Using the latest advancements from TensorFlow including the Accelerated Linear Algebra (XLA) Framework, JIT/AOT Compiler, and Graph Transform Tool, I’ll demonstrate how to optimize, profile, and deploy TensorFlow Models in GPU-based production environment.
This talk is contains many Spark ML and TensorFlow AI demos using PipelineIO's 100% Open Source Community Edition. All code and Docker images are available to reproduce on your own CPU or GPU-based cluster.
Chris Fregly is Founder and Research Engineer at PipelineIO, a Streaming Machine Learning and Artificial Intelligence Startup based in San Francisco. He is also an Apache Spark Contributor, a Netflix Open Source Committer, founder of the Global Advanced Spark and TensorFlow Meetup, author of the O’Reilly Training and Video Series titled, "High Performance TensorFlow in Production."
Previously, Chris was a Distributed Systems Engineer at Netflix, a Data Solutions Engineer at Databricks, and a Founding Member and Principal Engineer at the IBM Spark Technology Center in San Francisco.
https://www.meetup.com/TensorFlow-Chicago/events/240267321/
https://www.meetup.com/Advanced-Spark-and-TensorFlow-Meetup/events/240587698/
http://pipeline.io
https://github.com/fluxcapacitor/pipeline
Optimizing, Profiling, and Deploying TensorFlow AI Models in Production with ...Chris Fregly
Using the latest advancements from TensorFlow including the Accelerated Linear Algebra (XLA) Framework, JIT AOT Compiler, and Graph Transform Tool , I’ll 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.
http://pipeline.ai
Optimize + Deploy Distributed Tensorflow, Spark, and Scikit-Learn Models on G...Chris Fregly
Optimize + Deploy Distributed Tensorflow, Spark, and Scikit-Learn Models on GPUs - Advanced Spark and TensorFlow Meetup May 23 2017 @ Hotels.com London
We'll discuss how to deploy TensorFlow, Spark, and Sciki-learn models on GPUs with Kubernetes across multiple cloud providers including AWS, Google, and Azure - as well as on-premise.
In addition, we'll discuss how to optimize TensorFlow models for high-performance inference using the latest TensorFlow XLA (Accelerated Linear Algebra) framework including the JIT and AOT Compilers.
Github Repo (100% Open Source!)
https://github.com/fluxcapacitor/pipeline
http://pipeline.io
High Performance Distributed TensorFlow with GPUs - Nvidia GPU Tech Conferenc...Chris Fregly
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.
https://github.com/fluxcapacitor/pipeline/gpu.ml
http://pipeline.io
High Performance Distributed TensorFlow in Production with GPUs - NIPS 2017 -...Chris Fregly
Online Workshop
Note: A GPU-based cloud instance will be provided to each attendee for the duration of this event!!
At 8am PT on the morning of this workshop, we will email the Webinar details to your email address registered with Eventbrite.
If this email address is not up to date - or you do not get the email by 8am PT - please email your Eventbrite confirmation to help@pipeline.ai and we'll send you the details.
http://pipeline.ai
Title
PipelineAI Distributed Spark ML + Tensorflow AI + GPU Workshop
Time
Start: 9am PT Time
End: 1pm PT Time
Highlights
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!
Pre-requisites
Just a modern browser, internet connection, and a good night's sleep! We'll provide the rest.
Agenda
Spark ML
TensorFlow AI
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 Accelerated Linear Algebra Framework (XLA)
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)
More Info including GitHub and Docker Repos
http://pipeline.ai
High Performance TensorFlow in Production -- Sydney ML / AI Train Workshop @ ...Chris Fregly
http://pipeline.ai
Title
PipelineAI Distributed Spark ML + Tensorflow AI + GPU Workshop
*A GPU-based cloud instance will be provided to each attendee as part of this event
Highlights
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!
Agenda
Spark ML
Tensorflow AI
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)
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 Microsservices using Request Batching and Circuit Breakers (NetflixOSS)
Github Repo
https://github.com/fluxcapacitor/pipeline
High Performance Distributed TensorFlow with GPUs - NYC Workshop - July 9 2017Chris Fregly
http://pipeline.io
Title
PipelineAI Distributed Spark ML + Tensorflow AI + GPU Workshop
*A GPU-based cloud instance will be provided to each attendee as part of this event
Highlights
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!
Pre-requisites
Just a modern browser, internet connection, and a good night's sleep! We'll provide the rest.
Agenda
Spark ML
TensorFlow AI
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 Accelerated Linear Algebra Framework (XLA)
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)
Bio
Chris Fregly is Founder and Research Engineer at PipelineIO, a Streaming Machine Learning and Artificial Intelligence Startup based in San Francisco. He is also an Apache Spark Contributor, a Netflix Open Source Committer, founder of the Global Advanced Spark and TensorFlow Meetup, author of the O’Reilly Training and Video Series titled, "High Performance TensorFlow in Production."
Previously, Chris was a Distributed Systems Engineer at Netflix, a Data Solutions Engineer at Databricks, and a Founding Member and Principal Engineer at the IBM Spark Technology Center in San Francisco.
Github Repo
https://github.com/fluxcapacitor/pipeline
Video
https://youtu.be/oNf3I1fVmg8
Building Google Cloud ML Engine From Scratch on AWS with PipelineAI - ODSC Lo...Chris Fregly
http://pipeline.ai
Applying my Netflix experience to a real-world problem in the ML and AI world, I will demonstrate a full-featured, open-source, end-to-end TensorFlow Model Training and Deployment System using the latest advancements from Kubernetes, Istio, and TensorFlow.
In addition to training and hyper-parameter tuning, our model deployment pipeline will include continuous canary deployments of our TensorFlow Models into a live, hybrid-cloud production environment.
This is the holy grail of data science - rapid and safe experiments of ML / AI models directly in production.
Following the Successful Netflix Culture that I lived and breathed (https://www.slideshare.net/reed2001/culture-1798664/2-Netflix_CultureFreedom_Responsibility2), I give Data Scientists the Freedom and Responsibility to extend their ML / AI pipelines and experiments safely into production.
Offline, batch training and validation is for the slow and weak. Online, real-time training and validation on live production data is for the fast and strong.
Learn to be fast and strong by attending this talk.
http://pipeline.ai
Optimizing, Profiling, and Deploying TensorFlow AI Models in Production with ...Chris Fregly
Using the latest advancements from TensorFlow including the Accelerated Linear Algebra (XLA) Framework, JIT AOT Compiler, and Graph Transform Tool , I’ll 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.
http://pipeline.ai
Optimize + Deploy Distributed Tensorflow, Spark, and Scikit-Learn Models on G...Chris Fregly
Optimize + Deploy Distributed Tensorflow, Spark, and Scikit-Learn Models on GPUs - Advanced Spark and TensorFlow Meetup May 23 2017 @ Hotels.com London
We'll discuss how to deploy TensorFlow, Spark, and Sciki-learn models on GPUs with Kubernetes across multiple cloud providers including AWS, Google, and Azure - as well as on-premise.
In addition, we'll discuss how to optimize TensorFlow models for high-performance inference using the latest TensorFlow XLA (Accelerated Linear Algebra) framework including the JIT and AOT Compilers.
Github Repo (100% Open Source!)
https://github.com/fluxcapacitor/pipeline
http://pipeline.io
High Performance Distributed TensorFlow with GPUs - Nvidia GPU Tech Conferenc...Chris Fregly
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.
https://github.com/fluxcapacitor/pipeline/gpu.ml
http://pipeline.io
High Performance Distributed TensorFlow in Production with GPUs - NIPS 2017 -...Chris Fregly
Online Workshop
Note: A GPU-based cloud instance will be provided to each attendee for the duration of this event!!
At 8am PT on the morning of this workshop, we will email the Webinar details to your email address registered with Eventbrite.
If this email address is not up to date - or you do not get the email by 8am PT - please email your Eventbrite confirmation to help@pipeline.ai and we'll send you the details.
http://pipeline.ai
Title
PipelineAI Distributed Spark ML + Tensorflow AI + GPU Workshop
Time
Start: 9am PT Time
End: 1pm PT Time
Highlights
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!
Pre-requisites
Just a modern browser, internet connection, and a good night's sleep! We'll provide the rest.
Agenda
Spark ML
TensorFlow AI
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 Accelerated Linear Algebra Framework (XLA)
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)
More Info including GitHub and Docker Repos
http://pipeline.ai
High Performance TensorFlow in Production -- Sydney ML / AI Train Workshop @ ...Chris Fregly
http://pipeline.ai
Title
PipelineAI Distributed Spark ML + Tensorflow AI + GPU Workshop
*A GPU-based cloud instance will be provided to each attendee as part of this event
Highlights
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!
Agenda
Spark ML
Tensorflow AI
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)
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 Microsservices using Request Batching and Circuit Breakers (NetflixOSS)
Github Repo
https://github.com/fluxcapacitor/pipeline
High Performance Distributed TensorFlow with GPUs - NYC Workshop - July 9 2017Chris Fregly
http://pipeline.io
Title
PipelineAI Distributed Spark ML + Tensorflow AI + GPU Workshop
*A GPU-based cloud instance will be provided to each attendee as part of this event
Highlights
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!
Pre-requisites
Just a modern browser, internet connection, and a good night's sleep! We'll provide the rest.
Agenda
Spark ML
TensorFlow AI
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 Accelerated Linear Algebra Framework (XLA)
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)
Bio
Chris Fregly is Founder and Research Engineer at PipelineIO, a Streaming Machine Learning and Artificial Intelligence Startup based in San Francisco. He is also an Apache Spark Contributor, a Netflix Open Source Committer, founder of the Global Advanced Spark and TensorFlow Meetup, author of the O’Reilly Training and Video Series titled, "High Performance TensorFlow in Production."
Previously, Chris was a Distributed Systems Engineer at Netflix, a Data Solutions Engineer at Databricks, and a Founding Member and Principal Engineer at the IBM Spark Technology Center in San Francisco.
Github Repo
https://github.com/fluxcapacitor/pipeline
Video
https://youtu.be/oNf3I1fVmg8
Building Google Cloud ML Engine From Scratch on AWS with PipelineAI - ODSC Lo...Chris Fregly
http://pipeline.ai
Applying my Netflix experience to a real-world problem in the ML and AI world, I will demonstrate a full-featured, open-source, end-to-end TensorFlow Model Training and Deployment System using the latest advancements from Kubernetes, Istio, and TensorFlow.
In addition to training and hyper-parameter tuning, our model deployment pipeline will include continuous canary deployments of our TensorFlow Models into a live, hybrid-cloud production environment.
This is the holy grail of data science - rapid and safe experiments of ML / AI models directly in production.
Following the Successful Netflix Culture that I lived and breathed (https://www.slideshare.net/reed2001/culture-1798664/2-Netflix_CultureFreedom_Responsibility2), I give Data Scientists the Freedom and Responsibility to extend their ML / AI pipelines and experiments safely into production.
Offline, batch training and validation is for the slow and weak. Online, real-time training and validation on live production data is for the fast and strong.
Learn to be fast and strong by attending this talk.
http://pipeline.ai
Using the latest advancements from TensorFlow including the Accelerated Linear Algebra (XLA) Framework, JIT/AOT Compiler, and Graph Transform Tool , I’ll demonstrate how to optimize, profile, and deploy TensorFlow Models in GPU-based production environment.
This talk contains 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.
Optimizing, Profiling, and Deploying TensorFlow AI Models with GPUs - San Fra...Chris Fregly
http://pipeline.ai
Using the latest advancements from TensorFlow including the Accelerated Linear Algebra (XLA) Framework, JIT/AOT Compiler, and Graph Transform Tool, I’ll demonstrate how to optimize, profile, and deploy TensorFlow Models - and the TensorFlow Runtime - in GPU-based production environment. This talk is 100% demo based on open source tools and completely reproducible through Docker on your own GPU cluster.
Bio
Chris Fregly is Founder and Research Engineer at PipelineAI, a Streaming Machine Learning and Artificial Intelligence Startup based in San Francisco. He is also an Apache Spark Contributor, a Netflix Open Source Committer, founder of the Global Advanced Spark and TensorFlow Meetup, author of the O’Reilly Training and Video Series titled, "High-Performance TensorFlow in Production."
Previously, Chris was a Distributed Systems Engineer at Netflix, a Data Solutions Engineer at Databricks, and a Founding Member and Principal Engineer at the IBM Spark Technology Center in San Francisco.
http://pipeline.ai
Optimize + Deploy Distributed Tensorflow, Spark, and Scikit-Learn Models on GPUsChris Fregly
Optimize + Deploy Distributed Tensorflow, Spark, and Scikit-Learn Models on GPUs @ Strata London, May 24 2017
Optimize + Deploy Distributed Tensorflow, Spark, and Scikit-Learn Models on GPUs - Advanced Spark and TensorFlow Meetup May 23 2017 @ Hotels.com London
We'll discuss how to deploy TensorFlow, Spark, and Sciki-learn models on GPUs with Kubernetes across multiple cloud providers including AWS, Google, and Azure - as well as on-premise.
In addition, we'll discuss how to optimize TensorFlow models for high-performance inference using the latest TensorFlow XLA (Accelerated Linear Algebra) framework including the JIT and AOT Compilers.
Github Repo (100% Open Source!)
https://github.com/fluxcapacitor/pipeline
http://pipeline.io
PipelineAI Optimizes Your Enterprise AI Pipeline from Distributed Training to...Chris Fregly
https://pipeline.ai
With PipelineAI, You Can…
* Generate Hardware-Specific Model Optimizations
* Deploy and Compare Models in Live Production
* Optimize Complete AI Pipeline Across Many Models
* Hyper-Parameter Tune Both Training & Predicting Phases
Building Google's ML Engine from Scratch on AWS with GPUs, Kubernetes, Istio,...Chris Fregly
Applying my Netflix experience to a real-world problem in the ML and AI world, I will demonstrate a full-featured, open-source, end-to-end TensorFlow Model Training and Deployment System using the latest advancements from Kubernetes, Istio, and TensorFlow.
In addition to training and hyper-parameter tuning, our model deployment pipeline will include continuous canary deployments of our TensorFlow Models into a live, hybrid-cloud production environment.
This is the holy grail of data science - rapid and safe experiments of ML / AI models directly in production.
Following the Successful Netflix Culture that I lived and breathed (https://www.slideshare.net/reed2001/culture-1798664/2-Netflix_CultureFreedom_Responsibility2), I give Data Scientists the Freedom and Responsibility to extend their ML / AI pipelines and experiments safely into production.
Offline, batch training and validation is for the slow and weak. Online, real-time training and validation on live production data is for the fast and strong.
Learn to be fast and strong by attending this talk.
Bio:
Chris Fregly is Founder and Research Engineer at PipelineAI, a Streaming Machine Learning and Artificial Intelligence Startup based in San Francisco. He is also an Apache Spark Contributor, a Netflix Open Source Committer, founder of the Global Advanced Spark and TensorFlow Meetup, author of the O’Reilly Training and Video Series titled, "High Performance TensorFlow in Production."
Previously, Chris was a Distributed Systems Engineer at Netflix, a Data Solutions Engineer at Databricks, and a Founding Member and Principal Engineer at the IBM Spark Technology Center in San Francisco.
http://pipeline.ai
Hyper-Parameter Tuning Across the Entire AI Pipeline GPU Tech Conference San ...Chris Fregly
Chris Fregly, Founder @ PipelineAI, will walk you through a real-world, complete end-to-end Pipeline-optimization example. We highlight hyper-parameters - and model pipeline phases - that have never been exposed until now.
While most Hyperparameter Optimizers stop at the training phase (ie. learning rate, tree depth, ec2 instance type, etc), we extend model validation and tuning into a new post-training optimization phase including 8-bit reduced precision weight quantization and neural network layer fusing - among many other framework and hardware-specific optimizations.
Next, we introduce hyperparameters at the prediction phase including request-batch sizing and chipset (CPU v. GPU v. TPU).
Lastly, we determine a PipelineAI Efficiency Score of our overall Pipeline including Cost, Accuracy, and Time. We show techniques to maximize this PipelineAI Efficiency Score using our massive PipelineDB along with the Pipeline-wide hyper-parameter tuning techniques mentioned in this talk.
Bio
Chris Fregly is Founder and Applied AI Engineer at PipelineAI, a Real-Time Machine Learning and Artificial Intelligence Startup based in San Francisco.
He is also an Apache Spark Contributor, a Netflix Open Source Committer, founder of the Global Advanced Spark and TensorFlow Meetup, author of the O’Reilly Training and Video Series titled, "High Performance TensorFlow in Production with Kubernetes and GPUs."
Previously, Chris was a Distributed Systems Engineer at Netflix, a Data Solutions Engineer at Databricks, and a Founding Member and Principal Engineer at the IBM Spark Technology Center in San Francisco.
Speaker: Umayah Abdennabi
Agenda
* Intro Grammarly (Umayah Abdennabi, 5 mins)
* Meetup Updates and Announcements (Chris, 5 mins)
* Custom Functions in Spark SQL (30 mins)
Speaker: Umayah Abdennabi
Spark comes with a rich Expression library that can be extended to make custom expressions. We will look into custom expressions and why you would want to use them.
* TF 2.0 + Keras (30 mins)
Speaker: Francesco Mosconi
Tensorflow 2.0 was announced at the March TF Dev Summit, and it brings many changes and upgrades. The most significant change is the inclusion of Keras as the default model building API. In this talk, we'll review the main changes introduced in TF 2.0 and highlight the differences between open source Keras and tf.keras
* SQUAD Deep-Dive: Question & Answer with Context (45 mins)
Speaker: Brett Koonce (https://quarkworks.co)
SQuAD (Stanford Question Answer Dataset) is an NLP challenge based around answering questions by reading Wikipedia articles, designed to be a real-world machine learning benchmark. We will look at several different ways to tackle the SQuAD problem, building up to state of the art approaches in terms of time, complexity, and accuracy.
https://rajpurkar.github.io/SQuAD-explorer/
https://dawn.cs.stanford.edu/benchmark/#squad
Food and drinks will be provided. The event will be held at Grammarly's office at One Embarcadero Center on the 9th floor. When you arrive at One Embarcadero, take the escalator to the second floor where you will find the lobby and elevators to the office suites. Come on up to the 9th floor (no need to check in at security), and ring the Grammarly doorbell.
This is an introduction to polyaxon and why I use polyaxon.
Polyaxon enables me to leverage kubernetes to achieve the objectives:
- Make the lead time of experiments as short as possible.
- Make the financial cost to train models as cheap as possible.
- Make the experiments reproducible.
Kernel Recipes 2017: Using Linux perf at NetflixBrendan Gregg
Talk for Kernel Recipes 2017 by Brendan Gregg. "Linux perf is a crucial performance analysis tool at Netflix, and is used by a self-service GUI for generating CPU flame graphs and other reports. This sounds like an easy task, however, getting perf to work properly in VM guests running Java, Node.js, containers, and other software, has been at times a challenge. This talk summarizes Linux perf, how we use it at Netflix, the various gotchas we have encountered, and a summary of advanced features."
Now that you have your apps running on K8s, wondering how to get the response time that you need ? Tuning applications to get the performance that you need can be challenging. When you have to tune a number of microservices in Kubernetes to fix a response time or a throughput issue, it can get really overwhelming. This talk looks at some common performance issues and ways to solve them and more importantly the tools that can help you. We will also be specifically looking at Kruize that helps to not only right size your containers but also optimize the runtimes.
Using the latest advancements from TensorFlow including the Accelerated Linear Algebra (XLA) Framework, JIT/AOT Compiler, and Graph Transform Tool , I’ll demonstrate how to optimize, profile, and deploy TensorFlow Models in GPU-based production environment.
This talk contains 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.
Optimizing, Profiling, and Deploying TensorFlow AI Models with GPUs - San Fra...Chris Fregly
http://pipeline.ai
Using the latest advancements from TensorFlow including the Accelerated Linear Algebra (XLA) Framework, JIT/AOT Compiler, and Graph Transform Tool, I’ll demonstrate how to optimize, profile, and deploy TensorFlow Models - and the TensorFlow Runtime - in GPU-based production environment. This talk is 100% demo based on open source tools and completely reproducible through Docker on your own GPU cluster.
Bio
Chris Fregly is Founder and Research Engineer at PipelineAI, a Streaming Machine Learning and Artificial Intelligence Startup based in San Francisco. He is also an Apache Spark Contributor, a Netflix Open Source Committer, founder of the Global Advanced Spark and TensorFlow Meetup, author of the O’Reilly Training and Video Series titled, "High-Performance TensorFlow in Production."
Previously, Chris was a Distributed Systems Engineer at Netflix, a Data Solutions Engineer at Databricks, and a Founding Member and Principal Engineer at the IBM Spark Technology Center in San Francisco.
http://pipeline.ai
Optimize + Deploy Distributed Tensorflow, Spark, and Scikit-Learn Models on GPUsChris Fregly
Optimize + Deploy Distributed Tensorflow, Spark, and Scikit-Learn Models on GPUs @ Strata London, May 24 2017
Optimize + Deploy Distributed Tensorflow, Spark, and Scikit-Learn Models on GPUs - Advanced Spark and TensorFlow Meetup May 23 2017 @ Hotels.com London
We'll discuss how to deploy TensorFlow, Spark, and Sciki-learn models on GPUs with Kubernetes across multiple cloud providers including AWS, Google, and Azure - as well as on-premise.
In addition, we'll discuss how to optimize TensorFlow models for high-performance inference using the latest TensorFlow XLA (Accelerated Linear Algebra) framework including the JIT and AOT Compilers.
Github Repo (100% Open Source!)
https://github.com/fluxcapacitor/pipeline
http://pipeline.io
PipelineAI Optimizes Your Enterprise AI Pipeline from Distributed Training to...Chris Fregly
https://pipeline.ai
With PipelineAI, You Can…
* Generate Hardware-Specific Model Optimizations
* Deploy and Compare Models in Live Production
* Optimize Complete AI Pipeline Across Many Models
* Hyper-Parameter Tune Both Training & Predicting Phases
Building Google's ML Engine from Scratch on AWS with GPUs, Kubernetes, Istio,...Chris Fregly
Applying my Netflix experience to a real-world problem in the ML and AI world, I will demonstrate a full-featured, open-source, end-to-end TensorFlow Model Training and Deployment System using the latest advancements from Kubernetes, Istio, and TensorFlow.
In addition to training and hyper-parameter tuning, our model deployment pipeline will include continuous canary deployments of our TensorFlow Models into a live, hybrid-cloud production environment.
This is the holy grail of data science - rapid and safe experiments of ML / AI models directly in production.
Following the Successful Netflix Culture that I lived and breathed (https://www.slideshare.net/reed2001/culture-1798664/2-Netflix_CultureFreedom_Responsibility2), I give Data Scientists the Freedom and Responsibility to extend their ML / AI pipelines and experiments safely into production.
Offline, batch training and validation is for the slow and weak. Online, real-time training and validation on live production data is for the fast and strong.
Learn to be fast and strong by attending this talk.
Bio:
Chris Fregly is Founder and Research Engineer at PipelineAI, a Streaming Machine Learning and Artificial Intelligence Startup based in San Francisco. He is also an Apache Spark Contributor, a Netflix Open Source Committer, founder of the Global Advanced Spark and TensorFlow Meetup, author of the O’Reilly Training and Video Series titled, "High Performance TensorFlow in Production."
Previously, Chris was a Distributed Systems Engineer at Netflix, a Data Solutions Engineer at Databricks, and a Founding Member and Principal Engineer at the IBM Spark Technology Center in San Francisco.
http://pipeline.ai
Hyper-Parameter Tuning Across the Entire AI Pipeline GPU Tech Conference San ...Chris Fregly
Chris Fregly, Founder @ PipelineAI, will walk you through a real-world, complete end-to-end Pipeline-optimization example. We highlight hyper-parameters - and model pipeline phases - that have never been exposed until now.
While most Hyperparameter Optimizers stop at the training phase (ie. learning rate, tree depth, ec2 instance type, etc), we extend model validation and tuning into a new post-training optimization phase including 8-bit reduced precision weight quantization and neural network layer fusing - among many other framework and hardware-specific optimizations.
Next, we introduce hyperparameters at the prediction phase including request-batch sizing and chipset (CPU v. GPU v. TPU).
Lastly, we determine a PipelineAI Efficiency Score of our overall Pipeline including Cost, Accuracy, and Time. We show techniques to maximize this PipelineAI Efficiency Score using our massive PipelineDB along with the Pipeline-wide hyper-parameter tuning techniques mentioned in this talk.
Bio
Chris Fregly is Founder and Applied AI Engineer at PipelineAI, a Real-Time Machine Learning and Artificial Intelligence Startup based in San Francisco.
He is also an Apache Spark Contributor, a Netflix Open Source Committer, founder of the Global Advanced Spark and TensorFlow Meetup, author of the O’Reilly Training and Video Series titled, "High Performance TensorFlow in Production with Kubernetes and GPUs."
Previously, Chris was a Distributed Systems Engineer at Netflix, a Data Solutions Engineer at Databricks, and a Founding Member and Principal Engineer at the IBM Spark Technology Center in San Francisco.
Speaker: Umayah Abdennabi
Agenda
* Intro Grammarly (Umayah Abdennabi, 5 mins)
* Meetup Updates and Announcements (Chris, 5 mins)
* Custom Functions in Spark SQL (30 mins)
Speaker: Umayah Abdennabi
Spark comes with a rich Expression library that can be extended to make custom expressions. We will look into custom expressions and why you would want to use them.
* TF 2.0 + Keras (30 mins)
Speaker: Francesco Mosconi
Tensorflow 2.0 was announced at the March TF Dev Summit, and it brings many changes and upgrades. The most significant change is the inclusion of Keras as the default model building API. In this talk, we'll review the main changes introduced in TF 2.0 and highlight the differences between open source Keras and tf.keras
* SQUAD Deep-Dive: Question & Answer with Context (45 mins)
Speaker: Brett Koonce (https://quarkworks.co)
SQuAD (Stanford Question Answer Dataset) is an NLP challenge based around answering questions by reading Wikipedia articles, designed to be a real-world machine learning benchmark. We will look at several different ways to tackle the SQuAD problem, building up to state of the art approaches in terms of time, complexity, and accuracy.
https://rajpurkar.github.io/SQuAD-explorer/
https://dawn.cs.stanford.edu/benchmark/#squad
Food and drinks will be provided. The event will be held at Grammarly's office at One Embarcadero Center on the 9th floor. When you arrive at One Embarcadero, take the escalator to the second floor where you will find the lobby and elevators to the office suites. Come on up to the 9th floor (no need to check in at security), and ring the Grammarly doorbell.
This is an introduction to polyaxon and why I use polyaxon.
Polyaxon enables me to leverage kubernetes to achieve the objectives:
- Make the lead time of experiments as short as possible.
- Make the financial cost to train models as cheap as possible.
- Make the experiments reproducible.
Kernel Recipes 2017: Using Linux perf at NetflixBrendan Gregg
Talk for Kernel Recipes 2017 by Brendan Gregg. "Linux perf is a crucial performance analysis tool at Netflix, and is used by a self-service GUI for generating CPU flame graphs and other reports. This sounds like an easy task, however, getting perf to work properly in VM guests running Java, Node.js, containers, and other software, has been at times a challenge. This talk summarizes Linux perf, how we use it at Netflix, the various gotchas we have encountered, and a summary of advanced features."
Now that you have your apps running on K8s, wondering how to get the response time that you need ? Tuning applications to get the performance that you need can be challenging. When you have to tune a number of microservices in Kubernetes to fix a response time or a throughput issue, it can get really overwhelming. This talk looks at some common performance issues and ways to solve them and more importantly the tools that can help you. We will also be specifically looking at Kruize that helps to not only right size your containers but also optimize the runtimes.
Secure Because Math: A Deep-Dive on Machine Learning-Based Monitoring (#Secur...Alex Pinto
We could all have predicted this with our magical Big Data analytics platforms, but it seems that Machine Learning is the new hotness in Information Security. A great number of startups with ‘cy’ and ‘threat’ in their names that claim that their product will defend or detect more effectively than their neighbour's product "because math". And it should be easy to fool people without a PhD or two that math just works.
Indeed, math is powerful and large scale machine learning is an important cornerstone of much of the systems that we use today. However, not all algorithms and techniques are born equal. Machine Learning is a most powerful tool box, but not every tool can be applied to every problem and that’s where the pitfalls lie.
This presentation will describe the different techniques available for data analysis and machine learning for information security, and discuss their strengths and caveats. The Ghost of Marketing Past will also show how similar the unfulfilled promises of deterministic and exploratory analysis were, and how to avoid making the same mistakes again.
Finally, the presentation will describe the techniques and feature sets that were developed by the presenter on the past year as a part of his ongoing research project on the subject, in particular present some interesting results obtained since the last presentation on DefCon 21, and some ideas that could improve the application of machine learning for use in information security, especially in its use as a helper for security analysts in incident detection and response.
Big Data Spain - Nov 17 2016 - Madrid Continuously Deploy Spark ML and Tensor...Chris Fregly
In this talk, I describe some recent advancements in Streaming ML and AI Pipelines to enable data scientists to rapidly train and test on streaming data - and ultimately deploy models directly into production on their own with low friction and high impact.
With proper tooling and monitoring, data scientist have the freedom and responsibility to experiment rapidly on live, streaming data - and deploy directly into production as often as necessary. I’ll describe this tooling - and demonstrate a real production pipeline using Jupyter Notebook, Docker, Kubernetes, Spark ML, Kafka, TensorFlow, Jenkins, and Netflix Open Source.
Deploy Spark ML and Tensorflow AI Models from Notebooks to Microservices - No...Chris Fregly
In this completely 100% Open Source demo-based talk, Chris Fregly from PipelineIO will be addressing an area of machine learning and artificial intelligence that is often overlooked: the real-time, end-user-facing "serving” layer in a hybrid-cloud and on-premise deployment environment using Jupyter, NetflixOSS, Docker, and Kubernetes.
Serving models to end-users in real-time in a highly-scalable, fault-tolerant manner requires not only an understanding of machine learning fundamentals, but also an understanding of distributed systems and scalable microservices.
Chris will combine his work experience from both Databricks and Netflix to present a 100% open source, real-world, hybrid-cloud, on-premise, and NetflixOSS-based production-ready environment to serve your notebook-based Spark ML and TensorFlow AI models with highly-scalable and highly-available robustness.
Speaker Bio
Chris Fregly is a Research Scientist at PipelineIO - a Streaming Analytics and Machine Learning Startup in San Francisco.
Chris is an Apache Spark Contributor, Netflix Open Source Committer, Founder of the Global Advanced Spark and TensorFlow Meetup, and Author of the upcoming book, Advanced Spark, and Creator of the upcoming O'Reilly video series, Scaling TensorFlow Distributed in Production.
Previously, Chris was an engineer at Databricks and Netflix - as well as a Founding Member of the IBM Spark Technology Center in San Francisco.
Advanced Spark and TensorFlow Meetup 08-04-2016 One Click Spark ML Pipeline D...Chris Fregly
Empowering the Data Scientist with "1-Click" Production Deployment and Canary Testing of High-Performance and Highly-Scalable Spark ML and TensorFlow Models directly from Jupyter/iPython Notebooks using Docker, Kubernetes, Netflix OSS, Microservices, and Spinnaker.
With proper tooling and metrics, Data Scientists can directly deploy, analyze, A/B test, rollback, and scale out their Spark ML and TensorFlow model into live production serving with zero friction.
We will show you the open source tools that we've built based on Docker, Kubernetes, Netflix Open Source, Microservices, Spinnaker - and even Chaos Monkey!
Speaker: Chris Fregly @ PipelineIO, formerly Databricks and Netflix
Gradient Descent, Back Propagation, and Auto Differentiation - Advanced Spark...Chris Fregly
Advanced Spark and TensorFlow Meetup 08-04-2016
Fundamental Algorithms of Neural Networks including Gradient Descent, Back Propagation, Auto Differentiation, Partial Derivatives, Chain Rule
Feature Talk: Real-time Aggregations, Approximations, Similarities, and Recommendations at Scale using Spark Streaming, ML, GraphX, Kafka, Cassandra, Docker, CoreNLP, Word2Vec, LDA, and Twitter Algebird
Talk Abstract: Starting with a live, interactive demo generating audience-specific recommendations, we'll dive deep into each of the key components including NiFi, Kafka, Stanford CoreNLP, Docker, Word2Vec, LDA, Twitter Algebird, Spark Streaming, SQL, ML, GraphX. As a bonus, we'll discuss the latest Netflix Recommendations Pipeline and related open source projects.
Talk Agenda:
• Intro
• Live, Interactive Recommendations Demo
• Spark Streaming, ML, GraphX, Kafka, Cassandra, Docker, CoreNLP, Word2Vec, LDA, and Twitter Algebird (advancedspark.com)
• Types of Similarity
• Euclidean vs. Non-Euclidean Similarity
• Jaccard Similarity
• Cosine Similarity
• LogLikelihood Similarity
• Edit Distance
• Text-based Similarities and Analytics
• Word2Vec
• LDA Topic Extraction
• TextRank
• Similarity-based Recommendations
• User-to-User
• Content-based, Item-to-Item (Amazon)
• Collaborative-based, User-to-Item (Netflix)
• Graph-based, Item-to-Item "Pathways" (Spotify)
• Aggregations, Approximations, and Similarities at Scale
• Twitter Algebird
• MinHash and Bucketing
• Locality Sensitive Hashing (LSH)
• BloomFilters
• CountMin Sketch
• HyperLogLog
• Q & A
Speaker Bio: Chris Fregly is a Research Engineer @ Flux Capacitor AI in SF, an Apache Spark Contributor, and a Netflix Open Source Committer.
Chris is also the founder of the global Advanced Apache Spark Meetup and author of the upcoming book, Advanced Spark @ advancedspark.com.
Previously, Chris was a Data Solutions Engineer at Databricks and a Streaming Data Engineer at Netflix.
Machine Learning without the Math: An overview of Machine LearningArshad Ahmed
A brief overview of Machine Learning and its associated tasks from a high level. This presentation discusses key concepts without the maths.The more mathematically inclined are referred to Bishops book on Pattern Recognition and Machine Learning.
qconsf 2013: Top 10 Performance Gotchas for scaling in-memory Algorithms - Sr...Sri Ambati
Top 10 Performance Gotchas in scaling in-memory Algorithms
Abstract:
Math Algorithms have primarily been the domain of desktop data science. With the success of scalable algorithms at Google, Amazon, and Netflix, there is an ever growing demand for sophisticated algorithms over big data. In this talk, we get a ringside view in the making of the world's most scalable and fastest machine learning framework, H2O, and the performance lessons learnt scaling it over EC2 for Netflix and over commodity hardware for other power users.
Top 10 Performance Gotchas is about the white hot stories of i/o wars, S3 resets, and muxers, as well as the power of primitive byte arrays, non-blocking structures, and fork/join queues. Of good data distribution & fine-grain decomposition of Algorithms to fine-grain blocks of parallel computation. It's a 10-point story of the rage of a network of machines against the tyranny of Amdahl while keeping the statistical properties of the data and accuracy of the algorithm.
Track: Scalability, Availability, and Performance: Putting It All Together
Time: Wednesday, 11:45am - 12:35pm
Optimizing, profiling and deploying high performance Spark ML and TensorFlow ...DataWorks Summit
Using the latest advancements from TensorFlow including the Accelerated Linear Algebra (XLA) Framework, JIT/AOT Compiler, and Graph Transform Tool , I’ll demonstrate how to optimize, profile, and deploy TensorFlow Models in GPU-based production environment.
This talk is contains many Spark ML and TensorFlow AI demos using PipelineIO's 100% Open Source Community Edition. All code and Docker images are available to reproduce on your own CPU or GPU-based cluster.
* Bio *
Chris Fregly is Founder and Research Engineer at PipelineIO, a Streaming Machine Learning and Artificial Intelligence Startup based in San Francisco. He is also an Apache Spark Contributor, a Netflix Open Source Committer, founder of the Global Advanced Spark and TensorFlow Meetup, author of the O’Reilly Video Series High Performance TensorFlow in Production.
Previously, Chris was a Distributed Systems Engineer at Netflix, a Data Solutions Engineer at Databricks, and a Founding Member of the IBM Spark Technology Center in San Francisco.
Despite the increase of deep learning practitioners and researchers, many of them do not use GPUs, this may lead to long training/evaluation cycles and non-practical research.
In his talk, Lior shares how to get started with GPUs and some of the best practices that helped him during research and work. The talk is for everyone who works with machine learning (deep learning experience is NOT mandatory!), It covers the very basics of how GPU works, CUDA drivers, IDE configuration, training, inference, and multi-GPU training.
Deep Learning with Apache Spark and GPUs with Pierce SpitlerDatabricks
Apache Spark is a powerful, scalable real-time data analytics engine that is fast becoming the de facto hub for data science and big data. However, in parallel, GPU clusters are fast becoming the default way to quickly develop and train deep learning models. As data science teams and data savvy companies mature, they will need to invest in both platforms if they intend to leverage both big data and artificial intelligence for competitive advantage.
This session will cover:
– How to leverage Spark and TensorFlow for hyperparameter tuning and for deploying trained models
– DeepLearning4J, CaffeOnSpark, IBM’s SystemML and Intel’s BigDL
– Sidecar GPU cluster architecture and Spark-GPU data reading patterns
– The pros, cons and performance characteristics of various approaches
You’ll leave the session better informed about the available architectures for Spark and deep learning, and Spark with and without GPUs for deep learning. You’ll also learn about the pros and cons of deep learning software frameworks for various use cases, and discover a practical, applied methodology and technical examples for tackling big data deep learning.
In this deck from Switzerland HPC Conference, Gunter Roeth from NVIDIA presents: Deep Learning on the SaturnV Cluster.
"Machine Learning is among the most important developments in the history of computing. Deep learning is one of the fastest growing areas of machine learning and a hot topic in both academia and industry. It has dramatically improved the state-of-the-art in areas such as speech recognition, computer vision, predicting the activity of drug molecules, and many other machine learning tasks. The basic idea of deep learning is to automatically learn to represent data in multiple layers of increasing abstraction, thus helping to discover intricate structure in large datasets. NVIDIA has invested in SaturnV, a large GPU-accelerated cluster, (#28 on the November 2016 Top500 list) to support internal machine learning projects. After an introduction to deep learning on GPUs, we will address a selection of open questions programmers and users may face when using deep learning for their work on these clusters."
Watch the video: http://wp.me/p3RLHQ-gDv
Learn more: http://www.nvidia.com/object/dgx-saturnv.html
and
http://hpcadvisorycouncil.com/events/2017/swiss-workshop/agenda.php
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Spark is a powerful, scalable real-time data analytics engine that is fast becoming the de facto hub for data science and big data. However, in parallel, GPU clusters is fast becoming the default way to quickly develop and train deep learning models. As data science teams and data savvy companies mature, they will need to invest in both platforms if they intend to leverage both big data and artificial intelligence for competitive advantage.
This talk will discuss and show in action:
* Leveraging Spark and Tensorflow for hyperparameter tuning
* Leveraging Spark and Tensorflow for deploying trained models
* An examination of DeepLearning4J, CaffeOnSpark, IBM's SystemML, and Intel's BigDL
* Sidecar GPU cluster architecture and Spark-GPU data reading patterns
* Pros, cons, and performance characteristics of various approaches
Attendees will leave this session informed on:
* The available architectures for Spark and Deep Learning and Spark with and without GPUs for Deep Learning
* Several deep learning software frameworks, their pros and cons in the Spark context and for various use cases, and their performance characteristics
* A practical, applied methodology and technical examples for tackling big data deep learning
Scaling out Tensorflow-as-a-Service on Spark and Commodity GPUsJim Dowling
Scaling out Tensorflow-as-a-Service on Spark and Commodity GPUs, including AllReduce, Horovod, and how commodity GPU servers, such as DeepLearning11, will gain adoption.
Apache Spark and Tensorflow as a Service with Jim DowlingSpark Summit
In Sweden, from the Rise ICE Data Center at www.hops.site, we are providing to reseachers both Spark-as-a-Service and, more recently, Tensorflow-as-a-Service as part of the Hops platform. In this talk, we examine the different ways in which Tensorflow can be included in Spark workflows, from batch to streaming to structured streaming applications. We will analyse the different frameworks for integrating Spark with Tensorflow, from Tensorframes to TensorflowOnSpark to Databrick’s Deep Learning Pipelines. We introduce the different programming models supported and highlight the importance of cluster support for managing different versions of python libraries on behalf of users. We will also present cluster management support for sharing GPUs, including Mesos and YARN (in Hops Hadoop). Finally, we will perform a live demonstration of training and inference for a TensorflowOnSpark application written on Jupyter that can read data from either HDFS or Kafka, transform the data in Spark, and train a deep neural network on Tensorflow. We will show how to debug the application using both Spark UI and Tensorboard, and how to examine logs and monitor training.
Apache Spark and Tensorflow as a Service with Jim DowlingSpark Summit
In Sweden, from the Rise ICE Data Center at www.hops.site, we are providing to reseachers both Spark-as-a-Service and, more recently, Tensorflow-as-a-Service as part of the Hops platform. In this talk, we examine the different ways in which Tensorflow can be included in Spark workflows, from batch to streaming to structured streaming applications. We will analyse the different frameworks for integrating Spark with Tensorflow, from Tensorframes to TensorflowOnSpark to Databrick’s Deep Learning Pipelines. We introduce the different programming models supported and highlight the importance of cluster support for managing different versions of python libraries on behalf of users. We will also present cluster management support for sharing GPUs, including Mesos and YARN (in Hops Hadoop). Finally, we will perform a live demonstration of training and inference for a TensorflowOnSpark application written on Jupyter that can read data from either HDFS or Kafka, transform the data in Spark, and train a deep neural network on Tensorflow. We will show how to debug the application using both Spark UI and Tensorboard, and how to examine logs and monitor training.
Accelerated Machine Learning with RAPIDS and MLflow, Nvidia/RAPIDSDatabricks
Accelerated Machine Learning with RAPIDS and MLflow, Nvidia/RAPIDS
Abstract: We will introduce RAPIDS, a suite of open source libraries for GPU-accelerated data science, and illustrate how it operates seamlessly with MLflow to enable reproducible training, model storage, and deployment. We will walk through a baseline example that incorporates MLflow locally, with a simple SQLite backend, and briefly introduce how the same workflow can be deployed in the context of GPU enabled Kubernetes clusters.
https://telecombcn-dl.github.io/2017-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or image captioning.
Distributed Deep Learning with Apache Spark and TensorFlow with Jim DowlingDatabricks
Methods that scale with available computation are the future of AI. Distributed deep learning is one such method that enables data scientists to massively increase their productivity by (1) running parallel experiments over many devices (GPUs/TPUs/servers) and (2) massively reducing training time by distributing the training of a single network over many devices. Apache Spark is a key enabling platform for distributed deep learning, as it enables different deep learning frameworks to be embedded in Spark workflows in a secure end-to-end pipeline. In this talk, we examine the different ways in which Tensorflow can be included in Spark workflows to build distributed deep learning applications.
We will analyse the different frameworks for integrating Spark with Tensorflow, from Horovod to TensorflowOnSpark to Databrick’s Deep Learning Pipelines. We will also look at where you will find the bottlenecks when training models (in your frameworks, the network, GPUs, and with your data scientists) and how to get around them. We will look at how to use Spark Estimator model to perform hyper-parameter optimization with Spark/TensorFlow and model-architecture search, where Spark executors perform experiments in parallel to automatically find good model architectures.
The talk will include a live demonstration of training and inference for a Tensorflow application embedded in a Spark pipeline written in a Jupyter notebook on the Hops platform. We will show how to debug the application using both Spark UI and Tensorboard, and how to examine logs and monitor training. The demo will be run on the Hops platform, currently used by over 450 researchers and students in Sweden, as well as at companies such as Scania and Ericsson.
Intro - End to end ML with Kubeflow @ SignalConf 2018Holden Karau
There are many great tools for training machine learning tools, ranging from sci-kit to Apache Spark, and tensorflow. However many of these systems largely leave open the question how to use our models outside of the batch world (like in a reactive application). Different options exist for persisting the results and using them for live training, and we will explore the trade-offs of the different formats and their corresponding serving/prediction layers.
TensorFlow meetup: Keras - Pytorch - TensorFlow.jsStijn Decubber
Slides from the TensorFlow meetup hosted on October 9th at the ML6 offices in Ghent. Join our Meetup group for updates and future sessions: https://www.meetup.com/TensorFlow-Belgium/
Pandas on AWS - Let me count the ways.pdfChris Fregly
Chris Fregly (Principal Solution Architect, AI and machine learning at AWS) will give a brief presentation on the various ways to perform scalable Pandas, Modin, and Ray on AWS. He will then answer questions from the audience and moderator, Alejandro Herrera (whatever he is) at Ponder.
Chris Fregly is a Principal Solution Architect for AI and Machine Learning at Amazon Web Services (AWS) based in San Francisco, California. He is the organizer of the Global Data Science on AWS meetup. He is co-author of the O'Reilly Book, "Data Science on AWS."
Related Links
O'Reilly Book: https://www.amazon.com/dp/1492079391/
Website: https://datascienceonaws.com
Meetup: https://meetup.datascienceonaws.com
GitHub Repo: https://github.com/data-science-on-aws/
YouTube: https://youtube.datascienceonaws.com
Slideshare: https://slideshare.datascienceonaws.com
Ray AI Runtime (AIR) on AWS - Data Science On AWS MeetupChris Fregly
RSVP Webinar: https://www.eventbrite.com/e/webinarkubeflow-tensorflow-tfx-pytorch-gpu-spark-ml-amazonsagemaker-tickets-45852865154
Talk #0: Introductions and Meetup Announcements By Chris Fregly and Antje Barth
Talk #1: Ray Overview, Ray AI Runtime on AWS using Amazon SageMaker, EC2, EMR, EKS by Chris Fregly, Principal Specialist Solution Architect, AI and Machine Learning @ AWS
Talk #2: Deep-dive Blueprints for Amazon Elastic Kubernetes Service (EKS) including Ray and Spark by Apoorva Kulkarni, Sr. Specialist Solution Architect, Containers and Kubernetes @ AWS
RSVP Webinar: https://www.eventbrite.com/e/webinarkubeflow-tensorflow-tfx-pytorch-gpu-spark-ml-amazonsagemaker-tickets-45852865154
Zoom link: https://us02web.zoom.us/j/82308186562
Related Links
O'Reilly Book: https://www.amazon.com/dp/1492079391/
Website: https://datascienceonaws.com
Meetup: https://meetup.datascienceonaws.com
GitHub Repo: https://github.com/data-science-on-aws/
YouTube: https://youtube.datascienceonaws.com
Slideshare: https://slideshare.datascienceonaws.com
Amazon reInvent 2020 Recap: AI and Machine LearningChris Fregly
Amazon reInvent 2020 Recap: AI and Machine Learning
Video here: https://youtu.be/YSXe02Y5pHM
NEW RELEASE! Build, Automate, Manage, and Scale ML Workflows with the NEW Amazon SageMaker Pipelines by Hallie Crosby Weishahn.
Description of Talk and Demo
AWS recently announced Amazon SageMaker Pipelines (https://aws.amazon.com/sagemaker/pipelines/), the first purpose-built, easy-to-use Continuous Integration and Continuous Delivery (CI/CD) service for machine learning.
SageMaker Pipelines has three main components which improve the operational resilience and reproducibility of your workflows: 1) pipelines, 2) model registry, and 3) projects.
In this talk and demo, Hallie will walk us through the new Amazon SageMaker Pipelines feature including MLOps support.
Date/Time
9-10am US Pacific Time (Third Monday of Every Month)
RSVP: https://www.eventbrite.com/e/1-hr-free-workshop-pipelineai-gpu-tpu-spark-ml-tensorflow-ai-kubernetes-kafka-scikit-tickets-45852865154
Meetup:
https://www.meetup.com/Data-Science-on-AWS/
Zoom:
https://zoom.us/j/690414331
Webinar ID: 690 414 331
Phone:
+1 646 558 8656 (US Toll) or +1 408 638 0968 (US Toll)
Related Links
Meetup: https://meetup.datascienceonaws.com
GitHub Repo: https://github.com/data-science-on-aws/
O'Reilly Book: https://datascienceonaws.com
YouTube: https://youtube.datascienceonaws.com
Slideshare: https://slideshare.datascienceonaws.com
Support: https://support.pipeline.ai
Monthly Workshop: https://www.eventbrite.com/e/full-day-workshop-kubeflow-gpu-kerastensorflow-20-tf-extended-tfx-kubernetes-pytorch-xgboost-tickets-63362929227
RSVP: https://www.eventbrite.com/e/1-hr-free-workshop-pipelineai-gpu-tpu-spark-ml-tensorflow-ai-kubernetes-kafka-scikit-tickets-45852865154
Waking the Data Scientist at 2am: Detect Model Degradation on Production Mod...Chris Fregly
Waking the Data Scientist at 2am:
Detect Model Degradation on Production Models with Amazon SageMaker Endpoints & Model Monitor
In this talk, I describe how to deploy a model into production and monitor its performance using SageMaker Model Monitor. With Model Monitor, I can detect if a model's predictive performance has degraded - and alert an on-call data scientist to take action and improve the model at 2am while the DevOps folks sleep soundly through the night.
Topics: AI and Machine Learning, Model Deployment, Anomaly Detection, Amazon SageMaker Endpoints, and Model Monitor
Quantum Computing with Amazon Braket
In this talk, I describe some fundamental principles of quantum computing including qu-bits, superposition, and entanglement. I will demonstrate how to perform secure quantum computing tasks across many Quantum Processing Units (QPUs) using Amazon Braket, IAM, and S3.
AI and Machine Learning, Quantum Computing, Amazon Braket, QPU
15 Tips to Scale a Large AI/ML Workshop - Both Online and In-PersonChris Fregly
In this talk, we present tips and best practices for scaling a large workshop for 1,000's of simultaneous attendees - both online and in-person. While our workshop is focused on AI and machine learning on AWS, we generalize our learnings for any domain or specialization.
Video: https://youtu.be/T0L0JxDaPkc
RSVP Here: https://www.eventbrite.com/e/full-day-workshop-kubeflow-kerastensorflow-20-tf-extended-tfx-kubernetes-pytorch-xgboost-airflow-tickets-63362929227
Description
In this workshop, we build real-world machine learning pipelines using TensorFlow Extended (TFX), KubeFlow, Airflow, and MLflow.
Described in the 2017 paper, TFX is used internally by thousands of Google data scientists and engineers across every major product line within Google.
KubeFlow is a modern, end-to-end pipeline orchestration framework that embraces the latest AI best practices including hyper-parameter tuning, distributed model training, and model tracking.
Airflow is the most-widely used pipeline orchestration framework in machine learning and data engineering.
MLflow is a lightweight experiment-tracking system recently open-sourced by Databricks, the creators of Apache Spark. MLflow supports Python, Java/Scala, and R - and offers native support for TensorFlow, Keras, and Scikit-Learn.
Pre-requisites
Modern browser - and that's it!
Every attendee will receive a cloud instance
Nothing will be installed on your local laptop
Everything can be downloaded at the end of the workshop
Location
Online Workshop
The link will be sent a few hours before the start of the workshop.
Only registered users will receive the link.
If you do not receive the link a few hours before the start of the workshop, please send your Eventbrite registration confirmation to support@pipeline.ai for help.
Agenda
1. Create a Kubernetes cluster
2. Install KubeFlow, Airflow, TFX, and Jupyter
3. Setup ML Training Pipelines with KubeFlow and Airflow
4. Transform Data with TFX Transform
5. Validate Training Data with TFX Data Validation
6. Train Models with Jupyter, Keras/TensorFlow 2.0, PyTorch, XGBoost, and KubeFlow
7. Run a Notebook Directly on Kubernetes Cluster with KubeFlow
8. Analyze Models using TFX Model Analysis and Jupyter
9. Perform Hyper-Parameter Tuning with KubeFlow
10. Select the Best Model using KubeFlow Experiment Tracking
11. Run Multiple Experiments with MLflow Experiment Tracking
12. Reproduce Model Training with TFX Metadata Store
13. Deploy the Model to Production with TensorFlow Serving and Istio
14. Save and Download your Workspace
Key Takeaways
Attendees will gain experience training, analyzing, and serving real-world Keras/TensorFlow 2.0 models in production using model frameworks and open-source tools.
RSVP Here: https://www.eventbrite.com/e/full-day-workshop-kubeflow-kerastensorflow-20-tf-extended-tfx-kubernetes-pytorch-xgboost-airflow-tickets-63362929227
https://youtu.be/T0L0JxDaPkc
Title
Hands-on Learning with KubeFlow + Keras/TensorFlow 2.0 + TF Extended (TFX) + Kubernetes + PyTorch + XGBoost + Airflow + MLflow + Spark + Jupyter + TPU
Video
https://youtu.be/vaB4IM6ySD0
Description
In this workshop, we build real-world machine learning pipelines using TensorFlow Extended (TFX), KubeFlow, and Airflow.
Described in the 2017 paper, TFX is used internally by thousands of Google data scientists and engineers across every major product line within Google.
KubeFlow is a modern, end-to-end pipeline orchestration framework that embraces the latest AI best practices including hyper-parameter tuning, distributed model training, and model tracking.
Airflow is the most-widely used pipeline orchestration framework in machine learning.
Pre-requisites
Modern browser - and that's it!
Every attendee will receive a cloud instance
Nothing will be installed on your local laptop
Everything can be downloaded at the end of the workshop
Location
Online Workshop
Agenda
1. Create a Kubernetes cluster
2. Install KubeFlow, Airflow, TFX, and Jupyter
3. Setup ML Training Pipelines with KubeFlow and Airflow
4. Transform Data with TFX Transform
5. Validate Training Data with TFX Data Validation
6. Train Models with Jupyter, Keras/TensorFlow 2.0, PyTorch, XGBoost, and KubeFlow
7. Run a Notebook Directly on Kubernetes Cluster with KubeFlow
8. Analyze Models using TFX Model Analysis and Jupyter
9. Perform Hyper-Parameter Tuning with KubeFlow
10. Select the Best Model using KubeFlow Experiment Tracking
11. Reproduce Model Training with TFX Metadata Store and Pachyderm
12. Deploy the Model to Production with TensorFlow Serving and Istio
13. Save and Download your Workspace
Key Takeaways
Attendees will gain experience training, analyzing, and serving real-world Keras/TensorFlow 2.0 models in production using model frameworks and open-source tools.
Related Links
1. PipelineAI Home: https://pipeline.ai
2. PipelineAI Community Edition: http://community.pipeline.ai
3. PipelineAI GitHub: https://github.com/PipelineAI/pipeline
4. Advanced Spark and TensorFlow Meetup (SF-based, Global Reach): https://www.meetup.com/Advanced-Spark-and-TensorFlow-Meetup
5. YouTube Videos: https://youtube.pipeline.ai
6. SlideShare Presentations: https://slideshare.pipeline.ai
7. Slack Support: https://joinslack.pipeline.ai
8. Web Support and Knowledge Base: https://support.pipeline.ai
9. Email Support: support@pipeline.ai
PipelineAI Continuous Machine Learning and AI - Rework Deep Learning Summit -...Chris Fregly
Traditional machine learning pipelines end with life-less models sitting on disk in the research lab. These traditional models are typically trained on stale, offline, historical batch data. Static models and stale data are not sufficient to power today's modern, AI-first Enterprises that require continuous model training, continuous model optimizations, and lightning-fast model experiments directly in production. Through a series of open source, hands-on demos and exercises, we will use PipelineAI to breathe life into these models using 4 new techniques that we’ve pioneered:
* Continuous Validation (V)
* Continuous Optimizing (O)
* Continuous Training (T)
* Continuous Explainability (E).
The Continuous "VOTE" techniques has proven to maximize pipeline efficiency, minimize pipeline costs, and increase pipeline insight at every stage from continuous model training (offline) to live model serving (online.)
Attendees will learn to create continuous machine learning pipelines in production with PipelineAI, TensorFlow, and Kafka.
PipelineAI Real-Time Machine Learning - Global Artificial Intelligence Confer...Chris Fregly
Perform Online Predictions using Slack
A/B and multi-armed bandit model compare
Train Online Models with Kafka Streams
Create new models quickly
Deploy to production safely
Mirror traffic to validate online performance
Any Framework, Any Hardware, Any Cloud
Dashboard to manage the lifecycle of models from local development to live production
Generates optimized runtimes for the models
Custom targeting rules, shadow mode, and percentage-based rollouts to safely test features in live production
Continuous model training, model validation, and pipeline optimization
https://youtu.be/zpkH9oiIovU
https://www.meetup.com/Advanced-Spark-and-TensorFlow-Meetup/events/258276286/
Related Links
PipelineAI Home: https://pipeline.ai
PipelineAI Community Edition: https://community.pipeline.ai
PipelineAI GitHub: https://github.com/PipelineAI/pipeline
PipelineAI Quick Start: https://quickstart.pipeline.ai
Advanced Spark and TensorFlow Meetup (SF-based, Global Reach): https://www.meetup.com/Advanced-Spark-and-TensorFlow-Meetup
YouTube Videos: https://youtube.pipeline.ai
SlideShare Presentations: https://slideshare.pipeline.ai
Slack Support:
https://joinslack.pipeline.ai
Web Support and Knowledge Base: https://support.pipeline.ai
Email Support: help@pipeline.ai
Advanced Spark and TensorFlow Meetup - Dec 12 2017 - Dong Meng, MapR + Kubern...Chris Fregly
https://www.meetup.com/Advanced-Spark-and-TensorFlow-Meetup/events/244971261/
Based on this blog post: https://mengdong.github.io/2017/07/15/distributed-tensorflow-with-gpu-on-kubernetes-and-mapr/
youtube video:
https://www.youtube.com/watch?v=3phz1_B-rR4
http://pipeline.ai
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
✅Use Our AI-powered Traffic To Get Hundreds Of Customers
✅No Complicated Setup: Get Up And Running In 2 Minutes
✅99.99% Up-Time Guaranteed
✅30 Days Money-Back Guarantee
✅ZERO Upfront Cost
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
Understanding Globus Data Transfers with NetSageGlobus
NetSage is an open privacy-aware network measurement, analysis, and visualization service designed to help end-users visualize and reason about large data transfers. NetSage traditionally has used a combination of passive measurements, including SNMP and flow data, as well as active measurements, mainly perfSONAR, to provide longitudinal network performance data visualization. It has been deployed by dozens of networks world wide, and is supported domestically by the Engagement and Performance Operations Center (EPOC), NSF #2328479. We have recently expanded the NetSage data sources to include logs for Globus data transfers, following the same privacy-preserving approach as for Flow data. Using the logs for the Texas Advanced Computing Center (TACC) as an example, this talk will walk through several different example use cases that NetSage can answer, including: Who is using Globus to share data with my institution, and what kind of performance are they able to achieve? How many transfers has Globus supported for us? Which sites are we sharing the most data with, and how is that changing over time? How is my site using Globus to move data internally, and what kind of performance do we see for those transfers? What percentage of data transfers at my institution used Globus, and how did the overall data transfer performance compare to the Globus users?
Cyaniclab : Software Development Agency Portfolio.pdfCyanic lab
CyanicLab, an offshore custom software development company based in Sweden,India, Finland, is your go-to partner for startup development and innovative web design solutions. Our expert team specializes in crafting cutting-edge software tailored to meet the unique needs of startups and established enterprises alike. From conceptualization to execution, we offer comprehensive services including web and mobile app development, UI/UX design, and ongoing software maintenance. Ready to elevate your business? Contact CyanicLab today and let us propel your vision to success with our top-notch IT solutions.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Navigating the Metaverse: A Journey into Virtual Evolution"Donna Lenk
Join us for an exploration of the Metaverse's evolution, where innovation meets imagination. Discover new dimensions of virtual events, engage with thought-provoking discussions, and witness the transformative power of digital realms."
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
Custom Healthcare Software for Managing Chronic Conditions and Remote Patient...Mind IT Systems
Healthcare providers often struggle with the complexities of chronic conditions and remote patient monitoring, as each patient requires personalized care and ongoing monitoring. Off-the-shelf solutions may not meet these diverse needs, leading to inefficiencies and gaps in care. It’s here, custom healthcare software offers a tailored solution, ensuring improved care and effectiveness.
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
Enterprise Resource Planning System includes various modules that reduce any business's workload. Additionally, it organizes the workflows, which drives towards enhancing productivity. Here are a detailed explanation of the ERP modules. Going through the points will help you understand how the software is changing the work dynamics.
To know more details here: https://blogs.nyggs.com/nyggs/enterprise-resource-planning-erp-system-modules/
Software Engineering, Software Consulting, Tech Lead.
Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
Spring Transaction, Spring MVC,
Log4j, REST/SOAP WEB-SERVICES.
OpenFOAM solver for Helmholtz equation, helmholtzFoam / helmholtzBubbleFoamtakuyayamamoto1800
In this slide, we show the simulation example and the way to compile this solver.
In this solver, the Helmholtz equation can be solved by helmholtzFoam. Also, the Helmholtz equation with uniformly dispersed bubbles can be simulated by helmholtzBubbleFoam.
Providing Globus Services to Users of JASMIN for Environmental Data AnalysisGlobus
JASMIN is the UK’s high-performance data analysis platform for environmental science, operated by STFC on behalf of the UK Natural Environment Research Council (NERC). In addition to its role in hosting the CEDA Archive (NERC’s long-term repository for climate, atmospheric science & Earth observation data in the UK), JASMIN provides a collaborative platform to a community of around 2,000 scientists in the UK and beyond, providing nearly 400 environmental science projects with working space, compute resources and tools to facilitate their work. High-performance data transfer into and out of JASMIN has always been a key feature, with many scientists bringing model outputs from supercomputers elsewhere in the UK, to analyse against observational or other model data in the CEDA Archive. A growing number of JASMIN users are now realising the benefits of using the Globus service to provide reliable and efficient data movement and other tasks in this and other contexts. Further use cases involve long-distance (intercontinental) transfers to and from JASMIN, and collecting results from a mobile atmospheric radar system, pushing data to JASMIN via a lightweight Globus deployment. We provide details of how Globus fits into our current infrastructure, our experience of the recent migration to GCSv5.4, and of our interest in developing use of the wider ecosystem of Globus services for the benefit of our user community.
Listen to the keynote address and hear about the latest developments from Rachana Ananthakrishnan and Ian Foster who review the updates to the Globus Platform and Service, and the relevance of Globus to the scientific community as an automation platform to accelerate scientific discovery.
Large Language Models and the End of ProgrammingMatt Welsh
Talk by Matt Welsh at Craft Conference 2024 on the impact that Large Language Models will have on the future of software development. In this talk, I discuss the ways in which LLMs will impact the software industry, from replacing human software developers with AI, to replacing conventional software with models that perform reasoning, computation, and problem-solving.
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...Juraj Vysvader
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I didn't get rich from it but it did have 63K downloads (powered possible tens of thousands of websites).
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...
High Performance Distributed TensorFlow with GPUs - TensorFlow Chicago Meetup - June 22 2017
1. HIGH PERFORMANCE TENSORFLOW
IN PRODUCTION + GPUS!
CHRIS FREGLY, RESEARCH ENGINEER
@ PIPELINE.AI
TENSORFLOW CHICAGO MEETUP
JUNE 22, 2017 @ DEPAUL UNIVERSITY
I MISS YOU, CHICAGO!! (IN THE SUMMER…)
3. INTRODUCTIONS: ME
§ Chris Fregly, Research Engineer @
§ Formerly Netflix and Databricks
§ Advanced Spark and TensorFlow Meetup
Please Join Our 18,000+ Members Globally!!
* San Francisco
* Chicago
* Washington DC
* London
Please
Join!!
4. INTRODUCTIONS: YOU
§ Software Engineer or Data Scientist interested in optimizing
and deploying TensorFlow models to production
§ Assume you have a working knowledge of TensorFlow
6. CONTENT BREAKDOWN
§ 50% Training Optimizations (TensorFlow, XLA, Tools)
§ 50% Deployment and Inference Optimizations (Serving)
§ Why Heavy Focus on Inference?
§ Training: boring batch, O(num_data_scientists)
§ Inference: exciting realtime, O(num_users_of_app)
§ We Use Simple Models to Highlight Optimizations
§
Warning: This is not introductory TensorFlow material!
7. 100% OPEN SOURCE CODE
§ https://github.com/fluxcapacitor/pipeline/
§ Please Star this Repo! J
§ Slides, code, notebooks, Docker images available here:
https://github.com/fluxcapacitor/pipeline/
gpu.ml
8. YOU WILL LEARN…
§ TensorFlow Best Practices
§ To Inspect and Debug Models
§ To Distribute Training Across a Cluster
§ To Optimize Training with Queue Feeders
§ To Optimize Training with XLA JIT Compiler
§ To Optimize Inference with AOT and Graph Transform Tool (GTT)
§ Key Components of TensorFlow Serving
§ To Deploy Models with TensorFlow Serving
§ To Optimize Inference by Tuning TensorFlow Serving
9. AGENDA
§ Setup Environment
§ Train and Debug TensorFlow Model
§ Train with Distributed TensorFlow Cluster
§ Optimize Model with XLA JIT Compiler
§ Optimize Model with XLA AOT and Graph Transforms
§ Deploy Model to TensorFlow Serving Runtime
§ Optimize TensorFlow Serving Runtime
§ Wrap-up and Q&A
11. GPU HALF-PRECISION SUPPORT
§ FP16, INT8 are “Half Precision”
§ Supported by Pascal P100 (2016) and Volta V100 (2017)
§ Flexible FP32 GPU Cores Can Fit 2 FP16’s for 2x Throughput!
§ Half-Precision is OK for Approximate Deep Learning Use Cases
12. VOLTA V100 RECENTLY ANNOUNCED
§ 84 Streaming Multiprocessors (SM’s)
§ 5,376 GPU Cores
§ 672 Tensor Cores (ie. Google TPU)
§ Mixed FP16/FP32 Precision
§ More Shared Memory
§ New L0 Instruction Cache
§ Faster L1 Data Cache
§ V100 vs. P100 Performance
§ 12x TFLOPS @ Peak Training
§ 6x Inference Throughput
13. V100 AND CUDA 9
§ Independent Thread Scheduling - Finally!!
§ Similar to CPU fine-grained thread synchronization semantics
§ Allows GPU to yield execution of any thread
§ Still Optimized for SIMT (Same Instruction Multiple Thread)
§ SIMT units automatically scheduled together
§ Explicit Synchronization
P100 V100
14. GPU CUDA PROGRAMMING
§ Barbaric, But Fun Barbaric!
§ Must Know Underlying Hardware Very Well
§ Many Great Debuggers/Profilers
§ Hardware Changes are Painful!
§ Newer CUDA compiler
automatically JIT-compilesold
CUDA code to new NVPTX
§ Not optimal, of course
15. CUDA STREAMS
§ Asynchronous I/O Transfer
§ Overlap Compute and I/O
§ Keeps GPUs Saturated
§ Fundamental to Queue Framework in TensorFlow
16. AGENDA
§ Setup Environment
§ Train and Debug TensorFlow Model
§ Train with Distributed TensorFlow Cluster
§ Optimize Model with XLA JIT Compiler
§ Optimize Model with XLA AOT and Graph Transforms
§ Deploy Model to TensorFlow Serving Runtime
§ Optimize TensorFlow Serving Runtime
§ Wrap-up and Q&A
17. TRAINING TERMINOLOGY
§ Tensors: N-Dimensional Arrays
§ ie. Scalar, Vector, Matrix
§ Operations: MatMul, Add, SummaryLog,…
§ Graph: Graph of Operations (DAG)
§ Session: ContainsGraph(s)
§ Feeds: Feed inputs into Operation
§ Fetches: Fetch output from Operation
§ Variables: What we learn through training
§ aka “weights”, “parameters”
§ Devices: Hardware device on which we train
-TensorFlow-
Trains
Variables
-User-
Fetches
Outputs
-User-
Feeds
Inputs
-TensorFlow-
Performs
Operations
-TensorFlow-
Flows
Tensors
with tf.device(“worker:0/device/gpu:0,worker:1/device/gpu:0”)
18. TRAINING DEVICES
§ cpu:0
§ By default, all CPUs
§ Requires extra config to target a CPU
§ gpu:0..n
§ Each GPU has a unique id
§ TF usually prefers a single GPU
§ xla_cpu:0, xla_gpu:0..n
§ “JIT Compiler Device”
§ Hints TensorFlow to attempt JIT Compile
with tf.device(“/cpu:0”):
with tf.device(“/gpu:0”):
with tf.device(“/gpu:1”):
19. TRAINING METRICS: TENSORBOARD
§ Summary Ops
§ Event Files
/root/tensorboard/linear/<version>/events…
§ Tags
§ Organize data within Tensorboard UI
loss_summary_op = tf.summary.scalar('loss',
loss)
merge_all_summary_op = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(
'/root/tensorboard/linear/<version>',
graph=sess.graph)
20. TRAINING ON EXISTING INFRASTRUCTURE
§ Data Processing
§ HDFS/Hadoop
§ Spark
§ Containers
§ Docker
§ Schedulers
§ Kubernetes
§ Mesos
<dependency>
<groupId>org.tensorflow</groupId>
<artifactId>tensorflow-hadoop</artifactId>
<version>1.0-SNAPSHOT</version>
</dependency>
https://github.com/tensorflow/ecosystem
21. TRAINING PIPELINES
§ Don’t Use feed_dict for Production Workloads!!
§ feed_dict Requires Python <-> C++ Serialization
§ Retrieval is Single-threaded, Synchronous, SLOW!
§ Can’t Retrieve Until Current Batch is Complete
§ CPUs/GPUs Not Fully Utilized!
§ Use Queue or Dataset API
22. QUEUES
§ More than Just a Traditional Queue
§ Perform I/O, pre-processing, cropping, shuffling
§ Pulls from HDFS, S3, Google Storage, Kafka, ...
§ Combine many small files into large TFRecord files
§ Typically use CPUs to focus GPUs on compute
§ Uses CUDA Streams
23. DATA MOVEMENT WITH QUEUES
§ GPU Pulls Batch from Queue (CUDA Streams)
§ GPU pulls next batch while processing current batch
GPUs Stay Fully Utilized!
24. QUEUE CAPACITY PLANNING
§ batch_size
§ # examples / batch (ie. 64 jpg)
§ Limited by GPU RAM
§ num_processing_threads
§ CPU threads pull and pre-process batches of data
§ Limited by CPU Cores
§ queue_capacity
§ Limited by CPU RAM (ie. 5 * batch_size)
25. DETECT UNDERUTILIZED CPUS, GPUS
§ Instrument training code to generate “timelines”
§ Analyze with Google Web
Tracing Framework (WTF)
§ Monitor CPU with `top`, GPU with `nvidia-smi`
http://google.github.io/tracing-framework/
from tensorflow.python.client import timeline
trace =
timeline.Timeline(step_stats=run_metadata.step_stats)
with open('timeline.json', 'w') as trace_file:
trace_file.write(
trace.generate_chrome_trace_format(show_memory=True))
26. TENSORFLOW MODEL
§ MetaGraph
§ Combines GraphDef and Metadata
§ GraphDef
§ Architecture of your model (nodes, edges)
§ Metadata
§ Asset: Accompanying assets to your model
§ SignatureDef: Maps external : internal tensors
§ Variables
§ Stored separately during training (checkpoint)
§ Allows training to continue from any checkpoint
§ Variables are “frozen” into Constants when deployed for inference
GraphDef
x
W
mul add
b
MetaGraph
Metadata
Assets
SignatureDef
Tags
Version
Variables:
“W” : 0.328
“b” : -1.407
28. TENSORFLOW DEBUGGER
§ Step through Operations
§ Inspect Inputs and Outputs
§ Wrap Session in Debug Session
sess = tf.Session(config=config)
sess =
tf_debug.LocalCLIDebugWrapperSession(sess)
29. BATCH NORMALIZATION
§ Each Mini-Batch May Have Wildly Different Distributions
§ Normalize per batch (and layer)
§ Speeds up Training!!
§ Weights are Learned Quicker
§ Final Model is More Accurate
§ Final mean and variance will be folded into Graph later
-- Always Use Batch Normalization! --
z = tf.matmul(a_prev, W)
a = tf.nn.relu(z)
a_mean, a_var = tf.nn.moments(a, [0])
scale = tf.Variable(tf.ones([depth/channels]))
beta = tf.Variable(tf.zeros ([depth/channels]))
bn = tf.nn.batch_normalizaton(a, a_mean, a_var,
beta, scale, 0.001)
30. MULTI-GPU TRAINING (SINGLE NODE)
§ Variables stored on CPU (cpu:0)
§ Model graph (aka “replica”, “tower”)
is copied to each GPU(gpu:0, gpu:1, …)
Multi-GPU Training Steps:
1. CPU transfersmodel to each GPU
2. CPU waitson all GPUs to finish batch
3. CPU copiesall gradientsback from all GPUs
4. CPU synchronizesand averagesall gradientsfrom GPUs
5. CPU updatesGPUs with new variables/weights
6. Repeat Step 1 until reaching stop condition (ie. max_epochs)
31. DISTRIBUTED, MULTI-NODE TRAINING
§ TensorFlow Automatically Inserts Send and Receive Ops into Graph
§ Parameter Server Synchronously AggregatesUpdates to Variables
§ Nodes with Multiple GPUs will Pre-Aggregate Before Sending to PS
Worker0 Worker0
Worker1
Worker0 Worker1 Worker2
gpu0 gpu1
gpu2 gpu3
gpu0 gpu1
gpu2 gpu3
gpu0 gpu1
gpu2 gpu3
gpu0
gpu1
gpu0
gpu0
32. SYNCHRONOUS VS. ASYNCHRONOUS
§ Synchronous
§ Nodes compute gradients
§ Nodes update Parameter Server (PS)
§ Nodes sync on PS for latest gradients
§ Asynchronous
§ Some nodes delay in computing gradients
§ Nodes don’t update PS
§ Nodes get stale gradients from PS
§ May not converge due to stale reads!
33. DATA PARALLEL VS MODEL PARALLEL
§ Data Parallel (“Between-Graph Replication”)
§ Send exact same model to each device
§ Each device operates on its partition of data
§ ie. Spark sends same function to many workers
§ Each worker operateson their partition of data
§ Model Parallel (“In-Graph Replication”)
§ Send different partition of model to each device
§ Each device operates on all data
Very Difficult!!
Requiredfor Large Models.
(GPU RAM Limitation)
34. DISTRIBUTED TENSORFLOW CONCEPTS
§ Client
§ Program that builds a TF Graph, constructs a session,interacts with the cluster
§ Written in Python, C++
§ Cluster
§ Set of distributed nodes executing a graph
§ Nodes can play any role
§ Jobs (“Roles”)
§ Parameter Server (“ps”)stores andupdates variables
§ Worker (“worker”) performs compute-intensive tasks (stateless)
§ Assigned 0..* tasks
§ Task (“Server Process”)
“ps” and “worker” are
conventional names
35. CHIEF WORKER
§ Worker Task 0 is Chosen by Default
§ Task 0 is guaranteed to exist
§ Implements Maintenance Tasks
§ Writes checkpoints
§ Initializes parameters at start of training
§ Writes log summaries
§ Parameter Server health checks
36. NODE AND PROCESS FAILURES
§ Checkpoint to Persistent Storage (HDFS, S3)
§ Use MonitoredTrainingSession and Hooks
§ Use a Good Cluster Orchestrator (ie. Kubernetes,Mesos)
§ Understand Failure Modes and Recovery States
Stateless, Not Bad: Training Continues Stateful, Bad: TrainingMustStop Dios Mio! Long NightAhead…
37. VALIDATING DISTRIBUTED MODEL
§ Separate Training and Validation Clusters
§ Validate using Saved Checkpoints from Parameter Servers
§ Avoids Resource Contention
Training
Cluster
Validation
Cluster
Parameter Server
Cluster
38. EXPERIMENT AND ESTIMATOR API
§ Higher-Level APIs Simplify Distributed Training
§ Picks Up Configuration from Environment
§ Supports Custom Models (ie. Keras)
§ Used for Training, Validation, and Prediction
§ API is Changing, but Patterns Remain the Same
§ Works Well with Google Cloud ML (Surprised?!)
39. AGENDA
§ Setup Environment
§ Train and Debug TensorFlow Model
§ Train with Distributed TensorFlow Cluster
§ Optimize Model with XLA JIT Compiler
§ Optimize Model with XLA AOT and Graph Transforms
§ Deploy Model to TensorFlow Serving Runtime
§ Optimize TensorFlow Serving Runtime
§ Wrap-up and Q&A
40. XLA FRAMEWORK
§ Accelerated Linear Algebra (XLA)
§ Goals:
§ Reduce reliance on custom operators
§ Improve execution speed
§ Improve memory usage
§ Reduce mobile footprint
§ Improve portability
§ Helps TF Stay Flexible and Performant
41. XLA HIGH LEVEL OPTIMIZER (HLO)
§ Compiler Intermediate Representation (IR)
§ Independent of source and target language
§ Define Graphs using HLO Language
§ XLA Step 1 Emits Target-IndependentHLO
§ XLA Step 2 Emits Target-DependentLLVM
§ LLVM Emits Native Code Specific to Target
§ Supports x86-64, ARM64 (CPU), and NVPTX (GPU)
42. JIT COMPILER
§ Just-In-Time Compiler
§ Built on XLA Framework
§ Goals:
§ Reduce memory movement – especiallyuseful on GPUs
§ Reduce overhead of multiple function calls
§ Similar to Spark Operator Fusing in Spark 2.0
§ Unroll Loops, Fuse Operators, Fold Constants, …
§ Scope to session, device, or `with jit_scope():`
43. VISUALIZING JIT COMPILER IN ACTION
Before After
Google Web Tracing Framework:
http://google.github.io/tracing-framework/
from tensorflow.python.client import timeline
trace =
timeline.Timeline(step_stats=run_metadata.step_stats)
with open('timeline.json', 'w') as trace_file:
trace_file.write(
trace.generate_chrome_trace_format(show_memory=True))
45. IT’S WORTH HIGHLIGHTING…
§ From Now On, We Optimize Trained Models For Inference
§ In Other Words,
We’re Done with Training! Yeah!!
46. AGENDA
§ Setup Environment
§ Train and Debug TensorFlow Model
§ Train with Distributed TensorFlow Cluster
§ Optimize Model with XLA JIT Compiler
§ Optimize Model with XLA AOT and Graph Transforms
§ Deploy Model to TensorFlow Serving Runtime
§ Optimize TensorFlow Serving Runtime
§ Wrap-up and Q&A
47. AOT COMPILER
§ Standalone, Ahead-Of-Time (AOT) Compiler
§ Built on XLA framework
§ tfcompile
§ Creates executable with minimal TensorFlow Runtime needed
§ Includes only dependenciesneeded by subgraph computation
§ Creates functions with feeds (inputs) and fetches (outputs)
§ Packaged ascc_libary header and object filesto link into your app
§ Commonly used for mobile device inference graph
§ Currently, only CPU x86-64 and ARM are supported - no GPU
48. GRAPH TRANSFORM TOOL (GTT)
§ Optimize Trained Models for Inference
§ Remove training-only Ops (checkpoint, drop out, logs)
§ Remove unreachable nodes between given feed -> fetch
§ Fuse adjacent operators to improve memory bandwidth
§ Fold final batch norm mean and variance into variables
§ Round weights/variables improves compression (ie. 70%)
§ Quantize weights and activations simplifies model
§ FP32 down to INT8
57. ACTIVATION QUANTIZATION
§ Activations Not Known Ahead of Time
§ Dependson input, not easy to quantize
§ Requires Additional Calibration Step
§ Use a “representative”dataset
§ Per Neural Network Layer…
§ Collect histogram of activation values
§ Generate many quantized distributionswith different saturation thresholds
§ Choose threshold to minimize…
KL_divergence(ref_distribution, quant_distribution)
§ Not Much Time or Data is Required (Minutes on Commodity Hardware)
60. AGENDA
§ Setup Environment
§ Train and Debug TensorFlow Model
§ Train with Distributed TensorFlow Cluster
§ Optimize Model with XLA JIT Compiler
§ Optimize Model with XLA AOT and Graph Transforms
§ Deploy Model to TensorFlow Serving Runtime
§ Optimize TensorFlow Serving Runtime
§ Wrap-up and Q&A
61. MODEL SERVING TERMINOLOGY
§ Inference
§ Only Forward Propagation through Network
§ Predict, Classify, Regress, …
§ Bundle
§ GraphDef, Variables, Metadata, …
§ Assets
§ ie. Map of ClassificationID -> String
§ {9283: “penguin”, 9284: “bridge”, …}
§ Version
§ Every Model Has a Version Number (Integers Only?!)
§ Version Policy
§ ie. Serve Only Latest (Highest), Serve both Latest and Previous, …
62. TENSORFLOW SERVING FEATURES
§ Low-latency or High-throughput Tuning
§ Supports Auto-Scaling
§ DifferentModels/Versions Served in Same Process
§ Custom Loaders beyond File-based
§ Custom Serving Models beyond HashMap and TensorFlow
§ Custom Version Policies for A/B and Bandit Tests
§ Drain Requests for Graceful Model Shutdown / Update
§ Extensible Request Batching Strategies for Diff Use Cases and HW
§ Uses Highly-Efficient GRPC and Protocol Buffers
63. PREDICTION SERVICE
§ Predict (Original, Generic)
§ Input: List of Tensors
§ Output: List of Tensors
§ Classify
§ Input: List of `tf.Example` (key, value) pairs
§ Output: List of (class_label: String, score: float)
§ Regress
§ Input: List of `tf.Example` (key, value) pairs
§ Output: List of (label: String, score: float)
65. MULTI-HEADED INFERENCE
§ Multiple “Heads” of Model
§ Return class and scores to be fed into another model
§ Inputs Propagated Forward Only Once
§ Optimizes Bandwidth, CPU, Latency, Memory, Coolness
66. BUILD YOUR OWN MODEL SERVER (?!)
§ Adapt GRPC(Google) <-> HTTP (REST of the World)
§ Perform Batch Inference vs. Request/Response
§ Handle Requests Asynchronously
§ Support Mobile, Embedded Inference
§ Customize Request Batching
§ Add Circuit Breakers, Fallbacks
§ Control Latency Requirements
§ Reduce Number of Moving Parts
#include “tensorflow_serving/model_servers/server_core.h”
…
class MyTensorFlowModelServer {
ServerCore::Options options;
// set options (model name, path, etc)
std::unique_ptr<ServerCore> core;
TF_CHECK_OK(
ServerCore::Create(std::move(options), &core)
);
}
Compile and Link with
libtensorflow.so
67. FREEZING MODEL FOR DEPLOYMENT
§ Optimizations
§ freeze_graph
§ Results
§ Variables -> Constants
Finally!!
We’re Ready to Deploy…
68. AGENDA
§ Setup Environment
§ Train and Debug TensorFlow Model
§ Train with Distributed TensorFlow Cluster
§ Optimize Model with XLA JIT Compiler
§ Optimize Model with XLA AOT and Graph Transforms
§ Deploy Model to TensorFlow Serving Runtime
§ Optimize TensorFlow Serving Runtime
§ Wrap-up and Q&A
69. REQUEST BATCH TUNING
§ max_batch_size
§ Enables throughput/latency tradeoff
§ Bounded by RAM
§ batch_timeout_micros
§ Defines batch time window, latency upper-bound
§ Bounded by RAM
§ num_batch_threads
§ Defines parallelism
§ Bounded by CPU cores
§ max_enqueued_batches
§ Defines queue upper bound, throttling
§ Bounded by RAM
Reaching either threshold
will trigger a batch
70. BATCH SCHEDULER STRATEGIES
§ BasicBatchScheduler
§ Best for homogeneous request types (ie. always classify or always regress)
§ Async callback upon max_batch_size or batch_timeout_micros
§ BatchTask encapsulates unit of work to be batched
§ SharedBatchScheduler
§ Best for heterogeneous request types, multi-step inference, ensembles, …
§ Groups BatchTasks into separate queues to form homogenous batches
§ Processes batches fairly through interleaving
§ StreamingBatchScheduler
§ Mixed CPU/GPU/IO-bound workloads
§ Provides fine-grained control for complex, multi-phase inference logic
Must Experiment to Find
the Best Strategy for You!!
72. AGENDA
§ Setup Environment
§ Train and Debug TensorFlow Model
§ Train with Distributed TensorFlow Cluster
§ Optimize Model with XLA JIT Compiler
§ Optimize Model with XLA AOT and Graph Transforms
§ Deploy Model to TensorFlow Serving Runtime
§ Optimize TensorFlow Serving Runtime
§ Wrap-up and Q&A
73. YOU JUST LEARNED…
§ TensorFlow Best Practices
§ To Inspect and Debug Models
§ To Distribute Training Across a Cluster
§ To Optimize Training with Queue Feeders
§ To Optimize Training with XLA JIT Compiler
§ To Optimize Inference with AOT and Graph Transform Tool (GTT)
§ Key Components of TensorFlow Serving
§ To Deploy Models with TensorFlow Serving
§ To Optimize Inference by Tuning TensorFlow Serving