SEARCH SESSIONS

Search All
 
Refine Results:
 
Year(s)

SOCIAL MEDIA

EMAIL SUBSCRIPTION

 
 

GTC On-Demand

AI Application Deployment and Inference
Presentation
Media
Deploying, Profiling, and Optimizing Distributed TensorFlow in Production with GPUs
Using the latest advancements from TensorFlow including the Accelerated Linear Algebra (XLA) Framework, JIT/AOT Compiler, and Graph Transform Tool, we'll demonstrate how to optimize, profile, and deploy TensorFlow models in GPU-based production environments. We'll cover many demos based on open source tools. You can completely reproduce all demos through Docker on your own GPU cluster. See http://pipeline.ai for links to the GitHub Repo.
Using the latest advancements from TensorFlow including the Accelerated Linear Algebra (XLA) Framework, JIT/AOT Compiler, and Graph Transform Tool, we'll demonstrate how to optimize, profile, and deploy TensorFlow models in GPU-based production environments. We'll cover many demos based on open source tools. You can completely reproduce all demos through Docker on your own GPU cluster. See http://pipeline.ai for links to the GitHub Repo.  Back
 
Keywords:
AI Application Deployment and Inference, NVIDIA Inception Program, Deep Learning and AI Frameworks, GTC Silicon Valley 2018 - ID S8621
Streaming:
Share:
Accelerated Analytics
Presentation
Media
Optimizing, Profiling, and Deploying TensorFlow AI Models in Production with GPUs

Using the latest advancements from TensorFlow including the Accelerated Linear Algebra (XLA) Framework, JITundefinedAOT Compiler, and Graph Transform Tool , Ill demonstrate how to optimize, profile, and deploy TensorFlow Models in GPU-based production environment. This talk is 100% demo based with open source tools and completely reproducible through Docker on your own GPU cluster. In addition, I spin up a GPU cloud instance for every attendee in the audience. We go through the notebooks together as I demonstrate the process of continuously training, optimizing, deploying, and serving a TensorFlow model on a large, distributed cluster of Nvidia GPUs managed by the attendees.

Using the latest advancements from TensorFlow including the Accelerated Linear Algebra (XLA) Framework, JITundefinedAOT Compiler, and Graph Transform Tool , Ill demonstrate how to optimize, profile, and deploy TensorFlow Models in GPU-based production environment. This talk is 100% demo based with open source tools and completely reproducible through Docker on your own GPU cluster. In addition, I spin up a GPU cloud instance for every attendee in the audience. We go through the notebooks together as I demonstrate the process of continuously training, optimizing, deploying, and serving a TensorFlow model on a large, distributed cluster of Nvidia GPUs managed by the attendees.

  Back
 
Keywords:
Accelerated Analytics, Performance Optimization, Tools and Libraries, GTC Europe 2017 - ID 23363
Download:
Share:
Deep Learning and AI
Presentation
Media
Optimizing, Profiling, and Deploying TensorFlow AI Models in Production with GPUs
Using the latest advancements from TensorFlow including the Accelerated Linear Algebra (XLA) Framework, JIT/AOT Compiler, and Graph Transform Tool, Chris will demonstrate how to optimize, profile, and deploy TensorFlow Models in GPU-based production environment. This talk is 100% demo based with open source tools and completely reproducible through Docker on your own GPU cluster.
Using the latest advancements from TensorFlow including the Accelerated Linear Algebra (XLA) Framework, JIT/AOT Compiler, and Graph Transform Tool, Chris will demonstrate how to optimize, profile, and deploy TensorFlow Models in GPU-based production environment. This talk is 100% demo based with open source tools and completely reproducible through Docker on your own GPU cluster.  Back
 
Keywords:
Deep Learning and AI, AI Startup, Federal, Data Center and Cloud Infrastructure, GTC Silicon Valley 2017 - ID S7568
Download:
Share:
 
Optimizing, Profiling, and Deploying TensorFlow AI Models in Production with GPUs
Using the latest advancements from TensorFlow, including the accelerated linear algebra (XLA) framework, JIT/AOT compiler, and graph transform tool, Chris will demonstrate how to optimize, profile, and deploy TensorFlow models in GPU-based production environments. This talk is 100 percent demo based with open source tools and completely reproducible through Docker on your own GPU cluster. We'll provide a GPU for every attendee to follow along during the talk.
Using the latest advancements from TensorFlow, including the accelerated linear algebra (XLA) framework, JIT/AOT compiler, and graph transform tool, Chris will demonstrate how to optimize, profile, and deploy TensorFlow models in GPU-based production environments. This talk is 100 percent demo based with open source tools and completely reproducible through Docker on your own GPU cluster. We'll provide a GPU for every attendee to follow along during the talk.  Back
 
Keywords:
Deep Learning and AI, Data Center and Cloud Infrastructure, Deep Learning and AI Frameworks, GTC Washington D.C. 2017 - ID DC7102
Download:
Share: