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#Distributed Training

Showing: 64 courses
MLOps World: Machine Learning in Production

Learn Your Codebase: Fine-tuning CodeLlama with Flyte to Learn Flyte

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Discover how to fine-tune LLMs on specific codebases using Flyte, an open-source orchestration platform. Explore multi-node, multi-gpu distributed training for efficient model adaptation with limited resources.
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1 Lesons
1 hour 27 minutes
On-Demand
Free-Video
TensorFlow

Inside TensorFlow

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Deep dive into TensorFlow's internal workings, covering advanced topics like parameter servers, quantization, debugging, and optimization, presented by the framework's developers.
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23 Lesons
17 hours
On-Demand
Free-Video
Amazon Web Services

Amazon SageMaker Technical Deep Dive Series

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Comprehensive deep dive into Amazon SageMaker, covering notebook instances, algorithms, custom models, training, deployment, tuning, and advanced features for efficient machine learning workflows.
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16 Lesons
4 hours 30 minutes
On-Demand
Free-Video
TensorFlow

Deep Learning for Fundamental Sciences Using High-Performance Computing

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Exploring deep learning applications in fundamental sciences using high-performance computing to uncover universe secrets and improve discovery sensitivity.
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20 Lesons
27 minutes
On-Demand
Free-Video
TensorFlow

Distributed TensorFlow Training - Google I/O 2018

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Learn to scale TensorFlow training across multiple GPUs and machines efficiently. Explore distribution strategies, performance optimization techniques, and practical implementation using Google Cloud.
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23 Lesons
35 minutes
On-Demand
Free-Video
Anyscale

Scaling Ray Train to 10,000 Kubernetes Nodes on GKE

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Discover how to scale distributed machine learning workloads across 10,000 Kubernetes nodes using Ray Train and GKE, featuring architectural insights and practical solutions for massive AI model training.
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1 Lesons
35 minutes
On-Demand
Free-Video
CNCF [Cloud Native Computing Foundation]

VeScale - A PyTorch Native LLM Training Framework for Automatic Parallelism

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探索PyTorch原生框架veScale,实现大规模语言模型的自动并行训练。结合易用性和性能,助力开发者高效扩展LLM训练,无需复杂并行编程。
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1 Lesons
24 minutes
On-Demand
Free-Video
Linux Foundation

Boosting LLM Development and Training Efficiency: Automated Parallelization with MindSpore

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Explore automated parallelization for efficient LLM development and training using MindSpore. Learn to optimize distributed training on large-scale clusters without intrusive model modifications, accelerating AI research and implementation.
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1 Lesons
44 minutes
On-Demand
Free-Video
Linux Foundation

Efficient Data Parallel Distributed Training with Flyte, Spark and Horovod

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Learn to optimize distributed training using Flyte, Spark, and Horovod for efficient data parallelism in machine learning workflows.
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18 Lesons
41 minutes
On-Demand
Free-Video
Open Data Science

Build NLP Model with Azure ML AutoML NLP Capability

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Learn to build NLP models using Azure ML AutoML for tasks like classification and named entity recognition. Explore multilingual support, data labeling, and distributed training for efficient model development.
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12 Lesons
33 minutes
On-Demand
Free-Video
The Full Stack

Infrastructure & Tooling - Full Stack Deep Learning - Spring 2021

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Comprehensive overview of ML infrastructure and tools, covering software engineering, computing, resource management, frameworks, experiment management, and hyperparameter optimization for practitioners.
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11 Lesons
1 hour 7 minutes
On-Demand
Free-Video
The Full Stack

Data Science at Uber - Full Stack Deep Learning - August 2018

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Explore Uber's data science approach with Jai Ranganathan, covering model lifecycle, customer-centric problem-solving, and practical challenges in AI implementation at scale.
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31 Lesons
44 minutes
On-Demand
Free-Video
The Full Stack

Development Infrastructure & Tooling - FSDL

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Comprehensive overview of deep learning development infrastructure, covering software engineering, frameworks, distributed training, GPUs, resource management, and experiment tracking.
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8 Lesons
52 minutes
On-Demand
Free-Video
1littlecoder

PyTorch NLP Model Training and Fine-Tuning on Colab TPU Multi-GPU with Accelerate

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Learn to train and fine-tune PyTorch NLP models on Colab TPU using Hugging Face's Accelerate library for efficient multi-GPU/TPU implementation with minimal code changes.
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1 Lesons
23 minutes
On-Demand
Free-Video
Databricks

Object Detection with Transformers - From Training to Deployment

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Explore cutting-edge object detection using transformers, from fundamental concepts to advanced techniques. Learn scalable training with Determined AI and deployment using MLflow.
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29 Lesons
32 minutes
On-Demand
Free-Video
Databricks

Scaling Up AI Research to Production with PyTorch and MLFlow

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Explore PyTorch's latest advancements in AI research and production, including distributed training, model optimization, and deployment using MLFlow, with insights on scaling and efficiency.
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30 Lesons
44 minutes
On-Demand
Free-Video
Databricks

Generalized Pipeline Parallelism for DNN Training - PipeDream System Overview

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Efficient multi-accelerator parallelization for DNN training using PipeDream, combining intra-batch parallelism with inter-batch pipelining to improve throughput and overcome challenges in bi-directional training.
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10 Lesons
21 minutes
On-Demand
Free-Video
Databricks

Automated Background Removal Using PyTorch - Wehkamp's Image Processing Pipeline

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Discover an automated pipeline for efficient background removal in product images using PyTorch and machine learning, enhancing visual experiences for e-commerce customers.
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8 Lesons
23 minutes
On-Demand
Free-Video
CNCF [Cloud Native Computing Foundation]

Machine Learning with Kubernetes

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Explore MLOps with Kubernetes: Learn to build scalable ML pipelines using Kubeflow, optimize cluster design, and leverage Istio for enhanced observability and security in production environments.
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1 Lesons
25 minutes
On-Demand
Free-Video
USENIX

Accelerating Neural Recommendation Training with Embedding Scheduling

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Optimizing deep learning recommendation models through embedding scheduling, reducing communication overhead and improving training performance without compromising accuracy.
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1 Lesons
15 minutes
On-Demand
Free-Video
CNCF [Cloud Native Computing Foundation]

Training and Optimisation of Large Transformer Models - An ATLAS and CERN Use Case

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Explore CERN's implementation of transformer models for Jet Flavour Tagging, utilizing Kubeflow for machine learning and Katib for hyperparameter optimization in large-scale particle physics data analysis.
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1 Lesons
37 minutes
On-Demand
Free-Video
CNCF [Cloud Native Computing Foundation]

AutoML and Training Working Group Updates - Kubeflow Developments

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Explore AutoML and Training Working Group updates for Kubeflow, including Katib's V1 API graduation, advanced parameter distribution, and LLM tuning, plus Training Operator's roadmap for enhanced ML framework support.
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1 Lesons
36 minutes
On-Demand
Free-Video
CNCF [Cloud Native Computing Foundation]

A Scalable Platform for Training and Inference Using Kubeflow at CERN

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Explore CERN's Kubeflow-based machine learning platform for data preparation, analysis, training, and inference in physics research, with insights from the ATLAS experiment.
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1 Lesons
29 minutes
On-Demand
Free-Video
CNCF [Cloud Native Computing Foundation]

AutoML and Training Working Group Updates for Kubeflow

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Explore AutoML and Training Working Group updates for Kubeflow, including Katib's 2024 roadmap, Training Operator enhancements, and advancements in distributed ML training on Kubernetes.
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1 Lesons
36 minutes
On-Demand
Free-Video
LLMOps Space

Building the Fine-Tuning Pipeline for Alignment of LLMs

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Explore key aspects of aligning LLMs and set up infrastructure for a versatile alignment pipeline. Learn data collection, prompt tuning, and workflow management techniques.
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1 Lesons
45 minutes
On-Demand
Free-Video
Docker

Democratizing Machine Learning on Kubernetes

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Learn to scale machine learning on Kubernetes, exploring data and model parallelization, distributed training, and performance optimization techniques for large-scale AI projects.
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20 Lesons
38 minutes
On-Demand
Free-Video
CNCF [Cloud Native Computing Foundation]

Building and Managing a Centralized ML Platform with Kubeflow at CERN

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Explore CERN's centralized ML platform using Kubeflow, covering data preparation, model training, and serving. Learn about resource optimization, integration challenges, and cloud expansion possibilities.
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19 Lesons
31 minutes
On-Demand
Free-Video
USENIX

TACCL - Guiding Collective Algorithm Synthesis Using Communication Sketches

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Innovative tool for synthesizing efficient communication algorithms in multi-GPU machine learning, outperforming NCCL and accelerating model training across various hardware configurations.
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1 Lesons
15 minutes
On-Demand
Free-Video
USENIX

TopoOpt - Co-optimizing Network Topology and Parallelization Strategy for Distributed Training Jobs

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Novel direct-connect fabric optimizing distributed DNN training across computation, communication, and network topology. Reduces training time by up to 3.4x compared to similar-cost Fat-Tree interconnects.
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1 Lesons
17 minutes
On-Demand
Free-Video
Devoxx

Machine Intelligence at Google Scale

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Explore Google's scalable machine learning infrastructure, including pre-trained APIs and distributed training with TensorFlow, accelerating custom model development by 10x-40x.
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1 Lesons
59 minutes
On-Demand
Free-Video
GOTO Conferences

Using Kubernetes for Machine Learning Frameworks

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Explore Kubernetes for machine learning frameworks, covering setup, training, and inference phases. Learn to deploy TensorFlow, Apache MXNet, and PyTorch on Kubernetes clusters for efficient ML model production.
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26 Lesons
53 minutes
On-Demand
Free-Video
USENIX

Unlocking the Power of Inline Floating-Point Operations on Programmable Switches

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Explore FPISA, a novel floating-point representation for programmable switches, enabling efficient acceleration of machine learning and query processing applications in network hardware.
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1 Lesons
16 minutes
On-Demand
Free-Video
CNCF [Cloud Native Computing Foundation]

Operating Deep Learning Pipelines Anywhere Using Kubeflow

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Explore building and operating production-grade deep learning pipelines using Kubeflow, covering data preparation, storage, automation, and infrastructure management for multiple tenants.
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18 Lesons
39 minutes
On-Demand
Free-Video
DevConf

Scale Your Batch, Big Data, and AI Workloads Beyond the Kubernetes Scheduler

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Explore solutions for scaling batch, big data, and AI workloads on Kubernetes. Compare projects like Karmada, Koordinator, and Kueue to optimize cluster utilization and overcome scheduling challenges.
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1 Lesons
25 minutes
On-Demand
Free-Video
MLOps.community

Enabling Efficient Trillion Parameter Scale Training for Deep Learning Models

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Explore DeepSpeed's optimizations for efficient distributed training of large-scale deep learning models, addressing memory, compute, and data challenges in extreme model scaling.
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1 Lesons
28 minutes
On-Demand
Free-Video
Anyscale

Simplified Training, Tuning and Deployment of Foundational Models as a Service with Red Hat OpenShift

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Simplify end-to-end lifecycle of foundation models using Red Hat OpenShift Data Science. Learn about cloud-native, scalable stack for training, tuning, and inferencing with integrated open-source components.
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1 Lesons
28 minutes
On-Demand
Free-Video
Linux Foundation

VeScale - A PyTorch Native LLM Training Framework

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Explore a novel PyTorch-native framework for LLM training, combining automatic parallelism and ease of use. Scale your models efficiently with single-device code and transparent nD parallelization.
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1 Lesons
24 minutes
On-Demand
Free-Video
USENIX

Metis - Fast Automatic Distributed Training on Heterogeneous GPUs

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Efficient distributed training system for heterogeneous GPUs. Optimizes parallelism plans, improves resource utilization, and achieves significant speed-ups for large deep learning models.
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1 Lesons
20 minutes
On-Demand
Free-Video
Databricks

Supercharging AI Training with Mosaic Streaming and Lance Columnar Format

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Explore innovative data infrastructure for efficient AI model training, featuring Mosaic StreamingDataset and Lance columnar format for improved performance and cost-effectiveness in large-scale datasets.
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1 Lesons
28 minutes
On-Demand
Free-Video
Devoxx

Machine Intelligence at Google Scale - Vision - Speech API

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Explore Google's scalable Machine Learning solutions, including Vision and Speech APIs, TensorFlow, and Cloud ML. Learn about distributed training and pre-trained services for efficient AI development.
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25 Lesons
53 minutes
On-Demand
Free-Video
Devoxx

Machine Learning Using Kubeflow and Kubernetes

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Explore machine learning workflows on Kubernetes using Kubeflow, covering deployment, training, inference, and advanced features like hyperparameter tuning and automated pipelines.
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20 Lesons
51 minutes
On-Demand
Free-Video
TensorFlow

Inside TensorFlow - tf.data + tf.distribute

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Explore best practices for tf.data and tf.distribute in TensorFlow, covering input pipelines, performance optimization, and distributed training strategies for multi-GPU and TPU setups.
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24 Lesons
46 minutes
On-Demand
Free-Video
Meta

Introducing PyTorch 1.0 - A Research-Focused, Production-Ready Deep Learning Framework

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Explore PyTorch 1.0, a powerful deep learning framework that bridges research and production, accelerating AI development with innovative features and tools.
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19 Lesons
40 minutes
On-Demand
Free-Video
NDC Conferences

Taking Models to the Next Level with Azure Machine Learning

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Explore Azure Machine Learning for custom model training, from pre-built cognitive services to AutoML and hyperparameter tuning, with practical examples for text analysis and sentiment analysis.
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23 Lesons
57 minutes
On-Demand
Free-Video
Federated Learning One World Seminar

Recently Engineered Variants of Split Learning - Seminar 87

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Explore advanced variants of split learning techniques in federated systems, focusing on recent engineering developments and applications in distributed machine learning architectures.
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1 Lesons
49 minutes
On-Demand
Free-Video
Toronto Machine Learning Series (TMLS)

Scaling Deep Learning Model Training

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Learn techniques for scaling deep learning model training across distributed systems, reducing training time from days to hours while maintaining cost efficiency.
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1 Lesons
32 minutes
On-Demand
Free-Video
MIT HAN Lab

Distributed Training and Gradient Compression - Lecture 14

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Explore distributed training challenges, focusing on bandwidth and latency bottlenecks. Learn gradient compression techniques and delayed gradient averaging to optimize communication in deep learning systems.
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15 Lesons
57 minutes
On-Demand
Free-Video
MIT HAN Lab

Distributed Training and Gradient Compression - Lecture 14

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Explore distributed training challenges, focusing on bandwidth and latency bottlenecks. Learn gradient compression techniques and delayed gradient averaging to optimize communication in deep learning.
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1 Lesons
58 minutes
On-Demand
Free-Video
MIT HAN Lab

Distributed Training: Techniques and Strategies - Part II - Lecture 18

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Explore advanced distributed training techniques for machine learning, focusing on efficiency and scalability in large-scale AI systems.
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1 Lesons
55 minutes
On-Demand
Free-Video
MIT HAN Lab

Distributed Training for Efficient Machine Learning - Part II - Lecture 18

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Explore advanced distributed training techniques for machine learning, focusing on efficiency and scalability in large-scale systems.
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1 Lesons
55 minutes
On-Demand
Free-Video
MLOps.community

Systems Engineering in Machine Learning - Navigating Low-Level Challenges

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Explore systems engineering in ML with Andrew Dye, discussing low-level work, blending disciplines, and ML infrastructure challenges. Gain insights on distributed training, managing scale, and bridging gaps between firmware and MLOps.
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23 Lesons
48 minutes
On-Demand
Free-Video
MLOps.community

Accelerating Multimodal AI - From Tabular ML to Generative Video Systems

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Explore challenges and solutions in multimodal AI, from data management to efficient inference. Learn about systems supporting research and productionization in generative video and language models.
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18 Lesons
55 minutes
On-Demand
Free-Video
Anyscale

Scalable Training of Language Models Using Ray, JAX, and TPUv4

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Explore challenges and design decisions for scalable language model training, with quantitative analysis of efficiency improvements using Ray, JAX pjit, and TPUv4.
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1 Lesons
34 minutes
On-Demand
Free-Video
Anyscale

Best Practices for Productionizing Distributed Training with Ray Train

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Explore fault-tolerant distributed training techniques using Ray Train, covering experiment restoration, node failure recovery, cloud storage snapshots, and large model checkpointing for efficient AI workloads.
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1 Lesons
30 minutes
On-Demand
Free-Video
Scalable Parallel Computing Lab, SPCL @ ETH Zurich

Next-Generation Networks for Machine Learning

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Explore next-gen networks for machine learning, focusing on optical fabrics and scheduling techniques to accelerate distributed DNN training workloads and overcome datacenter network bottlenecks.
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3 Lesons
50 minutes
On-Demand
Free-Video
Linux Foundation

Detecting and Overcoming GPU Failures During ML Training

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Explore GPU failure challenges in ML training, learn proactive detection methods, and discover fault-tolerant distributed training principles to mitigate issues and optimize performance.
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1 Lesons
43 minutes
On-Demand
Free-Video
CNCF [Cloud Native Computing Foundation]

Leverage Topology Modeling and Topology-Aware Scheduling to Accelerate LLM Training

0 rewiews
Explore topology modeling and scheduling techniques to optimize LLM training performance. Learn to address network bottlenecks and enhance resource allocation for AI workloads in Kubernetes environments.
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1 Lesons
45 minutes
On-Demand
Free-Video
MIT HAN Lab

Distributed Training and Gradient Compression - Part I - Lecture 13

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Explore distributed training of neural networks, focusing on data parallelism and model parallelism techniques for efficient machine learning on resource-constrained devices.
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1 Lesons
1 hour 1 minute
On-Demand
Free-Video
MIT HAN Lab

Distributed Training and Gradient Compression - Part I - Lecture 13

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Explore distributed training of neural networks, focusing on data parallelism and model parallelism techniques for efficient machine learning on resource-constrained devices.
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1 Lesons
1 hour 1 minute
On-Demand
Free-Video
MIT HAN Lab

Distributed Training for Efficient Machine Learning - Part I - Lecture 17

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Explore distributed training techniques for machine learning, focusing on data and model parallelism strategies to enhance efficiency and scalability.
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1 Lesons
1 hour 1 minute
On-Demand
Free-Video
MIT HAN Lab

Distributed Training - Part I - Lecture 17

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Explore distributed training techniques for efficient machine learning, covering key concepts and strategies for scaling ML models across multiple devices.
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1 Lesons
1 hour 1 minute
On-Demand
Free-Video
Microsoft

Azure AI Training and Fine-tuning Custom LLMs with Distributed Training

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Master distributed training techniques for custom LLMs using Azure AI infrastructure, focusing on performance optimization, scalability, and efficient deployment with ND H100 and H200 series.
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1 Lesons
44 minutes
On-Demand
Free-Video
MIT HAN Lab

Distributed Training Methods and Parallelization Techniques - Lecture 19

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Explore advanced distributed training concepts, from data parallelism and communication primitives to pipeline, tensor, and sequence parallelism methods for efficient machine learning systems.
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1 Lesons
1 hour 10 minutes
On-Demand
Free-Video
MIT HAN Lab

Distributed Training: Hybrid Parallelism and Gradient Optimization - Lecture 20

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Delve into advanced distributed training concepts, exploring hybrid parallelism, gradient compression techniques, and strategies to overcome bandwidth and latency bottlenecks in ML systems.
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1 Lesons
59 minutes
On-Demand
Free-Video