Train

Train is a set Amazon Web Services CLI tools (packaged in a Docker container) used to manage AWS users, VPC's, user keys/security, AMI's, custom labs (one or more AWS instances grouped by tag), and sets of labs to be used for demos, testing, and training.

train: Primary tool - Manages all VPC Objects and instances
train-users: Mananges additional users allowed to use train
train-images: Manages associated lab AMI's

The tools provide a simple way to quickly create, manage, and destroy:

VPC Objects

VPC
IAM profile, role, policy
Gateway
Route table
Network ACL
Subnets
Default security group and rules
Key pairs for multiple users
Labs (Grouping of one or more instances with specific configurations)
Termination and clean-up of all created objects/files

AWS IAM Users

Manage (Create, list, and delete) AWS IAM Users

AMIs

Manage (Create, list, and delete) AMI's associated with custom labs.

Registration for training/testing

Bulk registration (Launch labs for users listed in a text file)
Registration mode: A 'Kiosk' style registration mode that provides a welcome message and prompts for email address used to launch labs.

Requirements

AWS Permissions

It's recommended you create an AWS account that is separate from your prod, dev, staging accounts. Train requires extensive IAM roles/permissions. (Create, list, and destroy: VPC's, EC2 instances/objects, IAM users, etc...)

Any user created with the train-users tool has the following policy: https://github.com/kizbitz/train/blob/master/train/vpc/users.py#L12-L41

Environment Variables

Environment variables can be set in the container by:

Creating/using a Docker environment file (Used in the following examples)
Pass them in when running the container
Export them directly in the container

Required Environment Variables

# Username
TRAINER=jbaker

# AWS Authentication
AWS_ACCESS_KEY_ID=<id>
AWS_SECRET_ACCESS_KEY=<key>

# AWS EC2 Configuration
AWS_REGION=<ec2-region>

Available AWS EC2 Regions

Region Name	Region
US East (N. Virginia)	us-east-1
US West (Oregon)	us-west-2
US West (N. California)	us-west-1
EU (Ireland)	eu-west-1
EU (Frankfurt)	eu-central-1
Asia Pacific (Singapore)	ap-southeast-1
Asia Pacific (Tokyo)	ap-northeast-1
Asia Pacific (Sydney)	ap-southeast-2
Asia Pacific (Seoul)	ap-northeast-2
South America (Sao Paulo)	sa-east-1

Note: TRAINER (username) is only used for tagging VCP objects only. It is not tied to any permissions.

Host Volume

A local host volume needs to be mounted inside the container to /host when running the container. The scripts will output all user keys and user instance information into a /host/<VPC> directory.

Optional

Optional Environment Variables

# AWS SES
SES_REGION=<ses-region>
SES_FROM_EMAIL=<email-address>
SES_FROM_NAME=<alternate-from-text>

# Root lab directory
LAB_DIR=<lab-directory>

# Full path to user configuration file
USER_FILE=<config-file>

# Tag for VPC, labs, instances, etc... (Recommended for different environments)
VPC=<vpc-tag>

# Template file for registration emails
EMAIL_TEMPLATE=<path-to-template-file-in-container>

# Optional user defined tag, default value is NOTSET
USERTAG=<string>

AWS SES Minimum Requirements

In order to send emails with train you must (at a minimum):

Set the SES_REGION and SES_FROM_EMAIL environment variables.
Verify a sending email address on your AWS account. See: http://docs.aws.amazon.com/ses/latest/DeveloperGuide/verify-email-addresses.html#verify-email-addresses-procedure
Request a limit increase and have your account migrated out of sandboxed mode. See: http://docs.aws.amazon.com/ses/latest/DeveloperGuide/request-production-access.html

Recommended AWS SES configuration:

Use a custom MAIL FROM domain: http://docs.aws.amazon.com/ses/latest/DeveloperGuide/mail-from.html
Authenticate your emails. See: http://docs.aws.amazon.com/ses/latest/DeveloperGuide/authentication.html

Available AWS SES Regions

Region Name	Region
US East (N. Virginia)	us-east-1
US West (Oregon)	us-west-2
EU (Ireland)	eu-west-1

Walk-through - Personal Use

The following section is a walk-through of usage for personal use (Useful for demos and individuals involved in QA/documentation/support teams)

Pull the Docker image:

vagrant@dockertest:~$ docker pull kizbitz/train
Using default tag: latest
latest: Pulling from kizbitz/train
843e2bded498: Pull complete
  *content removed*
81395f1294cc: Pull complete
Digest: sha256:146ff25a5b7fa8a4dd9914359c93f0be2c35ff0ca4a19045a1585dc07e005e0b
Status: Downloaded newer image for kizbitz/train:latest

Create a temp working directory:

vagrant@dockertest:~$ mkdir sandbox
vagrant@dockertest:~$ cd sandbox/

Create a Docker environment file with required environment variables:

vagrant@dockertest:~/sandbox$ vim train.env
vagrant@dockertest:~/sandbox$ cat train.env

TRAINER=jbaker
VPC=demo
AWS_REGION=us-east-1
AWS_ACCESS_KEY_ID=<your-aws-access-key>
AWS_SECRET_ACCESS_KEY=<your-aws-secret-access-key>

# SES Configuration
SES_REGION=us-east-1
SES_FROM_NAME=Docker Training
SES_FROM_EMAIL=no-reply@train.docker.com

Run the container with the environment file and mount a host volume:

vagrant@dockertest:~/sandbox$ ls -al
total 12
drwxrwxr-x  2 vagrant vagrant 4096 Jan 12 10:46 .
drwxr-xr-x 10 vagrant vagrant 4096 Jan 12 10:46 ..
-rw-------  1 vagrant vagrant  219 Jan 12 10:46 train.env
vagrant@dockertest:~/sandbox$ docker run -ti --rm --env-file='train.env' -v $(pwd):/host kizbitz/train
jbaker-demo:us-east-1:~$

After entering the container note that the prompt is displaying the: TRAINER-VPC:AWS_REGION environment variables.

jbaker-demo:us-east-1:~$

Executing train without any arguments will display help:

jbaker-demo:us-east-1:~$ train
usage: train [-h] [-v] [-k] [-a] [-i <lab>] [-x <lab>] [-r <lab>] [-e] [-l]
             [-d <tag>] [-p] [-t]

Train: AWS CLI Tool

optional arguments:
  -h, --help  show this help message and exit
  -v          Create AWS VPC
  -k          Create AWS key pairs
  -a          List all available labs
  -i <lab>    View description for an available lab
  -x <lab>    Execute a lab
  -r <lab>    Execute a lab in registration mode
  -e          Email instance information and credentials to users
  -l          List running labs and instances in AWS
  -d <tag>    Delete a lab from AWS
  -p          Purge/Delete all instances in VPC
  -t          Terminate environment (VPC and local files)
jbaker-demo:us-east-1:~$

Create the new VPC (along with the required VCP objects) and generate your key pair (from the TRAINER environment variable) using the -v and -k flags:

jbaker-demo:us-east-1:~$ train -vk
Creating AWS VPC ...
Creating IAM Profile: jbaker-demo ...
IAM profile, role, and policy created ...
Creating VPC: jbaker-demo ...
Creating gateway: jbaker-demo-igw ...
Creating route table: jbaker-demo-route-table ...
Configuring network ACL: jbaker-demo-network-acl ...
10.0.0.0/20
Creating subnet: jbaker-demo-us-east-1b ...
10.0.16.0/20
Creating subnet: jbaker-demo-us-east-1c ...
10.0.32.0/20
Creating subnet: jbaker-demo-us-east-1d ...
10.0.48.0/20
Creating subnet: jbaker-demo-us-east-1e ...
Configuring default security group ...
Adding default egress rules ...
Checking for existing key pair: jbaker-demo ...
Creating key pair: jbaker-demo ...
Key 'jbaker-demo' created and saved ...

Key pairs (*.pem for *nix users and a *.pem for Windows/PuTTY users) are are created and saved in a /host/<VPC>/users/<username> directory.

The key-pairs.txt file is used to track all key pairs created for this VPC. users.cfg is covered later in the Registration section.

jbaker-demo:us-east-1:~$ tree /host
/host
├── demo
│   ├── key-pairs.txt
│   ├── users
│   │   └── jbaker
│   │       ├── jbaker-demo.pem
│   │       └── jbaker-demo.ppk
│   └── users.cfg
└── train.env

List available labs:

jbaker-demo:us-east-1:~$ train -a

Available Labs:
  base
  dtr-volume
  template
  training-atp
  ucp

jbaker-demo:us-east-1:~$

Display lab information:

jbaker-demo:us-east-1:~$ train -i dtr-volume

Name: dtr-volume

Description:

DTR with image storage configured on a separate volume.

- Ubuntu 14.04
- 4.0.23 Kernel
- Latest Docker CS Release

jbaker-demo:us-east-1:~$

This particular lab launches a base Ubuntu 14.04 instances with an attached volume, upgrades the kernel to 4.0.23, installs the latest Docker CS engine and Docker Trusted registry, then configures the DTR storage option to point to the attached volume. See: https://github.com/kizbitz/train/tree/master/train/labs/dtr-volume

Note that launching a lab may cause updates, configuration changes or the installation of tools (such as Docker) which may require some time to complete and may result in the instance rebooting. It is advisable to wait until this process has fully completed before attempting to use the launched lab.

Launch this lab:

jbaker-demo:us-east-1:~$ train -x dtr-volume
Launching 'dtr-volume' lab with tag: dtr-volume-1
Launching instance: jbaker-dtr ...
Waiting for instances to initialize ...
Waiting for instance 'jbaker-dtr' to initialize ...
Waiting for instance 'jbaker-dtr' to initialize ...
Waiting for instance 'jbaker-dtr' to initialize ...
Waiting for instance 'jbaker-dtr' to initialize ...
Waiting for instance 'jbaker-dtr' to initialize ...
Waiting for instance 'jbaker-dtr' to initialize ...
Waiting for instance 'jbaker-dtr' to initialize ...
Creating instance tags for: jbaker-dtr...

Lab 'dtr-volume' launched with tag 'dtr-volume-1':

  Instances:

    Name:         jbaker-dtr
      Lab:        dtr-volume-1
      Region:     us-east-1
      IP:         54.84.220.19
      Private IP: 10.0.14.143
      Public DNS: ec2-54-84-220-19.compute-1.amazonaws.com

Launch another lab:

jbaker-demo:us-east-1:~$ train -x base
Available configurations for the 'base' lab:

 1) amazon-linux
 2) centos-7.0
 3) debian-jessie
 4) debian-wheez
 5) rhel-6.5
 6) rhel-6.6
 7) rhel-7.0
 8) rhel-7.1
 9) suse-sles-12
 10) ubuntu


Which configuration would you like to execute?: 10
How many instances would you like to launch: 2
Enter a custom AWS 'Name' tag: demo
Launching 'base' lab with tag: base-1
Launching instance: jbaker-demo-0 ...
Launching instance: jbaker-demo-1 ...
Waiting for instances to initialize ...
Waiting for instance 'jbaker-demo-0' to initialize ...
Waiting for instance 'jbaker-demo-0' to initialize ...
Waiting for instance 'jbaker-demo-1' to initialize ...
Creating instance tags for: jbaker-demo-0...
Creating instance tags for: jbaker-demo-1...

Lab 'base' launched with tag 'base-1':

  Instances:

    Name:         jbaker-demo-0
      Lab:        base-1
      Region:     us-east-1
      IP:         54.175.198.174
      Private IP: 10.0.7.117
      Public DNS: ec2-54-175-198-174.compute-1.amazonaws.com

    Name:         jbaker-demo-1
      Lab:        base-1
      Region:     us-east-1
      IP:         52.23.199.253
      Private IP: 10.0.20.29
      Public DNS: ec2-52-23-199-253.compute-1.amazonaws.com

jbaker-demo:us-east-1:~$

List running labs and instances:

jbaker-demo:us-east-1:~$ train -l

Running labs:
  base-1
  dtr-volume-1

Instances running in lab 'base-1':

    Name:         jbaker-demo-0
      Lab:        base-1
      Region:     us-east-1
      IP:         54.175.198.174
      Private IP: 10.0.7.117
      Public DNS: ec2-54-175-198-174.compute-1.amazonaws.com

    Name:         jbaker-demo-1
      Lab:        base-1
      Region:     us-east-1
      IP:         52.23.199.253
      Private IP: 10.0.20.29
      Public DNS: ec2-52-23-199-253.compute-1.amazonaws.com

Instances running in lab 'dtr-volume-1':

    Name:         jbaker-dtr
      Lab:        dtr-volume-1
      Region:     us-east-1
      IP:         54.84.220.19
      Private IP: 10.0.14.143
      Public DNS: ec2-54-84-220-19.compute-1.amazonaws.com

jbaker-demo:us-east-1:~$

Lab/Instance information is also saved in text files in the /host/<VCP>/users/<user> directory

jbaker-demo:us-east-1:~$ tree /host
/host
├── demo
│   ├── key-pairs.txt
│   ├── users
│   │   └── jbaker
│   │       ├── base-1.txt
│   │       ├── dtr-volume-1.txt
│   │       ├── jbaker-demo.pem
│   │       └── jbaker-demo.ppk
│   └── users.cfg
└── train.env

3 directories, 7 files
jbaker-demo:us-east-1:~$

Delete a lab:

jbaker-demo:us-east-1:~$ train -d dtr-volume-1

Terminate request sent for all lab instances ...
Lab 'dtr-volume-1' has been deleted ...

jbaker-demo:us-east-1:~$

Purge all instances in the VPC:

jbaker-demo:us-east-1:~$ train -p

Terminate request sent for all instances ...

jbaker-demo:us-east-1:~$

Confirmation:

jbaker-demo:us-east-1:~$ train -l

No labs running ...

jbaker-demo:us-east-1:~$ tree /host/share
/host/share
└── jbaker
    ├── jbaker-demo.pem
    └── jbaker-demo.ppk

1 directory, 2 files
jbaker-demo:us-east-1:~$

Terminate environment. Removes all VPC objects, labs, instances, and user keys:

jbaker-demo:us-east-1:~$ train -t
Terminating environment ...

VPC has no instances ...
Deleting IAM Profile: jbaker-demo ...
Deleting key pair for user: jbaker ...
Deleting VPC: jbaker-demo ...
Environment deleted ...
Finished ...
jbaker-demo:us-east-1:~$

Walk-through - Registration Modes

In addition to all of the above functionality, train provides two registration modes that can be used for providing instances to particpants in training events or hands-on lab scenarios.

Bulk registration
Registration (Kiosk) mode

Bulk Registration

To use the bulk registration mode you create a user config file. By default, train will use a user config file:

/host/<VPC>/users.cfg

Or, you can optionally set a custom file path with the USER_FILE environment variable: https://github.com/kizbitz/train/blob/master/train/vpc/config.py#L52

Create a users.cfg file in your the root of your VPC directory with a list of usernames and emails that will be used when creating key pairs and launching any lab instances.

One user per line in the format: <username>,<email>

vagrant@docker:~/sandbox$ tree
.
├── demo
│   ├── key-pairs.txt
│   ├── users
│   │   └── jbaker
│   │       ├── base-1.txt
│   │       ├── dtr-volume-1.txt
│   │       ├── jbaker-demo.pem
│   │       └── jbaker-demo.ppk
│   └── users.cfg
└── train.env

3 directories, 7 files

vagrant@docker:~/sandbox$ vim demo/users.cfg
vagrant@docker:~/sandbox$ cat demo/users.cfg
jbaker,jbaker@docker.com
mrcotton,mrcotton@simpledove.com
vagrant@docker:~/sandbox$

Note: This example only has two users specified but there is no limit except for your AWS limits.

Run the container with the environment file and mount a host volume:

vagrant@docker:~/sandbox$ ls -al
total 16
drwxr-xr-x 3 vagrant vagrant 4096 Jan 20 11:18 .
drwxr-xr-x 5 vagrant vagrant 4096 Jan 20 11:40 ..
drwxr-xr-x 3 vagrant vagrant 4096 Jan 20 11:40 demo
-rw------- 1 vagrant vagrant  183 Jan 20 11:18 train.env
vagrant@docker:~/sandbox$ docker run -ti --rm --env-file='train.env' -v $(pwd):/host kizbitz/train
jbaker-dev:us-east-1:~$

Create all keys and VPC:

jbaker-demo:us-east-1:~$ train -vk
Creating AWS VPC ...
Creating IAM Profile: jbaker-demo ...
IAM profile, role, and policy created ...
Creating VPC: jbaker-demo ...
Creating gateway: jbaker-demo-igw ...
Creating route table: jbaker-demo-route-table ...
Configuring network ACL: jbaker-demo-network-acl ...
10.0.0.0/20
Creating subnet: jbaker-demo-us-east-1b ...
10.0.16.0/20
Creating subnet: jbaker-demo-us-east-1c ...
10.0.32.0/20
Creating subnet: jbaker-demo-us-east-1d ...
10.0.48.0/20
Creating subnet: jbaker-demo-us-east-1e ...
Configuring default security group ...
Adding default egress rules ...
Checking for existing key pair: jbaker-demo ...
Creating key pair: jbaker-demo ...
Key 'jbaker-demo' created and saved ...
Checking for existing key pair: mrcotton-demo ...
Creating key pair: mrcotton-demo ...
Key 'mrcotton-demo' created and saved ...

Launch a lab for all users:

jbaker-demo:us-east-1:~$ train -x dtr-volume
Launching 'dtr-volume' lab with tag: dtr-volume-1
Launching instance: jbaker-dtr ...
Launching instance: mrcotton-dtr ...
Waiting for instances to initialize ...
Waiting for instance 'jbaker-dtr' to initialize ...
Waiting for instance 'jbaker-dtr' to initialize ...
Waiting for instance 'jbaker-dtr' to initialize ...
Waiting for instance 'mrcotton-dtr' to initialize ...
Creating instance tags for: jbaker-dtr...
Creating instance tags for: mrcotton-dtr...

Lab 'dtr-volume' launched with tag 'dtr-volume-1':

  Instances:

    Name:         jbaker-dtr
      Lab:        dtr-volume-1
      Region:     us-east-1
      IP:         54.173.183.121
      Private IP: 10.0.4.30
      Public DNS: ec2-54-173-183-121.compute-1.amazonaws.com

    Name:         mrcotton-dtr
      Lab:        dtr-volume-1
      Region:     us-east-1
      IP:         54.165.187.76
      Private IP: 10.0.1.129
      Public DNS: ec2-54-165-187-76.compute-1.amazonaws.com

jbaker-demo:us-east-1:~$

At this point all user keys and the lab instance information within saved in the container in /host/share (and in the host directory):

Note: On Windows open the *.txt files with Wordpad (or some other app that will render the \n's correctly)

jbaker-demo:us-east-1:~$ tree /host/share
/host/share
├── jbaker
│   ├── dtr-volume-1.txt
│   ├── jbaker-demo.pem
│   └── jbaker-demo.ppk
└── mrcotton
    ├── dtr-volume-1.txt
    ├── mrcotton-demo.pem
    └── mrcotton-demo.ppk

2 directories, 6 files
jbaker-demo:us-east-1:~$

List running labs and instances:

jbaker-demo:us-east-1:~$ train -l

Running labs:
  dtr-volume-1

Instances running in lab 'dtr-volume-1':

    Name:         jbaker-dtr
      Lab:        dtr-volume-1
      Region:     us-east-1
      IP:         54.173.183.121
      Private IP: 10.0.4.30
      Public DNS: ec2-54-173-183-121.compute-1.amazonaws.com

    Name:         mrcotton-dtr
      Lab:        dtr-volume-1
      Region:     us-east-1
      IP:         54.165.187.76
      Private IP: 10.0.1.129
      Public DNS: ec2-54-165-187-76.compute-1.amazonaws.com

jbaker-demo:us-east-1:~$

Email all users their lab instance information and keys:

Note:

Requires that you have SES set up correctly on your account - See requirements section above.
Recommended that you use a customized email template. See: https://github.com/kizbitz/train/blob/master/train/vpc/config.py#L58
- By default train will look for an email template in: /host/<VPC>/email.py
- If /host/<VPC>/email does not exist the template that will be used is located here: https://github.com/kizbitz/train/blob/master/train/templates/email.py
- You can also specify the email template to use with an EMAIL_TEMPLATE environment variable. See: https://github.com/kizbitz/train/blob/master/train/vpc/config.py#L58

jbaker-demo:us-east-1:~$ train -e
Emailing user information and credentials ...
Enter the 'from email address' for the outgoing message: no-reply@docker.com
Enter the 'from name' for the outgoing message: Docker Training
Enter the 'Subject' for the outgoing message: Docker Training Demo
Welcome email sent to: 'jbaker' <jbaker@docker.com> ...
Welcome email sent to: 'mrcotton' <mrcotton@simpledove.com> ...

After training is complete terminate/purge all instances and VPC.

Registration Mode

The alternative method of registration is using the -r flag to launch a lab.

This mode:

Prompts for a welcome message
Displays the welcome message and prompts for an email
Creates a username from the email and creates the key pairs
Launches the lab and then emails the user the connection info and keys

Notes:

It's recommend to use a custom email template (with no prompts) that sets:
- from_email, from_name, and subject variables with static entries.
  - See '/home/train/train/templates/email.py' in the container
Specify 'EMAIL_TEMPLATE' environment variable with the path to your template
The welcome message entered below will be shown to each individial before prompting for email.
To exit registration mode type 'exit' during the email prompt.

Example usage:

jbaker-demo:us-east-1:~$ train -r dtr-volume

Registration Mode:

- It's recommend to use a custom email template (with no prompts) that sets:
    - from_email, from_name, and subject variables with static entries.
        - See '/home/train/train/templates/email.py'
    - Specify 'EMAIL_TEMPLATE' environment variable with the path to your template

- The welcome message entered below will be shown to each individial before prompting for email.
- To exit registration mode type 'exit' during the email prompt.

Enter a welcome message: Welcome to Docker Training!

After the welcome message is entered the registration loop starts:

Welcome to Docker Training!

Please enter a valid email address: jbaker@docker.com
Checking for existing key pair: jbaker-demo ...
Creating key pair: jbaker-demo ...
Key 'jbaker-demo' created and saved ...
Launching 'dtr-volume' lab with tag: dtr-volume-1
Launching instance: jbaker-dtr ...
Waiting for instances to initialize ...
Waiting for instance 'jbaker-dtr' to initialize ...
Waiting for instance 'jbaker-dtr' to initialize ...
Waiting for instance 'jbaker-dtr' to initialize ...
Creating instance tags for: jbaker-dtr...

Lab 'dtr-volume' launched with tag 'dtr-volume-1':

  Instances:

    Name:         jbaker-dtr
      Lab:        dtr-volume-1
      Region:     us-east-1
      IP:         52.90.51.247
      Private IP: 10.0.4.100
      Public DNS: ec2-52-90-51-247.compute-1.amazonaws.com

Emailing user information and credentials ...
Welcome email sent to: 'jbaker' <jbaker@docker.com> ...


-----------------------------------

Instances launched successfully ...

Press 'Enter' to continue

After enter is pressed the welcome message is displayed again and prompts for another email:

Welcome to Docker Training!

Please enter a valid email address:

When using the -r flag for registration usernames and emails are collected in the file /host/registered-users.txt for use with post-training surveys, etc...:

jbaker-demo:us-east-1:~$ cat /host/registered-users.txt
jbaker,jbaker@docker.com
mrcotton,mrcotton@simpledove.com

train-images

The train-images CLI tool is used to manage AMI's for labs. Depending on how long it takes the cloud-init script to finish it can be quicker to create an AMI of the final instance to launch instead of the specified cloud-init script. (Especially during training events and hands-on labs)

Notes:

Only one AMI is created per instance definition in instances.cfg. This will be the <name>-0 instance when NAME is used in the definition or the zero index of the NAMES list. See: https://github.com/kizbitz/train/blob/master/train/labs/template/instances.cfg#L16-L22
When launching a lab train checks for existing AMI images for the lab and will use those if present. If not, the standard cloud-init script is used.

Example usage

Executing train-images without any arguments will display help:

jbaker-demo:us-east-1:~$ train-images
usage: train-images [-h] [-c <lab>] [-d <lab>] [-l] [-r]

Train: AWS CLI AMI Management

optional arguments:
  -h, --help  show this help message and exit
  -c <lab>    Create lab AMI's
  -d <lab>    Deregister lab AMI's
  -l          List all AMI's
  -r          List running labs
jbaker-demo:us-east-1:~$

List running labs:

jbaker-demo:us-east-1:~$ train-images -r

Running labs:
  dtr-volume-1

jbaker-demo:us-east-1:~$

Create AMI's for a lab:

jbaker-demo:us-east-1:~$ train-images -c dtr-volume-1
Creating AMI's for lab: dtr-volume-1
Completed ...

Note: Depending on the current state of the AWS workers it could take a while before the final image is available.

List all AMI's

jbaker-demo:us-east-1:~$ train-images -l

AWS AMI's:

  ID: ami-94bfe4fe
  Name: jbaker-demo-dtr-volume-ubuntu-0
  Description: dtr-volume lab AMI
  Region: us-east-1
  Tags:
     Name: jbaker-demo-dtr-volume-ubuntu-0
     AMI-Key: 0
     Lab: dtr-volume

jbaker-demo:us-east-1:~$

Deregistering an AMI:

jbaker-demo:us-east-1:~$ train-images -d dtr-volume
Degristering jbaker-demo-dtr-volume-ubuntu-0 ...
Completed ...
jbaker-demo:us-east-1:~$

train-users

train-users is a CLI tool for current AWS administrators to manage new train users.

Executing train-users without any arguments will display help:

jbaker-demo:us-east-1:~$ train-users
usage: train-users [-h] [-c <user>] [-d <user>] [-l]

Train: AWS CLI IAM/User Management

optional arguments:
  -h, --help  show this help message and exit
  -c <user>   Create IAM/User
  -d <user>   Delete AWS user
  -l          List all users

Using the -c flag will create a new user account and return the credentials that you can pass on.

baker-demo:us-east-1:~$ train-users -c mrcotton
Allow user to log into the AWS Console? [y/n]: y

'mrcotton' user created succesfully ...

Username: mrcotton
Password: 7F9@Yy2x33ye(Wwh4
Access Key ID: ABCDJEKAAUUBSXYZ2W7YB7A
Secret Access Key: KsappDockerfCUlc-SyuWdx9xBxROUof9nnSj3uz

jbaker-demo:us-east-1:~$

List all users:

jbaker-demo:us-east-1:~$ train-users -l

Current AWS user accounts:

- jbaker
- mrcotton

jbaker-demo:us-east-1:~$

Delete a user:

jbaker-demo:us-east-1:~$ train-users -d mrcotton
Are you sure you want to delete user: 'mrcotton'? [y/n]: y
'mrcotton' AWS user deleted ...
jbaker-demo:us-east-1:~$

WARNING: Security for your environment is your responsibility. Users created with this tool have a lot of permissions. Remember to audit your users and rotate keys/passwords to keep your environment secure.

Custom Labs

A template lab is provided that you can use as a starting base.
All current labs for reference: https://github.com/kizbitz/train/tree/master/train/labs

Tips

If you switch VPCs often you can create a helper function and pass in your VPC for launching your container.

Example function for Bash (~/.bashrc on your Docker host):

function train {
  if [ -z ${1} ]; then
    docker run -ti --rm --env-file='train.env' -v $(pwd):/host kizbitz/train
  else
    docker run -ti --rm --env-file='train.env' -e VPC=${1} -v $(pwd):/host kizbitz/train
  fi
}

Finally

Thoughts, comments, suggestions, bug reports, and pull requests welcome....

Name		Name	Last commit message	Last commit date
Latest commit History 179 Commits
bin		bin
train		train
.gitignore		.gitignore
Dockerfile		Dockerfile
LICENSE		LICENSE
README.md		README.md

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