Frequently Asked Questions¶
- When is the general availability release of Anaconda Enterprise v5?
- Which notebooks or editors does Anaconda Enterprise support?
- Can I deploy multiple data science applications to Anaconda Enterprise?
- Does Anaconda Enterprise support high availability deployments?
- Which identity management and authentication protocols does Anaconda Enterprise support?
- Does Anaconda Enterprise support two-factor authentication (including one-time passwords)?
- System requirements
- What operating systems are supported for Anaconda Enterprise?
- What are the minimum system requirements for Anaconda Enterprise nodes?
- Which browsers are supported for Anaconda Enterprise?
- Does Anaconda Enterprise come with a version control system?
- Can Anaconda Enterprise integrate with my own Git server?
- How do I install Anaconda Enterprise?
- Can Anaconda Enterprise be installed on-premises?
- Can Anaconda Enterprise be installed on cloud environments?
- Does Anaconda Enterprise support air gapped (off-line) environments?
- Can I build Docker images for the install of Anaconda Enterprise?
- Can I install Anaconda Enterprise on my own instance of Kubernetes?
- Can I get the AE installer packaged as a virtual machine (VM), Amazon Machine Image (AMI) or other installation package?
- Which ports are externally accessible from Anaconda Enterprise?
- Can I use Anaconda Enterprise to connect to my Hadoop/Spark cluster?
- How can I manage Anaconda packages on my Hadoop/Spark cluster?
- On how many nodes can I install Anaconda Enterprise?
- Anaconda Project
- What operating systems and Python versions are supported for Anaconda Project?
- How is encapsulation with Anaconda Project different from creating a workspace or project in Spyder, PyCharm, or other IDEs?
- What types of projects can I deploy?
- Does Anaconda Enterprise include Docker images for my data science projects?
- Are the deployed, self-service notebooks read-only?
- What happens when other people run the notebook? Does it overwrite any file, if notebook is writing to a file?
- Can I define environment variables as part of my data science project?
- How are Anaconda Project and Anaconda Enterprise available?
- Where can I find example projects for Anaconda Enterprise?
- Does Anaconda Enterprise support batch scoring with REST APIs?
- Does Anaconda Enterprise provide tools to help define and implement REST APIs?
- Help and training
Our GA release was August 31, 2017 (Version 5.0.3).
Anaconda Enterprise supports the use of Jupyter Notebooks and JupyterLab, which are the most popular integrated data science environments for working with Python and R notebooks.
In future releases we will add support for other editors including RStudio and Apache Zeppelin.
Yes, you can deploy multiple data science applications and languages across an Anaconda Enterprise cluster. Each data science application runs in a secure and isolated environment with all of the dependencies from Anaconda that it requires.
A single node can run multiple applications based on the amount of compute resources (CPU and RAM) available on a given node. Anaconda Enterprise handles all of the resource allocation and application scheduling for you.
Partially. Some of the Anaconda Enterprise services and user-deployed apps will be automatically configured when installed to three or more nodes. Anaconda Enterprise provides several automatic mechanisms for fault tolerance and service continuity, including automatic restarts, health checks, and service migration.
For more information, see Fault Tolerance.
Yes, Anaconda Enterprise supports single sign-on (SSO) and two-factor authentication (2FA) using FreeOTP, Google Authenticator or Google Authenticator compatible 2FA.
For more information, see One-time passwords.
Please see operating system requirements.
NOTE: Linux distributions other than those listed in the documentation can be supported on request.
Please see system requirements.
Yes, Anaconda Enterprise includes an internal git server, which allows users to save and commit versions of their projects.
In a future version of Anaconda Enterprise 5.x, you will be able to connect to external git servers.
The Anaconda Enterprise installer is a single tarball that includes Docker, Kubernetes, system dependencies, and all of the components and images necessary to run Anaconda Enterprise. The system administrator runs one command on each node.
Yes, the Anaconda Enterprise installer includes Docker, Kubernetes, system dependencies, and all of the components and images necessary to run Anaconda Enterprise on-premises or on a private cloud, with or without internet connectivity. We can deliver the installer to you on a USB drive.
No. The installation of Anaconda Enterprise is supported only by using the single-file installer. The Anaconda Enterprise installer includes Docker, Kubernetes, system dependencies, and all of the components and images necessary for Anaconda Enterprise.
No. The Anaconda Enterprise installer already includes Kubernetes.
Can I get the AE installer packaged as a virtual machine (VM), Amazon Machine Image (AMI) or other installation package?¶
No. The installation of Anaconda Enterprise is supported only by using the single-file installer.
Yes. Anaconda Enterprise supports connectivity from notebooks to local or remote Spark clusters by using the sparkmagic client and a Livy REST API server. Anaconda Enterprise provides sparkmagic and other Spark clients in the notebook editor and deployment environments. You can install the Livy server on the Spark cluster manually. A future version of Anaconda Enterprise will enable installing the Livy server on the Spark cluster using custom Anaconda parcels for Cloudera or custom Anaconda management packs for Hortonworks.
An IT administrator can generate custom Anaconda parcels for Cloudera CDH or custom Anaconda management packs for Hortonworks HDP using Anaconda Repository 4.x. A future version of Anaconda Enterprise 5.x will also implement this functionality. A data scientist can use these Anaconda libraries from a notebook as part of a Spark job.
You can install Anaconda Enterprise in the following configurations during the initial installation:
- One node (one master node)
- Two nodes (one master node, one worker node)
- Three nodes (one master node, two worker nodes)
- Four nodes (one master node, three worker nodes)
After the initial installation, you can add or remove worker nodes from the Anaconda Enterprise cluster at any time.
One node serves as the master node and writes storage to disk, and the other nodes serve as worker nodes. Anaconda Enterprise services and user-deployed applications run seamlessly on the master and worker nodes.
Anaconda Project supports Windows, macOS and Linux, and tracks the latest Anaconda releases with Python 2.7, 3.5 and 3.6.
How is encapsulation with Anaconda Project different from creating a workspace or project in Spyder, PyCharm, or other IDEs?¶
A workspace or project in an IDE is a directory of files on your desktop. Anaconda Project encapsulates those files, but also includes additional parameters to describe how to run a project with its dependencies. Anaconda Project is portable and allows users to run, share, and deploy applications across different operating systems.
Anaconda Project is very flexible and can deploy many types of projects with conda or pip dependencies. Deployable projects include:
- Notebooks (Python and R)
- Bokeh applications and dashboards
- REST APIs in Python and R (including machine learning scoring and predictions)
- Python and R scripts
- Third-party apps, web frameworks, and visualization tools such as Tensorboard, Flask, Falcon, deck.gl, plot.ly Dash, and more.
Any generic Python and R script or webapp can be configured to serve on port 8086, which will show the app in Anaconda Enterprise when deployed.
Anaconda Enterprise includes data science application images for the editor and deployments. You can install additional packages in either environment using Anaconda Project. Anaconda Project includes the information required to reproduce the project environment with Anaconda, including Python, R, or any other conda package or pip dependencies.
Yes, the deployed versions of self-service notebooks are read-only, but they can be executed by collaborators or viewers. Owners of the project that contain the notebooks can edit the notebook and deploy (or re-deploy) them.
What happens when other people run the notebook? Does it overwrite any file, if notebook is writing to a file?¶
A deployed, self-service notebook is read-only but can be executed by other collaborators or viewers. If multiple users are running a notebook that writes to a file, the file will be overwritten unless the notebook is configured to write data based on a username or other environment variable.
Yes, Anaconda Project supports environment variables that can be defined when deploying a data science application. Only project collaborators can view or edit environment variables, and they cannot be accessed by viewers.
Anaconda Project is free and open-source. Anaconda Enterprise is a commercial product.
Sample projects are included as part of the Anaconda Enterprise installation, which include sample workflows and notebooks for Python and R such as financial modeling, natural language processing, machine learning models with REST APIs, interactive Bokeh applications and dashboards, image classification, and more.
The sample projects include examples with visualization tools (Bokeh, deck.gl), pandas, scipy, Shiny, Tensorflow, Tensorboard, xgboost, and many other libraries. Users can save the sample projects to their Anaconda Enterprise account or download the sample projects to their local machine.
Yes, Anaconda Enterprise can be used to deploy machine learning models with REST APIs (including Python and R) that can be queried for batch scoring workflows. The REST APIs can be made available to other users and accessed with an API token.
Yes, a data scientist can basically create a model without much work for the API development. Anaconda Enterprise includes an API wrapper for Python frameworks that builds on top of existing web frameworks in Anaconda, making it easy to expose your existing data science models with minimal code. You can also deploy REST APIs using existing API frameworks for Python and R.
Yes, we offer product training for collaborative, end-to-end data science workflows with Anaconda Enterprise.