Open Source Python Networking Software for Windows

PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, DataPipe support, distributed graph learning via Quiver, a large number of common benchmark datasets (based on simple interfaces to create your own), the GraphGym experiment manager, and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. All it takes is 10-20 lines of code to get started with training a GNN model (see the next section for a quick tour).

Python API library and shell utilities to monitor file system events. A simple program that uses watchdog to monitor directories specified as command-line arguments and logs events generated. Watchdog comes with an optional utility script called watchmedo. Please type watchmedo --help at the shell prompt to know more about this tool. You can use the shell-command subcommand to execute shell commands in response to events. watchmedo can read tricks.yaml files and execute tricks within them in response to file system events. Tricks are actually event handlers that subclass watchdog.tricks.Trick and are written by plugin authors. Trick classes are augmented with a few additional features that regular event handlers don't need. The directory containing the tricks.yaml file will be monitored. Each trick class is initialized with its corresponding keys in the tricks.yaml file as arguments and events are fed to an instance of this class as they arrive.

Implementation of Imagen, Google's Text-to-Image Neural Network that beats DALL-E2, in Pytorch. It is the new SOTA for text-to-image synthesis. Architecturally, it is actually much simpler than DALL-E2. It consists of a cascading DDPM conditioned on text embeddings from a large pre-trained T5 model (attention network). It also contains dynamic clipping for improved classifier-free guidance, noise level conditioning, and a memory-efficient unit design. It appears neither CLIP nor prior network is needed after all. And so research continues. For simpler training, you can directly supply text strings instead of precomputing text encodings. (Although for scaling purposes, you will definitely want to precompute the textual embeddings + mask)

CTypes-based python bindings to the GSASL library (www.gnu.org/software/gsasl ). This package provides a parser that generates python code w/ types checking and more pythonish feel than just plain usage of the library through ctypes.

The Validating Stub Resolver (vsResolver) is a DNS stub resolver that implements the Domain Name System Security Extensions (DNSSEC) specified in RFC 4033, RFC 4034 and RFC 4035. These add data origin authentication and data integrity to the Domain Name System. vsResolver extends the dnspython toolkit (http://www.dnspython.org/) and uses the pycrypto library for its underlying crypto implementation(https://www.dlitz.net/software/pycrypto/). Click on this link for a ridiculously simple python page demonstrating vsResolver working - http://superawesum.novas.us/py/hello.py. Try entering some domains and click submit. Then, enable details, click submit to get the details,and compare with what you get for that domain at http://dnssec-debugger.verisignlabs.com.