DLAB Overview

What is DLAB?

DLab is an essential toolset for analytics. It is a self-service Web Console, used to create and manage exploratory environments. It allows teams to spin up analytical environments with best of breed open-source tools just with a single click of the mouse. Once established, environment can be managed by an analytical team itself, leveraging simple and easy-to-use Web Interface.

See more at dlab.apache.org.

Logical architecture

The following diagram demonstrate high-level logical architecture.

The diagram shows main components of DLab, which is a self-service for the infrastructure deployment and interaction with it. The purpose of each component is described below.

Self-Service

Self-Service is a service, which provides RESTful user API with Web User Interface for data scientist. It tightly interacts with Provisioning Service and Database. Self-Service delegates all user`s requests to Provisioning Service. After execution of certain request from Self-service, Provisioning Service returns response about corresponding action happened with particular resource. Self-service, then, saves this response into Database. So, each time Self-Service receives request about status of provisioned infrastructure resources – it loads it from Database and propagates to Web UI.

Billing

Billing is a module, which provides a loading of the billing report for the environment to the database. It can be running as part of the Self-Service or a separate process.

Provisioning Service

The Provisioning Service is a RESTful service, which provides APIs for provisioning of the user’s infrastructure. Provisioning Service receives the request from Self-Service, afterwards it forms and sends a command to the docker to execute requested action. Docker executes the command and generates a response.json file. Provisioning service analyzes response.json and responds to initial request of Self-Service, providing status-related information of the instance.

Security service

Security Service is RESTful service, which provides authorization API for Self-Service and Provisioning Service via LDAP.

Docker

Docker is an infrastructure-provisioning module based on Docker service, which provides low-level actions for infrastructure management.

Database

Database serves as a storage with description of user infrastructure, user’s settings and service information.

Physical architecture

The following diagrams demonstrate high-level physical architecture of DLab in AWS, GCP and Azure.

Diagram of Dlab physical architecture on AWS:

Diagram of Dlab physical architecture on GCP:

Diagram of Dlab physical architecture on Azure:

Main components

Self-service node (SSN)
Endpoint node
Edge node
Notebook node (Jupyter, Rstudio, etc.)
Data engine cluster
Data engine cluster as a service provided with Cloud

Self-service node (SSN)

Creation of self-service node – is the first step for deploying DLab. SSN is a main server with following pre-installed services:

DLab Web UI – is Web user interface for managing/deploying all components of DLab. It is accessible by the following URL: http[s]://SSN_Public_IP_or_Public_DNS
MongoDB – is a database, which contains part of DLab’s configuration, user’s exploratory environments description as well as user’s preferences.
Docker – used for building DLab Docker containers, which will be used for provisioning other components.

Elastic(Static) IP address is assigned to an SSN Node, so you are free to stop|start it and and SSN node's IP address won’t change.

Endpoint

This is a node which serves as a provisioning endpoint for Dlab resources. Endpoint machine is deployed separately from Dlab installation and can be even deployed on a different cloud.

Edge node

This node is used as a reverse-proxy server for the user. Through Edge node users can access Notebook via HTTPS. Edge Node has a Nginx reverse-proxy pre-installed.

Notebook node

The next step is setting up a Notebook node (or a Notebook server). It is a server with pre-installed applications and libraries for data processing, data cleaning and transformations, numerical simulations, statistical modeling, machine learning, etc. Following analytical tools are currently supported in DLab and can be installed on a Notebook node:

Jupyter
Jupyterlab
RStudio
Apache Zeppelin
TensorFlow + Jupyter
TensorFlow + RStudio
Deep Learning + Jupyter

Apache Spark is also installed for each of the analytical tools above.

Note: terms 'Apache Zeppelin' and 'Apache Spark' hereinafter may be referred to as 'Zeppelin' and 'Spark' respectively or may have original reference.

Data engine cluster

After deploying Notebook node, user can create one of the cluster for it:

Data engine - Spark standalone cluster
Data engine service - cloud managed cluster platform (EMR for AWS or Dataproc for GCP) That simplifies running big data frameworks, such as Apache Hadoop and Apache Spark to process and analyze vast amounts of data. Adding cluster is not mandatory and is only needed in case additional computational resources are required for job execution.

DLab Deployment

Structure of main DLab directory

DLab’s SSN node main directory structure is as follows:

/opt  
 └───dlab  
     ├───conf  
     ├───sources  
     ├───template  
     ├───tmp  
     │   └───result  
     └───webapp

conf – contains configuration for DLab Web UI and back-end services;
sources – contains all Docker/Python scripts, templates and files for provisioning;
template – docker’s templates;
tmp –temporary directory of DLab;
tmp/result – temporary directory for Docker’s response files;
webapp – contains all .jar files for DLab Web UI and back-end services.

Structure of log directory

SSN node structure of log directory is as follows:

/var
 └───opt
     └───dlab
         └───log
             ├───dataengine
             ├───dateengine-service
             ├───edge
             ├───notebook
             ├───project
             └───ssn

These directories contain the log files for each template and for DLab back-end services.

ssn – contains logs of back-end services;
provisioning.log – Provisioning Service log file;
security.log – Security Service log file;
selfservice.log – Self-Service log file;
edge, notebook, dataengine, dataengine-service – contains logs of Python scripts.

Keycloak server

Keycloak is used to manage user authentication instead of the aplication. To use existing server following parameters must be specified either when running Dlab deployment script or in /opt/dlab/conf/self-service.yml and /opt/dlab/conf/provisioning.yml files on SSN node.

Parameter	Description/Value
keycloak_realm_name	Keycloak Realm name
keycloak_auth_server_url	Keycloak auth server URL
keycloak_client_name	Keycloak client name
keycloak_client_secret	Keycloak client secret
keycloak_user	Keycloak user
keycloak_user_password	Keycloak user password

Preparing environment for Keycloak deployment

Keycloak can be deployed with Nginx proxy on instance using deploy_keycloak.py script. Currently it only works with HTTP.

Preparation steps for deployment:

Create an VM instance with the following settings:
- The instance should have access to Internet in order to install required prerequisites
- Boot disk OS Image - Ubuntu 18.04
Put private key that is used to connect to instance where Keycloak will be deployed somewhere on the instance where deployment script will be executed.
Install Git and clone DLab repository

Executing deployment script

To build Keycloak node, following steps should be executed:

Connect to the instance via SSH and run the following commands:

sudo su
apt-get update
apt-get install -y python-pip
pip install fabric==1.14.0

Go to dlab directory
Run infrastructure-provisioning/scripts/deploy_keycloak/deploy_keycloak.py deployment script:

/usr/bin/python infrastructure-provisioning/scripts/deploy_keycloak/deploy_keycloak.py --os_user ubuntu --keyfile ~/.ssh/key.pem --keycloak_realm_name test_realm_name  --keycloak_user admin --keycloak_user_password admin_password --public_ip_address XXX.XXX.XXX.XXX

List of parameters for Keycloak node deployment:

Parameter	Description/Value
os_user	username, used to connect to the instance
keyfile	/path_to_key/private_key.pem, used to connect to instance
keycloak_realm_name	Keycloak realm name that will be created
keycloak_user	initial keycloak admin username
keycloak_user_password	password for initial keycloak admin user
public_ip_address	Public IP address of the instance (if not specified, keycloak will be deployed on localhost)* (On AWS try to specify Public DNS (IPv4) instead of IPv4 if unable to connect)*

Self-Service Node

Preparing environment for DLab deployment

Deployment of DLab starts from creating Self-Service(SSN) node. DLab can be deployed in AWS, Azure and Google cloud.

For each cloud provider, prerequisites are different.

In Amazon cloud (click to expand)

Prerequisites:

DLab can be deployed using the following two methods:

IAM user: DLab deployment script is executed on local machine and uses IAM user permissions to create resources in AWS.
EC2 instance: DLab deployment script is executed on EC2 instance prepared in advance and with attached IAM role. Deployment script uses the attached IAM role to create resources in AWS.

'IAM user' method prerequisites:

IAM user with created AWS access key ID and secret access key. These keys are provided as arguments for the deployment script and are used to create resources in AWS.
Amazon EC2 Key Pair. This key is system and is used for configuring DLab instances.
The following IAM policy should be attached to the IAM user in order to deploy DLab.

'EC2 instance' method prerequisites:

Amazon EC2 Key Pair. This key is system and is used for configuring DLab instances.
EC2 instance where DLab deployment script is executed.
IAM role with the following IAM policy should be attached to the EC2 instance.

Optional prerequisites for both methods:

VPC ID. If VPC where DLab should be deployed is already in place, then "VPC ID" should be provided for deployment script. DLab instances are deployed in this VPC.
Subnet ID. If Subnet where DLab should be deployed is already in place, then "Subnet ID" should be provided for deployment script. DLab SSN node and users' Edge nodes are deployed in this Subnet.

DLab IAM Policy

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Action": [
                "iam:CreatePolicy",
                "iam:AttachRolePolicy",
                "iam:DetachRolePolicy",
                "iam:DeletePolicy",
                "iam:DeleteRolePolicy",
                "iam:GetRolePolicy",
                "iam:GetPolicy",
                "iam:GetUser",
                "iam:ListUsers",
                "iam:ListAccessKeys",
                "iam:ListUserPolicies",
                "iam:ListAttachedRolePolicies",
                "iam:ListPolicies",
                "iam:ListRolePolicies",
                "iam:ListRoles",
                "iam:CreateRole",
                "iam:CreateInstanceProfile",
                "iam:PutRolePolicy",
                "iam:AddRoleToInstanceProfile",
                "iam:PassRole",
                "iam:GetInstanceProfile",
                "iam:ListInstanceProfilesForRole",
                "iam:RemoveRoleFromInstanceProfile",
                "iam:DeleteInstanceProfile",
                "iam:ListInstanceProfiles",
                "iam:DeleteRole",
                "iam:GetRole"
            ],
            "Effect": "Allow",
            "Resource": "*"
        },
        {
            "Action": [
                "ec2:AuthorizeSecurityGroupEgress",
                "ec2:AuthorizeSecurityGroupIngress",
                "ec2:DeleteRouteTable",
                "ec2:DeleteSubnet",
                "ec2:DeleteTags",
                "ec2:DescribeSubnets",
                "ec2:DescribeVpcs",
                "ec2:DescribeInstanceStatus",
                "ec2:ModifyInstanceAttribute",
                "ec2:RevokeSecurityGroupIngress",
                "ec2:DescribeImages",
                "ec2:CreateTags",
                "ec2:DescribeRouteTables",
                "ec2:CreateRouteTable",
                "ec2:AssociateRouteTable",
                "ec2:DescribeVpcEndpoints",
                "ec2:CreateVpcEndpoint",
                "ec2:ModifyVpcEndpoint",
                "ec2:DescribeInstances",
                "ec2:RunInstances",
                "ec2:DescribeAddresses",
                "ec2:AllocateAddress",
                "ec2:AssociateAddress",
                "ec2:DisassociateAddress",
                "ec2:ReleaseAddress",
                "ec2:TerminateInstances",
                "ec2:AuthorizeSecurityGroupIngress",
                "ec2:AuthorizeSecurityGroupEgress",
                "ec2:DescribeSecurityGroups",
                "ec2:CreateSecurityGroup",
                "ec2:DeleteSecurityGroup",
                "ec2:RevokeSecurityGroupEgress"
                
            ],
            "Effect": "Allow",
            "Resource": "*"
        },
        {
            "Action": [
                "s3:GetBucketLocation",
                "s3:PutBucketPolicy",
                "s3:GetBucketPolicy",
                "s3:DeleteBucket",
                "s3:DeleteObject",
                "s3:GetObject",
                "s3:ListBucket",
                "s3:PutEncryptionConfiguration"
                "s3:ListAllMyBuckets",
                "s3:CreateBucket",
                "s3:PutBucketTagging",
                "s3:GetBucketTagging"
            ],
            "Effect": "Allow",
            "Resource": "*"
        }
    ]
}

Preparation steps for deployment:

Create an EC2 instance with the following settings:
- The instance should have access to Internet in order to install required prerequisites
- The instance should have access to further DLab installation
- AMI - Ubuntu 16.04
- IAM role with policy should be assigned to the instance
Put SSH key file created through Amazon Console on the instance with the same name
Install Git and clone DLab repository

In Azure cloud (click to expand)

Prerequisites:

IAM user with Contributor permissions.
Service principal and JSON based auth file with clientId, clientSecret and tenantId.

Note: The following permissions should be assigned to the service principal:

Windows Azure Active Directory
Microsoft Graph
Windows Azure Service Management API

Preparation steps for deployment:

Create a VM instance with the following settings:
- The instance should have access to Internet in order to install required prerequisites
- Image - Ubuntu 16.04
Generate SSH key pair and rename private key with .pem extension
Put JSON auth file to users home directory

In Google cloud (GCP) (click to expand)

Prerequisites:

IAM user
Service account and JSON auth file for it. In order to get JSON auth file, Key should be created for service account through Google cloud console.
Google Cloud Storage JSON API should be enabled

Preparation steps for deployment:

Create an VM instance with the following settings:
- The instance should have access to Internet in order to install required prerequisites
- Boot disk OS Image - Ubuntu 16.04
Generate SSH key pair and rename private key with .pem extension
Put JSON auth file created through Google cloud console to users home directory
Install Git and clone DLab repository

Executing deployment script

To build SSN node, following steps should be executed:

Connect to the instance via SSH and run the following commands:

sudo su
apt-get update
apt-get install git
git clone https://github.com/apache/incubator-dlab.git
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | apt-key add -
add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable"
apt-get update
apt-cache policy docker-ce
apt-get install -y docker-ce=17.06.2~ce-0~ubuntu
usermod -a -G docker *username*
apt-get install -y python-pip
pip install fabric==1.14.0
cd incubator-dlab

Go to dlab directory
Run infrastructure-provisioning/scripts/deploy_dlab.py deployment script:

This python script will build front-end and back-end part of DLab, create SSN docker image and run Docker container for creating SSN node.

In Amazon cloud (click to expand)

/usr/bin/python infrastructure-provisioning/scripts/deploy_dlab.py --conf_service_base_name dlab-test --aws_access_key XXXXXXX --aws_secret_access_key XXXXXXXXXX --aws_region xx-xxxxx-x --conf_os_family debian --conf_cloud_provider aws --aws_vpc_id vpc-xxxxx --aws_subnet_id subnet-xxxxx --aws_security_groups_ids sg-xxxxx,sg-xxxx --key_path /path/to/key/ --conf_key_name key_name --conf_tag_resource_id dlab --aws_account_id xxxxxxxx --aws_billing_bucket billing_bucket --aws_report_path /billing/directory/ --action create

List of parameters for SSN node deployment:

Parameter	Description/Value
conf_service_base_name	Any infrastructure value (should be unique if multiple SSN’s have been deployed before)
aws_access_key	AWS user access key
aws_secret_access_key	AWS user secret access key
aws_region	AWS region
conf_os_family	Name of the Linux distributive family, which is supported by DLab (Debian/RedHat)
conf_cloud_provider	Name of the cloud provider, which is supported by DLab (AWS)
conf_duo_vpc_enable	"true" - for installing DLab into two Virtual Private Clouds (VPCs) or "false" - for installing DLab into one VPC. Also this parameter isn't required when deploy DLab in one VPC
aws_vpc_id	ID of the VPC (optional)
aws_subnet_id	ID of the public subnet (optional)
aws_security_groups_ids	One or more ID`s of AWS Security Groups, which will be assigned to SSN node (optional)
key_path	Path to admin key (without key name)
conf_key_name	Name of the uploaded SSH key file (without “.pem” extension)
conf_tag_resource_id	The name of tag for billing reports
aws_account_id	The The ID of Amazon account
aws_billing_bucket	The name of S3 bucket where billing reports will be placed
aws_report_path	The path to billing reports directory in S3 bucket. This parameter isn't required when billing reports are placed in the root of S3 bucket.
action	In case of SSN node creation, this parameter should be set to “create”
workspace_path	Path to DLab sources root
conf_image_enabled	Enable or Disable creating image at first time

Note: If the following parameters are not specified, they will be created automatically:

aws_vpc_id
aws_subnet_id
aws_sg_ids

Note: If billing won't be used, the following parameters are not required:

aws_account_id
aws_billing_bucket
aws_report_path

SSN deployment creates following AWS resources:

SSN EC2 instance
Elastic IP for SSN instance
IAM role and EC2 Instance Profile for SSN
Security Group for SSN node (if it was specified, script will attach the provided one)
VPC, Subnet (if they have not been specified) for SSN and EDGE nodes S3 bucket – its name will be <service_base_name>-ssn-bucket. This bucket will contain necessary dependencies and configuration files for Notebook nodes (such as .jar files, YARN configuration, etc.)
S3 bucket for for collaboration between Dlab users. Its name will be <service_base_name>-<endpoint_name>-shared-bucket

In Azure cloud (click to expand)

/usr/bin/python infrastructure-provisioning/scripts/deploy_dlab.py --conf_service_base_name dlab_test --azure_region westus2 --conf_os_family debian --conf_cloud_provider azure --azure_vpc_name vpc-test --azure_subnet_name subnet-test --azure_security_group_name sg-test1,sg-test2 --key_path /root/ --conf_key_name Test --azure_auth_path /dir/file.json  --action create

List of parameters for SSN node deployment:

Parameter	Description/Value
conf_service_base_name	Any infrastructure value (should be unique if multiple SSN’s have been deployed before)
azure_resource_group_name	Resource group name (can be the same as service base name
azure_region	Azure region
conf_os_family	Name of the Linux distributive family, which is supported by DLab (Debian/RedHat)
conf_cloud_provider	Name of the cloud provider, which is supported by DLab (Azure)
azure_vpc_name	Name of the Virtual Network (VN) (optional)
azure_subnet_name	Name of the Azure subnet (optional)
azure_security_groups_name	One or more Name`s of Azure Security Groups, which will be assigned to SSN node (optional)
azure_ssn_instance_size	Instance size of SSN instance in Azure
key_path	Path to admin key (without key name)
conf_key_name	Name of the uploaded SSH key file (without “.pem” extension)
azure_auth_path	Full path to auth json file
azure_offer_number	Azure offer id number
azure_currency	Currency that is used for billing information(e.g. USD)
azure_locale	Locale that is used for billing information(e.g. en-US)
azure_region_info	Region info that is used for billing information(e.g. US)
azure_datalake_enable	Support of Azure Data Lake (true/false)
azure_oauth2_enabled	Defines if Azure OAuth2 authentication mechanisms is enabled(true/false)
azure_validate_permission_scope	Defines if DLab verifies user's permission to the configured resource(scope) during login with OAuth2 (true/false). If Data Lake is enabled default scope is Data Lake Store Account, else Resource Group, where DLab is deployed, is default scope. If user does not have any role in scope he/she is forbidden to log in
azure_application_id	Azure application ID that is used to log in users in DLab
azure_ad_group_id	ID of group in Active directory whose members have full access to shared folder in Azure Data Lake Store
action	In case of SSN node creation, this parameter should be set to “create”
conf_image_enabled	Enable or Disable creating image at first time

Note: If the following parameters are not specified, they will be created automatically:

azure_vpc_nam
azure_subnet_name
azure_security_groups_name

Note: Billing configuration:

To know azure_offer_number open Azure Portal, go to Subscriptions and open yours, then click Overview and you should see it under Offer ID property:

Please see RateCard API to get more details about azure_offer_number, azure_currency, azure_locale, azure_region_info. These DLab deploy properties correspond to RateCard API request parameters.

To have working billing functionality please review Billing configuration note and use proper parameters for SSN node deployment.

To use Data Lake Store please review Azure Data Lake usage pre-requisites note and use proper parameters for SSN node deployment.

Note: Azure Data Lake usage pre-requisites:

Configure application in Azure portal and grant proper permissions to it.

Open Azure Active Directory tab, then App registrations and click New application registration
Fill in ui form with the following parameters Name - put name of the new application, Application type - select Native, Sign-on URL put any valid url as it will be updated later
Grant proper permissions to the application. Select the application you just created on App registration view, then click Required permissions, then Add->Select an API-> In search field type MicrosoftAzureQueryService and press Select, then check the box Have full access to the Azure Data Lake service and save the changes. Repeat the same actions for Windows Azure Active Directory API (available on Required permissions->Add->Select an API) and the box Sign in and read user profile
Get Application ID from application properties it will be used as azure_application_id for deploy_dlap.py script

Usage of Data Lake resource predicts shared folder where all users can write or read any data. To manage access to this folder please create ot use existing group in Active Directory. All users from this group will have RW access to the shared folder. Put ID(in Active Directory) of the group as azure_ad_group_id parameter to deploy_dlab.py script
After execution of deploy_dlab.py script go to the application created in step 1 and change Redirect URIs value to the https://SSN_HOSTNAME/ where SSN_HOSTNAME - SSN node hostname

After SSN node deployment following Azure resources will be created:

Resource group where all DLAb resources will be provisioned
SSN Virtual machine
Static public IP address dor SSN virtual machine
Network interface for SSN node
Security Group for SSN node (if it was specified, script will attach the provided one)
Virtual network and Subnet (if they have not been specified) for SSN and EDGE nodes
Storage account and blob container for necessary further dependencies and configuration files for Notebook nodes (such as .jar files, YARN configuration, etc.)
Storage account and blob container for collaboration between Dlab users
If support of Data Lake is enabled: Data Lake and shared directory will be created

In Google cloud (GCP) (click to expand)

/usr/bin/python infrastructure-provisioning/scripts/deploy_dlab.py --conf_service_base_name dlab-test --gcp_region xx-xxxxx --gcp_zone xxx-xxxxx-x --conf_os_family debian --conf_cloud_provider gcp --key_path /path/to/key/ --conf_key_name key_name --gcp_ssn_instance_size n1-standard-1 --gcp_project_id project_id --gcp_service_account_path /path/to/auth/file.json --action create

List of parameters for SSN node deployment:

Parameter	Description/Value
conf_service_base_name	Any infrastructure value (should be unique if multiple SSN’s have been deployed before)
gcp_region	GCP region
gcp_zone	GCP zone
conf_os_family	Name of the Linux distributive family, which is supported by DLab (Debian/RedHat)
conf_cloud_provider	Name of the cloud provider, which is supported by DLab (GCP)
gcp_vpc_name	Name of the Virtual Network (VN) (optional)
gcp_subnet_name	Name of the GCP subnet (optional)
gcp_firewall_name	One or more Name`s of GCP Security Groups, which will be assigned to SSN node (optional)
key_path	Path to admin key (without key name)
conf_key_name	Name of the uploaded SSH key file (without “.pem” extension)
gcp_service_account_path	Full path to auth json file
gcp_ssn_instance_size	Instance size of SSN instance in GCP
gcp_project_id	ID of GCP project
action	In case of SSN node creation, this parameter should be set to “create”
conf_image_enabled	Enable or Disable creating image at first time
billing_dataset_name	Name of GCP dataset (BigQuery service)

Note: If you gonna use Dataproc cluster, be aware that Dataproc has limited availability in GCP regions. Cloud Dataproc availability by Region in GCP

After SSN node deployment following GCP resources will be created:

SSN VM instance
External IP address for SSN instance
IAM role and Service account for SSN
Security Groups for SSN node (if it was specified, script will attach the provided one)
VPC, Subnet (if they have not been specified) for SSN and EDGE nodes
Bucket for for collaboration between Dlab users. Its name will be <service_base_name>-<endpoint_name>-shared-bucket

Terminating Self-Service Node

Terminating SSN node will also remove all nodes and components related to it. Basically, terminating Self-service node will terminate all DLab’s infrastructure. Example of command for terminating DLab environment:

In Amazon (click to expand)

/usr/bin/python infrastructure-provisioning/scripts/deploy_dlab.py --conf_service_base_name dlab-test --aws_access_key XXXXXXX --aws_secret_access_key XXXXXXXX --aws_region xx-xxxxx-x --key_path /path/to/key/ --conf_key_name key_name --conf_os_family debian --conf_cloud_provider aws --action terminate

List of parameters for SSN node termination:

Parameter	Description/Value
conf_service_base_name	Unique infrastructure value
aws_access_key	AWS user access key
aws_secret_access_key	AWS user secret access key
aws_region	AWS region
key_path	Path to admin key (without key name)
conf_key_name	Name of the uploaded SSH key file (without “.pem” extension)
conf_os_family	Name of the Linux distributive family, which is supported by DLab (Debian/RedHat)
conf_cloud_provider	Name of the cloud provider, which is supported by DLab (AWS)
action	terminate

In Azure (click to expand)

/usr/bin/python infrastructure-provisioning/scripts/deploy_dlab.py --conf_service_base_name dlab-test --azure_vpc_name vpc-test --azure_resource_group_name resource-group-test --azure_region westus2 --key_path /root/ --conf_key_name Test --conf_os_family debian --conf_cloud_provider azure --azure_auth_path /dir/file.json --action terminate

List of parameters for SSN node termination:

Parameter	Description/Value
conf_service_base_name	Unique infrastructure value
azure_region	Azure region
conf_os_family	Name of the Linux distributive family, which is supported by DLab (Debian/RedHat)
conf_cloud_provider	Name of the cloud provider, which is supported by DLab (Azure)
azure_vpc_name	Name of the Virtual Network (VN)
key_path	Path to admin key (without key name)
conf_key_name	Name of the uploaded SSH key file (without “.pem” extension)
azure_auth_path	Full path to auth json file
action	terminate

In Google cloud (click to expand)

/usr/bin/python infrastructure-provisioning/scripts/deploy_dlab.py --gcp_project_id project_id --conf_service_base_name dlab-test --gcp_region xx-xxxxx --gcp_zone xx-xxxxx-x --key_path /path/to/key/ --conf_key_name key_name --conf_os_family debian --conf_cloud_provider gcp --gcp_service_account_path /path/to/auth/file.json --action terminate

List of parameters for SSN node termination:

Parameter	Description/Value
conf_service_base_name	Any infrastructure value (should be unique if multiple SSN’s have been deployed before)
gcp_region	GCP region
gcp_zone	GCP zone
conf_os_family	Name of the Linux distributive family, which is supported by DLab (Debian/RedHat)
conf_cloud_provider	Name of the cloud provider, which is supported by DLab (GCP)
gcp_vpc_name	Name of the Virtual Network (VN) (optional)
gcp_subnet_name	Name of the GCP subnet (optional)
key_path	Path to admin key (without key name)
conf_key_name	Name of the uploaded SSH key file (without “.pem” extension)
gcp_service_account_path	Full path to auth json file
gcp_project_id	ID of GCP project
action	In case of SSN node termination, this parameter should be set to “terminate”

Note: It is required to enter gcp_vpc_name and gcp_subnet_name parameters if Self-Service Node was deployed in pre-defined VPC and Subnet.

Edge Node

Gateway node (or an Edge node) is an instance(virtual machine) provisioned in a public subnet. It serves as an entry point for accessing user’s personal analytical environment. It is created by an end-user, whose public key will be uploaded there. Only via Edge node, DLab user can access such application resources as notebook servers and dataengine clusters. Also, Edge Node is used to setup SOCKS proxy to access notebook servers via Web UI and SSH. Elastic(Static) IP address is assigned to an Edge Node.

Create

In order to create Edge node using DLab Web UI – login and, click on the button “Upload” (Depending on authorization provider that was chosen on deployment stage, user may be taken from LDAP or from Azure AD (Oauth2)). Choose user’s SSH public key and after that click on the button “Create”. Edge node will be deployed and corresponding instance (virtual machine) will be started.

In Amazon (click to expand)

The following AWS resources will be created:

Edge EC2 instance
Elastic IP address for Edge EC2 instance
User's S3 bucket
Security Group for user's Edge instance
Security Group for all further user's Notebook instances
Security Groups for all further user's master nodes of data engine cluster
Security Groups for all further user's slave nodes of data engine cluster
IAM Roles and Instance Profiles for user's Edge instance
IAM Roles and Instance Profiles all further user's Notebook instances
User private subnet. All further nodes (Notebooks, EMR clusters) will be provisioned in different subnet than SSN.

List of parameters for Edge node creation:

Parameter	Description/Value
conf_resource	edge
conf_os_family	Name of the Linux distributive family, which is supported by DLAB (debian/redhat)
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
edge_user_name	Name of the user
aws_vpc_id	ID of AWS VPC where infrastructure is being deployed
aws_region	AWS region where infrastructure was deployed
aws_security_groups_ids	One or more id’s of the SSN instance security group
aws_subnet_id	ID of the AWS public subnet where Edge will be deployed
aws_private_subnet_prefix	Prefix of the private subnet
conf_tag_resource_id	The name of tag for billing reports
action	create

In Azure (click to expand)

The following Azure resources will be created:

Edge virtual machine
Static public IP address for Edge virtual machine
Network interface for Edge node
Security Group for user's Edge instance
Security Group for all further user's Notebook instances
Security Groups for all further user's master nodes of data engine cluster
Security Groups for all further user's slave nodes of data engine cluster
User's private subnet. All further nodes (Notebooks, data engine clusters) will be provisioned in different subnet than SSN.
User's storage account and blob container

List of parameters for Edge node creation:

Parameter	Description/Value
conf_resource	edge
conf_os_family	Name of the Linux distributive family, which is supported by DLAB (debian/redhat)
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
edge_user_name	Name of the user
azure_resource_group_name	Name of the resource group where all DLAb resources are being provisioned
azure_region	Azure region where infrastructure was deployed
azure_vpc_name	Name of Azure Virtual network where all infrastructure is being deployed
azure_subnet_name	Name of the Azure public subnet where Edge will be deployed
action	create

In Google cloud (click to expand)

The following GCP resources will be created:

Edge VM instance
External static IP address for Edge VM instance
Security Group for user's Edge instance
Security Group for all further user's Notebook instances
Security Groups for all further user's master nodes of data engine cluster
Security Groups for all further user's slave nodes of data engine cluster
User's private subnet. All further nodes (Notebooks, data engine clusters) will be provisioned in different subnet than SSN.
User's bucket

List of parameters for Edge node creation:

Parameter	Description/Value
conf_resource	edge
conf_os_family	Name of the Linux distributive family, which is supported by DLAB (debian/redhat)
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
edge_user_name	Name of the user
gcp_region	GCP region where infrastructure was deployed
gcp_zone	GCP zone where infrastructure was deployed
gcp_vpc_name	Name of Azure Virtual network where all infrastructure is being deployed
gcp_subnet_name	Name of the Azure public subnet where Edge will be deployed
gcp_project_id	ID of GCP project
action	create

Start/Stop

To start/stop Edge node, click on the button which looks like a cycle on the top right corner, then click on the button which is located in “Action” field and in the drop-down menu click on the appropriate action.

In Amazon (click to expand)

List of parameters for Edge node starting/stopping:

Parameter	Description/Value
conf_resource	edge
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
edge_user_name	Name of the user
aws_region	AWS region where infrastructure was deployed
action	start/stop

In Azure (click to expand)

List of parameters for Edge node starting:

Parameter	Description/Value
conf_resource	edge
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
edge_user_name	Name of the user
azure_resource_group_name	Name of the resource group where all DLAb resources are being provisioned
azure_region	Azure region where infrastructure was deployed
action	start

List of parameters for Edge node stopping:

Parameter	Description/Value
conf_resource	edge
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
edge_user_name	Name of the user
azure_resource_group_name	Name of the resource group where all DLAb resources are being provisioned
action	stop

In Google cloud (click to expand)

List of parameters for Edge node starting/stopping:

Parameter	Description/Value
conf_resource	edge
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
edge_user_name	Name of the user
gcp_region	GCP region where infrastructure was deployed
gcp_zone	GCP zone where infrastructure was deployed
gcp_project_id	ID of GCP project
action	start/stop

Notebook node

Notebook node is an instance (virtual machine), with preinstalled analytical software, needed dependencies and with pre-configured kernels and interpreters. It is the main part of personal analytical environment, which is setup by a data scientist. It can be Created, Stopped and Terminated. To support variety of analytical needs - Notebook node can be provisioned on any of cloud supported instance shape for your particular region. From analytical software, which is already pre-installed on a notebook node, end users can access (read/write) data stored on buckets/containers.

Create

To create Notebook node, click on the “Create new” button. Then, in drop-down menu choose template type (jupyter/rstudio/zeppelin/tensor/etc.), enter notebook name and choose instance shape. After clicking the button “Create”, notebook node will be deployed and started.

List of parameters for Notebook node creation:

In Amazon (click to expand)

Parameter	Description/Value
conf_resource	notebook
conf_os_family	Name of the Linux distributive family, which is supported by DLAB (debian/redhat)
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
edge_user_name	Value that previously was used when Edge being provisioned
aws_notebook_instance_type	Value of the Notebook EC2 instance shape
aws_region	AWS region where infrastructure was deployed
aws_security_groups_ids	ID of the SSN instance's security group
application	Type of the notebook template (jupyter/rstudio/zeppelin/tensor/deeplearning)
conf_tag_resource_id	The name of tag for billing reports
git_creds	User git credentials in JSON format
action	Create

Note: For format of git_creds see "Manage git credentials" lower.

In Azure (click to expand)

Parameter	Description/Value
conf_resource	notebook
conf_os_family	Name of the Linux distributive family, which is supported by DLAB (debian/redhat)
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
edge_user_name	Value that previously was used when Edge being provisioned
azure_notebook_instance_size	Value of the Notebook virtual machine shape
azure_region	Azure region where infrastructure was deployed
azure_vpc_name	NAme of Azure Virtual network where all infrastructure is being deployed
azure_resource_group_name	Name of the resource group where all DLAb resources are being provisioned
application	Type of the notebook template (jupyter/rstudio/zeppelin/tensor/deeplearning)
git_creds	User git credentials in JSON format
action	Create

In Google cloud (click to expand)

Parameter	Description/Value
conf_resource	notebook
conf_os_family	Name of the Linux distributive family, which is supported by DLAB (debian/redhat)
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
edge_user_name	Value that previously was used when Edge being provisioned
gcp_vpc_name	Name of Azure Virtual network where all infrastructure is being deployed
gcp_project_id	ID of GCP project
gcp_notebook_instance_size	Value of the Notebook VM instance size
gcp_region	GCP region where infrastructure was deployed
gcp_zone	GCP zone where infrastructure was deployed
application	Type of the notebook template (jupyter/rstudio/zeppelin/tensor/deeplearning)
git_creds	User git credentials in JSON format
action	Create

Stop

In order to stop Notebook node, click on the “gear” button in Actions column. From the drop-down menu click on “Stop” action.

List of parameters for Notebook node stopping:

In Amazon (click to expand)

Parameter	Description/Value
conf_resource	notebook
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
edge_user_name	Value that previously was used when Edge being provisioned
notebook_instance_name	Name of the Notebook instance to terminate
aws_region	AWS region where infrastructure was deployed
action	Stop

In Azure (click to expand)

Parameter	Description/Value
conf_resource	notebook
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
edge_user_name	Value that previously was used when Edge being provisioned
notebook_instance_name	Name of the Notebook instance to terminate
azure_resource_group_name	Name of the resource group where all DLAb resources are being provisioned
action	Stop

In Google cloud (click to expand)

Parameter	Description/Value
conf_resource	notebook
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
edge_user_name	Value that previously was used when Edge being provisioned
notebook_instance_name	Name of the Notebook instance to terminate
gcp_region	GCP region where infrastructure was deployed
gcp_zone	GCP zone where infrastructure was deployed
gcp_project_id	ID of GCP project
action	Stop

Start

In order to start Notebook node, click on the button, which looks like gear in “Action” field. Then in drop-down menu choose “Start” action.

List of parameters for Notebook node start:

In Amazon (click to expand)

Parameter	Description/Value
conf_resource	notebook
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
edge_user_name	Value that previously was used when Edge being provisioned
notebook_instance_name	Name of the Notebook instance to terminate
aws_region	AWS region where infrastructure was deployed
git_creds	User git credentials in JSON format
action	start

Note: For format of git_creds see "Manage git credentials" lower.

In Azure (click to expand)

Parameter	Description/Value
conf_resource	notebook
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
edge_user_name	Value that previously was used when Edge being provisioned
notebook_instance_name	Name of the Notebook instance to terminate
azure_resource_group_name	Name of the resource group where all DLAb resources are being provisioned
azure_region	Azure region where infrastructure was deployed
git_creds	User git credentials in JSON format
action	start

In Google cloud (click to expand)

Parameter	Description/Value
conf_resource	notebook
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
edge_user_name	Value that previously was used when Edge being provisioned
notebook_instance_name	Name of the Notebook instance to terminate
gcp_region	GCP region where infrastructure was deployed
gcp_zone	GCP zone where infrastructure was deployed
gcp_project_id	ID of GCP project
git_creds	User git credentials in JSON format
action	Stop

Terminate

In order to terminate Notebook node, click on the button, which looks like gear in “Action” field. Then in drop-down menu choose “Terminate” action.

List of parameters for Notebook node termination:

In Amazon (click to expand)

Parameter	Description/Value
conf_resource	notebook
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
edge_user_name	Value that previously was used when Edge being provisioned
notebook_instance_name	Name of the Notebook instance to terminate
aws_region	AWS region where infrastructure was deployed
action	terminate

Note: If terminate action is called, all connected data engine clusters will be removed.

In Azure (click to expand)

Parameter	Description/Value
conf_resource	notebook
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
edge_user_name	Value that previously was used when Edge being provisioned
notebook_instance_name	Name of the Notebook instance to terminate
azure_resource_group_name	Name of the resource group where all DLAb resources are being provisioned
action	terminate

In Google cloud (click to expand)

Parameter	Description/Value
conf_resource	notebook
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
edge_user_name	Value that previously was used when Edge being provisioned
notebook_instance_name	Name of the Notebook instance to terminate
gcp_region	GCP region where infrastructure was deployed
gcp_zone	GCP zone where infrastructure was deployed
gcp_project_id	ID of GCP project
git_creds	User git credentials in JSON format
action	Stop

List/Install additional libraries

In order to list available libraries (OS/Python2/Python3/R/Others) on Notebook node, click on the button, which looks like gear in “Action” field. Then in drop-down menu choose “Manage libraries” action.

In Amazon (click to expand)

List of parameters for Notebook node to get list of available libraries:

Parameter	Description/Value
conf_resource	notebook
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
edge_user_name	Value that previously was used when Edge being provisioned
notebook_instance_name	Name of the Notebook instance to terminate
aws_region	AWS region where infrastructure was deployed
application	Type of the notebook template (jupyter/rstudio/zeppelin/tensor/deeplearning)
action	lib_list

Note: This operation will return a file with response [edge_user_name]_[application]_[request_id]_all_pkgs.json

Example of available libraries in response (type->library->version):

{
  "os_pkg": {"htop": "2.0.1-1ubuntu1", "python-mysqldb": "1.3.7-1build2"},
  "pip2": {"requests": "N/A", "configparser": "N/A"},
  "pip3": {"configparser": "N/A"},
  "r_pkg": {"rmarkdown": "1.5"},
  "others": {"Keras": "N/A"}
}

List of parameters for Notebook node to install additional libraries:

Parameter	Description/Value
conf_resource	notebook
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
edge_user_name	Value that previously was used when Edge being provisioned
notebook_instance_name	Name of the Notebook instance to terminate
aws_region	AWS region where infrastructure was deployed
application	Type of the notebook template (jupyter/rstudio/zeppelin/tensor/deeplearning)
libs	List of additional libraries in JSON format with type (os_pkg/pip2/pip3/r_pkg/others)
action	lib_install

Example of additional_libs parameter:

{
  ...
  "libs": [
    {"group": "os_pkg", "name": "nmap"},
    {"group": "os_pkg", "name": "htop"},
    {"group": "pip2", "name": "requests"},
    {"group": "pip3", "name": "configparser"},
    {"group": "r_pkg", "name": "rmarkdown"},
    {"group": "others", "name": "Keras"}
  ]
  ...
}

In Azure (click to expand)

List of parameters for Notebook node to get list of available libraries:

Parameter	Description/Value
conf_resource	notebook
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
edge_user_name	Value that previously was used when Edge being provisioned
notebook_instance_name	Name of the Notebook instance to terminate
azure_resource_group_name	Name of the resource group where all DLAb resources are being provisioned
application	Type of the notebook template (jupyter/rstudio/zeppelin/tensor/deeplearning)
action	lib_list

List of parameters for Notebook node to install additional libraries:

Parameter	Description/Value
conf_resource	notebook
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
edge_user_name	Value that previously was used when Edge being provisioned
notebook_instance_name	Name of the Notebook instance to terminate
azure_resource_group_name	Name of the resource group where all DLAb resources are being provisioned
application	Type of the notebook template (jupyter/rstudio/zeppelin/tensor/deeplearning)
libs	List of additional libraries in JSON format with type (os_pkg/pip2/pip3/r_pkg/others)
action	lib_install

In Google cloud (click to expand)

List of parameters for Notebook node to get list of available libraries:

Parameter	Description/Value
conf_resource	notebook
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
edge_user_name	Value that previously was used when Edge being provisioned
notebook_instance_name	Name of the Notebook instance to terminate
application	Type of the notebook template (jupyter/rstudio/zeppelin/tensor/deeplearning)
gcp_project_id	ID of GCP project
gcp_zone	GCP zone name
action	lib_list

List of parameters for Notebook node to install additional libraries:

Parameter	Description/Value
conf_resource	notebook
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
edge_user_name	Value that previously was used when Edge being provisioned
notebook_instance_name	Name of the Notebook instance to terminate
gcp_project_id	ID of GCP project
gcp_zone	GCP zone name
application	Type of the notebook template (jupyter/rstudio/zeppelin/tensor/deeplearning)
libs	List of additional libraries in JSON format with type (os_pkg/pip2/pip3/r_pkg/others)
action	lib_install

Manage git credentials

In order to manage git credentials on Notebook node, click on the button “Git credentials”. Then in menu you can add or edit existing credentials.

In Amazon (click to expand)

List of parameters for Notebook node to manage git credentials:

Parameter	Description/Value
conf_resource	notebook
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
edge_user_name	Value that previously was used when Edge being provisioned
notebook_instance_name	Name of the Notebook instance to terminate
aws_region	AWS region where infrastructure was deployed
git_creds	User git credentials in JSON format
action	git_creds

Example of git_creds parameter:

[{
  "username": "Test User",
  "email": "test@example.com",
  "hostname": "github.com",
  "login": "testlogin",
  "password": "testpassword"
}, ...]

Note: Fields "username" and "email" are used for commits (displays Author in git log).

Note: Leave "hostname" field empty to apply login/password by default for all services.

Note: Also your can use "Personal access tokens" against passwords.

In Azure (click to expand)

Parameter	Description/Value
conf_resource	notebook
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
edge_user_name	Value that previously was used when Edge being provisioned
notebook_instance_name	Name of the Notebook instance to terminate
azure_resource_group_name	Name of the resource group where all DLAb resources are being provisioned
git_creds	User git credentials in JSON format
action	git_creds

In Google cloud (click to expand)

Parameter	Description/Value
conf_resource	notebook
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
edge_user_name	Value that previously was used when Edge being provisioned
gcp_project_id	ID of GCP project
gcp_region	GCP region name
gcp_zone	GCP zone name
notebook_instance_name	Name of the Notebook instance to terminate
git_creds	User git credentials in JSON format
action	git_creds

Dataengine-service cluster

Dataengine-service is a cluster provided by cloud as a service (EMR on AWS) can be created if more computational resources are needed for executing analytical algorithms and models, triggered from analytical tools. Jobs execution will be scaled to a cluster mode increasing the performance and decreasing execution time.

Create

To create dataengine-service cluster click on the “gear” button in Actions column, and click on “Add computational resources”. Specify dataengine-service version, fill in dataengine-service name, specify number of instances and instance shapes. Click on the “Create” button.

List of parameters for dataengine-service cluster creation:

In Amazon (click to expand)

Parameter	Description/Value
conf_resource	dataengine-service
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
emr_timeout	Value of timeout for dataengine-service during build.
emr_instance_count	Amount of instance in dataengine-service cluster
emr_master_instance_type	Value for dataengine-service EC2 master instance shape
emr_slave_instance_type	Value for dataengine-service EC2 slave instances shapes
emr_version	Available versions of dataengine-service (emr-5.2.0/emr-5.3.1/emr-5.6.0)
notebook_instance_name	Name of the Notebook dataengine-service will be linked to
edge_user_name	Value that previously was used when Edge being provisioned
aws_region	AWS region where infrastructure was deployed
conf_tag_resource_id	The name of tag for billing reports
action	create

Note: If “Spot instances” is enabled, dataengine-service Slave nodes will be created as EC2 Spot instances.

In Google cloud (click to expand)

Parameter	Description/Value
conf_resource	dataengine-service
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
notebook_instance_name	Name of the Notebook dataengine-service will be linked to
edge_user_name	Value that previously was used when Edge being provisioned
gcp_subnet_name	Name of subnet
dataproc_version	Version of Dataproc
dataproc_master_count	Number of master nodes
dataproc_slave_count	Number of slave nodes
dataproc_preemptible_count	Number of preemptible nodes
dataproc_master_instance_type	Size of master node
dataproc_slave_instance_type	Size of slave node
gcp_project_id	ID of GCP project
gcp_region	GCP region where infrastructure was deployed
gcp_zone	GCP zone name
conf_tag_resource_id	The name of tag for billing reports
action	create

Terminate

In order to terminate dataengine-service cluster, click on “x” button which is located in “Computational resources” field.

List of parameters for dataengine-service cluster termination:

In Amazon (click to expand)

Parameter	Description/Value
conf_resource	dataengine-service
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
edge_user_name	Value that previously was used when Edge being provisioned
emr_cluster_name	Name of the dataengine-service to terminate
notebook_instance_name	Name of the Notebook instance which dataengine-service is linked to
aws_region	AWS region where infrastructure was deployed
action	Terminate

In Google cloud (click to expand)

Parameter	Description/Value
conf_resource	dataengine-service
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
edge_user_name	Value that previously was used when Edge being provisioned
notebook_instance_name	Name of the Notebook instance which dataengine-service is linked to
gcp_project_id	ID of GCP project
gcp_region	GCP region where infrastructure was deployed
gcp_zone	GCP zone name
dataproc_cluster_name	Dataproc cluster name
action	Terminate

List/Install additional libraries

In order to list available libraries (OS/Python2/Python3/R/Others) on Dataengine-service, click on the button, which looks like gear in “Action” field. Then in drop-down menu choose “Manage libraries” action.

In Amazon (click to expand)

List of parameters for Dataengine-service node to get list of available libraries:

Parameter	Description/Value
conf_resource	dataengine-service
conf_key_name	Name of the uploaded SSH key file (without ".pem")
computational_id	Name of Dataengine-service
edge_user_name	Value that previously was used when Edge being provisioned
aws_region	AWS region where infrastructure was deployed
application	Type of the notebook template (jupyter/rstudio/zeppelin/tensor/deeplearning)
action	lib_list

Note: This operation will return a file with response [edge_user_name]_[application]_[request_id]_all_pkgs.json

Example of available libraries in response (type->library->version):

{
  "os_pkg": {"htop": "2.0.1-1ubuntu1", "python-mysqldb": "1.3.7-1build2"},
  "pip2": {"requests": "N/A", "configparser": "N/A"},
  "pip3": {"configparser": "N/A"},
  "r_pkg": {"rmarkdown": "1.5"},
  "others": {"Keras": "N/A"}
}

List of parameters for Dataengine-service to install additional libraries:

Parameter	Description/Value
conf_resource	dataengine-service
conf_key_name	Name of the uploaded SSH key file (without ".pem")
edge_user_name	Value that previously was used when Edge being provisioned
computational_id	Name of Dataengine-service
aws_region	AWS region where infrastructure was deployed
application	Type of the notebook template (jupyter/rstudio/zeppelin/tensor/deeplearning)
libs	List of additional libraries in JSON format with type (os_pkg/pip2/pip3/r_pkg/others)
action	lib_install

Example of additional_libs parameter:

{
  ...
  "libs": [
    {"group": "os_pkg", "name": "nmap"},
    {"group": "os_pkg", "name": "htop"},
    {"group": "pip2", "name": "requests"},
    {"group": "pip3", "name": "configparser"},
    {"group": "r_pkg", "name": "rmarkdown"},
    {"group": "others", "name": "Keras"}
  ]
  ...
}

In Google cloud (click to expand)

List of parameters for Dataengine-service node to get list of available libraries:

Parameter	Description/Value
conf_resource	dataengine-service
conf_key_name	Name of the uploaded SSH key file (without ".pem")
edge_user_name	Value that previously was used when Edge being provisioned
application	Type of the notebook template (jupyter/rstudio/zeppelin/tensor/deeplearning)
gcp_project_id	ID of GCP project
gcp_region	GCP region name
gcp_zone	GCP zone name
action	lib_list

List of parameters for Dataengine-service node to install additional libraries:

Parameter	Description/Value
conf_resource	dataengine-service
conf_key_name	Name of the uploaded SSH key file (without ".pem")
edge_user_name	Value that previously was used when Edge being provisioned
application	Type of the notebook template (jupyter/rstudio/zeppelin/tensor/deeplearning)
gcp_project_id	ID of GCP project
gcp_region	GCP region name
gcp_zone	GCP zone name
action	lib_install

Dataengine cluster

Dataengine is cluster based on Standalone Spark framework can be created if more computational resources are needed for executing analytical algorithms, but without additional expenses for cloud provided service.

Create

To create Spark standalone cluster click on the “gear” button in Actions column, and click on “Add computational resources”. Specify dataengine version, fill in dataengine name, specify number of instances and instance shapes. Click on the “Create” button.

List of parameters for dataengine cluster creation:

In Amazon (click to expand)

Parameter	Description/Value
conf_resource	dataengine
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
conf_os_family	Name of the Linux distributive family, which is supported by DLab (Debian/RedHat)
notebook_instance_name	Name of the Notebook dataengine will be linked to
dataengine_instance_count	Number of nodes in cluster
edge_user_name	Value that previously was used when Edge being provisioned
aws_region	Amazon region where all infrastructure was deployed
aws_dataengine_master_size	Size of master node
aws_dataengine_slave_size	Size of slave node
action	create

In Azure (click to expand)

Parameter	Description/Value
conf_resource	dataengine
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
conf_os_family	Name of the Linux distributive family, which is supported by DLab (Debian/RedHat)
notebook_instance_name	Name of the Notebook dataengine will be linked to
dataengine_instance_count	Number of nodes in cluster
edge_user_name	Value that previously was used when Edge being provisioned
azure_vpc_name	Name of Azure Virtual network where all infrastructure is being deployed
azure_region	Azure region where all infrastructure was deployed
azure_dataengine_master_size	Size of master node
azure_dataengine_slave_size	Size of slave node
azure_resource_group_name	Name of the resource group where all DLAb resources are being provisioned
azure_subnet_name	Name of the Azure public subnet where Edge was deployed
action	create

In Google cloud (click to expand)

Parameter	Description/Value
conf_resource	dataengine
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
conf_os_family	Name of the Linux distributive family, which is supported by DLab (Debian/RedHat)
notebook_instance_name	Name of the Notebook dataengine will be linked to
gcp_vpc_name	GCP VPC name
gcp_subnet_name	GCP subnet name
dataengine_instance_count	Number of nodes in cluster
gcp_dataengine_master_size	Size of master node
gcp_dataengine_slave_size	Size of slave node
gcp_project_id	ID of GCP project
gcp_region	GCP region where infrastructure was deployed
gcp_zone	GCP zone name
edge_user_name	Value that previously was used when Edge being provisioned
action	create

Terminate

In order to terminate dataengine cluster, click on “x” button which is located in “Computational resources” field.

List of parameters for dataengine cluster termination:

In Amazon (click to expand)

Parameter	Description/Value
conf_resource	dataengine
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
edge_user_name	Value that previously was used when Edge being provisioned
notebook_instance_name	Name of the Notebook instance which dataengine is linked to
computational_name	Name of cluster
aws_region	AWS region where infrastructure was deployed
action	Terminate

In Azure (click to expand)

Parameter	Description/Value
conf_resource	dataengine
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
edge_user_name	Value that previously was used when Edge being provisioned
computational_name	Name of cluster
notebook_instance_name	Name of the Notebook instance which dataengine is linked to
azure_region	Azure region where infrastructure was deployed
azure_resource_group_name	Name of the resource group where all DLAb resources are being provisioned
action	Terminate

In Google cloud (click to expand)

Parameter	Description/Value
conf_resource	dataengine
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
edge_user_name	Value that previously was used when Edge being provisioned
notebook_instance_name	Name of the Notebook instance which dataengine is linked to
computational_name	Name of cluster
gcp_project_id	ID of GCP project
gcp_region	GCP region where infrastructure was deployed
gcp_zone	GCP zone name
action	Terminate

List/Install additional libraries

In order to list available libraries (OS/Python2/Python3/R/Others) on Dataengine, click on the button, which looks like gear in “Action” field. Then in drop-down menu choose “Manage libraries” action.

In Amazon (click to expand)

List of parameters for Dataengine node to get list of available libraries:

Parameter	Description/Value
conf_resource	dataengine
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
edge_user_name	Value that previously was used when Edge being provisioned
computational_id	Name of cluster
application	Type of the notebook template (jupyter/rstudio/zeppelin/tensor/deeplearning)
action	lib_list

Note: This operation will return a file with response [edge_user_name]_[application]_[request_id]_all_pkgs.json

Example of available libraries in response (type->library->version):

{
  "os_pkg": {"htop": "2.0.1-1ubuntu1", "python-mysqldb": "1.3.7-1build2"},
  "pip2": {"requests": "N/A", "configparser": "N/A"},
  "pip3": {"configparser": "N/A"},
  "r_pkg": {"rmarkdown": "1.5"},
  "others": {"Keras": "N/A"}
}

List of parameters for Dataengine node to install additional libraries:

Parameter	Description/Value
conf_resource	dataengine
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
edge_user_name	Value that previously was used when Edge being provisioned
computational_id	Name of cluster
application	Type of the notebook template (jupyter/rstudio/zeppelin/tensor/deeplearning)
action	lib_install

Example of additional_libs parameter:

{
  ...
  "libs": [
    {"group": "os_pkg", "name": "nmap"},
    {"group": "os_pkg", "name": "htop"},
    {"group": "pip2", "name": "requests"},
    {"group": "pip3", "name": "configparser"},
    {"group": "r_pkg", "name": "rmarkdown"},
    {"group": "others", "name": "Keras"}
  ]
  ...
}

In Azure (click to expand)

List of parameters for Dataengine node to get list of available libraries:

Parameter	Description/Value
conf_resource	dataengine
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
edge_user_name	Value that previously was used when Edge being provisioned
azure_resource_group_name	Name of the resource group where all DLAb resources are being provisioned
computational_id	Name of cluster
application	Type of the notebook template (jupyter/rstudio/zeppelin/tensor/deeplearning)
action	lib_list

List of parameters for Dataengine node to install additional libraries:

Parameter	Description/Value
conf_resource	dataengine
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
edge_user_name	Value that previously was used when Edge being provisioned
azure_resource_group_name	Name of the resource group where all DLAb resources are being provisioned
computational_id	Name of cluster
application	Type of the notebook template (jupyter/rstudio/zeppelin/tensor/deeplearning)
action	lib_install

In Google cloud (click to expand)

List of parameters for Dataengine node to get list of available libraries:

Parameter	Description/Value
conf_resource	dataengine
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
edge_user_name	Value that previously was used when Edge being provisioned
application	Type of the notebook template (jupyter/rstudio/zeppelin/tensor/deeplearning)
gcp_project_id	ID of GCP project
gcp_zone	GCP zone name
computational_id	Name of cluster
action	lib_list

List of parameters for Dataengine node to install additional libraries:

Parameter	Description/Value
conf_resource	dataengine
conf_service_base_name	Unique infrastructure value, specified during SSN deployment
conf_key_name	Name of the uploaded SSH key file (without ".pem")
edge_user_name	Value that previously was used when Edge being provisioned
application	Type of the notebook template (jupyter/rstudio/zeppelin/tensor/deeplearning)
gcp_project_id	ID of GCP project
gcp_zone	GCP zone name
computational_id	Name of cluster
action	lib_install

Configuration files

DLab configuration files are located on SSN node by following path:

/opt/dlab/conf ssn.yml – basic configuration for all java services;
provisioning.yml – Provisioning Service configuration file;for
security.yml – Security Service configuration file;
self-service.yml – Self-Service configuration file.

Starting/Stopping services

All DLab services running as OS services and have next syntax for starting and stopping:

sudo supervisorctl {start | stop | status} [all | provserv | secserv | ui]

start – starting service or services;
stop – stopping service or services;
status – show status of service or services;
all – execute command for all services, this option is default;
provserv – execute command for Provisioning Service;
secserv – execute command for Security Service;
ui – execute command for Self-Service.

DLab Web UI

DLab self service is listening to the secure 8443 port. This port is used for secure local communication with provisioning service.

There is also Nginx proxy server running on Self-Service node, which proxies remote connection to local 8443 port. Nginx server is listening to both 80 and 443 ports by default. It means that you could access self-service Web UI using non-secure connections (80 port) or secure (443 port).

Establishing connection using 443 port you should take into account that DLab uses self-signed certificate from the box, however you are free to switch Nginx to use your own domain-verified certificate.

To disable non-secure connection please do the following:

uncomment at /etc/nginx/conf.d/nginx_proxy.conf file rule that rewrites all requests from 80 to 443 port;
reload/restart Nginx web server.

To use your own certificate please do the following:

upload your certificate and key to Self-Service node;
specify at /etc/nginx/conf.d/nginx_proxy.conf file the correct path to your new ssl_certificate and ssl_certificate_key;
reload/restart Nginx web server.

Billing report

AWS (click to expand)

Billing module is implemented as a separate jar file and can be running in the follow modes:

part of Self-Service;
separate system process;
manual loading or use external scheduler;

The billing module is running as part of the Self-Service (if billing was switched ON before SSN deployment). For details please refer to section Self-Service Node. Otherwise, you should manually configure file billing.yml. See the descriptions how to do this in the configuration file. Please also note, that you should also add an entry in the Mongo database into collection:

{
    "_id": "conf_tag_resource_id",
    "Value": "<CONF_TAG_RESOURCE_ID>"
}

After you have configured the billing, you can run it as a process of Self-Service. To do this, in the configuration file self-service.yml set the property BillingSchedulerEnabled to true and restart the Self-Service:

sudo supervisorctl stop ui
sudo supervisorctl start ui

If you want to load report manually, or use external scheduler use following command:

java -jar /opt/dlab/webapp/lib/billing/billing-aws.x.y.jar --conf /opt/dlab/conf/billing.yml
or
java -cp /opt/dlab/webapp/lib/billing/billing-aws.x.y.jar com.epam.dlab.BillingTool --conf /opt/dlab/conf/billing.yml

If you want billing to work as a separate process from the Self-Service use following command:

java -cp /opt/dlab/webapp/lib/billing/billing-aws.x.y.jar com.epam.dlab.BillingScheduler --conf /opt/dlab/conf/billing.yml

Azure (click to expand)

Billing module is implemented as a separate jar file and can be running in the follow modes:

part of Self-Service;
separate system process;

If you want to start billing module as a separate process use the following command:

java -jar /opt/dlab/webapp/lib/billing/billing-azure.x.y.jar /opt/dlab/conf/billing.yml

Backup and Restore

All DLab configuration files, keys, certificates, jars, database and logs can be saved to backup file.

Scripts for backup and restore is located in dlab_path/tmp/. Default: /opt/dlab/tmp/

List of parameters for run backup:

Parameter	Description/Value
--dlab_path	Path to DLab. Default: /opt/dlab/
--configs	Comma separated names of config files, like "security.yml", etc. Default: all
--keys	Comma separated names of keys, like "user_name.pub". Default: all
--certs	Comma separated names of SSL certificates and keys, like "dlab.crt", etc. Also available: skip. Default: all
--jars	Comma separated names of jar application, like "self-service" (without .jar), etc. Also available: all. Default: skip
--db	Mongo DB. Key without arguments. Default: disable
--logs	All logs (include docker). Key without arguments. Default: disable

List of parameters for run restore:

Parameter	Description/Value
--dlab_path	Path to DLab. Default: /opt/dlab/
--configs	Comma separated names of config files, like "security.yml", etc. Default: all
--keys	Comma separated names of keys, like "user_name.pub". Default: all
--certs	Comma separated names of SSL certificates and keys, like "dlab.crt", etc. Also available: skip. Default: all
--jars	Comma separated names of jar application, like "self-service" (without .jar), etc. Also available: all. Default: skip
--db	Mongo DB. Key without arguments. Default: disable
--file	Full or relative path to backup file or folder. Required field
--force	Force mode. Without any questions. Key without arguments. Default: disable

Note: You can type -h or --help for usage details.

Note: Restore process required stopping services.

GitLab server

Own GitLab server can be deployed from SSN node with script, which located in:

dlab_path/tmp/gitlab. Default: /opt/dlab/tmp/gitlab

All initial configuration parameters located in gitlab.ini file.

Some of parameters are already setuped from SSN provisioning.

GitLab uses the same LDAP server as DLab.

To deploy Gitlab server, set all needed parameters in gitlab.ini and run script:

./gitlab_deploy.py --action [create/terminate]

Note: Terminate process uses node_name to find instance.

Note: GitLab wouldn't be terminated with all environment termination process.

Troubleshooting

If the parameter dlab_path of configuration file dlab.ini wasn’t changed, the path to DLab service would default to:

/opt/dlab/ - main directory of DLab service
/var/opt/dlab/log/ or /var/log/dlab/ - path to log files

To check logs of Docker containers run the following commands:

docker ps -a – to get list of containers which were executed.
...
a85d0d3c27aa docker.dlab-dataengine:latest "/root/entrypoint...." 2 hours ago Exited (0) 2 hours ago infallible_gallileo
6bc2afeb888e docker.dlab-jupyter:latest "/root/entrypoint...." 2 hours ago Exited (0) 2 hours ago practical_cori
51b71c5d4aa3 docker.dlab-zeppelin:latest "/root/entrypoint...." 2 hours ago Exited (0) 2 hours ago determined_knuth
...
docker logs <container_id> – to get log for particular Docker container.

To change Docker images on existing environment, you can run script on SSN node that rebuilds docker images with the command:

docker-build all  #to rebuild all images

or

docker-build <notebook_name> #to rebuild certain images

You can also rebuild images manually by executing the following steps:

SSH to SSN instance
go to /opt/dlab/sources/
Modify needed files [4]. [ONLY FOR AZURE] Copy service principal json file with credentials to base/azure_auth.json
Rebuild proper Docker images, using one or several commands (depending on what files you’ve changed):

docker build --build-arg OS=<os_family> --file general/files/<cloud_provider>/base_Dockerfile -t docker.dlab-base .
docker build --build-arg OS=<os_family> --file general/files/<cloud_provider>/edge_Dockerfile -t docker.dlab-edge .
docker build --build-arg OS=<os_family> --file general/files/<cloud_provider>/jupyter_Dockerfile -t docker.dlab-jupyter .
docker build --build-arg OS=<os_family> --file general/files/<cloud_provider>/jupyterlab_Dockerfile -t docker.dlab-jupyterlab .
docker build --build-arg OS=<os_family> --file general/files/<cloud_provider>/rstudio_Dockerfile -t docker.dlab-rstudio .
docker build --build-arg OS=<os_family> --file general/files/<cloud_provider>/zeppelin_Dockerfile -t docker.dlab-zeppelin .
docker build --build-arg OS=<os_family> --file general/files/<cloud_provider>/tensor_Dockerfile -t docker.dlab-tensor .
docker build --build-arg OS=<os_family> --file general/files/<cloud_provider>/tensor-rstudio_Dockerfile -t docker.dlab-tensor-rstudio .
docker build --build-arg OS=<os_family> --file general/files/<cloud_provider>/deeplearning_Dockerfile -t docker.dlab-deeplearning .
docker build --build-arg OS=<os_family> --file general/files/<cloud_provider>/dataengine_Dockerfile -t docker.dlab-dataengine .

Development

DLab services could be ran in development mode. This mode emulates real work an does not create any resources on cloud provider environment.

Folder structure

dlab
├───infrastructure-provisioning
└───services
    ├───billing
    ├───common
    ├───provisioning-service
    ├───security-service
    ├───self-service
    └───settings

infrastructure-provisioning – code of infrastructure-provisioning module;
services – back-end services source code;
- billing – billing module for AWS cloud provider only;
- common – reusable code for all services;
- provisioning-service – Provisioning Service;
- security-service – Security Service;
- self-service – Self-Service and UI;
- settings – global settings that are stored in mongo database in development mode;

Pre-requisites

In order to start development of Front-end Web UI part of DLab - Git repository should be cloned and the following packages should be installed:

Git 1.7 or higher
Python 2.7 with library Fabric v1.14.0
Docker 1.12 - Infrastructure provisioning

Java back-end services

Java components description

Common

Common is a module, which wraps set of reusable code over services. Commonly reused functionality is as follows:

Models
REST client
Mongo persistence DAO
Security models and DAO

Self-Service

Self-Service provides REST based API’s. It tightly interacts with Provisioning Service and Security Service and actually delegates most of user`s requests for execution.

API class name	Supported actions	Description
BillingResource	Get billing invoice Export billing invoice in CSV file	Provides billing information.
ComputationalResource	Configuration limits Create Terminate	Used for computational resources management.
EdgeResource	Start Stop Status	Manage EDGE node.
ExploratoryResource	Create Status Start Stop Terminate	Used for exploratory environment management.
GitCredsResource	Update credentials Get credentials	Used for exploratory environment management.
InfrastructureInfoResource	Get info of environment Get status of environment	Used for obtaining statuses and additional information about provisioned resources
InfrastructureTemplatesResource	Get computation resources templates Get exploratory environment templates	Used for getting exploratory/computational templates
KeyUploaderResource	Check key Upload key Recover	Used for Gateway/EDGE node public key upload and further storing of this information in Mongo DB.
LibExploratoryResource	Lib groups Lib list Lib search Lib install	User’s authentication.
SecurityResource	Login Authorize Logout	User’s authentication.
UserSettingsResource	Get settings Save settings	User’s preferences.

Some class names may have endings like Aws or Azure(e.g. ComputationalResourceAws, ComputationalResourceAzure, etc...). It means that it's cloud specific class with a proper API

Provisioning Service

The Provisioning Service is key, REST based service for management of cloud specific or Docker based environment resources like computational, exploratory, edge, etc.

API class name	Supported actions	Description
ComputationalResource	Create Terminate	Docker actions for computational resources management.
DockerResource	Get Docker image Run Docker image	Requests and describes Docker images and templates.
EdgeResource	Create Start Stop	Provides Docker actions for EDGE node management.
ExploratoryResource	Create Start Stop Terminate	Provides Docker actions for working with exploratory environment management.
GitExploratoryResource	Update git greds	Docker actions to provision git credentials to running notebooks
InfrastructureResource	Status	Docker action for obtaining status of DLab infrastructure instances.
LibExploratoryResource	Lib list Install lib	Docker actions to install libraries on netobboks

Some class names may have endings like Aws or Azure(e.g. ComputationalResourceAws, ComputationalResourceAzure, etc...). It means that it's cloud specific class with a proper API

Security service

Security service is REST based service for user authentication against LDAP/LDAP + AWS/Azure OAuth2 depending on module configuration and cloud provider. LDAP only provides with authentication end point that allows to verify authenticity of users against LDAP instance. If you use AWS cloud provider LDAP + AWS authentication could be useful as it allows to combine LDAP authentication and verification if user has any role in AWS account

DLab provides OAuth2(client credentials and authorization code flow) security authorization mechanism for Azure users. This kind of authentication is required when you are going to use Data Lake. If Data Lake is not enabled you have two options LDAP or OAuth2 If OAuth2 is in use security-service validates user's permissions to configured permission scope(resource in Azure). If Data Lake is enabled default permission scope(can be configured manually after deploy DLab) is Data Lake Store account so only if user has any role in scope of Data Lake Store Account resource he/she will be allowed to log in If Data Lake is disabled but Azure OAuth2 is in use default permission scope will be Resource Group where DLab is created and only users who have any roles in the resource group will be allowed to log in.

Front-end

Front-end components description

Web UI sources are part of Self-Service.

Sources are located in dlab/services/self-service/src/main/resources/webapp

Main pages	Components and Services
Login page	LoginComponent applicationSecurityService handles http calls and stores authentication tokens on the client and attaches the token to authenticated calls; healthStatusService and appRoutingService check instances states and redirect to appropriate page.
Home page (list of resources)	HomeComponent nested several main components like ResourcesGrid for notebooks data rendering and filtering, using custom MultiSelectDropdown component; multiple modal dialogs components used for new instances creation, displaying detailed info and actions confirmation.
Health Status page	HealthStatusComponent HealthStatusGridComponent displays list of instances, their types, statutes, ID’s and uses healthStatusService for handling main actions.
Help pages	Static pages that contains information and instructions on how to access Notebook Server and generate SSH key pair. Includes only NavbarComponent.
Error page	Simple static page letting users know that opened page does not exist. Includes only NavbarComponent.
Reporting page	ReportingComponent ReportingGridComponent displays billing detailed info with built-in filtering and DateRangePicker component for custom range filtering; uses BillingReportService for handling main actions and exports report data to .csv file.

How to setup local development environment

The development environment setup description is written with assumption that user already has installed Java8 (JDK), Maven3 and set environment variables (JAVA_HOME, M2_HOME). The description will cover Mongo installation, Mongo user creation, filling initial data into Mongo, Node.js installation

Install Mongo database

Download MongoDB from https://www.mongodb.com/download-center
Install database based on MongoDB instructions
Start DB server and create accounts

use admin
db.createUser(
   {
     user: "admin",
     pwd: "<password>",
     roles: [ { role: "dbAdminAnyDatabase", db: "admin" },
              { role: "userAdminAnyDatabase", db: "admin" },
              { role: "readWriteAnyDatabase", db: "admin" } ]
   }
)
use <database_name>
db.createUser(
   {
     user: "admin",
     pwd: "<password>",
     roles: [ "dbAdmin", "userAdmin", "readWrite" ]
   }
)

Load collections form file dlab/services/settings/(aws|azure)/mongo_settings.json

mongoimport -u admin -p <password> -d <database_name> -c settings mongo_settings.json

Load collections form file dlab/infrastructure-provisioning/src/ssn/files/mongo_roles.json

mongoimport -u admin -p <password> -d <database_name> --jsonArray -c roles mongo_roles.json

Setting up environment options

Set option CLOUD_TYPE to aws/azure, DEV_MODE to true, mongo database name and password in configuration file dlab/infrastructure-provisioning/src/ssn/templates/ssn.yml

<#assign CLOUD_TYPE="aws">
...
<#assign DEV_MODE="true">
...
mongo:
  database: <database_name>
  password: <password>

Add system environment variable DLAB_CONF_DIR=<dlab_root_folder>/dlab/infrastructure-provisioning/src/ssn/templates/ssn.yml or create two symlinks in dlab/services/provisioning-service and dlab/services/self-service folders for file dlab/infrastructure-provisioning/src/ssn/templates/ssn.yml.

Unix

ln -s ../../infrastructure-provisioning/src/ssn/templates/ssn.yml ssn.yml

Windows

mklink ssn.yml ..\\..\\infrastructure-provisioning\\src\\ssn\\templates\\ssn.yml

For Unix system create two folders and grant permission for writing:

/var/opt/dlab/log/ssn
/opt/dlab/tmp/result

Install Node.js

Download Node.js from https://nodejs.org/en
Install Node.js
Make sure that the installation folder of Node.js has been added to the system environment variable PATH
Install latest packages

npm install npm@latest -g

Build Web UI components

Change folder to dlab/services/self-service/src/main/resources/webapp and install the dependencies from a package.json manifest

npm install

Replace CLOUD_PROVIDER options with aws|azure in dictionary file
dlab/services/self-service/src/main/resources/webapp/src/dictionary/global.dictionary.ts

import { NAMING_CONVENTION } from './(aws|azure).dictionary';

export * from './(aws|azure).dictionary';

Build web application

npm run build.prod

Prepare HTTPS prerequisites

To enable a SSL connection the web server should have a Digital Certificate. To create a server certificate, follow these steps:

Create the keystore.
Export the certificate from the keystore.
Sign the certificate.
Import the certificate into a truststore: a repository of certificates used for verifying the certificates. A truststore typically contains more than one certificate.

Please find below set of commands to create certificate, depending on OS.

Create Unix/Ubuntu server certificate

Pay attention that the last command has to be executed with administrative permissions.

keytool -genkeypair -alias dlab -keyalg RSA -storepass KEYSTORE_PASSWORD -keypass KEYSTORE_PASSWORD -keystore ~/keys/dlab.keystore.jks -keysize 2048 -dname "CN=localhost"
keytool -exportcert -alias dlab -storepass KEYSTORE_PASSWORD -file ~/keys/dlab.crt -keystore ~/keys/dlab.keystore.jks
sudo keytool -importcert -trustcacerts -alias dlab -file ~/keys/dlab.crt -noprompt -storepass changeit -keystore ${JRE_HOME}/lib/security/cacerts

Create Windows server certificate

Pay attention that the last command has to be executed with administrative permissions. To achieve this the command line (cmd) should be ran with administrative permissions.

"%JRE_HOME%\bin\keytool" -genkeypair -alias dlab -keyalg RSA -storepass KEYSTORE_PASSWORD -keypass KEYSTORE_PASSWORD -keystore <DRIVE_LETTER>:\home\%USERNAME%\keys\dlab.keystore.jks -keysize 2048 -dname "CN=localhost"
"%JRE_HOME%\bin\keytool" -exportcert -alias dlab -storepass KEYSTORE_PASSWORD -file <DRIVE_LETTER>:\home\%USERNAME%\keys\dlab.crt -keystore <DRIVE_LETTER>:\home\%USERNAME%\keys\dlab.keystore.jks
"%JRE_HOME%\bin\keytool" -importcert -trustcacerts -alias dlab -file <DRIVE_LETTER>:\home\%USERNAME%\keys\dlab.crt -noprompt -storepass changeit -keystore "%JRE_HOME%\lib\security\cacerts"

Useful command
"%JRE_HOME%\bin\keytool" -list -alias dlab -storepass changeit -keystore "%JRE_HOME%\lib\security\cacerts"
"%JRE_HOME%\bin\keytool" -delete -alias dlab -storepass changeit -keystore "%JRE_HOME%\lib\security\cacerts"

Where the <DRIVE_LETTER> must be the drive letter where you run the DLab.

How to run locally

There is a possibility to run Self-Service and Provisioning Service locally. All requests from Provisioning Service to Docker are mocked and instance creation status will be persisted to Mongo (only without real impact on Docker and AWS). Security Service can`t be running on local machine because of local LDAP mocking complexity.

Both services, Self-Service and Provisioning Service are dependent on dlab/provisioning-infrastructure/ssn/templates/ssn.yml configuration file. Both services have main functions as entry point, SelfServiceApplication for Self-Service and ProvisioningServiceApplication for Provisioning Service. Services could be started by running main methods of these classes. Both main functions require two arguments:

Run mode (“server”)
Configuration file name (“self-service.yml” or “provisioning.yml” depending on the service). Both files are located in root service directory. These configuration files contain service settings and are ready to use.

The services start up order does matter. Since Self-Service depends on Provisioning Service, the last should be started first and Self-Service afterwards. Services could be started from local IDEA (Eclipse or Intellij Idea) “Run” functionality of toolbox.

Run application flow is following:

Run provisioning-service passing 2 arguments: server, provisioning.yml
Run self-service passing 2 arguments: server, self-service.yml
Try to access self-service Web UI by https://localhost:8443

User: test
Password: <any>

Infrastructure provisioning

DevOps components overview

The following list shows common structure of scripts for deploying DLab

Folder structure

dlab
└───infrastructure-provisioning
    └───src
        ├───base
        ├───dataengine
        ├───dataengine-service
        ├───deeplearning            
        ├───edge
        ├───general
        ├───jupyter
        ├───jupyterlab
        ├───project
        ├───rstudio
        ├───ssn
        ├───superset
        ├───tensor
        ├───tensor-rstudio
        └───zeppelin

Each directory except general contains Python scripts, Docker files, templates, files for appropriate Docker image.

base – Main Docker image. It is a common/base image for other ones.
edge – Docker image for Edge node.
dataengine – Docker image for dataengine cluster.
dataengine-service – Docker image for dataengine-service cluster.
general – OS and CLOUD dependent common source.
ssn – Docker image for Self-Service node (SSN).
jupyter/rstudio/zeppelin/tensor/deeplearning – Docker images for Notebook nodes.

All Python scripts, Docker files and other files, which are located in these directories, are OS and CLOUD independent.

OS, CLOUD dependent and common for few templates scripts, functions, files are located in general directory.

general
├───api – all available API
├───conf – DLab configuration
├───files – OS/Cloud dependent files
├───lib – OS/Cloud dependent functions
├───scripts – OS/Cloud dependent Python scripts
└───templates – OS/Cloud dependent templates

These directories may contain differentiation by operating system (Debian/RedHat) or cloud provider (AWS).

Directories of templates (SSN, Edge etc.) contain only scripts, which are OS and CLOUD independent.

If script/function is OS or CLOUD dependent, it should be located in appropriate directory/library in general folder.

The following table describes mostly used scripts:

Script name/Path	Description
Dockerfile	Used for building Docker images and represents which Python scripts, templates and other files are needed. Required for each template.
base/entrypoint.py	This file is executed by Docker. It is responsible for setting environment variables, which are passed from Docker and for executing appropriate actions (script in general/api/).
base/scripts/*.py	Scripts, which are OS independent and are used in each template.
general/api/*.py	API scripts, which execute appropriate function from fabfile.py.
template_name/fabfile.py	Is the main file for template and contains all functions, which can be used as template actions.
template_name/scripts/*.py	Python scripts, which are used for template. They are OS and CLOUD independent.
general/lib/aws/*.py	Contains all functions related to AWS.
general/lib/os/	This directory is divided by type of OS. All OS dependent functions are located here.
general/lib/os/fab.py	Contains OS independent functions used for multiple templates.
general/scripts/	Directory is divided by type of Cloud provider and OS.
general/scripts/aws/*.py	Scripts, which are executed from fabfiles and AWS-specific. The first part of file name defines to which template this script is related to. For example: common_.py – can be executed from more than one template. ssn_.py – are used for SSN template. edge_*.py – are used for Edge template.
general/scripts/os/*.py	Scripts, which are OS independent and can be executed from more than one template.

Docker actions overview

Available Docker images and their actions:

Docker image	Actions
ssn	create, terminate
edge	create, terminate, status, start, stop, recreate
jupyter/rstudio/zeppelin/tensor/deeplearning	create, terminate, start, stop, configure, list_libs, install_libs, git_creds
dataengine/dataengine-service	create, terminate

Docker and python execution workflow on example of SSN node

Docker command for building images docker.dlab-base and docker.dlab-ssn:

sudo docker build --build-arg OS=debian  --file general/files/aws/base_Dockerfile -t docker.dlab-base . ;
sudo docker build --build-arg OS=debian  --file general/files/aws/ssn_Dockerfile -t docker.dlab-ssn . ;

Example of SSN Docker file:

FROM docker.dlab-base:latest

ARG OS

COPY ssn/ /root/
COPY general/scripts/aws/ssn_* /root/scripts/
COPY general/lib/os/${OS}/ssn_lib.py /usr/lib/python2.7/dlab/ssn_lib.py
COPY general/files/aws/ssn_policy.json /root/files/
COPY general/templates/aws/jenkins_jobs /root/templates/jenkins_jobs

RUN chmod a+x /root/fabfile.py; \
    chmod a+x /root/scripts/*

RUN mkdir /project_tree
COPY . /project_tree

Using this Docker file, all required scripts and files will be copied to Docker container.

Docker command for building SSN:

docker run -i -v /root/KEYNAME.pem:/root/keys/KEYNAME.pem –v /web_app:/root/web_app -e "conf_os_family=debian" -e "conf_cloud_provider=aws" -e "conf_resource=ssn" -e "aws_ssn_instance_size=t2.medium" -e "aws_region=us-west-2" -e "aws_vpc_id=vpc-111111" -e "aws_subnet_id=subnet-111111" -e "aws_security_groups_ids=sg-11111,sg-22222,sg-33333" -e "conf_key_name=KEYNAME" -e "conf_service_base_name=dlab_test" -e "aws_access_key=Access_Key_ID" -e "aws_secret_access_key=Secret_Access_Key" -e "conf_tag_resource_id=dlab" docker.dlab-ssn --action create ;

Docker executes entrypoint.py script with action create. Entrypoint.py will set environment variables, which were provided from Docker and execute general/api/create.py script:

    elif args.action == 'create':
        with hide('running'):
            local("/bin/create.py")

general/api/create.py will execute Fabric command with run action:

  try:
        local('cd /root; fab run')

Function run() in file ssn/fabfile.py will be executed. It will run two scripts general/scripts/aws/ssn_prepare.py and general/scripts/aws/ssn_configure.py:

    try:
        local("~/scripts/{}.py".format('ssn_prepare'))
    except Exception as err:
        traceback.print_exc()
        append_result("Failed preparing SSN node. Exception: " + str(err))
        sys.exit(1)

    try:
        local("~/scripts/{}.py".format('ssn_configure'))
    except Exception as err:
        traceback.print_exc()
        append_result("Failed configuring SSN node. Exception: " + str(err))
        sys.exit(1)

The scripts general/scripts/<cloud_provider>/ssn_prepare.py an general/scripts/<cloud_provider>/ssn_configure.py will execute other Python scripts/functions for:

ssn_prepate.py: 1. Creating configuration file (for AWS) 2. Creating Cloud resources.
ssn_configure.py: 1. Installing prerequisites 2. Installing required packages 3. Configuring Docker 4. Configuring DLab Web UI

If all scripts/function are executed successfully, Docker container will stop and SSN node will be created.

Example of Docker commands

SSN:

docker run -i -v <key_path><key_name>.pem:/root/keys/<key_name>.pem -e "region=<region>" -e "conf_service_base_name=<Infrastructure_Tag>" -e  “conf_resource=ssn" -e "aws_access_key=<Access_Key_ID>" -e "aws_secret_access_key=<Secret_Access_Key>" docker.dlab-ssn --action <action>

All parameters are listed in section "Self-ServiceNode" chapter.

Other images:

docker run -i -v /home/<user>/keys:/root/keys  -v /opt/dlab/tmp/result:/response -v /var/opt/dlab/log/<image>:/logs/<image>  -e <variable1> –e <variable2> docker.dlab-<image> --action <action>

How to add a new template

First of all, a new directory should be created in infrastructure-provisioning/src/.

For example: infrastructure-provisioning/src/my-tool/

The following scripts/directories are required to be created in the template directory:

my-tool
├───scripts
└───fabfile.py

fabfile.py – the main script, which contains main functions for this template such as run, stop, terminate, etc.

Here is example of run() function for Jupyter Notebook node:

Path: infrastructure-provisioning/src/jupyter/fabfile.py

def run():
    local_log_filename = "{}_{}_{}.log".format(os.environ['conf_resource'], os.environ['edge_user_name'], os.environ['request_id'])
    local_log_filepath = "/logs/" + os.environ['conf_resource'] + "/" + local_log_filename
    logging.basicConfig(format='%(levelname)-8s [%(asctime)s]  %(message)s',
                        level=logging.DEBUG,
                        filename=local_log_filepath)

    notebook_config = dict()
    notebook_config['uuid'] = str(uuid.uuid4())[:5]

    try:
        params = "--uuid {}".format(notebook_config['uuid'])
        local("~/scripts/{}.py {}".format('common_prepare_notebook', params))
    except Exception as err:
        traceback.print_exc()
        append_result("Failed preparing Notebook node.", str(err))
        sys.exit(1)

    try:
        params = "--uuid {}".format(notebook_config['uuid'])
        local("~/scripts/{}.py {}".format('jupyter_configure', params))
    except Exception as err:
        traceback.print_exc()
        append_result("Failed configuring Notebook node.", str(err))
        sys.exit(1)

This function describes process of creating Jupyter node. It is divided into two parts – prepare and configure. Prepare part is common for all notebook templates and responsible for creating of necessary cloud resources, such as EC2 instances, etc. Configure part describes how the appropriate services will be installed.

To configure Jupyter node, the script jupyter_configure.py is executed. This script describes steps for configuring Jupyter node. In each step, the appropriate Python script is executed.

For example:

Path: infrastructure-provisioning/src/general/scripts/aws/jupyter_configure.py

    try:
        logging.info('[CONFIGURE JUPYTER NOTEBOOK INSTANCE]')
        print('[CONFIGURE JUPYTER NOTEBOOK INSTANCE]')
        params = "--hostname {} --keyfile {} --region {} --spark_version {} --hadoop_version {} --os_user {} --scala_version {}".\
            format(instance_hostname, keyfile_name, os.environ['aws_region'], os.environ['notebook_spark_version'],
                   os.environ['notebook_hadoop_version'], os.environ['conf_os_user'],
                   os.environ['notebook_scala_version'])
        try:
            local("~/scripts/{}.py {}".format('configure_jupyter_node', params))

In this step, the script infrastructure-provisioning/src/jupyter/scripts/configure_jupyter_node.py will be executed.

Example of script infrastructure-provisioning/src/jupyter/scripts/configure_jupyter_node.py:

if __name__ == "__main__":
    print("Configure connections")
    env['connection_attempts'] = 100
    env.key_filename = [args.keyfile]
    env.host_string = args.os_user + '@' + args.hostname

    print("Configuring notebook server.")
    try:
        if not exists('/home/' + args.os_user + '/.ensure_dir'):
            sudo('mkdir /home/' + args.os_user + '/.ensure_dir')
    except:
        sys.exit(1)

    print("Mount additional volume")
    prepare_disk(args.os_user)

    print("Install Java")
    ensure_jre_jdk(args.os_user)

This script call functions for configuring Jupyter node. If this function is OS dependent, it will be placed in infrastructure-provisioning/src/general/lib/<OS_family>/debian/notebook_lib.py

All functions in template directory (e.g. infrastructure-provisioning/src/my-tool/) should be OS and cloud independent.

All OS or cloud dependent functions should be placed in infrastructure-provisioning/src/general/lib/ directory.

The following steps are required for each Notebook node:

Configure proxy on Notebook instance – the script infrastructure-provisioning/src/general/scripts/os/notebook_configure_proxy.py
Installing user’s key – the script infrastructure-provisioning/src/base/scripts/install_user_key.py

Other scripts, responsible for configuring Jupyter node are placed in infrastructure-provisioning/src/jupyter/scripts/

scripts directory – contains all required configuration scripts.
infrastructure-provisioning/src/general/files/<cloud_provider>/my-tool_Dockerfile – used for building template Docker image and describes which files, scripts, templates are required and will be copied to template Docker image.
infrastructure-provisioning/src/general/files/<cloud_provider>/my-tool_descriptsion.json – JSON file for DLab Web UI. In this file you can specify:

exploratory_environment_shapes – list of EC2 shapes
exploratory_environment_versions – description of template

Example of this file for Jupyter node for AWS cloud:

{
  "exploratory_environment_shapes" :
  {
    "For testing" : [
      {"Size": "S", "Description": "Standard_DS1_v2", "Type": "Standard_DS1_v2","Ram": "3.5 GB","Cpu": "1", "Spot": "true", "SpotPctPrice": "70"}
    ],
    "Memory optimized" : [
      {"Size": "S", "Description": "Standard_E4s_v3", "Type": "Standard_E4s_v3","Ram": "32 GB","Cpu": "4"},
      {"Size": "M", "Description": "Standard_E16s_v3", "Type": "Standard_E16s_v3","Ram": "128 GB","Cpu": "16"},
      {"Size": "L", "Description": "Standard_E32s_v3", "Type": "Standard_E32s_v3","Ram": "256 GB","Cpu": "32"}
    ],
    "Compute optimized": [
      {"Size": "S", "Description": "Standard_F2s", "Type": "Standard_F2s","Ram": "4 GB","Cpu": "2"},
      {"Size": "M", "Description": "Standard_F8s", "Type": "Standard_F8s","Ram": "16.0 GB","Cpu": "8"},
      {"Size": "L", "Description": "Standard_F16s", "Type": "Standard_F16s","Ram": "32.0 GB","Cpu": "16"}
    ]
  },
  "exploratory_environment_versions" :
  [
    {
      "template_name": "Jupyter notebook 5.7.4",
      "description": "Base image with jupyter node creation routines",
      "environment_type": "exploratory",
      "version": "jupyter_notebook-5.7.4",
      "vendor": "Azure"
    }
  ]
}

Additionally, following directories could be created:

templates – directory for new templates;
files – directory for files used by newly added templates only;

All Docker images are being built while creating SSN node. To add newly created template, add it to the list of images in the following script:

Path: infrastructure-provisioning/src/general/scripts/aws/ssn_configure.py

    try:
        logging.info('[CONFIGURING DOCKER AT SSN INSTANCE]')
        print('[CONFIGURING DOCKER AT SSN INSTANCE]')
        additional_config = [{"name": "base", "tag": "latest"},
                             {"name": "edge", "tag": "latest"},
                             {"name": "jupyter", "tag": "latest"},
                             {"name": "rstudio", "tag": "latest"},
                             {"name": "zeppelin", "tag": "latest"},
                             {"name": "tensor", "tag": "latest"},
                             {"name": "emr", "tag": "latest"}]

For example:

...

   {"name": "my-tool", "tag": "latest"},

...

LDAP Authentication

Unified logging and group management

There are a few popular LDAP distributions on the market like Active Directory, Open LDap. That’s why some differences in configuration appear. Also depending on customization, there might be differences in attributes configuration. For example the DN(distinguished name) may contain different attributes:

DN=CN=Name Surname,OU=groups,OU=EPAM,DC=Company,DC=Cloud
DN=UID=UID#53,OU=groups,OU=Company,DC=Company,DC=Cloud

CN vs UID.

The relation between users and groups also varies from vendor to vendor.

For example, in Open LDAP the group object may contain set (from 0 to many) attributes "memberuid" with values equal to user`s attribute “uid”.

However, in Active Directory the mappings are done based on other attributes. On a group size there is attribute "member" (from 0 to many values) and its value is user`s DN (distinguished name).

To fit the unified way of LDAP usage, we introduced configuration file with set of properties and customized scripts (python and JavaScript based). On backend side, all valuable attributes are further collected and passed to these scripts. To apply some customization it is required to update a few properties in security.yml and customize the scripts.

Properties overview

There are just a few properties based in which the customization could be done:

ldapBindTemplate: uid=%s,ou=People,dc=example,dc=com
ldapBindAttribute: uid
ldapSearchAttribute: uid

Where the:

ldapBindTemplate is a user`s DN template which should be filed with custom value. Here the template could be changed: uid=%s,ou=People,dc=example,dc=com -> cn=%s,ou=People,dc=example,dc=com.
ldapBindAttribute - this is a major attribute, on which the DN is based on. Usually it is any of: uid or cn, or email.
ldapSearchAttribute - another attribute, based on which users will be looked up in LDAP.

Additional parameters that are populated during deployment and may be changed in future are:

ldapConnectionConfig.name: ldap user name
ldapConnectionConfig.ldapHost: ldap host
ldapConnectionConfig.ldapPort: ldap port
ldapConnectionConfig.credentials: ldap credentials

Scripts overview

There are 3 scripts in security.yml:

userLookUp (python based) - responsible for user lookup in LDap and returns additional user`s attributes;
userInfo (python based) - enriches user with additional data;
groupInfo (javascript based) – responsible for mapping between users and groups;

Script structure

The scripts above were created to flexibly manage user`s security configuration. They all are part of security.yml configuration. All scripts have following structure: - name - cache - expirationTimeMsec - scope - attributes - timeLimit - base - filter - searchResultProcessor: - language - code

Major properties are:

attributes - list of attributes that will be retrieved from LDAP (-name, -cn, -uid, -member, etc);
filter - the filter, based on which the object will be retrieved from LDAP;
searchResultProcessor - optional. If only LDAP object attributes retrieving is required, this property should be empty. For example, “userLookup” script only retrieves list of "attributes". Otherwise, code customization (like user enrichment, user to groups matching, etc.) should be added into sub-properties below:
- language - the script language - "python" or "JavaScript"
- code - the script code.

"userLookUp" script

Configuration properties:

ldapBindTemplate: 'cn=%s,ou=users,ou=alxn,dc=alexion,dc=cloud'
ldapBindAttribute: cn
ldapSearchAttribute: mail

Script code:

name: userLookUp
cache: true
expirationTimeMsec: 600000
scope: SUBTREE
attributes:
  - cn
  - gidNumber
  - mail
  - memberOf
timeLimit: 0
base: ou=users,ou=alxn,dc=alexion,dc=cloud
filter: "(&(objectCategory=person)(objectClass=user)(mail=%mail%))"

In the example above, the user login passed from GUI is a mail (ldapSearchAttribute: mail) and based on the filer (filter: "(&(objectCategory=person)(objectClass=user)(mail=%mail%))") so, the service would search user by its “mail”. If corresponding users are found - the script will return additional user`s attributes:

cn
gidNumber
mail
memberOf

User`s authentication into LDAP would be done for DN with following template ldapBindTemplate: 'cn=%s,ou=users,ou=alxn, dc=alexion,dc=cloud', where CN is attribute retrieved by “userLookUp” script.

Azure OAuth2 Authentication

DLab supports OAuth2 authentication that is configured automatically in Security Service and Self Service after DLab deployment. Please see explanation details about configuration parameters for Self Service and Security Service below. DLab supports client credentials(username + password) and authorization code flow for authentication.

Azure OAuth2 Self Service configuration

azureLoginConfiguration:
    useLdap: false
    tenant: xxxx-xxxx-xxxx-xxxx
    authority: https://login.microsoftonline.com/
    clientId: xxxx-xxxx-xxxx-xxxx
    redirectUrl: https://dlab.azure.cloudapp.azure.com/
    responseMode: query
    prompt: consent
    silent: true
    loginPage: https://dlab.azure.cloudapp.azure.com/
    maxSessionDurabilityMilliseconds: 288000000

where:

useLdap - defines if LDAP authentication is enabled(true/false). If false Azure OAuth2 takes place with configuration properties below
tenant - tenant id of your company
authority - Microsoft login endpoint
clientId - id of the application that users log in through
redirectUrl - redirect URL to DLab application after try to login to Azure using OAuth2
responseMode - defines how Azure sends authorization code or error information to DLab during log in procedure
prompt - defines kind of prompt during Oauth2 login
silent - defines if DLab tries to log in user without interaction(true/false), if false DLab tries to login user with configured prompt
loginPage - start page of DLab application
maxSessionDurabilityMilliseconds - max user session durability. user will be asked to login after this period of time and when he/she creates ot starts notebook/cluster. This operation is needed to update refresh_token that is used by notebooks to access Data Lake Store

To get more info about responseMode, prompt parameters please visit Authorize access to web applications using OAuth 2.0 and Azure Active Directory

Azure OAuth2 Security Service configuration

azureLoginConfiguration:
    useLdap: false
    tenant: xxxx-xxxx-xxxx-xxxx
    authority: https://login.microsoftonline.com/
    clientId: xxxx-xxxx-xxxx-xxxx
    redirectUrl: https://dlab.azure.cloudapp.azure.com/
    validatePermissionScope: true
    permissionScope: subscriptions/xxxx-xxxx-xxxx-xxxx/resourceGroups/xxxx-xxxx/providers/Microsoft.DataLakeStore/accounts/xxxx/providers/Microsoft.Authorization/
    managementApiAuthFile: /dlab/keys/azure_authentication.json

where:

useLdap - defines if LDAP authentication is enabled(true/false). If false Azure OAuth2 takes place with configuration properties below
tenant - tenant id of your company
authority - Microsoft login endpoint
clientId - id of the application that users log in through
redirectUrl - redirect URL to DLab application after try to login to Azure using OAuth2
validatePermissionScope - defines(true/false) if user's permissions should be validated to resource that is provided in permissionScope parameter. User will be logged in onlu in case he/she has any role in resource IAM described with permissionScope parameter
permissionScope - describes Azure resource where user should have any role to pass authentication. If user has no role in resource IAM he/she will not be logged in
managementApiAuthFile - authentication file that is used to query Microsoft Graph API to check user roles in resource described in permissionScope

Files

README.md

Latest commit

History

README.md

File metadata and controls

DLAB Overview

CONTENTS

What is DLAB?

Logical architecture

Self-Service

Billing

Provisioning Service

Security service

Docker

Database

Physical architecture

Main components

Self-service node (SSN)

Endpoint

Edge node

Notebook node

Data engine cluster

DLab Deployment

Structure of main DLab directory

Structure of log directory

Keycloak server

Preparing environment for Keycloak deployment

Executing deployment script

Self-Service Node

Preparing environment for DLab deployment

Executing deployment script

Terminating Self-Service Node

Edge Node

Create

Start/Stop

Notebook node

Create

Stop

Start

Terminate

List/Install additional libraries

Manage git credentials

Dataengine-service cluster

Create

Terminate

List/Install additional libraries

Dataengine cluster

Create

Terminate

List/Install additional libraries

Configuration files

Starting/Stopping services

DLab Web UI

Billing report

Backup and Restore

GitLab server

Troubleshooting

Development

Folder structure

Pre-requisites

Java back-end services

Java components description

Common

Self-Service

Provisioning Service

Security service

Front-end

Front-end components description

How to setup local development environment

Install Mongo database

Setting up environment options

Install Node.js

Build Web UI components

Prepare HTTPS prerequisites

Create Unix/Ubuntu server certificate

Create Windows server certificate

How to run locally

Infrastructure provisioning

DevOps components overview