Re: Tutorials Download
Alke Stilwell
? As everything created by the GIMP project, these tutorials are fullycommunity-written. Therefore if you find any tutorial incomplete,or if it contains errors or outdated information, the best thing is tocontribute a fix. Moreover we warmly welcome new tutorials. See belowfor the full contributionprocedure.?
How to set the tile cache
The tile cache tells GIMP how much memory it can use before swapping some imagedata to disk. Setting the size of the tile cache can be critical for theperformance of your system.
The only thing we ask of contributors is to use Libre licenses, whichallow anyone to share, redistribute and modify the tutorials. It enablesothers to fix tutorials, even years later, even if the original authoris not around anymore. This is the same rules as for our code, makingGIMP a Free Software.
So you want to contribute? The process is simple, here is an example forthe hypothetical case where you found an error in an existing tutorial.In such a case of editing existing text, your change will be automaticallyconsidered to be in the same license as chosen by the first author:
For more complicated changes, or if you want to propose new tutorials,it might be necessary to understand at least the basics of the gitsource versionning system we use.Alternatively, you may simply write your tutorial in a new text file onyour machine (following Markdown syntax), then simply open areportand upload all the files (the text file as well as images and otherresources) in this report. Do not forget to tell us the license youchose from the list given above.
NLP from Scratch: Classifying Names with a Character-level RNNBuild and train a basic character-level RNN to classify word from scratch without the use of torchtext. First in a series of three tutorials.
(optional) Exporting a PyTorch model to ONNX using TorchDynamo backend and Running it using ONNX RuntimeBuild a image classifier model in PyTorch and convert it to ONNX before deploying it with ONNX Runtime.
Deploying PyTorch in Python via a REST API with FlaskDeploy a PyTorch model using Flask and expose a REST API for model inference using the example of a pretrained DenseNet 121 model which detects the image.
Loading a TorchScript Model in C++Learn how PyTorch provides to go from an existing Python model to a serialized representation that can be loaded and executed purely from C++, with no dependency on Python.
Extending TorchScript with Custom C++ OperatorsImplement a custom TorchScript operator in C++, how to build it into a shared library, how to use it in Python to define TorchScript models and lastly how to load it into a C++ application for inference workloads.
Registering a Dispatched Operator in C++The dispatcher is an internal component of PyTorch which is responsible for figuring out what code should actually get run when you call a function like torch::add.
Extending Dispatcher For a New Backend in C++Learn how to extend the dispatcher to add a new device living outside of the pytorch/pytorch repo and maintain it to keep in sync with native PyTorch devices.
(beta) Implementing High-Performance Transformers with SCALED DOT PRODUCT ATTENTIONThis tutorial explores the new torch.nn.functional.scaled_dot_product_attention and how it can be used to construct Transformer components.
Single-Machine Model Parallel Best PracticesLearn how to implement model parallel, a distributed training technique which splits a single model onto different GPUs, rather than replicating the entire model on each GPU
Training Transformer models using Distributed Data Parallel and Pipeline ParallelismWalk through a through a simple example of how to train a transformer model using Distributed Data Parallel and Pipeline Parallelism
Building an ExecuTorch iOS Demo AppExplore how to set up the ExecuTorch iOS Demo App, which uses the MobileNet v3 model to process live camera images leveraging three different backends: XNNPACK, Core ML, and Metal Performance Shaders (MPS).
Lowering a Model as a DelegateLearn to accelerate your program using ExecuTorch by applying delegates through three methods: lowering the whole module, composing it with another module, and partitioning parts of a module.
  This tutorial describes a prototype feature. Prototype features are typically not available as part of binary distributions like PyPI or Conda, except sometimes behind run-time flags, and are at an early stage for feedback and testing.
Copyright The Linux Foundation. The PyTorch Foundation is a project of The Linux Foundation. For web site terms of use, trademark policy and other policies applicable to The PyTorch Foundation please see www.linuxfoundation.org/policies/. The PyTorch Foundation supports the PyTorch open source project, which has been established as PyTorch Project a Series of LF Projects, LLC. For policies applicable to the PyTorch Project a Series of LF Projects, LLC, please see www.lfprojects.org/policies/.
United States citizens who are currently enrolled in undergraduate or graduate degree programs are eligible to apply.If you are currently enrolled in an undergraduate or graduate program at a U.S. college or university, you will apply through that institution, even if you are not currently a resident there. Find the Fulbright Program Adviser on your campus.
The Fulbright U.S. Student Program welcomes applications in the creative and performing arts. Arts candidates for the U.S. Student Program should have relatively limited professional experience in the fields (typically 7 years or less) in which they are applying. Artists with more experience should consider applying for the Fulbright Scholar Program.
If you are a non-U.S. citizen interested in applying for a Fulbright Award to the United States, you will need to apply through the Fulbright Commission or U.S. Embassy in your home country. Find out more information on the Fulbright Visiting Scholar Program or Fulbright Foreign Student Program.
The Fulbright U.S. Student Program Tutorials offer a "Fulbright 101" -- providing basic information about the program, applicant qualifications, and application components. Each tutorial focuses on a portion of the greater Fulbright U.S. Student Program. Accessible 24/7, these tutorials help applicants begin the process of determining which Fulbright U.S. Student Program offering is right for them and beginning the application process.
This tutorial discusses the Fulbright U.S. Student Program, how it fits within the larger Fulbright constellation of programs, eligibility, award types, funding and other benefits, application components, selection, and general next steps.
This tutorial provides an overview of the Fulbright U.S. Student Program Study/Research grants - the academic award opportunity where a candidate designs a research or graduate degree enrollment proposal for a specific country.
This tutorial provides an overview of the Fulbright U.S. Student Program Arts grants - the artistic award opportunity where a candidate designs a practicing or performing arts proposal for a specific country. This tutorial details application and grant components unique to Arts candidates applying within the study/research category.
This tutorial provides an overview of the Fulbright U.S Student Program English Teaching Assistant (ETA) award programs, which place grantees in schools overseas to supplement English language instruction and to provide a fluent speaker presence in the classrooms.
This tutorial provides an in-depth introduction to the Fulbright-Nat Geo Fellowship, which provides a unique platform for American Fulbright students to develop global narratives and discuss commonalities across borders around a common issue or theme.
This section of the Kubernetes documentation contains tutorials.A tutorial shows how to accomplish a goal that is larger than a singletask. Typically a tutorial has several sections,each of which has a sequence of steps.Before walking through each tutorial, you may want to bookmark theStandardized Glossary page for later references.
These tutorials provide step-by-step instructions for using SEER*Stat to calculate statistics, view database values, and export results. These tutorials are designed for SEER*Stat Version 8.3.9 and the 1975-2018 SEER Data. See the SEER*Stat help system for a description of the statistics calculated by SEER*Stat.
FEMA develops multimedia tutorials to provide in-depth training on different facets of the National Flood Insurance Program (NFIP) and to support public education and outreach efforts as part of the Risk Mapping, Assessment and Planning (Risk MAP) strategy.
These resources are intended for the general public (home, property, business owners), mapping professionals, mitigation planners and community officials interested in learning about available NFIP tools, including how to read a Flood Insurance Rate Map (FIRM) and Flood Insurance Study (FIS) Report and how to use FEMA flood hazard mapping software applications utilized in the NFIP. View system requirements.
cHECk-RAS is a program designed to verify the validity of an assortment of parameters found in the HEC-RAS hydraulics program. The cHECk-RAS tutorial will demonstrate the many functions of the program, including basic information about the U.S. Army Corps of Engineers HEC-RAS program.
RASPLOT Version 2.1.0 replaces FEMA's FISPLOT program, which was previously used to develop flood profiles. The RASPLOT tutorial demonstrates the use of the software to generate flood profiles from HEC-2 and HEC-RAS models.
The LOMA/LOMR-F Tutorial Series explains these map change application processes, including all required forms and information. The LOMA/LOMR-F Tutorial series also provides users with an online tool to complete portions of the required forms.
The Online LOMC is a convenient way to request a LOMA from FEMA. Applicants may easily upload all information, supporting documentation and check the status of their LOMA application online. In this PowerPoint presentation, you will find information about LOMCs and a step-by-step guide through the online request submission process.
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