PipelineAI High Performance TensorFlow + GPU + Kubernetes + Jupyter Workshop
PipelineAI Distributed TensorFlow AI + GPU Workshop
MUST RSVP!! A GPU-based cloud instance will be provided to each registered attendee as part of this event.
We will each build an end-to-end, continuous TensorFlow AI model training and deployment pipeline on our own GPU-based cloud instance.
At the end, we will combine our cloud instances to create the LARGEST Distributed TensorFlow AI Training and Serving Cluster in the WORLD!
Just a modern browser and an internet connection. We’ll provide the rest!
Storing and Serving Models with HDFS
Trade-offs of CPU vs. *GPU, Scale Up vs. Scale Out
CUDA + cuDNN GPU Development Overview
TensorFlow Model Checkpointing, Saving, Exporting, and Importing
Distributed TensorFlow AI Model Training (Distributed TensorFlow)
TensorFlow’s Just-in-Time (JIT) Compiler, Ahead of Time (AOT) Compiler
Centralized Logging and Visualizing of Distributed TensorFlow Training (Tensorboard)
Distributed TensorFlow AI Model Serving/Predicting (TensorFlow Serving)
Centralized Logging and Metrics Collection (Prometheus, Grafana)
Continuous TensorFlow AI Model Deployment (TensorFlow, Airflow)
Hybrid Cross-Cloud and On-Premise Deployments (Kubernetes)
High-Performance and Fault-Tolerant Micro-services (NetflixOSS)
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