# MAIA Toolkit
[](https://github.com/kthcloud/MAIA/actions/workflows/build.yaml)
[](https://maia-toolkit.readthedocs.io/en/latest/?badge=latest)
[](https://opensource.org/licenses/GPL-3.0)
MAIA Toolkit is a python package to interact with a Kubernetes cluster, to create custom environments and deploy
applications in MAIA (including pods, services and ingresses).
# Installation
The requirements for the package are ``Helm`` and ``kubectl``. To install
the package, clone the repository and run:
```shell
pip install maia-toolkit
```
To install ``Helm`` follow the instructions in the [Helm documentation](https://helm.sh/docs/intro/install/).
To install ``kubectl`` follow the instructions in
the [Kubernetes documentation](https://kubernetes.io/docs/tasks/tools/install-kubectl/).
# Deploying a MAIA Namespace
To deploy a MAIA namespace in a Kubernetes cluster, the script `MAIA_deploy_MAIA_namespace` can be used.
The script requires a configuration file with the following parameters:
```yaml
group_subdomain: <> # The group subdomain to be used in the URLs
group_ID: <> # The group ID in Keycloak, following the format MAIA:<group_ID>
users: # List of user emails to be added to the group
-
-
resources_limits: # List of resources limits to be used in the namespace
memory:
- "4G" # Memory usage lower limit
- "8G" # Memory usage upper limit
cpu:
- 4.0 # CPU usage lower limit
- 4.0 # CPU usage upper limit
gpu_request: "1" # Number of GPUs to be requested per user ( omit the field if no GPU is needed)
```
And, additionally, a cluster-specific configuration file with the following parameters:
```yaml
docker_server: "" # Docker server URL
docker_username: "" # Docker username
docker_password: "" # Docker password
storage_class: "" # k8s Storage class to be used
shared_storage_class: "" # k8s Storage class to be used for shared storage
traefik_resolver: "" # Traefik resolver to be used for k8s Ingress (only for Traefik)
hub_storage_class": "" # k8s Storage class to be used for JupyterHub storage
url_type: "subdomain" # URL type to be used for the MAIA Applications (subdomain or path)
domain: "" # k8s cluster domain
imagePullSecrets: "" # Image pull secrets to be used
admins: # List of admin emails
- ""
- ""
ssh_port_type: "" # SSH port type to be used. It can be either "NodePort" or "LoadBalancer"
ssh_hostname: "" # SSH hostname to be used
port_range: # Port range to be used for SSH ports, according to the cluster configuration for NodePort or LoadBalancer
- MIN_PORT
- MAX_PORT
keycloack: # Keycloak configuration for Authentication
client_id: "" # Keycloak client ID
issuer_url: "" # Keycloak issuer URL
client_secret: "" # Keycloak client secret
authorize_url: "" # Keycloak authorize URL
token_url: "" # Keycloak token URL
userdata_url: "" # Keycloak user data URL
```
Finally, a MAIA configuration file with the following parameters:
```yaml
orthanc_ohif:
image: "" # Orthanc-OHIF-MONAI Label image
tag: "" # Orthanc-OHIF-MONAI Label tag
nginx_proxy_image: "" # MAIA Nginx proxy image
maia_addons_version: "" # MAIA Addons chart version
maia_workspace_version: "" # MAIA Workspace tag
maia_workspace_image: "" # MAIA Workspace image
```
In order to deploy the MAIA namespace, the `minio` and `kustomize` CLI should be installed locally, to be able to
interact with the cluster.
To install the `minio` CLI, run:
```shell
curl https://dl.min.io/client/mc/release/linux-amd64/mc --create-dirs -o /usr/local/bin/mc
chmod +x /usr/local/bin/mc
```
To install the `kustomize` CLI, run:
```shell
cd /usr/local/bin && curl -s "https://raw.githubusercontent.com/kubernetes-sigs/kustomize/master/hack/install_kustomize.sh" | bash
```
To deploy the MAIA namespace, run:
```shell
export KUBECONFIG=<PATH/TO/KUBECONFIG>
MAIA_deploy_MAIA_namespace --namespace-config-file <PATH/TO/CONFIG/FILE> --cluster-config-file <PATH/TO/CLUSTER/CONFIG/FILE> --config-folder <PATH/TO/CONFIG/FOLDER> --maia-config-file <PATH/TO/MAIA/CONFIG/FILE>
```
## Offline Deployment
If you only want to create a deployment script, to review and run it later, you can use the `--create-script` flag:
```shell
MAIA_deploy_MAIA_namespace --namespace-config-file <PATH/TO/CONFIG/FILE> --cluster-config-file <PATH/TO/CLUSTER/CONFIG/FILE> --config-folder <PATH/TO/CONFIG/FOLDER> --maia-config-file <PATH/TO/MAIA/CONFIG/FILE> --create-script
```
## Minimal Installation
A minimal installation can be done, only deploying the JupyterHub interface and the required SSH services.
To install the MAIA namespace with the minimal configuration, you can use the `--minimal` flag:
```shell
MAIA_deploy_MAIA_namespace --namespace-config-file <PATH/TO/CONFIG/FILE> --cluster-config-file <PATH/TO/CLUSTER/CONFIG/FILE> --config-folder <PATH/TO/CONFIG/FOLDER> --maia-config-file <PATH/TO/MAIA/CONFIG/FILE> --minimal
```
# Deploy an Application in MAIA Namespace
The script to deploy custom applications uses Helm charts to deploy the applications, and it is available as a Helm
chart: [MAIA](https://github.com/kthcloud/MAIA).
With the **MAIA** chart it is possible to deploy any *Docker Image* as a Pod, expose the required ports as
services, mount persistent volumes on the specified locations and optionally create Ingress resources to expose the
application to the external traffic using the HTTPS protocol.
To add the chart to Helm, run:
```
helm repo add maia https://kthcloud.github.io/MAIA/
helm repo update
```
## Custom Helm values
A number of custom parameters can be specified for the Helm chart, including the Docker image to deploy, the port to
expose, etc.
The custom configuration is set in a JSON configuration file, following the conventions described below.
### General Configuration
#### Namespace [Required]
Specify the Cluster Namespace where to deploy the resources
```json
{
"namespace": "NAMESPACE_NAME"
}
```
#### Chart Name [Required]
Specify the Helm Chart Release name
```json
{
"chart_name": "Helm_Chart_name"
}
```
#### Docker image [Required]
To specify the Docker image to deploy
```json
{
"docker_image": "DOCKER_IMAGE"
}
```
#### Requested Resources
To request resources (RAM,CPU and optionally GPU).
```json
{
"memory_request": "REQUESTED_RAM_SIZE",
"cpu_request": "REQUESTED_CPUs"
}
```
Optionally, to request GPU usage:
```json
{
"gpu_request": "NUMBER_OF_GPUs"
}
```
#### Allocation Time [Required]
Since each environment is deployed as a Job with a fixed allocation time, the user can specify the requested allocation
time (default in days) in the following field:
```json
{
"allocationTime": "2"
}
```
#### Services
To specify which ports (and corresponding services) can be reached from outside the pod.
```json
{
"ports": {
"SERVICE_NAME_1": [
"PORT_NUMBER"
],
"SERVICE_NAME_2": [
"PORT_NUMBER"
]
}
}
```
The default *Service Type* is **ClusterIP**. To expose a service as a type **NodePort**:
```json
{
"service_type": "NodePort",
"ports": {
"SERVICE_NAME_1": [
"PORT_NUMBER",
"NODE_PORT_NUMBER"
],
"SERVICE_NAME_2": [
"PORT_NUMBER",
"NODE_PORT_NUMBER"
]
}
}
```
#### Persistent Volumes
2 different types of persistent volumes are available: **hostPath** (local folder) and **nfs** (shared nfs folder).
For each of these types, it is possible to request a Persistent Volume via a Persistent Volume Claim.
The *"readOnly"* options can be added to specify the mounted folder as read-only.
Request PVC:
```json
{
"persistent_volume": [
{
"mountPath": "/mount/path_1",
"size": "VOLUME_SIZE",
"access_mode": "ACCESS_TYPE",
"pvc_type": "STORAGE_CLASS"
},
{
"mountPath": "/mount/path_2",
"size": "VOLUME_SIZE",
"access_mode": "ACCESS_TYPE",
"pvc_type": "STORAGE_CLASS"
}
]
}
```
**"STORAGE_CLASS"** can be any of the storage classes available on the cluster:
```
kubectl get sc
```
#### Existing Persistent Volumes
Previously created pv can be mounted into multiple pods (ONLY if the *access mode* was previously set to **ReadWriteMany
** )
```json
{
"existing_persistent_volume": [
{
"name": "EXISTING_PVC_NAME",
"mountPath": "/mount/path"
}
]
}
```
#### Mounted files
Single files can be mounted inside the Pod. First, a ConfigMap including the file is created, and then it is mounted
into the Pod.
```json
{
"mount_files": {
"file_name": [
"/local/file/path",
"/file/mount/path"
]
}
}
```
#### Node Selection
To optionally select which node in the cluster to use for deploying the application.
```json
{
"node_selector": "NODE_NAME"
}
```
#### GPU Selection
To optionally select which available GPUs in the cluster to request. `product` attribute can be specified.
Example: `product: "RTX-2070-Super"`
```json
{
"gpu_selector": {
"product": "GPU_TYPE"
}
}
```
#### Ingress
Used to create an Ingress resources to access the application at the specified port by using an HTTPS address.
Two types of Ingress are currently supported: **NGINX** and **TRAEFIK**.
IMPORTANT! The specified DNS needs to be active and connected to the cluster DNS (**".maia.cloud.cbh.kth.se"**)
IMPORTANT! When working with the **TRAEFIK** Ingress, the *traefik_middleware* and *traefik_resolver*
should be explicitly specified, since only oauth-based authenticated users can be authorized
through the ingress.
Contact the MAIA admin to retrieve this information.
IMPORTANT! When working with the **NGINX** Ingress, the *oauth_url* and *nginx_issuer* should be explicitly specified,
since only oauth-based authenticated users can be authorized through the ingress.
Contact the MAIA admin to retrieve this information.
```json
{
"ingress": {
"host": "SUBDOMAIN.maia.cloud.cbh.kth.se",
"port": "SERVICE_PORT",
"path": "/<PATH>",
"oauth_url": "SUBDOMAIN.maia.cloud.cbh.kth.se",
"nginx_issuer": "<NGINX_ISSUER_NAME>"
}
}
```
```json
{
"ingress": {
"host": "SUBDOMAIN.maia.cloud.cbh.kth.se",
"port": "SERVICE_PORT",
"path": "/<PATH>",
"traefik_middleware": "<MIDDLEWARE_NAME>",
"traefik_resolver": "<TRAEFIK_RESOLVER_NAME>"
}
}
```
#### Environment variables
To add environment variables, used during the creation and deployment of the pod (i.e., environment variables to specify
for the Docker Image).
```json
{
"env_variables": {
"KEY_1": "VAL_1",
"KEY_2": "VAL_2"
}
}
```
#### Deployment
By default, the deployment is done as a Job. To deploy as a Deployment, the following field should be added:
```json
{
"deployment": "true"
}
```
#### Commmand
To specify a custom command to run inside the container:
```json
{
"command": [
"command",
"arg1",
"arg2"
]
}
```
#### Image Pull Secret
If the Docker image is stored in a private repository, the user can specify the secret to use to pull the image.
```json
{
"image_pull_secret": "SECRET NAME"
}
```
#### User info
When deploying MAIA-based applications, it is possible to create single/multiple user account in the environment.
For each of the users, *username*, *password*, and, optionally, an *ssh public key* are required.
This information is stored inside Secrets:
```
USER_1_SECRET:
user: USER_1
password: pw
ssh_publickey [Optional]: "ssh-rsa ..."
```
To provide the user information to the Pod:
```json
{
"user_secret": [
"user-1-secret",
"user-2-secret"
],
"user_secret_params": [
"user",
"password",
"ssh_publickey"
]
}
```
## Configuration File Example
```json
{
"namespace": "demo",
"chart_name": "jupyterlab-1-v1",
"docker_image": "jupyter/scipy-notebook",
"tag": "latest",
"memory_request": "4Gi",
"allocationTime": "2",
"cpu_request": "5000m",
"ports": {
"jupyter": [
8888
]
},
"persistent_volume": [
{
"mountPath": "/home/jovyan",
"size": "100Gi",
"access_mode": "ReadWriteOnce",
"pvc_type": "microk8s-hostpath"
}
]
}
```
# Tools
Install the **MAIA** package running:
```
pip install maia-tookit
```
Requirements:
```
kubectl # Kubernetes CLI
helm # Kubernetes Package Manager
```
## Deploy Charts
To deploy a Hive Chart, first create a config file according to the specific requirements (as
described [above](#Custom Helm values)).
After creating the config file, run:
```shell
MAIA_deploy_helm_chart --config-file <PATH/TO/CONFIG/FILE>
```
Raw data
{
"_id": null,
"home_page": "https://github.com/kthcloud/MAIA.git",
"name": "maia-toolkit",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": null,
"keywords": "helm, kubernetes, maia, resource deployment",
"author": "Bendazzoli Simone",
"author_email": "simben@kth.se",
"download_url": "https://files.pythonhosted.org/packages/27/17/40fd85657b58d03acc72cded36439ff9c10605b7f03aba508d9ff95ef213/maia_toolkit-1.1.2.tar.gz",
"platform": "OS Independent",
"description": "# MAIA Toolkit\n\n[](https://github.com/kthcloud/MAIA/actions/workflows/build.yaml)\n\n[](https://maia-toolkit.readthedocs.io/en/latest/?badge=latest)\n\n[](https://opensource.org/licenses/GPL-3.0)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMAIA Toolkit is a python package to interact with a Kubernetes cluster, to create custom environments and deploy\napplications in MAIA (including pods, services and ingresses).\n\n# Installation\n\nThe requirements for the package are ``Helm`` and ``kubectl``. To install\nthe package, clone the repository and run:\n\n```shell\npip install maia-toolkit\n```\n\nTo install ``Helm`` follow the instructions in the [Helm documentation](https://helm.sh/docs/intro/install/).\nTo install ``kubectl`` follow the instructions in\nthe [Kubernetes documentation](https://kubernetes.io/docs/tasks/tools/install-kubectl/).\n\n# Deploying a MAIA Namespace\n\nTo deploy a MAIA namespace in a Kubernetes cluster, the script `MAIA_deploy_MAIA_namespace` can be used.\nThe script requires a configuration file with the following parameters:\n\n```yaml\ngroup_subdomain: <> # The group subdomain to be used in the URLs\ngroup_ID: <> # The group ID in Keycloak, following the format MAIA:<group_ID>\nusers: # List of user emails to be added to the group\n -\n -\nresources_limits: # List of resources limits to be used in the namespace\n memory:\n - \"4G\" # Memory usage lower limit\n - \"8G\" # Memory usage upper limit\n cpu:\n - 4.0 # CPU usage lower limit\n - 4.0 # CPU usage upper limit \ngpu_request: \"1\" # Number of GPUs to be requested per user ( omit the field if no GPU is needed)\n```\n\nAnd, additionally, a cluster-specific configuration file with the following parameters:\n\n```yaml\ndocker_server: \"\" # Docker server URL\ndocker_username: \"\" # Docker username\ndocker_password: \"\" # Docker password\nstorage_class: \"\" # k8s Storage class to be used\nshared_storage_class: \"\" # k8s Storage class to be used for shared storage\ntraefik_resolver: \"\" # Traefik resolver to be used for k8s Ingress (only for Traefik)\nhub_storage_class\": \"\" # k8s Storage class to be used for JupyterHub storage\nurl_type: \"subdomain\" # URL type to be used for the MAIA Applications (subdomain or path)\ndomain: \"\" # k8s cluster domain\nimagePullSecrets: \"\" # Image pull secrets to be used\nadmins: # List of admin emails\n - \"\"\n - \"\"\nssh_port_type: \"\" # SSH port type to be used. It can be either \"NodePort\" or \"LoadBalancer\"\nssh_hostname: \"\" # SSH hostname to be used \nport_range: # Port range to be used for SSH ports, according to the cluster configuration for NodePort or LoadBalancer\n - MIN_PORT\n - MAX_PORT\nkeycloack: # Keycloak configuration for Authentication\n client_id: \"\" # Keycloak client ID\n issuer_url: \"\" # Keycloak issuer URL\n client_secret: \"\" # Keycloak client secret\n authorize_url: \"\" # Keycloak authorize URL\n token_url: \"\" # Keycloak token URL\n userdata_url: \"\" # Keycloak user data URL\n```\n\nFinally, a MAIA configuration file with the following parameters:\n\n```yaml\northanc_ohif:\n image: \"\" # Orthanc-OHIF-MONAI Label image\n tag: \"\" # Orthanc-OHIF-MONAI Label tag\nnginx_proxy_image: \"\" # MAIA Nginx proxy image\nmaia_addons_version: \"\" # MAIA Addons chart version\nmaia_workspace_version: \"\" # MAIA Workspace tag\nmaia_workspace_image: \"\" # MAIA Workspace image\n```\n\nIn order to deploy the MAIA namespace, the `minio` and `kustomize` CLI should be installed locally, to be able to\ninteract with the cluster.\n\nTo install the `minio` CLI, run:\n\n```shell\ncurl https://dl.min.io/client/mc/release/linux-amd64/mc --create-dirs -o /usr/local/bin/mc\nchmod +x /usr/local/bin/mc\n```\n\nTo install the `kustomize` CLI, run:\n\n```shell\ncd /usr/local/bin && curl -s \"https://raw.githubusercontent.com/kubernetes-sigs/kustomize/master/hack/install_kustomize.sh\" | bash\n```\n\nTo deploy the MAIA namespace, run:\n\n```shell\nexport KUBECONFIG=<PATH/TO/KUBECONFIG>\n\nMAIA_deploy_MAIA_namespace --namespace-config-file <PATH/TO/CONFIG/FILE> --cluster-config-file <PATH/TO/CLUSTER/CONFIG/FILE> --config-folder <PATH/TO/CONFIG/FOLDER> --maia-config-file <PATH/TO/MAIA/CONFIG/FILE>\n```\n\n## Offline Deployment\n\nIf you only want to create a deployment script, to review and run it later, you can use the `--create-script` flag:\n\n```shell\nMAIA_deploy_MAIA_namespace --namespace-config-file <PATH/TO/CONFIG/FILE> --cluster-config-file <PATH/TO/CLUSTER/CONFIG/FILE> --config-folder <PATH/TO/CONFIG/FOLDER> --maia-config-file <PATH/TO/MAIA/CONFIG/FILE> --create-script\n```\n\n## Minimal Installation\n\nA minimal installation can be done, only deploying the JupyterHub interface and the required SSH services.\nTo install the MAIA namespace with the minimal configuration, you can use the `--minimal` flag:\n\n```shell\nMAIA_deploy_MAIA_namespace --namespace-config-file <PATH/TO/CONFIG/FILE> --cluster-config-file <PATH/TO/CLUSTER/CONFIG/FILE> --config-folder <PATH/TO/CONFIG/FOLDER> --maia-config-file <PATH/TO/MAIA/CONFIG/FILE> --minimal\n```\n\n# Deploy an Application in MAIA Namespace\n\nThe script to deploy custom applications uses Helm charts to deploy the applications, and it is available as a Helm\nchart: [MAIA](https://github.com/kthcloud/MAIA).\n\nWith the **MAIA** chart it is possible to deploy any *Docker Image* as a Pod, expose the required ports as\nservices, mount persistent volumes on the specified locations and optionally create Ingress resources to expose the\napplication to the external traffic using the HTTPS protocol.\n\nTo add the chart to Helm, run:\n\n```\nhelm repo add maia https://kthcloud.github.io/MAIA/\nhelm repo update\n```\n## Custom Helm values\n\nA number of custom parameters can be specified for the Helm chart, including the Docker image to deploy, the port to\nexpose, etc.\n\nThe custom configuration is set in a JSON configuration file, following the conventions described below.\n\n### General Configuration\n\n#### Namespace [Required]\n\nSpecify the Cluster Namespace where to deploy the resources\n\n```json\n{\n \"namespace\": \"NAMESPACE_NAME\"\n}\n```\n\n#### Chart Name [Required]\n\nSpecify the Helm Chart Release name\n\n```json\n{\n \"chart_name\": \"Helm_Chart_name\"\n}\n```\n\n#### Docker image [Required]\n\nTo specify the Docker image to deploy\n\n```json\n{\n \"docker_image\": \"DOCKER_IMAGE\"\n}\n```\n\n#### Requested Resources\n\nTo request resources (RAM,CPU and optionally GPU).\n\n```json\n{\n \"memory_request\": \"REQUESTED_RAM_SIZE\",\n \"cpu_request\": \"REQUESTED_CPUs\"\n}\n```\n\nOptionally, to request GPU usage:\n\n```json\n{\n \"gpu_request\": \"NUMBER_OF_GPUs\"\n}\n```\n\n#### Allocation Time [Required]\n\nSince each environment is deployed as a Job with a fixed allocation time, the user can specify the requested allocation\ntime (default in days) in the following field:\n\n```json\n{\n \"allocationTime\": \"2\"\n}\n```\n\n#### Services\n\nTo specify which ports (and corresponding services) can be reached from outside the pod.\n\n```json\n{\n \"ports\": {\n \"SERVICE_NAME_1\": [\n \"PORT_NUMBER\"\n ],\n \"SERVICE_NAME_2\": [\n \"PORT_NUMBER\"\n ]\n }\n}\n\n```\n\nThe default *Service Type* is **ClusterIP**. To expose a service as a type **NodePort**:\n\n```json\n{\n \"service_type\": \"NodePort\",\n \"ports\": {\n \"SERVICE_NAME_1\": [\n \"PORT_NUMBER\",\n \"NODE_PORT_NUMBER\"\n ],\n \"SERVICE_NAME_2\": [\n \"PORT_NUMBER\",\n \"NODE_PORT_NUMBER\"\n ]\n }\n}\n\n```\n\n#### Persistent Volumes\n\n2 different types of persistent volumes are available: **hostPath** (local folder) and **nfs** (shared nfs folder).\nFor each of these types, it is possible to request a Persistent Volume via a Persistent Volume Claim.\n\nThe *\"readOnly\"* options can be added to specify the mounted folder as read-only.\n\nRequest PVC:\n\n```json\n{\n \"persistent_volume\": [\n {\n \"mountPath\": \"/mount/path_1\",\n \"size\": \"VOLUME_SIZE\",\n \"access_mode\": \"ACCESS_TYPE\",\n \"pvc_type\": \"STORAGE_CLASS\"\n },\n {\n \"mountPath\": \"/mount/path_2\",\n \"size\": \"VOLUME_SIZE\",\n \"access_mode\": \"ACCESS_TYPE\",\n \"pvc_type\": \"STORAGE_CLASS\"\n }\n ]\n}\n```\n\n**\"STORAGE_CLASS\"** can be any of the storage classes available on the cluster:\n\n```\nkubectl get sc\n```\n\n#### Existing Persistent Volumes\n\nPreviously created pv can be mounted into multiple pods (ONLY if the *access mode* was previously set to **ReadWriteMany\n** )\n\n```json\n{\n \"existing_persistent_volume\": [\n {\n \"name\": \"EXISTING_PVC_NAME\",\n \"mountPath\": \"/mount/path\"\n }\n ]\n}\n```\n\n#### Mounted files\n\nSingle files can be mounted inside the Pod. First, a ConfigMap including the file is created, and then it is mounted\ninto the Pod.\n\n```json\n{\n \"mount_files\": {\n \"file_name\": [\n \"/local/file/path\",\n \"/file/mount/path\"\n ]\n }\n}\n```\n\n#### Node Selection\n\nTo optionally select which node in the cluster to use for deploying the application.\n\n```json\n{\n \"node_selector\": \"NODE_NAME\"\n}\n```\n\n#### GPU Selection\n\nTo optionally select which available GPUs in the cluster to request. `product` attribute can be specified.\nExample: `product: \"RTX-2070-Super\"`\n\n```json\n{\n \"gpu_selector\": {\n \"product\": \"GPU_TYPE\"\n }\n}\n```\n\n#### Ingress\n\nUsed to create an Ingress resources to access the application at the specified port by using an HTTPS address.\nTwo types of Ingress are currently supported: **NGINX** and **TRAEFIK**.\n\nIMPORTANT! The specified DNS needs to be active and connected to the cluster DNS (**\".maia.cloud.cbh.kth.se\"**)\n\nIMPORTANT! When working with the **TRAEFIK** Ingress, the *traefik_middleware* and *traefik_resolver* \nshould be explicitly specified, since only oauth-based authenticated users can be authorized\nthrough the ingress.\nContact the MAIA admin to retrieve this information.\n\nIMPORTANT! When working with the **NGINX** Ingress, the *oauth_url* and *nginx_issuer* should be explicitly specified,\nsince only oauth-based authenticated users can be authorized through the ingress.\nContact the MAIA admin to retrieve this information.\n\n\n```json\n{\n \"ingress\": {\n \"host\": \"SUBDOMAIN.maia.cloud.cbh.kth.se\",\n \"port\": \"SERVICE_PORT\",\n \"path\": \"/<PATH>\",\n \"oauth_url\": \"SUBDOMAIN.maia.cloud.cbh.kth.se\",\n \"nginx_issuer\": \"<NGINX_ISSUER_NAME>\"\n }\n \n}\n```\n\n```json\n{\n \"ingress\": {\n \"host\": \"SUBDOMAIN.maia.cloud.cbh.kth.se\",\n \"port\": \"SERVICE_PORT\",\n \"path\": \"/<PATH>\",\n \"traefik_middleware\": \"<MIDDLEWARE_NAME>\",\n \"traefik_resolver\": \"<TRAEFIK_RESOLVER_NAME>\"\n }\n \n}\n```\n\n#### Environment variables\n\nTo add environment variables, used during the creation and deployment of the pod (i.e., environment variables to specify\nfor the Docker Image).\n\n```json\n{\n \"env_variables\": {\n \"KEY_1\": \"VAL_1\",\n \"KEY_2\": \"VAL_2\"\n }\n}\n```\n\n#### Deployment\nBy default, the deployment is done as a Job. To deploy as a Deployment, the following field should be added:\n\n```json\n{\n \"deployment\": \"true\"\n}\n```\n\n#### Commmand\nTo specify a custom command to run inside the container:\n\n```json\n{\n \"command\": [\n \"command\",\n \"arg1\",\n \"arg2\"\n ]\n}\n```\n\n#### Image Pull Secret\nIf the Docker image is stored in a private repository, the user can specify the secret to use to pull the image.\n\n```json\n{\n \"image_pull_secret\": \"SECRET NAME\"\n}\n```\n\n#### User info\n\nWhen deploying MAIA-based applications, it is possible to create single/multiple user account in the environment.\nFor each of the users, *username*, *password*, and, optionally, an *ssh public key* are required.\nThis information is stored inside Secrets:\n\n```\nUSER_1_SECRET:\n user: USER_1\n password: pw\n ssh_publickey [Optional]: \"ssh-rsa ...\" \n```\n\nTo provide the user information to the Pod:\n\n```json\n{\n \"user_secret\": [\n \"user-1-secret\",\n \"user-2-secret\"\n ],\n \"user_secret_params\": [\n \"user\",\n \"password\",\n \"ssh_publickey\"\n ]\n}\n```\n\n## Configuration File Example\n\n```json\n{\n \"namespace\": \"demo\",\n \"chart_name\": \"jupyterlab-1-v1\",\n \"docker_image\": \"jupyter/scipy-notebook\",\n \"tag\": \"latest\",\n \"memory_request\": \"4Gi\",\n \"allocationTime\": \"2\",\n \"cpu_request\": \"5000m\",\n \"ports\": {\n \"jupyter\": [\n 8888\n ]\n },\n \"persistent_volume\": [\n {\n \"mountPath\": \"/home/jovyan\",\n \"size\": \"100Gi\",\n \"access_mode\": \"ReadWriteOnce\",\n \"pvc_type\": \"microk8s-hostpath\"\n }\n ]\n}\n```\n\n# Tools\n\nInstall the **MAIA** package running:\n\n```\npip install maia-tookit\n```\n\nRequirements:\n\n```\nkubectl # Kubernetes CLI\nhelm # Kubernetes Package Manager\n```\n\n## Deploy Charts\n\nTo deploy a Hive Chart, first create a config file according to the specific requirements (as\ndescribed [above](#Custom Helm values)).\n\nAfter creating the config file, run:\n\n```shell\nMAIA_deploy_helm_chart --config-file <PATH/TO/CONFIG/FILE>\n```\n",
"bugtrack_url": null,
"license": "GPLv3",
"summary": "Python interface to MAIA. It can be used as interface to any Kubernetes-based platform.",
"version": "1.1.2",
"project_urls": {
"Bug Tracker": "https://github.com/kthcloud/MAIA",
"Documentation": "https://github.com/kthcloud/MAIA",
"Homepage": "https://github.com/kthcloud/MAIA.git",
"Source Code": "https://github.com/kthcloud/MAIA/issues"
},
"split_keywords": [
"helm",
" kubernetes",
" maia",
" resource deployment"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "31c971e727c347246bdd01a7c2f230c6f3bbd89d496bcf203d746174395bf0c6",
"md5": "dec09c4f9bc965ba84babb44800019f0",
"sha256": "8029db4fbb96be0d2b17087f02a705f23c490f8d25e25621360633f3d04f04ba"
},
"downloads": -1,
"filename": "maia_toolkit-1.1.2-py3-none-any.whl",
"has_sig": false,
"md5_digest": "dec09c4f9bc965ba84babb44800019f0",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.8",
"size": 45946,
"upload_time": "2024-11-06T16:08:32",
"upload_time_iso_8601": "2024-11-06T16:08:32.488119Z",
"url": "https://files.pythonhosted.org/packages/31/c9/71e727c347246bdd01a7c2f230c6f3bbd89d496bcf203d746174395bf0c6/maia_toolkit-1.1.2-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "271740fd85657b58d03acc72cded36439ff9c10605b7f03aba508d9ff95ef213",
"md5": "35140f66d3a99e83cb896f1c2b094ed2",
"sha256": "f279e44152b40f4fcfed82dbd34b7cc910ed5ceff0f3e6bb372f01d91b32c7ae"
},
"downloads": -1,
"filename": "maia_toolkit-1.1.2.tar.gz",
"has_sig": false,
"md5_digest": "35140f66d3a99e83cb896f1c2b094ed2",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.8",
"size": 60826,
"upload_time": "2024-11-06T16:08:33",
"upload_time_iso_8601": "2024-11-06T16:08:33.578927Z",
"url": "https://files.pythonhosted.org/packages/27/17/40fd85657b58d03acc72cded36439ff9c10605b7f03aba508d9ff95ef213/maia_toolkit-1.1.2.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-11-06 16:08:33",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "kthcloud",
"github_project": "MAIA",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"requirements": [
{
"name": "ruamel_yaml",
"specs": []
},
{
"name": "kubernetes",
"specs": []
},
{
"name": "minio",
"specs": []
},
{
"name": "click",
"specs": []
},
{
"name": "toml",
"specs": []
}
],
"tox": true,
"lcname": "maia-toolkit"
}
JSON
