Frequently asked questions¶
When was the general availability release of Anaconda Enterprise v5?
Our GA release was August 31, 2017 (version 5.0.3). Our most recent version was released January 2nd, 2019 (version 5.2.3).
Which notebooks or editors does Anaconda Enterprise support?
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 version 5.2.2 we added support for Apache Zeppelin, a web-based notebook that enables data-driven, interactive data analytics and collaborative documents with interpreters for Python, R, Spark, Hive, HDFS, SQL, and more.
Can I deploy multiple data science applications to Anaconda Enterprise?
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.
Does Anaconda Enterprise support high availability deployments?
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 in Anaconda Enterprise.
Which identity management and authentication protocols does Anaconda Enterprise support?
Anaconda Enterprise comes with out-of-the-box support for the following:
- LDAP / AD
For more information, see Connecting to external identity providers.
Does Anaconda Enterprise support two-factor authentication (including one-time passwords)?
Yes, Anaconda Enterprise supports single sign-on (SSO) and two-factor authentication (2FA) using FreeOTP, Google Authenticator or Google Authenticator compatible 2FA.
You can configure one-time password policies in Anaconda Enterprise by navigating to the authentication center and clicking on Authentication and then OTP Policy.
What operating systems are supported for Anaconda Enterprise?
Please see operating system requirements.
NOTE: Linux distributions other than those listed in the documentation can be supported on request.
What are the minimum system requirements for Anaconda Enterprise nodes?
Please see system requirements.
Which browsers are supported for Anaconda Enterprise?
Please see browser requirements.
Does Anaconda Enterprise come with a version control system?
Yes, Anaconda Enterprise includes an internal Git server, which allows users to save and commit versions of their projects.
Can Anaconda Enterprise integrate with my own Git server?
Yes, as described in Connecting to an external version control repository.
How do I install Anaconda Enterprise?
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.
Can Anaconda Enterprise be installed on-premises?
Yes, including airgapped environments.
Can Anaconda Enterprise be installed on cloud environments?
Yes, including Amazon AWS, Microsoft Azure, and Google Cloud Platform.
Does Anaconda Enterprise support air gapped (off-line) environments?
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.
Can I build Docker images for the install of Anaconda Enterprise?
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.
Can I install Anaconda Enterprise on my own instance of Kubernetes?
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.
Which ports are externally accessible from Anaconda Enterprise?
Please see network requirements.
Can I use Anaconda Enterprise to connect to my Hadoop/Spark cluster?
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, which inlcudes Spark, PySpark, and SparkR notebook kernels for deployment.
How can I manage Anaconda packages on my Hadoop/Spark cluster?
An administrator can generate custom Anaconda parcels for Cloudera CDH or custom Anaconda management packs for Hortonworks HDP using Anaconda Enterprise. A data scientist can use these Anaconda libraries from a notebook as part of a Spark job.
On how many nodes can I install Anaconda Enterprise?
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.
Can I generate certificates manually?
Yes, if automatic TLS/SSL certificate generation fails for any reason, you can generate the certificates manually. Follow these steps:
Generate self-signed temporary certificates. On the master node, run:
cd path/to/Anaconda/Enterprise/unpacked/installer cd DIY-SSL-CA bash create_noprompt.sh DESIRED_FQDN cp out/DESIRED_FQDN/secret.yaml /var/lib/gravity/planet/share/secrets.yaml
DESIRED_FQDNwith the fully-qualified domain of the cluster to which you are installing Anaconda Enterprise.
Saving this file as
/var/lib/gravity/planet/share/secrets.yamlon the Anaconda Enterprise master node makes it accessible as
/ext/share/secrets.yamlwithin the Anaconda Enterprise environment which can be accessed with the command
sudo gravity enter.
Replace the built-in
certssecret with the contents of
secrets.yaml. Enter the Anaconda Enterprise environment and run these commands:
$ kubectl delete secrets certs secret "certs" deleted $ kubectl create -f /ext/share/secrets.yaml secret "certs" created
How can I make GPUs available to my team of data scientists?
If your data science team plans to use version 5.2 of the Anaconda Enterprise AI enablement platform, here are a few approaches to consider when planning your GPU cluster:
Build a dedicated GPU-only cluster.
If GPUs will be used by specific teams only, creating a separate cluster allows you to more carefully control GPU access.
Build a heterogeneous cluster.
Not all projects require GPUs, so a cluster containing a mix of worker nodes—with and without GPUs—can serve a variety of use cases in a cost-effective way.
Add GPU nodes to an existing cluster.
If your team’s resource requirements aren’t clearly defined, you can start with a CPU-only cluster, and add GPU nodes to create a heterogeneous cluster when the need arises.
Anaconda Enterprise supports heterogeneous clusters by allowing you to create different “resource profiles” for projects. Each resource profile describes the number of CPU cores, the amount of memory, and the number of GPUs the project needs. Administrators typically will create “Regular”, “Large”, and “Large + GPU” resource profiles for users to select from when running their project. If a project requires a GPU, AE will run it on only those cluster nodes with an available GPU.
What software is GPU accelerated?
Anaconda provides a number of GPU-accelerated packages for data science. For deep learning, these include:
- Keras (
- TensorFlow (
- Caffe (
- PyTorch (
- MXNet (
For boosted decision tree models:
- XGBoost (
For more general array programming, custom algorithm development, and simulations:
- CuPy (
- Numba (
NOTE: Unless a package has been specifically optimized for GPUs (by the authors) and built by Anaconda with GPU support, it will not be GPU-accelerated, even if the hardware is present.
What hardware does each of my cluster nodes require?
Anaconda recommends installing Anaconda Enterprise in a cluster configuration. Each installation should have an odd number of master nodes, and we recommend at least one worker node. The master node runs all Anaconda Enterprise core services and does not need a GPU.
Using EC2 instances, a minimal configuration is one master node running on a
m4.4xlarge instance and one GPU worker node running on a
p3.2xlarge instance. More users will require more worker nodes—and possibly a mix of CPU and GPU worker nodes.
See Installation requirements for the baseline hardware requirements for Anaconda Enterprise.
How many GPUs does my cluster need?
A best practice for machine learning is for each user to have exclusive use of their GPU(s) while their project is running. This ensures they have sufficient GPU memory available for training, and provides more consistent performance.
When an Anaconda Enterprise user launches a notebook session or deployment that requires GPUs, those resources are reserved for as long as the project is running. When the notebook session or deployment is stopped, the GPUs are returned to the available pool for another user to claim.
The number of GPUs required in the cluster can therefore be determined by the number of concurrently running notebook sessions and deployments that are expected. Adding nodes to an Anaconda Enterprise cluster is straightforward, so organizations can start with a conservative number of GPUs and grow as demand increases.
To get more out of your GPU resources, Anaconda Enterprise supports scheduling and running unattended jobs. This enables you to execute periodic retraining tasks—or other resource-intensive tasks—after regular business hours, or at times GPUs would otherwise be idle.
What kind of GPUs should I use?
Although the Anaconda Distribution supports a wide range of NVIDIA GPUs, enterprise deployments for data science teams developing models should use one of the following GPUs:
- Tesla K80
- Tesla P100
- Tesla V100
The K80 is the oldest option, and only makes sense for budget cloud deployments (see below). We recommend using the V100 or P100 for new on-premise installations, as these GPUs are significantly faster—and therefore better for deep learning.
Can I mix GPU models in one cluster?
Kubernetes cannot currently distinguish between different GPU models in the same cluster node, so Anaconda Enterprise requires all GPU-enabled nodes within a given cluster to have the same GPU model (for example, all Tesla V100). Different clusters (e.g., “production” and “development”) can use different GPU models, of course.
Can I use cloud GPUs?
Yes, Anaconda Enterprise 5.2 can be installed on cloud VMs with GPU support. Amazon Web Services (AWS), Google Cloud Platform, and Microsoft Azure all offer Tesla GPU options.
What operating systems and Python versions are supported for Anaconda Project?
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.
What types of projects can I deploy?
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.
Does Anaconda Enterprise include Docker images for my data science projects?
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.
Are the deployed, self-service notebooks read-only?
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.
Can I define environment variables as part of my data science project?
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.
How are Anaconda Project and Anaconda Enterprise available?
Anaconda Project is free and open-source. Anaconda Enterprise is a commercial product.
Where can I find example projects for Anaconda Enterprise?
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.
Does Anaconda Enterprise support batch scoring with REST APIs?
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.
Does Anaconda Enterprise provide tools to help define and implement REST APIs?
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.
Help and training¶
Do you offer support for Anaconda Enterprise?
Yes, we offer full support with Anaconda Enterprise.
Do you offer training for Anaconda Enterprise?
Yes, we offer product training for collaborative, end-to-end data science workflows with Anaconda Enterprise.
Do you have a question not answered here?
Please contact us for more information.