CLI
The Pipekit CLI allows you to interact with Pipekit services without leaving your terminal.
Last updated
The Pipekit CLI allows you to interact with Pipekit services without leaving your terminal.
Last updated
To install the Pipekit CLI, fetch the precompiled binaries or packages from our page, or use the following install instructions, depending on your OS:
Release notes are published on the .
We produce a -based Docker image that contains the Pipekit CLI. To use it you can run a docker run
command appended with the pipekit arguments you require. You can by providing the required environment variables:
It would be more common to use the container within a workflow. An example of this is shown below:
You need to authenticate before using the Pipekit CLI to submit workflows. This is done using your username & password, or using single sign-on (SSO), depending on how your account was created. Running the login
command, you're prompted to choose one of the two mentioned methods:
After you've successfully logged in, either you provided your username and password or you've gone through the SSO pathway, selecting your identity provider and following the redirects through your browser, you should see the following message in your terminal:
Pipekit CLI stores your access token in your home directory, under ~/.pipekit/
.
You can also log in non-interactively, by providing your username and password as arguments to the login
command:
or
Alternatively, you can provide your credentials using environment variables:
To submit a workflow yaml for execution, use the submit
command. Upon submission, Pipekit will run this workflow on the specified cluster; creating a new Pipe if none of the existing Pipes have the required name. Pipes can be automatically created when a workflow is submitted, in that case, they will be named after the value of Argo Workflow's generateName
property. All future submissions of the workflow with the same generateName
will create new runs grouped under the same Pipe.
To submit
a workflow, you need to provide the path to the workflow yaml as an argument, and a list of flags, out of which only cluster-name
is required, example:
--namespace
-n
string
name of the namespace to submit the workflow to
--cluster-name
-c
string
name of the cluster to submit the workflow to
--open-ui
bool
open UI for the submitted workflow
--parameter
-p
stringArray (key=value)
pass input parameter(s)
--wait
-w
bool
wait for the submitted workflow to complete
--pipe-name
-d
string
name of the pipe to submit the workflow to
--json
bool
output the run details in JSON format
To get information about a run, use the get
command. For example:
You can also get information about a run by providing the cluster name, namespace and workflow name. In case there are multiple runs for the given workflow name, the latest one will be returned. For example:
--run-uuid
-r
string
run UUID of the workflow you want to get
--cluster-name
-c
string
name of the cluster where workflow is located
--namespace
-n
string
k8s namespace where workflow is/was running
--workflow-name
-w
string
name of the workflow
The Pipekit CLI offers a list
command that can be used to fetch and list pipekit entities, such as cluster, Pipes and runs.
To list all Pipes that have at least one run on a given cluster:
--all
-A
bool
get all Pipes, both enabled and disabled
--cluster-name
-c
string
the name of the cluster to list from (required)
--enabled
bool
enabled/disabled switch (default true)
To list all runs on a given cluster:
--cluster-name
-c
string
name of the cluster to list from
--all
-A
bool
get all runs, regardless of the status
--statuses
-s
stringArray
get runs with a given status (defaults to running)
There are certain actions that can be applied to runs after the workflow is submitted, those are stop
, terminate
and restart
.
To stop a running workflow:
To terminate a running workflow (stop immediately without running the exit handlers):
Both of these commands have the same flags:
--run-uuid
-r
string
run UUID of the running workflow you want to stop/terminate (required)
To restart a run, resubmitting the workflow for execution:
--run-uuid
-r
string
run UUID of the run you want to restart (required)
--open-ui
bool
open the UI of the resubmitted workflow
--wait
-w
bool
wait for the workflow to complete
Use the import workflows
command in the Pipekit CLI:
--cluster-name
-c
string
name of the cluster to assign workflows to (required)
--argoServerURI
string
Argo Server URI (defaults to localhost:2746)
--argoAuthToken
string
Argo Server authentication token
--batchSize
integer
number of workflows to fetch from the Workflow Archive per API call (defaults to 10)
--argoServerSubPath
string
sub path used when connecting to Argo Server
--insecure
-k
bool
skip TLS verification (defaults to false)
--useSSL
bool
use SSL when connecting to Argo Server (defaults to true)
Use the hera
command in the pipekit CLI:
To observe pod logs directly in the Pipekit CLI, use the logs
command:
Logs of a given pipe run can be filtered by the pod name and container name.
--run-uuid
-r
string
UUID of the run (required)
--container
-c
string
name of the container to filter logs by
--pod
-p
string
name of the pod to filter logs by
--node-id
-n
string
id of the workflow node to filter logs by
--follow
-f
bool
follow logs stream until run is finished
The latest
tag is always pinned to the latest released version of the Pipekit CLI. To find the a specific version, .
Containers are available in linux/amd64 and linux/arm64 variants. Both use Linux as the base image.
Or you can copy the binary into a container that you control. See "" for an example.
You can also use the Pipekit CLI within another container. This example Dockerfile shows how you could install the Pipekit CLI into a :
An SBOM for the CLI container is embedded within the container image in SPDX format. Further information is available on the page.
An admin can import archived workflows from an to Pipekit. This action will import the workflows and their metadata into Pipekit.
The import of is not supported.
Users can run the pipekit hera
command to get a token to run their workflows (and CronWorkflows) through Pipekit using the .
See the for more details and examples.