The document discusses techniques for distributed TensorFlow on Hops. It discusses how distributed deep learning is important for improving models through increased computation and larger training datasets. It describes how Hops provides an integrated platform for machine learning pipelines that supports distributed training, hyperparameter optimization, model serving, and data processing using Spark, TensorFlow and Kubernetes. Hops addresses limitations of other platforms by providing integrated security, high performance distributed storage, and ease of use through fully managed services.
Hopsworks at Google AI Huddle, SunnyvaleJim Dowling
Hopsworks is a platform for designing and operating End to End Machine Learning using PySpark and TensorFlow/PyTorch. Early access is now available on GCP. Hopsworks includes the industry's first Feature Store. Hopsworks is open-source.
Tom Peters, Software Engineer, Ufora at MLconf ATL 2016MLconf
Say What You Mean: Scaling Machine Learning Algorithms Directly from Source Code: Scaling machine learning applications is hard. Even with powerful systems like Spark, Tensor Flow, and Theano, the code you write has more to do with getting these systems to work at all than it does with your algorithm itself. But it doesn’t have to be this way!
In this talk, I’ll discuss an alternate approach we’ve taken with Pyfora, an open-source platform for scalable machine learning and data science in Python. I’ll show how it produces efficient, large scale machine learning implementations directly from the source code of single-threaded Python programs. Instead of programming to a complex API, you can simply say what you mean and move on. I’ll show some classes of problem where this approach truly shines, discuss some practical realities of developing the system, and I’ll talk about some future directions for the project.
PyMADlib - A Python wrapper for MADlib : in-database, parallel, machine learn...Srivatsan Ramanujam
These are slides from my talk @ DataDay Texas, in Austin on 30 Mar 2013
(http://2013.datadaytexas.com/schedule)
Favorite and Fork PyMADlib on GitHub: https://github.com/gopivotal/pymadlib
MADlib: http://madlib.net
Hussein Mehanna, Engineering Director, ML Core - Facebook at MLconf ATL 2016MLconf
Applying Deep Learning at Facebook Scale: Facebook leverages Deep Learning for various applications including event prediction, machine translation, natural language understanding and computer vision at a very large scale. There are more than a billion users logging on to Facebook every daily generating thousands of posts per second and uploading more than a billion images and videos every day. This talk will explain how Facebook scaled Deep Learning inference for realtime applications with latency budgets in the milliseconds.
Separating Hype from Reality in Deep Learning with Sameer FarooquiDatabricks
Deep Learning is all the rage these days, but where does the reality of what Deep Learning can do end and the media hype begin? In this talk, I will dispel common myths about Deep Learning that are not necessarily true and help you decide whether you should practically use Deep Learning in your software stack.
I’ll begin with a technical overview of common neural network architectures like CNNs, RNNs, GANs and their common use cases like computer vision, language understanding or unsupervised machine learning. Then I’ll separate the hype from reality around questions like:
• When should you prefer traditional ML systems like scikit learn or Spark.ML instead of Deep Learning?
• Do you no longer need to do careful feature extraction and standardization if using Deep Learning?
• Do you really need terabytes of data when training neural networks or can you ‘steal’ pre-trained lower layers from public models by using transfer learning?
• How do you decide which activation function (like ReLU, leaky ReLU, ELU, etc) or optimizer (like Momentum, AdaGrad, RMSProp, Adam, etc) to use in your neural network?
• Should you randomly initialize the weights in your network or use more advanced strategies like Xavier or He initialization?
• How easy is it to overfit/overtrain a neural network and what are the common techniques to ovoid overfitting (like l1/l2 regularization, dropout and early stopping)?
Arun Rathinasabapathy, Senior Software Engineer, LexisNexis at MLconf ATL 2016MLconf
Big Data Processing Above and Beyond Hadoop: Data-intensive computing represents a new computing paradigm to address Big Data processing requirements using high-performance architectures supporting scalable parallel processing to allow government, commercial organizations, and research environments to process massive amounts of data and implement new applications previously thought to be impractical or infeasible. The fundamental challenges of data-intensive computing are managing and processing exponentially growing data volumes, significantly reducing associated data analysis cycles to support practical, timely applications, and developing new algorithms which can scale to search and process massive amounts of data. The open source HPCC (High-Performance Computing Cluster) Systems platform offers a unified approach to Big Data processing requirements: (1) a scalable, integrated computer systems hardware and software architecture designed for parallel processing of data-intensive computing applications, and (2) a new programming paradigm in the form of a high-level, declarative, data-centric programming language designed specifically for big data processing. This presentation explores the challenges of data-intensive computing from a programming perspective, and describes the ECL programming language and the HPCC architecture designed for data-intensive computing applications. HPCC is an alternative to the Hadoop platform, and ECL is compared to Pig Latin, a high-level language developed for the Hadoop MapReduce architecture.
Hopsworks at Google AI Huddle, SunnyvaleJim Dowling
Hopsworks is a platform for designing and operating End to End Machine Learning using PySpark and TensorFlow/PyTorch. Early access is now available on GCP. Hopsworks includes the industry's first Feature Store. Hopsworks is open-source.
Tom Peters, Software Engineer, Ufora at MLconf ATL 2016MLconf
Say What You Mean: Scaling Machine Learning Algorithms Directly from Source Code: Scaling machine learning applications is hard. Even with powerful systems like Spark, Tensor Flow, and Theano, the code you write has more to do with getting these systems to work at all than it does with your algorithm itself. But it doesn’t have to be this way!
In this talk, I’ll discuss an alternate approach we’ve taken with Pyfora, an open-source platform for scalable machine learning and data science in Python. I’ll show how it produces efficient, large scale machine learning implementations directly from the source code of single-threaded Python programs. Instead of programming to a complex API, you can simply say what you mean and move on. I’ll show some classes of problem where this approach truly shines, discuss some practical realities of developing the system, and I’ll talk about some future directions for the project.
PyMADlib - A Python wrapper for MADlib : in-database, parallel, machine learn...Srivatsan Ramanujam
These are slides from my talk @ DataDay Texas, in Austin on 30 Mar 2013
(http://2013.datadaytexas.com/schedule)
Favorite and Fork PyMADlib on GitHub: https://github.com/gopivotal/pymadlib
MADlib: http://madlib.net
Hussein Mehanna, Engineering Director, ML Core - Facebook at MLconf ATL 2016MLconf
Applying Deep Learning at Facebook Scale: Facebook leverages Deep Learning for various applications including event prediction, machine translation, natural language understanding and computer vision at a very large scale. There are more than a billion users logging on to Facebook every daily generating thousands of posts per second and uploading more than a billion images and videos every day. This talk will explain how Facebook scaled Deep Learning inference for realtime applications with latency budgets in the milliseconds.
Separating Hype from Reality in Deep Learning with Sameer FarooquiDatabricks
Deep Learning is all the rage these days, but where does the reality of what Deep Learning can do end and the media hype begin? In this talk, I will dispel common myths about Deep Learning that are not necessarily true and help you decide whether you should practically use Deep Learning in your software stack.
I’ll begin with a technical overview of common neural network architectures like CNNs, RNNs, GANs and their common use cases like computer vision, language understanding or unsupervised machine learning. Then I’ll separate the hype from reality around questions like:
• When should you prefer traditional ML systems like scikit learn or Spark.ML instead of Deep Learning?
• Do you no longer need to do careful feature extraction and standardization if using Deep Learning?
• Do you really need terabytes of data when training neural networks or can you ‘steal’ pre-trained lower layers from public models by using transfer learning?
• How do you decide which activation function (like ReLU, leaky ReLU, ELU, etc) or optimizer (like Momentum, AdaGrad, RMSProp, Adam, etc) to use in your neural network?
• Should you randomly initialize the weights in your network or use more advanced strategies like Xavier or He initialization?
• How easy is it to overfit/overtrain a neural network and what are the common techniques to ovoid overfitting (like l1/l2 regularization, dropout and early stopping)?
Arun Rathinasabapathy, Senior Software Engineer, LexisNexis at MLconf ATL 2016MLconf
Big Data Processing Above and Beyond Hadoop: Data-intensive computing represents a new computing paradigm to address Big Data processing requirements using high-performance architectures supporting scalable parallel processing to allow government, commercial organizations, and research environments to process massive amounts of data and implement new applications previously thought to be impractical or infeasible. The fundamental challenges of data-intensive computing are managing and processing exponentially growing data volumes, significantly reducing associated data analysis cycles to support practical, timely applications, and developing new algorithms which can scale to search and process massive amounts of data. The open source HPCC (High-Performance Computing Cluster) Systems platform offers a unified approach to Big Data processing requirements: (1) a scalable, integrated computer systems hardware and software architecture designed for parallel processing of data-intensive computing applications, and (2) a new programming paradigm in the form of a high-level, declarative, data-centric programming language designed specifically for big data processing. This presentation explores the challenges of data-intensive computing from a programming perspective, and describes the ECL programming language and the HPCC architecture designed for data-intensive computing applications. HPCC is an alternative to the Hadoop platform, and ECL is compared to Pig Latin, a high-level language developed for the Hadoop MapReduce architecture.
Funda Gunes, Senior Research Statistician Developer & Patrick Koch, Principal...MLconf
Local Search Optimization for Hyper-Parameter Tuning: Many machine learning algorithms are sensitive to their hyper-parameter settings, lacking good universal rule-of-thumb defaults. In this talk we discuss the use of black-box local search optimization (LSO) for machine learning hyper-parameter tuning. Viewed as a black-box objective function of hyper-parameters, machine learning algorithms create a difficult class of optimization problems. The corresponding objective functions involved tend to be nonsmooth, discontinuous, unpredictably computationally expensive, requiring support for both continuous, categorical, and integer variables. Further evaluations can fail for a variety of reasons such as early exits due to node failure or hitting max time. Additionally, not all hyper-parameter combinations are compatible (creating so called “hidden constraints”). In this context, we apply a parallel hybrid derivative-free optimization algorithm that can make progress despite these difficulties providing significantly improved results over default settings with minimal user interaction. Further, we will address efficient parallel paradigms for different types of machine learning problems, while exploring the importance of validation to avoid overfitting and emphasizing that even for small data problems, the need to perform cross validations can create computationally intense functions that benefit from a distributed/threaded environment.
How to use Apache TVM to optimize your ML modelsDatabricks
Apache TVM is an open source machine learning compiler that distills the largest, most powerful deep learning models into lightweight software that can run on the edge. This allows the outputed model to run inference much faster on a variety of target hardware (CPUs, GPUs, FPGAs & accelerators) and save significant costs.
In this deep dive, we’ll discuss how Apache TVM works, share the latest and upcoming features and run a live demo of how to optimize a custom machine learning model.
Josh Patterson, Advisor, Skymind – Deep learning for Industry at MLconf ATL 2016MLconf
DL4J and DataVec for Enterprise Deep Learning Workflows: Applications in NLP, sensor processing (IoT), image processing, and audio processing have all emerged as prime deep learning applications. In this session we will take a look at a practical review of building practical and secure Deep Learning workflows in the enterprise. We’ll see how DL4J’s DataVec tool enables scalable ETL and vectorization pipelines to be created for a single machine or scale out to Spark on Hadoop. We’ll also see how Deep Networks such as Recurrent Neural Networks are able to leverage DataVec to more quickly process data for modeling.
Erin LeDell, Machine Learning Scientist, H2O.ai at MLconf ATL 2016MLconf
Multi-algorithm Ensemble Learning at Scale: Software, Hardware and Algorithmic Approaches: Multi-algorithm ensemble machine learning methods are often used when the true prediction function is not easily approximated by a single algorithm. The Super Learner algorithm, also known as stacking, combines multiple, typically diverse, base learning algorithms into a single, powerful prediction function through a secondary learning process called metalearning. Although ensemble methods offer superior performance over their singleton counterparts, there is an implicit computational cost to ensembles, as it requires training and cross-validating multiple base learning algorithms.
We will demonstrate a variety of software- and hardware-based approaches that lead to more scalable ensemble learning software, including a highly scalable implementation of stacking called “H2O Ensemble”, built on top of the open source, distributed machine learning platform, H2O. H2O Ensemble scales across multi-node clusters and allows the user to create ensembles of deep neural networks, Gradient Boosting Machines, Random Forest, and others. As for algorithm-based approaches, we will present two algorithmic modifications to the original stacking algorithm that further reduce computation time — Subsemble algorithm and the Online Super Learner algorithm. This talk will also include benchmarks of the implementations of these new stacking variants.
On-Prem Solution for the Selection of Wind Energy ModelsDatabricks
The renewable energy industry has only recently started to rely on data-driven models on applications that have traditionally required complex physical solutions. In this talk, we would like to show how we leverage Spark, Keras and (in our case, on-prem) high performance computing (HPC) infrastructure to potentially tackle common and interesting problems in the wind-related industry (saving hours of CPU-consuming simulations).
We use:
Apache Spark and Hive for data preparation and a combination of different data sources (some of them in the range of the petabytes scale).
Keras for model training/generation.
HPC for coordination and node-wide training of hyperparameters.
A Pipeline for Distributed Topic and Sentiment Analysis of Tweets on Pivotal ...Srivatsan Ramanujam
Unstructured data is everywhere - in the form of posts, status updates, bloglets or news feeds in social media or in the form of customer interactions Call Center CRM. While many organizations study and monitor social media for tracking brand value and targeting specific customer segments, in our experience blending the unstructured data with the structured data in supplementing data science models has been far more effective than working with it independently.
In this talk we will show case an end-to-end topic and sentiment analysis pipeline we've built on the Pivotal Greenplum Database platform for Twitter feeds from GNIP, using open source tools like MADlib and PL/Python. We've used this pipeline to build regression models to predict commodity futures from tweets and in enhancing churn models for telecom through topic and sentiment analysis of call center transcripts. All of this was possible because of the flexibility and extensibility of the platform we worked with.
Hardware Acceleration of SVM Training for Real-time Embedded Systems: An Over...Ilham Amezzane
Support Vector Machines (SVMs) have proven to yield high accuracy and have been used widespread in recent years. However, the standard versions of the SVM algorithm are very time-consuming and computationally intensive; which places a challenge on engineers to explore other hardware architectures than CPU, capable of performing real-time training and classifications while maintaining low power consumption in embedded systems. This paper proposes an overview of works based on the two most popular parallel processing devices: GPU and FPGA, with a focus on multiclass training process. Since different techniques have been evaluated using different experimentation platforms and methodologies, we only focus on the improvements realized in each study.
Avi Pfeffer, Principal Scientist, Charles River Analytics at MLconf SEA - 5/2...MLconf
Practical Probabilistic Programming with Figaro: Probabilistic reasoning enables you to predict the future, infer the past, and learn from experience. Probabilistic programming enables users to build and reason with a wide variety of probabilistic models without machine learning expertise. In this talk, I will present Figaro, a mature probabilistic programming system with many applications. I will describe the main design principles of the language and show example applications. I will also discuss our current efforts to fully automate and optimize the inference process.
Profiling PyTorch for Efficiency & Sustainabilitygeetachauhan
From my talk at the Data & AI summit - latest update on the PyTorch Profiler and how you can use it for optimizations for efficiency. Talk also dives into the future and what we need to do together as an industry to move towards Sustainable AI
Funda Gunes, Senior Research Statistician Developer & Patrick Koch, Principal...MLconf
Local Search Optimization for Hyper-Parameter Tuning: Many machine learning algorithms are sensitive to their hyper-parameter settings, lacking good universal rule-of-thumb defaults. In this talk we discuss the use of black-box local search optimization (LSO) for machine learning hyper-parameter tuning. Viewed as a black-box objective function of hyper-parameters, machine learning algorithms create a difficult class of optimization problems. The corresponding objective functions involved tend to be nonsmooth, discontinuous, unpredictably computationally expensive, requiring support for both continuous, categorical, and integer variables. Further evaluations can fail for a variety of reasons such as early exits due to node failure or hitting max time. Additionally, not all hyper-parameter combinations are compatible (creating so called “hidden constraints”). In this context, we apply a parallel hybrid derivative-free optimization algorithm that can make progress despite these difficulties providing significantly improved results over default settings with minimal user interaction. Further, we will address efficient parallel paradigms for different types of machine learning problems, while exploring the importance of validation to avoid overfitting and emphasizing that even for small data problems, the need to perform cross validations can create computationally intense functions that benefit from a distributed/threaded environment.
How to use Apache TVM to optimize your ML modelsDatabricks
Apache TVM is an open source machine learning compiler that distills the largest, most powerful deep learning models into lightweight software that can run on the edge. This allows the outputed model to run inference much faster on a variety of target hardware (CPUs, GPUs, FPGAs & accelerators) and save significant costs.
In this deep dive, we’ll discuss how Apache TVM works, share the latest and upcoming features and run a live demo of how to optimize a custom machine learning model.
Josh Patterson, Advisor, Skymind – Deep learning for Industry at MLconf ATL 2016MLconf
DL4J and DataVec for Enterprise Deep Learning Workflows: Applications in NLP, sensor processing (IoT), image processing, and audio processing have all emerged as prime deep learning applications. In this session we will take a look at a practical review of building practical and secure Deep Learning workflows in the enterprise. We’ll see how DL4J’s DataVec tool enables scalable ETL and vectorization pipelines to be created for a single machine or scale out to Spark on Hadoop. We’ll also see how Deep Networks such as Recurrent Neural Networks are able to leverage DataVec to more quickly process data for modeling.
Erin LeDell, Machine Learning Scientist, H2O.ai at MLconf ATL 2016MLconf
Multi-algorithm Ensemble Learning at Scale: Software, Hardware and Algorithmic Approaches: Multi-algorithm ensemble machine learning methods are often used when the true prediction function is not easily approximated by a single algorithm. The Super Learner algorithm, also known as stacking, combines multiple, typically diverse, base learning algorithms into a single, powerful prediction function through a secondary learning process called metalearning. Although ensemble methods offer superior performance over their singleton counterparts, there is an implicit computational cost to ensembles, as it requires training and cross-validating multiple base learning algorithms.
We will demonstrate a variety of software- and hardware-based approaches that lead to more scalable ensemble learning software, including a highly scalable implementation of stacking called “H2O Ensemble”, built on top of the open source, distributed machine learning platform, H2O. H2O Ensemble scales across multi-node clusters and allows the user to create ensembles of deep neural networks, Gradient Boosting Machines, Random Forest, and others. As for algorithm-based approaches, we will present two algorithmic modifications to the original stacking algorithm that further reduce computation time — Subsemble algorithm and the Online Super Learner algorithm. This talk will also include benchmarks of the implementations of these new stacking variants.
On-Prem Solution for the Selection of Wind Energy ModelsDatabricks
The renewable energy industry has only recently started to rely on data-driven models on applications that have traditionally required complex physical solutions. In this talk, we would like to show how we leverage Spark, Keras and (in our case, on-prem) high performance computing (HPC) infrastructure to potentially tackle common and interesting problems in the wind-related industry (saving hours of CPU-consuming simulations).
We use:
Apache Spark and Hive for data preparation and a combination of different data sources (some of them in the range of the petabytes scale).
Keras for model training/generation.
HPC for coordination and node-wide training of hyperparameters.
A Pipeline for Distributed Topic and Sentiment Analysis of Tweets on Pivotal ...Srivatsan Ramanujam
Unstructured data is everywhere - in the form of posts, status updates, bloglets or news feeds in social media or in the form of customer interactions Call Center CRM. While many organizations study and monitor social media for tracking brand value and targeting specific customer segments, in our experience blending the unstructured data with the structured data in supplementing data science models has been far more effective than working with it independently.
In this talk we will show case an end-to-end topic and sentiment analysis pipeline we've built on the Pivotal Greenplum Database platform for Twitter feeds from GNIP, using open source tools like MADlib and PL/Python. We've used this pipeline to build regression models to predict commodity futures from tweets and in enhancing churn models for telecom through topic and sentiment analysis of call center transcripts. All of this was possible because of the flexibility and extensibility of the platform we worked with.
Hardware Acceleration of SVM Training for Real-time Embedded Systems: An Over...Ilham Amezzane
Support Vector Machines (SVMs) have proven to yield high accuracy and have been used widespread in recent years. However, the standard versions of the SVM algorithm are very time-consuming and computationally intensive; which places a challenge on engineers to explore other hardware architectures than CPU, capable of performing real-time training and classifications while maintaining low power consumption in embedded systems. This paper proposes an overview of works based on the two most popular parallel processing devices: GPU and FPGA, with a focus on multiclass training process. Since different techniques have been evaluated using different experimentation platforms and methodologies, we only focus on the improvements realized in each study.
Avi Pfeffer, Principal Scientist, Charles River Analytics at MLconf SEA - 5/2...MLconf
Practical Probabilistic Programming with Figaro: Probabilistic reasoning enables you to predict the future, infer the past, and learn from experience. Probabilistic programming enables users to build and reason with a wide variety of probabilistic models without machine learning expertise. In this talk, I will present Figaro, a mature probabilistic programming system with many applications. I will describe the main design principles of the language and show example applications. I will also discuss our current efforts to fully automate and optimize the inference process.
Profiling PyTorch for Efficiency & Sustainabilitygeetachauhan
From my talk at the Data & AI summit - latest update on the PyTorch Profiler and how you can use it for optimizations for efficiency. Talk also dives into the future and what we need to do together as an industry to move towards Sustainable AI
ExtremeEarth: Hopsworks, a data-intensive AI platform for Deep Learning with ...Big Data Value Association
The main goal of the session is to showcase approaches that greatly simplify the work of a data analyst when performing data analytics, or when employing machine learning algorithms, over Big Data. The session will include presentations on
(a) How data analytics workflows can be easily and graphically composed, and then optimized for execution,
(b) How raw data with great variety can be easily queried using SQL interfaces, and
(c) How complex machine learning operations can be performed efficiently in distributed settings.
After these presentations, the speakers will participate in a discussion with the audience, in order to discuss further tools that could make the work of a data analyst more simple.
Alluxio Monthly Webinar - Accelerate AI Path to ProductionAlluxio, Inc.
Alluxio Product School Webinar
Aug. 15, 2023
For more Alluxio Events: https://www.alluxio.io/events/
Speaker: Roland Theron (Senior Solutions Engineer, Alluxio)
Organizations are retooling their enterprise data infrastructure in the race for AI/ML. However, growing datasets, extensive data engineering overhead, high GPU costs, and expensive specialized storage can make it difficult to get fast results from model development.
The data access layer is the key to accelerating your path to AI/ML. In this webinar, Roland Theron, Senior Solutions Engineer at Alluxio, discusses how the data access layer can help you:
- Build AI architecture on your existing data lake without the need for specialized hardware.
- Streamline the time-consuming process of managing data copies in data engineering.
- Speed up training workloads with high GPU utilization.
- Achieve optimal concurrency to deliver models to inference clusters for demanding applications
Alluxio Webinar - Maximize GPU Utilization for Model TrainingAlluxio, Inc.
Alluxio Webinar
June 26. 2023
For more Alluxio Events: https://www.alluxio.io/events/
Speaker:
Tarik Bennett (Senior Solutions Engineer, Alluxio)
Beinan Wang (Tech Lead, Alluxio)
When training models on ultra-large datasets, one of the biggest challenges is low GPU utilization. These powerful processors are often underutilized due to inefficient I/O and data access. This mismatch between computation and storage leads to wasted GPU resources, low performance, and high cloud storage costs. The rise of generative AI and GPU scarcity is only making this problem worse.
In this webinar, Tarik and Beinan discuss strategies for transforming idle GPUs into optimal powerhouses. They will focus on cost-effective management of ultra-large datasets for AI and analytics.
Deep learning beyond the learning - Jörg Schad - Codemotion Amsterdam 2018Codemotion
Open Source frameworks such as TensorFlow, MXNet, or PyTorch enable anyone to model and train Deep Neural Networks. While there are many great tutorials and talks showing us the best ways for training models, there is few information on what happens after we have trained our model? How can we store, utilize, and update it? In this talk, we look at the complete Deep Learning Pipeline and looks at topics such as deployments, multi-tenancy, jupyter notebooks, model serving, and more.
Deep learning beyond the learning - Jörg Schad - Codemotion Rome 2018 Codemotion
Open Source frameworks such as TensorFlow, MXNet, or PyTorch enable anyone to model and train Deep Neural Networks. While there are many great tutorials and talks showing us the best ways for training models, there is few information on what happens after we have trained our model? How can we store, utilize, and update it? In this talk, we look at the complete Deep Learning Pipeline and looks at topics such as deployments, multi-tenancy, jupyter notebooks, model serving, and more.
The common perception of applying deep learning is that you take an open source or research model, train it on raw data, and deploy the result as a fully self-contained artefact. The reality is far more complex.
For the training phase, users face an array of challenges including handling varied deep learning frameworks, hardware requirements and configurations, not to mention code quality, consistency, and packaging. For the deployment phase, they face another set of challenges, ranging from custom requirements for data pre- and postprocessing, inconsistencies across frameworks, and lack of standardization in serving APIs.
The goal of the IBM Developer Model Asset eXchange (MAX) is to remove these barriers to entry for developers to obtain, train, and deploy open source deep learning models for their business applications. In building the exchange, we encountered all these challenges and more.
For the training phase, we leverage the Fabric for Deep Learning (FfDL), an open source project providing framework-independent training of deep learning models on Kubernetes. For the deployment phase, MAX provides standardized container-based, fully self-contained model artifacts encompassing the end-to-end deep learning predictive pipeline.
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.
In this talk we'll look at simple building-block techniques for predicting metrics over time based on past data, taking into account trend, seasonality and noise, using Python with Tensorflow.
How to create an enterprise data lake for enterprise-wide information storage and sharing? The data lake concept, architecture principles, support for data science and some use case review.
Den Datenschatz heben und Zeit- und Energieeffizienz steigern: Mathematik und...Joachim Schlosser
In einer Gesellschaft, in der das Sammeln von personenbezogenen Daten mittlerweile alltäglich geworden ist, ist es nicht weiter verwunderlich, dass auch der innovative Maschinenbauer Daten sammelt, wo er nur kann. Produktdaten, Maschinendaten, Statistikdaten – in einer durchschnittlichen Produktionsanlage fallen bereits heute jeden Tag Gigabytes an Daten an. „Big Data“ wurde eines der Schlagworte der Industrie 4.0.
Doch was verspricht man sich davon? Welche Information steckt in den aufgezeichneten Maschinen- und Produktdaten? Und wie erfolgt die Auswertung?
Im Rahmen des Vortrags wird aufgezeigt, wie Unternehmen auf Basis einer etablierten Plattform wie MATLAB® ihre Auswertealgorithmen entwickeln, testen und ausrollen können. Die kontinuierliche Auswertung selbst erfolgt dann wahlweise auf einem Anlagenserver oder aber auch in Echtzeit direkt an der Maschine. Veranschaulicht wird dies anhand von Beispielen aus der Praxis.
Doch neben der gesammelten Daten kommt auch den Steuerungseinheiten in der Produktion in der Industrie 4.0 eine größere Bedeutung zu.
Wenn Werkstücke demnächst selbst wissen, wo sie im Produktionsablauf hin möchten und welcher Verarbeitungsschritt ihnen angedeihen soll, dann bedeutet das auch für die einzelnen Komponenten und Module in Produktion und Logistik ein mehr an Funktionalität, da sie auf diese Eingaben ebenfalls reagieren sollen.
Wie stellen Sie sicher, dass diese zusätzliche Funktionalität nicht zu Lasten der Energiebilanz gehen? Wie fahren Sie die Motoren und anderen aktiven Komponenten Ihrer Fertigung so, dass sie flexibel auf veränderte Routen der Werkstücke reagieren und dennoch im optimalen Bereich fahren?
Mehr denn je brauchen Sie gesteuerte und geregelte Komponenten und Module. Das sollte schon seit Industrie 3.0 vorhanden sein, jedoch ist auch hier noch viel ganz konkretes Potential zur Steigerung von Produktivität und Einsparung von Energie und Produktionszeit vorhanden.
Sie sehen im Vortrag, wie Sie ihre Komponenten besser beschalten, dass die vernetzten dynamischen Anforderungen von Industrie 4.0 lokal effizient umgesetzt werden können.
Metadata and Provenance for ML Pipelines with Hopsworks Jim Dowling
This talk describes the scale-out, consistent metadata architecture of Hopsworks and how we use it to support custom metadata and provenance for ML Pipelines with Hopsworks Feature Store, NDB, and ePipe . The talk is here: https://www.youtube.com/watch?v=oPp8PJ9QBnU&feature=emb_logo
PyData Berlin 2023 - Mythical ML Pipeline.pdfJim Dowling
This talk is a mental map for building ML systems as ML Pipelines that are factored into Feature Pipelines, Training Pipelines, and Inference Pipelines.
Building Hopsworks, a cloud-native managed feature store for machine learning Jim Dowling
Cloud Native London talk about the control layer of Hopsworks.ai and our choice of cloud native services. We built our own multi-tenant services as cloud native services, for the most part.
Asynchronous Hyperparameter Search with Spark on Hopsworks and MaggyJim Dowling
Spark AI Summit Europe 2019 talk: Asynchronous Hyperparameter Search with Spark on Hopsworks and Maggy. How can you do directed search efficiently with Spark? The answer is Maggy - asynchronous directed search on PySpark.
Hopsworks in the cloud Berlin Buzzwords 2019 Jim Dowling
This talk, given at Berlin Buzzwords 2019, describes the recent progress in making Hopsworks a cloud-native platform, with HA data-center support added for HopsFS.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Distributed TensorFlow on Hops (Papis London, April 2018)
1. Techniques for Distributed TensorFlow on Hops
Jim Dowling
CEO, Logical Clocks AB
Assoc Prof, KTH Stockholm
Senior Researcher, RISE SICS
jim_dowling
Europe 2018
9. • Model Architecture Search*
- Explore on smaller datasets, then scale to
larger datasets => enables more searches.
• SOTA on CIFAR10 (2.13% top 1)
SOTA on ImageNet (3.8% top 5)
- 450 GPU / 7 days
- 900 TPU / 5 days
Parallel Experiments to Find Better Models
*https://arxiv.org/abs/1802.01548
9/45
26. Why not Kubeflow?
•Operational Reasons
-No Integrated Enterprise Security Framework
• Encryption-in-Transit, Encryption-at-Rest
-Stateful services not designed for Kubernetes
• Distributed Storage, Kafka, Databases
•Usability Reasons
-Not a Fully Managed Platform
• Write YML files and restart just to install a new Python library
-Slow startup times for applications/notebooks
26/45
33. Hops API
•Python (also Java/Scala)
-Manage tensorboard, Load/save models in HDFS
-Horovod, TensorFlowOnSpark
-Parallel experiments
• Gridsearch
• Model Architecture Search with Genetic Algorithms
-Secure Streaming Analytics with Kafka/Spark/Flink
• SSL/TLS certs, Avro Schema, Endpoints for Kafka/Zookeeper/etc
33/45
Feature Extraction
Experimentation
Training
Test + Serve
Data Acquisition
Clean/Transform Data
44. Summary
•The future of Deep Learning is Distributed
https://www.oreilly.com/ideas/distributed-tensorflow
•Hops is a new Data Platform with first-class support for
Python / Deep Learning / ML / Data Governance / GPUs
*https://twitter.com/karpathy/status/972701240017633281
“It is starting to look like deep learning workflows of the future
feature autotuned architectures running with autotuned
compute schedules across arbitrary backends.”
Andrej Karpathy - Head of AI @ Tesla