TIM DETTMERS

Deep Learning Hardware Limbo. 2017-12-21 by Tim Dettmers 91 Comments. With the release of the Titan V, we now entered deep learning hardware limbo. It is unclear if NVIDIA will be able to keep its spot as the main deep learning hardware vendor in 2018 and both AMD and Intel Nervana will have a shot at overtaking NVIDIA. ABOUT ME — TIM DETTMERS About Me. I am a PhD student at the University of Washington advised by Luke Zettlemoyer working on representation learning, and neuro-inspired and hardware optimized deep learning. Previously I interned at the UC L Machine Reading Group where I was advised by Sebastian Riedel working on information retrieval and link prediction

in knowledge

THE BEST GPUS FOR DEEP LEARNING IN 2020 The Quadro M6000 has 24 GB of memory and goes for $400 on eBay. The Tesla K80 has a 2-in-1 GPU with 2x 12 GB of memory for about $200. These cards are slow compared to more modern cards, but the extra memory can come in handy for specific projects where memory is

paramount.

A FULL HARDWARE GUIDE TO DEEP LEARNING Typical monitor layout when I do deep learning: Left: Papers, Google searches, gmail, stackoverflow; middle: Code; right: Output windows, R, folders, systems monitors, GPU monitors, to-do list, and other small applications. Some words on building a PC. Many people are scared to build computers. The hardware components are expensive and you do not want to do something wrong. HOW TO PARALLELIZE DEEP LEARNING ON GPUS PART 2/2: MODEL In my last blog post I explained what model and data parallelism is and analysed how to use data parallelism effectively in deep learning.In this blog post I will focus on model parallelism. To recap, model parallelism is, when you split the model among GPUs and use the same data for each model; so each GPU works on a part of the model rather than a part of the data. DEEP LEARNING ARCHIVES 2015-07-27 by Tim Dettmers 181 Comments. In this blog post I will delve into the brain and explain its basic information processing machinery and compare it to deep learning. I do this by moving step-by-step along with the brains electrochemical and biological information processing pipeline and relating it directly to the

architecture of

UNDERSTANDING CONVOLUTION IN DEEP LEARNING Convolution operation for one pixel of the resulting feature map: One image patch (red) of the original image (RAM) is multiplied by the kernel, and its sum is written to the feature map pixel (Buffer RAM).Gif by Glen Williamson who runs a website that features many technical gifs.. As you can see there is also a normalization procedure where the output value is normalized by the size of the HOW TO PARALLELIZE DEEP LEARNING ON GPUS PART 1/2: DATA If you have six GPUs in two nodes you need to pass the data to five other GPUs, three of which need to go through the network card (3x 0.75ms), while two can use PCIe 3.0 to pass the data to the other two GPUs (about three times as fast; 2x 0.25ms). However, the PCIe pass is independent of the network card pass, so the time needed is determined HOW TO BUILD AND USE A MULTI GPU SYSTEM FOR DEEP LEARNING Hello Tim, Congrats for your excellent articles! I would like your advice on a setup for deep learning with images. I have 2 PCs currently with GTX 1060 and thought to replace those for 2x 2080 Ti in each PC and make a cluster with them (potentially adding more later), also will connect the GPUs with a fast direct link like you did if

it’s worth it.

CREDIT ASSIGNMENT IN DEEP LEARNING

TIM DETTMERS

Deep Learning Hardware Limbo. 2017-12-21 by Tim Dettmers 91 Comments. With the release of the Titan V, we now entered deep learning hardware limbo. It is unclear if NVIDIA will be able to keep its spot as the main deep learning hardware vendor in 2018 and both AMD and Intel Nervana will have a shot at overtaking NVIDIA. ABOUT ME — TIM DETTMERS About Me. I am a PhD student at the University of Washington advised by Luke Zettlemoyer working on representation learning, and neuro-inspired and hardware optimized deep learning. Previously I interned at the UC L Machine Reading Group where I was advised by Sebastian Riedel working on information retrieval and link prediction

in knowledge

THE BEST GPUS FOR DEEP LEARNING IN 2020 The Quadro M6000 has 24 GB of memory and goes for $400 on eBay. The Tesla K80 has a 2-in-1 GPU with 2x 12 GB of memory for about $200. These cards are slow compared to more modern cards, but the extra memory can come in handy for specific projects where memory is

paramount.

A FULL HARDWARE GUIDE TO DEEP LEARNING Typical monitor layout when I do deep learning: Left: Papers, Google searches, gmail, stackoverflow; middle: Code; right: Output windows, R, folders, systems monitors, GPU monitors, to-do list, and other small applications. Some words on building a PC. Many people are scared to build computers. The hardware components are expensive and you do not want to do something wrong. HOW TO PARALLELIZE DEEP LEARNING ON GPUS PART 2/2: MODEL In my last blog post I explained what model and data parallelism is and analysed how to use data parallelism effectively in deep learning.In this blog post I will focus on model parallelism. To recap, model parallelism is, when you split the model among GPUs and use the same data for each model; so each GPU works on a part of the model rather than a part of the data. DEEP LEARNING ARCHIVES 2015-07-27 by Tim Dettmers 181 Comments. In this blog post I will delve into the brain and explain its basic information processing machinery and compare it to deep learning. I do this by moving step-by-step along with the brains electrochemical and biological information processing pipeline and relating it directly to the

architecture of

UNDERSTANDING CONVOLUTION IN DEEP LEARNING Convolution operation for one pixel of the resulting feature map: One image patch (red) of the original image (RAM) is multiplied by the kernel, and its sum is written to the feature map pixel (Buffer RAM).Gif by Glen Williamson who runs a website that features many technical gifs.. As you can see there is also a normalization procedure where the output value is normalized by the size of the HOW TO PARALLELIZE DEEP LEARNING ON GPUS PART 1/2: DATA If you have six GPUs in two nodes you need to pass the data to five other GPUs, three of which need to go through the network card (3x 0.75ms), while two can use PCIe 3.0 to pass the data to the other two GPUs (about three times as fast; 2x 0.25ms). However, the PCIe pass is independent of the network card pass, so the time needed is determined HOW TO BUILD AND USE A MULTI GPU SYSTEM FOR DEEP LEARNING Hello Tim, Congrats for your excellent articles! I would like your advice on a setup for deep learning with images. I have 2 PCs currently with GTX 1060 and thought to replace those for 2x 2080 Ti in each PC and make a cluster with them (potentially adding more later), also will connect the GPUs with a fast direct link like you did if

it’s worth it.

CREDIT ASSIGNMENT IN DEEP LEARNING ABOUT ME — TIM DETTMERS About Me. I am a PhD student at the University of Washington advised by Luke Zettlemoyer working on representation learning, and neuro-inspired and hardware optimized deep learning. Previously I interned at the UC L Machine Reading Group where I was advised by Sebastian Riedel working on information retrieval and link prediction

in knowledge

DATA SCIENCE PORTFOLIO Abstract. I took part in the Crowdflower Kaggle competition in which it was the task to predict weather labels (e.g. sunny, cloudy, cold, hot) from twitter data. I used a deep neural network with rectified linear units and placed 2nd. The data was preprocessed with

regularized tf-idf.

TIM DETTMERS

At the beginning, deep learning is a lot of trial and error: You have to get a feel what parameters need to be adjusted, or what puzzle piece is missing in order to get a good result. A GPU helps you to fail quickly and learn important lessons so that you can keep improving. Soon my deep learning skills were sufficient to take the 2

nd place in

HARDWARE ARCHIVES

Deep Learning Hardware Limbo. 2017-12-21 by Tim Dettmers 91 Comments. With the release of the Titan V, we now entered deep learning hardware limbo. It is unclear if NVIDIA will be able to keep its spot as the main deep learning hardware vendor in 2018 and both AMD and Intel Nervana will have a shot at overtaking NVIDIA. MACHINE LEARNING PHD APPLICATIONS Machine Learning PhD Applications — Everything You Need to Know. 2018-11-26 by Tim Dettmers 130 Comments. I studied in depth how to be successful in my PhD applications and it paid off: I got admitted to Stanford, University of Washington, UCL, CMU, and NYU. This blog post is a mish-mash of how to proceed in your PhD applications from A to Z. TPUS VS GPUS FOR TRANSFORMERS (BERT) TPUs are about 32% to 54% faster for training BERT-like models. One can expect to replicate BERT base on an 8 GPU machine within about 10 to 17 days. On a standard, affordable GPU machine with 4 GPUs one can expect to train BERT base for about 34 days using 16-bit or about 11 days using 8-bit. Reader Interactions. SPARSE NETWORKS FROM SCRATCH: FASTER TRAINING WITHOUT This blog post is about my work, Sparse Networks from Scratch: Faster Training without Losing Performance, with Luke Zettlemoyer on fast training of neural networks which we keep sparse throughout training. We show that by developing an algorithm, sparse momentum, we can initialize a neural network with sparse random weights and train it to dense performance levels — all while doing DEEP LEARNING HARDWARE LIMBO Deep Learning Hardware Limbo. 2017-12-21 by Tim Dettmers 91 Comments. With the release of the Titan V, we now entered deep learning hardware limbo. It is unclear if NVIDIA will be able to keep its spot as the main deep learning hardware vendor in 2018 and both AMD and Intel Nervana will have a shot at overtaking NVIDIA. THE BRAIN VS. DEEP LEARNING VS. SINGULARITY In this blog post I will delve into the brain and explain its basic information processing machinery and compare it to deep learning. I do this by moving step-by-step along with the brains electrochemical and biological information processing pipeline and relating it directly to the architecture of convolutional nets.

TIM DETTMERS

This blog post is about my work with Luke Zettlemoyer on fast training of neural networks which we keep sparse throughout training. We show that by developing an algorithm, sparse momentum, we can initialize a neural network with sparse random weights and train it to dense performance levels — all while doing just a single training run.

TIM DETTMERS

Deep Learning Hardware Limbo. 2017-12-21 by Tim Dettmers 91 Comments. With the release of the Titan V, we now entered deep learning hardware limbo. It is unclear if NVIDIA will be able to keep its spot as the main deep learning hardware vendor in 2018 and both AMD and Intel Nervana will have a shot at overtaking NVIDIA. ABOUT ME — TIM DETTMERS About Me. I am a PhD student at the University of Washington advised by Luke Zettlemoyer working on representation learning, and neuro-inspired and hardware optimized deep learning. Previously I interned at the UC L Machine Reading Group where I was advised by Sebastian Riedel working on information retrieval and link prediction

in knowledge

A FULL HARDWARE GUIDE TO DEEP LEARNING Typical monitor layout when I do deep learning: Left: Papers, Google searches, gmail, stackoverflow; middle: Code; right: Output windows, R, folders, systems monitors, GPU monitors, to-do list, and other small applications. Some words on building a PC. Many people are scared to build computers. The hardware components are expensive and you do not want to do something wrong. THE BEST GPUS FOR DEEP LEARNING IN 2020 The Quadro M6000 has 24 GB of memory and goes for $400 on eBay. The Tesla K80 has a 2-in-1 GPU with 2x 12 GB of memory for about $200. These cards are slow compared to more modern cards, but the extra memory can come in handy for specific projects where memory is

paramount.

HOW TO PARALLELIZE DEEP LEARNING ON GPUS PART 2/2: MODEL In my last blog post I explained what model and data parallelism is and analysed how to use data parallelism effectively in deep learning.In this blog post I will focus on model parallelism. To recap, model parallelism is, when you split the model among GPUs and use the same data for each model; so each GPU works on a part of the model rather than a part of the data. UNDERSTANDING CONVOLUTION IN DEEP LEARNING Convolution operation for one pixel of the resulting feature map: One image patch (red) of the original image (RAM) is multiplied by the kernel, and its sum is written to the feature map pixel (Buffer RAM).Gif by Glen Williamson who runs a website that features many technical gifs.. As you can see there is also a normalization procedure where the output value is normalized by the size of the HOW TO BUILD AND USE A MULTI GPU SYSTEM FOR DEEP LEARNING Hello Tim, Congrats for your excellent articles! I would like your advice on a setup for deep learning with images. I have 2 PCs currently with GTX 1060 and thought to replace those for 2x 2080 Ti in each PC and make a cluster with them (potentially adding more later), also will connect the GPUs with a fast direct link like you did if

it’s worth it.

HOW TO PARALLELIZE DEEP LEARNING ON GPUS PART 1/2: DATA If you have six GPUs in two nodes you need to pass the data to five other GPUs, three of which need to go through the network card (3x 0.75ms), while two can use PCIe 3.0 to pass the data to the other two GPUs (about three times as fast; 2x 0.25ms). However, the PCIe pass is independent of the network card pass, so the time needed is determined THE BRAIN VS. DEEP LEARNING VS. SINGULARITY In this blog post I will delve into the brain and explain its basic information processing machinery and compare it to deep learning. I do this by moving step-by-step along with the brains electrochemical and biological information processing pipeline and relating it directly to the architecture of convolutional nets. TPUS VS GPUS FOR TRANSFORMERS (BERT)

TIM DETTMERS

Deep Learning Hardware Limbo. 2017-12-21 by Tim Dettmers 91 Comments. With the release of the Titan V, we now entered deep learning hardware limbo. It is unclear if NVIDIA will be able to keep its spot as the main deep learning hardware vendor in 2018 and both AMD and Intel Nervana will have a shot at overtaking NVIDIA. ABOUT ME — TIM DETTMERS About Me. I am a PhD student at the University of Washington advised by Luke Zettlemoyer working on representation learning, and neuro-inspired and hardware optimized deep learning. Previously I interned at the UC L Machine Reading Group where I was advised by Sebastian Riedel working on information retrieval and link prediction

in knowledge

A FULL HARDWARE GUIDE TO DEEP LEARNING Typical monitor layout when I do deep learning: Left: Papers, Google searches, gmail, stackoverflow; middle: Code; right: Output windows, R, folders, systems monitors, GPU monitors, to-do list, and other small applications. Some words on building a PC. Many people are scared to build computers. The hardware components are expensive and you do not want to do something wrong. THE BEST GPUS FOR DEEP LEARNING IN 2020 The Quadro M6000 has 24 GB of memory and goes for $400 on eBay. The Tesla K80 has a 2-in-1 GPU with 2x 12 GB of memory for about $200. These cards are slow compared to more modern cards, but the extra memory can come in handy for specific projects where memory is

paramount.

HOW TO PARALLELIZE DEEP LEARNING ON GPUS PART 2/2: MODEL In my last blog post I explained what model and data parallelism is and analysed how to use data parallelism effectively in deep learning.In this blog post I will focus on model parallelism. To recap, model parallelism is, when you split the model among GPUs and use the same data for each model; so each GPU works on a part of the model rather than a part of the data. UNDERSTANDING CONVOLUTION IN DEEP LEARNING Convolution operation for one pixel of the resulting feature map: One image patch (red) of the original image (RAM) is multiplied by the kernel, and its sum is written to the feature map pixel (Buffer RAM).Gif by Glen Williamson who runs a website that features many technical gifs.. As you can see there is also a normalization procedure where the output value is normalized by the size of the HOW TO BUILD AND USE A MULTI GPU SYSTEM FOR DEEP LEARNING Hello Tim, Congrats for your excellent articles! I would like your advice on a setup for deep learning with images. I have 2 PCs currently with GTX 1060 and thought to replace those for 2x 2080 Ti in each PC and make a cluster with them (potentially adding more later), also will connect the GPUs with a fast direct link like you did if

it’s worth it.

HOW TO PARALLELIZE DEEP LEARNING ON GPUS PART 1/2: DATA If you have six GPUs in two nodes you need to pass the data to five other GPUs, three of which need to go through the network card (3x 0.75ms), while two can use PCIe 3.0 to pass the data to the other two GPUs (about three times as fast; 2x 0.25ms). However, the PCIe pass is independent of the network card pass, so the time needed is determined THE BRAIN VS. DEEP LEARNING VS. SINGULARITY In this blog post I will delve into the brain and explain its basic information processing machinery and compare it to deep learning. I do this by moving step-by-step along with the brains electrochemical and biological information processing pipeline and relating it directly to the architecture of convolutional nets. TPUS VS GPUS FOR TRANSFORMERS (BERT) ABOUT ME — TIM DETTMERS About Me. I am a PhD student at the University of Washington advised by Luke Zettlemoyer working on representation learning, and neuro-inspired and hardware optimized deep learning. Previously I interned at the UC L Machine Reading Group where I was advised by Sebastian Riedel working on information retrieval and link prediction

in knowledge

DATA SCIENCE PORTFOLIO Abstract. I took part in the Crowdflower Kaggle competition in which it was the task to predict weather labels (e.g. sunny, cloudy, cold, hot) from twitter data. I used a deep neural network with rectified linear units and placed 2nd. The data was preprocessed with

regularized tf-idf.

TIM DETTMERS

At the beginning, deep learning is a lot of trial and error: You have to get a feel what parameters need to be adjusted, or what puzzle piece is missing in order to get a good result. A GPU helps you to fail quickly and learn important lessons so that you can keep improving. Soon my deep learning skills were sufficient to take the 2

nd place in

HARDWARE ARCHIVES

Deep Learning Hardware Limbo. 2017-12-21 by Tim Dettmers 91 Comments. With the release of the Titan V, we now entered deep learning hardware limbo. It is unclear if NVIDIA will be able to keep its spot as the main deep learning hardware vendor in 2018 and both AMD and Intel Nervana will have a shot at overtaking NVIDIA. DEEP LEARNING ARCHIVES 2015-07-27 by Tim Dettmers 181 Comments. In this blog post I will delve into the brain and explain its basic information processing machinery and compare it to deep learning. I do this by moving step-by-step along with the brains electrochemical and biological information processing pipeline and relating it directly to the

architecture of

HOW TO PICK YOUR GRAD SCHOOL So it is crucial to look at the social environment when you pick a grad school. Usually, within a grad school, the social environments are the office, research group meetings, other group meetings, lunchtime, and social activities organized by the department or by grad organizations. Fewer research groups have social outings as a

group, but

MACHINE LEARNING PHD APPLICATIONS Machine Learning PhD Applications — Everything You Need to Know. 2018-11-26 by Tim Dettmers 130 Comments. I studied in depth how to be successful in my PhD applications and it paid off: I got admitted to Stanford, University of Washington, UCL, CMU, and NYU. This blog post is a mish-mash of how to proceed in your PhD applications from A to Z. CREDIT ASSIGNMENT IN DEEP LEARNING 1 Ideas: 1/3 credit. 1000 Communication: 1/3000 credit each. 1000 Implementation: 1/3000 credit each. We see in this case the one person with the idea should receive the largest amount of credit. Similarly, if we weight the numbers differently, and if we assume contributions of individuals in groups are equal, then this credit assignment holds DEEP LEARNING HARDWARE LIMBO Deep Learning Hardware Limbo. 2017-12-21 by Tim Dettmers 91 Comments. With the release of the Titan V, we now entered deep learning hardware limbo. It is unclear if NVIDIA will be able to keep its spot as the main deep learning hardware vendor in 2018 and both AMD and Intel Nervana will have a shot at overtaking NVIDIA.

TIM DETTMERS

In this blog post I will delve into the brain and explain its basic information processing machinery and compare it to deep learning. I do this by moving step-by-step along with the brains electrochemical and biological information processing pipeline and relating it directly to the architecture of convolutional nets.

TIM DETTMERS

Making deep learning accessible. This blog post is about my work, Sparse Networks from Scratch: Faster Training without Losing Performance, with Luke Zettlemoyer on fast training of neural networks which we keep sparse throughout training. We show that by developing an algorithm, sparse momentum, we can initialize a neural network with sparse random weights and train it to dense ABOUT ME — TIM DETTMERS Research Interests Publications Awards & Honors Research Internships Service I am a PhD student at the University of Washington advised by Luke Zettlemoyer working on representation learning, and neuro-inspired and hardware optimized deep learning. Previously I interned at the UCL Machine Reading Group where I was advised by Sebastian Riedel working on information retrieval and link prediction THE BEST GPUS FOR DEEP LEARNING IN 2020 Overview. This blog post is structured in the following way. First, I will explain what makes a GPU fast. I will discuss CPUs vs GPUs, Tensor Cores, memory bandwidth, and the memory hierarchy of GPUs and how these relate to deep learning performance. A FULL HARDWARE GUIDE TO DEEP LEARNING Typical monitor layout when I do deep learning: Left: Papers, Google searches, gmail, stackoverflow; middle: Code; right: Output windows, R, folders, systems monitors, GPU monitors, to-do list, and other small applications. Some words on building a PC. Many people are scared to build computers. The hardware components are expensive and you do not want to do something wrong. HOW TO PARALLELIZE DEEP LEARNING ON GPUS PART 2/2: MODEL In my last blog post I explained what model and data parallelism is and analysed how to use data parallelism effectively in deep learning.In this blog post I will focus on model parallelism. To recap, model parallelism is, when you split the model among GPUs and use the same data for each model; so each GPU works on a part of the model rather than a part of the data. UNDERSTANDING CONVOLUTION IN DEEP LEARNING Convolution operation for one pixel of the resulting feature map: One image patch (red) of the original image (RAM) is multiplied by the kernel, and its sum is written to the feature map pixel (Buffer RAM).Gif by Glen Williamson who runs a website that features many technical gifs.. As you can see there is also a normalization procedure where the output value is normalized by the size of the HOW TO PARALLELIZE DEEP LEARNING ON GPUS PART 1/2: DATA Yeah, I noticed the same thing. The equation listed in the article is incorrect, but the one Baru listed isn’t quite right either. For example, the equation in the article divides by 40 twice, which is wrong, and the last 1024 is a 102, which is also wrong. HOW TO BUILD AND USE A MULTI GPU SYSTEM FOR DEEP LEARNING Hello Tim, Congrats for your excellent articles! I would like your advice on a setup for deep learning with images. I have 2 PCs currently with GTX 1060 and thought to replace those for 2x 2080 Ti in each PC and make a cluster with them (potentially adding more later), also will connect the GPUs with a fast direct link like you did if

it’s worth it.

HOW TO PICK YOUR GRAD SCHOOL The Career Perspective: Picking Based on Expected Success. The career perspective looks at the most critical factors for your academic success and success beyond that and picks the school that is best according to these factors. CREDIT ASSIGNMENT IN DEEP LEARNING

TIM DETTMERS

Making deep learning accessible. This blog post is about my work, Sparse Networks from Scratch: Faster Training without Losing Performance, with Luke Zettlemoyer on fast training of neural networks which we keep sparse throughout training. We show that by developing an algorithm, sparse momentum, we can initialize a neural network with sparse random weights and train it to dense ABOUT ME — TIM DETTMERS Research Interests Publications Awards & Honors Research Internships Service I am a PhD student at the University of Washington advised by Luke Zettlemoyer working on representation learning, and neuro-inspired and hardware optimized deep learning. Previously I interned at the UCL Machine Reading Group where I was advised by Sebastian Riedel working on information retrieval and link prediction THE BEST GPUS FOR DEEP LEARNING IN 2020 Overview. This blog post is structured in the following way. First, I will explain what makes a GPU fast. I will discuss CPUs vs GPUs, Tensor Cores, memory bandwidth, and the memory hierarchy of GPUs and how these relate to deep learning performance. A FULL HARDWARE GUIDE TO DEEP LEARNING Typical monitor layout when I do deep learning: Left: Papers, Google searches, gmail, stackoverflow; middle: Code; right: Output windows, R, folders, systems monitors, GPU monitors, to-do list, and other small applications. Some words on building a PC. Many people are scared to build computers. The hardware components are expensive and you do not want to do something wrong. HOW TO PARALLELIZE DEEP LEARNING ON GPUS PART 2/2: MODEL In my last blog post I explained what model and data parallelism is and analysed how to use data parallelism effectively in deep learning.In this blog post I will focus on model parallelism. To recap, model parallelism is, when you split the model among GPUs and use the same data for each model; so each GPU works on a part of the model rather than a part of the data. UNDERSTANDING CONVOLUTION IN DEEP LEARNING Convolution operation for one pixel of the resulting feature map: One image patch (red) of the original image (RAM) is multiplied by the kernel, and its sum is written to the feature map pixel (Buffer RAM).Gif by Glen Williamson who runs a website that features many technical gifs.. As you can see there is also a normalization procedure where the output value is normalized by the size of the HOW TO PARALLELIZE DEEP LEARNING ON GPUS PART 1/2: DATA Yeah, I noticed the same thing. The equation listed in the article is incorrect, but the one Baru listed isn’t quite right either. For example, the equation in the article divides by 40 twice, which is wrong, and the last 1024 is a 102, which is also wrong. HOW TO BUILD AND USE A MULTI GPU SYSTEM FOR DEEP LEARNING Hello Tim, Congrats for your excellent articles! I would like your advice on a setup for deep learning with images. I have 2 PCs currently with GTX 1060 and thought to replace those for 2x 2080 Ti in each PC and make a cluster with them (potentially adding more later), also will connect the GPUs with a fast direct link like you did if

it’s worth it.

HOW TO PICK YOUR GRAD SCHOOL The Career Perspective: Picking Based on Expected Success. The career perspective looks at the most critical factors for your academic success and success beyond that and picks the school that is best according to these factors. CREDIT ASSIGNMENT IN DEEP LEARNING ABOUT ME — TIM DETTMERS Research Interests Publications Awards & Honors Research Internships Service I am a PhD student at the University of Washington advised by Luke Zettlemoyer working on representation learning, and neuro-inspired and hardware optimized deep learning. Previously I interned at the UCL Machine Reading Group where I was advised by Sebastian Riedel working on information retrieval and link prediction

TIM DETTMERS

In my last blog post I explained what model and data parallelism is and analysed how to use data parallelism effectively in deep learning.In this blog post I will focus on model parallelism. about How to Parallelize Deep Learning on GPUs Part 2/2: Model

Parallelism

HARDWARE ARCHIVES

In this blog post I will delve into the brain and explain its basic information processing machinery and compare it to deep learning. I do this by moving step-by-step along with the brains electrochemical and biological information processing pipeline and relating it directly to the architecture of convolutional nets. HOW TO PICK YOUR GRAD SCHOOL The Career Perspective: Picking Based on Expected Success. The career perspective looks at the most critical factors for your academic success and success beyond that and picks the school that is best according to these factors.

SCIENCE ARCHIVES

I recently had a discussion about creativity with a colleague. We were discussing music and how creative many bands and groups are. At the end of our conversation, my friend told me, half-sarcastic-half-serious, how much more creative the people in the music industry are than him and that he just cannot find good ideas in his area of research even though he tried so hard for such a long

time.

MACHINE LEARNING PHD APPLICATIONS Hi Tim, Thanks for sharing. I was confident that I can get an PhD offer until I read your paper. Currently, I study Data Science at Fordham University with Merit-Based Scholarship and Graduate Assistantship, and work with a Professor on Natural language processing and deep learning research projects (may have publication this coming summer). THE BRAIN VS. DEEP LEARNING VS. SINGULARITY In this blog post I will delve into the brain and explain its basic information processing machinery and compare it to deep learning. I do this by moving step-by-step along with the brains electrochemical and biological information processing pipeline and relating it directly to the architecture of convolutional nets. DEEP LEARNING HARDWARE LIMBO Source: Wikipedia AMDs cards are incredible. The Vega Frontier Edition series clearly outmatches NVIDIA counterparts, and, from unbiased benchmarks of Volta vs Pascal, it seems that the Vega Frontier will be on-a-par or better compared to a Titan V if it is liquid cooled. Note that the Vega is based on an old architecture while the Titan V is

brand new.

TPUS VS GPUS FOR TRANSFORMERS (BERT) This is a great question, thank you! The number of tiles is determined by the amount of shared memory you have available. You have been 64kb to 96kb per streaming multiprocessor (SM), but of that memory you usually need to reserve half of it to do double buffered loads since the memory load latency is pretty high.

TIM DETTMERS

In this blog post I will delve into the brain and explain its basic information processing machinery and compare it to deep learning. I do this by moving step-by-step along with the brains electrochemical and biological information processing pipeline and relating it directly to the architecture of convolutional nets.

TIM DETTMERS

Deep Learning Hardware Limbo. 2017-12-21 by Tim Dettmers 91 Comments. With the release of the Titan V, we now entered deep learning hardware limbo. It is unclear if NVIDIA will be able to keep its spot as the main deep learning hardware vendor in 2018 and both AMD and Intel Nervana will have a shot at overtaking NVIDIA. ABOUT ME — TIM DETTMERS About Me. I am a PhD student at the University of Washington advised by Luke Zettlemoyer working on representation learning, and neuro-inspired and hardware optimized deep learning. Previously I interned at the UC L Machine Reading Group where I was advised by Sebastian Riedel working on information retrieval and link prediction

in knowledge

THE BEST GPUS FOR DEEP LEARNING IN 2020 The Quadro M6000 has 24 GB of memory and goes for $400 on eBay. The Tesla K80 has a 2-in-1 GPU with 2x 12 GB of memory for about $200. These cards are slow compared to more modern cards, but the extra memory can come in handy for specific projects where memory is

paramount.

A FULL HARDWARE GUIDE TO DEEP LEARNING Typical monitor layout when I do deep learning: Left: Papers, Google searches, gmail, stackoverflow; middle: Code; right: Output windows, R, folders, systems monitors, GPU monitors, to-do list, and other small applications. Some words on building a PC. Many people are scared to build computers. The hardware components are expensive and you do not want to do something wrong. HOW TO PARALLELIZE DEEP LEARNING ON GPUS PART 2/2: MODEL In my last blog post I explained what model and data parallelism is and analysed how to use data parallelism effectively in deep learning.In this blog post I will focus on model parallelism. To recap, model parallelism is, when you split the model among GPUs and use the same data for each model; so each GPU works on a part of the model rather than a part of the data. UNDERSTANDING CONVOLUTION IN DEEP LEARNING Convolution operation for one pixel of the resulting feature map: One image patch (red) of the original image (RAM) is multiplied by the kernel, and its sum is written to the feature map pixel (Buffer RAM).Gif by Glen Williamson who runs a website that features many technical gifs.. As you can see there is also a normalization procedure where the output value is normalized by the size of the HOW TO PARALLELIZE DEEP LEARNING ON GPUS PART 1/2: DATA If you have six GPUs in two nodes you need to pass the data to five other GPUs, three of which need to go through the network card (3x 0.75ms), while two can use PCIe 3.0 to pass the data to the other two GPUs (about three times as fast; 2x 0.25ms). However, the PCIe pass is independent of the network card pass, so the time needed is determined HOW TO BUILD AND USE A MULTI GPU SYSTEM FOR DEEP LEARNING Hello Tim, Congrats for your excellent articles! I would like your advice on a setup for deep learning with images. I have 2 PCs currently with GTX 1060 and thought to replace those for 2x 2080 Ti in each PC and make a cluster with them (potentially adding more later), also will connect the GPUs with a fast direct link like you did if

it’s worth it.

HOW TO PICK YOUR GRAD SCHOOL So it is crucial to look at the social environment when you pick a grad school. Usually, within a grad school, the social environments are the office, research group meetings, other group meetings, lunchtime, and social activities organized by the department or by grad organizations. Fewer research groups have social outings as a

group, but

THE BRAIN VS. DEEP LEARNING VS. SINGULARITY In this blog post I will delve into the brain and explain its basic information processing machinery and compare it to deep learning. I do this by moving step-by-step along with the brains electrochemical and biological information processing pipeline and relating it directly to the architecture of convolutional nets.

TIM DETTMERS

Deep Learning Hardware Limbo. 2017-12-21 by Tim Dettmers 91 Comments. With the release of the Titan V, we now entered deep learning hardware limbo. It is unclear if NVIDIA will be able to keep its spot as the main deep learning hardware vendor in 2018 and both AMD and Intel Nervana will have a shot at overtaking NVIDIA. ABOUT ME — TIM DETTMERS About Me. I am a PhD student at the University of Washington advised by Luke Zettlemoyer working on representation learning, and neuro-inspired and hardware optimized deep learning. Previously I interned at the UC L Machine Reading Group where I was advised by Sebastian Riedel working on information retrieval and link prediction

in knowledge

THE BEST GPUS FOR DEEP LEARNING IN 2020 The Quadro M6000 has 24 GB of memory and goes for $400 on eBay. The Tesla K80 has a 2-in-1 GPU with 2x 12 GB of memory for about $200. These cards are slow compared to more modern cards, but the extra memory can come in handy for specific projects where memory is

paramount.

A FULL HARDWARE GUIDE TO DEEP LEARNING Typical monitor layout when I do deep learning: Left: Papers, Google searches, gmail, stackoverflow; middle: Code; right: Output windows, R, folders, systems monitors, GPU monitors, to-do list, and other small applications. Some words on building a PC. Many people are scared to build computers. The hardware components are expensive and you do not want to do something wrong. HOW TO PARALLELIZE DEEP LEARNING ON GPUS PART 2/2: MODEL In my last blog post I explained what model and data parallelism is and analysed how to use data parallelism effectively in deep learning.In this blog post I will focus on model parallelism. To recap, model parallelism is, when you split the model among GPUs and use the same data for each model; so each GPU works on a part of the model rather than a part of the data. UNDERSTANDING CONVOLUTION IN DEEP LEARNING Convolution operation for one pixel of the resulting feature map: One image patch (red) of the original image (RAM) is multiplied by the kernel, and its sum is written to the feature map pixel (Buffer RAM).Gif by Glen Williamson who runs a website that features many technical gifs.. As you can see there is also a normalization procedure where the output value is normalized by the size of the HOW TO PARALLELIZE DEEP LEARNING ON GPUS PART 1/2: DATA If you have six GPUs in two nodes you need to pass the data to five other GPUs, three of which need to go through the network card (3x 0.75ms), while two can use PCIe 3.0 to pass the data to the other two GPUs (about three times as fast; 2x 0.25ms). However, the PCIe pass is independent of the network card pass, so the time needed is determined HOW TO BUILD AND USE A MULTI GPU SYSTEM FOR DEEP LEARNING Hello Tim, Congrats for your excellent articles! I would like your advice on a setup for deep learning with images. I have 2 PCs currently with GTX 1060 and thought to replace those for 2x 2080 Ti in each PC and make a cluster with them (potentially adding more later), also will connect the GPUs with a fast direct link like you did if

it’s worth it.

HOW TO PICK YOUR GRAD SCHOOL So it is crucial to look at the social environment when you pick a grad school. Usually, within a grad school, the social environments are the office, research group meetings, other group meetings, lunchtime, and social activities organized by the department or by grad organizations. Fewer research groups have social outings as a

group, but

THE BRAIN VS. DEEP LEARNING VS. SINGULARITY In this blog post I will delve into the brain and explain its basic information processing machinery and compare it to deep learning. I do this by moving step-by-step along with the brains electrochemical and biological information processing pipeline and relating it directly to the architecture of convolutional nets. DATA SCIENCE PORTFOLIO Abstract. I took part in the Crowdflower Kaggle competition in which it was the task to predict weather labels (e.g. sunny, cloudy, cold, hot) from twitter data. I used a deep neural network with rectified linear units and placed 2nd. The data was preprocessed with

regularized tf-idf.

TIM DETTMERS

At the beginning, deep learning is a lot of trial and error: You have to get a feel what parameters need to be adjusted, or what puzzle piece is missing in order to get a good result. A GPU helps you to fail quickly and learn important lessons so that you can keep improving. Soon my deep learning skills were sufficient to take the 2

nd place in

HARDWARE ARCHIVES

Deep Learning Hardware Limbo. 2017-12-21 by Tim Dettmers 91 Comments. With the release of the Titan V, we now entered deep learning hardware limbo. It is unclear if NVIDIA will be able to keep its spot as the main deep learning hardware vendor in 2018 and both AMD and Intel Nervana will have a shot at overtaking NVIDIA.

SCIENCE ARCHIVES

I recently had a discussion about creativity with a colleague. We were discussing music and how creative many bands and groups are. At the end of our conversation, my friend told me, half-sarcastic-half-serious, how much more creative the people in the music industry are than him and that he just cannot find good ideas in his area of research even though he tried so hard for such a long

time.

HOW TO BUILD AND USE A MULTI GPU SYSTEM FOR DEEP LEARNING Hello Tim, Congrats for your excellent articles! I would like your advice on a setup for deep learning with images. I have 2 PCs currently with GTX 1060 and thought to replace those for 2x 2080 Ti in each PC and make a cluster with them (potentially adding more later), also will connect the GPUs with a fast direct link like you did if

it’s worth it.

CREDIT ASSIGNMENT IN DEEP LEARNING 1 Ideas: 1/3 credit. 1000 Communication: 1/3000 credit each. 1000 Implementation: 1/3000 credit each. We see in this case the one person with the idea should receive the largest amount of credit. Similarly, if we weight the numbers differently, and if we assume contributions of individuals in groups are equal, then this credit assignment holds CREATIVITY IN ACADEMIA I recently had a discussion about creativity with a colleague. We were discussing music and how creative many bands and groups are. At the end of our conversation, my friend told me, half-sarcastic-half-serious, how much more creative the people in the music industry are than him and that he just cannot find good ideas in his area of research even though he tried so hard for such a long

time.

TPUS VS GPUS FOR TRANSFORMERS (BERT) TPUs are about 32% to 54% faster for training BERT-like models. One can expect to replicate BERT base on an 8 GPU machine within about 10 to 17 days. On a standard, affordable GPU machine with 4 GPUs one can expect to train BERT base for about 34 days using 16-bit or about 11 days using 8-bit. Reader Interactions. DEEP LEARNING HARDWARE LIMBO Deep Learning Hardware Limbo. 2017-12-21 by Tim Dettmers 91 Comments. With the release of the Titan V, we now entered deep learning hardware limbo. It is unclear if NVIDIA will be able to keep its spot as the main deep learning hardware vendor in 2018 and both AMD and Intel Nervana will have a shot at overtaking NVIDIA.

TIM DETTMERS

In this blog post I will delve into the brain and explain its basic information processing machinery and compare it to deep learning. I do this by moving step-by-step along with the brains electrochemical and biological information processing pipeline and relating it directly to the architecture of convolutional nets.

TIM DETTMERS

Deep Learning Hardware Limbo. 2017-12-21 by Tim Dettmers 91 Comments. With the release of the Titan V, we now entered deep learning hardware limbo. It is unclear if NVIDIA will be able to keep its spot as the main deep learning hardware vendor in 2018 and both AMD and Intel Nervana will have a shot at overtaking NVIDIA. ABOUT ME — TIM DETTMERS About Me. I am a PhD student at the University of Washington advised by Luke Zettlemoyer working on representation learning, and neuro-inspired and hardware optimized deep learning. Previously I interned at the UC L Machine Reading Group where I was advised by Sebastian Riedel working on information retrieval and link prediction

in knowledge

THE BEST GPUS FOR DEEP LEARNING IN 2020 The Quadro M6000 has 24 GB of memory and goes for $400 on eBay. The Tesla K80 has a 2-in-1 GPU with 2x 12 GB of memory for about $200. These cards are slow compared to more modern cards, but the extra memory can come in handy for specific projects where memory is

paramount.

A FULL HARDWARE GUIDE TO DEEP LEARNING Typical monitor layout when I do deep learning: Left: Papers, Google searches, gmail, stackoverflow; middle: Code; right: Output windows, R, folders, systems monitors, GPU monitors, to-do list, and other small applications. Some words on building a PC. Many people are scared to build computers. The hardware components are expensive and you do not want to do something wrong. HOW TO PARALLELIZE DEEP LEARNING ON GPUS PART 2/2: MODEL In my last blog post I explained what model and data parallelism is and analysed how to use data parallelism effectively in deep learning.In this blog post I will focus on model parallelism. To recap, model parallelism is, when you split the model among GPUs and use the same data for each model; so each GPU works on a part of the model rather than a part of the data. UNDERSTANDING CONVOLUTION IN DEEP LEARNING Convolution operation for one pixel of the resulting feature map: One image patch (red) of the original image (RAM) is multiplied by the kernel, and its sum is written to the feature map pixel (Buffer RAM).Gif by Glen Williamson who runs a website that features many technical gifs.. As you can see there is also a normalization procedure where the output value is normalized by the size of the HOW TO PARALLELIZE DEEP LEARNING ON GPUS PART 1/2: DATA If you have six GPUs in two nodes you need to pass the data to five other GPUs, three of which need to go through the network card (3x 0.75ms), while two can use PCIe 3.0 to pass the data to the other two GPUs (about three times as fast; 2x 0.25ms). However, the PCIe pass is independent of the network card pass, so the time needed is determined HOW TO BUILD AND USE A MULTI GPU SYSTEM FOR DEEP LEARNING Hello Tim, Congrats for your excellent articles! I would like your advice on a setup for deep learning with images. I have 2 PCs currently with GTX 1060 and thought to replace those for 2x 2080 Ti in each PC and make a cluster with them (potentially adding more later), also will connect the GPUs with a fast direct link like you did if

it’s worth it.

HOW TO PICK YOUR GRAD SCHOOL So it is crucial to look at the social environment when you pick a grad school. Usually, within a grad school, the social environments are the office, research group meetings, other group meetings, lunchtime, and social activities organized by the department or by grad organizations. Fewer research groups have social outings as a

group, but

THE BRAIN VS. DEEP LEARNING VS. SINGULARITY In this blog post I will delve into the brain and explain its basic information processing machinery and compare it to deep learning. I do this by moving step-by-step along with the brains electrochemical and biological information processing pipeline and relating it directly to the architecture of convolutional nets.

TIM DETTMERS

Deep Learning Hardware Limbo. 2017-12-21 by Tim Dettmers 91 Comments. With the release of the Titan V, we now entered deep learning hardware limbo. It is unclear if NVIDIA will be able to keep its spot as the main deep learning hardware vendor in 2018 and both AMD and Intel Nervana will have a shot at overtaking NVIDIA. ABOUT ME — TIM DETTMERS About Me. I am a PhD student at the University of Washington advised by Luke Zettlemoyer working on representation learning, and neuro-inspired and hardware optimized deep learning. Previously I interned at the UC L Machine Reading Group where I was advised by Sebastian Riedel working on information retrieval and link prediction

in knowledge

THE BEST GPUS FOR DEEP LEARNING IN 2020 The Quadro M6000 has 24 GB of memory and goes for $400 on eBay. The Tesla K80 has a 2-in-1 GPU with 2x 12 GB of memory for about $200. These cards are slow compared to more modern cards, but the extra memory can come in handy for specific projects where memory is

paramount.

A FULL HARDWARE GUIDE TO DEEP LEARNING Typical monitor layout when I do deep learning: Left: Papers, Google searches, gmail, stackoverflow; middle: Code; right: Output windows, R, folders, systems monitors, GPU monitors, to-do list, and other small applications. Some words on building a PC. Many people are scared to build computers. The hardware components are expensive and you do not want to do something wrong. HOW TO PARALLELIZE DEEP LEARNING ON GPUS PART 2/2: MODEL In my last blog post I explained what model and data parallelism is and analysed how to use data parallelism effectively in deep learning.In this blog post I will focus on model parallelism. To recap, model parallelism is, when you split the model among GPUs and use the same data for each model; so each GPU works on a part of the model rather than a part of the data. UNDERSTANDING CONVOLUTION IN DEEP LEARNING Convolution operation for one pixel of the resulting feature map: One image patch (red) of the original image (RAM) is multiplied by the kernel, and its sum is written to the feature map pixel (Buffer RAM).Gif by Glen Williamson who runs a website that features many technical gifs.. As you can see there is also a normalization procedure where the output value is normalized by the size of the HOW TO PARALLELIZE DEEP LEARNING ON GPUS PART 1/2: DATA If you have six GPUs in two nodes you need to pass the data to five other GPUs, three of which need to go through the network card (3x 0.75ms), while two can use PCIe 3.0 to pass the data to the other two GPUs (about three times as fast; 2x 0.25ms). However, the PCIe pass is independent of the network card pass, so the time needed is determined HOW TO BUILD AND USE A MULTI GPU SYSTEM FOR DEEP LEARNING Hello Tim, Congrats for your excellent articles! I would like your advice on a setup for deep learning with images. I have 2 PCs currently with GTX 1060 and thought to replace those for 2x 2080 Ti in each PC and make a cluster with them (potentially adding more later), also will connect the GPUs with a fast direct link like you did if

it’s worth it.

HOW TO PICK YOUR GRAD SCHOOL So it is crucial to look at the social environment when you pick a grad school. Usually, within a grad school, the social environments are the office, research group meetings, other group meetings, lunchtime, and social activities organized by the department or by grad organizations. Fewer research groups have social outings as a

group, but

THE BRAIN VS. DEEP LEARNING VS. SINGULARITY In this blog post I will delve into the brain and explain its basic information processing machinery and compare it to deep learning. I do this by moving step-by-step along with the brains electrochemical and biological information processing pipeline and relating it directly to the architecture of convolutional nets. DATA SCIENCE PORTFOLIO Abstract. I took part in the Crowdflower Kaggle competition in which it was the task to predict weather labels (e.g. sunny, cloudy, cold, hot) from twitter data. I used a deep neural network with rectified linear units and placed 2nd. The data was preprocessed with

regularized tf-idf.

TIM DETTMERS

At the beginning, deep learning is a lot of trial and error: You have to get a feel what parameters need to be adjusted, or what puzzle piece is missing in order to get a good result. A GPU helps you to fail quickly and learn important lessons so that you can keep improving. Soon my deep learning skills were sufficient to take the 2

nd place in

HARDWARE ARCHIVES

Deep Learning Hardware Limbo. 2017-12-21 by Tim Dettmers 91 Comments. With the release of the Titan V, we now entered deep learning hardware limbo. It is unclear if NVIDIA will be able to keep its spot as the main deep learning hardware vendor in 2018 and both AMD and Intel Nervana will have a shot at overtaking NVIDIA.

SCIENCE ARCHIVES

I recently had a discussion about creativity with a colleague. We were discussing music and how creative many bands and groups are. At the end of our conversation, my friend told me, half-sarcastic-half-serious, how much more creative the people in the music industry are than him and that he just cannot find good ideas in his area of research even though he tried so hard for such a long

time.

HOW TO BUILD AND USE A MULTI GPU SYSTEM FOR DEEP LEARNING Hello Tim, Congrats for your excellent articles! I would like your advice on a setup for deep learning with images. I have 2 PCs currently with GTX 1060 and thought to replace those for 2x 2080 Ti in each PC and make a cluster with them (potentially adding more later), also will connect the GPUs with a fast direct link like you did if

it’s worth it.

CREDIT ASSIGNMENT IN DEEP LEARNING 1 Ideas: 1/3 credit. 1000 Communication: 1/3000 credit each. 1000 Implementation: 1/3000 credit each. We see in this case the one person with the idea should receive the largest amount of credit. Similarly, if we weight the numbers differently, and if we assume contributions of individuals in groups are equal, then this credit assignment holds CREATIVITY IN ACADEMIA I recently had a discussion about creativity with a colleague. We were discussing music and how creative many bands and groups are. At the end of our conversation, my friend told me, half-sarcastic-half-serious, how much more creative the people in the music industry are than him and that he just cannot find good ideas in his area of research even though he tried so hard for such a long

time.

TPUS VS GPUS FOR TRANSFORMERS (BERT) TPUs are about 32% to 54% faster for training BERT-like models. One can expect to replicate BERT base on an 8 GPU machine within about 10 to 17 days. On a standard, affordable GPU machine with 4 GPUs one can expect to train BERT base for about 34 days using 16-bit or about 11 days using 8-bit. Reader Interactions. DEEP LEARNING HARDWARE LIMBO Deep Learning Hardware Limbo. 2017-12-21 by Tim Dettmers 91 Comments. With the release of the Titan V, we now entered deep learning hardware limbo. It is unclear if NVIDIA will be able to keep its spot as the main deep learning hardware vendor in 2018 and both AMD and Intel Nervana will have a shot at overtaking NVIDIA.

TIM DETTMERS

In this blog post I will delve into the brain and explain its basic information processing machinery and compare it to deep learning. I do this by moving step-by-step along with the brains electrochemical and biological information processing pipeline and relating it directly to the architecture of convolutional nets.

TIM DETTMERS

Deep Learning Hardware Limbo. 2017-12-21 by Tim Dettmers 91 Comments. With the release of the Titan V, we now entered deep learning hardware limbo. It is unclear if NVIDIA will be able to keep its spot as the main deep learning hardware vendor in 2018 and both AMD and Intel Nervana will have a shot at overtaking NVIDIA. ABOUT ME — TIM DETTMERS About Me. I am a PhD student at the University of Washington advised by Luke Zettlemoyer working on representation learning, and neuro-inspired and hardware optimized deep learning. Previously I interned at the UC L Machine Reading Group where I was advised by Sebastian Riedel working on information retrieval and link prediction

in knowledge

THE BEST GPUS FOR DEEP LEARNING IN 2020 The Quadro M6000 has 24 GB of memory and goes for $400 on eBay. The Tesla K80 has a 2-in-1 GPU with 2x 12 GB of memory for about $200. These cards are slow compared to more modern cards, but the extra memory can come in handy for specific projects where memory is

paramount.

A FULL HARDWARE GUIDE TO DEEP LEARNING Typical monitor layout when I do deep learning: Left: Papers, Google searches, gmail, stackoverflow; middle: Code; right: Output windows, R, folders, systems monitors, GPU monitors, to-do list, and other small applications. Some words on building a PC. Many people are scared to build computers. The hardware components are expensive and you do not want to do something wrong. HOW TO PARALLELIZE DEEP LEARNING ON GPUS PART 2/2: MODELDEEP LEARNING PARALLELBEYOND DATA AND MODEL PARALLELISMDATA PARALLEL VS MODEL PARALLELDATA PARALLELISM MODEL PARALLELISMMODEL PARALLEL TRAINING In my last blog post I explained what model and data parallelism is and analysed how to use data parallelism effectively in deep learning.In this blog post I will focus on model parallelism. To recap, model parallelism is, when you split the model among GPUs and use the same data for each model; so each GPU works on a part of the model rather than a part of the data. UNDERSTANDING CONVOLUTION IN DEEP LEARNING Convolution operation for one pixel of the resulting feature map: One image patch (red) of the original image (RAM) is multiplied by the kernel, and its sum is written to the feature map pixel (Buffer RAM).Gif by Glen Williamson who runs a website that features many technical gifs.. As you can see there is also a normalization procedure where the output value is normalized by the size of the HOW TO PARALLELIZE DEEP LEARNING ON GPUS PART 1/2: DATA If you have six GPUs in two nodes you need to pass the data to five other GPUs, three of which need to go through the network card (3x 0.75ms), while two can use PCIe 3.0 to pass the data to the other two GPUs (about three times as fast; 2x 0.25ms). However, the PCIe pass is independent of the network card pass, so the time needed is determined HOW TO BUILD AND USE A MULTI GPU SYSTEM FOR DEEP LEARNINGHOW TO CHECK GPU CONNECTIONHOW TO USE MULTIPLE GPUSGPU FOR SERVERSMULTI GPU CONFIGURATIONMULTI GPU PCMULTI GPU PYTORCH Hello Tim, Congrats for your excellent articles! I would like your advice on a setup for deep learning with images. I have 2 PCs currently with GTX 1060 and thought to replace those for 2x 2080 Ti in each PC and make a cluster with them (potentially adding more later), also will connect the GPUs with a fast direct link like you did if

it’s worth it.

HOW TO PICK YOUR GRAD SCHOOL So it is crucial to look at the social environment when you pick a grad school. Usually, within a grad school, the social environments are the office, research group meetings, other group meetings, lunchtime, and social activities organized by the department or by grad organizations. Fewer research groups have social outings as a

group, but

THE BRAIN VS. DEEP LEARNING VS. SINGULARITY In this blog post I will delve into the brain and explain its basic information processing machinery and compare it to deep learning. I do this by moving step-by-step along with the brains electrochemical and biological information processing pipeline and relating it directly to the architecture of convolutional nets.

TIM DETTMERS

Deep Learning Hardware Limbo. 2017-12-21 by Tim Dettmers 91 Comments. With the release of the Titan V, we now entered deep learning hardware limbo. It is unclear if NVIDIA will be able to keep its spot as the main deep learning hardware vendor in 2018 and both AMD and Intel Nervana will have a shot at overtaking NVIDIA. ABOUT ME — TIM DETTMERS About Me. I am a PhD student at the University of Washington advised by Luke Zettlemoyer working on representation learning, and neuro-inspired and hardware optimized deep learning. Previously I interned at the UC L Machine Reading Group where I was advised by Sebastian Riedel working on information retrieval and link prediction

in knowledge

THE BEST GPUS FOR DEEP LEARNING IN 2020 The Quadro M6000 has 24 GB of memory and goes for $400 on eBay. The Tesla K80 has a 2-in-1 GPU with 2x 12 GB of memory for about $200. These cards are slow compared to more modern cards, but the extra memory can come in handy for specific projects where memory is

paramount.

A FULL HARDWARE GUIDE TO DEEP LEARNING Typical monitor layout when I do deep learning: Left: Papers, Google searches, gmail, stackoverflow; middle: Code; right: Output windows, R, folders, systems monitors, GPU monitors, to-do list, and other small applications. Some words on building a PC. Many people are scared to build computers. The hardware components are expensive and you do not want to do something wrong. HOW TO PARALLELIZE DEEP LEARNING ON GPUS PART 2/2: MODELDEEP LEARNING PARALLELBEYOND DATA AND MODEL PARALLELISMDATA PARALLEL VS MODEL PARALLELDATA PARALLELISM MODEL PARALLELISMMODEL PARALLEL TRAINING In my last blog post I explained what model and data parallelism is and analysed how to use data parallelism effectively in deep learning.In this blog post I will focus on model parallelism. To recap, model parallelism is, when you split the model among GPUs and use the same data for each model; so each GPU works on a part of the model rather than a part of the data. UNDERSTANDING CONVOLUTION IN DEEP LEARNING Convolution operation for one pixel of the resulting feature map: One image patch (red) of the original image (RAM) is multiplied by the kernel, and its sum is written to the feature map pixel (Buffer RAM).Gif by Glen Williamson who runs a website that features many technical gifs.. As you can see there is also a normalization procedure where the output value is normalized by the size of the HOW TO PARALLELIZE DEEP LEARNING ON GPUS PART 1/2: DATA If you have six GPUs in two nodes you need to pass the data to five other GPUs, three of which need to go through the network card (3x 0.75ms), while two can use PCIe 3.0 to pass the data to the other two GPUs (about three times as fast; 2x 0.25ms). However, the PCIe pass is independent of the network card pass, so the time needed is determined HOW TO BUILD AND USE A MULTI GPU SYSTEM FOR DEEP LEARNINGHOW TO CHECK GPU CONNECTIONHOW TO USE MULTIPLE GPUSGPU FOR SERVERSMULTI GPU CONFIGURATIONMULTI GPU PCMULTI GPU PYTORCH Hello Tim, Congrats for your excellent articles! I would like your advice on a setup for deep learning with images. I have 2 PCs currently with GTX 1060 and thought to replace those for 2x 2080 Ti in each PC and make a cluster with them (potentially adding more later), also will connect the GPUs with a fast direct link like you did if

it’s worth it.

HOW TO PICK YOUR GRAD SCHOOL So it is crucial to look at the social environment when you pick a grad school. Usually, within a grad school, the social environments are the office, research group meetings, other group meetings, lunchtime, and social activities organized by the department or by grad organizations. Fewer research groups have social outings as a

group, but

THE BRAIN VS. DEEP LEARNING VS. SINGULARITY In this blog post I will delve into the brain and explain its basic information processing machinery and compare it to deep learning. I do this by moving step-by-step along with the brains electrochemical and biological information processing pipeline and relating it directly to the architecture of convolutional nets. DATA SCIENCE PORTFOLIO Abstract. I took part in the Crowdflower Kaggle competition in which it was the task to predict weather labels (e.g. sunny, cloudy, cold, hot) from twitter data. I used a deep neural network with rectified linear units and placed 2nd. The data was preprocessed with

regularized tf-idf.

TIM DETTMERS

At the beginning, deep learning is a lot of trial and error: You have to get a feel what parameters need to be adjusted, or what puzzle piece is missing in order to get a good result. A GPU helps you to fail quickly and learn important lessons so that you can keep improving. Soon my deep learning skills were sufficient to take the 2

nd place in

HARDWARE ARCHIVES

Deep Learning Hardware Limbo. 2017-12-21 by Tim Dettmers 91 Comments. With the release of the Titan V, we now entered deep learning hardware limbo. It is unclear if NVIDIA will be able to keep its spot as the main deep learning hardware vendor in 2018 and both AMD and Intel Nervana will have a shot at overtaking NVIDIA.

SCIENCE ARCHIVES

I recently had a discussion about creativity with a colleague. We were discussing music and how creative many bands and groups are. At the end of our conversation, my friend told me, half-sarcastic-half-serious, how much more creative the people in the music industry are than him and that he just cannot find good ideas in his area of research even though he tried so hard for such a long

time.

HOW TO BUILD AND USE A MULTI GPU SYSTEM FOR DEEP LEARNING Hello Tim, Congrats for your excellent articles! I would like your advice on a setup for deep learning with images. I have 2 PCs currently with GTX 1060 and thought to replace those for 2x 2080 Ti in each PC and make a cluster with them (potentially adding more later), also will connect the GPUs with a fast direct link like you did if

it’s worth it.

CREDIT ASSIGNMENT IN DEEP LEARNING 1 Ideas: 1/3 credit. 1000 Communication: 1/3000 credit each. 1000 Implementation: 1/3000 credit each. We see in this case the one person with the idea should receive the largest amount of credit. Similarly, if we weight the numbers differently, and if we assume contributions of individuals in groups are equal, then this credit assignment holds CREATIVITY IN ACADEMIA I recently had a discussion about creativity with a colleague. We were discussing music and how creative many bands and groups are. At the end of our conversation, my friend told me, half-sarcastic-half-serious, how much more creative the people in the music industry are than him and that he just cannot find good ideas in his area of research even though he tried so hard for such a long

time.

TPUS VS GPUS FOR TRANSFORMERS (BERT) TPUs are about 32% to 54% faster for training BERT-like models. One can expect to replicate BERT base on an 8 GPU machine within about 10 to 17 days. On a standard, affordable GPU machine with 4 GPUs one can expect to train BERT base for about 34 days using 16-bit or about 11 days using 8-bit. Reader Interactions. DEEP LEARNING HARDWARE LIMBO Deep Learning Hardware Limbo. 2017-12-21 by Tim Dettmers 91 Comments. With the release of the Titan V, we now entered deep learning hardware limbo. It is unclear if NVIDIA will be able to keep its spot as the main deep learning hardware vendor in 2018 and both AMD and Intel Nervana will have a shot at overtaking NVIDIA.

TIM DETTMERS

In this blog post I will delve into the brain and explain its basic information processing machinery and compare it to deep learning. I do this by moving step-by-step along with the brains electrochemical and biological information processing pipeline and relating it directly to the architecture of convolutional nets.

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WHICH GPU(S) TO GET FOR DEEP LEARNING: MY EXPERIENCE AND ADVICE FOR USING GPUS IN DEEP LEARNING 2020-09-07 by Tim Dettmers

1,536 Comments

Deep learning is a field with intense computational requirements, and your choice of GPU will fundamentally determine your deep learning experience. But what features are important if you want to buy a new GPU? GPU RAM, cores, tensor cores? How to make a cost-efficient choice? This blog post will delve into these questions, tackle common misconceptions, give you an intuitive understanding of how to think about GPUs, and will lend you advice, which will help you to make a choice that is right for you. about Which GPU(s) to Get for Deep Learning: My Experience and Advice for Using GPUs in Deep Learning Filed Under: Deep Learning

, Hardware

Tagged With: AMD

, CPU

, High Performance Computing

, Matrix

Multiplication ,

Parallel Computing , PCIe Lanes , Sparse Training HOW TO PICK YOUR GRAD SCHOOL 2020-03-10 by Tim Dettmers

11 Comments

If you are reading this, then you probably finished the long and arduous journey to grad school. You emerged victoriously, and this success is well-deserved. But which school should you pick? How to make a right choice if all schools look great in their own way? This blog post is centered around these questions. It is most useful if you are a computer science student aiming to study machine learning and, in particular, natural language processing in the US, but most of the information here is equally valid for any field of research and any

country.

The choice of grad school that is right for you can be tricky and confusing. We live in a time of hyper-competitiveness, where even undergrads need to optimize for metrics like paper count to make it to the next level — grad school. This heavily career-centered perspective was probably advantageous to get you into grad school, and it remains crucial to get you to the level after that: a great job in industry or academia. So picking the school which is best for your career can feel like an obvious choice. However, a PhD is a very long journey, and choosing your grad school based on this perspective alone might make you more vulnerable to burn-out, disillusionment, and general dissatisfaction. In this blog post, I will discuss this career-centered perspective in detail, but I also provide you with three other views that hopefully help you make a balanced choice that not only leads to academic success but long-term satisfaction and a full and rich life. Balancing your decision based on all four perspectives probably leads to a better choice than looking at one angle alone. Before I go into the details, let me briefly introduce these four perspectives: The Career Perspective, the Identity Perspective, the Stability Perspective, and the Variability Perspective. about How to Pick Your Grad School Filed Under: Academia ,

PhD Life Tagged

With: Advisors , Grad school

, PhD

ON CREATIVITY IN ACADEMIA 2019-09-03 by Tim Dettmers

5 Comments

I recently had a discussion about creativity with a colleague. We were discussing music and how creative many bands and groups are. At the end of our conversation, my friend told me, half-sarcastic-half-serious, how much more creative the people in the music industry are than him and that he just cannot find good ideas in his area of research even though he tried so hard for such a long time. I was a bit surprised because I thought of him as someone very creative. However, it is not uncommon to hear scientists lament about their lack of creativity compared to academic superstars. I think about creativity in academia is a bit distorted and a straight view can help to feel less bad about one’s own creativity. about On Creativity in Academia Filed Under: Academia ,

PhD Life ,

Science Tagged With: PhD SPARSE NETWORKS FROM SCRATCH: FASTER TRAINING WITHOUT LOSING

PERFORMANCE

2019-07-11 by Tim Dettmers

38 Comments

This blog post is about my work, Sparse Networks from Scratch: Faster Training without Losing Performance , with Luke Zettlemoyer

on fast training

of neural networks which we keep sparse throughout training. We show that by developing an algorithm, sparse momentum, we can initialize a neural network with sparse random weights and train it to dense performance levels — all while doing just a single training run. Furthermore, If we use optimized sparse convolution algorithms, we can speed up training between 3.5x for VGG to 12x for Wide Residual Networks. This stands in stark contrast to computationally expensive methods which require repetitive prune-and-retrain cycles as used by the Lottery Ticket Hypothesis (Frankle and Carbin, 2019) and other work. Thus we show that training sparse networks to dense performance levels does not require “winning the initialization lottery” but can be done reliably from random weights if combined with a method that moves weights around the network in a smart way. We call the paradigm that maintains sparsity throughout training while maintaining dense performance levels _sparse learning_. While this work shows that sparse learning is possible, future work holds the promise to train larger and deep networks on more data while requiring the same or less computational resources as current dense networks. about Sparse Networks from Scratch: Faster Training without Losing Performance Filed Under: Deep Learning

Tagged With: Sparse

Training

A FULL HARDWARE GUIDE TO DEEP LEARNING 2018-12-16 by Tim Dettmers

909 Comments

Deep Learning is very computationally intensive, so you will need a fast CPU with many cores, right? Or is it maybe wasteful to buy a fast CPU? One of the worst things you can do when building a deep learning system is to waste money on hardware that is unnecessary. Here I will guide you step by step through the hardware you will need for a cheap high-performance system. about A Full Hardware Guide to Deep Learning Filed Under: Hardware Tagged With: AMD , CPU

, GPU

, Intel

, PCIe Lanes

MACHINE LEARNING PHD APPLICATIONS — EVERYTHING YOU NEED TO KNOW 2018-11-26 by Tim Dettmers

120 Comments

I studied in depth how to be successful in my PhD applications and it paid off: I got admitted to Stanford, University of Washington, UCL, CMU, and NYU. This blog post is a mish-mash of how to proceed in your PhD applications from A to Z. It discusses what is important and what is not. It discusses application materials like the statement of purpose (SoP) and how to make sense of these application materials. about Machine Learning PhD Applications — Everything

You Need to Know

Filed Under: Academia ,

PhD Life Tagged

With: Advisors , Grad school

, PhD

TPUS VS GPUS FOR TRANSFORMERS (BERT) 2018-10-17 by Tim Dettmers

26 Comments

On the computational side, there have been confusions about how TPUs and GPUs relate to BERT . BERT base was trained with 4 TPU pods (16 TPU chips) in 4 days and BERT large with 16 TPUs (64 TPU chips) in 4 days. Does this mean only Google can train a BERT model? Does this mean that GPUs are dead? There are two fundamental things to understand here: (1) A TPU is a matrix multiplication engine — it does matrix multiplication and matrix operations, but not much else. It is fast at computing matrix multiplication, but one has to understand that (2) the slowest thing in matrix multiplication is to get the elements from the main memory and load it into the processing unit. In other words, the most expensive part in matrix multiplication is memory loads. Note the computational load for BERT should be about 90% for matrix multiplication. From these facts, we can do a small technical analysis

on this topic.

about TPUs vs GPUs for Transformers (BERT) Filed Under: Hardware Tagged With: Accelerators , GPU , Matrix Multiplication DEEP LEARNING HARDWARE LIMBO 2017-12-21 by Tim Dettmers

91 Comments

With the release of the Titan V, we now entered deep learning hardware limbo. It is unclear if NVIDIA will be able to keep its spot as the main deep learning hardware vendor in 2018 and both AMD and Intel Nervana will have a shot at overtaking NVIDIA. So for consumers, I cannot recommend buying any hardware right now. The most prudent choice is to wait until the hardware limbo passes. This might take as little as 3 months or as long as 9 months. So why did we enter deep learning hardware limbo just now? about Deep Learning Hardware Limbo Filed Under: Hardware Tagged With: Accelerators ,

AMD , GPU

, Intel

CREDIT ASSIGNMENT IN DEEP LEARNING 2017-09-16 by Tim Dettmers

15 Comments

This morning I got an email about my blog post discussing the history

of deep learning

which

rattled me back into a time of my academic career which I rather not think about. It was a low point which nearly ended my Master studies at the University of Lugano, and it made me feel so bad about blogging that I took two long years to recover. So what has happened? about Credit Assignment in Deep Learning Filed Under: Academia , Science Tagged With: PhD THE BRAIN VS DEEP LEARNING PART I: COMPUTATIONAL COMPLEXITY — OR WHY THE SINGULARITY IS NOWHERE NEAR 2015-07-27 by Tim Dettmers

181 Comments

In this blog post I will delve into the brain and explain its basic information processing machinery and compare it to deep learning. I do this by moving step-by-step along with the brains electrochemical and biological information processing pipeline and relating it directly to the architecture of convolutional nets. Thereby we will see that a neuron and a convolutional net are very similar information processing machines. While performing this comparison, I will also discuss the computational complexity of these processes and thus derive an estimate for the brains overall computational power. I will use these estimates, along with knowledge from high performance computing, to show that it is unlikely that there will be a technological singularity in this century. about The Brain vs Deep Learning Part I: Computational Complexity — Or Why the Singularity Is Nowhere Near Filed Under: Deep Learning

, Hardware

, Neuroscience

Tagged With:

Convolution , GPU

, High Performance Computing

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