Tubidy MP3 & MP4

En popüler MP3 müziklerinizi ve MP4 videolarınızı ücretsiz indirin. Geniş bir multimedya içeriği seçkisini keşfedin ve sorunsuz indirmelerin tadını çıkarın.

"Fast, Efficient Infrastructure for Distributed Deep Learning", Amar Phanisayee, Microsoft Research
"Fast, Efficient Infrastructure for Distributed Deep Learning", Amar Phanisayee, Microsoft Research
|
Yükleniyor...
 Hızlı erişim için Tubidy'yi favorilerinize ekleyin.
Lütfen bekleyiniz...
Type
Size

İlgili Videolar

"Fast, Efficient Infrastructure for Distributed Deep Learning", Amar Phanisayee, Microsoft Research

"Fast, Efficient Infrastructure for Distributed Deep Learning", Amar Phanisayee, Microsoft Research

|
Tech Showcase: Project Fiddle

Tech Showcase: Project Fiddle

|
Azure Container for PyTorch: An optimized container for large scale distributed training workloads

Azure Container for PyTorch: An optimized container for large scale distributed training workloads

|
Efficient Large-Scale AI Workshop | Session 2: Training and inference efficiency

Efficient Large-Scale AI Workshop | Session 2: Training and inference efficiency

|
HC33-T1.3: Machine Learning Performance and Challenges, Part 3

HC33-T1.3: Machine Learning Performance and Challenges, Part 3

|
Distributed Deep Leaning DeepSpeed

Distributed Deep Leaning DeepSpeed

|
Developer Tech Minutes: AI at Scale

Developer Tech Minutes: AI at Scale

|
USENIX ATC '20 - Daydream: Accurately Estimating the Efficacy of Optimizations for DNN Training

USENIX ATC '20 - Daydream: Accurately Estimating the Efficacy of Optimizations for DNN Training

|
DeepSpeed on AzureML

DeepSpeed on AzureML

|
[서울대 AI 여름학교] Microsoft Research Deep Speed Team - DeepSpeed: Training and Inference ...

[서울대 AI 여름학교] Microsoft Research Deep Speed Team - DeepSpeed: Training and Inference ...

|
ONNX Roadmap Discussion #4 20200923

ONNX Roadmap Discussion #4 20200923

|
Самописный DeepStream, переводим пайплайн обработки с CPU на GPU // Александр Гращенков, RoadAR

Самописный DeepStream, переводим пайплайн обработки с CPU на GPU // Александр Гращенков, RoadAR

|

Favorilere Ekle