What is DataLab?

    How to Contribute

Logical architecture

Physical architecture

DataLab Deployment

        Structure of main DataLab directory

        Structure of log directory

        Preparing environment for DataLab deployment

        Keycloak server

        Self-Service Node

        Endpoint Node

        Edge Node

        Notebook node

        Dataengine-service cluster

        Dataengine cluster

        Configuration files

        Starting/Stopping services

        Billing report

        Backup and Restore

        GitLab server

        Troubleshooting

Development

        Folder structure

        Pre-requisites

        Java back-end services

        Front-end

        How to setup local development environment

        How to run locally

        Infrastructure provisioning

        LDAP Authentication

        Azure OAuth2 Authentication

DataLab 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 datalab.incubator.apache.org.

The following diagram demonstrate high-level logical architecture.

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

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 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.

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

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

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

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

• 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

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

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

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

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.

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 DataLab 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.

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.

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

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

• conf – contains configuration for DataLab 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 DataLab;
• tmp/result – temporary directory for Docker’s response files;
• webapp – contains all .jar files for DataLab Web UI and back-end services.

SSN node structure of log directory is as follows:

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

These directories contain the log files for each template and for DataLab 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 is used to manage user authentication instead of the aplication. To use existing server following parameters must be specified either when running DataLab deployment script or in /opt/datalab/conf/self-service.yml and /opt/datalab/conf/provisioning.yml files on SSN node.

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 DataLab repository

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 python3-pip
pip3 install fabric

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

/usr/bin/python3 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:

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

For each cloud provider, prerequisites are different.

{
    "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 a VM instance with the following settings:
  • The instance should have access to Internet in order to install required prerequisites
  • Image - Ubuntu 20.04
• Generate SSH key pair and rename private key with .pem extension
• Put JSON auth file to users home directory

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-datalab.git -b develop
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.io
apt-get install -y docker.io=20.10.7-0ubuntu1~20.04.1
usermod -a -G docker *username*
apt-get install -y python3-pip
pip3 install fabric
cd incubator-datalab

• Go to datalab directory
• Run infrastructure-provisioning/scripts/deploy_datalab.py deployment script:

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

/usr/bin/python3 infrastructure-provisioning/scripts/deploy_datalab.py \
--conf_service_base_name datalab-test \
--conf_os_family debian \
--key_path /path/to/key/ \
--conf_key_name key_name \
--conf_tag_resource_id datalab \
--action create \
aws \
--aws_access_key XXXXXXX \
--aws_secret_access_key XXXXXXXXXX \
--aws_region xx-xxxxx-x \
--aws_vpc_id vpc-xxxxx \
--aws_subnet_id subnet-xxxxx \
--aws_security_groups_ids sg-xxxxx,sg-xxxx \
--aws_account_id xxxxxxxx \
--aws_billing_bucket billing_bucket \
--aws_report_path /billing/directory/

/usr/bin/python3 infrastructure-provisioning/scripts/deploy_datalab.py \
--conf_service_base_name datalab_test \
--conf_os_family debian \
--key_path /root/ \
--conf_key_name Test \
--azure_auth_path /dir/file.json  \
--action create \
azure \
--azure_vpc_name vpc-test \
--azure_subnet_name subnet-test \
--azure_security_group_name sg-test1,sg-test2 \
--azure_region westus2

/usr/bin/python3 infrastructure-provisioning/scripts/deploy_datalab.py \
--conf_service_base_name datalab-test \
--conf_os_family debian \
--key_path /path/to/key/ \
--conf_key_name key_name \
--action create
gcp \
--gcp_ssn_instance_size n1-standard-1 \
--gcp_project_id project_id \
--gcp_service_account_path /path/to/auth/file.json \
--gcp_region xx-xxxxx \
--gcp_zone xxx-xxxxx-x \

Note: Optionally Nexus repository can be used to store DataLab jars and Docker images

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

/usr/bin/python3 infrastructure-provisioning/scripts/deploy_datalab.py --conf_service_base_name datalab-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

/usr/bin/python3 infrastructure-provisioning/scripts/deploy_datalab.py --conf_service_base_name datalab-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

/usr/bin/python3 infrastructure-provisioning/scripts/deploy_datalab.py --gcp_project_id project_id --conf_service_base_name datalab-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

This node allows you to create Edge nodes and Notebooks on other cloud providers (AWS, Microsoft Azure or GCP). The exception is the option in which Edge nodes and Notebooks are created on the same cloud provider as Self-Service Node, in which case endpoint is already provided locally.

source /venv/bin/activate
/venv/bin/python3 infrastructure-provisioning/terraform/bin/datalab.py deploy aws endpoint \
--access_key_id access_key \
--secret_access_key secret_access_key \
--key_name datalab-key \
--pkey /path/to/private/key.pem \
--path_to_pub_key /path/key.pub \
--service_base_name datalab-test \
--endpoint_id awstest \
--region xx-xxx \
--zone xxxx \
--cloud_provider aws \
--vpc_id xxxxxxx \
--subnet_id xxxxxxx \
--ssn_ui_host 0.0.0.0 \
--billing_enable true/false \
--billing_bucket xxxxxxx \
--report_path xxxxxxxxx \
--billing_aws_account_id xxxxxxxxx \
--billing_tag xxxxxxx \
--mongo_password xxxxxxxxxx
deactivate

source /venv/bin/activate
/venv/bin/python3 infrastructure-provisioning/terraform/bin/datalab.py deploy azure endpoint \
--auth_file_path /path/to/auth.json
--key_name datalab-key \
--pkey /path/to/private/key.pem \
--path_to_pub_key /path/key.pub \
--service_base_name datalab-test \
--resource_group_name datalab-test-group \
--endpoint_id azuretest \
--region xx-xxx \
--zone xxxx \
--cloud_provider azure \
--vpc_id xxxxxxx \
--subnet_id xxxxxxx \
--ssn_ui_host 0.0.0.0 \
--billing_enable true/false \
--billing_bucket xxxxxxx \
--report_path xxxxxxxxx \
--billing_aws_account_id xxxxxxxxx \
--billing_tag xxxxxxx \
--mongo_password xxxxxxxxxx \
--offer_number xxxxxxxx \
--currency "USD"  \
--locale "en-US" \
--region_info "US"
deactivate

source /venv/bin/activate
/venv/bin/python3 infrastructure-provisioning/terraform/bin/datalab.py deploy gcp endpoint \
--gcp_project_id xxx-xxxx-xxxxxx \
--creds_file /path/to/auth.json \
--key_name datalab-key \
--pkey /path/to/private/key.pem \
--service_base_name datalab-test \
--path_to_pub_key /path/key.pub \
--endpoint_id gcptest \
--region xx-xxx \
--zone xxxx \
--cloud_provider gcp \
--vpc_id xxxxxxx \
--subnet_id xxxxxxx \
--ssn_ui_host 0.0.0.0 \
--mongo_password xxxxxxxxxx \
--billing_dataset_name xxxxxxxx \
--billing_enable true/false
deactivate

source /venv/bin/activate
/venv/bin/python3 infrastructure-provisioning/terraform/bin/datalab.py destroy aws endpoint \
--access_key_id access_key \
--secret_access_key secret_access_key \
--key_name datalab-key \
--pkey /path/to/private/key.pem \
--path_to_pub_key /path/key.pub \
--service_base_name datalab-test \
--endpoint_id awstest \
--region xx-xxx \
--zone xxxx \
--cloud_provider aws \
--vpc_id xxxxxxx \
--subnet_id xxxxxxx \
--ssn_ui_host 0.0.0.0 \
--billing_enable true/false \
--billing_bucket xxxxxxx \
--report_path xxxxxxxxx \
--billing_aws_account_id xxxxxxxxx \
--billing_tag xxxxxxx \
--mongo_password xxxxxxxxxx
deactivate

source /venv/bin/activate
/venv/bin/python3 infrastructure-provisioning/terraform/bin/datalab.py destroy azure endpoint \
--auth_file_path /path/to/auth.json
--key_name datalab-key \
--pkey /path/to/private/key.pem \
--path_to_pub_key /path/key.pub \
--service_base_name datalab-test \
--resource_group_name datalab-test-group \
--endpoint_id azuretest \
--region xx-xxx \
--zone xxxx \
--cloud_provider azure \
--vpc_id xxxxxxx \
--subnet_id xxxxxxx \
--ssn_ui_host 0.0.0.0 \
--billing_enable true/false \
--billing_bucket xxxxxxx \
--report_path xxxxxxxxx \
--billing_aws_account_id xxxxxxxxx \
--billing_tag xxxxxxx \
--mongo_password xxxxxxxxxx \
--offer_number xxxxxxxx \
--currency "USD"  \
--locale "en-US" \
--region_info "US"
deactivate

source /venv/bin/activate
/venv/bin/python3 infrastructure-provisioning/terraform/bin/datalab.py destroy gcp endpoint \
--gcp_project_id xxx-xxxx-xxxxxx \
--creds_file /path/to/auth.json \
--key_name datalab-key \
--pkey /path/to/private/key.pem \
--service_base_name datalab-test \
--path_to_pub_key /path/key.pub \
--endpoint_id gcptest \
--region xx-xxx \
--zone xxxx \
--cloud_provider gcp \
--vpc_id xxxxxxx \
--subnet_id xxxxxxx \
--ssn_ui_host 0.0.0.0 \
--mongo_password xxxxxxxxxx \
--billing_dataset_name xxxxxxxx \
--billing_enable true/false
deactivate

After creating endpoint, you must set the endpoint in Datalab UI. To do this, go to the tab "Resources" - "Endpoints". In the Name field, you should specify the name of the endpoint, in the URL, specify the network address of the endpoint (taking as an example https://0.0.0.0:8084/). In the Account field, you need to specify the "endpoint_id" value that was specified when creating the endpoint (for example, awstest, azuretest or gcptest), in the Endpoint tag field, specify the endpoint tag (optional).

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, DataLab 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.

In order to create Edge node using DataLab 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.

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.

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.

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 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 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 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 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.

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

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

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.

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

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.

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 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 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.

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

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

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.

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

List of parameters for dataengine cluster termination:

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.

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

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

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

• /opt/datalab/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.

All DataLab 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.

DataLab 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 DataLab 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.

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

sudo supervisorctl stop ui
sudo supervisorctl start ui

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

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

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

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

Scripts for backup and restore is located in datalab_path/tmp/. Default: /opt/datalab/tmp/

List of parameters for run backup:

List of parameters for run restore:

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

Note: Restore process required stopping services.

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

datalab_path/tmp/gitlab. Default: /opt/datalab/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 DataLab.

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.

If the parameter datalab_path of configuration file datalab.ini wasn’t changed, the path to DataLab service would default to:

• /opt/datalab/ - main directory of DataLab service
• /var/opt/datalab/log/ or /var/log/datalab/ - 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.datalab-dataengine:latest "/root/entrypoint...." 2 hours ago Exited (0) 2 hours ago infallible_gallileo
6bc2afeb888e docker.datalab-jupyter:latest "/root/entrypoint...." 2 hours ago Exited (0) 2 hours ago practical_cori
51b71c5d4aa3 docker.datalab-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:

1. SSH to SSN instance
2. go to /opt/datalab/sources/
3. Modify needed files [4]. [ONLY FOR AZURE] Copy service principal json file with credentials to base/azure_auth.json
4. 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.datalab-base .
docker build --build-arg OS=<os_family> --file general/files/<cloud_provider>/edge_Dockerfile -t docker.datalab-edge .
docker build --build-arg OS=<os_family> --file general/files/<cloud_provider>/jupyter_Dockerfile -t docker.datalab-jupyter .
docker build --build-arg OS=<os_family> --file general/files/<cloud_provider>/jupyterlab_Dockerfile -t docker.datalab-jupyterlab .
docker build --build-arg OS=<os_family> --file general/files/<cloud_provider>/rstudio_Dockerfile -t docker.datalab-rstudio .
docker build --build-arg OS=<os_family> --file general/files/<cloud_provider>/zeppelin_Dockerfile -t docker.datalab-zeppelin .
docker build --build-arg OS=<os_family> --file general/files/<cloud_provider>/tensor_Dockerfile -t docker.datalab-tensor .
docker build --build-arg OS=<os_family> --file general/files/<cloud_provider>/tensor-rstudio_Dockerfile -t docker.datalab-tensor-rstudio .
docker build --build-arg OS=<os_family> --file general/files/<cloud_provider>/deeplearning_Dockerfile -t docker.datalab-deeplearning .
docker build --build-arg OS=<os_family> --file general/files/<cloud_provider>/dataengine_Dockerfile -t docker.datalab-dataengine .

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

datalab
├───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;

In order to start development of Front-end Web UI part of DataLab - 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

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

1. Models
2. REST client
3. Mongo persistence DAO
4. Security models and DAO

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.

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

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

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 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

DataLab 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 DataLab) 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 DataLab is created and only users who have any roles in the resource group will be allowed to log in.

Web UI sources are part of Self-Service.

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

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

• Download MongoDB from https://www.mongodb.com/download-center
• Install database based on MongoDB instructions
• Download and install MongoDB Database Tools https://www.mongodb.com/try/download/database-tools
• 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 datalab/services/settings/(aws|azure)/mongo_settings.json

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

• Load collections form file datalab/infrastructure-provisioning/src/ssn/files/(aws|azure)/mongo_roles.json

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

If this command doesn't work for you, try to check https://docs.mongodb.com/v4.2/reference/program/mongoimport/ Or, use some UI client (f.e: MongoDB Compass )

• Set option CLOUD_TYPE to aws/azure, DEV_MODE to true, mongo database name and password in configuration file datalab/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 DATALAB_CONF_DIR=<datalab_root_folder>/datalab/infrastructure-provisioning/src/ssn/templates or create two symlinks in datalab/services/provisioning-service and datalab/services/self-service folders for file datalab/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/datalab/log/ssn
/opt/datalab/tmp/result

• Download Node.js LTS 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

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

npm install

• Build web application

npm run build.prod

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.

Pay attention that the last command has to be executed with administrative permissions. Use keytool bundled with JRE.

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

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. Path to <DRIVE_LETTER>:\home%USERNAME%\keys should exist.

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

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

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

Open infrastructure-provisioning/src/ssn/templates/ssn.yml

• (23) KEYS_DIR -> path to keys dir with backslash
• (30) CLOUD_TYPE -> CLOUD_PROVIDER to aws
• (34) DEV_MODE -> false to true
• (40-42) change user, pass, db to created in prev step

Open services/self-service/self-service.yml

• (170) keycloakConfiguration
  • (171) redirectUri -> https://localhost:8443/
  • (172) realm -> DLAB_bhliva
  • (174) auth-server-url -> http://52.11.45.11:8080/auth
  • (178) resource -> sss
  • credentials
    • (180) secret -> [ASK_YOUR_ONBOARDING_ENGINEER]

Open services/provisioning-service/provisioning.yml

• (93) keycloakConfiguration

  • (94) realm -> DLAB_bhliva
  • (96) auth-server-url -> http://52.11.45.11:8080/auth
  • (100) resource -> sss
  • credentials
    • (102) secret -> [ASK_YOUR_ONBOARDING_ENGINEER]

• (104) cloudProperties

  • (125) imageEnabled -> true
  • (133) stepCerts
    • (134) enabled -> true

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 datalab/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:

• Create and run provisioning-service configuration:

  • Create Application with name provisining-service-application

    • Main class: com.epam.datalab.backendapi.ProvisioningServiceApplication
    • VM options: -Ddocker.dir=[PATH_TO_PROJECT_DIR]\infrastructure-provisioning\src\general\files\aws
    • Program arguments : server [PATH_TO_PROJECT_DIR]\services\provisioning-service\provisioning.yml
    • Working directory:[PATH_TO_PROJECT_DIR]
    • Use classpath of module: provisioning-servise
    • PAY ATTENTION: JRE should be the same jre where created server certificate

  • Create and run self-service configuration:

    • Create Application with name self-service-application
      • Main class: com.epam.datalab.backendapi.SelfServiceApplication
      • Program arguments : server [PATH_TO_PROJECT_DIR]/services/self-service/self-service.yml
      • Working directory:[PATH_TO_PROJECT_DIR]
      • Use classpath of module: self-service
      • PAY ATTENTION: JRE should be the same jre where created server certificate
• Try to access self-service Web UI by https://localhost:8443

User: test
Password: <any>

The following list shows common structure of scripts for deploying DataLab

datalab
└───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 – DataLab 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:

Available Docker images and their actions:

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

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

Example of SSN Docker file:

FROM docker.datalab-base:latest

ARG OS

COPY ssn/ /root/
COPY general/scripts/aws/ssn_* /root/scripts/
COPY general/lib/os/${OS}/ssn_lib.py /usr/lib/python3.8/datalab/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=datalab_test" -e "aws_access_key=Access_Key_ID" -e "aws_secret_access_key=Secret_Access_Key" -e "conf_tag_resource_id=datalab" docker.datalab-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:

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

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

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.datalab-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/datalab/tmp/result:/response -v /var/opt/datalab/log/<image>:/logs/<image>  -e <variable1> –e <variable2> docker.datalab-<image> --action <action>

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(conn,'/home/' + args.os_user + '/.ensure_dir'):
            conn.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 DataLab 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"},

...

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.

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

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;

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.

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.

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

azureLoginConfiguration:
    useLdap: false
    tenant: xxxx-xxxx-xxxx-xxxx
    authority: https://login.microsoftonline.com/
    clientId: xxxx-xxxx-xxxx-xxxx
    redirectUrl: https://datalab.azure.cloudapp.azure.com/
    responseMode: query
    prompt: consent
    silent: true
    loginPage: https://datalab.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 DataLab application after try to login to Azure using OAuth2
• responseMode - defines how Azure sends authorization code or error information to DataLab during log in procedure
• prompt - defines kind of prompt during Oauth2 login
• silent - defines if DataLab tries to log in user without interaction(true/false), if false DataLab tries to login user with configured prompt
• loginPage - start page of DataLab 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

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