title stringlengths 34 95 | url stringlengths 39 106 | date stringlengths 10 10 | tags list | summary stringlengths 66 380 | content stringlengths 4.93k 25.2k |
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Google Colab the free GPU/TPU Jupyter Notebook Service | https://www.philschmid.de/google-cola-the-free-gpu-jupyter | 2020-02-26 | [
"Machine Learning"
] | A Short Introduction to Google Colab as a free Jupyter notebook service from Google. Learn how to use Accelerated Hardware like GPUs and TPUs to run your Machine learning completely for free in the cloud. | ## What is Google Colab
**Google Colaboratory** or „Colab“ for short is a free Jupyter notebook service from Google. It requires no setup and
runs entirely in the cloud. In Google Colab you can write, execute, save and share your Jupiter Notebooks. You access
powerful computing resources like TPUs and GPUs all for fre... |
Hugging Face Transformers Examples | https://www.philschmid.de/huggingface-transformers-examples | 2023-01-26 | [
"HuggingFace",
"Transformers",
"BERT",
"PyTorch"
] | Learn how to leverage Hugging Face Transformers to easily fine-tune your models. | <html class="max-w-none pt-6 pb-8 font-serif " itemscope itemtype="https://schema.org/FAQPage">
Machine learning and the adoption of the Transformer architecture are rapidly growing and will revolutionize the way we live and work. From self-driving cars to personalized medicine, the applications of [Transformers](https... |
BERT Text Classification in a different language | https://www.philschmid.de/bert-text-classification-in-a-different-language | 2020-05-22 | [
"NLP",
"Bert",
"HuggingFace"
] | Build a non-English (German) BERT multi-class text classification model with HuggingFace and Simple Transformers. | Currently, we have 7.5 billion people living on the world in around 200 nations. Only
[1.2 billion people of them are native English speakers](https://en.wikipedia.org/wiki/List_of_countries_by_English-speaking_population).
This leads to a lot of unstructured non-English textual data.
Most of the tutorials and blog po... |
Semantic Segmantion with Hugging Face's Transformers & Amazon SageMaker | https://www.philschmid.de/image-segmentation-sagemaker | 2022-05-03 | [
"AWS",
"SegFormer",
"Vision",
"Sagemaker"
] | Learn how to do image segmentation with Hugging Face Transformers, SegFormer and Amazon SageMaker. | Transformer models are changing the world of machine learning, starting with natural language processing, and now, with audio and computer vision. Hugging Face's mission is to democratize good machine learning and giving any one the opportunity to use these new state-of-the-art machine learning models.
Together with Am... |
Fine-tune FLAN-T5 for chat & dialogue summarization | https://www.philschmid.de/fine-tune-flan-t5 | 2022-12-27 | [
"T5",
"Summarization",
"HuggingFace",
"Chat"
] | Learn how to fine-tune Google's FLAN-T5 for chat & dialogue summarization using Hugging Face Transformers. | In this blog, you will learn how to fine-tune [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) for chat & dialogue summarization using Hugging Face Transformers. If you already know T5, FLAN-T5 is just better at everything. For the same number of parameters, these models have been fine-tuned on more th... |
Getting started with Pytorch 2.0 and Hugging Face Transformers | https://www.philschmid.de/getting-started-pytorch-2-0-transformers | 2023-03-16 | [
"Pytorch",
"BERT",
"HuggingFace",
"Optimization"
] | Learn how to get started with Pytorch 2.0 and Hugging Face Transformers and reduce your training time up to 2x. | On December 2, 2022, the PyTorch Team announced [PyTorch 2.0](https://pytorch.org/get-started/pytorch-2.0/) at the PyTorch Conference, focused on better performance, being faster, more pythonic, and staying as dynamic as before.
This blog post explains how to get started with PyTorch 2.0 and Hugging Face Transformers ... |
Deploy T5 11B for inference for less than $500 | https://www.philschmid.de/deploy-t5-11b | 2022-10-25 | [
"HuggingFace",
"Transformers",
"Endpoints",
"bnb"
] | Learn how to deploy T5 11B on a single GPU using Hugging Face Inference Endpoints. | This blog will teach you how to deploy [T5 11B](https://huggingface.co/t5-11b) for inference using [Hugging Face Inference Endpoints](https://huggingface.co/inference-endpoints). The T5 model was presented in [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://arxiv.org/pdf/1910.... |
Scalable, Secure Hugging Face Transformer Endpoints with Amazon SageMaker, AWS Lambda, and CDK | https://www.philschmid.de/huggingface-transformers-cdk-sagemaker-lambda | 2021-10-06 | [
"AWS",
"BERT",
"HuggingFace",
"Sagemaker"
] | Deploy Hugging Face Transformers to Amazon SageMaker and create an API for the Endpoint using AWS Lambda, API Gateway and AWS CDK. | Researchers, Data Scientists, Machine Learning Engineers are excellent at creating models to achieve new state-of-the-art performance on different tasks, but deploying those models in an accessible, scalable, and secure way is more of an art than science. Commonly, those skills are found in software engineering and Dev... |
Deploy FLAN-UL2 20B on Amazon SageMaker | https://www.philschmid.de/deploy-flan-ul2-sagemaker | 2023-03-20 | [
"GenerativeAI",
"SageMaker",
"HuggingFace",
"Inference"
] | Learn how to deploy Google's FLAN-UL 20B on Amazon SageMaker for inference. | Welcome to this Amazon SageMaker guide on how to deploy the [FLAN-UL2 20B](https://huggingface.co/google/flan-ul2) on Amazon SageMaker for inference. We will deploy [google/flan-ul2](https://huggingface.co/google/flan-ul2) to Amazon SageMaker for real-time inference using Hugging Face Inference Deep Learning Container.... |
Serverless Inference with Hugging Face's Transformers, DistilBERT and Amazon SageMaker | https://www.philschmid.de/sagemaker-serverless-huggingface-distilbert | 2022-04-21 | [
"HuggingFace",
"AWS",
"BERT",
"Serverless"
] | Learn how to deploy a Transformer model like BERT to Amazon SageMaker Serverless using the Python SageMaker SDK. | [Notebook: serverless_inference](https://github.com/huggingface/notebooks/blob/main/sagemaker/19_serverless_inference/sagemaker-notebook.ipynb)
Welcome to this getting started guide, you learn how to use the Hugging Face Inference DLCs and Amazon SageMaker Python SDK to create a [Serverless Inference](https://docs.aws... |
Efficient Large Language Model training with LoRA and Hugging Face | https://www.philschmid.de/fine-tune-flan-t5-peft | 2023-03-23 | [
"GenerativeAI",
"LoRA",
"HuggingFace",
"Training"
] | Learn how to fine-tune Google's FLAN-T5 XXL on a Single GPU using LoRA And Hugging Face Transformers. | In this blog, we are going to show you how to apply [Low-Rank Adaptation of Large Language Models (LoRA)](https://arxiv.org/abs/2106.09685) to fine-tune FLAN-T5 XXL (11 billion parameters) on a single GPU. We are going to leverage Hugging Face [Transformers](https://huggingface.co/docs/transformers/index), [Accelerate]... |
Setup Deep Learning environment for Hugging Face Transformers with Habana Gaudi on AWS | https://www.philschmid.de/getting-started-habana-gaudi | 2022-06-14 | [
"BERT",
"Habana",
"HuggingFace",
"Optimum"
] | Learn how to setup a Deep Learning Environment for Hugging Face Transformers with Habana Gaudi on AWS using the DL1 instance type. | This blog contains instructions for how to setup a Deep Learning Environment for Habana Gaudi on AWS using the DL1 instance type and Hugging Face libraries like [transformers](https://huggingface.co/docs/transformers/index), [optimum](https://huggingface.co/docs/optimum/index), [datasets](https://huggingface.co/docs/da... |
Workshop: Enterprise-Scale NLP with Hugging Face & Amazon SageMaker | https://www.philschmid.de/hugginface-sagemaker-workshop | 2021-12-29 | [
"HuggingFace",
"AWS",
"SageMaker"
] | In October and November, we held a workshop series on “Enterprise-Scale NLP with Hugging Face & Amazon SageMaker”. This workshop series consisted out of 3 parts and covers: Getting Started, Going Production & MLOps. | Earlier this year we announced a strategic collaboration with Amazon to make it easier for companies to use Hugging Face Transformers in Amazon SageMaker, and ship cutting-edge Machine Learning features faster. We introduced new Hugging Face Deep Learning Containers (DLCs) to train and deploy Hugging Face Transformers ... |
Fine-tune a non-English GPT-2 Model with Huggingface | https://www.philschmid.de/fine-tune-a-non-english-gpt-2-model-with-huggingface | 2020-09-06 | [
"NLP",
"GPT-2",
"Huggingface"
] | Fine-tune non-English, German GPT-2 model with Huggingface on German recipes. Using their Trainer class and Pipeline objects. | Unless you’re living under a rock, you probably have heard about [OpenAI](https://openai.com/)'s GPT-3 language model.
You might also have seen all the crazy demos, where the model writes `JSX`, `HTML` code, or its capabilities in the area
of zero-shot / few-shot learning. [Simon O'Regan](https://twitter.com/Simon_O_Re... |
An Amazon SageMaker Inference comparison with Hugging Face Transformers | https://www.philschmid.de/sagemaker-inference-comparison | 2022-05-17 | [
"HuggingFace",
"AWS",
"BERT",
"SageMaker"
] | Learn about the different existing Amazon SageMaker Inference options and and how to use them. | _"Amazon SageMaker is a fully managed machine learning service. With SageMaker, data scientists and developers can quickly and easily build and train machine learning models, and then directly deploy them into a production-ready hosted environment."_ - [AWS Documentation](https://docs.aws.amazon.com/sagemaker/latest/dg... |
MLOps: End-to-End Hugging Face Transformers with the Hub & SageMaker Pipelines | https://www.philschmid.de/mlops-sagemaker-huggingface-transformers | 2021-11-10 | [
"AWS",
"BERT",
"HuggingFace",
"Sagemaker"
] | Learn how to build an End-to-End MLOps Pipeline for Hugging Face Transformers from training to production using Amazon SageMaker. | Welcome to this getting started guide, we will use the new Hugging Face Inference DLCs and Amazon SageMaker Python SDK to create an End-to-End MLOps Pipeline for Hugging Face Transformers from training to production using Amazon SageMaker.
This blog posts demonstrates how to use [SageMaker Pipelines](https://docs.aws.a... |
Speed up BERT inference with Hugging Face Transformers and AWS Inferentia | https://www.philschmid.de/huggingface-bert-aws-inferentia | 2022-03-16 | [
"HuggingFace",
"AWS",
"BERT",
"Inferentia"
] | Learn how to accelerate BERT and Transformers inference using Hugging Face Transformers and AWS Inferentia. | notebook: [sagemaker/18_inferentia_inference](https://github.com/huggingface/notebooks/blob/master/sagemaker/18_inferentia_inference/sagemaker-notebook.ipynb)
The adoption of [BERT](https://huggingface.co/blog/bert-101) and [Transformers](https://huggingface.co/docs/transformers/index) continues to grow. Transformer-b... |
Image Classification with Hugging Face Transformers and `Keras` | https://www.philschmid.de/image-classification-huggingface-transformers-keras | 2022-01-04 | [
"HuggingFace",
"Keras",
"ViT",
"Tensorflow"
] | Learn how to fine-tune a Vision Transformer for Image Classification Example using vanilla `Keras`, `Transformers`, `Datasets`. | Welcome to this end-to-end Image Classification example using Keras and Hugging Face Transformers. In this demo, we will use the Hugging Faces `transformers` and `datasets` library together with `Tensorflow` & `Keras` to fine-tune a pre-trained vision transformer for image classification.
We are going to use the [Euro... |
Scaling Machine Learning from ZERO to HERO | https://www.philschmid.de/scaling-machine-learning-from-zero-to-hero | 2020-05-08 | [
"AWS",
"Serverless",
"Pytorch"
] | Scale your machine learning models by using AWS Lambda, the Serverless Framework, and PyTorch. I will show you how to build scalable deep learning inference architectures. | The workflow for building machine learning models often ends at the evaluation stage: you have achieved an acceptable
accuracy, which you can test and demonstrate in your "research environment" and “ta-da! Mission accomplished.” But this
is not all! The last - most important step in a machine learning workflow is **dep... |
K-Fold as Cross-Validation with a BERT Text-Classification Example | https://www.philschmid.de/k-fold-as-cross-validation-with-a-bert-text-classification-example | 2020-04-07 | [
"Python",
"BERT"
] | Using the K-Fold Cross-Validation to improve your Transformers model validation by the example of BERT Text-Classification | K-fold is a cross-validation method used to estimate the skill of a machine learning model on unseen data. It is
commonly used to validate a model, because it is easy to understand, to implement and results are having a higher
informative value than regular Validation Methods.
Cross-validation is a resampling procedur... |
Static Quantization with Hugging Face `optimum` for ~3x latency improvements | https://www.philschmid.de/static-quantization-optimum | 2022-06-07 | [
"BERT",
"OnnxRuntime",
"HuggingFace",
"Quantization"
] | Learn how to do post-training static quantization on Hugging Face Transformers model with `optimum` to achieve up to 3x latency improvements. | notebook: [optimum-static-quantization](https://github.com/philschmid/optimum-static-quantization/blob/master/notebook.ipynb)
In this session, you will learn how to do post-training static quantization on Hugging Face Transformers model. The session will show you how to quantize a DistilBERT model using [Hugging Face ... |
How to Set Up a CI/CD Pipeline for AWS Lambda With GitHub Actions and Serverless | https://www.philschmid.de/how-to-set-up-a-ci-cd-pipeline-for-aws-lambda-with-github-actions-and-serverless | 2020-04-01 | [
"AWS",
"Python",
"Github"
] | Automatically deploy your Python function with dependencies in less than five minutes | A CI/CD pipeline functional for your project is incredibly valuable as a developer. Thankfully, it’s not difficult to
set up such a pipeline with Github Actions.
In my previous
article, [Set up a CI/CD Pipeline for your Web app on AWS with Github Actions](https://www.philschmid.de/set-up-a-ci-cd-pipeline-for-your-web-... |
Accelerate GPT-J inference with DeepSpeed-Inference on GPUs | https://www.philschmid.de/gptj-deepspeed-inference | 2022-09-13 | [
"GPTJ",
"DeepSpeed",
"HuggingFace",
"Optimization"
] | Learn how to optimize GPT-J for GPU inference with a 1-line of code using Hugging Face Transformers and DeepSpeed. | In this session, you will learn how to optimize GPT-2/GPT-J for Inerence using [Hugging Face Transformers](https://huggingface.co/docs/transformers/index) and [DeepSpeed-Inference](https://www.deepspeed.ai/tutorials/inference-tutorial/). The session will show you how to apply state-of-the-art optimization techniques us... |
Document AI: Fine-tuning LayoutLM for document-understanding using Hugging Face Transformers | https://www.philschmid.de/fine-tuning-layoutlm | 2022-10-04 | [
"DocumentAI",
"HuggingFace",
"Transformers",
"LayoutLM"
] | Learn how to fine-tune LayoutLM for document-understand using Hugging Face Transformers. LayoutLM is a document image understanding and information extraction transformers. | In this blog, you will learn how to fine-tune [LayoutLM (v1)](https://huggingface.co/docs/transformers/model_doc/layoutlm) for document-understand using Hugging Face Transformers. LayoutLM is a document image understanding and information extraction transformers. LayoutLM (v1) is the only model in the LayoutLM family w... |
Document AI: LiLT a better language agnostic LayoutLM model | https://www.philschmid.de/fine-tuning-lilt | 2022-11-22 | [
"DocumentAI",
"HuggingFace",
"Transformers",
"LayoutLM"
] | Learn how to fine-tune LiLt (Language independent Layout Transformer) for document-understand/document-parsing using Hugging Face Transformers. | In this blog, you will learn how to fine-tune [LiLt](https://huggingface.co/docs/transformers/v4.24.0/en/model_doc/lilt#lilt) for document-understand using Hugging Face Transformers. LiLt or **L**anguage **i**ndependent **L**ayout **T**ransformer can combine any pre-trained [RoBERTa](https://huggingface.co/models?other... |
Multi-Model GPU Inference with Hugging Face Inference Endpoints | https://www.philschmid.de/multi-model-inference-endpoints | 2022-11-17 | [
"Inference",
"HuggingFace",
"BERT",
"MultiModel"
] | Learn how to deploy a multiple models on to a GPU with Hugging Face multi-model inference endpoints. | Multi-model inference endpoints provide a way to deploy multiple models onto the same infrastructure for a scalable and cost-effective inference. Multi-model inference endpoints load a list of models into memory, either CPU or GPU, and dynamically use them during inference.
This blog will cover how to create a multi-m... |
Distributed Training: Train BART/T5 for Summarization using 🤗 Transformers and Amazon SageMaker | https://www.philschmid.de/sagemaker-distributed-training | 2021-04-09 | [
"AWS",
"HuggingFace",
"BART"
] | Learn how to train distributed models for summarization using Hugging Face Transformers and Amazon SageMaker and upload them afterwards to huggingface.co. | In case you missed it: on March 25th [we announced a collaboration with Amazon SageMaker](https://huggingface.co/blog/the-partnership-amazon-sagemaker-and-hugging-face) to make it easier to create State-of-the-Art Machine Learning models, and ship cutting-edge NLP features faster.
Together with the SageMaker team, we ... |
Task-specific knowledge distillation for BERT using Transformers & Amazon SageMaker | https://www.philschmid.de/knowledge-distillation-bert-transformers | 2022-02-01 | [
"HuggingFace",
"AWS",
"BERT",
"PyTorch"
] | Learn how to run apply task-specific knowledge distillation for BERT and text-classification using Hugging Face Transformers & Amazon SageMaker including Hyperparameter search. | Welcome to this end-to-end task-specific knowledge distillation Text-Classification example using Transformers, PyTorch & Amazon SageMaker. Distillation is the process of training a small "student" to mimic a larger "teacher". In this example, we will use a [BERT-base](https://huggingface.co/textattack/bert-base-uncase... |
Stable Diffusion Inpainting example with Hugging Face inference Endpoints | https://www.philschmid.de/stable-diffusion-inpainting-inference-endpoints | 2022-12-15 | [
"Diffusion",
"Inference",
"HuggingFace",
"Generation"
] | Learn how to deploy Stable Diffusion 2.0 Inpainting on Hugging Face Inference Endpoints to manipulate images. | [Inpainting](https://en.wikipedia.org/wiki/Inpainting) refers to the process of replacing/deteriorating or filling in missing data in an artwork to complete the image. This process was commonly used in image restoration. But with the latest AI developments and breakthroughs, like [DALL-E by OpenAI](https://openai.com/d... |
Combine Amazon SageMaker and DeepSpeed to fine-tune FLAN-T5 XXL | https://www.philschmid.de/sagemaker-deepspeed | 2023-02-22 | [
"T5",
"DeepSpeed",
"HuggingFace",
"SageMaker"
] | Learn how to fine-tune Google's FLAN-T5 XXL on Amazon SageMaker using DeepSpeed and Hugging Face Transformers. | FLAN-T5, released with the[Scaling Instruction-Finetuned Language Models](https://arxiv.org/pdf/2210.11416.pdf)paper, is an enhanced version of T5 that has been fine-tuned in a mixture of tasks, or simple words, a better T5 model in any aspect. FLAN-T5 outperforms T5 by double-digit improvements for the same number of ... |
Financial Text Summarization with Hugging Face Transformers, Keras & Amazon SageMaker | https://www.philschmid.de/financial-summarizatio-huggingface-keras | 2022-01-19 | [
"HuggingFace",
"Keras",
"SageMaker",
"Tensorflow"
] | Learn how to fine-tune a a Hugging Face Transformer for Financial Text Summarization using vanilla `Keras`, `Tensorflow` , `Transformers`, `Datasets` & Amazon SageMaker. | Welcome to this end-to-end Financial Summarization (NLP) example using Keras and Hugging Face Transformers. In this demo, we will use the Hugging Faces `transformers` and `datasets` library together with `Tensorflow` & `Keras` to fine-tune a pre-trained seq2seq transformer for financial summarization.
We are going to ... |
Managed Transcription with OpenAI Whisper and Hugging Face Inference Endpoints | https://www.philschmid.de/whisper-inference-endpoints | 2022-12-20 | [
"Whisper",
"Transcription",
"HuggingFace",
"Inference"
] | Learn how to deploy OpenAI Whisper for speech recognition and transcription using Hugging Face Inference Endpoints. | In September, [OpenAI](hhttps://openai.com/blog/whisper/) announced and released [Whisper](https://openai.com/blog/whisper/), an automatic speech recognition (ASR) system trained on 680,000 hours of audio. Whisper achieved state-of-art performance and changed the status quo of speech recognition and transcription from ... |
Getting Started with AutoML and AWS AutoGluon | https://www.philschmid.de/getting-started-with-automl-and-aws-autogluon | 2020-04-20 | [
"AWS",
"AutoML",
"Computer Vision"
] | Built an Object Detection Model with AWS AutoML library AutoGluon. | Google [CEO Sundar Pichai wrote](https://blog.google/technology/ai/making-ai-work-for-everyone/), “... _designing neural
nets is extremely time intensive, and requires an expertise that limits its use to a smaller community of scientists and
engineers._" Shortly after this Google launched its service AutoML in early 20... |
Asynchronous Inference with Hugging Face Transformers and Amazon SageMaker | https://www.philschmid.de/sagemaker-huggingface-async-inference | 2022-02-15 | [
"HuggingFace",
"AWS",
"BERT",
"SageMaker"
] | Learn how to deploy an Asynchronous Inference model with Hugging Face Transformers and Amazon SageMaker, with autoscaling to zero. | Welcome to this getting started guide. We will use the Hugging Face Inference DLCs and Amazon SageMaker Python SDK to run an [Asynchronous Inference](https://docs.aws.amazon.com/sagemaker/latest/dg/async-inference.html) job.
Amazon SageMaker Asynchronous Inference is a new capability in SageMaker that queues incoming r... |
Hugging Face Transformers BERT fine-tuning using Amazon SageMaker and Training Compiler | https://www.philschmid.de/huggingface-amazon-sagemaker-training-compiler | 2021-12-07 | [
"AWS",
"BERT",
"HuggingFace",
"Sagemaker"
] | Learn how to Compile and fine-tune a Multi-Class Classification Transformers with `Trainer` and `emotion` dataset using Amazon SageMaker Training Compiler. | Last week at re:Invent 2021 [Swami Sivasubramanian](https://www.linkedin.com/in/swaminathansivasubramanian) has introducted the new [Amazon SageMaker Training Compiler](https://docs.aws.amazon.com/sagemaker/latest/dg/training-compiler.html), which optimizes DL models to accelerate training by more efficiently using Sag... |
Few-shot learning in practice with GPT-Neo | https://www.philschmid.de/few-shot-learning-gpt-neo | 2021-06-05 | [
"HuggingFace",
"GPT-Neo"
] | The latest developments in NLP show that you can overcome this limitation by providing a few examples at inference time with a large language model - a technique known as Few-Shot Learning. In this blog post, we'll explain what Few-Shot Learning is, and explore how a large language model called GPT-Neo. | > [Cross post from huggingface.co/blog](https://huggingface.co/blog/few-shot-learning-gpt-neo-and-inference-api)
In many Machine Learning applications, the amount of available labeled data is a barrier to producing a high-performing model. The latest developments in NLP show that you can overcome this limitation by pr... |
Custom Inference with Hugging Face Inference Endpoints | https://www.philschmid.de/custom-inference-handler | 2022-09-29 | [
"Inference",
"HuggingFace",
"BERT"
] | Welcome to this tutorial on how to create a custom inference handler for Hugging Face Inference Endpoints. | Welcome to this tutorial on how to create a custom inference handler for [Hugging Face Inference Endpoints](https://huggingface.co/inference-endpoints).
The tutorial will cover how to extend a default transformers pipeline with custom business logic, customize the request & response body, and add additional Python dep... |
Fine-tune FLAN-T5 XL/XXL using DeepSpeed & Hugging Face Transformers | https://www.philschmid.de/fine-tune-flan-t5-deepspeed | 2023-02-16 | [
"T5",
"DeepSpeed",
"HuggingFace",
"Summarization"
] | Learn how to fine-tune Google's FLAN-T5 XXL using DeepSpeed & Hugging Face Transformers. | FLAN-T5, released with the [Scaling Instruction-Finetuned Language Models](https://arxiv.org/pdf/2210.11416.pdf) paper, is an enhanced version of T5 that has been fine-tuned in a mixture of tasks, or simple words, a better T5 model in any aspect. FLAN-T5 outperforms T5 by double-digit improvements for the same number o... |
Deploy BERT with Hugging Face Transformers, Amazon SageMaker and Terraform module | https://www.philschmid.de/terraform-huggingface-amazon-sagemaker | 2022-02-08 | [
"HuggingFace",
"AWS",
"BERT",
"Terraform"
] | Learn how to deploy BERT/DistilBERT with Hugging Face Transformers using Amazon SageMaker and Terraform module. | _“Infrastructure as Code (IaC) is **the managing and provisioning of infrastructure through code instead of through manual processes**. With IaC, configuration files are created that contain your infrastructure specifications, which makes it easier to edit and distribute configurations. It also ensures that you provisi... |
Optimizing Transformers for GPUs with Optimum | https://www.philschmid.de/optimizing-transformers-with-optimum-gpu | 2022-07-13 | [
"BERT",
"OnnxRuntime",
"HuggingFace",
"Optimization"
] | Learn how to optimize Hugging Face Transformers models for NVIDIA GPUs using Optimum. You will learn how to optimize a DistilBERT for ONNX Runtime | In this session, you will learn how to optimize Hugging Face Transformers models for GPUs using Optimum. The session will show you how to convert you weights to fp16 weights and optimize a DistilBERT model using [Hugging Face Optimum](https://huggingface.co/docs/optimum/index) and [ONNX Runtime](https://onnxruntime.ai/... |
Deploy FLAN-T5 XXL on Amazon SageMaker | https://www.philschmid.de/deploy-flan-t5-sagemaker | 2023-02-08 | [
"T5",
"SageMaker",
"HuggingFace",
"Inference"
] | Learn how to deploy Google's FLAN-T5 XXL on Amazon SageMaker for inference. | Welcome to this Amazon SageMaker guide on how to deploy the [FLAN-T5-XXL](https://huggingface.co/google/flan-t5-xxl) on Amazon SageMaker for inference. We will deploy [philschmid/flan-t5-xxl-sharded-fp16](https://huggingface.co/philschmid/flan-t5-xxl-sharded-fp16) to Amazon SageMake for real-time inference using Huggin... |
Optimizing Transformers with Hugging Face Optimum | https://www.philschmid.de/optimizing-transformers-with-optimum | 2022-06-30 | [
"BERT",
"OnnxRuntime",
"HuggingFace",
"Optimization"
] | Learn how to optimize Hugging Face Transformers models using Optimum. The session will show you how to dynamically quantize and optimize a DistilBERT model using Hugging Face Optimum and ONNX Runtime. Hugging Face Optimum is an extension of 🤗 Transformers, providing a set of performance optimization tools enabling max... | _last update: 2022-11-18_
In this session, you will learn how to optimize Hugging Face Transformers models using Optimum. The session will show you how to dynamically quantize and optimize a DistilBERT model using [Hugging Face Optimum](https://huggingface.co/docs/optimum/index) and [ONNX Runtime](https://onnxruntime.... |
Accelerated document embeddings with Hugging Face Transformers and AWS Inferentia | https://www.philschmid.de/huggingface-sentence-transformers-aws-inferentia | 2022-04-19 | [
"HuggingFace",
"AWS",
"BERT",
"Inferentia"
] | Learn how to accelerate Sentence Transformers inference inference using Hugging Face Transformers and AWS Inferentia. | notebook: [sentence-transformers-huggingface-inferentia](https://github.com/philschmid/sentence-transformers-huggingface-inferentia/blob/main/sagemaker-notebook.ipynb)
The adoption of [BERT](https://huggingface.co/blog/bert-101) and [Transformers](https://huggingface.co/docs/transformers/index) continues to grow. Tran... |
Document AI: Fine-tuning Donut for document-parsing using Hugging Face Transformers | https://www.philschmid.de/fine-tuning-donut | 2022-09-06 | [
"DocumentAI",
"HuggingFace",
"Transformers",
"Donut"
] | Learn how to fine-tune Donut-base for document-understand/document-parsing using Hugging Face Transformers. Donut is a new document-understanding model achieving state-of-art performance and can be used for commercial applications. | In this blog, you will learn how to fine-tune [Donut-base](https://huggingface.co/naver-clova-ix/donut-base) for document-understand/document-parsing using Hugging Face Transformers. Donut is a new document-understanding model achieving state-of-art performance with an MIT-license, which allows it to be used for commer... |
A remote guide to re:Invent 2021 machine learning sessions | https://www.philschmid.de/re-invent-2021 | 2021-11-11 | [
"AWS",
"ReInvent"
] | If you are like me you are not from the USA and cannot easily travel to Las Vegas. I have the perfect remote guide for your perfect virtual re:Invent 2021 focused on NLP and Machine Learning. | After last year's amazing fully virtual event and improvement in the current life situation, Amazonians can finally gather back to Vegas for a somewhat traditional re:Invent. But this year will be the second year that re:Invent has a large virtual/remote part.
If you are like me you are not from the USA and cannot eas... |
Pre-Training BERT with Hugging Face Transformers and Habana Gaudi | https://www.philschmid.de/pre-training-bert-habana | 2022-08-24 | [
"BERT",
"Habana",
"HuggingFace",
"Optimum"
] | Learn how to pre-traing BERT from scratch using Hugging Face Transformers and Habana Gaudi. | In this Tutorial, you will learn how to pre-train [BERT-base](https://huggingface.co/bert-base-uncased) from scratch using a Habana Gaudi-based [DL1 instance](https://aws.amazon.com/ec2/instance-types/dl1/) on AWS to take advantage of the cost-performance benefits of Gaudi. We will use the Hugging Face [Transformers](h... |
Deploy BigScience T0_3B to AWS & Amazon SageMaker | https://www.philschmid.de/deploy-bigscience-t0-3b-to-aws-and-amazon-sagemaker | 2021-10-20 | [
"AWS",
"Shorts",
"HuggingFace",
"Sagemaker"
] | 🌸 BigScience released their first modeling paper introducing T0 which outperforms GPT-3 on many zero-shot tasks while being 16x smaller! Deploy BigScience the 3 Billion version (T0_3B) to Amazon SageMaker with a few lines of code to run a scalable production workload! | Earlier this week 🌸 [BigScience](https://bigscience.huggingface.co/) released their first [modeling paper](https://arxiv.org/abs/2110.08207) for the collaboration introducing [T0\*](https://huggingface.co/bigscience/T0_3B). For those of you who haven't heard about 🌸 [BigScience](https://bigscience.huggingface.co/) it... |
Stable Diffusion on Amazon SageMaker | https://www.philschmid.de/sagemaker-stable-diffusion | 2022-11-01 | [
"AWS",
"HuggingFace",
"SageMaker",
"Diffusion"
] | Learn how to deploy Stable Diffusion to Amazon SageMaker to generate images. | Welcome to this Amazon SageMaker guide on how to use the [Stable Diffusion](https://huggingface.co/blog/stable_diffusion) to generate image for a given input prompt. We will deploy [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4) to Amazon SageMake for real-time inference using Hugg... |
Going Production: Auto-scaling Hugging Face Transformers with Amazon SageMaker | https://www.philschmid.de/auto-scaling-sagemaker-huggingface | 2021-10-29 | [
"AWS",
"BERT",
"HuggingFace",
"Sagemaker"
] | Learn how to add auto-scaling to your Hugging Face Transformers SageMaker Endpoints. | Welcome to this getting started guide, we will use the new Hugging Face Inference DLCs and Amazon SageMaker Python SDK to deploy a transformer model for real-time inference.
In this example, we are going to deploy a trained Hugging Face Transformer model onto SageMaker for inference.
```python
!pip install "sagemaker>... |
Multi-Container Endpoints with Hugging Face Transformers and Amazon SageMaker | https://www.philschmid.de/sagemaker-huggingface-multi-container-endpoint | 2022-02-22 | [
"HuggingFace",
"AWS",
"BERT",
"SageMaker"
] | Learn how to deploy multiple Hugging Face Transformers for inference with Amazon SageMaker and Multi-Container Endpoints. | Welcome to this getting started guide. We will use the Hugging Face Inference DLCs and Amazon SageMaker to deploy multiple transformer models as [Multi-Container Endpoint](https://docs.aws.amazon.com/sagemaker/latest/dg/multi-container-endpoints.html).
Amazon SageMaker Multi-Container Endpoint is an inference option to... |
Automatic Speech Recogntion with Hugging Face's Transformers & Amazon SageMaker | https://www.philschmid.de/automatic-speech-recognition-sagemaker | 2022-04-28 | [
"AWS",
"Wav2vec2",
"Speech",
"Sagemaker"
] | Learn how to do automatic speech recognition/speech-to-text with Hugging Face Transformers, Wav2vec2 and Amazon SageMaker. | Transformer models are changing the world of machine learning, starting with natural language processing, and now, with audio and computer vision. Hugging Face's mission is to democratize good machine learning and give anyone the opportunity to use these new state-of-the-art machine learning models.
Together with Amazo... |
Save up to 90% training cost with AWS Spot Instances and Hugging Face Transformers | https://www.philschmid.de/sagemaker-spot-instance | 2022-03-22 | [
"AWS",
"HuggingFace",
"BERT",
"SageMaker"
] | Learn how to leverage AWS Spot Instances when training Hugging Face Transformers with Amazon SageMaker to save up to 90% training cost. | notebook: [sagemaker/05_spot_instances](https://github.com/huggingface/notebooks/blob/master/sagemaker/05_spot_instances/sagemaker-notebook.ipynb)
[Amazon EC2 Spot Instances](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/using-spot-instances.html) are a way to take advantage of unused EC2 capacity in the AWS clo... |
Stable Diffusion with Hugging Face Inference Endpoints | https://www.philschmid.de/stable-diffusion-inference-endpoints | 2022-11-28 | [
"Diffusion",
"Inference",
"HuggingFace",
"Generation"
] | Learn how to deploy Stable Diffusion 2.0 on Hugging Face Inference Endpoints to generate images based from text. | Welcome to this Hugging Face Inference Endpoints guide on how to deploy [Stable Diffusion](https://huggingface.co/blog/stable_diffusion)
to generate images for a given input prompt. We will deploy [stabilityai/stable-diffusion-2](https://huggingface.co/stabilityai/stable-diffusion-2)
to Inference Endpoints for real-tim... |
My path to become a certified solution architect | https://www.philschmid.de/my-path-to-become-a-certified-solution-architect | 2020-10-24 | [
"AWS",
"Certificate",
"Cloud"
] | This is the Story of how I became a certified solution architect within 28 hours of preparation. | Hello, my name is Philipp and I am working as a machine learning engineer at a technology incubation startup. At work I
design and implement cloud-native machine learning architectures for fin-tech and insurance companies.
I started to work with AWS 2 1/2 years ago. Since then I had built many projects both privately ... |
MLOps: Using the Hugging Face Hub as model registry with Amazon SageMaker | https://www.philschmid.de/huggingface-hub-amazon-sagemaker | 2021-11-16 | [
"AWS",
"BERT",
"HuggingFace",
"Sagemaker"
] | Learn how to automatically save your model weights, logs, and artifacts to the Hugging Face Hub using Amazon SageMaker and how to deploy the model afterwards for inference. | The [Hugging Face Hub](hf.co/models) is the largest collection of models, datasets, and metrics in order to democratize and advance AI for everyone 🚀. The Hugging Face Hub works as a central place where anyone can share and explore models and datasets.
In this blog post you will learn how to automatically save your m... |
AWS Lambda with custom docker images as runtime | https://www.philschmid.de/aws-lambda-with-custom-docker-image | 2020-12-02 | [
"AWS",
"Serverless",
"Docker"
] | Learn how to build and deploy an AWS Lambda function with a custom python docker container as runtime with the use of Amazon ECR. | It's the most wonderful time of the year. Of course, I'm not talking about Christmas but re:Invent. It is re:Invent
time. Due to the current situation in the world, re:Invent does not take place like every year in Las Vegas but is
entirely virtual and for free. This means that it is possible for everyone to attend. In ... |
Outperform OpenAI GPT-3 with SetFit for text-classification | https://www.philschmid.de/getting-started-setfit | 2022-10-18 | [
"GPT3",
"HuggingFace",
"Transformers",
"SetFit"
] | Learn how to use SetFit to create a text-classification model with only a `8` labeled samples per class, or `32` samples in total. You will also learn how to improve your model by using hyperparamter tuning. | In many Machine Learning applications, the amount of available labeled data is a barrier to producing a high-performing model.
In the last 2 years developments have shown that you can overcome this data limitation by using Large Language Models, like [OpenAI GPT-3](https://openai.com/blog/gpt-3-apps/) together wit a _f... |
Serverless BERT with HuggingFace and AWS Lambda | https://www.philschmid.de/serverless-bert-with-huggingface-and-aws-lambda | 2020-06-30 | [
"AWS",
"Serverless",
"Bert"
] | Build a serverless question-answering API with BERT, HuggingFace, the Serverless Framework and AWS Lambda. | "Serverless" and "BERT" are two topics that strongly influenced the world of computing.
[Serverless architecture](https://hackernoon.com/what-is-serverless-architecture-what-are-its-pros-and-cons-cc4b804022e9)
allows us to provide dynamically scale-in and -out the software without managing and provisioning computing po... |
Accelerate Stable Diffusion inference with DeepSpeed-Inference on GPUs | https://www.philschmid.de/stable-diffusion-deepspeed-inference | 2022-11-08 | [
"Diffusion",
"DeepSpeed",
"HuggingFace",
"Optimization"
] | Learn how to optimize Stable Diffusion for GPU inference with a 1-line of code using Hugging Face Diffusers and DeepSpeed. | In this session, you will learn how to optimize Stable Diffusion for Inerence using Hugging Face [🧨 Diffusers library](https://huggingface.co/docs/diffusers/index). and [DeepSpeed-Inference](https://www.deepspeed.ai/tutorials/inference-tutorial/). The session will show you how to apply state-of-the-art optimization te... |
Advanced PII detection and anonymization with Hugging Face Transformers and Amazon SageMaker | https://www.philschmid.de/pii-huggingface-sagemaker | 2022-05-31 | [
"BERT",
"PII",
"HuggingFace",
"SageMaker"
] | Learn how to do advanced PII detection and anonymization with Hugging Face Transformers and Amazon SageMaker. | repository [philschmid/advanced-pii-huggingface-sagemaker](https://github.com/philschmid/advanced-pii-huggingface-sagemaker)
PII or Personally identifiable information (PII) is any data that could potentially identify a specific individual, e.g. to distinguish one person from another. Below are a few examples of PII:
... |
Getting started with CNNs by calculating LeNet-Layer manually | https://www.philschmid.de/getting-started-with-cnn-by-calculating-lenet-layer-manually | 2020-02-28 | [
"Computer Vision"
] | Getting started explanation to CNNs by calculating Yann LeCun LeNet-5 manually for handwritten digits and learning about Padding and Stride. | The idea of CNNs is intelligently adapt to the properties of images by reducing the dimension. To achieve this
convolutional layer and pooling layer are used. Convolutional layers are reducing the dimensions by adding filters
(kernel windows) to the Input. The dimension can reduce by applying kernel windows to calculat... |
Convert Transformers to ONNX with Hugging Face Optimum | https://www.philschmid.de/convert-transformers-to-onnx | 2022-06-21 | [
"BERT",
"HuggingFace",
"ONNX",
"Optimum"
] | Introduction guide about ONNX and Transformers. Learn how to convert transformers like BERT to ONNX and what you can do with it. | Hundreds of Transformers experiments and models are uploaded to the [Hugging Face Hub](https://huggingface.co/) every single day. Machine learning engineers and students conducting those experiments use a variety of frameworks like PyTorch, TensorFlow/Keras, or others. These models are already used by thousands of comp... |
Controlled text-to-image generation with ControlNet on Inference Endpoints | https://www.philschmid.de/stable-diffusion-controlnet-endpoint | 2023-03-03 | [
"Diffusion",
"Inference",
"HuggingFace",
"Generation"
] | Learn how to deploy ControlNet Stable Diffusion Pipeline on Hugging Face Inference Endpoints to generate controlled images. | ControlNet is a neural network structure to control diffusion models by adding extra conditions.
With [ControlNet](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/controlnet), users can easily condition the generation with different spatial contexts such as a depth map, a segmentation map,... |
How to use Google Tag Manager and Google Analytics without Cookies | https://www.philschmid.de/how-to-use-google-tag-manager-and-google-analytics-without-cookies | 2020-06-06 | [
"Analytics",
"Web"
] | Connect your user behavior with technical insights without using cookies to improve your customer experience. | _"Web analytics is the measurement, collection, analysis, and reporting of web data for purposes of understanding and
optimizing web usage. However, Web analytics is not just a process for measuring web traffic but can be used as a tool
for business and market research, and to assess and improve the effectiveness of a ... |
Hugging Face Transformers and Habana Gaudi AWS DL1 Instances | https://www.philschmid.de/habana-distributed-training | 2022-07-05 | [
"BERT",
"Habana",
"HuggingFace",
"Optimum"
] | Learn how to learn how to fine-tune XLM-RoBERTa for multi-lingual multi-class text-classification using a Habana Gaudi-based DL1 instance. | In this blog, you will learn how to fine-tune [XLM-RoBERTa](https://huggingface.co/xlm-roberta-large) for multi-lingual multi-class text-classification using a Habana Gaudi-based [DL1 instance](https://aws.amazon.com/ec2/instance-types/dl1/) on AWS to take advantage of the cost performance benefits of Gaudi. We will us... |
Set up a CI/CD Pipeline for your Web app on AWS with Github Actions | https://www.philschmid.de/set-up-a-ci-cd-pipeline-for-your-web-app-on-aws-s3-with-github-actions | 2020-03-25 | [
"AWS",
"Vue",
"Github"
] | Automatic deploy your React, Vue, Angular or Svelte app on s3 and create a cache Invalidation with Github Actions. | Nat Friedman described Github Actions as an API *“… to orchestrate any workflow, based on any event, while GitHub
manages the execution, provides rich feedback and secures every step along the way. With GitHub Actions, workflows and
steps are just code in a repository, so you can create, share, reuse, and fork your sof... |
New Serverless BERT with Huggingface, AWS Lambda, and AWS EFS | https://www.philschmid.de/new-serverless-bert-with-huggingface-aws-lambda | 2020-11-15 | [
"AWS",
"Serverless",
"Bert"
] | Build a serverless Question-Answering API using the Serverless Framework, AWS Lambda, AWS EFS, efsync, Terraform, the transformers Library from HuggingFace, and a `mobileBert` model from Google fine-tuned on SQuADv2. | 4 months ago I wrote the article
["Serverless BERT with HuggingFace and AWS Lambda"](https://www.philschmid.de/serverless-bert-with-huggingface-and-aws-lambda),
which demonstrated how to use BERT in a serverless way with AWS Lambda and the Transformers Library from HuggingFace.
In this article, I already predicted tha... |
efsync my first open-source MLOps toolkit | https://www.philschmid.de/efsync-my-first-open-source-mlops-toolkit | 2020-11-04 | [
"Serverless",
"AWS",
"MLOps"
] | efsync is a CLI/SDK tool, which syncs files from S3 or local filesystem automatically to AWS EFS and enables you to install dependencies with the AWS Lambda runtime directly into your EFS filesystem. | Part of using Machine Learning successfully in production is the use of MLOps. MLOps enhances DevOps with continuous
training (CT). The main components of MLOps therefore include continuous integration (CI), continuous delivery (CD), and
continuous training (CT).
[Nvidia wrote an article about what MLOps is in detail.]... |
Creating document embeddings with Hugging Face's Transformers & Amazon SageMaker | https://www.philschmid.de/custom-inference-huggingface-sagemaker | 2022-03-08 | [
"HuggingFace",
"AWS",
"BERT",
"SageMaker"
] | Learn how to use a custom Inference script for creating document embeddings with Hugging Face's Transformers, Amazon SageMaker, and Sentence Transformers. | Welcome to this getting started guide. We will use the Hugging Face Inference DLCs and Amazon SageMaker Python SDK to create a [real-time inference endpoint](https://docs.aws.amazon.com/sagemaker/latest/dg/realtime-endpoints.html) running a Sentence Transformers for document embeddings. Currently, the [SageMaker Huggin... |
Deploy GPT-J 6B for inference using Hugging Face Transformers and Amazon SageMaker | https://www.philschmid.de/deploy-gptj-sagemaker | 2022-01-11 | [
"HuggingFace",
"AWS",
"SageMaker",
"GPTJ"
] | Learn how to deploy EleutherAIs GPT-J 6B for inference using Hugging Face Transformers and Amazon SageMaker. | Almost 6 months ago to the day, [EleutherAI](https://www.eleuther.ai/) released [GPT-J 6B](https://huggingface.co/EleutherAI/gpt-j-6B), an open-source alternative to [OpenAIs GPT-3](https://openai.com/blog/gpt-3-apps/). [GPT-J 6B](https://huggingface.co/EleutherAI/gpt-j-6B) is the 6 billion parameter successor to [Eleu... |
Accelerate BERT inference with DeepSpeed-Inference on GPUs | https://www.philschmid.de/bert-deepspeed-inference | 2022-08-16 | [
"BERT",
"DeepSpeed",
"HuggingFace",
"Optimization"
] | Learn how to optimize BERT for GPU inference with a 1-line of code using Hugging Face Transformers and DeepSpeed. | In this session, you will learn how to optimize Hugging Face Transformers models for GPU inference using [DeepSpeed-Inference](https://www.deepspeed.ai/tutorials/inference-tutorial/). The session will show you how to apply state-of-the-art optimization techniques using [DeepSpeed-Inference](https://www.deepspeed.ai/tut... |
Autoscaling BERT with Hugging Face Transformers, Amazon SageMaker and Terraform module | https://www.philschmid.de/terraform-huggingface-amazon-sagemaker-advanced | 2022-03-01 | [
"HuggingFace",
"AWS",
"BERT",
"Terraform"
] | Learn how to apply autoscaling to Hugging Face Transformers and Amazon SageMaker using Terraform. | A Few weeks ago we released a Terraform module [sagemaker-huggingface](https://registry.terraform.io/modules/philschmid/sagemaker-huggingface/aws/latest), which makes it super easy to deploy Hugging Face Transformers like BERT from Amazon S3 or the [Hugging Face Hub](http://hf.co/models) to Amazon SageMake for real-tim... |
Use Sentence Transformers with TensorFlow | https://www.philschmid.de/tensorflow-sentence-transformers | 2022-08-30 | [
"BERT",
"Tensorflow",
"HuggingFace",
"Keras"
] | Learn how to Sentence Transformers model with TensorFlow and Keras for creating document embeddings | In this blog, you will learn how to use a [Sentence Transformers](https://www.sbert.net/) model with TensorFlow and Keras. The blog will show you how to create a custom Keras model to load [Sentence Transformers](https://www.sbert.net/) models and run inference on it to create document embeddings.
[Sentence Transforme... |
Create custom Github Action in 4 steps | https://www.philschmid.de/create-custom-github-action-in-4-steps | 2020-09-25 | [
"Cloud",
"DevOps",
"Github"
] | Create a custom github action in 4 steps. Also learn how to test it offline and publish it in the Github Action marketplace. | Automation, complexity reduction, reproducibility, maintainability are all advantages that can be realized by a
continuous integration (CI) pipeline. With GitHub Actions, you can build these CI pipelines.
"You can create workflows using actions defined in your repository, open-source Actions in a public repository on
... |
Deploy LayoutLM with Hugging Face Inference Endpoints | https://www.philschmid.de/inference-endpoints-layoutlm | 2022-10-06 | [
"DocumentAI",
"HuggingFace",
"Transformers",
"LayoutLM"
] | Learn how to deploy LayoutLM for document-understand using Hugging Face Inference Endpoints. | In this blog, you will learn how to deploy a fine-tune [LayoutLM (v1)](https://huggingface.co/docs/transformers/model_doc/layoutlm) for document-understand using [Hugging Face Inference Endpoints](https://huggingface.co/inference-endpoints). LayoutLM is a multimodal Transformer model for document image understanding an... |
Distributed training on multilingual BERT with Hugging Face Transformers & Amazon SageMaker | https://www.philschmid.de/pytorch-distributed-training-transformers | 2022-01-25 | [
"HuggingFace",
"AWS",
"BERT",
"PyTorch"
] | Learn how to run large-scale distributed training using multilingual BERT on over 1 million data points with Hugging Face Transformers & Amazon SageMaker | Welcome to this end-to-end multilingual Text-Classification example using PyTorch. In this demo, we will use the Hugging Faces `transformers` and `datasets` library together with `Pytorch` to fine-tune a multilingual transformer for text-classification. This example is a derived version of the [text-classificiaton.ipyn... |
Serverless Machine Learning Applications with Hugging Face Gradio and AWS Lambda | https://www.philschmid.de/serverless-gradio | 2022-11-15 | [
"Serverless",
"HuggingFace",
"AWS",
"Gradio"
] | Learn how to deploy a Hugging Face Gradio Application using Hugging Face Transformers to AWS Lambda for serverless workloads. | _“Serverless computing is a method of providing backend services on an as-used basis. A serverless provider allows users to write and deploy code without […] worrying about the underlying infrastructure ”_ [[What is serverless computing?](https://www.cloudflare.com/en-gb/learning/serverless/what-is-serverless/)]
Serve... |
Accelerate Sentence Transformers with Hugging Face Optimum | https://www.philschmid.de/optimize-sentence-transformers | 2022-08-02 | [
"BERT",
"OnnxRuntime",
"HuggingFace",
"Optimization"
] | Learn how to optimize Sentence Transformers using Hugging Face Optimum. You will learn how dynamically quantize and optimize a Sentence Transformer for ONNX Runtime. | _last update: 2022-11-18_
In this session, you will learn how to optimize [Sentence Transformers](https://huggingface.co/sentence-transformers) using Optimum. The session will show you how to dynamically quantize and optimize a MiniLM [Sentence Transformers](https://huggingface.co/sentence-transformers) model using [H... |
Hugging Face Transformers with Keras: Fine-tune a non-English BERT for Named Entity Recognition | https://www.philschmid.de/huggingface-transformers-keras-tf | 2021-12-21 | [
"HuggingFace",
"Keras",
"BERT",
"Tensorflow"
] | Learn how to fine-tune a non-English BERT using Hugging Face Transformers and Keras/TF, Transformers, datasets. | Welcome to this end-to-end Named Entity Recognition example using Keras. In this tutorial, we will use the Hugging Faces `transformers` and `datasets` library together with `Tensorflow` & `Keras` to fine-tune a pre-trained non-English transformer for token-classification (ner).
If you want a more detailed example for ... |
Serverless BERT with HuggingFace, AWS Lambda, and Docker | https://www.philschmid.de/serverless-bert-with-huggingface-aws-lambda-docker | 2020-12-06 | [
"AWS",
"Serverless",
"BERT"
] | Learn how to use the newest cutting edge computing power of AWS with the benefits of serverless architectures to leverage Google's "State-of-the-Art" NLP Model. | It's the most wonderful time of the year. Of course, I'm not talking about Christmas but re:Invent. It is re:Invent
time.
photo from the keynote by Andy Jassy, rights belong to Amazon
In the opening keynote, Andy Jassy presente... |
New Serverless Transformers using Amazon SageMaker Serverless Inference and Hugging Face | https://www.philschmid.de/serverless-transformers-sagemaker-huggingface | 2021-12-15 | [
"AWS",
"BERT",
"Serverless",
"Sagemaker"
] | Learn how to deploy Hugging Face Transformers serverless using the new Amazon SageMaker Serverless Inference. | Last week at re:Invent 2021, AWS announced several new features for [Amazon SageMaker](https://aws.amazon.com/sagemaker/) to improve the Machine Learning experience using AWS and Amazon SageMaker. Amongst all the new announcements during [Swami Sivasubramanian](https://www.linkedin.com/in/swaminathansivasubramanian) Ma... |
Multilingual Serverless XLM RoBERTa with HuggingFace, AWS Lambda | https://www.philschmid.de/multilingual-serverless-xlm-roberta-with-huggingface | 2020-12-17 | [
"AWS",
"Serverless",
"BERT"
] | Learn how to build a Multilingual Serverless BERT Question Answering API with a model size of more than 2GB and then testing it in German and France. | Currently, we have 7.5 billion people living on the world in around 200 nations. Only
[1.2 billion people of them are native English speakers](https://en.wikipedia.org/wiki/List_of_countries_by_English-speaking_population).
This leads to a lot of unstructured non-English textual data.
Most of the tutorials and blog po... |
Accelerate Vision Transformer (ViT) with Quantization using Optimum | https://www.philschmid.de/optimizing-vision-transformer | 2022-07-19 | [
"ViT",
"OnnxRuntime",
"HuggingFace",
"Optimization"
] | Learn how to optimize Vision Transformer (ViT) using Hugging Face Optimum. You will learn how dynamically quantize a ViT model for ONNX Runtime. | _last update: 2022-11-18_
In this session, you will learn how to optimize Vision Transformers models using Optimum. The session will show you how to dynamically quantize and optimize a ViT model using [Hugging Face Optimum](https://huggingface.co/docs/optimum/index) and [ONNX Runtime](https://onnxruntime.ai/). Hugging... |
Mount your AWS EFS volume into AWS Lambda with the Serverless Framework | https://www.philschmid.de/mount-your-aws-efs-volume-into-aws-lambda-with-the-serverless-framework | 2020-08-12 | [
"AWS",
"Serverless"
] | Leverage your Serverless architectures with mounting your AWS EFS volume into your AWS Lambda with the Serverless Framework. | "_Just like wireless internet has wires somewhere, serverless architectures still have servers somewhere. What
‘serverless’ really means is that as a developer you don’t have to think about those servers. You just focus on
code." -_ [serverless.com](https://serverless.com/learn/overview/)
This focus is only possible i... |
Fine-tuning LayoutLM for document-understanding using Keras & Hugging Face Transformers | https://www.philschmid.de/fine-tuning-layoutlm-keras | 2022-10-13 | [
"Keras",
"HuggingFace",
"Transformers",
"LayoutLM"
] | Learn how to fine-tune LayoutLM for document-understand using Keras & Hugging Face Transformers. | In this blog, you will learn how to fine-tune [LayoutLM (v1)](https://huggingface.co/docs/transformers/model_doc/layoutlm) for document-understand using Tensorflow, Keras & Hugging Face Transformers. LayoutLM is a document image understanding and information extraction transformers and was originally published by Micro... |
Getting started with Transformers and TPU using PyTorch | https://www.philschmid.de/getting-started-tpu-transformers | 2023-01-16 | [
"Pytorch",
"BERT",
"HuggingFace",
"TPU"
] | Learn how to get started with Hugging Face Transformers and TPUs using PyTorch, fine-tune a BERT model for Text Classification using the newest Google Cloud TPUs. | Tensor Processing Units (TPU) are specialized accelerators developed by Google to speed up machine learning tasks. They are built from the ground up with a focus on machine & deep learning workloads.
TPUs are available on the [Google Cloud](https://cloud.google.com/tpu/docs/tpus) and can be used with popular deep lear... |
Deep Learning setup made easy with EC2 Remote Runner and Habana Gaudi | https://www.philschmid.de/habana-gaudi-ec2-runner | 2022-07-26 | [
"BERT",
"Habana",
"HuggingFace",
"AWS"
] | Learn how to migrate your training jobs to a Habana Gaudi-based DL1 instance on AWS using EC2 Remote Runner. | Going from experimenting and preparation in a local environment to managed cloud infrastructure is often times too complex and prevents data scientists from iterating quickly and efficiently on their Deep Learning projects and work.
A common workflow I see is that a DS/MLE starts virtual machines in the cloud, ssh int... |
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