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Conference: Transform X 2022
Authors: Varun Mohan
2022-10-19
Graphics Processing Units (GPUs) are used for training artificial intelligence and deep learning models, particularly those related to ML inference use cases. However, using GPUs to deploy models at scale can create several challenges for ML practitioners. In this session, Varun Mohan, CEO and Co-Founder of Exafunction, shared the best practices he’s learned to build an architecture that optimizes GPUs for deep learning workloads. Mohan explained the advantages for using GPUs for ML deployment, as well as where they might not have as many benefits. Mohan also discussed cost, memory, and other factors in the GPU-vs-CPU equation. He discussed inefficiencies that may arise in different scenarios and some of the issues related to network bandwidth and egress. Mohan offered techniques, including the importance of batching workloads and optimizing your models, to solve these problems. Finally, he discussed how some companies are using GPUs to run their recommendation and serving systems. Before Exafunction, Mohan was a technical lead and senior manager at Nuro, where he saw the power of deep learning and the large challenges of productionizing it at scale.
Conference: Transform X 2022
Authors: Emad Mostaque, Alexandr Wang
2022-10-19
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The speaker discusses the democratization of AI and the importance of diversity in data sets to ensure aligned artificial intelligence. They argue for the need to build smaller, more democratized models that impact a broader set of people and allow for adaptation to social issues. The speaker also emphasizes the importance of transparency in the development of AI models and the need for human feedback in reinforcement learning.
- AI democratization and diversity in data sets are crucial for aligned artificial intelligence
- Smaller, more democratized models are needed to impact a broader set of people and adapt to social issues
- Transparency in AI model development is necessary
- Human feedback is crucial in reinforcement learning
Conference: Transform X 2022
Authors: Susan Zhang, Faisal Siddiqi, Bryan Catanzaro, Erhan Bas, Elliot Branson
2022-10-19
Join this enterprise-focused, spirited discussion on how best to train, use, and fine-tune foundation models in the enterprise. Elliot Branson, Director of Machine Learning & Engineering, Scale AI, will moderate the panel with industry experts from AWS, NVIDIA, Netflix, and Meta.Erhan Bas, formerly Applied Scientist at Amazon Web Services and now at Scale, shares his perspective on training large language models (LLMs). Bryan Catanzaro, Vice President of Applied Deep Learning Research at NVIDIA, shares how the GPU manufacturer is targeting foundation models as a core workflow for enterprise customers. Faisal Siddiqi, Director of Machine Learning Platform at Netflix, will share how his company is using foundation models to analyze highly produced video content. Susan Zhang, Researcher at Facebook AI Research (FAIR), a division of Meta, will share insights from training and fine-tuning Meta’s OPT model.Members of the panel will share how they scale their training across multiple nodes, attempt to avoid overfitting by mitigating data quality issues early on, and address bias in models trained on a large internet-based text corpus. The panelists will discuss the compute cost inherent in training an LLM from scratch, how to avoid costly and tedious hyperparameter optimization, the need to mitigate training failure risk in clusters with thousands of GPUs, including sticking to synchronous gradient descent, and the need for extremely fast storage devices to save and load training checkpoints.
Conference: Transform X 2022
Authors: David Ha
2022-10-19
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Collective intelligence can be used to improve deep learning models by incorporating principles of self-organization and adaptability.
- Deep learning networks require sophisticated engineering and careful training schemes.
- Collective intelligence produces systems that are robust, adaptable, and have less rigid assumptions about their environment configurations.
- Active areas in modern deep learning research that incorporate collective intelligence include deep reinforcement, multi-agent, and meta learning.
- An example of collective intelligence in action is the annual Reddit art Place experiment where users collaborate and coordinate a strategy to create a meaningful design.
- Analog neural networks developed in the 1980s were much closer to natural adaptive systems and produced amazing results such as object extraction.
- Collective intelligence can be applied to image processing, generative models, deep reinforcement learning, multi-agent learning, and meta learning.
Conference: OpenAI + Data Forum 2022
Authors: Jeff Zemerick
2022-06-23
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Bringing NLP capabilities to Apache Solr through ONNX and OpenNLP
- Apache OpenNLP is a Java-based NLP tool that has been around for over a decade and offers various capabilities such as tokenization, document classification, and named entity recognition
- Apache Solr depends on Apache Lucene for search functionality, and Apache Lucene has a dependency on Apache OpenNLP for some NLP operations
- The ONNX Runtime allows for the use of deep learning models across programming languages, architectures, and platforms, enabling the use of NLP services created in other languages
- The speaker demonstrates how a deep learning model trained using PyTorch or Tensorflow can be used for inference from a Java search stack of Apache OpenNLP, Apache Lucene, and Apache Solr
- The speaker discusses the challenges and relationships between OpenNLP, Lucene, and Solr, and provides resources for attendees to get started with these open source projects
Conference: OpenAI + Data Forum 2022
Authors: Travis Addair, Nicolas Castet
2022-06-21
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The presentation discusses a full stack platform for optimizing data sharing and processing in a cluster environment.
- The platform uses the Hadoop Distributed File System (HDFS) and YARN for data storage and processing.
- The platform includes a peer-to-peer torrent-like protocol called Left-Right (Letbat) for efficient data transfer.
- The platform allows for easy sharing of data sets and includes a search function for finding relevant data.
- The platform is still in development and the team is looking for feedback and developers to contribute.