Geeps: Scalable deep learning on distributed gpus with a gpu-specialized parameter server. Scaling distributed machine learning with the parameter server. Language models are unsupervised multitask learners. Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters. Alpa: Automating inter-and intra-operator parallelism for distributed deep learning. Do the main claims made in the abstract and introduction accurately reflect the paper's If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Y es] Codes and how Did you specify all the training details (e.g., data splits, hyperparameters, how they Did you report error bars (e.g., with respect to the random seed after running experiments multiple times)?

Geeps: Scalable deep learning on distributed gpus with a gpu-specialized parameter server. Scaling distributed machine learning with the parameter server. Language models are unsupervised multitask learners. Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters. Alpa: Automating inter-and intra-operator parallelism for distributed deep learning. Do the main claims made in the abstract and introduction accurately reflect the paper's If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Y es] Codes and how Did you specify all the training details (e.g., data splits, hyperparameters, how they Did you report error bars (e.g., with respect to the random seed after running experiments multiple times)?

Scaling up model sizes can lead to fundamentally new capabilities in many machine learning (ML) tasks. However, training big models requires strong distributed system expertise to carefully design model-parallel execution strategies that suit the model architectures and cluster setups.

The Mixture-of-Experts (MoE) structure scales the Transformer-based large language models (LLMs) and improves their performance with only the sub-linear increase in computation resources. Recently, a fine-grained DeepSeekMoE structure is proposed, which can further improve the computing efficiency of MoE without performance degradation. However, the All-to-All communication introduced by MoE has become a bottleneck, especially for the fine-grained structure, which typically involves and activates more experts, hence contributing to heavier communication overhead. In this paper, we propose a novel MoE structure named BigMac, which is also fine-grained but with high communication efficiency. The innovation of BigMac is mainly due to that we abandon the \textbf{c}ommunicate-\textbf{d}escend-\textbf{a}scend-\textbf{c}ommunicate (CDAC) manner used by fine-grained MoE, which leads to the All-to-All communication always taking place at the highest dimension. Instead, BigMac designs an efficient \textbf{d}escend-\textbf{c}ommunicate-\textbf{c}ommunicate-\textbf{a}scend (DCCA) manner. Specifically, we add a descending and ascending projection at the entrance and exit of the expert, respectively, which enables the communication to perform at a very low dimension. Furthermore, to adapt to DCCA, we re-design the structure of small experts, ensuring that the expert in BigMac has enough complexity to address tokens. Experimental results show that BigMac achieves comparable or even better model quality than fine-grained MoEs with the same number of experts and a similar number of total parameters. Equally importantly, BigMac reduces the end-to-end latency by up to 3.09$\times$ for training and increases the throughput by up to 3.11$\times$ for inference on state-of-the-art AI computing frameworks including Megatron, Tutel, and DeepSpeed-Inference.

Training large-scale language models is increasingly critical in various domains, but it is hindered by frequent failures, leading to significant time and economic costs. Current failure recovery methods in cloud-based settings inadequately address the diverse and complex scenarios that arise, focusing narrowly on erasing downtime for individual tasks without considering the overall cost impact on a cluster. We introduce Unicron, a workload manager designed for efficient self-healing in large-scale language model training. Unicron optimizes the training process by minimizing failure-related costs across multiple concurrent tasks within a cluster. Its key features include in-band error detection for real-time error identification without extra overhead, a dynamic cost-aware plan generation mechanism for optimal reconfiguration, and an efficient transition strategy to reduce downtime during state changes. Deployed on a 128-GPU distributed cluster, Unicron demonstrates up to a 1.9x improvement in training efficiency over state-of-the-art methods, significantly reducing failure recovery costs and enhancing the reliability of large-scale language model training.

Yes, you have your R2-D2, your BB-8, Data (Brent Spiner), even WALL-E. So while we still can, take notes on these robots before they become our technological overlords. Not only are the Fem-bots evil, they are Evil's evil. Dr. Evil's (Mike Myers), to be precise. Attractive and seductive, the Fem-bots were a means of distracting, and killing, Austin Powers (Mike Myers), not only with their agility but with their "machine gun jubblies," guns protruding from their breasts.

Large language models (LLMs) are some of the most advanced deep learning algorithms that are capable of understanding written language. Many modern LLMs are built using the transformer network introduced by Google in 2017 in the Attention Is All You Need research paper. NVIDIA NeMo Megatron is an end-to-end GPU-accelerated framework for training and deploying transformer-based LLMs up to a trillion parameters. In September 2022, NVIDIA announced that NeMo Megatron is now available in Open Beta, allowing you to train and deploy LLMs using your own data. With this announcement, several pretrained checkpoints have been uploaded to HuggingFace, enabling anyone to deploy LLMs locally using GPUs.

Scaling up model sizes can lead to fundamentally new capabilities in many machine learning (ML) tasks. However, training big models requires strong distributed system expertise to carefully design model-parallel execution strategies that suit the model architectures and cluster setups. In this paper, we develop AMP, a framework that automatically derives such strategies. AMP identifies a valid space of model parallelism strategies and efficiently searches the space for high-performed strategies, by leveraging a cost model designed to capture the heterogeneity of the model and cluster specifications. Unlike existing methods, AMP is specifically tailored to support complex models composed of uneven layers and cluster setups with more heterogeneous accelerators and bandwidth. We evaluate AMP on popular models and cluster setups from public clouds and show that AMP returns parallel strategies that match the expert-tuned strategies on typical cluster setups. On heterogeneous clusters or models with heterogeneous architectures, AMP finds strategies with 1.54x and 1.77x higher throughput than state-of-the-art model-parallel systems, respectively.

AI will never be ethical... or will it? Megatron Transformer, an AI developed by the Applied Deep Research team at Nvidia, recently argued both positions and a host of others at the Oxford Union, a historically important debate club. The AI was trained with the entirety of Wikipedia and 63 million English news articles, as well as "gigabytes worth of Reddit discourse." Afterwards, it was asked to argue the position that AI will never be ethical. It responded by saying that AI is "not smart enough to make AI ethical."