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GTC On-Demand

Presentation
Media
Abstract:
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
 
Topics:
AI Application Deployment and Inference, NVIDIA Inception Program, Deep Learning and AI Frameworks
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8621
Streaming:
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Abstract:

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.

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Topics:
Accelerated Analytics, Performance Optimization, Tools and Libraries
Type:
Talk
Event:
GTC Europe
Year:
2017
Session ID:
23363
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Abstract:
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
 
Topics:
Deep Learning and AI, Data Center and Cloud Infrastructure, Deep Learning and AI Frameworks
Type:
Talk
Event:
GTC Washington D.C.
Year:
2017
Session ID:
DC7102
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Abstract:
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
 
Topics:
Deep Learning and AI, AI Startup, Federal, Data Center and Cloud Infrastructure
Type:
Tutorial
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7568
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