We’ve had a number of major product development efforts underway over the last year, and we’re pleased to share a lot of new announcements for 2017:
A New Chapter for the Enthought Python Distribution (EPD):
Python 3 and Intel MKL 2017
In 2004, Enthought released the first “Python: Enthought Edition,” a Python package distribution tailored for a scientific and analytic audience. In 2008 this became the Enthought Python Distribution (EPD), a self-contained installer with the
"enpkg" command-line tool to update and manage packages. Since then, over a million users have benefited from Enthought’s tested, pre-compiled set of Python packages, allowing them to focus on their science by eliminating the hassle of setting up tools.
Fast forward to 2017, and we now offer over 450 Python packages and a new era for the Enthought Python Distribution; access to all of the packages in the new EPD is completely free to all users and includes packages and runtimes for both Python 2 and Python 3 with some exciting new additions. Our ever-growing list of packages includes, for example, the 2017 release of the MKL (Math Kernel Library), the fruit of an ongoing collaboration with Intel.
The New Enthought Deployment Server:
Secure, Onsite Access to EPD and Private Packages
For those who are interested in having a private copy of the Enthought Python Distribution behind their firewall, as well as the ability to upload and manage internal private packages alongside it, we now offer the Enthought Deployment Server, an onsite version of the server we have been using for years to serve millions of Python packages to our users.
With a local Enthought Deployment Server, your private copy will periodically synchronize with our master repository, on a schedule of your choosing, to keep you up to date with the latest releases. You can also set up private package repositories and control access to them using your existing LDAP or Active Directory service in a way that suits your organization. We can even give you access to the packages (and their historical versions) inside of air-gapped networks! See our webinar introducing the Enthought Deployment Server.
Command Line Access to the New EPD and Flat Environments
via the Enthought Deployment Manager (EDM)
In 2013, we expanded the original EPD to introduce Enthought Canopy, coupling an integrated analysis environment with additional features such as a graphical package manager, documentation browser, and other user-friendly tools together with the Enthought Python Distribution to provide even more features to help “make science and analysis easy.”
With its MATLAB-like experience, Canopy has enabled countless engineers, scientists and analysts to perform sophisticated analysis, build models, and create cutting-edge data science algorithms. The all-in-one analysis platform for Python has also been widely adopted in organizations who want to provide a single, unified platform that can be used by everyone from data analysts to software engineers.
But we heard from a number of you that you also still wanted the capability to have flat, standalone environments not coupled to any editor or graphical tool. And we listened!
So last year, we finished building out our next-generation command-line tool that makes producing flat, standalone Python environments super easy. We call it the Enthought Deployment Manager (or EDM for short), because it’s a tool to quickly deploy one or multiple Python environments with the full control over package versions and runtime environments.
EDM is also a valuable tool for use cases such as command line deployment on local machines or servers, web application deployment on AWS using Ansible and Amazon CloudFormation, rapid environment setup on continuous integration systems such as Travis-CI, Appveyor, or Jenkins/TeamCity, and more.
Finally, a new state-of-the-art package dependency solver included in the tool guarantees the consistency of your environment, and if your workflow requires switching between different environments, its sandboxed architecture makes it a snap to switch contexts. All of this has also been designed with a focus on providing robust backward compatibility to our customers over time. Find out more about EDM here.
Enthought Canopy 2.0:
Python 3 packages and New EDM Back End Infrastructure
The new Enthought Python Distribution (EPD) and Enthought Deployment Manager (EDM) will also provide additional benefits for Canopy. Canopy 2.0 is just around the corner, which will be the first version to include Python 3 packages from EPD.
In addition, we have re-worked Canopy’s graphical package manager to use EDM as its back end, to take advantage of both the consistency and stability of the environments EDM provides, as well as its new package dependency solver. By itself, this will provide a big boost in stability for users (ever found yourself wrapped up in a tangle of inconsistent package versions?). Alongside the conversion of Canopy’s back end infrastructure to EDM, we have also included a substantial number of stability improvements and bug fixes.
Canopy’s Graphical Debugger adds external IPython kernel debugging support
On the integrated analysis environment side of Canopy, the graphical debugger and variable browser, first introduced in 2015, has gotten some nifty new features, including the ability to connect to and debug an external IPython kernel, in addition to a number of stability improvements. (Weren’t aware you could connect to an external process? Look for the context menu in the IPython console, use it to connect to the IPython kernel running, say, a Jupyter notebook, and debug away!)
Canopy Data Import Tool adds CSV exports and input file templates
Also, we’ve continued to add new features to the Canopy Data Import Tool since its initial release in May of 2016. The Data Import Tool allows users to quickly and easily import CSVs and other structured text files into Pandas DataFrames through a graphical interface, manipulate the data, and create reusable Python scripts to speed future data wrangling.
The latest version of the tool (v. 1.0.9, shipping with Canopy 2.0) has some nice new features like CSV exporting, input file templates, and more. See Enthought’s blog for some great examples of how the Data Import Tool speeds data loading, wrangling and analysis.
What to Look Forward to in 2017
So where are we headed in 2017? We have put a lot of effort into building a strong foundation with our core suite of products, and now we’re focused on continuing to deliver new value (our enterprise users in particular have a number of new features to look forward to). First up, for example, you can look for expanded capabilities around Python environments, making it easy to manage multiple environments, or even standardize and distribute them in your organization. With the tremendous advancements in our core products that took place in 2016, there are a lot of follow-on features we can deliver. Stay tuned for updates!
Have a specific feature you’d like to see in one of Enthought’s products? E-mail our product team at email@example.com and tell us about it!