# Deployment

Once you have started using `secator` locally, you can deploy it on remote instances.

***

## Amazon Web Services (AWS)

*Amazon Web Services* (AWS) is a subsidiary of Amazon that provides on-demand cloud computing platforms and APIs to individuals, companies.

### Elastic Compute Cloud

#### Deploy on a single EC2 instance

<figure><img src="https://3064929067-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F787L9q3BUjAZ71WVY90V%2Fuploads%2FxzrKO8jaGbPR2a99w0B5%2Fsecator-ec2-single.png?alt=media&#x26;token=1a30d692-0ee4-487b-a23b-0b33e7542f61" alt=""><figcaption><p>Deployment on a single EC2 instance</p></figcaption></figure>

To deploy `secator` on AWS on a single EC2 instance, we will install RabbitMQ, Redis and a Celery worker running `secator` on the instance.

<details>

<summary>Step 1: Create an EC2 instance with AMI Ubuntu</summary>

* Go to the AWS Management Console
* Create an EC2 instance using the Ubuntu AMI
* Configure **Security Groups:**
  * Allow port 6379 (Redis)
  * Allow port 5672 (RabitMQ)
* SSH to your created instance

</details>

<details>

<summary>Step 2: Install RabbitMQ as a task broker</summary>

Celery needs a task broker to send tasks to remote workers.

```bash
sudo apt-get install curl gnupg apt-transport-https -y
curl -1sLf "https://keys.openpgp.org/vks/v1/by-fingerprint/0A9AF2115F4687BD29803A206B73A36E6026DFCA" | sudo gpg --dearmor | sudo tee /usr/share/keyrings/com.rabbitmq.team.gpg > /dev/null
curl -1sLf "https://keyserver.ubuntu.com/pks/lookup?op=get&search=0xf77f1eda57ebb1cc" | sudo gpg --dearmor | sudo tee /usr/share/keyrings/net.launchpad.ppa.rabbitmq.erlang.gpg > /dev/null
curl -1sLf "https://packagecloud.io/rabbitmq/rabbitmq-server/gpgkey" | sudo gpg --dearmor | sudo tee /usr/share/keyrings/io.packagecloud.rabbitmq.gpg > /dev/null
sudo apt-get update -y
sudo apt-get install -y erlang-base \
    erlang-asn1 erlang-crypto erlang-eldap erlang-ftp erlang-inets \
    erlang-mnesia erlang-os-mon erlang-parsetools erlang-public-key \
    erlang-runtime-tools erlang-snmp erlang-ssl \
    erlang-syntax-tools erlang-tftp erlang-tools erlang-xmerl
sudo apt-get install rabbitmq-server -y --fix-missing
sudo rabbitmq-plugins enable rabbitmq_management
sudo rabbitmqctl add_user secator <RABBITMQ_PASSWORD>
sudo rabbitmqctl set_user_tags secator administrator
sudo rabbitmqctl set_permissions -p / secator ".*" ".*" ".*"
```

**Make sure your replace the \<RABBITMQ\_PASSWORD> by a strong password that you generate.**

</details>

<details>

<summary>Step 3: Install Redis as a storage backend</summary>

Celery needs a storage backend to store results. `secator` uses the storage backend to print results in real-time.

```bash
sudo apt install redis-server
sudo vi /etc/redis/redis.conf
# set requirepass to <REDIS_PASSWORD>
# comment the "bind 127.0.0.1 ::1" line
# change "protected-mode" to "no"

sudo /etc/init.d/redis-server restart
```

**Make sure your replace the \<REDIS\_PASSWORD> by a strong password that you generate.**

</details>

<details>

<summary>Step 4: Deploy a secator worker</summary>

First, setup `secator`using the all-in-one bash setup script:

```
wget -O - https://raw.githubusercontent.com/freelabz/secator/main/scripts/install.sh | sh
```

Then, set the RabbitMQ and Redis connection details in `secator`'s config:

```bash
secator config set celery.broker_url amqp://secator:<RABBITMQ_PASSWORD>@localhost:5672/
secator config set celery.result_backend redis://default:<REDIS_PASSWORD>@localhost:6379/0
```

Finally, run a `secator worker`:

```
nohup secator worker > worker.log 2>&1 &  # start in background and save logs
```

</details>

<details>

<summary>Step 5: Run a task from your local machine</summary>

Let's configure the worker with RabbitMQ and Redis connection details:

```bash
secator config set celery.broker_url amqp://secator:<RABBITMQ_PASSWORD>@<EC2_PUBLIC_IP>:5672/
secator config set celery.result_backend redis://default:<REDIS_PASSWORD>@<EC2_PUBLIC_IP>:6379/0
```

Run a test task:

```
secator x httpx wikipedia.org
```

You should get an output like the following:

```bash
                         __            
   ________  _________ _/ /_____  _____
  / ___/ _ \/ ___/ __ `/ __/ __ \/ ___/
 (__  /  __/ /__/ /_/ / /_/ /_/ / /    
/____/\___/\___/\__,_/\__/\____/_/     v0.0.1

                    freelabz.com

Celery worker is alive !
╭──────── Task httpx ─────────╮
│ 📜 Description: DotMap()    │
│ 👷 Workspace: default       │
│ 🍐 Targets:                 │
│    • wikipedia.org          │
│ 📌 Options:                 │
│    • follow_redirect: False │
│    • threads: 50            │
│    • debug_resp: False      │
╰─────────────────────────────╯
[10:20:54] 🎉 Task httpx sent to Celery worker...                                                                                                                                        _base.py:614
🏆 Live results:
🔗 https://wikipedia.org [301] [301 Moved Permanently] [mw1415.eqiad.wmnet] [HSTS] [text/html] [234]
```

</details>

#### Deploy on multiple EC2 instances

If you want a more scaleable architecture, we recommend deploying RabbitMQ, Redis, and Celery workers on different EC2 instances.

The steps are exactly the same as for [#deploy-on-a-single-ec2-instance](#deploy-on-a-single-ec2-instance "mention"), expect that steps 2, 3, and **4** will each be run on **separate EC2 instance**.&#x20;

You can repeat step 4 on more instances to increase the number of workers.

***

## Google Cloud Platform (GCP)

The *Google Cloud Platform* (GCP) is a suite of cloud services that offers server space on virtual machines, internal networks, VPN connections, disk storage, ...

### Google Compute Engine

#### Deploy on a single GCE instance

<figure><img src="https://3064929067-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2F787L9q3BUjAZ71WVY90V%2Fuploads%2FxzrKO8jaGbPR2a99w0B5%2Fsecator-ec2-single.png?alt=media&#x26;token=1a30d692-0ee4-487b-a23b-0b33e7542f61" alt=""><figcaption><p>Deployment on a single EC2 instance</p></figcaption></figure>

To deploy `secator` on a single GCE machine, we will install RabbitMQ, Redis and a Celery worker running `secator` on the instance.

<details>

<summary>Step 1: Create a GCE instance</summary>

* Go to the Google Cloud Console
* Create a GCE instance using the Debian image
* Create firewall rules in **Network** > **Firewall:**
  * Allow port 6379 (Redis)
  * Allow port 5672 (RabbitMQ)
* SSH to your created instance

</details>

<details>

<summary>Step 2: Install RabbitMQ as a task broker</summary>

Celery needs a task broker to send tasks to remote workers.

```bash
sudo apt-get install curl gnupg apt-transport-https -y
curl -1sLf "https://keys.openpgp.org/vks/v1/by-fingerprint/0A9AF2115F4687BD29803A206B73A36E6026DFCA" | sudo gpg --dearmor | sudo tee /usr/share/keyrings/com.rabbitmq.team.gpg > /dev/null
curl -1sLf "https://keyserver.ubuntu.com/pks/lookup?op=get&search=0xf77f1eda57ebb1cc" | sudo gpg --dearmor | sudo tee /usr/share/keyrings/net.launchpad.ppa.rabbitmq.erlang.gpg > /dev/null
curl -1sLf "https://packagecloud.io/rabbitmq/rabbitmq-server/gpgkey" | sudo gpg --dearmor | sudo tee /usr/share/keyrings/io.packagecloud.rabbitmq.gpg > /dev/null
sudo apt-get update -y
sudo apt-get install -y erlang-base \
    erlang-asn1 erlang-crypto erlang-eldap erlang-ftp erlang-inets \
    erlang-mnesia erlang-os-mon erlang-parsetools erlang-public-key \
    erlang-runtime-tools erlang-snmp erlang-ssl \
    erlang-syntax-tools erlang-tftp erlang-tools erlang-xmerl
sudo apt-get install rabbitmq-server -y --fix-missing
sudo rabbitmq-plugins enable rabbitmq_management
sudo rabbitmqctl add_user secator <RABBITMQ_PASSWORD>
sudo rabbitmqctl set_user_tags secator administrator
sudo rabbitmqctl set_permissions -p / secator ".*" ".*" ".*"
```

**Make sure your replace the \<RABBITMQ\_PASSWORD> by a strong password that you generate.**

</details>

<details>

<summary>Step 3: Install Redis as a storage backend</summary>

Celery needs a storage backend to store results. `secator` uses the storage backend to print results in real-time.

```bash
sudo apt install redis-server
sudo vi /etc/redis/redis.conf
# set requirepass to <REDIS_PASSWORD>
# comment the "bind 127.0.0.1 ::1" line
# change "protected-mode" to "no"

sudo /etc/init.d/redis-server restart
```

**Make sure your replace the \<REDIS\_PASSWORD> by a strong password that you generate.**

</details>

<details>

<summary>Step 4: Deploy a secator worker</summary>

First, setup `secator`using the all-in-one bash setup script:

```
wget -O - https://raw.githubusercontent.com/freelabz/secator/main/scripts/install.sh | sh
```

Then, set the RabbitMQ and Redis connection details in `secator`'s config:

```bash
secator config set celery.broker_url amqp://secator:<RABBITMQ_PASSWORD>@localhost:5672/
secator config set celery.result_backend redis://default:<REDIS_PASSWORD>@localhost:6379/0
```

Finally, run a `secator worker`:

```
nohup secator worker > worker.log 2>&1 &  # start in background and save logs
```

</details>

<details>

<summary>Step 5: Run a task from your local machine</summary>

First, set the RabbitMQ and Redis connection details in `secator`'s config:

```bash
secator config set celery.broker_url amqp://secator:<RABBITMQ_PASSWORD>@<GCE_PUBLIC_IP>:5672/
secator config set celery.result_backend redis://default:<REDIS_PASSWORD>@<GCE_PUBLIC_IP>:6379/0
```

Run a test task:

```
secator x httpx wikipedia.org
```

You should get an output like the following:

```bash
                         __            
   ________  _________ _/ /_____  _____
  / ___/ _ \/ ___/ __ `/ __/ __ \/ ___/
 (__  /  __/ /__/ /_/ / /_/ /_/ / /    
/____/\___/\___/\__,_/\__/\____/_/     v0.0.1

                    freelabz.com

Celery worker is alive !
╭──────── Task httpx ─────────╮
│ 📜 Description: DotMap()    │
│ 👷 Workspace: default       │
│ 🍐 Targets:                 │
│    • wikipedia.org          │
│ 📌 Options:                 │
│    • follow_redirect: False │
│    • threads: 50            │
│    • debug_resp: False      │
╰─────────────────────────────╯
[10:20:54] 🎉 Task httpx sent to Celery worker...                                                                                                                                        _base.py:614
🏆 Live results:
🔗 https://wikipedia.org [301] [301 Moved Permanently] [mw1415.eqiad.wmnet] [HSTS] [text/html] [234]
```

</details>

#### Deploy on multiple GCE instances

If you want a more scaleable architecture, we recommend deploying RabbitMQ, Redis, and Celery workers on different machines.

The steps are exactly the same as for the previous section, except that steps 2, 3, and **4** will each be run on **separate GCE instance**.&#x20;

You can repeat step 4 on more instances to increase the number of workers.

### Google Kubernetes Engine \[WIP]

### Cloud Run \[WIP]

***

## Axiom \[WIP]

**Axiom is a dynamic infrastructure framework** to efficiently work with multi-cloud environments, build and deploy repeatable infrastructure focused on offensive and defensive security.

***

## Bare metal

### Kubernetes

A Helm chart is available in the repository: <https://github.com/freelabz/secator/tree/main/helm>.

### Docker-compose \[WIP]

***