Microsoft Technical Computing Group has just announced the Python Tools for Visual Studio (PTVS) open source project. PTVS improves on the IronPython Tools for Visual Studio code base (introduced in IronPython 2.7) and adds CPython, Cluster support and new modules like NumPy and SciPy in .Net.
The major features announced are –
- Advanced editing, Intellisense, browsing, “Find all refs”, REPL, …
- Supports CPython and IronPython
- Local & Cluster/remote debugging
- Profiling with multiple views
- Interactive parallel computing via integrated IPython REPL
- Support for HPC clusters and MPI, including debugging support
- NumPy & SciPy for .Net
- Support for Cloud Computing (soon)
- Support for Dryad (large scale, data-intensive parallel programming) (soon)
- Free & Open Source (Apache 2.0)
The timing of this announcement has already created some confusion, especially about the difference between IronPython Tools for Visual Studio and Python Tools for Visual Studio. Microsoft clarified that PTVS improves on the former’s codebase and is meant as a replacement. The timeline for PTVS (which is currently in beta) meant that it could not ship with the IronPython 2.7 release. The two teams will work to make PTVS the preferred tool in the next major release for IronPython.
Dino Viehland, a Software Design Engineer from Microsoft, had this to say -
PTVS keeps all the features we had in IronPython Tools for VS and adds a bunch of new features. For starters we add support for both REPLs and debugging in CPython and other Python implementations. We also add profiling support for CPython, support for multiple language versions (2.5 through 3.2), and attach to process for CPython processes - both local and remote. Meanwhile we still support all the features we had before including great intellisense and drag and drop GUI development for IronPython. But we're also focusing on developing new features in a specific area - High Performance Computing. For our first beta we've focused on enabling two different scenarios - batch computing via MPI and interactive computing via IPython. For batch computing we support publishing to and running on the cluster via a simple "F5" scenario. If you're not yet ready to run on the cluster you can do the same multi-process launch on the local machine. For interactive development we are focusing on the bleeding edge of IPython (.11+) where the new architecture is designed from the ground up to support parallel computing and alternate REPL UIs.
The RTM version is scheduled for Fall of 2011.