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Guiding Text Generation with Constrained Beam Search in 🤗 Transformers | cwkeam | March 11, 2022 | constrained-beam-search | guide, nlp | https://huggingface.co/blog/constrained-beam-search | With Constrained Beam Search But what if we knew that we wanted a formal output instead of the informal one? What if we knew from prior knowledge what the generation must include, and we could inject it into the generation? The following is what is possible now with the force_words_ids keyword argument to model.generate(): python tokenizer = AutoTokenizer.from_pretrained("t5-base") model = AutoModelForSeq2SeqLM.from_pretrained("t5-base") encoder_input_str = "translate English to German: How old are you?" force_words = ["Sie"] input_ids = tokenizer(encoder_input_str, return_tensors="pt").input_ids force_words_ids = tokenizer(force_words, add_special_tokens=False).input_ids outputs = model.generate( input_ids, force_words_ids=force_words_ids, num_beams=5, num_return_sequences=1, no_repeat_ngram_size=1, remove_invalid_values=True, ) print("Output:\n" + 100 * '-') print(tokenizer.decode(outputs[0], skip_special_tokens=True)) Output: ---------------------------------------------------------------------------------------------------- Wie alt sind Sie? As you can see, we were able to guide the generation with prior knowledge about our desired output. Previously we would've had to generate a bunch of possible outputs, then filter the ones that fit our requirement. Now we can do that at the generation stage. | [
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Image search with 🤗 datasets | davanstrien | March 16, 2022 | image-search-datasets | cv | https://huggingface.co/blog/image-search-datasets | Out-of-Scope Use Cases > > Any deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP's performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case > currently potentially harmful. > source suggests that 'deployment' is not a good idea. Whilst the results I got are interesting, I haven't played around with the model enough yet (and haven't done anything more systematic to evaluate its performance and biases) to be confident about 'deploying' it. Another additional consideration is the target dataset itself. The images are drawn from books covering a variety of subjects and time periods. There are plenty of books which represent colonial attitudes and as a result some of the images included may represent certain groups of people in a negative way. This could potentially be a bad combo with a tool which allows any arbitrary text input to be encoded as a prompt. There may be ways around this issue but this will require a bit more thought. | [
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Image search with 🤗 datasets | davanstrien | March 16, 2022 | image-search-datasets | cv | https://huggingface.co/blog/image-search-datasets | Push all the things to the hub! One of the super awesome things about the 🤗 ecosystem is the Hugging Face Hub. We can use the Hub to access models and datasets. It is often used for sharing work with others, but it can also be a useful tool for work in progress. datasets recently added a push_to_hub method that allows you to push a dataset to the Hub with minimal fuss. This can be really helpful by allowing you to pass around a dataset with all the transforms etc. already done. For now, we'll push the dataset to the Hub and keep it private initially. Depending on where you are running the code, you may need to authenticate. You can either do this using the huggingface-cli login command or, if you are running in a notebook, using notebook_login python from huggingface_hub import notebook_login notebook_login() python dataset.push_to_hub('davanstrien/embellishments-sample', private=True) > Note: in a previous version of this blog post we had to do a few more steps to ensure images were embedded when using push_to_hub. Thanks to this pull request we no longer need to worry about these extra steps. We just need to make sure embed_external_files=True (which is the default behaviour). | [
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Image search with 🤗 datasets | davanstrien | March 16, 2022 | image-search-datasets | cv | https://huggingface.co/blog/image-search-datasets | Creating embeddings 🕸 We now have a dataset with a bunch of images in it. To begin creating our image search app, we need to embed these images. There are various ways to try and do this, but one possible way is to use the CLIP models via the sentence_transformers library. The CLIP model from OpenAI learns a joint representation for both images and text, which is very useful for what we want to do since we want to input text and get back an image. We can download the model using the SentenceTransformer class. python from sentence_transformers import SentenceTransformer model = SentenceTransformer('clip-ViT-B-32') This model will take as input either an image or some text and return an embedding. We can use the datasets map method to encode all our images using this model. When we call map, we return a dictionary with the key embeddings containing the embeddings returned by the model. We also pass device='cuda' when we call the model; this ensures that we're doing the encoding on the GPU. python ds_with_embeddings = dataset.map( lambda example: {'embeddings':model.encode(example['image'], device='cuda')}, batched=True, batch_size=32) We can 'save' our work by pushing back to the Hub using push_to_hub. python ds_with_embeddings.push_to_hub('davanstrien/embellishments-sample', private=True) If we were to move to a different machine, we could grab our work again by loading it from the Hub 😃 python from datasets import load_dataset ds_with_embeddings = load_dataset("davanstrien/embellishments-sample", use_auth_token=True) We now have a new column which contains the embeddings for our images. We could manually search through these and compare them to some input embedding but datasets has an add_faiss_index method. This uses the faiss library to create an efficient index for searching embeddings. For more background on this library, you can watch this YouTube video python ds_with_embeddings['train'].add_faiss_index(column='embeddings') Dataset({ features: ['fname', 'year', 'path', 'image', 'embeddings'], num_rows: 10000 }) | [
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Image search with 🤗 datasets | davanstrien | March 16, 2022 | image-search-datasets | cv | https://huggingface.co/blog/image-search-datasets | The dataset: "Digitised Books - Images identified as Embellishments. c. 1510 - c. 1900" This is a dataset of images which have been pulled from a collection of digitised books from the British Library. These images come from books across a wide time period and from a broad range of domains. The images were extracted using information contained in the OCR output for each book. As a result, it's known which book the images came from, but not necessarily anything else about that image i.e. what is shown in the image. Some attempts to help overcome this have included uploading the images to flickr. This allows people to tag the images or put them into various different categories. There have also been projects to tag the dataset using machine learning. This work makes it possible to search by tags, but we might want a 'richer' ability to search. For this particular experiment, we'll work with a subset of the collections which contain "embellishments". This dataset is a bit smaller, so it will be better for experimenting with. We can get the full data from the British Library's data repository: https://doi.org/10.21250/db17. Since the full dataset is still fairly large, you'll probably want to start with a smaller sample. | [
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Image search with 🤗 datasets | davanstrien | March 16, 2022 | image-search-datasets | cv | https://huggingface.co/blog/image-search-datasets | Image search > Note that these examples were generated from the full version of the dataset so you may get slightly different results. We now have everything we need to create a simple image search. We can use the same model we used to encode our images to encode some input text. This will act as the prompt we try and find close examples for. Let's start with 'a steam engine'. python prompt = model.encode("A steam engine") We can use another method from the datasets library get_nearest_examples to get images which have an embedding close to our input prompt embedding. We can pass in a number of results we want to get back. python scores, retrieved_examples = ds_with_embeddings['train'].get_nearest_examples('embeddings', prompt, k=9) We can index into the first example this retrieves: python retrieved_examples['image'][0] This isn't quite a steam engine, but it's also not a completely weird result. We can plot the other results to see what was returned. python import matplotlib.pyplot as plt python plt.figure(figsize=(20, 20)) columns = 3 for i in range(9): image = retrieved_examples['image'][i] plt.subplot(9 / columns + 1, columns, i + 1) plt.imshow(image) Some of these results look fairly close to our input prompt. We can wrap this in a function so we can more easily play around with different prompts python def get_image_from_text(text_prompt, number_to_retrieve=9): prompt = model.encode(text_prompt) scores, retrieved_examples = ds_with_embeddings['train'].get_nearest_examples('embeddings', prompt, k=number_to_retrieve) plt.figure(figsize=(20, 20)) columns = 3 for i in range(9): image = retrieved_examples['image'][i] plt.title(text_prompt) plt.subplot(9 / columns + 1, columns, i + 1) plt.imshow(image) python get_image_from_text("An illustration of the sun behind a mountain") | [
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Image search with 🤗 datasets | davanstrien | March 16, 2022 | image-search-datasets | cv | https://huggingface.co/blog/image-search-datasets | Trying a bunch of prompts ✨ Now we have a function for getting a few results, we can try a bunch of different prompts: - For some of these I'll choose prompts which are a broad 'category' i.e. 'a musical instrument' or 'an animal', others are specific i.e. 'a guitar'. - Out of interest I also tried a boolean operator: "An illustration of a cat or a dog". - Finally I tried something a little more abstract: \"an empty abyss\" python prompts = ["A musical instrument", "A guitar", "An animal", "An illustration of a cat or a dog", "an empty abyss"] python for prompt in prompts: get_image_from_text(prompt) We can see these results aren't always right, but they are usually reasonable. It already seems like this could be useful for searching for the semantic content of an image in this dataset. However we might hold off on sharing this as is... | [
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Image search with 🤗 datasets | davanstrien | March 16, 2022 | image-search-datasets | cv | https://huggingface.co/blog/image-search-datasets | # Image search with 🤗 datasets 🤗 datasets is a library that makes it easy to access and share datasets. It also makes it easy to process data efficiently -- including working with data which doesn't fit into memory. When datasets was first launched, it was associated mostly with text data. However, recently, datasets has added increased support for audio as well as images. In particular, there is now a datasets feature type for images. A previous blog post showed how datasets can be used with 🤗 transformers to train an image classification model. In this blog post, we'll see how we can combine datasets and a few other libraries to create an image search application. First, we'll install datasets. Since we're going to be working with images, we'll also install pillow. We'll also need sentence_transformers and faiss. We'll introduce those in more detail below. We also install rich - we'll only briefly use it here, but it's a super handy package to have around -- I'd really recommend exploring it further! python !pip install datasets pillow rich faiss-gpu sentence_transformers To start, let's take a look at the image feature. We can use the wonderful rich library to poke around python objects (functions, classes etc.) python from rich import inspect import datasets python inspect(datasets.Image, help=True) ╭───────────────────────── <class 'datasets.features.image.Image'> ─────────────────────────╮ │ class Image(decode: bool = True, id: Union[str, NoneType] = None) -> None: │ │ │ │ Image feature to read image data from an image file. │ │ │ │ Input: The Image feature accepts as input: │ │ - A :obj:str: Absolute path to the image file (i.e. random access is allowed). │ │ - A :obj:dict with the keys: │ │ │ │ - path: String with relative path of the image file to the archive file. │ │ - bytes: Bytes of the image file. │ │ │ │ This is useful for archived files with sequential access. │ │ │ │ - An :obj:np.ndarray: NumPy array representing an image. │ │ - A :obj:PIL.Image.Image: PIL image object. │ │ │ │ Args: │ │ decode (:obj:bool, default </span><span style="color: #00ff00; text-decoration-color: #00ff00; font-style: italic">True</span><span style="color: #008080; text-decoration-color: #008080">): Whether to decode the image data. If </span><span style="color: #ff0000; text-decoration-color: #ff0000; font-style: italic">False</span><span style="color: #008080; text-decoration-color: #008080">, │ │ returns the underlying dictionary in the format {"path": image_path, "bytes": │ │ image_bytes}. │ │ │ │ decode = True │ │ dtype = 'PIL.Image.Image' │ │ id = None │ │ pa_type = StructType(struct<bytes: binary, path: string>) │ ╰───────────────────────────────────────────────────────────────────────────────────────────╯ We can see there a few different ways in which we can pass in our images. We'll come back to this in a little while. A really nice feature of the datasets library (beyond the functionality for processing data, memory mapping etc.) is that you get some nice things 'for free'. One of these is the ability to add a faiss index to a dataset. faiss is a "library for efficient similarity search and clustering of dense vectors". The datasets docs shows an example of using a faiss index for text retrieval. In this post we'll see if we can do the same for images. | [
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Image search with 🤗 datasets | davanstrien | March 16, 2022 | image-search-datasets | cv | https://huggingface.co/blog/image-search-datasets | Conclusion Although we don't have a nice demo to show for it, we've seen how we can use datasets to: - load images into the new Image feature type - 'save' our work using push_to_hub and use this to move data between machines/sessions - create a faiss index for images that we can use to retrieve images from a text (or image) input. | [
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Image search with 🤗 datasets | davanstrien | March 16, 2022 | image-search-datasets | cv | https://huggingface.co/blog/image-search-datasets | Primary intended uses > > We primarily imagine the model will be used by researchers to better understand robustness, generalization, and other capabilities, biases, and constraints of computer vision models. > source This is fairly close to what we are interested in here. Particularly we might be interested in how well the model deals with the kinds of images in our dataset (illustrations from mostly 19th century books). The images in our dataset are (probably) fairly different from the training data. The fact that some of the images also contain text might help CLIP since it displays some OCR ability. However, looking at the out-of-scope use cases in the model card: > | [
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Image search with 🤗 datasets | davanstrien | March 16, 2022 | image-search-datasets | cv | https://huggingface.co/blog/image-search-datasets | Creating our dataset Our dataset consists of a folder containing subdirectories inside which are images. This is a fairly standard format for sharing image datasets. Thanks to a recently merged pull request we can directly load this dataset using datasets ImageFolder loader 🤯 python from datasets import load_dataset dataset = load_dataset("imagefolder", data_files="https://zenodo.org/record/6224034/files/embellishments_sample.zip?download=1") Let's see what we get back. python dataset DatasetDict({ train: Dataset({ features: ['image', 'label'], num_rows: 10000 }) }) We can get back a DatasetDict, and we have a Dataset with image and label features. Since we don't have any train/validation splits here, let's grab the train part of our dataset. Let's also take a look at one example from our dataset to see what this looks like. python dataset = dataset["train"] dataset[0] python {'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=358x461 at 0x7F9488DBB090>, 'label': 208} Let's start with the label column. It contains the parent folder for our images. In this case, the label column represents the year of publication for the books from which the images are taken. We can see the mappings for this using dataset.features: python dataset.features['label'] In this particular dataset, the image filenames also contain some metadata about the book from which the image was taken. There are a few ways we can get this information. When we look at one example from our dataset that the image feature was a PIL.JpegImagePlugin.JpegImageFile. Since PIL.Images have a filename attribute, one way in which we can grab our filenames is by accessing this. python dataset[0]['image'].filename python /root/.cache/huggingface/datasets/downloads/extracted/f324a87ed7bf3a6b83b8a353096fbd9500d6e7956e55c3d96d2b23cc03146582/embellishments_sample/1920/000499442_0_000579_1_[The Ring and the Book etc ]_1920.jpg Since we might want easy access to this information later, let's create a new column to extract the filename. For this, we'll use the map method. python dataset = dataset.map(lambda example: {"fname": example['image'].filename.split("/")[-1]}) We can look at one example to see what this looks like now. python dataset[0] python {'fname': '000499442_0_000579_1_[The Ring and the Book etc ]_1920.jpg', 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=358x461 at 0x7F94862A9650>, 'label': 208} We've got our metadata now. Let's see some pictures already! If we access an example and index into the image column we'll see our image 😃 python dataset[10]['image'] > Note in an earlier version of this blog post the steps to download and load the images was much more convoluted. The new ImageFolder loader makes this process much easier 😀 In particular, we don't need to worry about how to load our images since datasets took care of this for us. | [
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Image search with 🤗 datasets | davanstrien | March 16, 2022 | image-search-datasets | cv | https://huggingface.co/blog/image-search-datasets | Creating a Hugging Face Space? 🤷🏼 One obvious next step for this kind of project is to create a Hugging Face Space demo. This is what I've done for other models. It was a fairly simple process to get a Gradio app setup from the point we got to here. Here is a screenshot of this app: However, I'm a little bit vary about making this public straightaway. Looking at the model card for the CLIP model we can look at the primary intended uses: > | [
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Image search with 🤗 datasets | davanstrien | March 16, 2022 | image-search-datasets | cv | https://huggingface.co/blog/image-search-datasets | Switching machines At this point, we've created a dataset and moved it to the Hub. This means it is possible to pick up the work/dataset elsewhere. In this particular example, having access to a GPU is important. Using the Hub as a way to pass around our data we could start on a laptop and pick up the work on Google Colab. If we move to a new machine, we may need to login again. Once we've done this we can load our dataset python from datasets import load_dataset dataset = load_dataset("davanstrien/embellishments-sample", use_auth_token=True) | [
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Accelerate BERT inference with Hugging Face Transformers and AWS inferentia | philschmid | March 16, 2022 | bert-inferentia-sagemaker | partnerships, aws, guide, nlp | https://huggingface.co/blog/bert-inferentia-sagemaker | # Accelerate BERT inference with Hugging Face Transformers and AWS Inferentia notebook: sagemaker/18_inferentia_inference The adoption of BERT and Transformers continues to grow. Transformer-based models are now not only achieving state-of-the-art performance in Natural Language Processing but also for Computer Vision, Speech, and Time-Series. 💬 🖼 🎤 ⏳ Companies are now slowly moving from the experimentation and research phase to the production phase in order to use transformer models for large-scale workloads. But by default BERT and its friends are relatively slow, big, and complex models compared to the traditional Machine Learning algorithms. Accelerating Transformers and BERT is and will become an interesting challenge to solve in the future. AWS's take to solve this challenge was to design a custom machine learning chip designed for optimized inference workload called AWS Inferentia. AWS says that AWS Inferentia “delivers up to 80% lower cost per inference and up to 2.3X higher throughput than comparable current generation GPU-based Amazon EC2 instances.” The real value of AWS Inferentia instances compared to GPU comes through the multiple Neuron Cores available on each device. A Neuron Core is the custom accelerator inside AWS Inferentia. Each Inferentia chip comes with 4x Neuron Cores. This enables you to either load 1 model on each core (for high throughput) or 1 model across all cores (for lower latency). | [
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Accelerate BERT inference with Hugging Face Transformers and AWS inferentia | philschmid | March 16, 2022 | bert-inferentia-sagemaker | partnerships, aws, guide, nlp | https://huggingface.co/blog/bert-inferentia-sagemaker | 5. Run and evaluate Inference performance of BERT on Inferentia The .deploy() returns an HuggingFacePredictor object which can be used to request inference. python data = { "inputs": "the mesmerizing performances of the leads keep the film grounded and keep the audience riveted .", } res = predictor.predict(data=data) res We managed to deploy our neuron compiled BERT to AWS Inferentia on Amazon SageMaker. Now, let's test its performance. As a dummy load test, we will loop and send 10,000 synchronous requests to our endpoint. python # send 10000 requests for i in range(10000): resp = predictor.predict( data={"inputs": "it 's a charming and often affecting journey ."} ) Let's inspect the performance in cloudwatch. python print(f"https://console.aws.amazon.com/cloudwatch/home?region={sess.boto_region_name}#metricsV2:graph=~(metrics~(~(~'AWS*2fSageMaker~'ModelLatency~'EndpointName~'{predictor.endpoint_name}~'VariantName~'AllTraffic))~view~'timeSeries~stacked~false~region~'{sess.boto_region_name}~start~'-PT5M~end~'P0D~stat~'Average~period~30);query=~'*7bAWS*2fSageMaker*2cEndpointName*2cVariantName*7d*20{predictor.endpoint_name}") The average latency for our BERT model is 5-6ms for a sequence length of 128. Figure 1. Model Latency | [
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Accelerate BERT inference with Hugging Face Transformers and AWS inferentia | philschmid | March 16, 2022 | bert-inferentia-sagemaker | partnerships, aws, guide, nlp | https://huggingface.co/blog/bert-inferentia-sagemaker | Tutorial In this end-to-end tutorial, you will learn how to speed up BERT inference for text classification with Hugging Face Transformers, Amazon SageMaker, and AWS Inferentia. You can find the notebook here: sagemaker/18_inferentia_inference You will learn how to: - 1. Convert your Hugging Face Transformer to AWS Neuron - 2. Create a custom inference.py script for text-classification - 3. Create and upload the neuron model and inference script to Amazon S3 - 4. Deploy a Real-time Inference Endpoint on Amazon SageMaker - 5. Run and evaluate Inference performance of BERT on Inferentia Let's get started! 🚀 --- If you are going to use Sagemaker in a local environment (not SageMaker Studio or Notebook Instances), you need access to an IAM Role with the required permissions for Sagemaker. You can find here more about it. | [
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Accelerate BERT inference with Hugging Face Transformers and AWS inferentia | philschmid | March 16, 2022 | bert-inferentia-sagemaker | partnerships, aws, guide, nlp | https://huggingface.co/blog/bert-inferentia-sagemaker | 3. Create and upload the neuron model and inference script to Amazon S3 Before we can deploy our neuron model to Amazon SageMaker we need to create a model.tar.gz archive with all our model artifacts saved into tmp/, e.g. neuron_model.pt and upload this to Amazon S3. To do this we need to set up our permissions. python import sagemaker import boto3 sess = sagemaker.Session() # sagemaker session bucket -> used for uploading data, models and logs # sagemaker will automatically create this bucket if it not exists sagemaker_session_bucket=None if sagemaker_session_bucket is None and sess is not None: # set to default bucket if a bucket name is not given sagemaker_session_bucket = sess.default_bucket() try: role = sagemaker.get_execution_role() except ValueError: iam = boto3.client('iam') role = iam.get_role(RoleName='sagemaker_execution_role')['Role']['Arn'] sess = sagemaker.Session(default_bucket=sagemaker_session_bucket) print(f"sagemaker role arn: {role}") print(f"sagemaker bucket: {sess.default_bucket()}") print(f"sagemaker session region: {sess.boto_region_name}") Next, we create our model.tar.gz. The inference.py script will be placed into a code/ folder. python # copy inference.py into the code/ directory of the model directory. !cp -r code/ tmp/code/ # create a model.tar.gz archive with all the model artifacts and the inference.py script. %cd tmp !tar zcvf model.tar.gz * %cd .. Now we can upload our model.tar.gz to our session S3 bucket with sagemaker. python from sagemaker.s3 import S3Uploader # create s3 uri s3_model_path = f"s3://{sess.default_bucket()}/{model_id}" # upload model.tar.gz s3_model_uri = S3Uploader.upload(local_path="tmp/model.tar.gz",desired_s3_uri=s3_model_path) print(f"model artifcats uploaded to {s3_model_uri}") | [
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Accelerate BERT inference with Hugging Face Transformers and AWS inferentia | philschmid | March 16, 2022 | bert-inferentia-sagemaker | partnerships, aws, guide, nlp | https://huggingface.co/blog/bert-inferentia-sagemaker | Conclusion We successfully managed to compile a vanilla Hugging Face Transformers model to an AWS Inferentia compatible Neuron Model. After that we deployed our Neuron model to Amazon SageMaker using the new Hugging Face Inference DLC. We managed to achieve 5-6ms latency per neuron core, which is faster than CPU in terms of latency, and achieves a higher throughput than GPUs since we ran 4 models in parallel. If you or you company are currently using a BERT-like Transformer for encoder tasks (text-classification, token-classification, question-answering etc.), and the latency meets your requirements you should switch to AWS Inferentia. This will not only save costs, but can also increase efficiency and performance for your models. We are planning to do a more detailed case study on cost-performance of transformers in the future, so stay tuned! Also if you want to learn more about accelerating transformers you should also check out Hugging Face optimum. --- Thanks for reading! If you have any questions, feel free to contact me, through Github, or on the forum. You can also connect with me on Twitter or LinkedIn. | [
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Accelerate BERT inference with Hugging Face Transformers and AWS inferentia | philschmid | March 16, 2022 | bert-inferentia-sagemaker | partnerships, aws, guide, nlp | https://huggingface.co/blog/bert-inferentia-sagemaker | 1. Convert your Hugging Face Transformer to AWS Neuron We are going to use the AWS Neuron SDK for AWS Inferentia. The Neuron SDK includes a deep learning compiler, runtime, and tools for converting and compiling PyTorch and TensorFlow models to neuron compatible models, which can be run on EC2 Inf1 instances. As a first step, we need to install the Neuron SDK and the required packages. Tip: If you are using Amazon SageMaker Notebook Instances or Studio you can go with the conda_python3 conda kernel. python # Set Pip repository to point to the Neuron repository !pip config set global.extra-index-url https://pip.repos.neuron.amazonaws.com # Install Neuron PyTorch !pip install torch-neuron==1.9.1.* neuron-cc[tensorflow] sagemaker>=2.79.0 transformers==4.12.3 --upgrade After we have installed the Neuron SDK we can load and convert our model. Neuron models are converted using torch_neuron with its trace method similar to torchscript. You can find more information in our documentation. To be able to convert our model we first need to select the model we want to use for our text classification pipeline from hf.co/models. For this example, let's go with distilbert-base-uncased-finetuned-sst-2-english but this can be easily adjusted with other BERT-like models. python model_id = "distilbert-base-uncased-finetuned-sst-2-english" At the time of writing, the AWS Neuron SDK does not support dynamic shapes, which means that the input size needs to be static for compiling and inference. In simpler terms, this means that when the model is compiled with e.g. an input of batch size 1 and sequence length of 16, the model can only run inference on inputs with that same shape. When using a t2.medium instance the compilation takes around 3 minutes python import os import tensorflow # to workaround a protobuf version conflict issue import torch import torch.neuron from transformers import AutoTokenizer, AutoModelForSequenceClassification # load tokenizer and model tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForSequenceClassification.from_pretrained(model_id, torchscript=True) # create dummy input for max length 128 dummy_input = "dummy input which will be padded later" max_length = 128 embeddings = tokenizer(dummy_input, max_length=max_length, padding="max_length",return_tensors="pt") neuron_inputs = tuple(embeddings.values()) # compile model with torch.neuron.trace and update config model_neuron = torch.neuron.trace(model, neuron_inputs) model.config.update({"traced_sequence_length": max_length}) # save tokenizer, neuron model and config for later use save_dir="tmp" os.makedirs("tmp",exist_ok=True) model_neuron.save(os.path.join(save_dir,"neuron_model.pt")) tokenizer.save_pretrained(save_dir) model.config.save_pretrained(save_dir) | [
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Accelerate BERT inference with Hugging Face Transformers and AWS inferentia | philschmid | March 16, 2022 | bert-inferentia-sagemaker | partnerships, aws, guide, nlp | https://huggingface.co/blog/bert-inferentia-sagemaker | 2. Create a custom inference.py script for text-classification The Hugging Face Inference Toolkit supports zero-code deployments on top of the pipeline feature from 🤗 Transformers. This allows users to deploy Hugging Face transformers without an inference script [Example]. Currently, this feature is not supported with AWS Inferentia, which means we need to provide an inference.py script for running inference. If you would be interested in support for zero-code deployments for Inferentia let us know on the forum. --- To use the inference script, we need to create an inference.py script. In our example, we are going to overwrite the model_fn to load our neuron model and the predict_fn to create a text-classification pipeline. If you want to know more about the inference.py script check out this example. It explains amongst other things what model_fn and predict_fn are. python !mkdir code We are using the NEURON_RT_NUM_CORES=1 to make sure that each HTTP worker uses 1 Neuron core to maximize throughput. python %%writefile code/inference.py import os from transformers import AutoConfig, AutoTokenizer import torch import torch.neuron # To use one neuron core per worker os.environ["NEURON_RT_NUM_CORES"] = "1" # saved weights name AWS_NEURON_TRACED_WEIGHTS_NAME = "neuron_model.pt" def model_fn(model_dir): # load tokenizer and neuron model from model_dir tokenizer = AutoTokenizer.from_pretrained(model_dir) model = torch.jit.load(os.path.join(model_dir, AWS_NEURON_TRACED_WEIGHTS_NAME)) model_config = AutoConfig.from_pretrained(model_dir) return model, tokenizer, model_config def predict_fn(data, model_tokenizer_model_config): # destruct model, tokenizer and model config model, tokenizer, model_config = model_tokenizer_model_config # create embeddings for inputs inputs = data.pop("inputs", data) embeddings = tokenizer( inputs, return_tensors="pt", max_length=model_config.traced_sequence_length, padding="max_length", truncation=True, ) # convert to tuple for neuron model neuron_inputs = tuple(embeddings.values()) # run prediciton with torch.no_grad(): predictions = model(*neuron_inputs)[0] scores = torch.nn.Softmax(dim=1)(predictions) # return dictonary, which will be json serializable return [{"label": model_config.id2label[item.argmax().item()], "score": item.max().item()} for item in scores] | [
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Accelerate BERT inference with Hugging Face Transformers and AWS inferentia | philschmid | March 16, 2022 | bert-inferentia-sagemaker | partnerships, aws, guide, nlp | https://huggingface.co/blog/bert-inferentia-sagemaker | 4. Deploy a Real-time Inference Endpoint on Amazon SageMaker After we have uploaded our model.tar.gz to Amazon S3 can we create a custom HuggingfaceModel. This class will be used to create and deploy our real-time inference endpoint on Amazon SageMaker. python from sagemaker.huggingface.model import HuggingFaceModel # create Hugging Face Model Class huggingface_model = HuggingFaceModel( model_data=s3_model_uri, # path to your model and script role=role, # iam role with permissions to create an Endpoint transformers_version="4.12", # transformers version used pytorch_version="1.9", # pytorch version used py_version='py37', # python version used ) # Let SageMaker know that we've already compiled the model via neuron-cc huggingface_model._is_compiled_model = True # deploy the endpoint endpoint predictor = huggingface_model.deploy( initial_instance_count=1, # number of instances instance_type="ml.inf1.xlarge" # AWS Inferentia Instance ) | [
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Accelerate BERT inference with Hugging Face Transformers and AWS inferentia | philschmid | March 16, 2022 | bert-inferentia-sagemaker | partnerships, aws, guide, nlp | https://huggingface.co/blog/bert-inferentia-sagemaker | Delete model and endpoint To clean up, we can delete the model and endpoint. python predictor.delete_model() predictor.delete_endpoint() | [
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Fine-Tune a Semantic Segmentation Model with a Custom Dataset | segments-tobias | March 17, 2022 | fine-tune-segformer | guide, partnerships, cv | https://huggingface.co/blog/fine-tune-segformer | Image processor & data augmentation A SegFormer model expects the input to be of a certain shape. To transform our training data to match the expected shape, we can use SegFormerImageProcessor. We could use the ds.map function to apply the image processor to the whole training dataset in advance, but this can take up a lot of disk space. Instead, we'll use a transform, which will only prepare a batch of data when that data is actually used (on-the-fly). This way, we can start training without waiting for further data preprocessing. In our transform, we'll also define some data augmentations to make our model more resilient to different lighting conditions. We'll use the ColorJitter function from torchvision to randomly change the brightness, contrast, saturation, and hue of the images in the batch. python from torchvision.transforms import ColorJitter from transformers import SegformerImageProcessor processor = SegformerImageProcessor() jitter = ColorJitter(brightness=0.25, contrast=0.25, saturation=0.25, hue=0.1) def train_transforms(example_batch): images = [jitter(x) for x in example_batch['pixel_values']] labels = [x for x in example_batch['label']] inputs = processor(images, labels) return inputs def val_transforms(example_batch): images = [x for x in example_batch['pixel_values']] labels = [x for x in example_batch['label']] inputs = processor(images, labels) return inputs # Set transforms train_ds.set_transform(train_transforms) test_ds.set_transform(val_transforms) # 3. Fine-tune a SegFormer model | [
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Fine-Tune a Semantic Segmentation Model with a Custom Dataset | segments-tobias | March 17, 2022 | fine-tune-segformer | guide, partnerships, cv | https://huggingface.co/blog/fine-tune-segformer | Set up the labeling task on Segments.ai First, create an account at https://segments.ai/join. Next, create a new dataset and upload your images. You can either do this from the web interface or via the Python SDK (see the notebook). | [
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Fine-Tune a Semantic Segmentation Model with a Custom Dataset | segments-tobias | March 17, 2022 | fine-tune-segformer | guide, partnerships, cv | https://huggingface.co/blog/fine-tune-segformer | Use a dataset from the Hub If you don't want to create your own dataset, but found a suitable dataset for your use case on the Hugging Face Hub, you can define the identifier here. For example, you can use the full labeled sidewalk dataset. Note that you can check out the examples directly in your browser. python hf_dataset_identifier = "segments/sidewalk-semantic" # 2. Load and prepare the Hugging Face dataset for training Now that we've created a new dataset and pushed it to the Hugging Face Hub, we can load the dataset in a single line. python from datasets import load_dataset ds = load_dataset(hf_dataset_identifier) Let's shuffle the dataset and split the dataset in a train and test set. python ds = ds.shuffle(seed=1) ds = ds["train"].train_test_split(test_size=0.2) train_ds = ds["train"] test_ds = ds["test"] We'll extract the number of labels and the human-readable ids, so we can configure the segmentation model correctly later on. python import json from huggingface_hub import hf_hub_download repo_id = f"datasets/{hf_dataset_identifier}" filename = "id2label.json" id2label = json.load(open(hf_hub_download(repo_id=hf_dataset_identifier, filename=filename, repo_type="dataset"), "r")) id2label = {int(k): v for k, v in id2label.items()} label2id = {v: k for k, v in id2label.items()} num_labels = len(id2label) | [
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Fine-Tune a Semantic Segmentation Model with a Custom Dataset | segments-tobias | March 17, 2022 | fine-tune-segformer | guide, partnerships, cv | https://huggingface.co/blog/fine-tune-segformer | Load the model to fine-tune The SegFormer authors define 5 models with increasing sizes: B0 to B5. The following chart (taken from the original paper) shows the performance of these different models on the ADE20K dataset, compared to other models. Source Here, we'll load the smallest SegFormer model (B0), pre-trained on ImageNet-1k. It's only about 14MB in size! Using a small model will make sure that our model can run smoothly on our pizza delivery robot. python from transformers import SegformerForSemanticSegmentation pretrained_model_name = "nvidia/mit-b0" model = SegformerForSemanticSegmentation.from_pretrained( pretrained_model_name, id2label=id2label, label2id=label2id ) | [
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Fine-Tune a Semantic Segmentation Model with a Custom Dataset | segments-tobias | March 17, 2022 | fine-tune-segformer | guide, partnerships, cv | https://huggingface.co/blog/fine-tune-segformer | Push the result to the Hugging Face Hub When you're done labeling, create a new dataset release containing the labeled data. You can either do this on the releases tab on Segments.ai, or programmatically through the SDK as shown in the notebook. Note that creating the release can take a few seconds. You can check the releases tab on Segments.ai to check if your release is still being created. Now, we'll convert the release to a Hugging Face dataset via the Segments.ai Python SDK. If you haven't set up the Segments Python client yet, follow the instructions in the "Set up the labeling task on Segments.ai" section of the notebook. Note that the conversion can take a while, depending on the size of your dataset. python from segments.huggingface import release2dataset release = segments_client.get_release(dataset_identifier, release_name) hf_dataset = release2dataset(release) If we inspect the features of the new dataset, we can see the image column and the corresponding label. The label consists of two parts: a list of annotations and a segmentation bitmap. The annotation corresponds to the different objects in the image. For each object, the annotation contains an id and a category_id. The segmentation bitmap is an image where each pixel contains the id of the object at that pixel. More information can be found in the relevant docs. For semantic segmentation, we need a semantic bitmap that contains a category_id for each pixel. We'll use the get_semantic_bitmap function from the Segments.ai SDK to convert the bitmaps to semantic bitmaps. To apply this function to all the rows in our dataset, we'll use dataset.map. python from segments.utils import get_semantic_bitmap def convert_segmentation_bitmap(example): return { "label.segmentation_bitmap": get_semantic_bitmap( example["label.segmentation_bitmap"], example["label.annotations"], id_increment=0, ) } semantic_dataset = hf_dataset.map( convert_segmentation_bitmap, ) You can also rewrite the convert_segmentation_bitmap function to use batches and pass batched=True to dataset.map. This will significantly speed up the mapping, but you might need to tweak the batch_size to ensure the process doesn't run out of memory. The SegFormer model we're going to fine-tune later expects specific names for the features. For convenience, we'll match this format now. Thus, we'll rename the image feature to pixel_values and the label.segmentation_bitmap to label and discard the other features. python semantic_dataset = semantic_dataset.rename_column('image', 'pixel_values') semantic_dataset = semantic_dataset.rename_column('label.segmentation_bitmap', 'label') semantic_dataset = semantic_dataset.remove_columns(['name', 'uuid', 'status', 'label.annotations']) We can now push the transformed dataset to the Hugging Face Hub. That way, your team and the Hugging Face community can make use of it. In the next section, we'll see how you can load the dataset from the Hub. python hf_dataset_identifier = f"{hf_username}/{dataset_name}" semantic_dataset.push_to_hub(hf_dataset_identifier) | [
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Fine-Tune a Semantic Segmentation Model with a Custom Dataset | segments-tobias | March 17, 2022 | fine-tune-segformer | guide, partnerships, cv | https://huggingface.co/blog/fine-tune-segformer | Set up the Trainer To fine-tune the model on our data, we'll use Hugging Face's Trainer API. We need to set up the training configuration and an evalutation metric to use a Trainer. First, we'll set up the TrainingArguments. This defines all training hyperparameters, such as learning rate and the number of epochs, frequency to save the model and so on. We also specify to push the model to the hub after training (push_to_hub=True) and specify a model name (hub_model_id). python from transformers import TrainingArguments epochs = 50 lr = 0.00006 batch_size = 2 hub_model_id = "segformer-b0-finetuned-segments-sidewalk-2" training_args = TrainingArguments( "segformer-b0-finetuned-segments-sidewalk-outputs", learning_rate=lr, num_train_epochs=epochs, per_device_train_batch_size=batch_size, per_device_eval_batch_size=batch_size, save_total_limit=3, evaluation_strategy="steps", save_strategy="steps", save_steps=20, eval_steps=20, logging_steps=1, eval_accumulation_steps=5, load_best_model_at_end=True, push_to_hub=True, hub_model_id=hub_model_id, hub_strategy="end", ) Next, we'll define a function that computes the evaluation metric we want to work with. Because we're doing semantic segmentation, we'll use the mean Intersection over Union (mIoU), directly accessible in the evaluate library. IoU represents the overlap of segmentation masks. Mean IoU is the average of the IoU of all semantic classes. Take a look at this blogpost for an overview of evaluation metrics for image segmentation. Because our model outputs logits with dimensions height/4 and width/4, we have to upscale them before we can compute the mIoU. python import torch from torch import nn import evaluate metric = evaluate.load("mean_iou") def compute_metrics(eval_pred): with torch.no_grad(): logits, labels = eval_pred logits_tensor = torch.from_numpy(logits) # scale the logits to the size of the label logits_tensor = nn.functional.interpolate( logits_tensor, size=labels.shape[-2:], mode="bilinear", align_corners=False, ).argmax(dim=1) pred_labels = logits_tensor.detach().cpu().numpy() # currently using _compute instead of compute # see this issue for more info: https://github.com/huggingface/evaluate/pull/328#issuecomment-1286866576 metrics = metric._compute( predictions=pred_labels, references=labels, num_labels=len(id2label), ignore_index=0, reduce_labels=processor.do_reduce_labels, ) # add per category metrics as individual key-value pairs per_category_accuracy = metrics.pop("per_category_accuracy").tolist() per_category_iou = metrics.pop("per_category_iou").tolist() metrics.update({f"accuracy_{id2label[i]}": v for i, v in enumerate(per_category_accuracy)}) metrics.update({f"iou_{id2label[i]}": v for i, v in enumerate(per_category_iou)}) return metrics Finally, we can instantiate a Trainer object. python from transformers import Trainer trainer = Trainer( model=model, args=training_args, train_dataset=train_ds, eval_dataset=test_ds, compute_metrics=compute_metrics, ) Now that our trainer is set up, training is as simple as calling the train function. We don't need to worry about managing our GPU(s), the trainer will take care of that. python trainer.train() When we're done with training, we can push our fine-tuned model and the image processor to the Hub. This will also automatically create a model card with our results. We'll supply some extra information in kwargs to make the model card more complete. python kwargs = { "tags": ["vision", "image-segmentation"], "finetuned_from": pretrained_model_name, "dataset": hf_dataset_identifier, } processor.push_to_hub(hub_model_id) trainer.push_to_hub(**kwargs) # 4. Inference Now comes the exciting part, using our fine-tuned model! In this section, we'll show how you can load your model from the hub and use it for inference. However, you can also try out your model directly on the Hugging Face Hub, thanks to the cool widgets powered by the hosted inference API. If you pushed your model to the Hub in the previous step, you should see an inference widget on your model page. You can add default examples to the widget by defining example image URLs in your model card. See this model card as an example. | [
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Fine-Tune a Semantic Segmentation Model with a Custom Dataset | segments-tobias | March 17, 2022 | fine-tune-segformer | guide, partnerships, cv | https://huggingface.co/blog/fine-tune-segformer | Create your own dataset To create your semantic segmentation dataset, you'll need two things: 1. images covering the situations your model will encounter in the real world 2. segmentation labels, i.e. images where each pixel represents a class/category. We went ahead and captured a thousand images of sidewalks in Belgium. Collecting and labeling such a dataset can take a long time, so you can start with a smaller dataset and expand it if the model does not perform well enough. Some examples of the raw images in the sidewalk dataset. To obtain segmentation labels, we need to indicate the classes of all the regions/objects in these images. This can be a time-consuming endeavour, but using the right tools can speed up the task significantly. For labeling, we'll use Segments.ai, since it has smart labeling tools for image segmentation and an easy-to-use Python SDK. | [
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Fine-Tune a Semantic Segmentation Model with a Custom Dataset | segments-tobias | March 17, 2022 | fine-tune-segformer | guide, partnerships, cv | https://huggingface.co/blog/fine-tune-segformer | Use the model from the Hub We'll first load the model from the Hub using SegformerForSemanticSegmentation.from_pretrained(). python from transformers import SegformerImageProcessor, SegformerForSemanticSegmentation processor = SegformerImageProcessor.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512") model = SegformerForSemanticSegmentation.from_pretrained(f"{hf_username}/{hub_model_id}") Next, we'll load an image from our test dataset. python image = test_ds[0]['pixel_values'] gt_seg = test_ds[0]['label'] image To segment this test image, we first need to prepare the image using the image processor. Then we forward it through the model. We also need to remember to upscale the output logits to the original image size. In order to get the actual category predictions, we just have to apply an argmax on the logits. python from torch import nn inputs = processor(images=image, return_tensors="pt") outputs = model(**inputs) logits = outputs.logits # shape (batch_size, num_labels, height/4, width/4) # First, rescale logits to original image size upsampled_logits = nn.functional.interpolate( logits, size=image.size[::-1], # (height, width) mode='bilinear', align_corners=False ) # Second, apply argmax on the class dimension pred_seg = upsampled_logits.argmax(dim=1)[0] Now it's time to display the result. We'll display the result next to the ground-truth mask. What do you think? Would you send our pizza delivery robot on the road with this segmentation information? The result might not be perfect yet, but we can always expand our dataset to make the model more robust. We can now also go train a larger SegFormer model, and see how it stacks up. # 5. Conclusion That's it! You now know how to create your own image segmentation dataset and how to use it to fine-tune a semantic segmentation model. We introduced you to some useful tools along the way, such as: * Segments.ai for labeling your data * 🤗 datasets for creating and sharing a dataset * 🤗 transformers for easily fine-tuning a state-of-the-art segmentation model * Hugging Face Hub for sharing our dataset and model, and for creating an inference widget for our model We hope you enjoyed this post and learned something. Feel free to share your own model with us on Twitter (@TobiasCornille, @NielsRogge, and @huggingface). | [
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Fine-Tune a Semantic Segmentation Model with a Custom Dataset | segments-tobias | March 17, 2022 | fine-tune-segformer | guide, partnerships, cv | https://huggingface.co/blog/fine-tune-segformer | Label the images Now that the raw data is loaded, go to segments.ai/home and open the newly created dataset. Click "Start labeling" and create segmentation masks. You can use the ML-powered superpixel and autosegment tools to label faster. Tip: when using the superpixel tool, scroll to change the superpixel size, and click and drag to select segments. | [
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Fine-Tune a Semantic Segmentation Model with a Custom Dataset | segments-tobias | March 17, 2022 | fine-tune-segformer | guide, partnerships, cv | https://huggingface.co/blog/fine-tune-segformer | # Fine-Tune a Semantic Segmentation Model with a Custom Dataset This guide shows how you can fine-tune Segformer, a state-of-the-art semantic segmentation model. Our goal is to build a model for a pizza delivery robot, so it can see where to drive and recognize obstacles 🍕🤖. We'll first label a set of sidewalk images on Segments.ai. Then we'll fine-tune a pre-trained SegFormer model by using 🤗 transformers, an open-source library that offers easy-to-use implementations of state-of-the-art models. Along the way, you'll learn how to work with the Hugging Face Hub, the largest open-source catalog of models and datasets. Semantic segmentation is the task of classifying each pixel in an image. You can see it as a more precise way of classifying an image. It has a wide range of use cases in fields such as medical imaging and autonomous driving. For example, for our pizza delivery robot, it is important to know exactly where the sidewalk is in an image, not just whether there is a sidewalk or not. Because semantic segmentation is a type of classification, the network architectures used for image classification and semantic segmentation are very similar. In 2014, a seminal paper by Long et al. used convolutional neural networks for semantic segmentation. More recently, Transformers have been used for image classification (e.g. ViT), and now they're also being used for semantic segmentation, pushing the state-of-the-art further. SegFormer is a model for semantic segmentation introduced by Xie et al. in 2021. It has a hierarchical Transformer encoder that doesn't use positional encodings (in contrast to ViT) and a simple multi-layer perceptron decoder. SegFormer achieves state-of-the-art performance on multiple common datasets. Let's see how our pizza delivery robot performs for sidewalk images. Let's get started by installing the necessary dependencies. Because we're going to push our dataset and model to the Hugging Face Hub, we need to install Git LFS and log in to Hugging Face. The installation of git-lfs might be different on your system. Note that Google Colab has Git LFS pre-installed. bash pip install -q transformers datasets evaluate segments-ai apt-get install git-lfs git lfs install huggingface-cli login # 1. Create/choose a dataset The first step in any ML project is assembling a good dataset. In order to train a semantic segmentation model, we need a dataset with semantic segmentation labels. We can either use an existing dataset from the Hugging Face Hub, such as ADE20k, or create our own dataset. For our pizza delivery robot, we could use an existing autonomous driving dataset such as CityScapes or BDD100K. However, these datasets were captured by cars driving on the road. Since our delivery robot will be driving on the sidewalk, there will be a mismatch between the images in these datasets and the data our robot will see in the real world. We don't want our delivery robot to get confused, so we'll create our own semantic segmentation dataset using images captured on sidewalks. We'll show how you can label the images we captured in the next steps. If you just want to use our finished, labeled dataset, you can skip the "Create your own dataset" section and continue from "Use a dataset from the Hub". | [
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Announcing the 🤗 AI Research Residency Program | douwekiela | March 22, 2022 | ai-residency | community, research | https://huggingface.co/blog/ai-residency | # Announcing the 🤗 AI Research Residency Program 🎉 🎉 🎉 The 🤗 Research Residency Program is a 9-month opportunity to launch or advance your career in machine learning research 🚀. The goal of the residency is to help you grow into an impactful AI researcher. Residents will work alongside Researchers from our Science Team. Together, you will pick a research problem and then develop new machine learning techniques to solve it in an open & collaborative way, with the hope of ultimately publishing your work and making it visible to a wide audience. Applicants from all backgrounds are welcome! Ideally, you have some research experience and are excited about our mission to democratize responsible machine learning. The progress of our field has the potential to exacerbate existing disparities in ways that disproportionately hurt the most marginalized people in society — including people of color, people from working-class backgrounds, women, and LGBTQ+ people. These communities must be centered in the work we do as a research community. So we strongly encourage proposals from people whose personal experience reflects these identities.. We encourage applications relating to AI that demonstrate a clear and positive societal impact. | [
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Announcing the 🤗 AI Research Residency Program | douwekiela | March 22, 2022 | ai-residency | community, research | https://huggingface.co/blog/ai-residency | How to Apply Since the focus of your work will be on developing Machine Learning techniques, your application should show evidence of programming skills and of prerequisite courses, like calculus or linear algebra, or links to an open-source project that demonstrates programming and mathematical ability. More importantly, your application needs to present interest in effecting positive change through AI in any number of creative ways. This can stem from a topic that is of particular interest to you and your proposal would capture concrete ways in which machine learning can contribute. Thinking through the entire pipeline, from understanding where ML tools are needed to gathering data and deploying the resulting approach, can help make your project more impactful. We are actively working to build a culture that values diversity, equity, and inclusivity. We are intentionally building a workplace where people feel respected and supported—regardless of who you are or where you come from. We believe this is foundational to building a great company and community. Hugging Face is an equal opportunity employer and we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. Submit your application here. | [
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Announcing the 🤗 AI Research Residency Program | douwekiela | March 22, 2022 | ai-residency | community, research | https://huggingface.co/blog/ai-residency | FAQs * Can I complete the program part-time?No. The Residency is only offered as a full-time position. * I have been out of school for several years. Can I apply?Yes. We will consider applications from various backgrounds. * Can I be enrolled as a student at a university or work for another employer during the residency?No, the residency can’t be completed simultaneously with any other obligations. * Will I receive benefits during the Residency?Yes, residents are eligible for most benefits, including medical (depending on location). * Will I be required to relocate for this residency?Absolutely not! We are a distributed team and you are welcome to work from wherever you are currently located. * Is there a deadline?Applications close on April 3rd, 2022! | [
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Machine Learning Experts - Meg Mitchell Interview | britneymuller | March 23, 2022 | meg-mitchell-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/meg-mitchell-interview | What are you working on at Hugging Face? - Working on a few different tools designed for engineers. - Working on philosophical and social science research: Just did a deep dive into UDHR (Universal Declaration of Human Rights) and how those can be applied with AI. Trying to help bridge the gaps between AI, ML, law, and philosophy. - Trying to develop some statistical methods that are helpful for testing systems as well as understanding datasets. - We also recently put out a tool that shows how well a language maps to Zipfian distributions (how natural language tends to go) so you can test how well your model is matching with natural language that way. - Working a lot on the culture stuff: spending a lot of time on hiring and what processes we should have in place to be more inclusive. - Working on Big Science: a massive effort with people from all around the world, not just hugging face working on data governance (how can big data be used and examined without having it proliferate all over the world/being tracked with how it’s used). - Occasionally I’ll do an interview or talk to a Senator, so it’s all over the place. - Try to answer emails sometimes. Note: Everyone at Hugging Face wears several hats. :) | [
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Machine Learning Experts - Meg Mitchell Interview | britneymuller | March 23, 2022 | meg-mitchell-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/meg-mitchell-interview | You frequently include incredible examples of biased models in your Keynotes and interviews. One in particular that I love is the criminal detection model you've talked about that was using patterns of mouth angles to identify criminals (which you swiftly debunked). Meg: Yes, [the example is that] they were making this claim that there was this angle theta that was more indicative of criminals when it was a smaller angle. However, I was looking at the math and I realized that what they were talking about was a smile! Where you would have a wider angle for a smile vs a smaller angle associated with a straight face. They really missed the boat on what they were actually capturing there. Experimenter's bias: wanting to find things that aren’t there. | [
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Machine Learning Experts - Meg Mitchell Interview | britneymuller | March 23, 2022 | meg-mitchell-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/meg-mitchell-interview | Could you share a little bit about your background and what brought you to Hugging Face? Dr. Margaret Mitchell’s Background: - Bachelor’s in Linguistics at Reed College - Worked on NLP - Worked on assistive and augmentative technology after her Bachelor’s and also during her graduate studies - Master’s in Computational Linguistics at the University of Washington - PhD in Computer Science Meg: I did heavy statistical work as a postdoc at Johns Hopkins and then went to Microsoft Research where I continued doing vision to language generation that led to working on an app for people who are blind to navigate the world a bit easier called Seeing AI. After a few years at Microsoft, I left to work at Google to focus on big data problems inherent in deep learning. That’s where I started focusing on things like fairness, rigorous evaluation for different kinds of issues, and bias. While at Google, I founded and co-led the Ethical AI Team which focuses on inclusion and transparency. After four years at Google, I came over to Hugging Face where I was able to jump in and focus on coding. I’m helping to create protocols for ethical AI research, inclusive hiring, systems, and setting up a good culture here at Hugging Face. | [
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Machine Learning Experts - Meg Mitchell Interview | britneymuller | March 23, 2022 | meg-mitchell-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/meg-mitchell-interview | Best piece of advice for someone looking to get into AI? Meg: Depends on who the person is. If they have marginalized characteristics I would give very different advice. For example, if it was a woman I would say, 'Don’t listen to your supervisors saying you aren’t good at this. Chances are you are just thinking about things differently than they are used to so have confidence in yourself.' If it’s someone with more majority characteristics I’d say, 'Forget about the pipeline problem, pay attention to the people around you and make sure that you hold them up so that the pipeline you’re in now becomes less of a problem.' Also, 'Evaluate your systems'. | [
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Machine Learning Experts - Meg Mitchell Interview | britneymuller | March 23, 2022 | meg-mitchell-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/meg-mitchell-interview | How can ML teams be more aware of harmful bias? Meg: A primary issue is that these concepts haven't been taught and most teams simply aren’t aware. Another problem is the lack of a lexicon to contextualize and communicate what is going on. For example: - This is what marginalization is - This is what a power differential is - Here is what inclusion is - Here is how stereotypes work Having a better understanding of these pillars is really important. Another issue is the culture behind machine learning. It’s taken a bit of an ‘Alpha’ or ‘macho’ approach where the focus is on ‘beating’ the last numbers, making things ‘faster’, ‘bigger’, etc. There are lots of parallels that can be made to human anatomy. There’s also a very hostile competitiveness that comes out where you find that women are disproportionately treated as less than. Since women are often much more familiar with discrimination women are focusing a lot more on ethics, stereotypes, sexism, etc. within AI. This means it gets associated with women more and seen as less than which makes the culture a lot harder to penetrate. It’s generally assumed that I’m not technical. It’s something I have to prove over and over again. I’m called a linguist, an ethicist because these are things I care about and know about but that is treated as less-than. People say or think, “You don’t program, you don’t know about statistics, you are not as important,” and it’s often not until I start talking about things technically that people take me seriously which is unfortunate. There is a massive cultural barrier in ML. | [
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Machine Learning Experts - Meg Mitchell Interview | britneymuller | March 23, 2022 | meg-mitchell-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/meg-mitchell-interview | Decision thresholds & model transparency Meg: When Amazon first started putting out facial recognition and facial analysis technology it was found that the gender classification was disproportionately bad for black women and Amazon responded by saying “this was done using the wrong decision threshold”. And then one of the police agencies who had been using one of these systems had been asked what decision threshold they had been using and said, “Oh we’re not using a decision threshold,”. Which was like oh you really don’t understand how this works and are using this out of the box with default parameter settings?! That is a problem. So minimally having this documentary brings awareness to decisions around the various types of parameters. Machine learning models are so different from other things we put out into the public. Toys, medicine, and cars have all sorts of regulations to ensure products are safe and work as intended. We don’t have that in machine learning, partly because it’s new so the laws and regulations don’t exist yet. It’s a bit like the wild west, and that’s what we’re trying to change with model cards. | [
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Machine Learning Experts - Meg Mitchell Interview | britneymuller | March 23, 2022 | meg-mitchell-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/meg-mitchell-interview | Transcription: Note: Transcription has been slightly modified/reformatted to deliver the highest-quality reading experience. | [
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Machine Learning Experts - Meg Mitchell Interview | britneymuller | March 23, 2022 | meg-mitchell-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/meg-mitchell-interview | Diversity in AI - Isn’t there proof that having a more diverse set of people on an ML project results in better outcomes? Meg: Yes, since you have different perspectives you have a different distribution over options and thus, more options. One of the fundamental aspects of machine learning is that when you start training you can use a randomized starting point and what kind of distribution you want to sample from. Most engineers can agree that you don’t want to sample from one little piece of the distribution to have the best chance of finding a local optimum. You need to translate this approach to the people sitting at the table. Just how you want to have a Gaussian approach over different start states, so too do you want that at the table when you’re starting projects because it gives you this larger search space making it easier to attain a local optimum. | [
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Machine Learning Experts - Meg Mitchell Interview | britneymuller | March 23, 2022 | meg-mitchell-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/meg-mitchell-interview | Can you talk about Model Cards and how that project came to be? Meg: This project started at Google when I first started working on fairness and what a rigorous evaluation of fairness would look like. In order to do that you need to have an understanding of context and understanding of who would use it. This revolved around how to approach model biases and it wasn’t getting a lot of pick up. I was talking to Timnit Gebru who was at that time someone in the field with similar interest to me and she was talking about this idea of datasheets; a kind of documentation for data (based on her experience at Apple) doing engineering where you tend to have specifications of hardware. But we don’t have something similar for data and she was talking about how crazy that is. So Timnit had this idea of datasheets for datasets. It struck me that by having an ‘artifact’ people in tech who are motivated by launches would care a lot more about it. So if we say you have to produce this artifact and it will count as a launch suddenly people would be more incentivized to do it. The way we came up with the name was that a comparable word to ‘data sheet’ that could be used for models was card (plus it was shorter). Also decided to call it ‘model cards’ because the name was very generic and would have longevity over time. Timnit’s paper was called ‘Data Sheets for Datasets’. So we called ours ‘Model Cards for Model Reporting’ and once we had the published paper people started taking us more seriously. Couldn’t have done this without Timnit Gebru’s brilliance suggesting “You need an artifact, a standardized thing that people will want to produce.” | [
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Machine Learning Experts - Meg Mitchell Interview | britneymuller | March 23, 2022 | meg-mitchell-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/meg-mitchell-interview | Where are model cards headed? Meg: There’s a pretty big barrier to entry to do model cards in a way that is well informed by ethics. Partly because the people who need to fill this out are often engineers and developers who want to launch their model and don’t want to sit around thinking about documentation and ethics. Part of why I wanted to join Hugging Face is because it gave me an opportunity to standardize how these processes could be filled out and automated as much as possible. One thing I really like about Hugging Face is there is a focus on creating end-to-end machine learning processes that are as smooth as possible. Would love to do something like that with model cards where you could have something largely automatically generated as a function of different questions asked or even based on model specifications directly. We want to work towards having model cards as filled out as possible and interactive. Interactivity would allow you to see the difference in false-negative rate as you move the decision threshold. Normally with classification systems, you set some threshold at which you say yes or no, like .7, but in practice, you actually want to vary the decision threshold to trade off different errors. A static report of how well it works isn’t as informative as you want it to be because you want to know how well it works as different decision thresholds are chosen, and you could use that to decide what decision threshold to be used with your system. So we created a model card where you could interactively change the decision threshold and see how the numbers change. Moving towards that direction in further automation and interactivity is the way to go. | [
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Machine Learning Experts - Meg Mitchell Interview | britneymuller | March 23, 2022 | meg-mitchell-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/meg-mitchell-interview | Should people be afraid of AI taking over the world? Meg: There are a lot of things to be afraid of with AI. I like to see it as we have a distribution over different kinds of outcomes, some more positive than others, so there’s not one set one that we can know. There are a lot of different things where AI can be super helpful and more task-based over more generalized intelligence. You can see it going in another direction, similar to what I mentioned earlier about a model thinking something destructive is beautiful is one hop away from a system that is able to press a button to set off a missile. Don’t think people should be scared per se, but they should think about the best and worst-case scenarios and try to mitigate or stop the worst outcomes. I think the biggest thing right now is these systems can widen the divide between the haves and have nots. Further giving power to people who have power and further worsening things for people who don’t. The people designing these systems tend to be people with more power and wealth and they design things for their kinds of interest. I think that’s happening right now and something to think about in the future. Hopefully, we can focus on the things that are most beneficial and continue heading in that direction. | [
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Machine Learning Experts - Meg Mitchell Interview | britneymuller | March 23, 2022 | meg-mitchell-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/meg-mitchell-interview | Outro Britney: Meg had a hard stop on the hour but I was able to ask her my last question offline: What’s something you’ve been interested in lately? Meg’s response: "How to propagate and grow plants in synthetic/controlled settings." Just when I thought she couldn’t get any cooler. 🤯 I’ll leave you with a recent quote from Meg in a Science News article on Ethical AI: “The most pressing problem is the diversity and inclusion of who’s at the table from the start. All the other issues fall out from there.” -Meg Mitchell. Thank you for listening to Machine Learning Experts! Honorable mentions + links: - Emily Bender - Ehud Reiter - Abeba Birhane - Seeing AI - Data Sheets for Datasets - Model Cards - Model Cards Paper - Abeba Birhane - The Values Encoded in Machine Learning Research - Data and its (dis)contents: - Herbert Clark Follow Meg Online: - Twitter - Website - LinkedIn | [
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Machine Learning Experts - Meg Mitchell Interview | britneymuller | March 23, 2022 | meg-mitchell-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/meg-mitchell-interview | # Machine Learning Experts - Margaret Mitchell Hey friends! Welcome to Machine Learning Experts. I'm your host, Britney Muller and today’s guest is none other than Margaret Mitchell (Meg for short). Meg founded & co-led Google’s Ethical AI Group, is a pioneer in the field of Machine Learning, has published over 50 papers, and is a leading researcher in Ethical AI. You’ll hear Meg talk about the moment she realized the importance of ethical AI (an incredible story!), how ML teams can be more aware of harmful data bias, and the power (and performance) benefits of inclusion and diversity in ML. Very excited to introduce this powerful episode to you! Here’s my conversation with Meg Mitchell: | [
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Machine Learning Experts - Meg Mitchell Interview | britneymuller | March 23, 2022 | meg-mitchell-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/meg-mitchell-interview | What industries are you most excited to see ML applied (or ML Ethics be applied) Meg: The health and assistive domains continue to be areas I care a lot about and see a ton of potential. Also want to see systems that help people understand their own biases. Lots of technology is being created to screen job candidates for job interviews but I feel that technology should really be focused on the interviewer and how they might be coming at the situation with different biases. Would love to have more technology that assists humans to be more inclusive instead of assisting humans to exclude people. | [
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Machine Learning Experts - Meg Mitchell Interview | britneymuller | March 23, 2022 | meg-mitchell-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/meg-mitchell-interview | Lack of diversity and inclusion hurts everyone Meg: Diversity is when you have a lot of races, ethnicities, genders, abilities, statuses at the table. Inclusion is when each person feels comfortable talking, they feel welcome. One of the best ways to be more inclusive is to not be exclusive. Feels fairly obvious but is often missed. People get left out of meetings because we don’t find them helpful or find them annoying or combative (which is a function of various biases). To be inclusive you need to not be exclusive so when scheduling a meeting pay attention to the demographic makeup of the people you’re inviting. If your meeting is all-male, that’s a problem. It’s incredibly valuable to become more aware and intentional about the demographic makeup of the people you’re including in an email. But you’ll notice in tech, a lot of meetings are all male, and if you bring it up that can be met with a lot of hostility. Air on the side of including people. We all have biases but there are tactics to break some of those patterns. When writing an email I’ll go through their gender and ethnicities to ensure I’m being inclusive. It’s a very conscious effort. That sort of thinking through demographics helps. However, mention this before someone sends an email or schedules a meeting. People tend to not respond as well when you mention these things after the fact. | [
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Machine Learning Experts - Meg Mitchell Interview | britneymuller | March 23, 2022 | meg-mitchell-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/meg-mitchell-interview | Anything else you would like to mention? Meg: One of the things I’m working on, that I think other people should be working on, is lowering the barrier of entry to AI for people with different academic backgrounds. We have a lot of people developing technology, which is great, but we don’t have a lot of people in a situation where they can really question the technology because there is often a bottleneck. For example, if you want to know about data directly you have to be able to log into a server and write a SQL query. So there is a bottleneck where engineers have to do it and I want to remove that barrier. How can we take things that are fundamentally technical code stuff and open it up so people can directly query the data without knowing how to program? We will be able to make better technology when we remove the barriers that require engineers to be in the middle. | [
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Machine Learning Experts - Meg Mitchell Interview | britneymuller | March 23, 2022 | meg-mitchell-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/meg-mitchell-interview | Meg’s impact on AI Meg is featured in the book Genius Makers ‘The Mavericks who brought AI to Google, Facebook, and the World’. Cade Metz interviewed Meg for this while she was at Google. Meg’s pioneering research, systems, and work have played a pivotal role in the history of AI. (we are so lucky to have her at Hugging Face!) | [
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Machine Learning Experts - Meg Mitchell Interview | britneymuller | March 23, 2022 | meg-mitchell-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/meg-mitchell-interview | When did you recognize the importance of Ethical AI? Meg: This occurred when I was working at Microsoft while I was working on the assistance technology, Seeing AI. In general, I was working on generating language from images and I started to see was how lopsided data was. Data represents a subset of the world and it influences what a model will say. So I began to run into issues where white people would be described as ‘people’ and black people would be described as ‘black people’ as if white was a default and black was a marked characteristic. That was concerning to me. There was also an ah-ha moment when I was feeding my system a sequence of images, getting it to talk more about a story of what is happening. And I fed it some images of this massive blast where a lot of people worked, called the ‘Hebstad blast’. You could see that the person taking the picture was on the second or third story looking out on the blast. The blast was very close to this person. It was a very dire and intense moment and when I fed this to the system the system’s output was that “ this is awesome, this is a great view, this is beautiful’. And I thought.. this is a great view of this horrible scene but the important part here is that people may be dying. This is a massive destructive explosion. But the thing is, when you’re learning from images people don’t tend to take photos of terrible things, they take photos of sunsets, fireworks, etc., and a visual recognition model had learned on these images and believed that color in the sky was a positive, beautiful thing. At that moment, I realized that if a model with that sort of thinking had access to actions it would be just one hop away from a system that would blow up buildings because it thought it was beautiful. This was a moment for me when I realized I didn’t want to keep making these systems do better on benchmarks, I wanted to fundamentally shift how we were looking at these problems, how we were approaching data and analysis of data, how we were evaluating and all of the factors we were leaving out with these straightforward pipelines. So that really became my shift into ethical AI work. | [
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Machine Learning Experts - Meg Mitchell Interview | britneymuller | March 23, 2022 | meg-mitchell-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/meg-mitchell-interview | In what applications is data ethics most important? Meg: Human-centric technology that deals with people and identity (face recognition, pedestrian recognition). In NLP this would pertain more to the privacy of individuals, how individuals are talked about, and the biases models pick up with regards to descriptors used for people. | [
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Machine Learning Experts - Meg Mitchell Interview | britneymuller | March 23, 2022 | meg-mitchell-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/meg-mitchell-interview | Rapid Fire Questions: | [
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Machine Learning Experts - Meg Mitchell Interview | britneymuller | March 23, 2022 | meg-mitchell-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/meg-mitchell-interview | Fav ML papers? Meg: Most recently I’ve really loved what Abeba Birhane has been doing on values that are encoded in machine learning. My own team at Google had been working on data genealogies, bringing critical analysis on how ML data is handled which they have a few papers on - for example, Data and its (dis)contents: A survey of dataset development and use in machine learning research. Really love that work and might be biased because it included my team and direct reports, I’m very proud of them but it really is fundamentally good work. Earlier papers that I’m interested in are more reflective of what I was doing at that time. Really love the work of Herbert Clark who was a psycholinguistics/communications person and he did a lot of work that is easily ported to computational models about how humans communicate. Really love his work and cite him a lot throughout my thesis. | [
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Introducing Decision Transformers on Hugging Face 🤗 | edbeeching | March 28, 2022 | decision-transformers | open-source-collab, guide, rl | https://huggingface.co/blog/decision-transformers | Acknowledgements We would like to thank the paper’s first authors, Kevin Lu and Lili Chen, for their constructive conversations. | [
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Introducing Decision Transformers on Hugging Face 🤗 | edbeeching | March 28, 2022 | decision-transformers | open-source-collab, guide, rl | https://huggingface.co/blog/decision-transformers | Install the package python pip install git+https://github.com/huggingface/transformers | [
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Introducing Decision Transformers on Hugging Face 🤗 | edbeeching | March 28, 2022 | decision-transformers | open-source-collab, guide, rl | https://huggingface.co/blog/decision-transformers | # Introducing Decision Transformers on Hugging Face 🤗 At Hugging Face, we are contributing to the ecosystem for Deep Reinforcement Learning researchers and enthusiasts. Recently, we have integrated Deep RL frameworks such as Stable-Baselines3. And today we are happy to announce that we integrated the Decision Transformer, an Offline Reinforcement Learning method, into the 🤗 transformers library and the Hugging Face Hub. We have some exciting plans for improving accessibility in the field of Deep RL and we are looking forward to sharing them with you over the coming weeks and months. - What is Offline Reinforcement Learning? - Introducing Decision Transformers - Using the Decision Transformer in 🤗 Transformers - Conclusion - What's next? - References | [
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Introducing Decision Transformers on Hugging Face 🤗 | edbeeching | March 28, 2022 | decision-transformers | open-source-collab, guide, rl | https://huggingface.co/blog/decision-transformers | What is Offline Reinforcement Learning? Deep Reinforcement Learning (RL) is a framework to build decision-making agents. These agents aim to learn optimal behavior (policy) by interacting with the environment through trial and error and receiving rewards as unique feedback. The agent’s goal is to maximize its cumulative reward, called return. Because RL is based on the reward hypothesis: all goals can be described as the maximization of the expected cumulative reward. Deep Reinforcement Learning agents learn with batches of experience. The question is, how do they collect it?: A comparison between Reinforcement Learning in an Online and Offline setting, figure taken from this post In online reinforcement learning, the agent gathers data directly: it collects a batch of experience by interacting with the environment. Then, it uses this experience immediately (or via some replay buffer) to learn from it (update its policy). But this implies that either you train your agent directly in the real world or have a simulator. If you don’t have one, you need to build it, which can be very complex (how to reflect the complex reality of the real world in an environment?), expensive, and insecure since if the simulator has flaws, the agent will exploit them if they provide a competitive advantage. On the other hand, in offline reinforcement learning, the agent only uses data collected from other agents or human demonstrations. It does not interact with the environment. The process is as follows: 1. Create a dataset using one or more policies and/or human interactions. 2. Run offline RL on this dataset to learn a policy This method has one drawback: the counterfactual queries problem. What do we do if our agent decides to do something for which we don’t have the data? For instance, turning right on an intersection but we don’t have this trajectory. There’s already exists some solutions on this topic, but if you want to know more about offline reinforcement learning you can watch this video | [
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Introducing Decision Transformers on Hugging Face 🤗 | edbeeching | March 28, 2022 | decision-transformers | open-source-collab, guide, rl | https://huggingface.co/blog/decision-transformers | Loading the model Using the Decision Transformer is relatively easy, but as it is an autoregressive model, some care has to be taken in order to prepare the model’s inputs at each time-step. We have prepared both a Python script and a Colab notebook that demonstrates how to use this model. Loading a pretrained Decision Transformer is simple in the 🤗 transformers library: python from transformers import DecisionTransformerModel model_name = "edbeeching/decision-transformer-gym-hopper-expert" model = DecisionTransformerModel.from_pretrained(model_name) `````` | [
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Introducing Decision Transformers on Hugging Face 🤗 | edbeeching | March 28, 2022 | decision-transformers | open-source-collab, guide, rl | https://huggingface.co/blog/decision-transformers | Using the Decision Transformer in 🤗 Transformers The Decision Transformer model is now available as part of the 🤗 transformers library. In addition, we share nine pre-trained model checkpoints for continuous control tasks in the Gym environment. An “expert” Decision Transformers model, learned using offline RL in the Gym Walker2d environment. | [
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Introducing Decision Transformers on Hugging Face 🤗 | edbeeching | March 28, 2022 | decision-transformers | open-source-collab, guide, rl | https://huggingface.co/blog/decision-transformers | Evaluating the model In order to evaluate the model, we need some additional information; the mean and standard deviation of the states that were used during training. Fortunately, these are available for each of the checkpoint’s model card on the Hugging Face Hub! We also need a target return for the model. This is the power of return conditioned Offline Reinforcement Learning: we can use the target return to control the performance of the policy. This could be really powerful in a multiplayer setting, where we would like to adjust the performance of an opponent bot to be at a suitable difficulty for the player. The authors show a great plot of this in their paper! Sampled (evaluation) returns accumulated by Decision Transformer when conditioned on the specified target (desired) returns. Top: Atari. Bottom: D4RL medium-replay datasets. Figure from [1]. python TARGET_RETURN = 3.6 # This was normalized during training MAX_EPISODE_LENGTH = 1000 state_mean = np.array( [1.3490015, -0.11208222, -0.5506444, -0.13188992, -0.00378754, 2.6071432, 0.02322114, -0.01626922, -0.06840388, -0.05183131, 0.04272673,]) state_std = np.array( [0.15980862, 0.0446214, 0.14307782, 0.17629202, 0.5912333, 0.5899924, 1.5405099, 0.8152689, 2.0173461, 2.4107876, 5.8440027,]) state_mean = torch.from_numpy(state_mean) state_std = torch.from_numpy(state_std) state = env.reset() target_return = torch.tensor(TARGET_RETURN).float().reshape(1, 1) states = torch.from_numpy(state).reshape(1, state_dim).float() actions = torch.zeros((0, act_dim)).float() rewards = torch.zeros(0).float() timesteps = torch.tensor(0).reshape(1, 1).long() # take steps in the environment for t in range(max_ep_len): # add zeros for actions as input for the current time-step actions = torch.cat([actions, torch.zeros((1, act_dim))], dim=0) rewards = torch.cat([rewards, torch.zeros(1)]) # predicting the action to take action = get_action(model, (states - state_mean) / state_std, actions, rewards, target_return, timesteps) actions[-1] = action action = action.detach().numpy() # interact with the environment based on this action state, reward, done, _ = env.step(action) cur_state = torch.from_numpy(state).reshape(1, state_dim) states = torch.cat([states, cur_state], dim=0) rewards[-1] = reward pred_return = target_return[0, -1] - (reward / scale) target_return = torch.cat([target_return, pred_return.reshape(1, 1)], dim=1) timesteps = torch.cat([timesteps, torch.ones((1, 1)).long() * (t + 1)], dim=1) if done: break You will find a more detailed example, with the creation of videos of the agent in our Colab notebook. | [
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Introducing Decision Transformers on Hugging Face 🤗 | edbeeching | March 28, 2022 | decision-transformers | open-source-collab, guide, rl | https://huggingface.co/blog/decision-transformers | What’s next? In the coming weeks and months, we plan on supporting other tools from the ecosystem: - Integrating RL-baselines3-zoo - Uploading RL-trained-agents models into the Hub: a big collection of pre-trained Reinforcement Learning agents using stable-baselines3 - Integrating other Deep Reinforcement Learning libraries - Implementing Convolutional Decision Transformers For Atari - And more to come 🥳 The best way to keep in touch is to join our discord server to exchange with us and with the community. | [
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Introducing Decision Transformers on Hugging Face 🤗 | edbeeching | March 28, 2022 | decision-transformers | open-source-collab, guide, rl | https://huggingface.co/blog/decision-transformers | Autoregressive prediction function The model performs an [autoregressive prediction](https://en.wikipedia.org/wiki/Autoregressive_model); that is to say that predictions made at the current time-step **t** are sequentially conditioned on the outputs from previous time-steps. This function is quite meaty, so we will aim to explain it in the comments. `````python # Function that gets an action from the model using autoregressive prediction # with a window of the previous 20 timesteps. def get_action(model, states, actions, rewards, returns_to_go, timesteps): # This implementation does not condition on past rewards states = states.reshape(1, -1, model.config.state_dim) actions = actions.reshape(1, -1, model.config.act_dim) returns_to_go = returns_to_go.reshape(1, -1, 1) timesteps = timesteps.reshape(1, -1) # The prediction is conditioned on up to 20 previous time-steps states = states[:, -model.config.max_length :] actions = actions[:, -model.config.max_length :] returns_to_go = returns_to_go[:, -model.config.max_length :] timesteps = timesteps[:, -model.config.max_length :] # pad all tokens to sequence length, this is required if we process batches padding = model.config.max_length - states.shape[1] attention_mask = torch.cat([torch.zeros(padding), torch.ones(states.shape[1])]) attention_mask = attention_mask.to(dtype=torch.long).reshape(1, -1) states = torch.cat([torch.zeros((1, padding, state_dim)), states], dim=1).float() actions = torch.cat([torch.zeros((1, padding, act_dim)), actions], dim=1).float() returns_to_go = torch.cat([torch.zeros((1, padding, 1)), returns_to_go], dim=1).float() timesteps = torch.cat([torch.zeros((1, padding), dtype=torch.long), timesteps], dim=1) # perform the prediction state_preds, action_preds, return_preds = model( states=states, actions=actions, rewards=rewards, returns_to_go=returns_to_go, timesteps=timesteps, attention_mask=attention_mask, return_dict=False,) return action_preds[0, -1] | [
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Introducing Decision Transformers on Hugging Face 🤗 | edbeeching | March 28, 2022 | decision-transformers | open-source-collab, guide, rl | https://huggingface.co/blog/decision-transformers | References [1] Chen, Lili, et al. "Decision transformer: Reinforcement learning via sequence modeling." Advances in neural information processing systems 34 (2021). [2] Agarwal, Rishabh, Dale Schuurmans, and Mohammad Norouzi. "An optimistic perspective on offline reinforcement learning." International Conference on Machine Learning. PMLR, 2020. | [
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Introducing Decision Transformers on Hugging Face 🤗 | edbeeching | March 28, 2022 | decision-transformers | open-source-collab, guide, rl | https://huggingface.co/blog/decision-transformers | Creating the environment We provide pretrained checkpoints for the Gym Hopper, Walker2D and Halfcheetah. Checkpoints for Atari environments will soon be available.python import gym env = gym.make("Hopper-v3") state_dim = env.observation_space.shape[0] # state size act_dim = env.action_space.shape[0] # action size | [
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Introducing Decision Transformers on Hugging Face 🤗 | edbeeching | March 28, 2022 | decision-transformers | open-source-collab, guide, rl | https://huggingface.co/blog/decision-transformers | Introducing Decision Transformers The Decision Transformer model was introduced by “Decision Transformer: Reinforcement Learning via Sequence Modeling” by Chen L. et al. It abstracts Reinforcement Learning as a conditional-sequence modeling problem. The main idea is that instead of training a policy using RL methods, such as fitting a value function, that will tell us what action to take to maximize the return (cumulative reward), we use a sequence modeling algorithm (Transformer) that, given a desired return, past states, and actions, will generate future actions to achieve this desired return. It’s an autoregressive model conditioned on the desired return, past states, and actions to generate future actions that achieve the desired return. This is a complete shift in the Reinforcement Learning paradigm since we use generative trajectory modeling (modeling the joint distribution of the sequence of states, actions, and rewards) to replace conventional RL algorithms. It means that in Decision Transformers, we don’t maximize the return but rather generate a series of future actions that achieve the desired return. The process goes this way: 1. We feed the last K timesteps into the Decision Transformer with 3 inputs: - Return-to-go - State - Action 2. The tokens are embedded either with a linear layer if the state is a vector or CNN encoder if it’s frames. 3. The inputs are processed by a GPT-2 model which predicts future actions via autoregressive modeling. Decision Transformer architecture. States, actions, and returns are fed into modality specific linear embeddings and a positional episodic timestep encoding is added. Tokens are fed into a GPT architecture which predicts actions autoregressively using a causal self-attention mask. Figure from [1]. | [
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Introducing Decision Transformers on Hugging Face 🤗 | edbeeching | March 28, 2022 | decision-transformers | open-source-collab, guide, rl | https://huggingface.co/blog/decision-transformers | Conclusion In addition to Decision Transformers, we want to support more use cases and tools from the Deep Reinforcement Learning community. Therefore, it would be great to hear your feedback on the Decision Transformer model, and more generally anything we can build with you that would be useful for RL. Feel free to reach out to us. | [
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Don't repeat yourself - 🤗 Transformers Design Philosophy | patrickvonplaten | April 5, 2022 | transformers-design-philosophy | community | https://huggingface.co/blog/transformers-design-philosophy | Designing open-source libraries for modern machine learning | [
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Don't repeat yourself - 🤗 Transformers Design Philosophy | patrickvonplaten | April 5, 2022 | transformers-design-philosophy | community | https://huggingface.co/blog/transformers-design-philosophy | 🤗 Transformers Design Philosophy "Don't repeat yourself", or DRY, is a well-known principle of software development. The principle originates from "The pragmatic programmer", one of the most read books on code design. The principle's simple message makes obvious sense: Don't rewrite a logic that already exists somewhere else. This ensures the code remains in sync, making it easier to maintain and more robust. Any change to this logical pattern will uniformly affect all of its dependencies. At first glance, the design of Hugging Face's Transformers library couldn't be more contrary to the DRY principle. Code for the attention mechanism is more or less copied over 50 times into different model files. Sometimes code of the whole BERT model is copied into other model files. We often force new model contributions identical to existing models - besides a small logical tweak - to copy all of the existing code. Why do we do this? Are we just too lazy or overwhelmed to centralize all logical pieces into one place? No, we are not lazy - it's a very conscious decision not to apply the DRY design principle to the Transformers library. Instead, we decided to adopt a different design principle which we like to call the single model file policy. The single model file policy states that all code necessary for the forward pass of a model is in one and only one file - called the model file. If a reader wants to understand how BERT works for inference, she should only have to look into BERT's modeling_bert.py file. We usually reject any attempt to abstract identical sub-components of different models into a new centralized place. We don't want to have a attention_layer.py that includes all possible attention mechanisms. Again why do we do this? In short the reasons are: - 1. Transformers is built by and for the open-source community. - 2. Our product are models and our customers are users reading or tweaking model code. - 3. The field of machine learning evolves extremely fast. - 4. Machine Learning models are static. | [
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Don't repeat yourself - 🤗 Transformers Design Philosophy | patrickvonplaten | April 5, 2022 | transformers-design-philosophy | community | https://huggingface.co/blog/transformers-design-philosophy | 1. Built by and for the open-source community Transformers is built to actively incentivize external contributions. A contribution is often either a bug fix or a new model contribution. If a bug is found in one of the model files, we want to make it as easy as possible for the finder to fix it. There is little that is more demotivating than fixing a bug only to see that it caused 100 failures of other models. Because model code is independent from all other models, it's fairly easy for someone that only understands the one model she is working with to fix it. Similarly, it's easier to add new modeling code and review the corresponding PR if only a single new model file is added. The contributor does not have to figure out how to add new functionality to a centralized attention mechanism without breaking existing models. The reviewer can easily verify that none of the existing models are broken. | [
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Don't repeat yourself - 🤗 Transformers Design Philosophy | patrickvonplaten | April 5, 2022 | transformers-design-philosophy | community | https://huggingface.co/blog/transformers-design-philosophy | # ~~Don't~~ Repeat Yourself | [
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Don't repeat yourself - 🤗 Transformers Design Philosophy | patrickvonplaten | April 5, 2022 | transformers-design-philosophy | community | https://huggingface.co/blog/transformers-design-philosophy | 4. Machine Learning models are static The Transformers library is a unified and polished collection of machine learning models that different research teams have created. Every machine learning model is usually accompanied by a paper and its official GitHub repository. Once a machine learning model is published, it is rarely adapted or changed afterward. Instead, research teams tend to publish a new model built upon previous models but rarely make significant changes to already published code. This is an important realization when deciding on the design principles of the Transformers library. It means that once a model architecture has been added to Transformers, the fundamental components of the model don't change anymore. Bugs are often found and fixed, methods and variables might be renamed, and the output or input format of the model might be slightly changed, but the model's core components don't change anymore. Consequently, the need to apply global changes to all models in Transformers is significantly reduced, making it less important that every logical pattern only exists once since it's rarely changed. A second realization is that models do not depend on each other in a bidirectional way. More recent published models might depend on existing models, but it's quite obvious that an existing model cannot logically depend on its successor. E.g. T5 is partly built upon BERT and therefore T5's modeling code might logically depend on BERT's modeling code, but BERT cannot logically depend in any way on T5. Thus, it would not be logically sound to refactor BERT's attention function to also work with T5's attention function - someone reading through BERT's attention layer should not have to know anything about T5. Again, this advocates against centralizing components such as the attention layer into modules that all models can access. On the other hand, the modeling code of successor models can very well logically depend on its predecessor model. E.g., DeBERTa-v2 modeling code does logically depend to some extent on DeBERTa's modeling code. Maintainability is significantly improved by ensuring the modeling code of DeBERTa-v2 stays in sync with DeBERTa's. Fixing a bug in DeBERTa should ideally also fix the same bug in DeBERTa-v2. How can we maintain the single model file policy while ensuring that successor models stay in sync with their predecessor model? Now, we explain why we put the asterisk \( {}^{\textbf{}} \) after "Repeat Yourself". We don't blindly copy-paste all existing modeling code even if it looks this way. One of Transformers' core maintainers, Sylvain Gugger, found a great mechanism that respects both the single file policy and keeps maintainability cost in bounds. This mechanism, loosely called "the copying mechanism", allows us to mark logical components, such as an attention layer function, with a # Copied from <predecessor_model>.<function> statement, which enforces the marked code to be identical to the <function> of the <predecessor_model>. E.g., this line of over DeBERTa-v2's class enforces the whole class to be identical to DeBERTa's class except for the prefix DeBERTav2. This way, the copying mechanism keeps modeling code very easy to understand while significantly reducing maintenance. If some code is changed in a function of a predecessor model that is referred to by a function of its successor model, there are tools in place that automatically correct the successor model's function. | [
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Don't repeat yourself - 🤗 Transformers Design Philosophy | patrickvonplaten | April 5, 2022 | transformers-design-philosophy | community | https://huggingface.co/blog/transformers-design-philosophy | Conclusion All in all, at 🤗 Hugging Face we are convinced that the single file policy is the right coding philosophy for Transformers. What do you think? If you read until here, we would be more than interested in hearing your opinion! If you would like to leave a comment, please visit the corresponding forum post here. | [
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Don't repeat yourself - 🤗 Transformers Design Philosophy | patrickvonplaten | April 5, 2022 | transformers-design-philosophy | community | https://huggingface.co/blog/transformers-design-philosophy | 3. Machine Learning is evolving at a neck-breaking speed Research in the field of machine learning, and especially neural networks, evolves extremely fast. A model that was state-of-the-art a year ago might be outdated today. We don't know which attention mechanism, position embedding, or architecture will be the best in a year. Therefore, we cannot define standard logical patterns that apply to all models. As an example, two years ago, one might have defined BERT's self attention layer as the standard attention layer used by all Transformers models. Logically, a "standard" attention function could have been moved into a central attention.py file. But then came attention layers that added relative positional embeddings in each attention layer (T5), multiple different forms of chunked attention (Reformer, Longformer, BigBird), and separate attention mechanism for position and word embeddings (DeBERTa), etc... Every time we would have to have asked ourselves whether the "standard" attention function should be adapted or whether it would have been better to add a new attention function to attention.py. But then how do we name it? attention_with_positional_embd, reformer_attention, deberta_attention? It's dangerous to give logical components of machine learning models general names because the perception of what this component stands for might change or become outdated very quickly. E.g., does chunked attention corresponds to GPTNeo's, Reformer's, or BigBird's chunked attention? Is the attention layer a self-attention layer, a cross-attentional layer, or does it include both? However, if we name attention layers by their model's name, we should directly put the attention function in the corresponding modeling file. | [
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Don't repeat yourself - 🤗 Transformers Design Philosophy | patrickvonplaten | April 5, 2022 | transformers-design-philosophy | community | https://huggingface.co/blog/transformers-design-philosophy | 2. Modeling code is our product We assume that a significant amount of users of the Transformers library not only read the documentation, but also look into the actual modeling code and potentially modify it. This hypothesis is backed by the Transformers library being forked over 10,000 times and the Transformers paper being cited over a thousand times. Therefore it is of utmost importance that someone reading Transformers modeling code for the first time can easily understand and potentially adapt it. Providing all the necessary logical components in order in a single modeling file helps a lot to achieve improved readability and adaptability. Additionally, we care a great deal about sensible variable/method naming and prefer expressive/readable code over character-efficient code. | [
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Don't repeat yourself - 🤗 Transformers Design Philosophy | patrickvonplaten | April 5, 2022 | transformers-design-philosophy | community | https://huggingface.co/blog/transformers-design-philosophy | Drawbacks Clearly, there are also drawbacks to the single file policy two of which we quickly want to mention here. A major goal of Transformers is to provide a unified API for both inference and training for all models so that a user can quickly switch between different models in her setup. However, ensuring a unified API across models is much more difficult if modeling files are not allowed to use abstracted logical patterns. We solve this problem by running a lot of tests (ca. 20,000 tests are run daily at the time of writing this blog post) to ensure that models follow a consistent API. In this case, the single file policy requires us to be very rigorous when reviewing model and test additions. Second, there is a lot of research on just a single component of a Machine Learning model. E.g., research teams investigate new forms of an attention mechanism that would apply to all existing pre-trained models as has been done in the Rethinking Attention with Performers. How should we incorporate such research into the Transformers library? It is indeed problematic. Should we change all existing models? This would go against points 3. and 4. as written above. Should we add 100+ new modeling files each prefixed with Performer...? This seems absurd. In such a case there is sadly no good solution and we opt for not integrating the paper into Transformers in this case. If the paper would have gotten much more traction and included strong pre-trained checkpoints, we would have probably added new modeling files of the most important models such as modeling_performer_bert.py available. | [
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Habana Labs and Hugging Face Partner to Accelerate Transformer Model Training | susanlansing | April 12, 2022 | habana | partnerships | https://huggingface.co/blog/habana | # Habana Labs and Hugging Face Partner to Accelerate Transformer Model Training Santa Clara and San Francisco, CA, April 12th, 2022 Powered by deep learning, transformer models deliver state-of-the-art performance on a wide range of machine learning tasks, such as natural language processing, computer vision, speech, and more. However, training them at scale often requires a large amount of computing power, making the whole process unnecessarily long, complex, and costly. Today, Habana® Labs, a pioneer in high-efficiency, purpose-built deep learning processors, and Hugging Face, the home of Transformer models, are happy to announce that they’re joining forces to make it easier and quicker to train high-quality transformer models. Thanks to the integration of Habana’s SynapseAI software suite with the Hugging Face Optimum open-source library, data scientists and machine learning engineers can now accelerate their Transformer training jobs on Habana processors with just a few lines of code and enjoy greater productivity as well as lower training cost. Habana Gaudi training solutions, which power Amazon’s EC2 DL1 instances and Supermicro’s X12 Gaudi AI Training Server, deliver price/performance up to 40% lower than comparable training solutions and enable customers to train more while spending less. The integration of ten 100 Gigabit Ethernet ports onto every Gaudi processor enables system scaling from 1 to thousands of Gaudis with ease and cost-efficiency. Habana’s SynapseAI® is optimized—at inception—to enable Gaudi performance and usability, supports TensorFlow and PyTorch frameworks, with a focus on computer vision and natural language processing applications. With 60,000+ stars on Github, 30,000+ models, and millions of monthly visits, Hugging Face is one of the fastest-growing projects in open source software history, and the go-to place for the machine learning community. With its Hardware Partner Program, Hugging Face provides Gaudi’s advanced deep learning hardware with the ultimate Transformer toolset. This partnership will enable rapid expansion of the Habana Gaudi training transformer model library, bringing Gaudi efficiency and ease of use to a wide array of customer use cases like natural language processing, computer vision, speech, and more. “We’re excited to partner with Hugging Face and its many open-source developers to address the growing demand for transformer models that benefit from the efficiency, usability, and scalability of the Gaudi training platform”, said Sree Ganesan, head of software product management, Habana Labs. “Habana Gaudi brings a new level of efficiency to deep learning model training, and we’re super excited to make this performance easily accessible to Transformer users with minimal code changes through Optimum”, said Jeff Boudier, product director at Hugging Face. To learn how to get started training with Habana Gaudi, please visit https://developer.habana.ai. For more info on the Hugging Face and Habana Gaudi collaboration, please visit https://huggingface.co/Habana. | [
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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | Totally. Don't want to mess with a good thing! Lewis: Exactly. | [
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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | Should people be afraid of AI taking over the world? Lewis: Maybe. It’s a tough one because I think we have reasons to think that we may create systems that are quite dangerous in the sense that they could be used to cause a lot of harm. An analogy is perhaps with weapons you can use within the sports like archery and shooting, but you can also use them for war. One big risk is probably if we think about combining these techniques with the military perhaps this leads to some tricky situations. But, I'm not super worried about the Terminator. I'm more worried about, I don't know, a rogue agent on the financial stock market bankrupting the whole world. | [
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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | What are some of the industries you're most excited to see machine learning applied? Lewis: As I mentioned before, I think the natural sciences is the area I’m most excited about This is where I think that's most exciting. If we look at something, say at the industrial side, I guess some of the development of new drugs through machine learning is very exciting. Personally, I'd be really happy if there were advancements in robotics where I could finally have a robot to like fold my laundry because I really hate doing this and it would be nice if like there was an automated way of handling that. | [
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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | I'm definitely going to give that a listen. [Update: It’s one of my new favorite podcasts. :) Thank you, Lewis!] | [
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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | What is the meaning of life? Lewis: I think that the honest answer is, I don't know. And probably anyone who does tell you an answer probably is lying. That's a bit sarcastic. I dunno, I guess being a site scientist by training and especially a physicist, you develop this worldview that is very much that there isn't really some sort of deeper meaning to this. It's very much like the universe is quite random and I suppose the only thing you can take from that beyond being very sad is that you derive your own meaning, right? And most of the time this comes either from the work that you do or from the family or from your friends that you have. But I think when you find a way to derive your own meaning and discover what you do is actually interesting and meaningful that that's the best part. Life is very up and down, right? At least for me personally, the things that have always been very meaningful are generally in creating things. So, I used to be a musician, so that was a way of creating music for other people and there was great pleasure in doing that. And now I kind of, I guess, create code which is a form of creativity. | [
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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | That's a good point. Lewis: Sorry, that's a bit dark. | [
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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | You and other experts at Hugging Face have been working hard on the Hugging Face Course. How did that come about & where is it headed? Lewis: When I joined Hugging Face, Sylvian and Lysandre, two of the core maintainers of the transformers library, were developing a course to basically bridge the gap between people who are more like software engineers who are curious about natural language processing but specifically curious about the transformers revolution that's been happening. So I worked with them and others in the open-source team to create a free course called the Hugging Face Course. And this course is designed to really help people go from knowing kind of not so much about ML all the way through to having the ability to train models on many different tasks. And, we've released two parts of this course and planning to release the third part this year. I'm really excited about the next part that we're developing right now where we're going to explore different modalities where transformers are really powerful. Most of the time we think of transformers for NLP, but likely there's been this explosion where transformers are being used in things like audio or in computer vision and we're going to be looking at these in detail. | [
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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | I don't think so. Lewis: So when O’Reilly is telling you “We're going to get our illustrator now to design the cover,” it's a secret, right? They don't tell you what the logic is or you have no say in the matter. So, basically, the illustrator comes up with an idea and in one of the last chapters of the book we have a section where we basically train a GPT-2 like model on Python code, this was Thom's idea, and he decided to call it code parrot. I think the idea or the joke he had was that there's a lot of discussion in the community about this paper that Meg Mitchell and others worked on called, ‘Stochastic Parrots’. And the idea was that you have these very powerful language models which seem to exhibit human-like traits in their writing as we discussed earlier but deep down maybe they're just doing some sort of like parrot parenting thing. You know, if you talk to like a cockatoo it will swear at you or make jokes. That may not be a true measure of intelligence, right? So I think that the illustrator somehow maybe saw that and decided to put a parrot which I think is a perfect metaphor for the book. And the fact that there are transformers in it. | [
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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | I love the idea of had you done something differently maybe you wouldn't be at Hugging Face. Lewis: It’s like the butterfly effect movie, right? You go back in time and then you don't have any legs or something. | [
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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | # Machine Learning Experts - Lewis Tunstall | [
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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | Perfect. Thank you so much, Lewis. And I'll chat with you soon! Lewis: See ya, Britney. Bye. Thank you for listening to Machine Learning Experts! | [
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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | So glad you got that one in there. Well done! Look forward to many more in the next edition. Thank you so much for joining me today. I really appreciate it. Where can our listeners find you online? Lewis: I'm fairly active on Twitter. You can just find me my handle @_lewtun. LinkedIn is a strange place and I'm not really on there very much. And of course, there's Hugging Face, the Hugging Face Forums, and Discord. | [
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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | Exactly. Congratulations! Want to briefly read one endorsement that I love about this book; “Complexity made simple. This is a rare and precious book about NLP, transformers, and the growing ecosystem around them, Hugging Face. Whether these are still buzzwords to you or you already have a solid grasp of it all, the authors will navigate you with humor, scientific rigor, and plenty of code examples into the deepest secrets of the coolest technology around. From “off-the-shelf pre-trained” to “from-scratch custom” models, and from performance to missing labels issues, the authors address practically every real-life struggle of an ML engineer and provide state-of-the-art solutions, making this book destined to dictate the standards in the field for years to come.” —Luca Perrozi Ph.D., Data Science and Machine Learning Associate Manager at Accenture. Checkout Natural Language Processing with Transformers. | [
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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | No, that was great. The next question is a follow-up on your folding laundry robot. When will AI-assisted robots be in homes everywhere? Lewis: Honest answer. I don't know. Everyone, I know who's working on robotics says this is still an extremely difficult task in the sense that robotics hasn't quite experienced the same kind of revolutions that NLP and deep learning have had. But on the other hand, you can see some pretty exciting developments in the last year, especially around the idea of being able to transfer knowledge from a simulation into the real world. I think there's hope that in my lifetime I will have a laundry-folding robot. | [
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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | I love it. Well, it looks absolutely amazing. A lot of these types of books tend to be quite dry and technical and this one reads almost like a novel mixed with great applicable technical information, which is beautiful. Lewis: Thanks. Yeah, that’s one thing we realized afterward because it was the first time we were writing a book we thought we should be sort of serious, right? But if you sort of know me I'm like never really serious about anything. And in hindsight, we should have been even more silly in the book. I had to control my humor in various places but maybe there'll be a second edition one day and then we can just inject it with memes. | [
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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | What are some common mistakes machine learning engineers or teams make? Lewis: I can maybe tell you the ones that I've done. Probably a good representative of the rest of the things. So I think the biggest lesson I learned when I was starting out in the field is using baseline models when starting out. It’s a common problem that I did and then later saw other junior engineers doing is reaching for the fanciest state-of-the-art model. Although that may work, a lot of the time what happens is you introduce a lot of complexity into the problem and your state-of-the-art model may have a bug and you won't really know how to fix it because the model is so complex. It’s a very common pattern in industry and especially within NLP is that you can actually get quite far with regular expressions and linear models like logistic regression and these kinds of things will give you a good start. Then if you can build a better model then great, you should do that, but it's great to have a reference point. And then I think the second big lesson I’ve learned from building a lot of projects is that you can get a bit obsessed with the modeling part of the problem because that's the exciting bit when you're doing machine learning but there's this whole ecosystem. Especially if you work in a large company there'll be this whole ecosystem of services and things that are around your application. So the lesson there is you should really try to build something end to end that maybe doesn't even have any machine learning at all. But it's the scaffolding upon which you can build the rest of the system because you could spend all this time training an awesome mode, and then you go, oh, oops. It doesn't integrate with the requirements we have in our application. And then you've wasted all this time. | [
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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | 🤗 Welcome to Machine Learning Experts - Lewis Tunstall Hey friends! Welcome to Machine Learning Experts. I'm your host, Britney Muller and today’s guest is Lewis Tunstall. Lewis is a Machine Learning Engineer at Hugging Face where he works on applying Transformers to automate business processes and solve MLOps challenges. Lewis has built ML applications for startups and enterprises in the domains of NLP, topological data analysis, and time series. You’ll hear Lewis talk about his new book, transformers, large scale model evaluation, how he’s helping ML engineers optimize for faster latency and higher throughput, and more. In a previous life, Lewis was a theoretical physicist and outside of work loves to play guitar, go trail running, and contribute to open-source projects. Very excited to introduce this fun and brilliant episode to you! Here’s my conversation with Lewis Tunstall: Note: Transcription has been slightly modified/reformatted to deliver the highest-quality reading experience. | [
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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | No way?! Lewis: He's joining the Big Science team as an intern. So. I mean this is a super cool thing, right? When you learn something and then use that thing to apply which I thought was pretty awesome. | [
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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | That is incredible. How does it feel to have a copy of your book in your hands? Lewis: I have to say, I just became a parent about a year and a half ago and it feels kind of similar to my son being born. You're holding this thing that you created. It's quite an exciting feeling and so different to actually hold it (compared to reading a PDF). Confirms that it’s actually real and I didn't just dream about it. | [
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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | Best piece of advice for someone looking to get into AI/Machine Learning? Lewis: Just start. Just start coding. Just start contributing if you want to do open-source. You can always find reasons not to do it but you just have to get your hands dirty. | [
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Machine Learning Experts - Lewis Tunstall Interview | britneymuller | April 13, 2022 | lewis-tunstall-interview | expert-acceleration-program, ml-experts | https://huggingface.co/blog/lewis-tunstall-interview | What are some transformers applications that you're excited about? Lewis: So one that's kind of fun is in the course we had an event last year where we got people in the community to use the course material to build applications. And one of the participants in this event created a cover letter generator for jobs. So the idea is that when you apply for a job there's always this annoying thing you have to write a cover letter and it's always like a bit like you have to be witty. So this guy created a cover letter generator where you provide some information about yourself and then it generates it from that. And he actually used that to apply to Hugging Face. | [
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