Overview

The Whole Tale platform provides is a scalable, web-based, multi-user platform for the creation, publication, and execution of “tales” – executable research objects that capture data, code, and the complete software environment required for reproducibility. It is designed to enable researchers to publish their code and data along with required software dependencies to long-term research archives, simplifying the process of creating and verifying computational artifacts.

The Whole Tale platform includes the following primary components:

  • Identity and access management
  • Dashboard
  • Whole Tale API
  • Whole Tale Filesystem
  • Image registry
  • Provider API
  • User environments

The following diagram illustrates the logical relationship between key system components:

../_images/logical_overview.png

The Whole Tale platform leverages and extends a variety of standard components and services including the OpenStack cloud platform (via Jetstream and Chameleon), Docker Swarm container orchestration platform, Celery/Redis for distributed task management, MongoDB for data management, Traefik reverse proxy, Open Monitoring Distribution for monitoring, as well as interactive analysis environments such as RStudio and Jupyter. Whole Tale leverages and extends the Girder REST API framework.

../_images/logical_overview.png

Identity and Access Management

Identity and access management are implemented via OAuth 2.0/OpenID Connect. Via the Girder OAuth plugin, the platform can be configured to use common OAuth providers including Google, Github, Bitbucket, and Globus. The production WT service leverages Globus Auth for federated login because if provides support for:

  • InCommon IdPs via CILogon
  • XSEDE/Argonne, ORCID and other research-centric systems
  • Tokens that can be used to initiate Globus transfers

The publishing framework uses ORCID for authentication into the DataONE network.

Dashboard

The dashboard is the primary interface into the Whole Tale system for users to interactively launch, create, and share Tales. It is the reference interface for the Whole Tale API, built using the Ember JS open-source web framework.

../_images/dashboard.png

Whole Tale API

The Whole Tale API extends the Girder framework adding Whole Tale capabilities including:

  • Images, Tales and Instances
  • Distributed home and Tale workspace folders
  • Importing data from remote repositories
  • Publishing Tales to remote repositories
  • Remote data access and caching

Via Celery/Redis, the Whole Tale API provides a scalable framework for: * Building and manaaging Tale images * Launching Tale instances (e.g., RStudio, Jupyter environments) * Ingesting data from external sources

The following diagram provides an overview of key compoments of the Whole Tale API:

../_images/tale_instance_model.png

Each user has a home folder that is accessible via the Whole Tale filesystem to every running Tale instance (and also exposed via WebDav to be optionally mounted on their local system). Every Tale is defined by its environment (e.g., RStudio/Jupyter); a workspace folder containing code, data, and narrative; and an optional set of externally-referenced datasets. When a user runs/launches a Tale, they get a Tale instance – a running Docker container based on the defined environment with the Tale workspace, external data, and home directory mounted and accessible.

Girder

Girder is an open source web-based data management platform intended for developing new web services with a focus on data organization, user management, authentication and authorization. It has been adopted by several related projects including yt.Hub, the NSF-funded Renaissance Simulations Laboratory, Crops in silico, and the Einstein Toolkit DataVault.

Whole Tale leverages Girder for the following features:

  • OAuth flow for user authentication
  • User and group management including advanced access control models
  • Metadata management including file, folder, and collection abstractions
  • Job management framework including notifications
  • API key and token management
  • Lighweight and high-performance interface to MongoDB

Environment Customization

As of release v0.6, environment customization is implemented via the Recipe model

../_images/tale_image_model.png

A Tale image is defined by a “recipe”, which refers to a Github repository and commit ID that conforms to the Whole Tale image definition requirements. Future releases will include integration with Project Jupyter’s repo2docker framework.

Scalable task distribution (gwvolman)

The Whole Tale API implements a generic and scalable task distribution framework via the popular Celery system. The gwvolman implements tasks including:

  • Building and pushing images
  • Managing services (Swarm) including start/stop/update
  • Managing container volumes (mount/unmount)
  • Ingesting data from external providers
  • Publishing Tales to external providers (v0.7)

Whole Tale Filesystem

The Whole Tale filesystem provides distributed access to system data via a POSIX interface. This includes enabling access to home and Tale workspace data and managing access to and caching of externally registered data.

../_images/filesystem_overview.png

Distributed folder access (wt_home_dir)

The Whole Tale platform includes an integrated WebDAV server (via WsgiDav) to enable distributed access to home and Tale workspace folders. The WebDAV server is integrated with Girder for authentication and to synchronize fileystem metadata. This means that changes made via WebDAV or Girder (e.g., the WT Dashboard) are always reflected in the exposed filesystem.

../_images/webdav_overview.png

Data Management Service (girder_wt_data_manager)

The Whole Tale Data Management system is responsible for managing the data used in Tales. The main components include:

  • Transfer subsystem that managed movement of data from external data providers to local storage in Whole Tale. This is achieved through provider-specific transfer adapters.
  • Storage management system that acts as a local data cache that selectively caches or clears local copies of externally hosted data based on frequency of use.
  • Filesystem interface that allows tales to access cached data through a standard POSIX interface.
../_images/data_manager_overview.png

Python client (girderfs)

Whole Tale provides girerfs, a Python client/library to mount the Whole Tale filesystem volumes. This is an intermediate layer representing data in Whole Tale as a POSIX filesystem that interfaces with the Data Management system. This is based on fusepy, a thin python wrapper for FUSE development.

This component supports the following mount types: * remote: mount Girder folders via REST API * direct: mount local Girder assetstore * wt_dms: mount via Whole Tale DMS * wt_work: mount Tale workspace via davfs * wt_home: mount user home directory via davfs

Provider Framework

The Whole Tale provider framework is designed to enable easy extension to support new providers for data registration, “Analyze in WT” capabilities, and publishing.

The framework consists of the following interfaces:

  • ImportProvider: Search, register, and access data from external repositories
  • Integration: Translate requests for Analyze in Whole Tale
  • PublishProvider: Publish Tales to external repositories
  • TransferHandler: Protocol handlers for transferring data (e.g., HTTP, Globus)

Remote data registration and access

Combined with the Whole Tale filesystem and data management system, the provider model provides an abstraction over heterogenous data sources (APIs), exposing a consistent interface to both the Whole Tale dashboard and running tale instances. Datasets from DataONE, Dataverse, and Globus are exposed to running Jupyter and RStudio containers as elements of a POSIX filesystem. The registration process captures only the metadata of the remote dataset and the data management service retrieves the actual bits only when used. This means that only those portions of the remote dataset that are actually used are transferred and cached in Whole Tale.

../_images/registration_overview.png

User Environments

A fundamental design of the Whole Tale system is that users must be able to conduct and publish their analysis using their software environment of choice. Common environments such as RStudio and Jupyter should be provided by the system. Users must be able to customize these environments by selecting specific software versions. They must also be able to define and share new environments that may not be part of the base system.

In v0.6, the base environments are defined by the Recipe and Image models. Recipes refer to specific Github repositories and commit hashes. Imaages are the build Docker images stored in the Whole Tale image registry.

In v0.7, we will move to adopt the Binder repo2docker model where users can easily customize software in the environment.