zenika/ztraining2strigo Docker 镜像 - 轩辕镜像

Ztraining2strigo

Create Strigo class for Zenika training.

Install

Windows

1. Download the ztraining2strigo-x.y.z.exe from latest release.
2. Rename it to ztraining2strigo.exe
3. Add it to your PATH

An alternative is to follow the procedure for other OS.

Other OS

1. Make sure to have Python >= 3.11 installed.

2. Download the ztraining2strigo-x.y.z-py3-none-any.whl from latest release.

3. Install the wheel package:

   shell
   pip install ztraining2strigo-x.y.z-py3-none-any.whl
   

Docker

1. Get the image zenika/ztraining2strigo:x.y.z:

   shell
   docker image pull zenika/ztraining2strigo:x.y.z
   

2. Define an alias ztraining2strigo:

   shell
   alias ztraining2strigo='docker container run --rm --volume $(pwd):/training --user $(id -u):$(id -g) --env STRIGO_ORG_ID --env STRIGO_API_KEY --env Z2S_TRACE_HTTP --interactive --tty zenika/ztraining2strigo:x.y.z'
   

Strigo authentication

1. Go to [***]
2. Scroll to section "Authentication Credentials"
3. Define the environment variables:

• STRIGO_ORG_ID with the value of "Organization ID"
• STRIGO_API_KEY with the value of "API Key"

If the environment variables are not set, the Strigo credentials will be asked when launching the tool.

Usage

1. Go to the root of your training
2. Launch any command of the ztraining2strigo binary

shell
$ ztraining2strigo --help
usage: ztraining2strigo [-h] [--config CONFIG] COMMAND ...

positional arguments:
  COMMAND          sub-command help
    create         Create config for new Strigo class. The class parameters are asked interactively.
    retrieve       Retrieve config from existing Strigo class
    update         Update Strigo class from config

optional arguments:
  -h, --help       show this help message and exit
  --config CONFIG

Retrieve configuration from existing Strigo class

shell
$ ztraining2strigo retrieve --help
usage: ztraining2strigo retrieve [-h] CLASS_ID

positional arguments:
  CLASS_ID    Existing Strigo class ID

optional arguments:
  -h, --help  show this help message and exit

This command can be used to create the configuration from existing Strigo class.

• The configuration will be stored inside a strigo.json file at the root of your training (or one referenced by --config)
• The presentation filename will be searched recursively in the training directory and used in config if found, otherwise just prefixed with PDF/
• The presentation
• The init scripts will be downloaded into Installation/strigo/init_<machine_name>.sh
• The post launch scripts will be downloaded into Installation/strigo/post_launch_<machine_name>.sh

After launching this command, you can:

• Edit the generated configuration
• Reorganize the scripts to mutualize if possible/necessary

Create configuration from scratch

shell
$ ztraining2strigo create --help
usage: ztraining2strigo create [-h]

optional arguments:
  -h, --help  show this help message and exit

This command can be used to create a configuration and the corresponding Strigo class.

• All necessary elements of a Strigo class will be asked interactively
• The configuration will be stored inside a strigo.json file at the root of your training (or one referenced by --config)
• The class will be created on Strigo

Update Strigo class from configuration

shell
$ ztraining2strigo update --help
usage: ztraining2strigo update [-h] [--dry-run] [--diff]

optional arguments:
  -h, --help     show this help message and exit
  --dry-run, -n  Do not perform update
  --diff, -d     Display diff of changes to apply in machines scripts

This command can be used to update a Strigo class from local configuration.

• Update is idempotent: if Strigo class is already as described by configuration, nothing will be done
• It is possible to check if an updated should be performed by using the --dry-run option

Configuration

Configuration is stored in JSON format inside a strigo.json file at the root of your training (or one referenced by --config).

There is a JSON Schema available at <[***]>.

• id: the Strigo ID of the class, shouldn't be changed
• name: the name of the class
• description: the list of lines of description of the class (can be empty list [])
• labels: the list of labels the class (can be empty list [])
• presentations: the list of presentation materials, can only contains 1 element for now (Strigo model)
  • file: the path to presentation file (typically pdf/Zenika-Formation-xxx-Slides.pdf or pdf/Zenika-training-material-Slides.pdf)
  • notes_source: the path to the listing of slides for notes extraction (should be Slides/slides.json)
• resources: the list of lab machines
  • name: the display name of the machine
  • instance_type: the size of the machine (one of t3.medium, t3.large or t3.xlarge, see AWS EC2 T3 Instances)
  • image: the machine image, can be the normalized name of the preconfigured Strigo images (lower case, space replaced by simple hyphen -), or a custom image:
    • image_id: the AMI ID
    • image_user: the default user of the AMI
    • ec2_region: the region of the AMI
  • init_scripts: the list of init scripts to use for the machine, content of all the scripts will be concatenated into 1 init script in Strigo. Can be either:
    • a reference to a local script:
      • path: the path of the local script inside the training repository
    • a reference to a script from strigo-init-script-libs:
      • script: the filename of the script
      • version: the git version of the script to get (defaults to main)
      • env: the mapping of environment variables for the script
  • post_launch_scripts: the list of post launch batch scripts (Windows only) to use for the machine, content of all the scripts will be concatenated into 1 init script in Strigo. Same format as init_scripts
  • view_interface: the default interface of the machine (one of terminal or desktop, defaults to none)
  • webview_links: the list of web interfaces of the machine:
    • name: the name of the interface
    • url: the URL of the interface (something of the form [***]<port>)

Example:

json
{
  "$schema": "[***]",
  "name": "My training",
  "id": "43t8s3ZNSGwy89Ffo",
  "description": [
    "The description of the training",
    "",
    "Can be on multiple lines in a list"
  ],
  "presentations": [
    {
      "file": "pdf/Zenika-training-material-Slides.pdf",
      "notes_source": "Slides/slides.json"
    }
  ],
  "resources": [
    {
      "name": "machine1",
      "instance_type": "t2.medium",
      "image": "ubuntu-16.04.2",
      "init_scripts": [
        {
          "path": "Installation/strigo/init_all.sh"
        },
        {
          "path": "Installation/strigo/init_machine1.sh"
        },
        {
          "script": "code-server.sh",
          "env": {
            "code_server_version": "3.11.1",
            "code_server_extensions": "ms-azuretools.vscode-docker coenraads.bracket-pair-colorizer-2",
            "code_server_settings": "{\"workbench.colorTheme\": \"Default Dark+\"}"
          }
        }
      ],
      "post_launch_scripts": [],
      "webview_links": [
        {
          "name": "code-server",
          "url": "[***]"
        }
      ]
    },
    {
      "name": "machine2",
      "instance_type": "t2.xlarge",
      "image": {
        "image_id": "ami-0b209583a4a1146dd",
        "image_user": "ubuntu",
        "ec2_region": "eu-west-3"
      },
      "init_scripts": [
        {
          "path": "Installation/strigo/init_all.sh"
        }
      ],
      "post_launch_scripts": [],
      "webview_links": []
    }
  ]
}

Build package

Building requirement is a Python environment >= 3.11.

Python wheel

shell
./build.sh

Windows binary

powershell
.\build_windows.ps1

Docker image

shell
docker image build --tag zenika/ztraining2strigo .

Debugging

You can activate HTTP traces by setting the environment variable Z2S_TRACE_HTTP to 1 or True.