Infrastructure as Code (IaC) is a sizzling subject today, and the IaC software of selection is Terraform by HashiCorp. Terraform is a cloud provisioning product that gives infrastructure for any software. You’ll be able to confer with a protracted checklist of suppliers for any goal platform.
Terraform’s checklist of suppliers now consists of Cisco Modeling Labs (CML) 2, so we will use Terraform to regulate digital community infrastructure working on CML2. Preserve studying to learn to get began with Terraform and CML, from the preliminary configuration via its superior options.
How does Terraform work?
Terraform makes use of code to explain the specified state of the required infrastructure and monitor this state over the infrastructure’s lifetime. This code is written in HashiCorp Configuration Language (HCL). If it modifications, Terraform figures out all of the variations (state modifications) to replace the infrastructure and assist attain the brand new state. Ultimately, when the infrastructure isn’t wanted anymore, Terraform can destroy it.
A Terraform supplier presents assets (issues which have state) and knowledge sources (read-only knowledge with out state).
In CML2 phrases, examples embrace:
- Assets: Labs, nodes, hyperlinks
- Knowledge sources: Labs, nodes, and hyperlinks, in addition to out there nodes and picture definitions, out there bridges for exterior connectors, and consumer lists and teams, and so on.
NOTE: At present, only some knowledge sources are carried out.
Getting began with Terraform and CML
To get began with Terraform and CML, you’ll want the next:
Outline and initialize a workspace
First, we’ll create a brand new listing and alter it as follows:
$ mkdir tftest $ cd tftest
All of the configuration and state required by Terraform stays on this listing.
The code snippets offered want to enter a Terraform configuration file, sometimes a file referred to as most important.tf. Nonetheless, configuration blocks will also be unfold throughout a number of recordsdata, as Terraform will mix all recordsdata with the .tf extension within the present working listing.
The next code block tells Terraform that we wish to use the CML2 supplier. It’ll obtain and set up the most recent out there model from the registry at initialization. We add this to a brand new file referred to as most important.tf:
terraform { required_providers { cml2 = { supply = "registry.terraform.io/ciscodevnet/cml2" } } }
With the supplier outlined, we will now initialize the atmosphere. This can obtain the supplier binary from the Hashicorp registry and set up it on the native laptop. It’ll additionally create numerous recordsdata and a listing that holds further Terraform configuration and state.
$ terraform init Initializing the backend... Initializing supplier plugins... - Discovering newest model of ciscodevnet/cml2... - Putting in ciscodevnet/cml2 v0.4.1... - Put in ciscodevnet/cml2 v0.4.1 (self-signed, key ID A97E6292972408AB) Associate and group suppliers are signed by their builders. If you would like to know extra about supplier signing, you may examine it right here: https://www.terraform.io/docs/cli/plugins/signing.html Terraform has created a lock file .terraform.lock.hcl to document the supplier picks it made above. Embody this file in your model management repository in order that Terraform can assure to make the identical picks by default while you run "terraform init" sooner or later. Terraform has been efficiently initialized! You might now start working with Terraform. Strive working "terraform plan" to see any modifications which can be required on your infrastructure. All Terraform instructions ought to now work. For those who ever set or change modules or backend configuration for Terraform, rerun this command to reinitialize your working listing. For those who overlook, different instructions will detect it and remind you to take action if essential. $
Configure the supplier
The CML2 terraform supplier wants credentials to entry CML2. These credentials are configured as proven within the following instance. In fact, tackle, username and password must match the precise atmosphere:
supplier "cml2" { tackle = "https://cml-controller.cml.lab" username = "admin" password = "supersecret" # skip_verify = true }
The skip_verify is commented out within the instance. You would possibly wish to uncomment it to work with the default certificates that’s shipped with the product, which is signed by the Cisco CML CA. Contemplate putting in a trusted certificates chain on the controller.
Whereas the above works OK, it’s not advisable to configure clear-text credentials in recordsdata which may find yourself in supply code administration (SCM). A greater strategy is to make use of atmosphere variables, ideally together with some tooling like direnv. As a prerequisite, the variables should be outlined inside the configuration:
variable "tackle" { description = "CML controller tackle" sort = string default = "https://cml-controller.cml.lab" } variable "username" { description = "cml2 username" sort = string default = "admin" } variable "password" { description = "cml2 password" sort = string delicate = true }
NOTE: Including the “delicate” attribute ensures that this worth just isn’t printed in any output.
We now can create a direnv configuration to insert values from the atmosphere into our supplier configuration by making a .envrc file. You can too obtain this by manually “sourcing” this file utilizing supply .envrc. The good thing about direnv is that this routinely occurs when becoming the listing.
TF_VAR_address="https://cml-controller.cml.lab" TF_VAR_username="admin" TF_VAR_password="secret" export TF_VAR_username TF_VAR_password TF_VAR_address
This decouples the Terraform configuration recordsdata from the credentials/dynamic values in order that they’ll simply be added to SCM, like Git, with out exposing delicate values, akin to passwords or addresses.
Outline the CML2 lab infrastructure
With the essential configuration carried out, we will now describe our CML2 lab infrastructure. We have now two choices:
- Import-mode
- Outline-mode
Import-mode
This imports an present CML2 lab YAML topology file as a Terraform lifecycle useful resource. That is the “one-stop” answer, defining all nodes, hyperlinks and interfaces in a single go. As well as, you should use Terraform templating to switch properties of the imported lab (see beneath).
Import-mode instance
Right here’s a easy import-mode instance:
useful resource "cml2_lifecycle" "this" { topology = file("topology.yaml") }
The file topology.yaml can be imported into CML2 after which began. We now must “plan” the change:
$ terraform plan Terraform used the chosen suppliers to generate the next execution plan. Useful resource actions are indicated with the next symbols: + create Terraform will carry out the next actions: # cml2_lifecycle.this can be created + useful resource "cml2_lifecycle" "this" { + booted = (recognized after apply) + id = (recognized after apply) + lab_id = (recognized after apply) + nodes = { } -> (recognized after apply) + state = (recognized after apply) + topology = (delicate worth) } Plan: 1 so as to add, 0 to vary, 0 to destroy. $
Then apply it (-auto-approve is a short-cut and needs to be dealt with with care):
$ terraform apply -auto-approve
Terraform used the chosen suppliers to generate the next execution plan. Useful resource actions are indicated with the next symbols: + create
Terraform will carry out the next actions: # cml2_lifecycle.this can be created + useful resource "cml2_lifecycle" "this" { + booted = (recognized after apply) + id = (recognized after apply) + lab_id = (recognized after apply) + nodes = { } -> (recognized after apply) + state = (recognized after apply) + topology = (delicate worth) } Plan: 1 so as to add, 0 to vary, 0 to destroy. cml2_lifecycle.this: Creating... cml2_lifecycle.this: Nonetheless creating... [10s elapsed] cml2_lifecycle.this: Nonetheless creating... [20s elapsed] cml2_lifecycle.this: Creation full after 25s [id=b75992ec-d345-4638-a6fd-2c0b640a3c22] Apply full! Assets: 1 added, 0 modified, 0 destroyed. $
We will now take a look at the state:
$ terraform present # cml2_lifecycle.this: useful resource "cml2_lifecycle" "this" { booted = true id = "b75992ec-d345-4638-a6fd-2c0b640a3c22" nodes = { # (3 unchanged parts hidden) } state = "STARTED" topology = (delicate worth) } $ terraform console > keys(cml2_lifecycle.this.nodes) tolist([ "0504773c-5396-44ff-b545-ccb734e11691", "22271a81-1d3a-4403-97de-686ebf0f36bc", "2bccca61-d4ee-459a-81bd-96b32bdaeaed", ]) > cml2_lifecycle.this.nodes["0504773c-5396-44ff-b545-ccb734e11691"].interfaces[0].ip4[0] "192.168.122.227" > exit $
Easy import instance with a template
This instance is just like the one above, however this time we import the topology utilizing templatefile(), which permits templating of the topology. Assuming that the CML2 topology YAML file begins with
lab: description: "description" notes: "notes" timestamp: 1606137179.2951126 title: ${toponame} model: 0.0.4 nodes: - id: n0 [...]
then utilizing this HCL
useful resource "cml2_lifecycle" "this" { topology = templatefile("topology.yaml", { toponame = "yolo lab" }) }
will substitute the title: ${toponame} from the YAML with the content material of the string “yolo lab” at import time. Notice that as a substitute of a string literal, it’s completely high quality to make use of a variable like var.toponame or different HCL options!
Outline-mode utilization
Outline-mode begins with the definition of a lab useful resource after which provides node and hyperlink assets. On this mode, assets will solely be created. If we wish to management the runtime state (e.g., begin/cease/wipe the lab), then we have to hyperlink these parts to a lifecycle useful resource.
Right here’s an instance:
useful resource "cml2_lab" "this" { } useful resource "cml2_node" "ext" { lab_id = cml2_lab.this.id nodedefinition = "external_connector" label = "Web" configuration = "bridge0" } useful resource "cml2_node" "r1" { lab_id = cml2_lab.this.id label = "R1" nodedefinition = "alpine" } useful resource "cml2_link" "l1" { lab_id = cml2_lab.this.id node_a = cml2_node.ext.id node_b = cml2_node.r1.id }
This can create the lab, the nodes, and the hyperlink between them. With out additional configuration, nothing can be began. If these assets needs to be began, then you definately’ll want a CML2 lifecycle useful resource:
useful resource "cml2_lifecycle" "prime" { lab_id = cml2_lab.this.id parts = [ cml2_node.ext.id, cml2_node.r2.id, cml2_link.l1.id, ] }
Right here’s what this appears to be like like after making use of the mixed plan.
NOTE: For brevity, some attributes are omitted and have been changed by […]:
$ terraform apply -auto-approve Terraform used the chosen suppliers to generate the next execution plan. Useful resource actions are indicated with the next symbols: + create Terraform will carry out the next actions: # cml2_lab.this can be created + useful resource "cml2_lab" "this" { + created = (recognized after apply) + description = (recognized after apply) + teams = [ ] -> (recognized after apply) + id = (recognized after apply) [...] + title = (recognized after apply) } # cml2_lifecycle.prime can be created + useful resource "cml2_lifecycle" "prime" { + booted = (recognized after apply) + parts = [ + (known after apply), + (known after apply), + (known after apply), ] + id = (recognized after apply) + lab_id = (recognized after apply) + nodes = { } -> (recognized after apply) + state = (recognized after apply) } # cml2_link.l1 can be created + useful resource "cml2_link" "l1" { + id = (recognized after apply) + interface_a = (recognized after apply) + interface_b = (recognized after apply) + lab_id = (recognized after apply) + label = (recognized after apply) + link_capture_key = (recognized after apply) + node_a = (recognized after apply) + node_a_slot = (recognized after apply) + node_b = (recognized after apply) + node_b_slot = (recognized after apply) + state = (recognized after apply) } # cml2_node.ext can be created + useful resource "cml2_node" "ext" { + configuration = (recognized after apply) + cpu_limit = (recognized after apply) + cpus = (recognized after apply) [...] + x = (recognized after apply) + y = (recognized after apply) } # cml2_node.r1 can be created + useful resource "cml2_node" "r1" { + configuration = (recognized after apply) + cpu_limit = (recognized after apply) + cpus = (recognized after apply) [...] + x = (recognized after apply) + y = (recognized after apply) } Plan: 5 so as to add, 0 to vary, 0 to destroy. cml2_lab.this: Creating... cml2_lab.this: Creation full after 0s [id=306f3ebf-c819-4b89-a99d-138a58ca7195] cml2_node.ext: Creating... cml2_node.r2: Creating... cml2_node.ext: Creation full after 1s [id=32f187bf-4f53-462a-8e36-43cd9b6e17a4] cml2_node.r2: Creation full after 1s [id=5d59a0d3-70a1-45a1-9b2a-4cecd9a4e696] cml2_link.l1: Creating... cml2_link.l1: Creation full after 0s [id=a083c777-abab-47d2-95c3-09d897e01d2e] cml2_lifecycle.prime: Creating... cml2_lifecycle.prime: Nonetheless creating... [10s elapsed] cml2_lifecycle.prime: Nonetheless creating... [20s elapsed] cml2_lifecycle.prime: Creation full after 22s [id=306f3ebf-c819-4b89-a99d-138a58ca7195] Apply full! Assets: 5 added, 0 modified, 0 destroyed. $
The parts lifecycle attribute is required to tie the person nodes and hyperlinks into the lifecycle useful resource. This ensures the proper sequence of operations primarily based on the dependencies between the assets.
NOTE: It’s not attainable to make use of each import and parts on the similar time. As well as, when importing a topology utilizing the topology attribute, a lab_id can’t be set.
Superior utilization
The lifecycle useful resource has a couple of extra configuration parameters that management superior options. Right here’s an inventory of these parameters and what they do:
- configs is a map of strings. The keys are node labels, and the values are node configurations. When these are current, the supplier will examine for all node labels to see whether or not they’re matching and, if they’re, substitute the node’s configuration with the supplied configuration. This lets you “inject” configurations right into a topology file. The bottom topology file may haven’t any configurations, by which case the precise configurations can be supplied by way of an instance file(“node1-config”) or a literal configuration string, as proven right here:
configs = { "node-1": file("node1-config") "node-2": "hostname node2" }
- staging defines the node begin sequence when the lab is began. Node tags are used to realize this. Right here’s an instance:
staging = { phases = ["infra", "core", "site-1"] start_remaining = true }
The given instance ensures that nodes with the tag “infra” are began first. The supplier waits till all nodes with this tag are marked as “booted.” Then, all nodes with the tag “core” are began, and so forth. If, after the tip of the stage checklist, there are nonetheless stopped nodes, then the start_remaining flag determines whether or not they need to stay stopped or needs to be began as properly (the default is true, e.g., they are going to all be began).
- state defines the runtime state of the lab. By default that is STARTED, which suggests the lab can be began. Choices are STARTED, STOPPED, and DEFINED_ON_CORE
– STARTED is the default
– STOPPED could be set if the lab is at the moment began, in any other case it’s going to produce a failure
– DEFINED_ON_CORE is wiping the lab if the present state is both STARTED or STOPPED
- timeouts can be utilized to set totally different timeouts for operations. This is perhaps essential for giant labs that take a very long time to start out. The defaults are set to 2h .
- wait is a boolean flag, which defines whether or not the supplier ought to watch for convergence (for instance, when the lab begins, and that is set to false, then the supplier will begin the lab however won’t wait till all nodes inside the lab are “prepared”).
- id is a read-only computed attribute. A UUIDv4 can be auto-generated at create time and assigned to this ID.
CRUD operations
Of the 4 primary operations of useful resource administration, create, learn, replace, and delete (CRUD), the earlier sections primarily described the create and skim side. However Terraform also can take care of replace and delete.
Plans could be modified, new assets could be added, and present assets could be eliminated or modified. That is at all times a results of modifying/altering your Terraform configuration recordsdata after which having Terraform determine the required state modifications by way of the terraform plan adopted by a terraform apply as soon as you might be glad with these modifications.
Updating assets
It’s attainable to replace assets, however not each mixture is seamless. Right here are some things to think about:
- Only some node attributes could be modified seamlessly; examples are coordinates (x/y), label, and configuration
- Some plan modifications will re-create assets. For instance, working nodes can be destroyed and restarted is that if the node definition is modified
Deleting assets
Lastly, a terraform destroy will delete all created assets from the controller.
Knowledge Sources
Versus assets, knowledge sources don’t maintain any state. They’re used to learn knowledge from the controller. This knowledge can then be used to reference parts in different knowledge sources or assets. A superb instance, though not but carried out, can be an inventory of accessible node- and image-definitions. By studying these into a knowledge supply, the HCL defining the infrastructure may take out there definitions under consideration.
There are, nonetheless, a couple of knowledge sources carried out:
- Node: Reads a node by offering a lab and a node ID
- Lab: Reads a lab by offering both a lab ID or a lab title
Output
All knowledge in assets and knowledge sources can be utilized to drive output from Terraform. A helpful instance within the context of CML2 is the retrieval of IP addresses from working nodes. Right here’s the best way to do it, assuming that the lifecycle useful resource is named this and likewise assuming that R1 is ready to purchase an IP tackle by way of an exterior connector:
cml2_lifecycle.this.nodes["0504773c-5396-44ff-b545- ccb734e11691"].interfaces[0].ip4[0]
Notice, nonetheless, that output can be calculated when assets may not exist, so the above will give an error because of the node not being discovered or the interface checklist being empty. To protect in opposition to this, you should use HCL:
output "r1_ip_address" { worth = ( cml2_lifecycle.prime.nodes[cml2_node.r1.id].interfaces[0].ip4 == null ? "undefined" : ( size(cml2_lifecycle.prime.nodes[cml2_node.r1.id].interfaces[0].ip4) > 0 ? cml2_lifecycle.prime.nodes[cml2_node.r1.id].interfaces[0].ip4[0] : "no ip" ) ) }
Output:
r1_ip_address = "192.168.255.115"
Conclusion
The CML2 supplier matches properly into the general Terraform eco-system. With the flexibleness HCL offers and by combining it with different Terraform suppliers, it’s by no means been simpler to automate digital community infrastructure inside CML2. What is going to you do with these new capabilities? We’re curious to listen to about it! Let’s proceed the dialog on the Cisco Studying Community’s Cisco Modeling Labs Neighborhood.
Single customers should buy Cisco Modeling Labs – Private and Cisco Modeling Labs – Private Plus licenses from the Cisco Studying Community Retailer. For groups, discover CML – Enterprise and CML – Larger Schooling licensing and make contact with us to find out how Cisco Modeling Labs can energy your NetDevOps transformation.
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References
- https://developer.hashicorp.com/terraform/tutorials/aws-get-started/install-cli
- https://github.com/CiscoDevNet/terraform-provider-cml2
- https://registry.terraform.io/suppliers/CiscoDevNet/cml2
- https://developer.hashicorp.com/terraform/language
- https://direnv.web/
- Picture by Dall-E (https://labs.openai.com/)
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