PyTorch-BigGraph | Generate embeddings from large-scale graph-structured data | Machine Learning library
kandi X-RAY | PyTorch-BigGraph Summary
kandi X-RAY | PyTorch-BigGraph Summary
PyTorch-BigGraph is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. PyTorch-BigGraph has no bugs, it has no vulnerabilities, it has build file available and it has medium support. However PyTorch-BigGraph has a Non-SPDX License. You can install using 'pip install PyTorch-BigGraph' or download it from GitHub, PyPI.
PyTorch-BigGraph (PBG) is a distributed system for learning graph embeddings for large graphs, particularly big web interaction graphs with up to billions of entities and trillions of edges. PBG was introduced in the PyTorch-BigGraph: A Large-scale Graph Embedding Framework paper, presented at the SysML conference in 2019. PBG trains on an input graph by ingesting its list of edges, each identified by its source and target entities and, possibly, a relation type. It outputs a feature vector (embedding) for each entity, trying to place adjacent entities close to each other in the vector space, while pushing unconnected entities apart. Therefore, entities that have a similar distribution of neighbors will end up being nearby. It is possible to configure each relation type to calculate this "proximity score" in a different way, with the parameters (if any) learned during training. This allows the same underlying entity embeddings to be shared among multiple relation types. The generality and extensibility of its model allows PBG to train a number of models from the knowledge graph embedding literature, including TransE, RESCAL, DistMult and ComplEx.
PyTorch-BigGraph (PBG) is a distributed system for learning graph embeddings for large graphs, particularly big web interaction graphs with up to billions of entities and trillions of edges. PBG was introduced in the PyTorch-BigGraph: A Large-scale Graph Embedding Framework paper, presented at the SysML conference in 2019. PBG trains on an input graph by ingesting its list of edges, each identified by its source and target entities and, possibly, a relation type. It outputs a feature vector (embedding) for each entity, trying to place adjacent entities close to each other in the vector space, while pushing unconnected entities apart. Therefore, entities that have a similar distribution of neighbors will end up being nearby. It is possible to configure each relation type to calculate this "proximity score" in a different way, with the parameters (if any) learned during training. This allows the same underlying entity embeddings to be shared among multiple relation types. The generality and extensibility of its model allows PBG to train a number of models from the knowledge graph embedding literature, including TransE, RESCAL, DistMult and ComplEx.
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PyTorch-BigGraph has a medium active ecosystem.
It has 3245 star(s) with 442 fork(s). There are 91 watchers for this library.
It had no major release in the last 12 months.
There are 53 open issues and 137 have been closed. On average issues are closed in 218 days. There are 8 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of PyTorch-BigGraph is v1.0.0
Quality
PyTorch-BigGraph has 0 bugs and 0 code smells.
Security
PyTorch-BigGraph has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
PyTorch-BigGraph code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
PyTorch-BigGraph has a Non-SPDX License.
Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.
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PyTorch-BigGraph releases are available to install and integrate.
Deployable package is available in PyPI.
Build file is available. You can build the component from source.
Installation instructions, examples and code snippets are available.
PyTorch-BigGraph saves you 4290 person hours of effort in developing the same functionality from scratch.
It has 9095 lines of code, 676 functions and 52 files.
It has medium code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed PyTorch-BigGraph and discovered the below as its top functions. This is intended to give you an instant insight into PyTorch-BigGraph implemented functionality, and help decide if they suit your requirements.
- Perform a single training step
- Append a tensor to the buffer
- Flushes buffered data to the dataset
- Return the average statistics
- Converts input data into entities
- Collect relation types from edge files
- Given a list of entity types return a mapping of entities to entities
- Generates edge files from the edge storage
- Extract a gzipped file
- Read data from file
- Start the server
- Map a schema to a schema
- Adjusts the score of the edges
- Builds TSV for the given model
- Get the number of partitioned entities
- Concatenate a list of edge lists
- Parse a partial config dictionary
- Thread loop loop
- Performs a single step
- Acquire a bucket for the given rank
- Store a key in the store
- Get a value from the server
- Run a partition server
- Shuffle a random split
- Run the worker thread
- Download url to root
Get all kandi verified functions for this library.
PyTorch-BigGraph Key Features
No Key Features are available at this moment for PyTorch-BigGraph.
PyTorch-BigGraph Examples and Code Snippets
No Code Snippets are available at this moment for PyTorch-BigGraph.
Community Discussions
Trending Discussions on PyTorch-BigGraph
QUESTION
Use pre trained Nodes from past runs - Pytorch Biggraph
Asked 2020-Oct-29 at 12:28
After struggling with this amazing facebookresearch / PyTorch-BigGraph project, and its impossible API, I managed to get a grip on how to run it (thanks to stand alone simple example)
My system restrictions do not allow me to train the dense (embedding) representation of all edges, and I need from time to time to upload past embeddings and train the model using both new edges and existing nodes, notice that nodes in past and new edge list do not necessarily overlap.
I tried to understand from here: see the context section how to do it, so far with no success.
Following is a stand-alone PGD code, that turned batch_edges into an embedding node list, however, I need it to use pre-trained nodes list past_trained_nodes.
ANSWER
Answered 2020-Oct-29 at 12:28Since torchbiggraph is file based, you can modify the saved files to load pre-trained embeddings and add new nodes. I wrote a function to achieve this
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install PyTorch-BigGraph
To install the latest version of PBG run:.
The results of the paper can easily be reproduced by running the following command (which executes this script):. This will download the Freebase 15k knowledge base dataset, put it into the right format, train on it using the ComplEx model and finally perform an evaluation of the learned embeddings that calculates the MRR and other metrics that should match the paper. Another command, torchbiggraph_example_livejournal, does the same for the LiveJournal interaction graph dataset. To learn how to use PBG, let us walk through what the FB15k script does.
The results of the paper can easily be reproduced by running the following command (which executes this script):. This will download the Freebase 15k knowledge base dataset, put it into the right format, train on it using the ComplEx model and finally perform an evaluation of the learned embeddings that calculates the MRR and other metrics that should match the paper. Another command, torchbiggraph_example_livejournal, does the same for the LiveJournal interaction graph dataset. To learn how to use PBG, let us walk through what the FB15k script does.
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