This is the Linux app named Jraph whose latest release can be downloaded as v0.0.6.dev0sourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named Jraph with OnWorks for free.
इस ऐप को चलाने के लिए इन निर्देशों का पालन करें:
- 1. इस एप्लिकेशन को अपने पीसी में डाउनलोड करें।
- 2. हमारे फ़ाइल प्रबंधक में https://www.onworks.net/myfiles.php?username=XXXXX उस उपयोगकर्ता नाम के साथ दर्ज करें जो आप चाहते हैं।
- 3. इस एप्लिकेशन को ऐसे फाइल मैनेजर में अपलोड करें।
- 4. इस वेबसाइट से ऑनवर्क्स लिनक्स ऑनलाइन या विंडोज ऑनलाइन एमुलेटर या मैकोज़ ऑनलाइन एमुलेटर शुरू करें।
- 5. ऑनवर्क्स लिनक्स ओएस से आपने अभी शुरुआत की है, हमारे फाइल मैनेजर को https://www.onworks.net/myfiles.php?username=XXXXX उस यूजरनेम के साथ जाएं जो आप चाहते हैं।
- 6. एप्लिकेशन डाउनलोड करें, इसे इंस्टॉल करें और इसे चलाएं।
स्क्रीनशॉट
Ad
ज्राफ
वर्णन
Jraph (pronounced “giraffe”) is a lightweight JAX library developed by Google DeepMind for building and experimenting with graph neural networks (GNNs). It provides an efficient and flexible framework for representing, manipulating, and training models on graph-structured data. The core of Jraph is the GraphsTuple data structure, which enables users to define graphs with arbitrary node, edge, and global attributes, and to batch variable-sized graphs efficiently for JAX’s just-in-time compilation. The library includes a comprehensive set of utilities for batching, padding, masking, and partitioning graph data, making it ideal for distributed and large-scale GNN experiments. Jraph also comes with a model zoo—a collection of forkable reference implementations of common message-passing GNN architectures, such as Graph Networks, Graph Convolutional Networks, and Graph Attention Networks.
विशेषताएं
- Lightweight GraphsTuple data structure for flexible graph representation
- Distributed message-passing support for massive graphs across multiple devices
- Utilities for batching, masking, and padding to handle variable-sized graphs
- Modular model zoo of reusable graph neural network architectures
- Educational Colab tutorials and large-scale dataset examples (e.g., OGBG-MOLPCBA)
- Fully JAX-compatible for jit compilation, pmap parallelization, and scalability
प्रोग्रामिंग भाषा
अजगर
श्रेणियाँ
This is an application that can also be fetched from https://sourceforge.net/projects/jraph.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
