Top Tweets for #DAWNBench
After the #DAWNBench in July, Ping An Technology’s Occam Platform, an #AutoML based end-to-end platform for #AI application acceleration, won the 2019 China Computer Federation (CCF) Science Technology Award for Outstanding Progress for the first time!
スタンフォード大学のAIベンチマークコンペティション #DAWNBench にて、Alibaba Cloudが性能とコスト面での記録を更新しました。50,000枚の画像をわずか4.218ミリ秒で認識し、Amazonが保有していた記録を上回る結果となりました。
#アリババクラウド #AlibabaCloud
At #Stanford 's #DAWNBench #deeplearning competition, which requires participating platforms to accurately identify & classify 50,000 images, we set records for both low cost & high speed, identifying images in just 4.218ms. More on #AlibabaCloud #AI at https://t.co/GaKj30bigf
At #Stanford 's #DAWNBench #deeplearning competition, which requires participating platforms to accurately identify & classify 50,000 images, we set records for both low cost & high speed, identifying images in just 4.218ms. More on #AlibabaCloud #AI at https://t.co/GaKj30bigf
Full code and training blog for the top #DAWNBench #DeepLearning CIFAR10 Benchmark! Train your #resnet #NeuralNetworks, 2.5x faster, using 8x fewer GPUs and at a quarter the cost! Great work @dcpage3 and @myrtlesoftware https://t.co/xpZfve805y #fastai #pytorch
Since we opened #DAWNBench deep learning benchmark rolling submissions, there have been some cool entires. You can now train CIFAR10 for just $0.06 and ImageNet for $12.60 (4x less than in April!): https://t.co/K3A5UOa5Xz. MLPerf's deadline is also soon (https://t.co/1WTgZaaz8e).
Since we opened #DAWNBench deep learning benchmark rolling submissions, there have been some cool entires. You can now train CIFAR10 for just $0.06 and ImageNet for $12.60 (4x less than in April!): https://t.co/K3A5UOa5Xz. MLPerf's deadline is also soon (https://t.co/1WTgZaaz8e).
An #ArtificialIntelligence #Algorithm
created by #fastdotai has
beaten #Google’s code
as measured using a
benchmark called #DAWNBench
which uses a common
image classification task to
track the speed of a
#DeepLearning algorithm per dollar of compute power
https://t.co/247ZITEliz
#AVBytes A small team from Fast. ai, led by Jeremy Howard, outperformed Google's machine learning code, as per Stanford's #DAWNBench benchmark. Their model took 18 min to train & the entire process cost just $40!
https://t.co/51oAomccTc #FastAI #Imagenet
Small team of #AI coders beats #Google’s machine learning code https://t.co/A0zFFfN51P #MachineLearning #DAWNBench #ArticialIntelligence #FastAI #ImageNet #AWS
https://t.co/tS2q9jkRfO’s team built an algorithm that beats Google’s code, as determined by a Stanford benchmark called DAWNBench.
😜👩🏫 #ML #machinelearning #DawnBench
https://t.co/1nDGS7kGNy
An analysis of the entires at the #DAWNbench deep learning benchmark: https://t.co/nDIJVu2ThF . Which factors contributed to performance and do these entries actually saturate modern hardware?
'Think of #DAWNBench as an athletics meet for #AI engineers, with hurdles and long jump replaced by tasks like object recognition and reading comprehension' https://t.co/Wk53Mfhxhr via @Verge
Using Intel optimized Caffe on high-performance @awscloud instances, we reduced image latency to less than 10 milliseconds and processed 10,000 images for only $0.02 at Stanford’s first #DL benchmark & competition: #DAWNBench https://t.co/HujDK4Nd6L #intelAI
Stanford #DAWNBench v1 #DeepLearning Benchmark Results: The cheapest submission for ResNet50 on #ImageNet ran in 8 hours 53 minutes for a total of $58.53 on a Google TPUv2 machine using #TensorFlow 1.8.0-rc1 https://t.co/EU9q9BORQ3
Train Imagenet for $25 on AWS... “there is no ability to code directly for the TPU, which severely limits algorithmic creativity” https://t.co/3DYFGXf7l1 #dawnbench #deeplearning
Some awesome submissions coming into #DAWNbench. Training cost for ImageNet and CIFAR went down by 20x and 200x respectively over our original entries, which were GPU code from open source frameworks. Time is down to 3 min for CIFAR and 30min for ImageNet. https://t.co/CBPoz4qaAa
The #DawnBench deep learning benchmark is heating up with submissions from Intel as well as a machine-learned model architecture from Google! Final deadline is April 20th. https://t.co/K3A5UOa5Xz
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