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Based on old Pascal architecture, GTX 1080 Ti is surpassed by RTX 2080 Ti (you can refer to some previous post for comparison details 1. https://lambdalabs.com/blog/2080-ti-deep-learning-benchmarks/ 2.https://gpu.userbenchmark.com/Compare/Nvidia-RTX-2080-Ti-vs-Nvidia-GTX-1080-Ti/4027). We are very appreciated that NVIDIA supported us with a Titan RTX GPU without any constraints on writing. ... MXNET, CNTK, DeepLearning4J, or Chainer deserve to be discussed. The speed of mixed precision is nearly two times than the single precision except for PyTorch. TensorFlow and PyTorch have minor difference results with mixed precision a bit higher on the proposed CPU. When performing the VGG-16 tasks, all three frameworks have fully utilized the GPU, but TensorFlow achieves the fastest sample training speed while MXNet is the slowest. Keras provides plenty of nice examples in ~/keras/examples. Initially released in 2015 winter by Google Brain team, TensorFlow is Google Brain’s second-generation machine learning framework. In parallel, they’ve moved the projet to the Apache Incubator and are currently putting the finishing touches to MXNet 0.11. 2018 Fortune Global 500 Public Company AI Adaptivity Report is out!Purchase a Kindle-formatted report on Amazon. MXNET It does not appear to be as widely used at TensorFlow, but this framework is considered to have the potential to have exponential growth in the near future. It’s easy and free to post your thinking on any topic. So fare comparison would be mxnet vs Keras, not mxnet vs Theano. Until this report is written, MLPerf has not included the latest NVIDIA GPUs such as Titan RTX. It seems to me every Deep Learning practitioner ought to check MXNet out, especially now that it’s properly integrated with Keras: changing a line of configuration is all it takes :). Keras vs MXNet: What are the differences? Definitely an advantage when you’re experimenting. Followed by all set-up steps and experimental settings, we present the details of the result of CV tasks as follows: Settings:Experiment: ResNet-50 InferenceFramework: NGC TensorFlow 18.12/NGC PyTorch 19.01/NGC MXNet 19.01Batch size: 64 (inference), Settings:Experiment: ResNet-50 TrainingFramework: NGC TensorFlow 18.12/NGC PyTorch 19.01/NGC MXNet 19.01Batch size: 128 (training), Settings:Experiment: VGG16 InferenceFramework: NGC TensorFlow 18.12/NGC PyTorch 19.01/NGC MXNet 19.01Batch size: 64 (inference), Settings:Experiment: VGG-16 TrainingFramework: NGC TensorFlow 18.12/NGC PyTorch 19.01/NGC MXNet 19.01Batch size: 128 (training). There is a huge distributed performance advantages vs TensorFlow. For GNMT task, PyTorch has the highest GPU utilization, but in the meantime, its inference speed outperforms the others. What is MXNet? A few months, we took an early look at running Keras with Apache MXNet as its backend. We know you don’t want to miss any stories. It is very likely for our readers to just add RTX to their current home workstation that they use for works, study, as well as gaming. Settings:Experiment: Faster-RCNN InferenceFramework: NGC TensorFlow 18.12/NGC PyTorch 19.01/NGC MXNet 19.01Batch size: 1 (inference), Settings:Experiment: Faster-RCNN TrainingFramework: NGC TensorFlow 18.12/NGC PyTorch 19.01/NGC MXNet 19.01Batch size: 1 (training). Three Frameworks take full GPU utilization on VGG-16, PyTorch version FRCNN takes the least GPU utilization due to its code optimization. For Word2Vec task, TensorFlow outperforms the others, but it has a higher GPU utilization. In this section, we ran all CV tasks with single precision. PyTorch. As for evaluation metrics, we present GPU utilization percentage, Memory utilization percentage, GPU Memory used, CPU utilization percentage, Memory utilization percentage, CPU Memory used and training/inference speed. Whereas in mxnet you must have to use mx.nd.contrib.foreach, .while_loop, .cond in order to convert your code from dynamic to static. This suggests that training with mixed precision have the potential to become a new meta for deep learning tasks. TensorFlow is fairly easy to learn but there is a bit less magic than keras. Batch size of 1 is only set for the Faster-RCNN experiment due to the specification of this algorithm — it could be increased to 4 with some modification, but we decided to stay with the original implementation. Lambda, the AI infrastructure company, has released a blog on 2080 Ti TensorFlow GPU benchmarks (https://lambdalabs.com/blog/best-gpu-tensorflow-2080-ti-vs-v100-vs-titan-v-vs-1080-ti-benchmark/). We write down as much detail as possible to ensure our evaluation is reproducible. The experiments contains various types of Computer Vision and Natural Language Processing tasks. https://aws.amazon.com/evangelists/julien-simon/, https://aws.amazon.com/evangelists/julien-simon/, Training with Keras-MXNet on Amazon SageMaker, Four Deep Learning Papers to Read in May 2021, Everything you need to know about AutoML and Neural Architecture Search, Why you shouldn’t join the AI industry in 2021, Stochastic connections in Neural Networks using Tensorflow, Nengo: How to Simulate Network Signals with Python. Similar to the performance on GNMT task, the training speed on NCF task is accelerated with mixed precision. Figure 4.4.10: Memory utilization at training. Though we only have 16GB memory, it is still not the bottleneck for Titan RTX when performing training and inference of ResNet-50. A flexible and efficient library for deep learning. PyTorch has the highest GPU utilization in GNMT training while lowest in NCF training. As for explicit experiments result, we found TensorFlow and PyTorch may perform better on data-intensive computer vision tasks, and MxNet performs well on general small dataset training. Fortunately, no neighbour was injured in the process. Figure 4.4.10: Memory utilization at training. On average TensorFlow takes the most GPU utilization across all inference tasks. Finally, thanks a lot for the support from Synced Global Office and our friend in UofT Jack Luo. Each experiment follows its official settings from its original repository. We compared the performance and efficiency of the three frameworks when performing training and inference with mixed precision and single precision. Follow us on Twitter @Synced_Global for daily AI news! The high computation efficiency of GPUs drives the developers to include GPU support when designing distribution machine learning frameworks. TensorFlow vs. PyTorch. Results on Mixed Precision and Single Precision, https://lambdalabs.com/blog/best-gpu-tensorflow-2080-ti-vs-v100-vs-titan-v-vs-1080-ti-benchmark/, https://github.com/NVIDIA/DeepLearningExamples, https://lambdalabs.com/blog/2080-ti-deep-learning-benchmarks/, https://gpu.userbenchmark.com/Compare/Nvidia-RTX-2080-Ti-vs-Nvidia-GTX-1080-Ti/4027, http://developer.download.nvidia.com/compute/cuda/docs/CUDA_Architecture_Overview.pdf, https://github.com/dmlc/web-data/raw/master/mxnet/paper/mxnet-learningsys.pdf, https://www.tensorflow.org/guide/performance/benchmarks, https://github.com/tensorflow/models/tree/master/official/resnet, https://github.com/tensorflow/models/tree/master/research/slim, https://github.com/tensorflow/benchmarks/tree/master/scripts/tf_cnn_benchmarks, https://github.com/kuangliu/pytorch-cifar, https://github.com/pytorch/examples/tree/master/imagenet, https://github.com/ryujaehun/pytorch-gpu-benchmark/blob/master/benchmark_models.py, https://gist.github.com/tdeboissiere/12a5e814e9eff3d2cb2c29ff100a09f0, https://github.com/ruotianluo/pytorch-faster-rcnn, https://github.com/apache/incubator-mxnet/tree/master/example/image-classification, https://mxnet.incubator.apache.org/api/python/gluon/model_zoo.html, https://www.leadergpu.com/articles/432-mxnet-benchmark, https://mxnet.apache.org/model_zoo/index.html, https://www.tomshardware.com/news/nvidia-titan-rtx-specs-pricing,38184.html, https://www.hardwarezone.com.sg/feature-nvidia-geforce-rtx-2080-and-2080-ti-review-guess-who-has-fastest-cards-again/test-setup-gaming-performance, Toward a New Generation of Neuromorphic Computing: IBM & ETH Zurich’s Biologically Inspired…, Microsoft & Peking U Researchers Identify ‘Knowledge Neurons’ in Pretrained Transformers, Enabling…, Google’s 1.3 MiB On-Device Model Brings High-Performance Disfluency Detection Down to Size, ETH Zurich Leverages Spiking Neural Networks To Build Ultra-Low-Power Neuromorphic Processors, Google and UC Berkeley Propose Green Strategies for Large Neural Network Training, NVIDIA, Stanford & Microsoft Propose Efficient Trillion-Parameter Language Model Training on GPU…, Pieter Abbeel Team Proposes Task-Agnostic RL Method to Auto-Tune Simulations to the Real World, Yann LeCun Team’s Novel End-to-End Modulated Detector Captures Visual Concepts in Free-Form Text. Disadvantages of Apache MXNet. Let IT Central Station and our comparison database help you with your research. Still, with 8 GPUs and a well-known data set, MXNet is significantly faster, much more memory-efficient and more accurate than Tensorflow. Python bindings are installed in Python 3.6 on Windows 2016 and in Python 3.5 on Linux) R bindings are also included in the Ubuntu DSVM. Our evaluation will be based on the three frameworks to cover most machine learning practitioners. Check out latest performance benchmark by NVIDIA here, and you can see that MXNet is outperforming both Tensorflow and Pytorch by very large margins. Keras supports multiple backends for training and it’s very easy to switch from one to the other. We believe our testbed is representative and affordable for most of our readers. Also, CMU CS Dean Andrew Moore cited MXNet as "is the most scalable framework for deep learning I have seen" This is pretty impressive work in such a short time frame!

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