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Infrastructure as Code (IaC) is a scorching subject today, and the IaC software of alternative is Terraform by HashiCorp. Terraform is a cloud provisioning product that gives infrastructure for any software. You’ll be able to consult with a protracted record of suppliers for any goal platform.
Terraform’s record of suppliers now consists of Cisco Modeling Labs (CML) 2, so we will use Terraform to manage digital community infrastructure working on CML2. Hold studying to learn to get began with Terraform and CML, from the preliminary configuration by means of 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 adjustments, Terraform figures out all of the variations (state adjustments) to replace the infrastructure and assist attain the brand new state. Finally, when the infrastructure isn’t wanted anymore, Terraform can destroy it.
A Terraform supplier provides sources (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 accessible nodes and picture definitions, accessible bridges for exterior connectors, and person lists and teams, and so on.
NOTE: At present, just a few 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 introduced want to enter a Terraform configuration file, sometimes a file known as essential.tf. Nonetheless, configuration blocks can 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 need to use the CML2 supplier. It can obtain and set up the most recent accessible model from the registry at initialization. We add this to a brand new file known as essential.tf:
terraform { required_providers { cml2 = { supply = "registry.terraform.io/ciscodevnet/cml2" } } }
With the supplier outlined, we will now initialize the atmosphere. This may obtain the supplier binary from the Hashicorp registry and set up it on the native pc. It can 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) Accomplice and neighborhood suppliers are signed by their builders. If you would like to know extra about supplier signing, you possibly can 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. Embrace 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! It's possible you'll now start working with Terraform. Strive working "terraform plan" to see any adjustments which are required to your infrastructure. All Terraform instructions ought to now work. Should you ever set or change modules or backend configuration for Terraform, rerun this command to reinitialize your working listing. Should you overlook, different instructions will detect it and remind you to take action if obligatory. $
Configure the supplier
The CML2 terraform supplier wants credentials to entry CML2. These credentials are configured as proven within the following instance. After all, deal with, username and password have to match the precise atmosphere:
supplier "cml2" { deal with = "https://cml-controller.cml.lab" username = "admin" password = "supersecret" # skip_verify = true }
The skip_verify is commented out within the instance. You may need to uncomment it to work with the default certificates that’s shipped with the product, which is signed by the Cisco CML CA. Think about 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 that may find yourself in supply code administration (SCM). A greater method is to make use of atmosphere variables, ideally together with some tooling like direnv. As a prerequisite, the variables should be outlined throughout the configuration:
variable "deal with" { description = "CML controller deal with" kind = string default = "https://cml-controller.cml.lab" } variable "username" { description = "cml2 username" kind = string default = "admin" } variable "password" { description = "cml2 password" kind = 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. It’s also possible to obtain this by manually “sourcing” this file utilizing supply .envrc. The good thing about direnv is that this robotically occurs when turning into 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 will simply be added to SCM, like Git, with out exposing delicate values, resembling passwords or addresses.
Outline the CML2 lab infrastructure
With the essential configuration finished, we will now describe our CML2 lab infrastructure. We have now two choices:
- Import-mode
- Outline-mode
Import-mode
This imports an current 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 need to use Terraform templating to interchange 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 will probably be imported into CML2 after which began. We now have to “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 will probably 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 alter, 0 to destroy. $
Then apply it (-auto-approve is a short-cut and must be dealt with with care):
$ terraform apply -auto-approveTerraform 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 will probably 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 alter, 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 components 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 much 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. Observe that as an alternative of a string literal, it’s completely fantastic 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 sources. On this mode, sources will solely be created. If we need to management the runtime state (e.g., begin/cease/wipe the lab), then we have to hyperlink these components 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 may create the lab, the nodes, and the hyperlink between them. With out additional configuration, nothing will probably be began. If these sources must be began, then you definitely’ll want a CML2 lifecycle useful resource:
useful resource "cml2_lifecycle" "prime" { lab_id = cml2_lab.this.id components = [ 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 will probably 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 will probably be created + useful resource "cml2_lifecycle" "prime" { + booted = (recognized after apply) + components = [ + (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 will probably 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 will probably 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 will probably 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 alter, 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 components lifecycle attribute is required to tie the person nodes and hyperlinks into the lifecycle useful resource. This ensures the proper sequence of operations based mostly on the dependencies between the sources.
NOTE: It’s not doable to make use of each import and components 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 a listing 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 test for all node labels to see whether or not they’re matching and, if they’re, substitute the node’s configuration with the offered configuration. This lets you “inject” configurations right into a topology file. The bottom topology file might haven’t any configurations, by which case the precise configurations could be offered 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 top of the stage record, there are nonetheless stopped nodes, then the start_remaining flag determines whether or not they need to stay stopped or must be began as nicely (the default is true, e.g., they may all be began).
- state defines the runtime state of the lab. By default that is STARTED, which implies the lab will probably be began. Choices are STARTED, STOPPED, and DEFINED_ON_CORE
– STARTED is the default
– STOPPED will be set if the lab is presently began, in any other case it can 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 completely different timeouts for operations. This could be obligatory 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 anticipate convergence (for instance, when the lab begins, and that is set to false, then the supplier will begin the lab however is not going to wait till all nodes throughout the lab are “prepared”).
- id is a read-only computed attribute. A UUIDv4 will probably 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 facet. However Terraform may also take care of replace and delete.
Plans will be modified, new sources will be added, and current sources will be eliminated or modified. That is all the time a results of modifying/altering your Terraform configuration recordsdata after which having Terraform determine the required state adjustments by way of the terraform plan adopted by a terraform apply as soon as you might be glad with these adjustments.
Updating sources
It’s doable to replace sources, however not each mixture is seamless. Right here are some things to think about:
- Just a few node attributes will be modified seamlessly; examples are coordinates (x/y), label, and configuration
- Some plan adjustments will re-create sources. For instance, working nodes will probably be destroyed and restarted is that if the node definition is modified
Deleting sources
Lastly, a terraform destroy will delete all created sources from the controller.
Knowledge Sources
Versus sources, knowledge sources don’t maintain any state. They’re used to learn knowledge from the controller. This knowledge can then be used to reference components in different knowledge sources or sources. A great instance, though not but carried out, could be a listing of accessible node- and image-definitions. By studying these into an information supply, the HCL defining the infrastructure might take accessible 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 sources 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 way in which to do it, assuming that the lifecycle useful resource known as this and likewise assuming that R1 is ready to purchase an IP deal with by way of an exterior connector:
cml2_lifecycle.this.nodes["0504773c-5396-44ff-b545- ccb734e11691"].interfaces[0].ip4[0]
Observe, nonetheless, that output can be calculated when sources may not exist, so the above will give an error as a result of node not being discovered or the interface record being empty. To protect towards this, you need to 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 suits properly into the general Terraform eco-system. With the flexibleness HCL gives 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 purchase 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 get in touch with us to learn the way 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.internet/
- Picture by Dall-E (https://labs.openai.com/)
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