Introducing PyTorch BigGraph

Introducing PyTorch BigGraph

Facebook’s New Framework for Processing Large Graphs. Graphs are one of the fundamental data structures in machine learning applications.

Facebook’s New Framework for Processing Large Graphs. Graphs are one of the fundamental data structures in machine learning applications.

Specifically, graph-embedding methods are a form of unsupervised learning, in that they learn representations of nodes using the native graph structure. Training data in mainstream scenarios such as social media predictions, internet of things(IOT) pattern detection or drug-sequence modeling are naturally represented using graph structures. Any one of those scenarios can easily produce graphs with billions of interconnected nodes. While the richness and intrinsic navigation capabilities of graph structures is a great playground for machine learning models, their complexity posses massive scalability challenges. Not surprisingly, the support for large-scale graph data structures in modern deep learning frameworks is still quite limited. Recently, Facebook unveiled PyTorch BigGraph, a new framework that makes it much faster and easier to produce graph embeddings for extremely large graphs in PyTorch models.

To some extent, graph structures can be seen as an alternative to labeled training dataset as the connections between the nodes can be used to infer specific relationships. This is the approach followed by unsupervised graph embedding methods which learn a vector representation of each node in a graph by optimizing the objective that the embeddings for pairs of nodes with edges between them are closer together than pairs of nodes without a shared edge. This is similar to how word embeddings like word2vec are trained on text.

Most graph embedding methods result quite constrained when applied to large graph structures. To give a example, a model with two billion nodes and 100 embedding parameters per node (expressed as floats) would require 800GB of memory just to store its parameters, thus many standard methods exceed the memory capacity of typical commodity servers. To represents a major challenge for deep learning models and is the genesis of Facebook’s BigGraph framework.

PyTorch BigGraph

The goal of PyTorch BigGraph(PBG) is to enable graph embedding models to scale to graphs with billions of nodes and trillions of edges. PBG achieves that by enabling four fundamental building blocks:

  • graph partitioning, so that the model does not have to be fully loaded into memory
  • multi-threaded computation on each machine
  • distributed execution across multiple machines (optional), all simultaneously operating on disjoint parts of the graph
  • batched negative sampling, allowing for processing >1 million edges/sec/machine with 100 negatives per edge

PBG addresses some of the shortcomings of traditional graph embedding methods by partitioning the graph structure into randomly divided into P partitions that are sized so that two partitions can fit in memory. For example, if an edge has a source in partition p1 and destination in partition p2 then it is placed into bucket (p1, p2). In the same model, the graph edges are then divided into P2 buckets based on their source and destination node. Once the nodes and edges are partitioned, training can be performed on one bucket at a time. The training of bucket (p1, p2) only requires the embeddings for partitions p1 and p2 to be stored in memory. The PBG structure guarantees that buckets have at least one previously-trained embedding partition.

Another area in which PBG really innovates is the parallelization and distribution of the training mechanics. PBG uses PyTorch parallelization primitives to implement a distributed training model that leverages the block partition structure illustrated previously. In this model, individual machines coordinate to train on disjoint buckets using a lock server which parcels out buckets to the workers in order to minimize communication between the different machines. Each machine can train the model in parallel using different buckets.

In the previous figure, the Trainer module in machine 2 requests a bucket from the lock server on machine 1, which locks that bucket’s partitions. The trainer then saves any partitions that it is no longer using and loads new partitions that it needs to and from the sharded partition servers, at which point it can release its old partitions on the lock server. Edges are then loaded from a shared filesystem, and training occurs on multiple threads without inter-thread synchronization. In a separate thread, a small number of shared parameters are continuously synchronized with a sharded parameter server. Model checkpoints are occasionally written to the shared filesystem from the trainers. This model allows a set of P buckets to be parallelized using up to P/2 machines.

One of the indirect innovations of PBG is the use of batched negative sampling techniques. Traditional graph embedding models, construct random “false” edges as negative training examples along with the true positive edges. This significantly speeds training because only a small percentage of weights must be updated with each new sample. However, the negative samples end up introducing a performance overhead in the processing of the graph and end up “corrupting” true edges with random source or destination nodes. PBG introduces a method that reuses a single batch of N random nodes to produce corrupted negative samples for N training edges. In comparison to other embedding methods, this technique allows us to train on many negative examples per true edge at little computational cost.

To increase memory efficiency and computational resources on large graphs, PBG leverages a single batch of Bn sampled source or destination nodes to construct multiple negative examples.In a typical setup, PBG takes a batch of B = 1000 positive edges from the training set, and breaks it into chunks of 50 edges. The destination (equivalently, source) embeddings from each chunk is concatenated with 50 embeddings sampled uniformly from the tail entity type. The outer product of the 50 positives with the 200 sampled nodes equates to 9900 negative examples.

The batched negative sampling approach has a direct impact in the speed of the training of the models. Without batching, the speed of training is inversely proportional to the number of negative samples. Batched training improves that equation achieving constant training speed.

Facebook evaluated PGB using different graph datasets such as LiveJournal, Twitter data and YouTube user interaction data. Additionally, PBG was benchmarked using the Freebase knowledge graph, which contains more than 120 million nodes and 2.7 billion edges as well as a smaller subset of the Freebase graph, known as FB15k, which contains 15,000 nodes and 600,000 edges and is commonly used as a benchmark for multi-relation embedding methods. The FB15k experiments showed PBG performing similarly to state of the art graph embedding models. However, when evaluated against the full Freebase dataset, PBG show memory consumptions improves by over 88%.

PBG is one of the first methods that can scale and the training and processing of graph data to structures with billions of nodes and trillions of edges. The first implementation of PBG has been open sourced in GitHub and we should expect interesting contributions in the near future.

Machine Learning, Data Science and Deep Learning with Python

Machine Learning, Data Science and Deep Learning with Python

Complete hands-on Machine Learning tutorial with Data Science, Tensorflow, Artificial Intelligence, and Neural Networks. Introducing Tensorflow, Using Tensorflow, Introducing Keras, Using Keras, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Learning Deep Learning, Machine Learning with Neural Networks, Deep Learning Tutorial with Python

Machine Learning, Data Science and Deep Learning with Python

Complete hands-on Machine Learning tutorial with Data Science, Tensorflow, Artificial Intelligence, and Neural Networks

Explore the full course on Udemy (special discount included in the link):

In less than 3 hours, you can understand the theory behind modern artificial intelligence, and apply it with several hands-on examples. This is machine learning on steroids! Find out why everyone’s so excited about it and how it really works – and what modern AI can and cannot really do.

In this course, we will cover:
• Deep Learning Pre-requistes (gradient descent, autodiff, softmax)
• The History of Artificial Neural Networks
• Deep Learning in the Tensorflow Playground
• Deep Learning Details
• Introducing Tensorflow
• Using Tensorflow
• Introducing Keras
• Using Keras to Predict Political Parties
• Convolutional Neural Networks (CNNs)
• Using CNNs for Handwriting Recognition
• Recurrent Neural Networks (RNNs)
• Using a RNN for Sentiment Analysis
• The Ethics of Deep Learning
• Learning More about Deep Learning

At the end, you will have a final challenge to create your own deep learning / machine learning system to predict whether real mammogram results are benign or malignant, using your own artificial neural network you have learned to code from scratch with Python.

Separate the reality of modern AI from the hype – by learning about deep learning, well, deeply. You will need some familiarity with Python and linear algebra to follow along, but if you have that experience, you will find that neural networks are not as complicated as they sound. And how they actually work is quite elegant!

This is hands-on tutorial with real code you can download, study, and run yourself.

Best Python Libraries For Data Science & Machine Learning

Best Python Libraries For Data Science & Machine Learning

Best Python Libraries For Data Science & Machine Learning | Data Science Python Libraries

This video will focus on the top Python libraries that you should know to master Data Science and Machine Learning. Here’s a list of topics that are covered in this session:

  • Introduction To Data Science And Machine Learning
  • Why Use Python For Data Science And Machine Learning?
  • Python Libraries for Data Science And Machine Learning
  • Python libraries for Statistics
  • Python libraries for Visualization
  • Python libraries for Machine Learning
  • Python libraries for Deep Learning
  • Python libraries for Natural Language Processing

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Further reading about Python

Complete Python Bootcamp: Go from zero to hero in Python 3

Machine Learning A-Z™: Hands-On Python & R In Data Science

Python and Django Full Stack Web Developer Bootcamp

Complete Python Masterclass

Python Tutorial - Python GUI Programming - Python GUI Examples (Tkinter Tutorial)

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PyTorch Tutorial for Beginners

The Pandas Library for Python

Introduction To Data Analytics With Pandas

Python Programming for Data Science and Machine Learning

Python Programming for Data Science and Machine Learning

This article provides an overview of Python and its application to Data Science and Machine Learning and why it is important.

Originally published by Chris Kambala  at

Python is a general-purpose, high-level, object-oriented, and easy to learn programming language. It was created by Guido van Rossum who is known as the godfather of Python.

Python is a popular programming language because of its simplicity, ease of use, open source licensing, and accessibility — the foundation of its renowned community, which provides great support and help in creating tons of packages, tutorials, and sample programs.

Python can be used to develop a wide variety of applications — ranging from Web, Desktop GUI based programs/applications to science and mathematics programs, and Machine learning and other big data computing systems.

Let’s explore the use of Python in Machine Learning, Data Science, and Data Engineering.

Machine Learning

Machine learning is a relatively new and evolving system development paradigm that has quickly become a mandatory requirement for companies and programmers to understand and use. See our previous article on Machine Learning for the background. Due to the complex, scientific computing nature of machine learning applications, Python is considered the most suitable programming language. This is because of its extensive and mature collection of mathematics and statistics libraries, extensibility, ease of use and wide adoption within the scientific community. As a result, Python has become the recommended programming language for machine learning systems development.

Data Science

Data science combines cutting edge computer and storage technologies with data representation and transformation algorithms and scientific methodology to develop solutions for a variety of complex data analysis problems encompassing raw and structured data in any format. A Data Scientist possesses knowledge of solutions to various classes of data-oriented problems and expertise in applying the necessary algorithms, statistics, and mathematic models, to create the required solutions. Python is recognized among the most effective and popular tools for solving data science related problems.

Data Engineering

Data Engineers build the foundations for Data Science and Machine Learning systems and solutions. Data Engineers are technology experts who start with the requirements identified by the data scientist. These requirements drive the development of data platforms that leverage complex data extraction, loading, and transformation to deliver structured datasets that allow the Data Scientist to focus on solving the business problem. Again, Python is an essential tool in the Data Engineer’s toolbox — one that is used every day to architect and operate the big data infrastructure that is leveraged by the data scientist.

Use Cases for Python, Data Science, and Machine Learning

Here are some example Data Science and Machine Learning applications that leverage Python.

  • Netflix uses data science to understand user viewing pattern and behavioral drivers. This, in turn, helps Netflix to understand user likes/dislikes and predict and suggest relevant items to view.
  • Amazon, Walmart, and Target are heavily using data science, data mining and machine learning to understand users preference and shopping behavior. This assists in both predicting demands to drive inventory management and to suggest relevant products to online users or via email marketing.
  • Spotify uses data science and machine learning to make music recommendations to its users.
  • Spam programs are making use of data science and machine learning algorithm(s) to detect and prevent spam emails.

This article provided an overview of Python and its application to Data Science and Machine Learning and why it is important.

Originally published by Chris Kambala  at


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