik:/etc/traefik
      - /var/run/docker.sock:/var/run/docker.sock:ro
    # Add health check
    healthcheck:
      test: curl -s --fail http://127.0.0.1:8080/ping
      interval: 30s
      retries: 3
      timeout: 10s
      start_period: 10s

  # A container that exposes an API to show its IP address
  whoami:
    image: traefik/whoami
    container_name: whoami
    hostname: whoami
    depends_on:
      - traefik
    labels:
        - "traefik.enable=true"                                       # To enable whoami to Traefik
        - "traefik.http.routers.whoami.rule=Host(`whoami.localhost`)" # Declare the host rule for this service
        - "traefik.http.routers.whoami.entrypoints=web"               # Declare the EntryPoint

original post on github

a blog post series to my homelab

Part One

In this blog post, I will start by walking you through the process of setting up Traefik as a reverse proxy for your homelab. My setup involves using a virtual machine connected via a point-to-point VPN to a cloud VPS, along with several Docker containers on my homelab for various tasks and learning purposes. The goal is to expose only Traefik to the internet, which will then provide access to my internal homelab. For certain applications, I also use Tailscale, which I prefer not to expose directly to the internet. In short, I have a complex internal homelab setup, and in this post, we’ll simplify it!

I’ve made a short video to accompany this blog post:

docker compose

To begin, we need to set up a basic Docker Compose YAML file.

As of the time of writing this blog post, the latest Traefik Docker container image is version 3.3. It is best to declare a specific version instead of using “latest” tag.

image: traefik:v3.3

Using an .env file in a Docker Compose configuration is important for several reasons, as for configure variables, secrets and it is easy to reuse though several services and to avoid hardcoding values. For traefik is important so we can configure the docker GID in order traefil to be able to use the docker socket.

eg. .env

# This is my user id
PUID=1001
# This is my docker group id
PGID=142
UMASK="002"
TZ="Europe/Athens"
DNS="88.198.92.222"

Next interesting topic is the volumes section.

I would like to mount a local directory for the traefik configuration, which I will later use with the dynamic file provider. Additionally, to enable Traefik to recongize our (future) docker images, we need to mount the docker socket too.

    volumes:
      - ./traefik:/etc/traefik
      - /var/run/docker.sock:/var/run/docker.sock

to conclude, here is a very basic docker compose yaml file:

docker-compose.yml

---
services:
  traefik:
    image: traefik:v3.3
    container_name: traefik
    hostname: traefik
    env_file:
      - path: ./.env
        required: true
    restart: unless-stopped
    ports:
      # The Web UI (enabled by --api.insecure=true)
      - 8080:8080
      # The HTTP port
      - 80:80
    volumes:
      - ./traefik:/etc/traefik
      - /var/run/docker.sock:/var/run/docker.sock

pull traefik docker image

we can explicitly get the Traefik docker container image

docker compose pull traefik

traefik configuration file

we also need to create the configuration file by enabling the API and the dashboard for now.

Create the directory and file

traefik/traefik.yml

and write this:

# API and dashboard configuration
api:
  insecure: true

Start traefik docker

We are ready start and run our Traefik docker container:

docker compose up

result is something like:

[+] Running 2/2
 ✔ Network homelab_default  Created       0.3s
 ✔ Container traefik        Created       0.4s
Attaching to traefik

To stop traefik from docker compose, we need to open a new terminal and type from the same directory

docker compose down

or, we ca run the docker compose and detach it so it runs on the background:

docker compose up traefik -d

This is going to be useful for the future as we starting each service one by one.

Test traefik

Open your browser and click on: http://127.0.0.1:8080

you will see something like: