The accompanying slide presentation to a webinar I gave back in may showing the power of Python with open source tools like Quantum GIS (QGIS) and PostGIS.
Python and GIS: Improving Your WorkflowJohn Reiser
A 40 minute talk on using Python with GIS software. Integration with ArcGIS and open source software is demonstrated. Includes links to several Python-based projects on Github. Presented at the Delaware Valley Regional Planning Commission's Information Resource Exchange Group on December 9th, 2015.
This document provides a summary of a presentation on Python for Everyone. The presentation outline includes an introduction, overview of what Python is, why use Python, where it fits in, and how to automate workflows using Python for both desktop and server applications in ArcGIS. It also discusses ArcGIS integration with Python using ArcPy and resources for learning more about Python. The presentation includes demonstrations of automating tasks using Python for desktop and server applications. It promotes official Esri training courses on Python and provides resources for learning more about Python for GIS tasks.
This document provides an overview of a Python Programming for ArcGIS workshop, including:
- The workshop will teach Python skills to access ArcGIS commands, attribute tables, and geometries for geoprocessing.
- An outline of topics includes introductions to Python and ArcGIS, programming principles and modules, ModelBuilder, and reading and writing data.
- Examples of Python code are provided to demonstrate basic concepts like variables, conditionals, loops, importing modules, and file manipulation.
Python is an open source scripting language that can be used independently or within ArcGIS to automate geoprocessing and map creation tasks. It allows users to easily share and expand geoprocessing tools. Python code can be written in various integrated development environments (IDEs) or text editors and then run to create and automate workflows, extend existing tools with custom logic, write new tools, and access other modules to analyze geospatial data. Online training resources are available to help users learn Python scripting.
Kubeflow is an open-source project that makes it easy to deploy and manage machine learning workloads on Kubernetes. The Kubeflow organization on GitHub contains many repositories that provide tools and services for Kubeflow. These repositories include ones for the main Kubeflow deployment, documentation, examples, a CLI for deployment, metadata tracking, testing infrastructure, common libraries, a frontend dashboard, machine learning pipelines, TensorFlow and PyTorch operators, hyperparameter tuning, and serverless inferencing.
Infrastructure-as-Code with Pulumi- Better than all the others (like Ansible)?Jonas Hecht
There's a new Infrastructure-as-Code (IaC) kid on the block: Pulumi is there to frighten the established: Chef, Puppet, Terraform, Cloudformation, Ansible... But is it really the "better" tool and how could they be compared? Is it only hype-driven? We'll find out, incl. lot's of example code. (ContainerConf / Continuous Lifecycle 2019 Talk in Mannheim)
Example GitHub code: https://github.com/jonashackt/pulumi-python-aws-ansible
https://github.com/jonashackt/pulumi-typescript-aws-fargate
HKube is an open source framework that runs algorithms on Kubernetes as distributed pipelines. It handles scheduling algorithms across nodes, prioritizing tasks, and provides a simple API. The core modules include an API server, pipeline driver to build graphs and manage flow, and a resource manager to allocate pods. Algorithms run as containers managed by workers that communicate results. The current version provides dynamic allocation and monitoring, and future plans include additional orchestrator support, affinity, caching, and a public algorithm store.
Python and GIS: Improving Your WorkflowJohn Reiser
A 40 minute talk on using Python with GIS software. Integration with ArcGIS and open source software is demonstrated. Includes links to several Python-based projects on Github. Presented at the Delaware Valley Regional Planning Commission's Information Resource Exchange Group on December 9th, 2015.
This document provides a summary of a presentation on Python for Everyone. The presentation outline includes an introduction, overview of what Python is, why use Python, where it fits in, and how to automate workflows using Python for both desktop and server applications in ArcGIS. It also discusses ArcGIS integration with Python using ArcPy and resources for learning more about Python. The presentation includes demonstrations of automating tasks using Python for desktop and server applications. It promotes official Esri training courses on Python and provides resources for learning more about Python for GIS tasks.
This document provides an overview of a Python Programming for ArcGIS workshop, including:
- The workshop will teach Python skills to access ArcGIS commands, attribute tables, and geometries for geoprocessing.
- An outline of topics includes introductions to Python and ArcGIS, programming principles and modules, ModelBuilder, and reading and writing data.
- Examples of Python code are provided to demonstrate basic concepts like variables, conditionals, loops, importing modules, and file manipulation.
Python is an open source scripting language that can be used independently or within ArcGIS to automate geoprocessing and map creation tasks. It allows users to easily share and expand geoprocessing tools. Python code can be written in various integrated development environments (IDEs) or text editors and then run to create and automate workflows, extend existing tools with custom logic, write new tools, and access other modules to analyze geospatial data. Online training resources are available to help users learn Python scripting.
Kubeflow is an open-source project that makes it easy to deploy and manage machine learning workloads on Kubernetes. The Kubeflow organization on GitHub contains many repositories that provide tools and services for Kubeflow. These repositories include ones for the main Kubeflow deployment, documentation, examples, a CLI for deployment, metadata tracking, testing infrastructure, common libraries, a frontend dashboard, machine learning pipelines, TensorFlow and PyTorch operators, hyperparameter tuning, and serverless inferencing.
Infrastructure-as-Code with Pulumi- Better than all the others (like Ansible)?Jonas Hecht
There's a new Infrastructure-as-Code (IaC) kid on the block: Pulumi is there to frighten the established: Chef, Puppet, Terraform, Cloudformation, Ansible... But is it really the "better" tool and how could they be compared? Is it only hype-driven? We'll find out, incl. lot's of example code. (ContainerConf / Continuous Lifecycle 2019 Talk in Mannheim)
Example GitHub code: https://github.com/jonashackt/pulumi-python-aws-ansible
https://github.com/jonashackt/pulumi-typescript-aws-fargate
HKube is an open source framework that runs algorithms on Kubernetes as distributed pipelines. It handles scheduling algorithms across nodes, prioritizing tasks, and provides a simple API. The core modules include an API server, pipeline driver to build graphs and manage flow, and a resource manager to allocate pods. Algorithms run as containers managed by workers that communicate results. The current version provides dynamic allocation and monitoring, and future plans include additional orchestrator support, affinity, caching, and a public algorithm store.
This document outlines the Kubeflow pull request (PR) workflow, which involves four steps: 1) finding an issue to work on by reviewing issues on the Kubeflow GitHub repositories, 2) writing code and tests to address the issue, 3) submitting a PR with the code changes, and 4) having the PR reviewed and merged into the main repository once approved. It provides guidance on using tools like GitHub, writing tests, addressing code review comments, and resolving conflicts when merging approved PRs.
End to end Machine Learning using Kubeflow - Build, Train, Deploy and ManageAnimesh Singh
This document discusses Kubeflow, an end-to-end machine learning platform for Kubernetes. It covers various Kubeflow components like Jupyter notebooks, distributed training operators, hyperparameter tuning with Katib, model serving with KFServing, and orchestrating the full ML lifecycle with Kubeflow Pipelines. It also talks about IBM's contributions to Kubeflow and shows how Watson AI Pipelines can productize Kubeflow Pipelines using Tekton.
This document provides an overview of Pulumi, an infrastructure as code platform. It discusses what Pulumi is, the programming languages supported like TypeScript, JavaScript, Python, Go and .NET, and the cloud providers supported like AWS, Azure, GCP etc. It also provides a comparison of Pulumi with other infrastructure as code tools like Terraform, CloudFormation and ARM templates in terms of factors like supported languages, state management, stack management etc. Finally, it outlines the steps to get started with Pulumi including installing it, logging into Azure and creating a new Pulumi project.
Kubeflow provides several operators for distributed training including the TF operator, PyTorch operator, and MPI operator. The TF and PyTorch operators run distributed training jobs using the corresponding frameworks while the MPI operator allows for framework-agnostic distributed training. Katib is Kubeflow's built-in hyperparameter tuning service and provides a flexible framework for hyperparameter tuning and neural architecture search with algorithms like random search, grid search, hyperband, and Bayesian optimization.
Kubeflow: Machine Learning en Cloud para todosGlobant
Speaker: Juan Camilo Díaz
Video: https://youtu.be/jfH93vdRmTk
Kubeflow hace que implementar workflows de Machine Learning en Kubernetes sean simples, portátiles y escalables. Kubeflow es el kit de herramientas que permite implementar procesos de Machine Learning, ampliando la capacidad de Kubernetes para ejecutar pasos independientes y configurables, con bibliotecas y frameworks específicos.
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Hay trabajos y hay carreras. Las oportunidades vienen a golpear la puerta cuando menos lo esperas. La decisión es tuya. Desde tener la oportunidad de hacer algo significativo día tras día, hasta estar rodeado de gente supremamente inteligente y motivada.
¿Estás listo?
Descúbre todas nuestras oportunidades acá: https://bit.ly/2PWKky9
---------------------------------------------------------------------------------------------------------------------------------------------------------------
Síguenos en:
Facebook: https://www.facebook.com/Globant/
Twitter: https://twitter.com/Globant
Instagram: https://www.instagram.com/globantpics/
Linkedin: https://www.linkedin.com/company/globant
Introducing Kubeflow (w. Special Guests Tensorflow and Apache Spark)DataWorks Summit
Data Science, Machine Learning, and Artificial Intelligence has exploded in popularity in the last five years, but the nagging question remains, “How to put models into production?” Engineers are typically tasked to build one-off systems to serve predictions which must be maintained amid a quickly evolving back-end serving space which has evolved from single-machine, to custom clusters, to “serverless”, to Docker, to Kubernetes. In this talk, we present KubeFlow- an open source project which makes it easy for users to move models from laptop to ML Rig to training cluster to deployment. In this talk we will discuss, “What is KubeFlow?”, “why scalability is so critical for training and model deployment?”, and other topics.
Users can deploy models written in Python’s skearn, R, Tensorflow, Spark, and many more. The magic of Kubernetes allows data scientists to write models on their laptop, deploy to an ML-Rig, and then devOps can move that model into production with all of the bells and whistles such as monitoring, A/B tests, multi-arm bandits, and security.
TensorFlow London 14: Ben Hall 'Machine Learning Workloads with Kubernetes an...Seldon
This document discusses deploying machine learning workloads with Kubernetes and Kubeflow. It covers setting up a Kubeflow cluster, training a model using TFJob, serving the model with Seldon Core, querying the model, and using Ksonnet to generate and apply Kubernetes manifests for Kubeflow components like TF serving.
1. KFServing and Feast provide capabilities for serving machine learning models and managing features respectively.
2. The Feast feature store is proposed as a new type of transformer for KFServing to preprocess requests by retrieving online features from Feast to augment the input for models.
3. This would allow models deployed using KFServing to leverage curated features stored in Feast for more accurate inferences.
The magic behind your Lyft ride prices: A case study on machine learning and ...Karthik Murugesan
Rakesh Kumar and Thomas Weise explore how Lyft dynamically prices its rides with a combination of various data sources, ML models, and streaming infrastructure for low latency, reliability, and scalability—allowing the pricing system to be more adaptable to real-world changes.
This document provides an overview of Grafana, an open source metrics dashboard and graph editor for Graphite, InfluxDB and OpenTSDB. It discusses Grafana's features such as rich graphing, time series querying, templated queries, annotations, dashboard search and export/import. The document also covers Grafana's history and alternatives. It positions Grafana as providing richer features than Graphite Web and highlights features like multiple y-axes, unit formats, mixing graph types, thresholds and tooltips.
Torkel Ödegaard (Creator of Grafana) - Grafana at #DOXLONOutlyer
Video: http://youtu.be/tgdP1juFGVU
Torkel Ödegaard (Creator of Grafana) talking about his awesome Open-Source projects for monitoring, and how Grafana can be used.
Grafana: http://grafana.org
Join DevOps Exchange London here: http://www.meetup.com/DevOps-Exchange-London/
Follow DOXLON on twitter: twitter.com/doxlon
Apache Airflow is a platform for authoring, scheduling, and monitoring workflows or directed acyclic graphs (DAGs). It allows defining and monitoring cron jobs, automating DevOps tasks, moving data periodically, and building machine learning pipelines. Many large companies use Airflow for tasks like data ingestion, analytics automation, and machine learning workflows. The author proposes using Airflow to manage data movement and automate tasks for their organization to benefit business units. Instructions are provided on installing Airflow using pip, Docker, or Helm along with developing sample DAGs connecting to Azure services like Blob Storage, Cosmos DB, and Databricks.
Building an analytics workflow using Apache AirflowYohei Onishi
This document discusses using Apache Airflow to build an analytics workflow. It begins with an overview of Airflow and how it can be used to author workflows through Python code. Examples are shown of using Airflow to copy files between S3 buckets. The document then covers setting up a highly available Airflow cluster, implementing continuous integration/deployment, and monitoring workflows. It emphasizes that Google Cloud Composer can simplify deploying and managing Airflow clusters on Google Kubernetes Engine and integrating with other Google Cloud services.
Powering machine learning workflows with Apache Airflow and PythonTatiana Al-Chueyr
This document provides an overview of using Apache Airflow to power machine learning workflows with Python. It discusses Airflow concepts like DAGs, operators, relationships and visualizations. It also covers installing Airflow, common issues experienced like debugging and versioning, and using Airflow for machine learning tasks like model building and hyperparameter tuning. Examples of Airflow pipelines for data ingestion and machine learning are demonstrated. The presenter's background and the BBC Datalab team are briefly introduced.
End-to-End ML pipelines with Beam, Flink, TensorFlow and Hopsworks.Theofilos Kakantousis
This document summarizes an agenda for a presentation on end-to-end machine learning pipelines using Beam, Flink, TensorFlow, and Hopsworks. The presentation covers what Hopsworks is and how it enables Beam portability with the Flink runner. It also discusses how ML pipelines can be built with Beam and TensorFlow Extended on Hopsworks and provides a demo.
End to-end ml pipelines with beam, flink, tensor flow, and hopsworks (beam su...Theofilos Kakantousis
Apache Beam is a key technology for building scalable End-to-End ML pipelines, as it is the data preparation and model analysis engine for TensorFlow Extended (TFX), a framework for horizontally scalable Machine Learning (ML) pipelines based on TensorFlow. In this talk, we present TFX on Hopsworks, a fully open-source platform for running TFX pipelines on any cloud or on-premise. Hopsworks is a project-based multi-tenant platform for both data parallel programming and horizontally scalable machine learning pipelines. Hopsworks supports Apache Flink as a runner for Beam jobs and TFX pipelines are supported through Airflow support in Hopsworks. We will demonstrate how to build a ML pipeline with TFX, Beam’s Python API and the Flink Runner by using Jupyter notebooks, explain how security is transparently enabled with short-lived TLS certificates, and go through all the pipeline steps, from Data Validation, to Transformation, Model training with TensorFlow, Model Analysis, Model Serving and Monitoring with Kubernetes.
To the best of our knowledge, Hopsworks is the first fully open-source on-premise platform that supports both TFX pipelines and Apache Beam.
The document discusses using GraphQL to build a serverless API for a mobile app that detects construction errors. Key points include:
1. GraphQL provides an efficient way to fetch data from microservices in a single roundtrip and allows flexible field selection.
2. The previous monolithic REST API was split into microservices for data fetching, state management, and serving the API to improve performance and scalability.
3. Serverless is a good fit for the stateless GraphQL architecture as resolvers can trigger other serverless functions and the API can scale up and down easily.
This document discusses how Python can be used for geographic information systems (GIS). It outlines several Python modules that are useful for GIS, including Shapely for geometry operations, NumPy for array processing of map data, PyProj for projections, Mapnik for map rendering, GeoAlchemy for spatial databases, TileCache for tile servers, and GeoDjango as a full-stack framework. The document encourages experimenting with the many Python APIs available for GIS tasks.
This document outlines the Kubeflow pull request (PR) workflow, which involves four steps: 1) finding an issue to work on by reviewing issues on the Kubeflow GitHub repositories, 2) writing code and tests to address the issue, 3) submitting a PR with the code changes, and 4) having the PR reviewed and merged into the main repository once approved. It provides guidance on using tools like GitHub, writing tests, addressing code review comments, and resolving conflicts when merging approved PRs.
End to end Machine Learning using Kubeflow - Build, Train, Deploy and ManageAnimesh Singh
This document discusses Kubeflow, an end-to-end machine learning platform for Kubernetes. It covers various Kubeflow components like Jupyter notebooks, distributed training operators, hyperparameter tuning with Katib, model serving with KFServing, and orchestrating the full ML lifecycle with Kubeflow Pipelines. It also talks about IBM's contributions to Kubeflow and shows how Watson AI Pipelines can productize Kubeflow Pipelines using Tekton.
This document provides an overview of Pulumi, an infrastructure as code platform. It discusses what Pulumi is, the programming languages supported like TypeScript, JavaScript, Python, Go and .NET, and the cloud providers supported like AWS, Azure, GCP etc. It also provides a comparison of Pulumi with other infrastructure as code tools like Terraform, CloudFormation and ARM templates in terms of factors like supported languages, state management, stack management etc. Finally, it outlines the steps to get started with Pulumi including installing it, logging into Azure and creating a new Pulumi project.
Kubeflow provides several operators for distributed training including the TF operator, PyTorch operator, and MPI operator. The TF and PyTorch operators run distributed training jobs using the corresponding frameworks while the MPI operator allows for framework-agnostic distributed training. Katib is Kubeflow's built-in hyperparameter tuning service and provides a flexible framework for hyperparameter tuning and neural architecture search with algorithms like random search, grid search, hyperband, and Bayesian optimization.
Kubeflow: Machine Learning en Cloud para todosGlobant
Speaker: Juan Camilo Díaz
Video: https://youtu.be/jfH93vdRmTk
Kubeflow hace que implementar workflows de Machine Learning en Kubernetes sean simples, portátiles y escalables. Kubeflow es el kit de herramientas que permite implementar procesos de Machine Learning, ampliando la capacidad de Kubernetes para ejecutar pasos independientes y configurables, con bibliotecas y frameworks específicos.
---------------------------------------------------------------------------------------------------------------------------------------------------------------
Hay trabajos y hay carreras. Las oportunidades vienen a golpear la puerta cuando menos lo esperas. La decisión es tuya. Desde tener la oportunidad de hacer algo significativo día tras día, hasta estar rodeado de gente supremamente inteligente y motivada.
¿Estás listo?
Descúbre todas nuestras oportunidades acá: https://bit.ly/2PWKky9
---------------------------------------------------------------------------------------------------------------------------------------------------------------
Síguenos en:
Facebook: https://www.facebook.com/Globant/
Twitter: https://twitter.com/Globant
Instagram: https://www.instagram.com/globantpics/
Linkedin: https://www.linkedin.com/company/globant
Introducing Kubeflow (w. Special Guests Tensorflow and Apache Spark)DataWorks Summit
Data Science, Machine Learning, and Artificial Intelligence has exploded in popularity in the last five years, but the nagging question remains, “How to put models into production?” Engineers are typically tasked to build one-off systems to serve predictions which must be maintained amid a quickly evolving back-end serving space which has evolved from single-machine, to custom clusters, to “serverless”, to Docker, to Kubernetes. In this talk, we present KubeFlow- an open source project which makes it easy for users to move models from laptop to ML Rig to training cluster to deployment. In this talk we will discuss, “What is KubeFlow?”, “why scalability is so critical for training and model deployment?”, and other topics.
Users can deploy models written in Python’s skearn, R, Tensorflow, Spark, and many more. The magic of Kubernetes allows data scientists to write models on their laptop, deploy to an ML-Rig, and then devOps can move that model into production with all of the bells and whistles such as monitoring, A/B tests, multi-arm bandits, and security.
TensorFlow London 14: Ben Hall 'Machine Learning Workloads with Kubernetes an...Seldon
This document discusses deploying machine learning workloads with Kubernetes and Kubeflow. It covers setting up a Kubeflow cluster, training a model using TFJob, serving the model with Seldon Core, querying the model, and using Ksonnet to generate and apply Kubernetes manifests for Kubeflow components like TF serving.
1. KFServing and Feast provide capabilities for serving machine learning models and managing features respectively.
2. The Feast feature store is proposed as a new type of transformer for KFServing to preprocess requests by retrieving online features from Feast to augment the input for models.
3. This would allow models deployed using KFServing to leverage curated features stored in Feast for more accurate inferences.
The magic behind your Lyft ride prices: A case study on machine learning and ...Karthik Murugesan
Rakesh Kumar and Thomas Weise explore how Lyft dynamically prices its rides with a combination of various data sources, ML models, and streaming infrastructure for low latency, reliability, and scalability—allowing the pricing system to be more adaptable to real-world changes.
This document provides an overview of Grafana, an open source metrics dashboard and graph editor for Graphite, InfluxDB and OpenTSDB. It discusses Grafana's features such as rich graphing, time series querying, templated queries, annotations, dashboard search and export/import. The document also covers Grafana's history and alternatives. It positions Grafana as providing richer features than Graphite Web and highlights features like multiple y-axes, unit formats, mixing graph types, thresholds and tooltips.
Torkel Ödegaard (Creator of Grafana) - Grafana at #DOXLONOutlyer
Video: http://youtu.be/tgdP1juFGVU
Torkel Ödegaard (Creator of Grafana) talking about his awesome Open-Source projects for monitoring, and how Grafana can be used.
Grafana: http://grafana.org
Join DevOps Exchange London here: http://www.meetup.com/DevOps-Exchange-London/
Follow DOXLON on twitter: twitter.com/doxlon
Apache Airflow is a platform for authoring, scheduling, and monitoring workflows or directed acyclic graphs (DAGs). It allows defining and monitoring cron jobs, automating DevOps tasks, moving data periodically, and building machine learning pipelines. Many large companies use Airflow for tasks like data ingestion, analytics automation, and machine learning workflows. The author proposes using Airflow to manage data movement and automate tasks for their organization to benefit business units. Instructions are provided on installing Airflow using pip, Docker, or Helm along with developing sample DAGs connecting to Azure services like Blob Storage, Cosmos DB, and Databricks.
Building an analytics workflow using Apache AirflowYohei Onishi
This document discusses using Apache Airflow to build an analytics workflow. It begins with an overview of Airflow and how it can be used to author workflows through Python code. Examples are shown of using Airflow to copy files between S3 buckets. The document then covers setting up a highly available Airflow cluster, implementing continuous integration/deployment, and monitoring workflows. It emphasizes that Google Cloud Composer can simplify deploying and managing Airflow clusters on Google Kubernetes Engine and integrating with other Google Cloud services.
Powering machine learning workflows with Apache Airflow and PythonTatiana Al-Chueyr
This document provides an overview of using Apache Airflow to power machine learning workflows with Python. It discusses Airflow concepts like DAGs, operators, relationships and visualizations. It also covers installing Airflow, common issues experienced like debugging and versioning, and using Airflow for machine learning tasks like model building and hyperparameter tuning. Examples of Airflow pipelines for data ingestion and machine learning are demonstrated. The presenter's background and the BBC Datalab team are briefly introduced.
End-to-End ML pipelines with Beam, Flink, TensorFlow and Hopsworks.Theofilos Kakantousis
This document summarizes an agenda for a presentation on end-to-end machine learning pipelines using Beam, Flink, TensorFlow, and Hopsworks. The presentation covers what Hopsworks is and how it enables Beam portability with the Flink runner. It also discusses how ML pipelines can be built with Beam and TensorFlow Extended on Hopsworks and provides a demo.
End to-end ml pipelines with beam, flink, tensor flow, and hopsworks (beam su...Theofilos Kakantousis
Apache Beam is a key technology for building scalable End-to-End ML pipelines, as it is the data preparation and model analysis engine for TensorFlow Extended (TFX), a framework for horizontally scalable Machine Learning (ML) pipelines based on TensorFlow. In this talk, we present TFX on Hopsworks, a fully open-source platform for running TFX pipelines on any cloud or on-premise. Hopsworks is a project-based multi-tenant platform for both data parallel programming and horizontally scalable machine learning pipelines. Hopsworks supports Apache Flink as a runner for Beam jobs and TFX pipelines are supported through Airflow support in Hopsworks. We will demonstrate how to build a ML pipeline with TFX, Beam’s Python API and the Flink Runner by using Jupyter notebooks, explain how security is transparently enabled with short-lived TLS certificates, and go through all the pipeline steps, from Data Validation, to Transformation, Model training with TensorFlow, Model Analysis, Model Serving and Monitoring with Kubernetes.
To the best of our knowledge, Hopsworks is the first fully open-source on-premise platform that supports both TFX pipelines and Apache Beam.
The document discusses using GraphQL to build a serverless API for a mobile app that detects construction errors. Key points include:
1. GraphQL provides an efficient way to fetch data from microservices in a single roundtrip and allows flexible field selection.
2. The previous monolithic REST API was split into microservices for data fetching, state management, and serving the API to improve performance and scalability.
3. Serverless is a good fit for the stateless GraphQL architecture as resolvers can trigger other serverless functions and the API can scale up and down easily.
This document discusses how Python can be used for geographic information systems (GIS). It outlines several Python modules that are useful for GIS, including Shapely for geometry operations, NumPy for array processing of map data, PyProj for projections, Mapnik for map rendering, GeoAlchemy for spatial databases, TileCache for tile servers, and GeoDjango as a full-stack framework. The document encourages experimenting with the many Python APIs available for GIS tasks.
Solving Geophysics Problems with PythonPaige Bailey
This document discusses using Python for solving problems in geophysics. It begins by defining geophysics as the application of physics to the study of the Earth, its environments, and its processes. It then discusses various geophysical themes like gravity, heat flow, electricity, fluid dynamics, magnetism, radioactivity, and vibration. The rest of the document focuses on different geophysical libraries and software that can be used with Python, applications of geophysics to energy exploration and production, and challenges of dealing with big data in upstream oil and gas.
Milos Miljkovic - Analyzing satellite images with python scientific stackPyData
Python has a rich ecosystem of open source geographical information science (GIS) applications. Most of the GIS packages are Python bindings to binaries for data transformation and image manipulation. This makes it hard to study what the data processing encompasses and masks the underlying algorithms. This talk will use Landsat 8 satellite imagery and Python scientific stack to demonstrate a typical data-centric approach for GIS analysis and at the same time explain algorithmic underpinnings. Image recognition and machine learning techniques will be applied to satellite images to expose the data's openness to exploration.
Transformation of traditional village into eco-villageRamesh Bhandari
We have defined eco-village as a rural human settlement with all members committed to sustainably manage locally available natural resources with integrated comprehensive human right based approach to meet their social, spiritual, psychological, physical (including technological) and economic needs without any negative impact on natural ecosystems, resources, climate and health. Ecovillage thus addresses the social, spiritual or cultural, ecological and techno-economic discrepancies and instabilities through sustainable community based structures, practices and concepts from holistic right based perspectives.
Ecovillage has social, physical, spiritual or cultural and ecological (including techno-economic) structures or systems. Each system has subsystems that interact with each other.
http://worecnepal.org
A MAC URISA event. This talk is oriented to GIS users looking to learn more about the Python programming language. The Python language is incorporated into many GIS applications. Python also has a considerable installation base, with many freely available modules that help developers extend their software to do more.
The beginning third of the talk discusses the history and syntax of the language, along with why a GIS specialist would want to learn how to use the language. The middle of the talk discusses how Python is integrated with the ESRI ArcGIS Desktop suite. The final portion of the talk discusses two Python projects and how they can be used to extend your GIS capabilities and improve efficiency.
Recording of the talk: https://www.youtube.com/watch?v=F1_FqvbXHb4
Introduction to underlying technologies, the rationale of using Python and Qt as a development platform on Maemo and a short demo of a few projects built with these tools. Comparison of different bindings (PyQt vs PySide). PyQt/PySide development environments, how to develop most efficiently, how to debug, how to profile and optimize, platform caveats and gotchas.
PySide is a Python binding for the Qt framework that was developed by INdT to provide Python bindings under the LGPL license. It consists of PySide, which is imported, libpyside which handles Qt signals and slots, and libshiboken which helps interface Python and C++. Shiboken is the binding generator used to create the PySide bindings and can generate bindings for any C++ library. Currently, many Qt modules like QtCore and QtGui are supported. Future work includes improving the binding generation process and expanding platform support.
Kivy is open source Module or platform to develop cross platform application in python. it supports python 2 and python 3 both. It is fully implemented in Cython (python library to write c code)
- What are Internal Developer Portal (IDP) and Platform Engineering?
- What is Backstage?
- How Backstage can help dev to build developer portal to make their job easier
Jirayut Nimsaeng
Founder & CEO
Opsta (Thailand) Co., Ltd.
Youtube Record: https://youtu.be/u_nLbgWDwsA?t=850
Dev Mountain Tech Festival @ Chiang Mai
November 12, 2022
This webinar presents the official set of bindings to use Qt's API in your Python application. Using real examples, we will not only implement a beautiful UI, we'll illustrate how it interacts with your regular Python business logic.
This document provides an overview of Gradio, an open-source Python library for building machine learning demos and web applications. It discusses key features of Gradio like customizable input/output components, real-time feedback, and support for popular machine learning frameworks. The document also covers installing Gradio, setting up a development environment, integrating models, and deploying applications. Additional topics include handling errors, scaling applications, and the Gradio API.
Given on Tuesday, June 23, 2009 at the Greater Cleveland PC Users Group C#/VB.NET SIG. A very basic intro to Python given to a .NET crowd with the assumption of little to no Python experience.
Talk at PyCon2022 over building binary packages for Python. Covers an overview and an in-depth look into pybind11 for binding, scikit-build for creating the build, and build & cibuildwheel for making the binaries that can be distributed on PyPI.
Developing Apps with GPT-4 and ChatGPT_ Build Intelligent Chatbots, Content G...BIHI Oussama
Written in clear and concise language, Developing Apps with GPT-4 and ChatGPT includes easy-to-follow examples to help you understand and apply the concepts to your projects. Python code examples are available in a GitHub repository, and the book includes a glossary of key terms. Ready to harness the power of large language models in your applications? This book is a must.
You'll learn:
The fundamentals and benefits of ChatGPT and GPT-4 and how they work
How to integrate these models into Python-based applications for NLP tasks
How to develop applications using GPT-4 or ChatGPT APIs in Python for text generation, question answering, and content summarization, among other tasks
Advanced GPT topics including prompt engineering, fine-tuning models for specific tasks,
Matt "Grizz" Griswold and Chris Grundemann are both IX founders, internetworking experts, and automation proponents. With around 4 decades of combined experience they are now turning to sharing what they've learned about automating BGP and interconnection through a set of open source tools, along with support and services for those that need it.
This talk will share what they have learned both from personal experience as well as through dozens of recent interviews with IX operators and interconnection engineers over the past several months. Including common challenges and best practices.
The highlight of the talk will be announcing and describing two open source automation tools built to make interconnection and BGP easier for everyone. One is ixCtl, which is built to automate the most common and problematic tasks involved in running an internet exchange point, particularly configuring and managing secure route servers. The other is PeerCtl, which is built to automate the most common and problematic tasks involved in interconnecting an AS; from bilateral and multilateral peering to PNI and also transit connections.
Code for both (along with several other tools) is available on GitHub: https://github.com/fullctl
This half-day tutorial introduces Protocol Buffers, gRPC, and the open source tools that Google uses to publish and support some of the world's biggest APIs. We'll show how the Protocol Buffer language allows APIs to be described, reviewed, and implemented in a programming-language independent way, how gRPC enables high-performance streaming APIs, and how \ a few simple conventions can enable related tools to serve robust REST APIs and generate production-quality client libraries in seven popular programming languages. This is API publishing the Google way, but large teams aren't required. With shared open-source tooling, even the smallest developer can build scalable, usable APIs that delight.
https://apistrat18.sched.com/event/FTR3/usable-apis-at-scale-with-protocol-buffers-and-grpc-tim-burks-andrew-gunsch-google
Python is a widely used general-purpose, high-level programming language.Its design philosophy emphasizes code readability, and its syntax allows programmers to express concepts in fewer lines of code than would be possible in languages such as C.The language provides constructs intended to enable clear programs on both a small and large scale.Python supports multiple programming paradigms, including object-oriented, imperative and functional programming or procedural styles. It features a dynamic type system and automatic memory management and has a large and comprehensive standard library.
Fantasy cricket game using python(intershala project)Rr
This document describes a 6-week summer training project on developing a fantasy cricket game using Python. It includes an introduction to Python, the training contents on Python basics, OOP, databases and GUI development. It outlines the problem of creating the fantasy game, database design, and screenshots of the game interface. The coding and testing of the game are discussed. Finally, it concludes the potential of using Python for teaching programming concepts.
Tweepy is an open source Python package that gives you a very convenient way to access the Twitter API with Python. Tweepy includes a set of classes and methods that represent Twitter's models and API endpoints, and it transparently handles various implementation details, such as: Data encoding and decoding.
Biscuit, the cryptotoken you can share safely with your ap isQuentin Adam
Biscuit is a cryptotoken created by Clever Cloud that can be used to safely share access with APIs. It uses protocol buffers for encoding and symbol tables to reduce token size. Biscuit features a built-in ACL management system and uses datalog to determine access permissions in a flexible way based on actions rather than static roles. The specification and several implementations are open source and free to use, including for Clever Cloud's API and other projects.
Everybody is consuming NuGet packages these days. It’s easy, right? But how can we create and share our own packages? What is .NET Standard? How should we version, create, publish and share our package?
Once we have those things covered, we’ll look beyond what everyone is doing. How can we use the NuGet client API to fetch data from NuGet? Can we build an application plugin system based on NuGet? What hidden gems are there in the NuGet server API? Can we create a full copy of NuGet.org?
Good questions! In this talk, we will get them answered.
This document contains questions and answers related to DevOps concepts. It begins with definitions of DevOps and explains that DevOps aims to automate infrastructure and integrate development and operations teams. Key DevOps principles like infrastructure as code, continuous integration, deployment and monitoring are outlined. Popular DevOps tools like Git, Jenkins, Ansible, Docker and Nagios are listed. The document also includes questions on version control systems, Git, Ansible, Docker, Scrum methodology and more DevOps related topics.
This document provides an overview of the Python programming language, including its history, key features, and common uses. It discusses how Python is an interpreted, object-oriented language with dynamic typing and automatic memory management. Examples are given of Python's syntax for numbers, strings, modules, data structures like lists and dictionaries, and the interactive shell. Popular applications of Python like web development, science, and games are also mentioned.
Python doesn't have built-in mobile development capabilities, but there are packages you can use to create mobile applications, like Kivy, PyQt, or even Beeware's Toga library. These libraries are all major players in the Python mobile space.
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