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Create an Endpoint
After your first login, you will be directed to the [Endpoint creation page](https://ui.endpoints.huggingface.co/new). As an example, this guide will go through the steps to deploy [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) for text classification.
## 1. Enter the Hugging Face Repository ID and your desired endpoint name:
<img src="https://raw.githubusercontent.com/huggingface/hf-endpoints-documentation/main/assets/1_repository.png" alt="select repository" />
## 2. Select your Cloud Provider and region. Initially, only AWS will be available as a Cloud Provider with the `us-east-1` and `eu-west-1` regions. We will add Azure soon, and if you need to test Endpoints with other Cloud Providers or regions, please let us know.
<img src="https://raw.githubusercontent.com/huggingface/hf-endpoints-documentation/main/assets/1_region.png" alt="select region" />
## 3. Define the [Security Level](security) for the Endpoint:
<img src="https://raw.githubusercontent.com/huggingface/hf-endpoints-documentation/main/assets/1_security.png" alt="define security" />
## 4. Create your Endpoint by clicking **Create Endpoint**. By default, your Endpoint is created with a medium CPU (2 x 4GB vCPUs with Intel Xeon Ice Lake) The cost estimate assumes the Endpoint will be up for an entire month, and does not take autoscaling into account.
<img src="https://raw.githubusercontent.com/huggingface/hf-endpoints-documentation/main/assets/1_create_cost.png" alt="create endpoint" />
## 5. Wait for the Endpoint to build, initialize and run which can take between 1 to 5 minutes.
<img src="https://raw.githubusercontent.com/huggingface/hf-endpoints-documentation/main/assets/overview.png" alt="overview" />
## 6. Test your Endpoint in the overview with the Inference widget 🏁 🎉!
<img src="https://raw.githubusercontent.com/huggingface/hf-endpoints-documentation/main/assets/1_inference.png" alt="run inference" />
| huggingface/hf-endpoints-documentation/blob/main/docs/source/guides/create_endpoint.mdx |
Choosing a metric for your task
**So you've trained your model and want to see how well it’s doing on a dataset of your choice. Where do you start?**
There is no “one size fits all” approach to choosing an evaluation metric, but some good guidelines to keep in mind are:
## Categories of metrics
There are 3 high-level categories of metrics:
1. *Generic metrics*, which can be applied to a variety of situations and datasets, such as precision and accuracy.
2. *Task-specific metrics*, which are limited to a given task, such as Machine Translation (often evaluated using metrics [BLEU](https://huggingface.co/metrics/bleu) or [ROUGE](https://huggingface.co/metrics/rouge)) or Named Entity Recognition (often evaluated with [seqeval](https://huggingface.co/metrics/seqeval)).
3. *Dataset-specific metrics*, which aim to measure model performance on specific benchmarks: for instance, the [GLUE benchmark](https://huggingface.co/datasets/glue) has a dedicated [evaluation metric](https://huggingface.co/metrics/glue).
Let's look at each of these three cases:
### Generic metrics
Many of the metrics used in the Machine Learning community are quite generic and can be applied in a variety of tasks and datasets.
This is the case for metrics like [accuracy](https://huggingface.co/metrics/accuracy) and [precision](https://huggingface.co/metrics/precision), which can be used for evaluating labeled (supervised) datasets, as well as [perplexity](https://huggingface.co/metrics/perplexity), which can be used for evaluating different kinds of (unsupervised) generative tasks.
To see the input structure of a given metric, you can look at its metric card. For example, in the case of [precision](https://huggingface.co/metrics/precision), the format is:
```
>>> precision_metric = evaluate.load("precision")
>>> results = precision_metric.compute(references=[0, 1], predictions=[0, 1])
>>> print(results)
{'precision': 1.0}
```
### Task-specific metrics
Popular ML tasks like Machine Translation and Named Entity Recognition have specific metrics that can be used to compare models. For example, a series of different metrics have been proposed for text generation, ranging from [BLEU](https://huggingface.co/metrics/bleu) and its derivatives such as [GoogleBLEU](https://huggingface.co/metrics/google_bleu) and [GLEU](https://huggingface.co/metrics/gleu), but also [ROUGE](https://huggingface.co/metrics/rouge), [MAUVE](https://huggingface.co/metrics/mauve), etc.
You can find the right metric for your task by:
- **Looking at the [Task pages](https://huggingface.co/tasks)** to see what metrics can be used for evaluating models for a given task.
- **Checking out leaderboards** on sites like [Papers With Code](https://paperswithcode.com/) (you can search by task and by dataset).
- **Reading the metric cards** for the relevant metrics and see which ones are a good fit for your use case. For example, see the [BLEU metric card](https://github.com/huggingface/evaluate/tree/main/metrics/bleu) or [SQuaD metric card](https://github.com/huggingface/evaluate/tree/main/metrics/squad).
- **Looking at papers and blog posts** published on the topic and see what metrics they report. This can change over time, so try to pick papers from the last couple of years!
### Dataset-specific metrics
Some datasets have specific metrics associated with them -- this is especially in the case of popular benchmarks like [GLUE](https://huggingface.co/metrics/glue) and [SQuAD](https://huggingface.co/metrics/squad).
<Tip warning={true}>
💡
GLUE is actually a collection of different subsets on different tasks, so first you need to choose the one that corresponds to the NLI task, such as mnli, which is described as “crowdsourced collection of sentence pairs with textual entailment annotations”
</Tip>
If you are evaluating your model on a benchmark dataset like the ones mentioned above, you can use its dedicated evaluation metric. Make sure you respect the format that they require. For example, to evaluate your model on the [SQuAD](https://huggingface.co/datasets/squad) dataset, you need to feed the `question` and `context` into your model and return the `prediction_text`, which should be compared with the `references` (based on matching the `id` of the question) :
```
>>> from evaluate import load
>>> squad_metric = load("squad")
>>> predictions = [{'prediction_text': '1976', 'id': '56e10a3be3433e1400422b22'}]
>>> references = [{'answers': {'answer_start': [97], 'text': ['1976']}, 'id': '56e10a3be3433e1400422b22'}]
>>> results = squad_metric.compute(predictions=predictions, references=references)
>>> results
{'exact_match': 100.0, 'f1': 100.0}
```
You can find examples of dataset structures by consulting the "Dataset Preview" function or the dataset card for a given dataset, and you can see how to use its dedicated evaluation function based on the metric card.
| huggingface/evaluate/blob/main/docs/source/choosing_a_metric.mdx |
主要特点
让我们来介绍一下 Gradio 最受欢迎的一些功能！这里是 Gradio 的主要特点：
1. [添加示例输入](#example-inputs)
2. [传递自定义错误消息](#errors)
3. [添加描述内容](#descriptive-content)
4. [设置旗标](#flagging)
5. [预处理和后处理](#preprocessing-and-postprocessing)
6. [样式化演示](#styling)
7. [排队用户](#queuing)
8. [迭代输出](#iterative-outputs)
9. [进度条](#progress-bars)
10. [批处理函数](#batch-functions)
11. [在协作笔记本上运行](#colab-notebooks)
## 示例输入
您可以提供用户可以轻松加载到 "Interface" 中的示例数据。这对于演示模型期望的输入类型以及演示数据集和模型一起探索的方式非常有帮助。要加载示例数据，您可以将嵌套列表提供给 Interface 构造函数的 `examples=` 关键字参数。外部列表中的每个子列表表示一个数据样本，子列表中的每个元素表示每个输入组件的输入。有关每个组件的示例数据格式在[Docs](https://gradio.app/docs#components)中有说明。
$code_calculator
$demo_calculator
您可以将大型数据集加载到示例中，通过 Gradio 浏览和与数据集进行交互。示例将自动分页（可以通过 Interface 的 `examples_per_page` 参数进行配置）。
继续了解示例，请参阅[更多示例](https://gradio.app/more-on-examples)指南。
## 错误
您希望向用户传递自定义错误消息。为此，with `gr.Error("custom message")` 来显示错误消息。如果在上面的计算器示例中尝试除以零，将显示自定义错误消息的弹出模态窗口。了解有关错误的更多信息，请参阅[文档](https://gradio.app/docs#error)。
## 描述性内容
在前面的示例中，您可能已经注意到 Interface 构造函数中的 `title=` 和 `description=` 关键字参数，帮助用户了解您的应用程序。
Interface 构造函数中有三个参数用于指定此内容应放置在哪里：
- `title`：接受文本，并可以将其显示在界面的顶部，也将成为页面标题。
- `description`：接受文本、Markdown 或 HTML，并将其放置在标题正下方。
- `article`：也接受文本、Markdown 或 HTML，并将其放置在界面下方。
如果您使用的是 `Blocks` API，则可以 with `gr.Markdown(...)` 或 `gr.HTML(...)` 组件在任何位置插入文本、Markdown 或 HTML，其中描述性内容位于 `Component` 构造函数内部。
另一个有用的关键字参数是 `label=`，它存在于每个 `Component` 中。这修改了每个 `Component` 顶部的标签文本。还可以为诸如 `Textbox` 或 `Radio` 之类的表单元素添加 `info=` 关键字参数，以提供有关其用法的进一步信息。
```python
gr.Number(label='年龄', info='以年为单位，必须大于0')
```
## 旗标
默认情况下，"Interface" 将有一个 "Flag" 按钮。当用户测试您的 `Interface` 时，如果看到有趣的输出，例如错误或意外的模型行为，他们可以将输入标记为您进行查看。在由 `Interface` 构造函数的 `flagging_dir=` 参数提供的目录中，将记录标记的输入到一个 CSV 文件中。如果界面涉及文件数据，例如图像和音频组件，将创建文件夹来存储这些标记的数据。
例如，对于上面显示的计算器界面，我们将在下面的旗标目录中存储标记的数据：
```directory
+-- calculator.py
+-- flagged/
| +-- logs.csv
```
_flagged/logs.csv_
```csv
num1,operation,num2,Output
5,add,7,12
6,subtract,1.5,4.5
```
与早期显示的冷色界面相对应，我们将在下面的旗标目录中存储标记的数据：
```directory
+-- sepia.py
+-- flagged/
| +-- logs.csv
| +-- im/
| | +-- 0.png
| | +-- 1.png
| +-- Output/
| | +-- 0.png
| | +-- 1.png
```
_flagged/logs.csv_
```csv
im,Output
im/0.png,Output/0.png
im/1.png,Output/1.png
```
如果您希望用户提供旗标原因，可以将字符串列表传递给 Interface 的 `flagging_options` 参数。用户在进行旗标时必须选择其中一个字符串，这将作为附加列保存到 CSV 中。
## 预处理和后处理 (Preprocessing and Postprocessing)
如您所见，Gradio 包括可以处理各种不同数据类型的组件，例如图像、音频和视频。大多数组件都可以用作输入或输出。
当组件用作输入时，Gradio 自动处理*预处理*，将数据从用户浏览器发送的类型（例如网络摄像头快照的 base64 表示）转换为您的函数可以接受的形式（例如 `numpy` 数组）。
同样，当组件用作输出时，Gradio 自动处理*后处理*，将数据从函数返回的形式（例如图像路径列表）转换为可以在用户浏览器中显示的形式（例如以 base64 格式显示图像的 `Gallery`）。
您可以使用构建图像组件时的参数控制*预处理*。例如，如果您使用以下参数实例化 `Image` 组件，它将将图像转换为 `PIL` 类型，并将其重塑为`(100, 100)`，而不管提交时的原始大小如何：
```py
img = gr.Image(shape=(100, 100), type="pil")
```
相反，这里我们保留图像的原始大小，但在将其转换为 numpy 数组之前反转颜色：
```py
img = gr.Image(invert_colors=True, type="numpy")
```
后处理要容易得多！Gradio 自动识别返回数据的格式（例如 `Image` 是 `numpy` 数组还是 `str` 文件路径？），并将其后处理为可以由浏览器显示的格式。
请查看[文档](https://gradio.app/docs)，了解每个组件的所有与预处理相关的参数。
## 样式 (Styling)
Gradio 主题是自定义应用程序外观和感觉的最简单方法。您可以选择多种主题或创建自己的主题。要这样做，请将 `theme=` 参数传递给 `Interface` 构造函数。例如：
```python
demo = gr.Interface(..., theme=gr.themes.Monochrome())
```
Gradio 带有一组预先构建的主题，您可以从 `gr.themes.*` 加载。您可以扩展这些主题或从头开始创建自己的主题 - 有关更多详细信息，请参阅[主题指南](https://gradio.app/theming-guide)。
要增加额外的样式能力，您可以 with `css=` 关键字将任何 CSS 传递给您的应用程序。
Gradio 应用程序的基类是 `gradio-container`，因此以下是一个更改 Gradio 应用程序背景颜色的示例：
```python
with `gr.Interface(css=".gradio-container {background-color: red}") as demo:
...
```
## 队列 (Queuing)
如果您的应用程序预计会有大量流量，请 with `queue()` 方法来控制处理速率。这将排队处理调用，因此一次只处理一定数量的请求。队列使用 Websockets，还可以防止网络超时，因此如果您的函数的推理时间很长（> 1 分钟），应使用队列。
with `Interface`：
```python
demo = gr.Interface(...).queue()
demo.launch()
```
with `Blocks`：
```python
with gr.Blocks() as demo：
#...
demo.queue()
demo.launch()
```
您可以通过以下方式控制一次处理的请求数量：
```python
demo.queue(concurrency_count=3)
```
查看有关配置其他队列参数的[队列文档](/docs/#queue)。
在 Blocks 中指定仅对某些函数进行排队：
```python
with gr.Blocks() as demo2：
num1 = gr.Number()
num2 = gr.Number()
output = gr.Number()
gr.Button("Add").click(
lambda a, b: a + b, [num1, num2], output)
gr.Button("Multiply").click(
lambda a, b: a * b, [num1, num2], output, queue=True)
demo2.launch()
```
## 迭代输出 (Iterative Outputs)
在某些情况下，您可能需要传输一系列输出而不是一次显示单个输出。例如，您可能有一个图像生成模型，希望显示生成的每个步骤的图像，直到最终图像。或者您可能有一个聊天机器人，它逐字逐句地流式传输响应，而不是一次返回全部响应。
在这种情况下，您可以将**生成器**函数提供给 Gradio，而不是常规函数。在 Python 中创建生成器非常简单：函数不应该有一个单独的 `return` 值，而是应该 with `yield` 连续返回一系列值。通常，`yield` 语句放置在某种循环中。下面是一个简单示例，生成器只是简单计数到给定数字：
```python
def my_generator(x):
for i in range(x):
yield i
```
您以与常规函数相同的方式将生成器提供给 Gradio。例如，这是一个（虚拟的）图像生成模型，它在输出图像之前生成数个步骤的噪音：
$code_fake_diffusion
$demo_fake_diffusion
请注意，我们在迭代器中添加了 `time.sleep(1)`，以创建步骤之间的人工暂停，以便您可以观察迭代器的步骤（在真实的图像生成模型中，这可能是不必要的）。
将生成器提供给 Gradio **需要**在底层 Interface 或 Blocks 中启用队列（请参阅上面的队列部分）。
## 进度条
Gradio 支持创建自定义进度条，以便您可以自定义和控制向用户显示的进度更新。要启用此功能，只需为方法添加一个默认值为 `gr.Progress` 实例的参数即可。然后，您可以直接调用此实例并传入 0 到 1 之间的浮点数来更新进度级别，或者 with `Progress` 实例的 `tqdm()` 方法来跟踪可迭代对象上的进度，如下所示。必须启用队列以进行进度更新。
$code_progress_simple
$demo_progress_simple
如果您 with `tqdm` 库，并且希望从函数内部的任何 `tqdm.tqdm` 自动报告进度更新，请将默认参数设置为 `gr.Progress(track_tqdm=True)`！
## 批处理函数 (Batch Functions)
Gradio 支持传递*批处理*函数。批处理函数只是接受输入列表并返回预测列表的函数。
例如，这是一个批处理函数，它接受两个输入列表（一个单词列表和一个整数列表），并返回修剪过的单词列表作为输出：
```python
import time
def trim_words(words, lens):
trimmed_words = []
time.sleep(5)
for w, l in zip(words, lens):
trimmed_words.append(w[:int(l)])
return [trimmed_words]
for w, l in zip(words, lens):
```
使用批处理函数的优点是，如果启用了队列，Gradio 服务器可以自动*批处理*传入的请求并并行处理它们，从而可能加快演示速度。以下是 Gradio 代码的示例（请注意 `batch=True` 和 `max_batch_size=16` - 这两个参数都可以传递给事件触发器或 `Interface` 类）
with `Interface`：
```python
demo = gr.Interface(trim_words, ["textbox", "number"], ["output"],
batch=True, max_batch_size=16)
demo.queue()
demo.launch()
```
with `Blocks`：
```python
import gradio as gr
with gr.Blocks() as demo:
with gr.Row():
word = gr.Textbox(label="word")
leng = gr.Number(label="leng")
output = gr.Textbox(label="Output")
with gr.Row():
run = gr.Button()
event = run.click(trim_words, [word, leng], output, batch=True, max_batch_size=16)
demo.queue()
demo.launch()
```
在上面的示例中，可以并行处理 16 个请求（总推理时间为 5 秒），而不是分别处理每个请求（总推理时间为 80 秒）。许多 Hugging Face 的 `transformers` 和 `diffusers` 模型在 Gradio 的批处理模式下自然工作：这是[使用批处理生成图像的示例演示](https://github.com/gradio-app/gradio/blob/main/demo/diffusers_with_batching/run.py)
注意：使用 Gradio 的批处理函数 **requires** 在底层 Interface 或 Blocks 中启用队列（请参阅上面的队列部分）。
## Gradio 笔记本 (Colab Notebooks)
Gradio 可以在任何运行 Python 的地方运行，包括本地 Jupyter 笔记本和协作笔记本，如[Google Colab](https://colab.research.google.com/)。对于本地 Jupyter 笔记本和 Google Colab 笔记本，Gradio 在本地服务器上运行，您可以在浏览器中与之交互。（注意：对于 Google Colab，这是通过[服务工作器隧道](https://github.com/tensorflow/tensorboard/blob/master/docs/design/colab_integration.md)实现的，您的浏览器需要启用 cookies。）对于其他远程笔记本，Gradio 也将在服务器上运行，但您需要使用[SSH 隧道](https://coderwall.com/p/ohk6cg/remote-access-to-ipython-notebooks-via-ssh)在本地浏览器中查看应用程序。通常，更简单的选择是使用 Gradio 内置的公共链接，[在下一篇指南中讨论](/sharing-your-app/#sharing-demos)。
| gradio-app/gradio/blob/main/guides/cn/01_getting-started/02_key-features.md |
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# Training on TPU with TensorFlow
<Tip>
If you don't need long explanations and just want TPU code samples to get started with, check out [our TPU example notebook!](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/tpu_training-tf.ipynb)
</Tip>
### What is a TPU?
A TPU is a **Tensor Processing Unit.** They are hardware designed by Google, which are used to greatly speed up the tensor computations within neural networks, much like GPUs. They can be used for both network training and inference. They are generally accessed through Google’s cloud services, but small TPUs can also be accessed directly for free through Google Colab and Kaggle Kernels.
Because [all TensorFlow models in 🤗 Transformers are Keras models](https://huggingface.co/blog/tensorflow-philosophy), most of the methods in this document are generally applicable to TPU training for any Keras model! However, there are a few points that are specific to the HuggingFace ecosystem (hug-o-system?) of Transformers and Datasets, and we’ll make sure to flag them up when we get to them.
### What kinds of TPU are available?
New users are often very confused by the range of TPUs, and the different ways to access them. The first key distinction to understand is the difference between **TPU Nodes** and **TPU VMs.**
When you use a **TPU Node**, you are effectively indirectly accessing a remote TPU. You will need a separate VM, which will initialize your network and data pipeline and then forward them to the remote node. When you use a TPU on Google Colab, you are accessing it in the **TPU Node** style.
Using TPU Nodes can have some quite unexpected behaviour for people who aren’t used to them! In particular, because the TPU is located on a physically different system to the machine you’re running your Python code on, your data cannot be local to your machine - any data pipeline that loads from your machine’s internal storage will totally fail! Instead, data must be stored in Google Cloud Storage where your data pipeline can still access it, even when the pipeline is running on the remote TPU node.
<Tip>
If you can fit all your data in memory as `np.ndarray` or `tf.Tensor`, then you can `fit()` on that data even when using Colab or a TPU Node, without needing to upload it to Google Cloud Storage.
</Tip>
<Tip>
**🤗Specific Hugging Face Tip🤗:** The methods `Dataset.to_tf_dataset()` and its higher-level wrapper `model.prepare_tf_dataset()` , which you will see throughout our TF code examples, will both fail on a TPU Node. The reason for this is that even though they create a `tf.data.Dataset` it is not a “pure” `tf.data` pipeline and uses `tf.numpy_function` or `Dataset.from_generator()` to stream data from the underlying HuggingFace `Dataset`. This HuggingFace `Dataset` is backed by data that is on a local disc and which the remote TPU Node will not be able to read.
</Tip>
The second way to access a TPU is via a **TPU VM.** When using a TPU VM, you connect directly to the machine that the TPU is attached to, much like training on a GPU VM. TPU VMs are generally easier to work with, particularly when it comes to your data pipeline. All of the above warnings do not apply to TPU VMs!
This is an opinionated document, so here’s our opinion: **Avoid using TPU Node if possible.** It is more confusing and more difficult to debug than TPU VMs. It is also likely to be unsupported in future - Google’s latest TPU, TPUv4, can only be accessed as a TPU VM, which suggests that TPU Nodes are increasingly going to become a “legacy” access method. However, we understand that the only free TPU access is on Colab and Kaggle Kernels, which uses TPU Node - so we’ll try to explain how to handle it if you have to! Check the [TPU example notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/tpu_training-tf.ipynb) for code samples that explain this in more detail.
### What sizes of TPU are available?
A single TPU (a v2-8/v3-8/v4-8) runs 8 replicas. TPUs exist in **pods** that can run hundreds or thousands of replicas simultaneously. When you use more than a single TPU but less than a whole pod (for example, a v3-32), your TPU fleet is referred to as a **pod slice.**
When you access a free TPU via Colab, you generally get a single v2-8 TPU.
### I keep hearing about this XLA thing. What’s XLA, and how does it relate to TPUs?
XLA is an optimizing compiler, used by both TensorFlow and JAX. In JAX it is the only compiler, whereas in TensorFlow it is optional (but mandatory on TPU!). The easiest way to enable it when training a Keras model is to pass the argument `jit_compile=True` to `model.compile()`. If you don’t get any errors and performance is good, that’s a great sign that you’re ready to move to TPU!
Debugging on TPU is generally a bit harder than on CPU/GPU, so we recommend getting your code running on CPU/GPU with XLA first before trying it on TPU. You don’t have to train for long, of course - just for a few steps to make sure that your model and data pipeline are working like you expect them to.
<Tip>
XLA compiled code is usually faster - so even if you’re not planning to run on TPU, adding `jit_compile=True` can improve your performance. Be sure to note the caveats below about XLA compatibility, though!
</Tip>
<Tip warning={true}>
**Tip born of painful experience:** Although using `jit_compile=True` is a good way to get a speed boost and test if your CPU/GPU code is XLA-compatible, it can actually cause a lot of problems if you leave it in when actually training on TPU. XLA compilation will happen implicitly on TPU, so remember to remove that line before actually running your code on a TPU!
</Tip>
### How do I make my model XLA compatible?
In many cases, your code is probably XLA-compatible already! However, there are a few things that work in normal TensorFlow that don’t work in XLA. We’ve distilled them into three core rules below:
<Tip>
**🤗Specific HuggingFace Tip🤗:** We’ve put a lot of effort into rewriting our TensorFlow models and loss functions to be XLA-compatible. Our models and loss functions generally obey rule #1 and #2 by default, so you can skip over them if you’re using `transformers` models. Don’t forget about these rules when writing your own models and loss functions, though!
</Tip>
#### XLA Rule #1: Your code cannot have “data-dependent conditionals”
What that means is that any `if` statement cannot depend on values inside a `tf.Tensor`. For example, this code block cannot be compiled with XLA!
```python
if tf.reduce_sum(tensor) > 10:
tensor = tensor / 2.0
```
This might seem very restrictive at first, but most neural net code doesn’t need to do this. You can often get around this restriction by using `tf.cond` (see the documentation [here](https://www.tensorflow.org/api_docs/python/tf/cond)) or by removing the conditional and finding a clever math trick with indicator variables instead, like so:
```python
sum_over_10 = tf.cast(tf.reduce_sum(tensor) > 10, tf.float32)
tensor = tensor / (1.0 + sum_over_10)
```
This code has exactly the same effect as the code above, but by avoiding a conditional, we ensure it will compile with XLA without problems!
#### XLA Rule #2: Your code cannot have “data-dependent shapes”
What this means is that the shape of all of the `tf.Tensor` objects in your code cannot depend on their values. For example, the function `tf.unique` cannot be compiled with XLA, because it returns a `tensor` containing one instance of each unique value in the input. The shape of this output will obviously be different depending on how repetitive the input `Tensor` was, and so XLA refuses to handle it!
In general, most neural network code obeys rule #2 by default. However, there are a few common cases where it becomes a problem. One very common one is when you use **label masking**, setting your labels to a negative value to indicate that those positions should be ignored when computing the loss. If you look at NumPy or PyTorch loss functions that support label masking, you will often see code like this that uses [boolean indexing](https://numpy.org/doc/stable/user/basics.indexing.html#boolean-array-indexing):
```python
label_mask = labels >= 0
masked_outputs = outputs[label_mask]
masked_labels = labels[label_mask]
loss = compute_loss(masked_outputs, masked_labels)
mean_loss = torch.mean(loss)
```
This code is totally fine in NumPy or PyTorch, but it breaks in XLA! Why? Because the shape of `masked_outputs` and `masked_labels` depends on how many positions are masked - that makes it a **data-dependent shape.** However, just like for rule #1, we can often rewrite this code to yield exactly the same output without any data-dependent shapes.
```python
label_mask = tf.cast(labels >= 0, tf.float32)
loss = compute_loss(outputs, labels)
loss = loss * label_mask # Set negative label positions to 0
mean_loss = tf.reduce_sum(loss) / tf.reduce_sum(label_mask)
```
Here, we avoid data-dependent shapes by computing the loss for every position, but zeroing out the masked positions in both the numerator and denominator when we calculate the mean, which yields exactly the same result as the first block while maintaining XLA compatibility. Note that we use the same trick as in rule #1 - converting a `tf.bool` to `tf.float32` and using it as an indicator variable. This is a really useful trick, so remember it if you need to convert your own code to XLA!
#### XLA Rule #3: XLA will need to recompile your model for every different input shape it sees
This is the big one. What this means is that if your input shapes are very variable, XLA will have to recompile your model over and over, which will create huge performance problems. This commonly arises in NLP models, where input texts have variable lengths after tokenization. In other modalities, static shapes are more common and this rule is much less of a problem.
How can you get around rule #3? The key is **padding** - if you pad all your inputs to the same length, and then use an `attention_mask`, you can get the same results as you’d get from variable shapes, but without any XLA issues. However, excessive padding can cause severe slowdown too - if you pad all your samples to the maximum length in the whole dataset, you might end up with batches consisting endless padding tokens, which will waste a lot of compute and memory!
There isn’t a perfect solution to this problem. However, you can try some tricks. One very useful trick is to **pad batches of samples up to a multiple of a number like 32 or 64 tokens.** This often only increases the number of tokens by a small amount, but it hugely reduces the number of unique input shapes, because every input shape now has to be a multiple of 32 or 64. Fewer unique input shapes means fewer XLA compilations!
<Tip>
**🤗Specific HuggingFace Tip🤗:** Our tokenizers and data collators have methods that can help you here. You can use `padding="max_length"` or `padding="longest"` when calling tokenizers to get them to output padded data. Our tokenizers and data collators also have a `pad_to_multiple_of` argument that you can use to reduce the number of unique input shapes you see!
</Tip>
### How do I actually train my model on TPU?
Once your training is XLA-compatible and (if you’re using TPU Node / Colab) your dataset has been prepared appropriately, running on TPU is surprisingly easy! All you really need to change in your code is to add a few lines to initialize your TPU, and to ensure that your model and dataset are created inside a `TPUStrategy` scope. Take a look at [our TPU example notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/tpu_training-tf.ipynb) to see this in action!
### Summary
There was a lot in here, so let’s summarize with a quick checklist you can follow when you want to get your model ready for TPU training:
- Make sure your code follows the three rules of XLA
- Compile your model with `jit_compile=True` on CPU/GPU and confirm that you can train it with XLA
- Either load your dataset into memory or use a TPU-compatible dataset loading approach (see [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/tpu_training-tf.ipynb))
- Migrate your code either to Colab (with accelerator set to “TPU”) or a TPU VM on Google Cloud
- Add TPU initializer code (see [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/tpu_training-tf.ipynb))
- Create your `TPUStrategy` and make sure dataset loading and model creation are inside the `strategy.scope()` (see [notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/tpu_training-tf.ipynb))
- Don’t forget to take `jit_compile=True` out again when you move to TPU!
- 🙏🙏🙏🥺🥺🥺
- Call model.fit()
- You did it! | huggingface/transformers/blob/main/docs/source/en/perf_train_tpu_tf.md |
Gradio Demo: blocks_random_slider
```
!pip install -q gradio
```
```
import gradio as gr
def func(slider_1, slider_2):
return slider_1 * 5 + slider_2
with gr.Blocks() as demo:
slider = gr.Slider(minimum=-10.2, maximum=15, label="Random Slider (Static)", randomize=True)
slider_1 = gr.Slider(minimum=100, maximum=200, label="Random Slider (Input 1)", randomize=True)
slider_2 = gr.Slider(minimum=10, maximum=23.2, label="Random Slider (Input 2)", randomize=True)
slider_3 = gr.Slider(value=3, label="Non random slider")
btn = gr.Button("Run")
btn.click(func, inputs=[slider_1, slider_2], outputs=gr.Number())
if __name__ == "__main__":
demo.launch()
```
| gradio-app/gradio/blob/main/demo/blocks_random_slider/run.ipynb |
Git over SSH
You can access and write data in repositories on huggingface.co using SSH (Secure Shell Protocol). When you connect via SSH, you authenticate using a private key file on your local machine.
Some actions, such as pushing changes, or cloning private repositories, will require you to upload your SSH public key to your account on huggingface.co.
You can use a pre-existing SSH key, or generate a new one specifically for huggingface.co.
## Checking for existing SSH keys
If you have an existing SSH key, you can use that key to authenticate Git operations over SSH.
SSH keys are usually located under `~/.ssh` on Mac & Linux, and under `C:\\Users\\<username>\\.ssh` on Windows. List files under that directory and look for files of the form:
- id_rsa.pub
- id_ecdsa.pub
- id_ed25519.pub
Those files contain your SSH public key.
If you don't have such file under `~/.ssh`, you will have to [generate a new key](#generating-a-new-ssh-keypair). Otherwise, you can [add your existing SSH public key(s) to your huggingface.co account](#add-a-ssh-key-to-your-account).
## Generating a new SSH keypair
If you don't have any SSH keys on your machine, you can use `ssh-keygen` to generate a new SSH key pair (public + private keys):
```
$ ssh-keygen -t ed25519 -C "your.email@example.co"
```
We recommend entering a passphrase when you are prompted to. A passphrase is an extra layer of security: it is a password that will be prompted whenever you use your SSH key.
Once your new key is generated, add it to your SSH agent with `ssh-add`:
```
$ ssh-add ~/.ssh/id_ed25519
```
If you chose a different location than the default to store your SSH key, you would have to replace `~/.ssh/id_ed25519` with the file location you used.
## Add a SSH key to your account
To access private repositories with SSH, or to push changes via SSH, you will need to add your SSH public key to your huggingface.co account. You can manage your SSH keys [in your user settings](https://huggingface.co/settings/keys).
To add a SSH key to your account, click on the "Add SSH key" button.
Then, enter a name for this key (for example, "Personal computer"), and copy and paste the content of your **public** SSH key in the area below. The public key is located in the `~/.ssh/id_XXXX.pub` file you found or generated in the previous steps.
Click on "Add key", and voilà! You have added a SSH key to your huggingface.co account.
## Testing your SSH authentication
Once you have added your SSH key to your huggingface.co account, you can test that the connection works as expected.
In a terminal, run:
```
$ ssh -T git@hf.co
```
If you see a message with your username, congrats! Everything went well, you are ready to use git over SSH.
Otherwise, if the message states something like the following, make sure your SSH key is actually used by your SSH agent.
```
Hi anonymous, welcome to Hugging Face.
```
| huggingface/hub-docs/blob/main/docs/hub/security-git-ssh.md |
!---
Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
# Token classification with LayoutLMv3 (PyTorch version)
This directory contains a script, `run_funsd_cord.py`, that can be used to fine-tune (or evaluate) LayoutLMv3 on form understanding datasets, such as [FUNSD](https://guillaumejaume.github.io/FUNSD/) and [CORD](https://github.com/clovaai/cord).
The script `run_funsd_cord.py` leverages the 🤗 Datasets library and the Trainer API. You can easily customize it to your needs.
## Fine-tuning on FUNSD
Fine-tuning LayoutLMv3 for token classification on [FUNSD](https://guillaumejaume.github.io/FUNSD/) can be done as follows:
```bash
python run_funsd_cord.py \
--model_name_or_path microsoft/layoutlmv3-base \
--dataset_name funsd \
--output_dir layoutlmv3-test \
--do_train \
--do_eval \
--max_steps 1000 \
--evaluation_strategy steps \
--eval_steps 100 \
--learning_rate 1e-5 \
--load_best_model_at_end \
--metric_for_best_model "eval_f1" \
--push_to_hub \
--push_to_hub°model_id layoutlmv3-finetuned-funsd
```
👀 The resulting model can be found here: https://huggingface.co/nielsr/layoutlmv3-finetuned-funsd. By specifying the `push_to_hub` flag, the model gets uploaded automatically to the hub (regularly), together with a model card, which includes metrics such as precision, recall and F1. Note that you can easily update the model card, as it's just a README file of the respective repo on the hub.
There's also the "Training metrics" [tab](https://huggingface.co/nielsr/layoutlmv3-finetuned-funsd/tensorboard), which shows Tensorboard logs over the course of training. Pretty neat, huh?
## Fine-tuning on CORD
Fine-tuning LayoutLMv3 for token classification on [CORD](https://github.com/clovaai/cord) can be done as follows:
```bash
python run_funsd_cord.py \
--model_name_or_path microsoft/layoutlmv3-base \
--dataset_name cord \
--output_dir layoutlmv3-test \
--do_train \
--do_eval \
--max_steps 1000 \
--evaluation_strategy steps \
--eval_steps 100 \
--learning_rate 5e-5 \
--load_best_model_at_end \
--metric_for_best_model "eval_f1" \
--push_to_hub \
--push_to_hub°model_id layoutlmv3-finetuned-cord
```
👀 The resulting model can be found here: https://huggingface.co/nielsr/layoutlmv3-finetuned-cord. Note that a model card gets generated automatically in case you specify the `push_to_hub` flag. | huggingface/transformers/blob/main/examples/research_projects/layoutlmv3/README.md |
State in Blocks
We covered [State in Interfaces](https://gradio.app/interface-state), this guide takes a look at state in Blocks, which works mostly the same.
## Global State
Global state in Blocks works the same as in Interface. Any variable created outside a function call is a reference shared between all users.
## Session State
Gradio supports session **state**, where data persists across multiple submits within a page session, in Blocks apps as well. To reiterate, session data is _not_ shared between different users of your model. To store data in a session state, you need to do three things:
1. Create a `gr.State()` object. If there is a default value to this stateful object, pass that into the constructor.
2. In the event listener, put the `State` object as an input and output.
3. In the event listener function, add the variable to the input parameters and the return value.
Let's take a look at a game of hangman.
$code_hangman
$demo_hangman
Let's see how we do each of the 3 steps listed above in this game:
1. We store the used letters in `used_letters_var`. In the constructor of `State`, we set the initial value of this to `[]`, an empty list.
2. In `btn.click()`, we have a reference to `used_letters_var` in both the inputs and outputs.
3. In `guess_letter`, we pass the value of this `State` to `used_letters`, and then return an updated value of this `State` in the return statement.
With more complex apps, you will likely have many State variables storing session state in a single Blocks app.
Learn more about `State` in the [docs](https://gradio.app/docs#state).
| gradio-app/gradio/blob/main/guides/03_building-with-blocks/03_state-in-blocks.md |
如何使用地图组件绘制图表
Related spaces:
Tags: PLOTS, MAPS
## 简介
本指南介绍如何使用 Gradio 的 `Plot` 组件在地图上绘制地理数据。Gradio 的 `Plot` 组件可以与 Matplotlib、Bokeh 和 Plotly 一起使用。在本指南中，我们将使用 Plotly 进行操作。Plotly 可以让开发人员轻松创建各种地图来展示他们的地理数据。点击[这里](https://plotly.com/python/maps/)查看一些示例。
## 概述
我们将使用纽约市的 Airbnb 数据集，该数据集托管在 kaggle 上，点击[这里](https://www.kaggle.com/datasets/dgomonov/new-york-city-airbnb-open-data)。我已经将其上传到 Hugging Face Hub 作为一个数据集，方便使用和下载，点击[这里](https://huggingface.co/datasets/gradio/NYC-Airbnb-Open-Data)。使用这些数据，我们将在地图上绘制 Airbnb 的位置，并允许基于价格和位置进行筛选。下面是我们将要构建的演示。 ⚡️
$demo_map_airbnb
## 步骤 1-加载 CSV 数据 💾
让我们首先从 Hugging Face Hub 加载纽约市的 Airbnb 数据。
```python
from datasets import load_dataset
dataset = load_dataset("gradio/NYC-Airbnb-Open-Data", split="train")
df = dataset.to_pandas()
def filter_map(min_price, max_price, boroughs):
new_df = df[(df['neighbourhood_group'].isin(boroughs)) &
(df['price'] > min_price) & (df['price'] < max_price)]
names = new_df["name"].tolist()
prices = new_df["price"].tolist()
text_list = [(names[i], prices[i]) for i in range(0, len(names))]
```
在上面的代码中，我们先将 CSV 数据加载到一个 pandas dataframe 中。让我们首先定义一个函数，这将作为 gradio 应用程序的预测函数。该函数将接受最低价格、最高价格范围和筛选结果地区的列表作为参数。我们可以使用传入的值 (`min_price`、`max_price` 和地区列表) 来筛选数据框并创建 `new_df`。接下来，我们将创建包含每个 Airbnb 的名称和价格的 `text_list`，以便在地图上使用作为标签。
## 步骤 2-地图图表 🌐
Plotly 使得处理地图变得很容易。让我们看一下下面的代码，了解如何创建地图图表。
```python
import plotly.graph_objects as go
fig = go.Figure(go.Scattermapbox(
customdata=text_list,
lat=new_df['latitude'].tolist(),
lon=new_df['longitude'].tolist(),
mode='markers',
marker=go.scattermapbox.Marker(
size=6
),
hoverinfo="text",
hovertemplate='<b>Name</b>: %{customdata[0]}<br><b>Price</b>: $%{customdata[1]}'
))
fig.update_layout(
mapbox_style="open-street-map",
hovermode='closest',
mapbox=dict(
bearing=0,
center=go.layout.mapbox.Center(
lat=40.67,
lon=-73.90
),
pitch=0,
zoom=9
),
)
```
上面的代码中，我们通过传入经纬度列表来创建一个散点图。我们还传入了名称和价格的自定义数据，以便在鼠标悬停在每个标记上时显示额外的信息。接下来，我们使用 `update_layout` 来指定其他地图设置，例如缩放和居中。
有关使用 Mapbox 和 Plotly 创建散点图的更多信息，请点击[这里](https://plotly.com/python/scattermapbox/)。
## 步骤 3-Gradio 应用程序 ⚡️
我们将使用两个 `gr.Number` 组件和一个 `gr.CheckboxGroup` 组件，允许用户指定价格范围和地区位置。然后，我们将使用 `gr.Plot` 组件作为我们之前创建的 Plotly + Mapbox 地图的输出。
```python
with gr.Blocks() as demo:
with gr.Column():
with gr.Row():
min_price = gr.Number(value=250, label="Minimum Price")
max_price = gr.Number(value=1000, label="Maximum Price")
boroughs = gr.CheckboxGroup(choices=["Queens", "Brooklyn", "Manhattan", "Bronx", "Staten Island"], value=["Queens", "Brooklyn"], label="Select Boroughs:")
btn = gr.Button(value="Update Filter")
map = gr.Plot()
demo.load(filter_map, [min_price, max_price, boroughs], map)
btn.click(filter_map, [min_price, max_price, boroughs], map)
```
我们使用 `gr.Column` 和 `gr.Row` 布局这些组件，并为演示加载时和点击 " 更新筛选 " 按钮时添加了事件触发器，以触发地图更新新的筛选条件。
以下是完整演示代码：
$code_map_airbnb
## 步骤 4-部署 Deployment 🤗
如果你运行上面的代码，你的应用程序将在本地运行。
如果要获取临时共享链接，可以将 `share=True` 参数传递给 `launch`。
但如果你想要一个永久的部署解决方案呢？
让我们将我们的 Gradio 应用程序部署到免费的 HuggingFace Spaces 平台。
如果你以前没有使用过 Spaces，请按照之前的指南[这里](/using_hugging_face_integrations)。
## 结论 🎉
你已经完成了！这是构建地图演示所需的所有代码。
链接到演示：[地图演示](https://huggingface.co/spaces/gradio/map_airbnb)和[完整代码](https://huggingface.co/spaces/gradio/map_airbnb/blob/main/run.py)（在 Hugging Face Spaces）
| gradio-app/gradio/blob/main/guides/cn/05_tabular-data-science-and-plots/plot-component-for-maps.md |
SE-ResNet
**SE ResNet** is a variant of a [ResNet](https://www.paperswithcode.com/method/resnet) that employs [squeeze-and-excitation blocks](https://paperswithcode.com/method/squeeze-and-excitation-block) to enable the network to perform dynamic channel-wise feature recalibration.
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('seresnet152d', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `seresnet152d`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('seresnet152d', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@misc{hu2019squeezeandexcitation,
title={Squeeze-and-Excitation Networks},
author={Jie Hu and Li Shen and Samuel Albanie and Gang Sun and Enhua Wu},
year={2019},
eprint={1709.01507},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
<!--
Type: model-index
Collections:
- Name: SE ResNet
Paper:
Title: Squeeze-and-Excitation Networks
URL: https://paperswithcode.com/paper/squeeze-and-excitation-networks
Models:
- Name: seresnet152d
In Collection: SE ResNet
Metadata:
FLOPs: 20161904304
Parameters: 66840000
File Size: 268144497
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- Label Smoothing
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA Titan X GPUs
ID: seresnet152d
LR: 0.6
Epochs: 100
Layers: 152
Dropout: 0.2
Crop Pct: '0.94'
Momentum: 0.9
Batch Size: 1024
Image Size: '256'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/resnet.py#L1206
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet152d_ra2-04464dd2.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 83.74%
Top 5 Accuracy: 96.77%
- Name: seresnet50
In Collection: SE ResNet
Metadata:
FLOPs: 5285062320
Parameters: 28090000
File Size: 112621903
Architecture:
- 1x1 Convolution
- Batch Normalization
- Bottleneck Residual Block
- Convolution
- Global Average Pooling
- Max Pooling
- ReLU
- Residual Block
- Residual Connection
- Softmax
- Squeeze-and-Excitation Block
Tasks:
- Image Classification
Training Techniques:
- Label Smoothing
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 8x NVIDIA Titan X GPUs
ID: seresnet50
LR: 0.6
Epochs: 100
Layers: 50
Dropout: 0.2
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 1024
Image Size: '224'
Interpolation: bicubic
Code: https://github.com/rwightman/pytorch-image-models/blob/a7f95818e44b281137503bcf4b3e3e94d8ffa52f/timm/models/resnet.py#L1180
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/seresnet50_ra_224-8efdb4bb.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 80.26%
Top 5 Accuracy: 95.07%
--> | huggingface/pytorch-image-models/blob/main/hfdocs/source/models/se-resnet.mdx |
--
title: poseval
emoji: 🤗
colorFrom: blue
colorTo: red
sdk: gradio
sdk_version: 3.19.1
app_file: app.py
pinned: false
tags:
- evaluate
- metric
description: >-
The poseval metric can be used to evaluate POS taggers. Since seqeval does not work well with POS data
that is not in IOB format the poseval is an alternative. It treats each token in the dataset as independant
observation and computes the precision, recall and F1-score irrespective of sentences. It uses scikit-learns's
classification report to compute the scores.
---
# Metric Card for peqeval
## Metric description
The poseval metric can be used to evaluate POS taggers. Since seqeval does not work well with POS data (see e.g. [here](https://stackoverflow.com/questions/71327693/how-to-disable-seqeval-label-formatting-for-pos-tagging)) that is not in IOB format the poseval is an alternative. It treats each token in the dataset as independant observation and computes the precision, recall and F1-score irrespective of sentences. It uses scikit-learns's [classification report](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html) to compute the scores.
## How to use
Poseval produces labelling scores along with its sufficient statistics from a source against references.
It takes two mandatory arguments:
`predictions`: a list of lists of predicted labels, i.e. estimated targets as returned by a tagger.
`references`: a list of lists of reference labels, i.e. the ground truth/target values.
It can also take several optional arguments:
`zero_division`: Which value to substitute as a metric value when encountering zero division. Should be one of [`0`,`1`,`"warn"`]. `"warn"` acts as `0`, but the warning is raised.
```python
>>> predictions = [['INTJ', 'ADP', 'PROPN', 'NOUN', 'PUNCT', 'INTJ', 'ADP', 'PROPN', 'VERB', 'SYM']]
>>> references = [['INTJ', 'ADP', 'PROPN', 'PROPN', 'PUNCT', 'INTJ', 'ADP', 'PROPN', 'PROPN', 'SYM']]
>>> poseval = evaluate.load("poseval")
>>> results = poseval.compute(predictions=predictions, references=references)
>>> print(list(results.keys()))
['ADP', 'INTJ', 'NOUN', 'PROPN', 'PUNCT', 'SYM', 'VERB', 'accuracy', 'macro avg', 'weighted avg']
>>> print(results["accuracy"])
0.8
>>> print(results["PROPN"]["recall"])
0.5
```
## Output values
This metric returns a a classification report as a dictionary with a summary of scores for overall and per type:
Overall (weighted and macro avg):
`accuracy`: the average [accuracy](https://huggingface.co/metrics/accuracy), on a scale between 0.0 and 1.0.
`precision`: the average [precision](https://huggingface.co/metrics/precision), on a scale between 0.0 and 1.0.
`recall`: the average [recall](https://huggingface.co/metrics/recall), on a scale between 0.0 and 1.0.
`f1`: the average [F1 score](https://huggingface.co/metrics/f1), which is the harmonic mean of the precision and recall. It also has a scale of 0.0 to 1.0.
Per type (e.g. `MISC`, `PER`, `LOC`,...):
`precision`: the average [precision](https://huggingface.co/metrics/precision), on a scale between 0.0 and 1.0.
`recall`: the average [recall](https://huggingface.co/metrics/recall), on a scale between 0.0 and 1.0.
`f1`: the average [F1 score](https://huggingface.co/metrics/f1), on a scale between 0.0 and 1.0.
## Examples
```python
>>> predictions = [['INTJ', 'ADP', 'PROPN', 'NOUN', 'PUNCT', 'INTJ', 'ADP', 'PROPN', 'VERB', 'SYM']]
>>> references = [['INTJ', 'ADP', 'PROPN', 'PROPN', 'PUNCT', 'INTJ', 'ADP', 'PROPN', 'PROPN', 'SYM']]
>>> poseval = evaluate.load("poseval")
>>> results = poseval.compute(predictions=predictions, references=references)
>>> print(list(results.keys()))
['ADP', 'INTJ', 'NOUN', 'PROPN', 'PUNCT', 'SYM', 'VERB', 'accuracy', 'macro avg', 'weighted avg']
>>> print(results["accuracy"])
0.8
>>> print(results["PROPN"]["recall"])
0.5
```
## Limitations and bias
In contrast to [seqeval](https://github.com/chakki-works/seqeval), the poseval metric treats each token independently and computes the classification report over all concatenated sequences..
## Citation
```bibtex
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
```
## Further References
- [README for seqeval at GitHub](https://github.com/chakki-works/seqeval)
- [Classification report](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.classification_report.html)
- [Issues with seqeval](https://stackoverflow.com/questions/71327693/how-to-disable-seqeval-label-formatting-for-pos-tagging) | huggingface/evaluate/blob/main/metrics/poseval/README.md |
--
title: "Large Language Models: A New Moore's Law?"
thumbnail: /blog/assets/33_large_language_models/01_model_size.jpg
authors:
- user: juliensimon
---
# Large Language Models: A New Moore's Law?
A few days ago, Microsoft and NVIDIA [introduced](https://www.microsoft.com/en-us/research/blog/using-deepspeed-and-megatron-to-train-megatron-turing-nlg-530b-the-worlds-largest-and-most-powerful-generative-language-model/) Megatron-Turing NLG 530B, a Transformer-based model hailed as "*the world’s largest and most powerful generative language model*."
This is an impressive show of Machine Learning engineering, no doubt about it. Yet, should we be excited about this mega-model trend? I, for one, am not. Here's why.
<kbd>
<img src="assets/33_large_language_models/01_model_size.jpg">
</kbd>
### This is your Brain on Deep Learning
Researchers estimate that the human brain contains an average of [86 billion neurons](https://pubmed.ncbi.nlm.nih.gov/19226510/) and 100 trillion synapses. It's safe to assume that not all of them are dedicated to language either. Interestingly, GPT-4 is [expected](https://www.wired.com/story/cerebras-chip-cluster-neural-networks-ai/) to have about 100 trillion parameters... As crude as this analogy is, shouldn't we wonder whether building language models that are about the size of the human brain is the best long-term approach?
Of course, our brain is a marvelous device, produced by millions of years of evolution, while Deep Learning models are only a few decades old. Still, our intuition should tell us that something doesn't compute (pun intended).
### Deep Learning, Deep Pockets?
As you would expect, training a 530-billion parameter model on humongous text datasets requires a fair bit of infrastructure. In fact, Microsoft and NVIDIA used hundreds of DGX A100 multi-GPU servers. At $199,000 a piece, and factoring in networking equipment, hosting costs, etc., anyone looking to replicate this experiment would have to spend close to $100 million dollars. Want fries with that?
Seriously, which organizations have business use cases that would justify spending $100 million on Deep Learning infrastructure? Or even $10 million? Very few. So who are these models for, really?
### That Warm Feeling is your GPU Cluster
For all its engineering brilliance, training Deep Learning models on GPUs is a brute force technique. According to the spec sheet, each DGX server can consume up to 6.5 kilowatts. Of course, you'll need at least as much cooling power in your datacenter (or your server closet). Unless you're the Starks and need to keep Winterfell warm in winter, that's another problem you'll have to deal with.
In addition, as public awareness grows on climate and social responsibility issues, organizations need to account for their carbon footprint. According to this 2019 [study](https://arxiv.org/pdf/1906.02243.pdf) from the University of Massachusetts, "*training BERT on GPU is roughly equivalent to a trans-American flight*".
BERT-Large has 340 million parameters. One can only extrapolate what the footprint of Megatron-Turing could be... People who know me wouldn't call me a bleeding-heart environmentalist. Still, some numbers are hard to ignore.
### So?
Am I excited by Megatron-Turing NLG 530B and whatever beast is coming next? No. Do I think that the (relatively small) benchmark improvement is worth the added cost, complexity and carbon footprint? No. Do I think that building and promoting these huge models is helping organizations understand and adopt Machine Learning ? No.
I'm left wondering what's the point of it all. Science for the sake of science? Good old marketing? Technological supremacy? Probably a bit of each. I'll leave them to it, then.
Instead, let me focus on pragmatic and actionable techniques that you can all use to build high quality Machine Learning solutions.
### Use Pretrained Models
In the vast majority of cases, you won't need a custom model architecture. Maybe you'll *want* a custom one (which is a different thing), but there be dragons. Experts only!
A good starting point is to look for [models](https://huggingface.co/models) that have been pretrained for the task you're trying to solve (say, [summarizing English text](https://huggingface.co/models?language=en&pipeline_tag=summarization&sort=downloads)).
Then, you should quickly try out a few models to predict your own data. If metrics tell you that one works well enough, you're done! If you need a little more accuracy, you should consider fine-tuning the model (more on this in a minute).
### Use Smaller Models
When evaluating models, you should pick the smallest one that can deliver the accuracy you need. It will predict faster and require fewer hardware resources for training and inference. Frugality goes a long way.
It's nothing new either. Computer Vision practitioners will remember when [SqueezeNet](https://arxiv.org/abs/1602.07360) came out in 2017, achieving a 50x reduction in model size compared to [AlexNet](https://papers.nips.cc/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html), while meeting or exceeding its accuracy. How clever that was!
Downsizing efforts are also under way in the Natural Language Processing community, using transfer learning techniques such as [knowledge distillation](https://en.wikipedia.org/wiki/Knowledge_distillation). [DistilBERT](https://arxiv.org/abs/1910.01108) is perhaps its most widely known achievement. Compared to the original BERT model, it retains 97% of language understanding while being 40% smaller and 60% faster. You can try it [here](https://huggingface.co/distilbert-base-uncased). The same approach has been applied to other models, such as Facebook's [BART](https://arxiv.org/abs/1910.13461), and you can try DistilBART [here](https://huggingface.co/models?search=distilbart).
Recent models from the [Big Science](https://bigscience.huggingface.co/) project are also very impressive. As visible in this graph included in the [research paper](https://arxiv.org/abs/2110.08207), their T0 model outperforms GPT-3 on many tasks while being 16x smaller.
<kbd>
<img src="assets/33_large_language_models/02_t0.png">
</kbd>
You can try T0 [here](https://huggingface.co/bigscience/T0pp). This is the kind of research we need more of!
### Fine-Tune Models
If you need to specialize a model, there should be very few reasons to train it from scratch. Instead, you should fine-tune it, that is to say train it only for a few epochs on your own data. If you're short on data, maybe of one these [datasets](https://huggingface.co/datasets) can get you started.
You guessed it, that's another way to do transfer learning, and it'll help you save on everything!
* Less data to collect, store, clean and annotate,
* Faster experiments and iterations,
* Fewer resources required in production.
In other words: save time, save money, save hardware resources, save the world!
If you need a tutorial, the Hugging Face [course](https://huggingface.co/course) will get you started in no time.
### Use Cloud-Based Infrastructure
Like them or not, cloud companies know how to build efficient infrastructure. Sustainability studies show that cloud-based infrastructure is more energy and carbon efficient than the alternative: see [AWS](https://sustainability.aboutamazon.com/environment/the-cloud), [Azure](https://azure.microsoft.com/en-us/global-infrastructure/sustainability), and [Google](https://cloud.google.com/sustainability). Earth.org [says](https://earth.org/environmental-impact-of-cloud-computing/) that while cloud infrastructure is not perfect, "[*it's] more energy efficient than the alternative and facilitates environmentally beneficial services and economic growth.*"
Cloud certainly has a lot going for it when it comes to ease of use, flexibility and pay as you go. It's also a little greener than you probably thought. If you're short on GPUs, why not try fine-tune your Hugging Face models on [Amazon SageMaker](https://aws.amazon.com/sagemaker/), AWS' managed service for Machine Learning? We've got [plenty of examples](https://huggingface.co/docs/sagemaker/train) for you.
### Optimize Your Models
From compilers to virtual machines, software engineers have long used tools that automatically optimize their code for whatever hardware they're running on.
However, the Machine Learning community is still struggling with this topic, and for good reason. Optimizing models for size and speed is a devilishly complex task, which involves techniques such as:
* Specialized hardware that speeds up training ([Graphcore](https://www.graphcore.ai/), [Habana](https://habana.ai/)) and inference ([Google TPU](https://cloud.google.com/tpu), [AWS Inferentia](https://aws.amazon.com/machine-learning/inferentia/)).
* Pruning: remove model parameters that have little or no impact on the predicted outcome.
* Fusion: merge model layers (say, convolution and activation).
* Quantization: storing model parameters in smaller values (say, 8 bits instead of 32 bits)
Fortunately, automated tools are starting to appear, such as the [Optimum](https://huggingface.co/hardware) open source library, and [Infinity](https://huggingface.co/infinity), a containerized solution that delivers Transformers accuracy at 1-millisecond latency.
### Conclusion
Large language model size has been increasing 10x every year for the last few years. This is starting to look like another [Moore's Law](https://en.wikipedia.org/wiki/Moore%27s_law).
We've been there before, and we should know that this road leads to diminishing returns, higher cost, more complexity, and new risks. Exponentials tend not to end well. Remember [Meltdown and Spectre](https://meltdownattack.com/)? Do we want to find out what that looks like for AI?
Instead of chasing trillion-parameter models (place your bets), wouldn't all be better off if we built practical and efficient solutions that all developers can use to solve real-world problems?
*Interested in how Hugging Face can help your organization build and deploy production-grade Machine Learning solutions? Get in touch at [julsimon@huggingface.co](mailto:julsimon@huggingface.co) (no recruiters, no sales pitches, please).*
| huggingface/blog/blob/main/large-language-models.md |
!--Copyright 2021 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# VisionTextDualEncoder
## Overview
The [`VisionTextDualEncoderModel`] can be used to initialize a vision-text dual encoder model with
any pretrained vision autoencoding model as the vision encoder (*e.g.* [ViT](vit), [BEiT](beit), [DeiT](deit)) and any pretrained text autoencoding model as the text encoder (*e.g.* [RoBERTa](roberta), [BERT](bert)). Two projection layers are added on top of both the vision and text encoder to project the output embeddings
to a shared latent space. The projection layers are randomly initialized so the model should be fine-tuned on a
downstream task. This model can be used to align the vision-text embeddings using CLIP like contrastive image-text
training and then can be used for zero-shot vision tasks such image-classification or retrieval.
In [LiT: Zero-Shot Transfer with Locked-image Text Tuning](https://arxiv.org/abs/2111.07991) it is shown how
leveraging pre-trained (locked/frozen) image and text model for contrastive learning yields significant improvement on
new zero-shot vision tasks such as image classification or retrieval.
## VisionTextDualEncoderConfig
[[autodoc]] VisionTextDualEncoderConfig
## VisionTextDualEncoderProcessor
[[autodoc]] VisionTextDualEncoderProcessor
<frameworkcontent>
<pt>
## VisionTextDualEncoderModel
[[autodoc]] VisionTextDualEncoderModel
- forward
</pt>
<tf>
## FlaxVisionTextDualEncoderModel
[[autodoc]] FlaxVisionTextDualEncoderModel
- __call__
</tf>
<jax>
## TFVisionTextDualEncoderModel
[[autodoc]] TFVisionTextDualEncoderModel
- call
</jax>
</frameworkcontent>
| huggingface/transformers/blob/main/docs/source/en/model_doc/vision-text-dual-encoder.md |
hat is dynamic padding? In the "Batching Inputs together" video, we have seen that to be able to group inputs of different lengths in the same batch, we need to add padding tokens to all the short inputs until they are all of the same length. Here for instance, the longest sentence is the third one, and we need to add 5, 2 and 7 pad tokens to the other to have four sentences of the same lengths. When dealing with a whole dataset, there are various padding strategies we can apply. The most obvious one is to pad all the elements of the dataset to the same length: the length of the longest sample. This will then give us batches that all have the same shape determined by the maximum sequence length. The downside is that batches composed from short sentences will have a lot of padding tokens which introduce more computations in the model we ultimately don't need. To avoid this, another strategy is to pad the elements when we batch them together, to the longest sentence inside the batch. This way batches composed of short inputs will be smaller than the batch containing the longest sentence in the dataset. This will yield some nice speedup on CPU and GPU. The downside is that all batches will then have different shapes, which slows down training on other accelerators like TPUs. Let's see how to apply both strategies in practice. We have actually seen how to apply fixed padding in the Datasets Overview video, when we preprocessed the MRPC dataset: after loading the dataset and tokenizer, we applied the tokenization to all the dataset with padding and truncation to make all samples of length 128. As a result, if we pass this dataset to a PyTorch DataLoader, we get batches of shape batch size (here 16) by 128. To apply dynamic padding, we must defer the padding to the batch preparation, so we remove that part from our tokenize function. We still leave the truncation part so that inputs that are bigger than the maximum length accepted by the model (usually 512) get truncated to that length. Then we pad our samples dynamically by using a data collator. Those classes in the Transformers library are responsible for applying all the final processing needed before forming a batch, here DataCollatorWithPadding will pad the samples to the maximum length inside the batch of sentences. We pass it to the PyTorch DataLoader as a collate function, then observe that the batches generated have various lenghs, all way below the 128 from before. Dynamic batching will almost always be faster on CPUs and GPUs, so you should apply it if you can. Remember to switch back to fixed padding however if you run your training script on TPU or need batches of fixed shapes. | huggingface/course/blob/main/subtitles/en/raw/chapter3/02d_dynamic-padding.md |
!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# Kandinsky 3
Kandinsky 3 is created by [Vladimir Arkhipkin](https://github.com/oriBetelgeuse),[Anastasia Maltseva](https://github.com/NastyaMittseva),[Igor Pavlov](https://github.com/boomb0om),[Andrei Filatov](https://github.com/anvilarth),[Arseniy Shakhmatov](https://github.com/cene555),[Andrey Kuznetsov](https://github.com/kuznetsoffandrey),[Denis Dimitrov](https://github.com/denndimitrov), [Zein Shaheen](https://github.com/zeinsh)
The description from it's Github page:
*Kandinsky 3.0 is an open-source text-to-image diffusion model built upon the Kandinsky2-x model family. In comparison to its predecessors, enhancements have been made to the text understanding and visual quality of the model, achieved by increasing the size of the text encoder and Diffusion U-Net models, respectively.*
Its architecture includes 3 main components:
1. [FLAN-UL2](https://huggingface.co/google/flan-ul2), which is an encoder decoder model based on the T5 architecture.
2. New U-Net architecture featuring BigGAN-deep blocks doubles depth while maintaining the same number of parameters.
3. Sber-MoVQGAN is a decoder proven to have superior results in image restoration.
The original codebase can be found at [ai-forever/Kandinsky-3](https://github.com/ai-forever/Kandinsky-3).
<Tip>
Check out the [Kandinsky Community](https://huggingface.co/kandinsky-community) organization on the Hub for the official model checkpoints for tasks like text-to-image, image-to-image, and inpainting.
</Tip>
<Tip>
Make sure to check out the schedulers [guide](../../using-diffusers/schedulers) to learn how to explore the tradeoff between scheduler speed and quality, and see the [reuse components across pipelines](../../using-diffusers/loading#reuse-components-across-pipelines) section to learn how to efficiently load the same components into multiple pipelines.
</Tip>
## Kandinsky3Pipeline
[[autodoc]] Kandinsky3Pipeline
- all
- __call__
## Kandinsky3Img2ImgPipeline
[[autodoc]] Kandinsky3Img2ImgPipeline
- all
- __call__
| huggingface/diffusers/blob/main/docs/source/en/api/pipelines/kandinsky3.md |
Datasets server - worker
> Workers that pre-compute and cache the response to /splits, /first-rows, /parquet, /info and /size.
## Configuration
Use environment variables to configure the workers. The prefix of each environment variable gives its scope.
### Uvicorn
The following environment variables are used to configure the Uvicorn server (`WORKER_UVICORN_` prefix). It is used for the /healthcheck and the /metrics endpoints:
- `WORKER_UVICORN_HOSTNAME`: the hostname. Defaults to `"localhost"`.
- `WORKER_UVICORN_NUM_WORKERS`: the number of uvicorn workers. Defaults to `2`.
- `WORKER_UVICORN_PORT`: the port. Defaults to `8000`.
### Prometheus
- `PROMETHEUS_MULTIPROC_DIR`: the directory where the uvicorn workers share their prometheus metrics. See https://github.com/prometheus/client_python#multiprocess-mode-eg-gunicorn. Defaults to empty, in which case every uvicorn worker manages its own metrics, and the /metrics endpoint returns the metrics of a random worker.
## Worker configuration
Set environment variables to configure the worker.
- `WORKER_CONTENT_MAX_BYTES`: the maximum size in bytes of the response content computed by a worker (to prevent returning big responses in the REST API). Defaults to `10_000_000`.
- `WORKER_DIFFICULTY_MAX`: the maximum difficulty of the jobs to process. Defaults to None.
- `WORKER_DIFFICULTY_MIN`: the minimum difficulty of the jobs to process. Defaults to None.
- `WORKER_HEARTBEAT_INTERVAL_SECONDS`: the time interval between two heartbeats. Each heartbeat updates the job "last_heartbeat" field in the queue. Defaults to `60` (1 minute).
- `WORKER_JOB_TYPES_BLOCKED`: comma-separated list of job types that will not be processed, e.g. "dataset-config-names,dataset-split-names". If empty, no job type is blocked. Defaults to empty.
- `WORKER_JOB_TYPES_ONLY`: comma-separated list of the non-blocked job types to process, e.g. "dataset-config-names,dataset-split-names". If empty, the worker processes all the non-blocked jobs. Defaults to empty.
- `WORKER_KILL_LONG_JOB_INTERVAL_SECONDS`: the time interval at which the worker looks for long jobs to kill them. Defaults to `60` (1 minute).
- `WORKER_KILL_ZOMBIES_INTERVAL_SECONDS`: the time interval at which the worker looks for zombie jobs to kill them. Defaults to `600` (10 minutes).
- `WORKER_MAX_DISK_USAGE_PCT`: maximum disk usage of every storage disk in the list (in percentage) to allow a job to start. Set to 0 to disable the test. Defaults to 90.
- `WORKER_MAX_JOB_DURATION_SECONDS`: the maximum duration allowed for a job to run. If the job runs longer, it is killed (see `WORKER_KILL_LONG_JOB_INTERVAL_SECONDS`). Defaults to `1200` (20 minutes).
- `WORKER_MAX_LOAD_PCT`: maximum load of the machine (in percentage: the max between the 1m load and the 5m load divided by the number of CPUs \*100) allowed to start a job. Set to 0 to disable the test. Defaults to 70.
- `WORKER_MAX_MEMORY_PCT`: maximum memory (RAM + SWAP) usage of the machine (in percentage) allowed to start a job. Set to 0 to disable the test. Defaults to 80.
- `WORKER_MAX_MISSING_HEARTBEATS`: the number of hearbeats a job must have missed to be considered a zombie job. Defaults to `5`.
- `WORKER_SLEEP_SECONDS`: wait duration in seconds at each loop iteration before checking if resources are available and processing a job if any is available. Note that the loop doesn't wait just after finishing a job: the next job is immediately processed. Defaults to `15`.
- `WORKER_STORAGE_PATHS`: comma-separated list of paths to check for disk usage. Defaults to empty.
Also, it's possible to force the parent directory in which the temporary files (as the current job state file and its associated lock file) will be created by setting `TMPDIR` to a writable directory. If not set, the worker will use the default temporary directory of the system, as described in https://docs.python.org/3/library/tempfile.html#tempfile.gettempdir.
### Datasets based worker
Set environment variables to configure the datasets-based worker (`DATASETS_BASED_` prefix):
- `DATASETS_BASED_HF_DATASETS_CACHE`: directory where the `datasets` library will store the cached datasets' data. If not set, the datasets library will choose the default location. Defaults to None.
Also, set the modules cache configuration for the datasets-based worker. See [../../libs/libcommon/README.md](../../libs/libcommon/README.md). Note that this variable has no `DATASETS_BASED_` prefix:
- `HF_MODULES_CACHE`: directory where the `datasets` library will store the cached dataset scripts. If not set, the datasets library will choose the default location. Defaults to None.
Note that both directories will be appended to `WORKER_STORAGE_PATHS` (see [../../libs/libcommon/README.md](../../libs/libcommon/README.md)) to hold the workers when the disk is full.
### Numba library
Numba requires setting the `NUMBA_CACHE_DIR` environment variable to a writable directory to cache the compiled functions. Required on cloud infrastructure (see https://stackoverflow.com/a/63367171/7351594):
- `NUMBA_CACHE_DIR`: directory where the `numba` decorators (used by `librosa`) can write cache.
Note that this directory will be appended to `WORKER_STORAGE_PATHS` (see [../../libs/libcommon/README.md](../../libs/libcommon/README.md)) to hold the workers when the disk is full.
### Huggingface_hub library
If the Hub is not https://huggingface.co (i.e., if you set the `COMMON_HF_ENDPOINT` environment variable), you must set the `HF_ENDPOINT` environment variable to the same value. See https://github.com/huggingface/datasets/pull/5196#issuecomment-1322191411 for more details:
- `HF_ENDPOINT`: the URL of the Hub. Defaults to `https://huggingface.co`.
### First rows worker
Set environment variables to configure the `first-rows` worker (`FIRST_ROWS_` prefix):
- `FIRST_ROWS_MAX_BYTES`: the max size of the /first-rows response in bytes. Defaults to `1_000_000` (1 MB).
- `FIRST_ROWS_MAX_NUMBER`: the max number of rows fetched by the worker for the split and provided in the /first-rows response. Defaults to `100`.
- `FIRST_ROWS_MIN_CELL_BYTES`: the minimum size in bytes of a cell when truncating the content of a row (see `FIRST_ROWS_ROWS_MAX_BYTES`). Below this limit, the cell content will not be truncated. Defaults to `100`.
- `FIRST_ROWS_MIN_NUMBER`: the min number of rows fetched by the worker for the split and provided in the /first-rows response. Defaults to `10`.
- `FIRST_ROWS_COLUMNS_MAX_NUMBER`: the max number of columns (features) provided in the /first-rows response. If the number of columns is greater than the limit, an error is returned. Defaults to `1_000`.
Also, set the assets-related configuration for the first-rows worker. See [../../libs/libcommon/README.md](../../libs/libcommon/README.md).
### Parquet and info worker
Set environment variables to configure the `parquet-and-info` worker (`PARQUET_AND_INFO_` prefix):
- `PARQUET_AND_INFO_COMMIT_MESSAGE`: the git commit message when the worker uploads the parquet files to the Hub. Defaults to `Update parquet files`.
- `PARQUET_AND_INFO_COMMITTER_HF_TOKEN`: the HuggingFace token to commit the parquet files to the Hub. The token must be an app token associated with a user that has the right to 1. create the `refs/convert/parquet` branch (see `PARQUET_AND_INFO_TARGET_REVISION`) and 2. push commits to it on any dataset. [Datasets maintainers](https://huggingface.co/datasets-maintainers) members have these rights. The token must have permission to write. If not set, the worker will fail. Defaults to None.
- `PARQUET_AND_INFO_MAX_DATASET_SIZE_BYTES`: the maximum size in bytes of the dataset to pre-compute the parquet files. Bigger datasets, or datasets without that information, are partially streamed to get parquet files up to this value. Defaults to `100_000_000`.
- `PARQUET_AND_INFO_MAX_EXTERNAL_DATA_FILES`: the maximum number of external files of the datasets. Bigger datasets, or datasets without that information, are partially streamed to get parquet files up to `PARQUET_AND_INFO_MAX_DATASET_SIZE_BYTES` bytes. Defaults to `10_000`.
- `PARQUET_AND_INFO_MAX_ROW_GROUP_BYTE_SIZE_FOR_COPY`: the maximum size in bytes of the row groups of parquet datasets that are copied to the target revision. Bigger datasets, or datasets without that information, are partially streamed to get parquet files up to `PARQUET_AND_INFO_MAX_DATASET_SIZE_BYTES` bytes. Defaults to `100_000_000`.
- `PARQUET_AND_INFO_SOURCE_REVISION`: the git revision of the dataset to use to prepare the parquet files. Defaults to `main`.
- `PARQUET_AND_INFO_TARGET_REVISION`: the git revision of the dataset where to store the parquet files. Make sure the committer token (`PARQUET_AND_INFO_COMMITTER_HF_TOKEN`) has the permission to write there. Defaults to `refs/convert/parquet`.
- `PARQUET_AND_INFO_URL_TEMPLATE`: the URL template to build the parquet file URLs. Defaults to `/datasets/%s/resolve/%s/%s`.
### Duckdb Index worker
Set environment variables to configure the `duckdb-index` worker (`DUCKDB_INDEX_` prefix):
- `DUCKDB_INDEX_CACHE_DIRECTORY`: directory where the temporal duckdb index files are stored. Defaults to empty.
- `DUCKDB_INDEX_COMMIT_MESSAGE`: the git commit message when the worker uploads the duckdb index file to the Hub. Defaults to `Update duckdb index file`.
- `DUCKDB_INDEX_COMMITTER_HF_TOKEN`: the HuggingFace token to commit the duckdb index file to the Hub. The token must be an app token associated with a user that has the right to 1. create the `refs/convert/parquet` branch (see `DUCKDB_INDEX_TARGET_REVISION`) and 2. push commits to it on any dataset. [Datasets maintainers](https://huggingface.co/datasets-maintainers) members have these rights. The token must have permission to write. If not set, the worker will fail. Defaults to None.
- `DUCKDB_INDEX_MAX_DATASET_SIZE_BYTES`: the maximum size in bytes of the dataset's parquet files to index. Datasets with bigger size are ignored. Defaults to `100_000_000`.
- `DUCKDB_INDEX_TARGET_REVISION`: the git revision of the dataset where to store the duckdb index file. Make sure the committer token (`DUCKDB_INDEX_COMMITTER_HF_TOKEN`) has the permission to write there. Defaults to `refs/convert/parquet`.
- `DUCKDB_INDEX_URL_TEMPLATE`: the URL template to build the duckdb index file URL. Defaults to `/datasets/%s/resolve/%s/%s`.
- `DUCKDB_INDEX_EXTENSIONS_DIRECTORY`: directory where the duckdb extensions will be downloaded. Defaults to empty.
### Descriptive statistics worker
Set environment variables to configure the `descriptive-statistics` worker (`DESCRIPTIVE_STATISTICS_` prefix):
- `DESCRIPTIVE_STATISTICS_CACHE_DIRECTORY`: directory to which a dataset in parquet format is downloaded. Defaults to empty.
- `DESCRIPTIVE_STATISTICS_HISTOGRAM_NUM_BINS`: number of histogram bins (see examples below for more info).
- `DESCRIPTIVE_STATISTICS_MAX_PARQUET_SIZE_BYTES`: maximum size in bytes of the dataset's parquet files to compute statistics. Datasets with bigger size are ignored. Defaults to `100_000_000`.
#### How descriptive statistics are computed
Descriptive statistics are currently computed for the following data types: strings, floats, and ints (including `ClassLabel` int).
Response has two fields: `num_examples` and `statistics`. `statistics` field is a list of dicts with three keys: `column_name`, `column_type`, and `column_statistics`.
`column_type` is one of the following values:
* `class_label` - for `datasets.ClassLabel` feature
* `float` - for float dtypes ("float16", "float32", "float64")
* `int` - for integer dtypes ("int8", "int16", "int32", "int64", "uint8", "uint16", "uint32", "uint64")
* `string_label` - for string dtypes ("string", "large_string") - if there are less than or equal to `MAX_NUM_STRING_LABELS` unique values (hardcoded in worker's code, for now it's 30)
* `string_text` - for string dtypes ("string", "large_string") - if there are more than `MAX_NUM_STRING_LABELS` unique values
* `bool` - for boolean dtype ("bool")
`column_statistics` content depends on the feature type, see examples below.
##### class_label
<details><summary>example: </summary>
<p>
```python
{
"column_name": "class_col",
"column_type": "class_label",
"column_statistics": {
"nan_count": 0,
"nan_proportion": 0.0,
"no_label_count": 0, # number of -1 values - special value of the `datasets` lib to encode `no label`
"no_label_proportion": 0.0,
"n_unique": 5, # number of unique values (excluding `no label` and nan)
"frequencies": { # mapping value -> its count
"this": 19834,
"are": 20159,
"random": 20109,
"words": 20172,
"test": 19726
}
}
}
```
</p>
</details>
##### float
Bin size for histogram is counted as `(max_value - min_value) / DESCRIPTIVE_STATISTICS_HISTOGRAM_NUM_BINS`
<details><summary>example: </summary>
<p>
```python
{
"column_name": "delay",
"column_type": "float",
"column_statistics": {
"nan_count": 0,
"nan_proportion": 0.0,
"min": -10.206,
"max": 8.48053,
"mean": 2.10174,
"median": 3.4012,
"std": 3.12487,
"histogram": {
"hist": [
2,
34,
256,
15198,
9037,
2342,
12743,
45114,
14904,
370
],
"bin_edges": [
-10.206,
-8.33734,
-6.46869,
-4.60004,
-2.73139,
-0.86273,
1.00592,
2.87457,
4.74322,
6.61188,
8.48053 # includes maximum value, so len is always len(hist) + 1
]
}
}
}
```
</p>
</details>
##### int
As bin edges for integer values also must be integers, bin size is counted as `np.ceil((max_value - min_value + 1) / DESCRIPTIVE_STATISTICS_HISTOGRAM_NUM_BINS)`. Rounding up means that there might be smaller number of bins in response then provided `DESCRIPTIVE_STATISTICS_HISTOGRAM_NUM_BINS`. The last bin's size might be smaller than that of the others if the feature's range is not divisible by the rounded bin size.
<details><summary>examples: </summary>
<p>
```python
{
"column_name": "direction",
"column_type": "int",
"column_statistics": {
"nan_count": 0,
"nan_proportion": 0.0,
"min": 0,
"max": 1,
"mean": 0.49925,
"median": 0.0,
"std": 0.5,
"histogram": {
"hist": [
50075,
49925
],
"bin_edges": [
0,
1,
1 # if the last value is equal to the last but one, that means that this bin includes only this value
]
}
}
},
{
"column_name": "hour",
"column_type": "int",
"column_statistics": {
"nan_count": 0,
"nan_proportion": 0.0,
"min": 0,
"max": 23,
"mean": 13.44402,
"median": 14.0,
"std": 5.49455,
"histogram": {
"hist": [
2694,
2292,
16785,
16326,
16346,
17809,
16546,
11202
],
"bin_edges": [
0,
3,
6,
9,
12,
15,
18,
21,
23
]
}
}
},
{
"column_name": "humidity",
"column_type": "int",
"column_statistics": {
"nan_count": 0,
"nan_proportion": 0.0,
"min": 54,
"max": 99,
"mean": 83.89878,
"median": 85.0,
"std": 8.65174,
"histogram": {
"hist": [
554,
1662,
3823,
6532,
12512,
17536,
23871,
20355,
12896,
259
],
"bin_edges": [
54,
59,
64,
69,
74,
79,
84,
89,
94,
99,
99
]
}
}
},
{
"column_name": "weekday",
"column_type": "int",
"column_statistics": {
"nan_count": 0,
"nan_proportion": 0.0,
"min": 0,
"max": 6,
"mean": 3.08063,
"median": 3.0,
"std": 1.90347,
"histogram": {
"hist": [
10282,
15416,
15291,
15201,
15586,
15226,
12998
],
"bin_edges": [
0,
1,
2,
3,
4,
5,
6,
6
]
}
}
}
```
</p>
</details>
##### string_label
If the number of unique values in a column (within requested split) is <= `MAX_NUM_STRING_LABELS` (currently 30), the column is considered to be a category and the categories counts are computed.
<details><summary>examples: </summary>
<p>
```python
{
'column_name': 'string_col',
'column_type': 'string_label',
'column_statistics':
{
"nan_count": 0,
"nan_proportion": 0.0,
"n_unique": 5, # number of unique values (excluding nan)
"frequencies": { # mapping value -> its count
"this": 19834,
"are": 20159,
"random": 20109,
"words": 20172,
"test": 19726
}
}
}
```
</p>
</details>
##### string_text
If the number of unique values in a column (within requested split) is > `MAX_NUM_STRING_LABELS` (currently 30), the column is considered to be text and the distribution of text **lengths** is computed.
<details><summary>example: </summary>
<p>
```python
{
'column_name': 'text_col',
'column_type': 'string_text',
'column_statistics': {
'max': 296,
'mean': 97.46649,
'median': 88.0,
'min': 11,
'nan_count': 0,
'nan_proportion': 0.0,
'std': 55.82714,
'histogram': {
'bin_edges': [
11,
40,
69,
98,
127,
156,
185,
214,
243,
272,
296
],
'hist': [
171,
224,
235,
180,
102,
99,
53,
28,
10,
2
]
},
}
}
```
</p>
</details>
##### bool
<details><summary>example: </summary>
<p>
```python
{
'column_name': 'bool__nan_column',
'column_type': 'bool',
'column_statistics':
{
'nan_count': 3,
'nan_proportion': 0.15,
'frequencies': {
'False': 7,
'True': 10
}
}
}
```
</p>
</details>
### Splits worker
The `splits` worker does not need any additional configuration.
### Common
See [../../libs/libcommon/README.md](../../libs/libcommon/README.md) for more information about the common configuration.
| huggingface/datasets-server/blob/main/services/worker/README.md |
Differences between Dataset and IterableDataset
There are two types of dataset objects, a [`Dataset`] and an [`IterableDataset`].
Whichever type of dataset you choose to use or create depends on the size of the dataset.
In general, an [`IterableDataset`] is ideal for big datasets (think hundreds of GBs!) due to its lazy behavior and speed advantages, while a [`Dataset`] is great for everything else.
This page will compare the differences between a [`Dataset`] and an [`IterableDataset`] to help you pick the right dataset object for you.
## Downloading and streaming
When you have a regular [`Dataset`], you can access it using `my_dataset[0]`. This provides random access to the rows.
Such datasets are also called "map-style" datasets.
For example you can download ImageNet-1k like this and access any row:
```python
from datasets import load_dataset
imagenet = load_dataset("imagenet-1k", split="train") # downloads the full dataset
print(imagenet[0])
```
But one caveat is that you must have the entire dataset stored on your disk or in memory, which blocks you from accessing datasets bigger than the disk.
Because it can become inconvenient for big datasets, there exists another type of dataset, the [`IterableDataset`].
When you have an `IterableDataset`, you can access it using a `for` loop to load the data progressively as you iterate over the dataset.
This way, only a small fraction of examples is loaded in memory, and you don't write anything on disk.
For example, you can stream the ImageNet-1k dataset without downloading it on disk:
```python
from datasets import load_dataset
imagenet = load_dataset("imagenet-1k", split="train", streaming=True) # will start loading the data when iterated over
for example in imagenet:
print(example)
break
```
Streaming can read online data without writing any file to disk.
For example, you can stream datasets made out of multiple shards, each of which is hundreds of gigabytes like [C4](https://huggingface.co/datasets/c4), [OSCAR](https://huggingface.co/datasets/oscar) or [LAION-2B](https://huggingface.co/datasets/laion/laion2B-en).
Learn more about how to stream a dataset in the [Dataset Streaming Guide](./stream).
This is not the only difference though, because the "lazy" behavior of an `IterableDataset` is also present when it comes to dataset creation and processing.
## Creating map-style datasets and iterable datasets
You can create a [`Dataset`] using lists or dictionaries, and the data is entirely converted to Arrow so you can easily access any row:
```python
my_dataset = Dataset.from_dict({"col_1": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]})
print(my_dataset[0])
```
To create an `IterableDataset` on the other hand, you must provide a "lazy" way to load the data.
In Python, we generally use generator functions. These functions `yield` one example at a time, which means you can't access a row by slicing it like a regular `Dataset`:
```python
def my_generator(n):
for i in range(n):
yield {"col_1": i}
my_iterable_dataset = IterableDataset.from_generator(my_generator, gen_kwargs={"n": 10})
for example in my_iterable_dataset:
print(example)
break
```
## Loading local files entirely and progressively
It is possible to convert local or remote data files to an Arrow [`Dataset`] using [`load_dataset`]:
```python
data_files = {"train": ["path/to/data.csv"]}
my_dataset = load_dataset("csv", data_files=data_files, split="train")
print(my_dataset[0])
```
However, this requires a conversion step from CSV to Arrow format, which takes time and disk space if your dataset is big.
To save disk space and skip the conversion step, you can define an `IterableDataset` by streaming from the local files directly.
This way, the data is read progressively from the local files as you iterate over the dataset:
```python
data_files = {"train": ["path/to/data.csv"]}
my_iterable_dataset = load_dataset("csv", data_files=data_files, split="train", streaming=True)
for example in my_iterable_dataset: # this reads the CSV file progressively as you iterate over the dataset
print(example)
break
```
Many file formats are supported, like CSV, JSONL, and Parquet, as well as image and audio files.
You can find more information in the corresponding guides for loading [tabular](./tabular_load), [text](./nlp_load), [vision](./image_load), and [audio](./audio_load]) datasets.
## Eager data processing and lazy data processing
When you process a [`Dataset`] object using [`Dataset.map`], the entire dataset is processed immediately and returned.
This is similar to how `pandas` works for example.
```python
my_dataset = my_dataset.map(process_fn) # process_fn is applied on all the examples of the dataset
print(my_dataset[0])
```
On the other hand, due to the "lazy" nature of an `IterableDataset`, calling [`IterableDataset.map`] does not apply your `map` function over the full dataset.
Instead, your `map` function is applied on-the-fly.
Because of that, you can chain multiple processing steps and they will all run at once when you start iterating over the dataset:
```python
my_iterable_dataset = my_iterable_dataset.map(process_fn_1)
my_iterable_dataset = my_iterable_dataset.filter(filter_fn)
my_iterable_dataset = my_iterable_dataset.map(process_fn_2)
# process_fn_1, filter_fn and process_fn_2 are applied on-the-fly when iterating over the dataset
for example in my_iterable_dataset:
print(example)
break
```
## Exact and fast approximate shuffling
When you shuffle a [`Dataset`] using [`Dataset.shuffle`], you apply an exact shuffling of the dataset.
It works by taking a list of indices `[0, 1, 2, ... len(my_dataset) - 1]` and shuffling this list.
Then, accessing `my_dataset[0]` returns the row and index defined by the first element of the indices mapping that has been shuffled:
```python
my_dataset = my_dataset.shuffle(seed=42)
print(my_dataset[0])
```
Since we don't have random access to the rows in the case of an `IterableDataset`, we can't use a shuffled list of indices and access a row at an arbitrary position.
This prevents the use of exact shuffling.
Instead, a fast approximate shuffling is used in [`IterableDataset.shuffle`].
It uses a shuffle buffer to sample random examples iteratively from the dataset.
Since the dataset is still read iteratively, it provides excellent speed performance:
```python
my_iterable_dataset = my_iterable_dataset.shuffle(seed=42, buffer_size=100)
for example in my_iterable_dataset:
print(example)
break
```
But using a shuffle buffer is not enough to provide a satisfactory shuffling for machine learning model training. So [`IterableDataset.shuffle`] also shuffles the dataset shards if your dataset is made of multiple files or sources:
```python
# Stream from the internet
my_iterable_dataset = load_dataset("deepmind/code_contests", split="train", streaming=True)
my_iterable_dataset.n_shards # 39
# Stream from local files
data_files = {"train": [f"path/to/data_{i}.csv" for i in range(1024)]}
my_iterable_dataset = load_dataset("csv", data_files=data_files, split="train", streaming=True)
my_iterable_dataset.n_shards # 1024
# From a generator function
def my_generator(n, sources):
for source in sources:
for example_id_for_current_source in range(n):
yield {"example_id": f"{source}_{example_id_for_current_source}"}
gen_kwargs = {"n": 10, "sources": [f"path/to/data_{i}" for i in range(1024)]}
my_iterable_dataset = IterableDataset.from_generator(my_generator, gen_kwargs=gen_kwargs)
my_iterable_dataset.n_shards # 1024
```
## Speed differences
Regular [`Dataset`] objects are based on Arrow which provides fast random access to the rows.
Thanks to memory mapping and the fact that Arrow is an in-memory format, reading data from disk doesn't do expensive system calls and deserialization.
It provides even faster data loading when iterating using a `for` loop by iterating on contiguous Arrow record batches.
However as soon as your [`Dataset`] has an indices mapping (via [`Dataset.shuffle`] for example), the speed can become 10x slower.
This is because there is an extra step to get the row index to read using the indices mapping, and most importantly, you aren't reading contiguous chunks of data anymore.
To restore the speed, you'd need to rewrite the entire dataset on your disk again using [`Dataset.flatten_indices`], which removes the indices mapping.
This may take a lot of time depending of the size of your dataset though:
```python
my_dataset[0] # fast
my_dataset = my_dataset.shuffle(seed=42)
my_dataset[0] # up to 10x slower
my_dataset = my_dataset.flatten_indices() # rewrite the shuffled dataset on disk as contiguous chunks of data
my_dataset[0] # fast again
```
In this case, we recommend switching to an [`IterableDataset`] and leveraging its fast approximate shuffling method [`IterableDataset.shuffle`].
It only shuffles the shards order and adds a shuffle buffer to your dataset, which keeps the speed of your dataset optimal.
You can also reshuffle the dataset easily:
```python
for example in enumerate(my_iterable_dataset): # fast
pass
shuffled_iterable_dataset = my_iterable_dataset.shuffle(seed=42, buffer_size=100)
for example in enumerate(shuffled_iterable_dataset): # as fast as before
pass
shuffled_iterable_dataset = my_iterable_dataset.shuffle(seed=1337, buffer_size=100) # reshuffling using another seed is instantaneous
for example in enumerate(shuffled_iterable_dataset): # still as fast as before
pass
```
If you're using your dataset on multiple epochs, the effective seed to shuffle the shards order in the shuffle buffer is `seed + epoch`.
It makes it easy to reshuffle a dataset between epochs:
```python
for epoch in range(n_epochs):
my_iterable_dataset.set_epoch(epoch)
for example in my_iterable_dataset: # fast + reshuffled at each epoch using `effective_seed = seed + epoch`
pass
```
## Switch from map-style to iterable
If you want to benefit from the "lazy" behavior of an [`IterableDataset`] or their speed advantages, you can switch your map-style [`Dataset`] to an [`IterableDataset`]:
```python
my_iterable_dataset = my_dataset.to_iterable_dataset()
```
If you want to shuffle your dataset or [use it with a PyTorch DataLoader](./use_with_pytorch#stream-data), we recommend generating a sharded [`IterableDataset`]:
```python
my_iterable_dataset = my_dataset.to_iterable_dataset(num_shards=1024)
my_iterable_dataset.n_shards # 1024
```
| huggingface/datasets/blob/main/docs/source/about_mapstyle_vs_iterable.mdx |
Q-Learning Recap [[q-learning-recap]]
*Q-Learning* **is the RL algorithm that** :
- Trains a *Q-function*, an **action-value function** encoded, in internal memory, by a *Q-table* **containing all the state-action pair values.**
- Given a state and action, our Q-function **will search its Q-table for the corresponding value.**
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit3/Q-function-2.jpg" alt="Q function" width="100%"/>
- When the training is done, **we have an optimal Q-function, or, equivalently, an optimal Q-table.**
- And if we **have an optimal Q-function**, we
have an optimal policy, since we **know, for each state, the best action to take.**
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit3/link-value-policy.jpg" alt="Link value policy" width="100%"/>
But, in the beginning, our **Q-table is useless since it gives arbitrary values for each state-action pair (most of the time we initialize the Q-table to 0 values)**. But, as we explore the environment and update our Q-table it will give us a better and better approximation.
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/notebooks/unit2/q-learning.jpeg" alt="q-learning.jpeg" width="100%"/>
This is the Q-Learning pseudocode:
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit3/Q-learning-2.jpg" alt="Q-Learning" width="100%"/>
| huggingface/deep-rl-class/blob/main/units/en/unit2/q-learning-recap.mdx |
!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# Zero-shot object detection
[[open-in-colab]]
Traditionally, models used for [object detection](object_detection) require labeled image datasets for training,
and are limited to detecting the set of classes from the training data.
Zero-shot object detection is supported by the [OWL-ViT](../model_doc/owlvit) model which uses a different approach. OWL-ViT
is an open-vocabulary object detector. It means that it can detect objects in images based on free-text queries without
the need to fine-tune the model on labeled datasets.
OWL-ViT leverages multi-modal representations to perform open-vocabulary detection. It combines [CLIP](../model_doc/clip) with
lightweight object classification and localization heads. Open-vocabulary detection is achieved by embedding free-text queries with the text encoder of CLIP and using them as input to the object classification and localization heads.
associate images and their corresponding textual descriptions, and ViT processes image patches as inputs. The authors
of OWL-ViT first trained CLIP from scratch and then fine-tuned OWL-ViT end to end on standard object detection datasets using
a bipartite matching loss.
With this approach, the model can detect objects based on textual descriptions without prior training on labeled datasets.
In this guide, you will learn how to use OWL-ViT:
- to detect objects based on text prompts
- for batch object detection
- for image-guided object detection
Before you begin, make sure you have all the necessary libraries installed:
```bash
pip install -q transformers
```
## Zero-shot object detection pipeline
The simplest way to try out inference with OWL-ViT is to use it in a [`pipeline`]. Instantiate a pipeline
for zero-shot object detection from a [checkpoint on the Hugging Face Hub](https://huggingface.co/models?other=owlvit):
```python
>>> from transformers import pipeline
>>> checkpoint = "google/owlvit-base-patch32"
>>> detector = pipeline(model=checkpoint, task="zero-shot-object-detection")
```
Next, choose an image you'd like to detect objects in. Here we'll use the image of astronaut Eileen Collins that is
a part of the [NASA](https://www.nasa.gov/multimedia/imagegallery/index.html) Great Images dataset.
```py
>>> import skimage
>>> import numpy as np
>>> from PIL import Image
>>> image = skimage.data.astronaut()
>>> image = Image.fromarray(np.uint8(image)).convert("RGB")
>>> image
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/zero-sh-obj-detection_1.png" alt="Astronaut Eileen Collins"/>
</div>
Pass the image and the candidate object labels to look for to the pipeline.
Here we pass the image directly; other suitable options include a local path to an image or an image url. We also pass text descriptions for all items we want to query the image for.
```py
>>> predictions = detector(
... image,
... candidate_labels=["human face", "rocket", "nasa badge", "star-spangled banner"],
... )
>>> predictions
[{'score': 0.3571370542049408,
'label': 'human face',
'box': {'xmin': 180, 'ymin': 71, 'xmax': 271, 'ymax': 178}},
{'score': 0.28099656105041504,
'label': 'nasa badge',
'box': {'xmin': 129, 'ymin': 348, 'xmax': 206, 'ymax': 427}},
{'score': 0.2110239565372467,
'label': 'rocket',
'box': {'xmin': 350, 'ymin': -1, 'xmax': 468, 'ymax': 288}},
{'score': 0.13790413737297058,
'label': 'star-spangled banner',
'box': {'xmin': 1, 'ymin': 1, 'xmax': 105, 'ymax': 509}},
{'score': 0.11950037628412247,
'label': 'nasa badge',
'box': {'xmin': 277, 'ymin': 338, 'xmax': 327, 'ymax': 380}},
{'score': 0.10649408400058746,
'label': 'rocket',
'box': {'xmin': 358, 'ymin': 64, 'xmax': 424, 'ymax': 280}}]
```
Let's visualize the predictions:
```py
>>> from PIL import ImageDraw
>>> draw = ImageDraw.Draw(image)
>>> for prediction in predictions:
... box = prediction["box"]
... label = prediction["label"]
... score = prediction["score"]
... xmin, ymin, xmax, ymax = box.values()
... draw.rectangle((xmin, ymin, xmax, ymax), outline="red", width=1)
... draw.text((xmin, ymin), f"{label}: {round(score,2)}", fill="white")
>>> image
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/zero-sh-obj-detection_2.png" alt="Visualized predictions on NASA image"/>
</div>
## Text-prompted zero-shot object detection by hand
Now that you've seen how to use the zero-shot object detection pipeline, let's replicate the same
result manually.
Start by loading the model and associated processor from a [checkpoint on the Hugging Face Hub](https://huggingface.co/models?other=owlvit).
Here we'll use the same checkpoint as before:
```py
>>> from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection
>>> model = AutoModelForZeroShotObjectDetection.from_pretrained(checkpoint)
>>> processor = AutoProcessor.from_pretrained(checkpoint)
```
Let's take a different image to switch things up.
```py
>>> import requests
>>> url = "https://unsplash.com/photos/oj0zeY2Ltk4/download?ixid=MnwxMjA3fDB8MXxzZWFyY2h8MTR8fHBpY25pY3xlbnwwfHx8fDE2Nzc0OTE1NDk&force=true&w=640"
>>> im = Image.open(requests.get(url, stream=True).raw)
>>> im
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/zero-sh-obj-detection_3.png" alt="Beach photo"/>
</div>
Use the processor to prepare the inputs for the model. The processor combines an image processor that prepares the
image for the model by resizing and normalizing it, and a [`CLIPTokenizer`] that takes care of the text inputs.
```py
>>> text_queries = ["hat", "book", "sunglasses", "camera"]
>>> inputs = processor(text=text_queries, images=im, return_tensors="pt")
```
Pass the inputs through the model, post-process, and visualize the results. Since the image processor resized images before
feeding them to the model, you need to use the [`~OwlViTImageProcessor.post_process_object_detection`] method to make sure the predicted bounding
boxes have the correct coordinates relative to the original image:
```py
>>> import torch
>>> with torch.no_grad():
... outputs = model(**inputs)
... target_sizes = torch.tensor([im.size[::-1]])
... results = processor.post_process_object_detection(outputs, threshold=0.1, target_sizes=target_sizes)[0]
>>> draw = ImageDraw.Draw(im)
>>> scores = results["scores"].tolist()
>>> labels = results["labels"].tolist()
>>> boxes = results["boxes"].tolist()
>>> for box, score, label in zip(boxes, scores, labels):
... xmin, ymin, xmax, ymax = box
... draw.rectangle((xmin, ymin, xmax, ymax), outline="red", width=1)
... draw.text((xmin, ymin), f"{text_queries[label]}: {round(score,2)}", fill="white")
>>> im
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/zero-sh-obj-detection_4.png" alt="Beach photo with detected objects"/>
</div>
## Batch processing
You can pass multiple sets of images and text queries to search for different (or same) objects in several images.
Let's use both an astronaut image and the beach image together.
For batch processing, you should pass text queries as a nested list to the processor and images as lists of PIL images,
PyTorch tensors, or NumPy arrays.
```py
>>> images = [image, im]
>>> text_queries = [
... ["human face", "rocket", "nasa badge", "star-spangled banner"],
... ["hat", "book", "sunglasses", "camera"],
... ]
>>> inputs = processor(text=text_queries, images=images, return_tensors="pt")
```
Previously for post-processing you passed the single image's size as a tensor, but you can also pass a tuple, or, in case
of several images, a list of tuples. Let's create predictions for the two examples, and visualize the second one (`image_idx = 1`).
```py
>>> with torch.no_grad():
... outputs = model(**inputs)
... target_sizes = [x.size[::-1] for x in images]
... results = processor.post_process_object_detection(outputs, threshold=0.1, target_sizes=target_sizes)
>>> image_idx = 1
>>> draw = ImageDraw.Draw(images[image_idx])
>>> scores = results[image_idx]["scores"].tolist()
>>> labels = results[image_idx]["labels"].tolist()
>>> boxes = results[image_idx]["boxes"].tolist()
>>> for box, score, label in zip(boxes, scores, labels):
... xmin, ymin, xmax, ymax = box
... draw.rectangle((xmin, ymin, xmax, ymax), outline="red", width=1)
... draw.text((xmin, ymin), f"{text_queries[image_idx][label]}: {round(score,2)}", fill="white")
>>> images[image_idx]
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/zero-sh-obj-detection_4.png" alt="Beach photo with detected objects"/>
</div>
## Image-guided object detection
In addition to zero-shot object detection with text queries, OWL-ViT offers image-guided object detection. This means
you can use an image query to find similar objects in the target image.
Unlike text queries, only a single example image is allowed.
Let's take an image with two cats on a couch as a target image, and an image of a single cat
as a query:
```py
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image_target = Image.open(requests.get(url, stream=True).raw)
>>> query_url = "http://images.cocodataset.org/val2017/000000524280.jpg"
>>> query_image = Image.open(requests.get(query_url, stream=True).raw)
```
Let's take a quick look at the images:
```py
>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots(1, 2)
>>> ax[0].imshow(image_target)
>>> ax[1].imshow(query_image)
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/zero-sh-obj-detection_5.png" alt="Cats"/>
</div>
In the preprocessing step, instead of text queries, you now need to use `query_images`:
```py
>>> inputs = processor(images=image_target, query_images=query_image, return_tensors="pt")
```
For predictions, instead of passing the inputs to the model, pass them to [`~OwlViTForObjectDetection.image_guided_detection`]. Draw the predictions
as before except now there are no labels.
```py
>>> with torch.no_grad():
... outputs = model.image_guided_detection(**inputs)
... target_sizes = torch.tensor([image_target.size[::-1]])
... results = processor.post_process_image_guided_detection(outputs=outputs, target_sizes=target_sizes)[0]
>>> draw = ImageDraw.Draw(image_target)
>>> scores = results["scores"].tolist()
>>> boxes = results["boxes"].tolist()
>>> for box, score, label in zip(boxes, scores, labels):
... xmin, ymin, xmax, ymax = box
... draw.rectangle((xmin, ymin, xmax, ymax), outline="white", width=4)
>>> image_target
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/zero-sh-obj-detection_6.png" alt="Cats with bounding boxes"/>
</div>
If you'd like to interactively try out inference with OWL-ViT, check out this demo:
<iframe
src="https://adirik-owl-vit.hf.space"
frameborder="0"
width="850"
height="450"
></iframe>
| huggingface/transformers/blob/main/docs/source/en/tasks/zero_shot_object_detection.md |
Quiz
The best way to learn and [to avoid the illusion of competence](https://www.coursera.org/lecture/learning-how-to-learn/illusions-of-competence-BuFzf) **is to test yourself.** This will help you to find **where you need to reinforce your knowledge**.
### Q1: Which of the following interpretations of bias-variance tradeoff is the most accurate in the field of Reinforcement Learning?
<Question
choices={[
{
text: "The bias-variance tradeoff reflects how my model is able to generalize the knowledge to previously tagged data we give to the model during training time.",
explain: "This is the traditional bias-variance tradeoff in Machine Learning. In our specific case of Reinforcement Learning, we don't have previously tagged data, but only a reward signal.",
correct: false,
},
{
text: "The bias-variance tradeoff reflects how well the reinforcement signal reflects the true reward the agent should get from the enviromment",
explain: "",
correct: true,
},
]}
/>
### Q2: Which of the following statements are true, when talking about models with bias and/or variance in RL?
<Question
choices={[
{
text: "An unbiased reward signal returns rewards similar to the real / expected ones from the environment",
explain: "",
correct: true,
},
{
text: "A biased reward signal returns rewards similar to the real / expected ones from the environment",
explain: "If a reward signal is biased, it means the reward signal we get differs from the real reward we should be getting from an environment",
correct: false,
},
{
text: "A reward signal with high variance has much noise in it and gets affected by, for example, stochastic (non constant) elements in the environment",
explain: "",
correct: true,
},
{
text: "A reward signal with low variance has much noise in it and gets affected by, for example, stochastic (non constant) elements in the environment",
explain: "If a reward signal has low variance, then it's less affected by the noise of the environment and produce similar values regardless the random elements in the environment",
correct: false,
},
]}
/>
### Q3: Which of the following statements are true about Monte Carlo method?
<Question
choices={[
{
text: "It's a sampling mechanism, which means we don't analyze all the possible states, but a sample of those",
explain: "",
correct: true,
},
{
text: "It's very resistant to stochasticity (random elements in the trajectory)",
explain: "Monte Carlo randomly estimates everytime a sample of trajectories. However, even same trajectories can have different reward values if they contain stochastic elements",
correct: false,
},
{
text: "To reduce the impact of stochastic elements in Monte Carlo, we take `n` strategies and average them, reducing their individual impact",
explain: "",
correct: true,
},
]}
/>
### Q4: How would you describe, with your own words, the Actor-Critic Method (A2C)?
<details>
<summary>Solution</summary>
The idea behind Actor-Critic is that we learn two function approximations:
1. A `policy` that controls how our agent acts (π)
2. A `value` function to assist the policy update by measuring how good the action taken is (q)
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit8/step2.jpg" alt="Actor-Critic, step 2"/>
</details>
### Q5: Which of the following statements are true about the Actor-Critic Method?
<Question
choices={[
{
text: "The Critic does not learn any function during the training process",
explain: "Both the Actor and the Critic function parameters are updated during training time",
correct: false,
},
{
text: "The Actor learns a policy function, while the Critic learns a value function",
explain: "",
correct: true,
},
{
text: "It adds resistance to stochasticity and reduces high variance",
explain: "",
correct: true,
},
]}
/>
### Q6: What is `Advantage` in the A2C method?
<details>
<summary>Solution</summary>
Instead of using directly the Action-Value function of the Critic as it is, we could use an `Advantage` function. The idea behind an `Advantage` function is that we calculate the relative advantage of an action compared to the others possible at a state, averaging them.
In other words: how taking that action at a state is better compared to the average value of the state
<img src="https://huggingface.co/datasets/huggingface-deep-rl-course/course-images/resolve/main/en/unit8/advantage1.jpg" alt="Advantage in A2C"/>
</details>
Congrats on finishing this Quiz 🥳, if you missed some elements, take time to read the chapter again to reinforce (😏) your knowledge.
| huggingface/deep-rl-class/blob/main/units/en/unit6/quiz.mdx |
Access and read Logs
Hugging Face Endpoints provides access to the logs of your Endpoints through the UI in the “Logs” tab of your Endpoint.
You will have access to the build logs of your Image artifacts as well as access to the Container Logs during inference.
<img src="https://raw.githubusercontent.com/huggingface/hf-endpoints-documentation/main/assets/9_selection.png" alt="select logs" />
The Container Logs are only available when your Endpoint is in the “Running” state.
_Note: If your Endpoint creation is in the “Failed” state, you can check the Build Logs to see what the reason was, e.g. wrong version of a dependency, etc._
**Build Logs:**
<img src="https://raw.githubusercontent.com/huggingface/hf-endpoints-documentation/main/assets/9_build_logs.png" alt="build logs" />
**Container Logs:**
<img src="https://raw.githubusercontent.com/huggingface/hf-endpoints-documentation/main/assets/9_logs.png" alt="container logs" />
| huggingface/hf-endpoints-documentation/blob/main/docs/source/guides/logs.mdx |
Gradio Demo: examples_component
```
!pip install -q gradio
```
```
# Downloading files from the demo repo
import os
os.mkdir('images')
!wget -q -O images/cheetah1.jpg https://github.com/gradio-app/gradio/raw/main/demo/examples_component/images/cheetah1.jpg
!wget -q -O images/lion.jpg https://github.com/gradio-app/gradio/raw/main/demo/examples_component/images/lion.jpg
!wget -q -O images/lion.webp https://github.com/gradio-app/gradio/raw/main/demo/examples_component/images/lion.webp
!wget -q -O images/logo.png https://github.com/gradio-app/gradio/raw/main/demo/examples_component/images/logo.png
```
```
import gradio as gr
import os
def flip(i):
return i.rotate(180)
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
img_i = gr.Image(label="Input Image", type="pil")
with gr.Column():
img_o = gr.Image(label="Output Image")
with gr.Row():
btn = gr.Button(value="Flip Image")
btn.click(flip, inputs=[img_i], outputs=[img_o])
gr.Examples(
[
os.path.join(os.path.abspath(''), "images/cheetah1.jpg"),
os.path.join(os.path.abspath(''), "images/lion.jpg"),
],
img_i,
img_o,
flip,
)
demo.launch()
```
| gradio-app/gradio/blob/main/demo/examples_component/run.ipynb |
Additional Readings
These are **optional readings** if you want to go deeper.
## Introduction to Policy Optimization
- [Part 3: Intro to Policy Optimization - Spinning Up documentation](https://spinningup.openai.com/en/latest/spinningup/rl_intro3.html)
## Policy Gradient
- [https://johnwlambert.github.io/policy-gradients/](https://johnwlambert.github.io/policy-gradients/)
- [RL - Policy Gradient Explained](https://jonathan-hui.medium.com/rl-policy-gradients-explained-9b13b688b146)
- [Chapter 13, Policy Gradient Methods; Reinforcement Learning, an introduction by Richard Sutton and Andrew G. Barto](http://incompleteideas.net/book/RLbook2020.pdf)
## Implementation
- [PyTorch Reinforce implementation](https://github.com/pytorch/examples/blob/main/reinforcement_learning/reinforce.py)
- [Implementations from DDPG to PPO](https://github.com/MrSyee/pg-is-all-you-need)
| huggingface/deep-rl-class/blob/main/units/en/unit4/additional-readings.mdx |
!--Copyright 2022 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# Quantization
## ORTQuantizer
[[autodoc]] onnxruntime.quantization.ORTQuantizer
- all
| huggingface/optimum/blob/main/docs/source/onnxruntime/package_reference/quantization.mdx |
Gradio Demo: number_component
```
!pip install -q gradio
```
```
import gradio as gr
with gr.Blocks() as demo:
gr.Number()
demo.launch()
```
| gradio-app/gradio/blob/main/demo/number_component/run.ipynb |
Gradio Demo: map_airbnb
### Display an interactive map of AirBnB locations with Plotly. Data is hosted on HuggingFace Datasets.
```
!pip install -q gradio plotly
```
```
import gradio as gr
import plotly.graph_objects as go
from datasets import load_dataset
dataset = load_dataset("gradio/NYC-Airbnb-Open-Data", split="train")
df = dataset.to_pandas()
def filter_map(min_price, max_price, boroughs):
filtered_df = df[(df['neighbourhood_group'].isin(boroughs)) &
(df['price'] > min_price) & (df['price'] < max_price)]
names = filtered_df["name"].tolist()
prices = filtered_df["price"].tolist()
text_list = [(names[i], prices[i]) for i in range(0, len(names))]
fig = go.Figure(go.Scattermapbox(
customdata=text_list,
lat=filtered_df['latitude'].tolist(),
lon=filtered_df['longitude'].tolist(),
mode='markers',
marker=go.scattermapbox.Marker(
size=6
),
hoverinfo="text",
hovertemplate='<b>Name</b>: %{customdata[0]}<br><b>Price</b>: $%{customdata[1]}'
))
fig.update_layout(
mapbox_style="open-street-map",
hovermode='closest',
mapbox=dict(
bearing=0,
center=go.layout.mapbox.Center(
lat=40.67,
lon=-73.90
),
pitch=0,
zoom=9
),
)
return fig
with gr.Blocks() as demo:
with gr.Column():
with gr.Row():
min_price = gr.Number(value=250, label="Minimum Price")
max_price = gr.Number(value=1000, label="Maximum Price")
boroughs = gr.CheckboxGroup(choices=["Queens", "Brooklyn", "Manhattan", "Bronx", "Staten Island"], value=["Queens", "Brooklyn"], label="Select Boroughs:")
btn = gr.Button(value="Update Filter")
map = gr.Plot()
demo.load(filter_map, [min_price, max_price, boroughs], map)
btn.click(filter_map, [min_price, max_price, boroughs], map)
if __name__ == "__main__":
demo.launch()
```
| gradio-app/gradio/blob/main/demo/map_airbnb/run.ipynb |
Res2Net
**Res2Net** is an image model that employs a variation on bottleneck residual blocks, [Res2Net Blocks](https://paperswithcode.com/method/res2net-block). The motivation is to be able to represent features at multiple scales. This is achieved through a novel building block for CNNs that constructs hierarchical residual-like connections within one single residual block. This represents multi-scale features at a granular level and increases the range of receptive fields for each network layer.
## How do I use this model on an image?
To load a pretrained model:
```py
>>> import timm
>>> model = timm.create_model('res2net101_26w_4s', pretrained=True)
>>> model.eval()
```
To load and preprocess the image:
```py
>>> import urllib
>>> from PIL import Image
>>> from timm.data import resolve_data_config
>>> from timm.data.transforms_factory import create_transform
>>> config = resolve_data_config({}, model=model)
>>> transform = create_transform(**config)
>>> url, filename = ("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
>>> urllib.request.urlretrieve(url, filename)
>>> img = Image.open(filename).convert('RGB')
>>> tensor = transform(img).unsqueeze(0) # transform and add batch dimension
```
To get the model predictions:
```py
>>> import torch
>>> with torch.no_grad():
... out = model(tensor)
>>> probabilities = torch.nn.functional.softmax(out[0], dim=0)
>>> print(probabilities.shape)
>>> # prints: torch.Size([1000])
```
To get the top-5 predictions class names:
```py
>>> # Get imagenet class mappings
>>> url, filename = ("https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt", "imagenet_classes.txt")
>>> urllib.request.urlretrieve(url, filename)
>>> with open("imagenet_classes.txt", "r") as f:
... categories = [s.strip() for s in f.readlines()]
>>> # Print top categories per image
>>> top5_prob, top5_catid = torch.topk(probabilities, 5)
>>> for i in range(top5_prob.size(0)):
... print(categories[top5_catid[i]], top5_prob[i].item())
>>> # prints class names and probabilities like:
>>> # [('Samoyed', 0.6425196528434753), ('Pomeranian', 0.04062102362513542), ('keeshond', 0.03186424449086189), ('white wolf', 0.01739676296710968), ('Eskimo dog', 0.011717947199940681)]
```
Replace the model name with the variant you want to use, e.g. `res2net101_26w_4s`. You can find the IDs in the model summaries at the top of this page.
To extract image features with this model, follow the [timm feature extraction examples](../feature_extraction), just change the name of the model you want to use.
## How do I finetune this model?
You can finetune any of the pre-trained models just by changing the classifier (the last layer).
```py
>>> model = timm.create_model('res2net101_26w_4s', pretrained=True, num_classes=NUM_FINETUNE_CLASSES)
```
To finetune on your own dataset, you have to write a training loop or adapt [timm's training
script](https://github.com/rwightman/pytorch-image-models/blob/master/train.py) to use your dataset.
## How do I train this model?
You can follow the [timm recipe scripts](../scripts) for training a new model afresh.
## Citation
```BibTeX
@article{Gao_2021,
title={Res2Net: A New Multi-Scale Backbone Architecture},
volume={43},
ISSN={1939-3539},
url={http://dx.doi.org/10.1109/TPAMI.2019.2938758},
DOI={10.1109/tpami.2019.2938758},
number={2},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
publisher={Institute of Electrical and Electronics Engineers (IEEE)},
author={Gao, Shang-Hua and Cheng, Ming-Ming and Zhao, Kai and Zhang, Xin-Yu and Yang, Ming-Hsuan and Torr, Philip},
year={2021},
month={Feb},
pages={652–662}
}
```
<!--
Type: model-index
Collections:
- Name: Res2Net
Paper:
Title: 'Res2Net: A New Multi-scale Backbone Architecture'
URL: https://paperswithcode.com/paper/res2net-a-new-multi-scale-backbone
Models:
- Name: res2net101_26w_4s
In Collection: Res2Net
Metadata:
FLOPs: 10415881200
Parameters: 45210000
File Size: 181456059
Architecture:
- Batch Normalization
- Convolution
- Global Average Pooling
- ReLU
- Res2Net Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x Titan Xp GPUs
ID: res2net101_26w_4s
LR: 0.1
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L152
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net101_26w_4s-02a759a1.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.19%
Top 5 Accuracy: 94.43%
- Name: res2net50_14w_8s
In Collection: Res2Net
Metadata:
FLOPs: 5403546768
Parameters: 25060000
File Size: 100638543
Architecture:
- Batch Normalization
- Convolution
- Global Average Pooling
- ReLU
- Res2Net Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x Titan Xp GPUs
ID: res2net50_14w_8s
LR: 0.1
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L196
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_14w_8s-6527dddc.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.14%
Top 5 Accuracy: 93.86%
- Name: res2net50_26w_4s
In Collection: Res2Net
Metadata:
FLOPs: 5499974064
Parameters: 25700000
File Size: 103110087
Architecture:
- Batch Normalization
- Convolution
- Global Average Pooling
- ReLU
- Res2Net Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x Titan Xp GPUs
ID: res2net50_26w_4s
LR: 0.1
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L141
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_26w_4s-06e79181.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.99%
Top 5 Accuracy: 93.85%
- Name: res2net50_26w_6s
In Collection: Res2Net
Metadata:
FLOPs: 8130156528
Parameters: 37050000
File Size: 148603239
Architecture:
- Batch Normalization
- Convolution
- Global Average Pooling
- ReLU
- Res2Net Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x Titan Xp GPUs
ID: res2net50_26w_6s
LR: 0.1
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L163
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_26w_6s-19041792.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 78.57%
Top 5 Accuracy: 94.12%
- Name: res2net50_26w_8s
In Collection: Res2Net
Metadata:
FLOPs: 10760338992
Parameters: 48400000
File Size: 194085165
Architecture:
- Batch Normalization
- Convolution
- Global Average Pooling
- ReLU
- Res2Net Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x Titan Xp GPUs
ID: res2net50_26w_8s
LR: 0.1
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L174
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_26w_8s-2c7c9f12.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 79.19%
Top 5 Accuracy: 94.37%
- Name: res2net50_48w_2s
In Collection: Res2Net
Metadata:
FLOPs: 5375291520
Parameters: 25290000
File Size: 101421406
Architecture:
- Batch Normalization
- Convolution
- Global Average Pooling
- ReLU
- Res2Net Block
Tasks:
- Image Classification
Training Techniques:
- SGD with Momentum
- Weight Decay
Training Data:
- ImageNet
Training Resources: 4x Titan Xp GPUs
ID: res2net50_48w_2s
LR: 0.1
Epochs: 100
Crop Pct: '0.875'
Momentum: 0.9
Batch Size: 256
Image Size: '224'
Weight Decay: 0.0001
Interpolation: bilinear
Code: https://github.com/rwightman/pytorch-image-models/blob/d8e69206be253892b2956341fea09fdebfaae4e3/timm/models/res2net.py#L185
Weights: https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-res2net/res2net50_48w_2s-afed724a.pth
Results:
- Task: Image Classification
Dataset: ImageNet
Metrics:
Top 1 Accuracy: 77.53%
Top 5 Accuracy: 93.56%
--> | huggingface/pytorch-image-models/blob/main/hfdocs/source/models/res2net.mdx |
ow to slice and dice a dataset. Most of the time, the data you work with won’t be perfectly prepared for training models. In this video we’ll explore various features that Datasets provides to clean up your datasets. The Datasets library provides several built-in methods that allow you to wrangle your data. In this video we'll see how you can shuffle and split your data, select the rows you're interested in, tweak the columns, and apply processing functions with the map() method. Let's start with shuffling. It is generally a good idea to apply shuffling to the training set so that your model doesn't learn any artificial ordering in the data. If you want to shuffle the whole dataset, you can apply the appropriately named shuffle() method to your dataset. You can see an example of this method in action here, where we've downloaded the training split of the SQUAD dataset and shuffled all the rows randomly.Another way to shuffle the data is to create random train and test splits. This can be useful if you have to create your own test splits from raw data. To do this, you just apply the train_test_split method and specify how large the test split should be. In this example, we've specified that the test set should be 10% of the total dataset size. You can see that the output of train_test_split is a DatasetDict object, whose keys correspond to the new splits. Now that we know how to shuffle a dataset, let's take a look at returning the rows we're interested in. The most common way to do this is with the select method. This method expects a list or generator of the dataset's indices, and will then return a new Dataset object containing just those rows. If you want to create a random sample of rows, you can do this by chaining the shuffle and select methods together. In this example, we've created a sample of 5 elements from the SQuAD dataset. The last way to pick out specific rows in a dataset is by applying the filter method. This method checks whether each rows fulfills some condition or not. For example, here we've created a small lambda function that checks whether the title starts with the letter "L". Once we apply this function with the filter method, we get a subset of the data consisting of just these titles. So far we've been talking about the rows of a dataset, but what about the columns? The Datasets library has two main methods for transforming columns: a rename_column method to change the name of a column, and a remove_columns method to delete them. You can see examples of both these method here. Some datasets have nested columns and you can expand these by applying the flatten method. For example in the SQUAD dataset, the answers column contains a text and answer_start field. If we want to promote them to their own separate columns, we can apply flatten as shown here. Of course, no discussion of the Datasets library would be complete without mentioning the famous map method. This method applies a custom processing function to each row in the dataset. For example,here we first define a lowercase_title function that simply lowercases the text in the title column and then we feed that to the map method and voila! we now have lowercase titles. The map method can also be used to feed batches of rows to the processing function. This is especially useful for tokenization, where the tokenizers are backed by the Tokenizers library can use fast multithreading to process batches in parallel. | huggingface/course/blob/main/subtitles/en/raw/chapter5/03a_slice-and-dice.md |
Gradio Demo: question-answering
```
!pip install -q gradio torch transformers
```
```
import gradio as gr
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "deepset/roberta-base-squad2"
nlp = pipeline("question-answering", model=model_name, tokenizer=model_name)
context = "The Amazon rainforest, also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 square kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations. The majority of the forest is contained within Brazil, with 60% of the rainforest, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador, Bolivia, Guyana, Suriname and French Guiana. The Amazon represents over half of the planet's remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees divided into 16,000 species."
question = "Which continent is the Amazon rainforest in?"
def predict(context, question):
res = nlp({"question": question, "context": context})
return res["answer"], res["score"]
gr.Interface(
predict,
inputs=[
gr.Textbox(lines=7, value=context, label="Context Paragraph"),
gr.Textbox(lines=2, value=question, label="Question"),
],
outputs=[gr.Textbox(label="Answer"), gr.Textbox(label="Score")],
).launch()
```
| gradio-app/gradio/blob/main/demo/question-answering/run.ipynb |
!--Copyright 2023 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
-->
# IP-Adapter
[IP-Adapter](https://hf.co/papers/2308.06721) is a lightweight adapter that enables prompting a diffusion model with an image. This method decouples the cross-attention layers of the image and text features. The image features are generated from an image encoder. Files generated from IP-Adapter are only ~100MBs.
<Tip>
Learn how to load an IP-Adapter checkpoint and image in the [IP-Adapter](../../using-diffusers/loading_adapters#ip-adapter) loading guide.
</Tip>
## IPAdapterMixin
[[autodoc]] loaders.ip_adapter.IPAdapterMixin
| huggingface/diffusers/blob/main/docs/source/en/api/loaders/ip_adapter.md |
!--⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.
-->
# Configuration
[`PeftConfigMixin`] is the base configuration class for storing the adapter configuration of a [`PeftModel`], and [`PromptLearningConfig`] is the base configuration class for soft prompt methods (p-tuning, prefix tuning, and prompt tuning). These base classes contain methods for saving and loading model configurations from the Hub, specifying the PEFT method to use, type of task to perform, and model configurations like number of layers and number of attention heads.
## PeftConfigMixin
[[autodoc]] config.PeftConfigMixin
- all
## PeftConfig
[[autodoc]] PeftConfig
- all
## PromptLearningConfig
[[autodoc]] PromptLearningConfig
- all
| huggingface/peft/blob/main/docs/source/package_reference/config.md |
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