[**中文**](./README_CN.md) # VisualDL Guide ### Overview VisualDL is a visualization tool designed for Deep Learning. VisualDL provides a variety of charts to show the trends of parameters. It enables users to understand the training process and model structures of Deep Learning models more clearly and intuitively so as to optimize models efficiently. Currently, VisualDL provides **Fifteen Components**: scalar, image, audio, text, graph(dynamic, static), histogram, pr curve, ROC curve, high dimensional and hyperparameters, profiler, x2paddle, fastdeployserver, fastdeployclient. VisualDL iterates rapidly and new functions will be continuously added. | Component Name | Display Chart | Function | | :----------------------------------------------------------: | :---------------------------: | :----------------------------------------------------------- | | [ Scalar](#Scalar--Line-Chart) | Line Chart | Display scalar data such as loss and accuracy dynamically. | | [Image](#Image--Image-Visualization) | Image Visualization | Display images, visualizing the input and the output and making it easy to view the changes in the intermediate process. | | [Audio](#Audio--Audio-Play) | Audio Play | Play the audio during the training process, making it easy to monitor the process of speech recognition and text-to-speech. | | [Text](#Text) | Text Visualization | Visualize the text output of NLP models within any stage, aiding developers to compare the changes of outputs so as to deeply understand the training process and simply evaluate the performance of the model. | | [Graph](#Graph--Network-Structure) | Network Structure | Visualize network structures, node attributes and data flow, assisting developers to learn and to optimize network structures. | | [Histogram](#Histogram--Distribution-of-Tensors) | Distribution of Tensors | Present the changes of distributions of tensors, such as weights/gradients/bias, during the training process. | | [PR Curve](#PR-Curve) | Precision & Recall Curve | Display precision-recall curves across training steps, clarifying the tradeoff between precision and recall when comparing models. | | [ROC Curve](#ROC-Curve) | Receiver Operating Characteristic curve | Show the performance of a classification model at all classification thresholds. | | [High Dimensional](#High-Dimensional--Data-Dimensionality-Reduction) | Data Dimensionality Reduction | Project high-dimensional data into 2D/3D space for embedding visualization, making it convenient to observe the correlation between data. | | [Hyper Parameters](#hyperparameters--hyperparameter-visualization) | HyperParameter Visualization | Visualize the relationship between hyperparameters and model metrics (such as accuracy and loss) in a rich view, helping you identify the best hyperparameters in an efficient way. | |[Profiler](#Profiler--profiling-data-visualization)| Profiling data visualization | Analyse profiling data exported by paddle, helping users identify program bottlenecks and optimize performance | |[X2Paddle](#X2Paddle--model-format-transformation)| Model conversion| Convert onnx model to paddle format | |[FastDeployServer](#fastdeployserver--fastdeploy-serving-deployment-visualization) | fastdeploy serving deployment visualization | Provide the functions of loading and editing the model repository, fastdeployserver service management and monitoring | |[FastDeployClient](#fastdeployclient--fastdeploy-client-for-request-visualization)| fastdeploy client for request visualization | Access the fastdeployserver service, helping users visualize prediction requests and results | At the same time, VisualDL provides [VDL.service](#vdlservice) , which allows developers to easily save, track and share visualization results of experiments with anyone for free. ## Scalar--Line Chart ### Introduction The data type of the input is scalar values. Scalar is used to present the training parameters in the form of a line chart. By using Scalar to record loss and accuracy, developers are able to track the trend of changes easily through line charts. ### Record Interface The interface of the Scalar is shown as follows: ```python add_scalar(tag, value, step, walltime=None) ``` The interface parameters are described as follows: | parameter | format | meaning | | --------- | ------ | ------------------------------------------------------------ | | tag | string | Record the name of the scalar data，e.g.train/loss. Notice that the name cannot contain `%` | | value | float | Record the data, can't be `None` | | step | int | Record the training steps. The data will be sampled, meaning that only part of data will be displayed. (the sampling algorithm is reservoir sampling, details can be refered to [VisualDL sampling algorithm](../faq.md/#what-are-the-sampling-rules-of-visualdl)) | | walltime | int | Record the time-stamp of the data, the default is the current time-stamp | *Note that the rules of specifying tags (e.g.train/acc) are: 1. The tag before the first `/` is the parent tag and serves as the tag of the same raw 2. The tag after the first `/` is a child tag, the charts with child tag will be displayed under the parent tag. The data of the same parent tag but different child tags will be displayed in the same column, but not in the same picture. 3. Users can use multiple `/`, but the tag of a raw is the parent tag--the tag before the first `/` Here are three examples: - When 'train' is created as the parent tag and 'acc' and 'loss' are created as child tags：`train/acc`、 `train/loss`，the tag of a raw is 'train' , which includes two sub charts--'acc' and 'loss':

- When 'train' is created as the parent tag, and 'test/acc' and 'test/loss' are created as child tags：`train/test/acc`、 `train/test/loss`, the tag of a raw is 'train', which includes two sub charts--'test/acc' and 'test/loss':

- When two parent tags are created：`acc`、 `loss`， two rows of charts are named as 'acc' and 'loss' respectively.

### Demo - Fundamental Methods The following shows an example of using Scalar to record data, and the script can be found in [Scalar Demo](../../demo/components/scalar_test.py) ```python from visualdl import LogWriter if __name__ == '__main__': value = [i/1000.0 for i in range(1000)] # initialize a recorder with LogWriter(logdir="./log/scalar_test/train") as writer: for step in range(1000): # add accuracy with tag of 'acc' to the recorder writer.add_scalar(tag="acc", step=step, value=value[step]) # add loss with tag of 'loss' to the recorder writer.add_scalar(tag="loss", step=step, value=1/(value[step] + 1)) ``` After running the above program, developers can launch the panel by: ```shell visualdl --logdir ./log --port 8080 ``` Then, open the browser and enter the address: `http://127.0.0.1:8080`to view line charts:

- Advanced Usage--Comparison of Multiple Experiments The following shows the comparison of multiple sets of experiments using Scalar. There are two steps to achieve this function: 1. Create sub-log files to store the parameter data of each group of experiments 2. When recording data to the scalar component，developers can compare **the same type of parameters for different experiments** by **using the same tag**. Note that the log files you want to display must be placed in different directories because only one log file in a directory is valid and displayed. ```python from visualdl import LogWriter if __name__ == '__main__': value = [i/1000.0 for i in range(1000)] # Step 1: Create a parent folder: log and a child folder: scalar_test with LogWriter(logdir="./log/scalar_test") as writer: for step in range(1000): # Step 2: Add data with tag train/acc to the recorder writer.add_scalar(tag="train/acc", step=step, value=value[step]) # Step 2: Add data with tag train/loss to the recorder writer.add_scalar(tag="train/loss", step=step, value=1/(value[step] + 1)) # Step 1: Create a second child folder: scalar_test2 value = [i/500.0 for i in range(1000)] with LogWriter(logdir="./log/scalar_test2") as writer: for step in range(1000): # Step 2: Add the accuracy data of scalar_test2 under the same name `train/acc` writer.add_scalar(tag="train/acc", step=step, value=value[step]) # Step 2: Add the loss data of scalar_test2 under the same name as `train/loss` writer.add_scalar(tag="train/loss", step=step, value=1/(value[step] + 1)) ``` After running the above program, developers can launch the panel by: ```shell visualdl --logdir ./log --port 8080 ``` Then, open the browser and enter the address: `http://127.0.0.1:8080` to view line charts:

*For more specific details of how to compare multiple experiments, pleas refer to the project on AI Studio：[VisualDL 2.0--Visualization of eye disease recognition training](https://aistudio.baidu.com/aistudio/projectdetail/502834) It can be seen that the data of different experiments (determined by the path) are displayed in different pictures, and the data of the same tag is displayed on the same picture for comparison. ### Functional Instruction * Developers are allowed to zoom in, restore, transform of the coordinate axis (y-axis logarithmic coordinates), download the line chart.

* Details can be shown by hovering on specific data points.

* Developers can find target scalar charts by searching corresponded tags.

* Specific runs can be selected by searching for the corresponded experiment tags.

* Display the global extrema

* Only display smoothed data

* There are three measurement scales of X axis 1. Step: number of iterations 2. Walltime: absolute training time 3. Relative: training time

* The smoothness of the curve can be adjusted to better show the change of the overall trend.

## Image--Image Visualization ### Introduction The Image is used to present the change of image data during training. Developers can view images in different training stages by adding few lines of codes to record images in a log file. ### Record Interface The interface of the Image is shown as follows: ```python add_image(tag, img, step, walltime=None, dataformats="HWC") ``` The interface parameters are described as follows: | parameter | format | meaning | | --------- | ------------- | ------------------------------------------------------------ | | tag | string | Record the name of the image data，e.g.train/loss. Notice that the name cannot contain `%` | | img | numpy.ndarray | Images in ndarray format. The default `HWC` format dimension is [h, w, c], h and w are the height and width of the images, and c is the number of channels, which can be 1, 3, 4. Floating point data will be clipped to the range[0, 1), and note that the image data cannot be None. | | step | int | Record the training steps | | walltime | int | Record the time-stamp of the data, the default is the current time-stamp | | dataformats| string | Format of image，include `NCHW`、`NHWC`、`HWC`、`CHW`、`HW`，default is `HWC`. It will be converted to `HWC` format when stored.| ### Demo The following shows an example of using Image to record data, and the script can be found in [Image Demo](https://github.com/PaddlePaddle/VisualDL/blob/develop/demo/components/image_test.py). ```python import numpy as np from PIL import Image from visualdl import LogWriter def random_crop(img): """get random 100x100 slices of image """ img = Image.open(img) w, h = img.size random_w = np.random.randint(0, w - 100) random_h = np.random.randint(0, h - 100) r = img.crop((random_w, random_h, random_w + 100, random_h + 100)) return np.asarray(r) if __name__ == '__main__': # initialize a recorder with LogWriter(logdir="./log/image_test/train") as writer: for step in range(6): # add image data writer.add_image(tag="eye", img=random_crop("../../docs/images/eye.jpg"), step=step) ``` After running the above program, developers can launch the panel by: ```shell visualdl --logdir ./log --port 8080 ``` Then, open the browser and enter the address: `http://127.0.0.1:8080`to view:

### Functional Instructions - Developers can find target images by searching corresponded tags.

- Developers are allowed to view image data under different iterations by scrolling the Step/iteration slider.

## Audio--Audio Play ### Introduction Audio aims to allow developers to listen to the audio in real-time during the training process, helping developers to monitor the process of speech recognition and text-to-speech. ### Record Interface The interface of the Image is shown as follows: ```python add_audio(tag, audio_array, step, sample_rate) ``` The interface parameters are described as follows: | parameter | format | meaning | | --------- | ------------- | ------------------------------------------------------------ | | tag | string | Record the name of the audio，e.g.audoi/sample. Notice that the name cannot contain `%` | | audio_arry | numpy.ndarray | Audio in ndarray format, whose elements are float values, and the range should be normalized in [-1, 1] | | step | int | Record the training steps | | sample_rate | int | Sample rate，the default sampling rate is 8000. **Please note that the rate should be the rate of the original audio** | ### Demo The following shows an example of using Audio to record data, and the script can be found in [Audio Demo](https://github.com/PaddlePaddle/VisualDL/blob/develop/demo/components/audio_test.py). ```python from visualdl import LogWriter from scipy.io import wavfile if __name__ == '__main__': with LogWriter(logdir="./log/audio_test/train") as writer: sample_rate, audio_data = wavfile.read('./test.wav') writer.add_audio(tag="audio_tag", audio_array=audio_data, step=0, sample_rate=sample_rate) ``` After running the above program, developers can launch the panel by: ```shell visualdl --logdir ./log --port 8080 ``` Then, open the browser and enter the address: `http://127.0.0.1:8080`to view:

### Functional Instructions - Developers can find the target audio by searching corresponded tags.

- Developers are allowed to listen to the audio under different iterations by scrolling the Step/iteration slider.

- Play/Pause the audio

- Adjust the volume

- Download the audio

## Text ### Introduction visualizes the text output of NLP models within any stage, aiding developers to compare the changes of outputs so as to deeply understand the training process and simply evaluate the performance of the model. ### Record Interface The interface of the Text is shown as follows: ```python add_text(tag, text_string, step=None, walltime=None) ``` The interface parameters are described as follows: | parameter | format | meaning | | -------------- | --------------------- | ------------------------------------------- | | tag | string | Record the name of the text data，e.g.train/loss. Notice that the name cannot contain `%` | | text_string | string | Value of text | | step | int | Record the training steps | | walltime | int | Record the time-stamp of the data, and the default is the current time-stamp | ### Demo The following shows an example of how to use Text component, and script can be found in [Text Demo](../../demo/components/text_test.py) ```python from visualdl import LogWriter if __name__ == '__main__': texts = [ '上联: 众 佛 群 灵 光 圣 地 下联: 众 生 一 念 证 菩 提', '上联: 乡 愁 何 处 解 下联: 故 事 几 时 休', '上联: 清 池 荷 试 墨 下联: 碧 水 柳 含 情', '上联: 既 近 浅 流 安 笔 砚 下联: 欲 将 直 气 定 乾 坤', '上联: 日 丽 萱 闱 祝 无 量 寿 下联: 月 明 桂 殿 祝 有 余 龄', '上联: 一 地 残 红 风 拾 起 下联: 半 窗 疏 影 月 窥 来' ] with LogWriter(logdir="./log/text_test/train") as writer: for step in range(len(texts)): writer.add_text(tag="output", step=step, text_string=texts[step]) ``` After running the above program, developers can launch the panel by: ```shell visualdl --logdir ./log --port 8080 ``` Then, open the browser and enter the address`http://127.0.0.1:8080` to view:

### Functional Instrucions - Developers can find the target text by searching corresponded tags.

- Developers can find the target runs by searching corresponded tags.

- Developers can fold the tab of text.

## Graph--Network Structure ### Introduction Graph can visualize the network structure of the model by one click. It enables developers to view the model attributes, node information, searching node and so on. These functions help developers analyze model structures and understand the directions of data flow quickly. ### Record Interface The interface of the Graph is shown as follows: ```python add_graph(model, input_spec, verbose=False): ``` The interface parameters are described as follows: | parameter | format | meaning | | -------------- | --------------------- | ------------------------------------------- | | model | paddle.nn.Layer | Dynamic model of paddle | | input_spec | list\[paddle.static.InputSpec\|Tensor\] | Describes the input of the saved model's [forward arguments](https://www.paddlepaddle.org.cn/documentation/docs/zh/api/paddle/static/InputSpec_cn.html) | | verbose | bool | Whether to print graph statistic information in console. | **Note** If you want to use add_graph interface, paddle package is required. Please refer to website of [PaddlePaddle](https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/en/install/pip/linux-pip_en.html)。 ### Demo The following shows an example of how to use Graph component, and script can be found in [Graph Demo](https://github.com/PaddlePaddle/VisualDL/blob/develop/demo/components/graph_test.py) There are two methods to launch this component: ```python import paddle import paddle.nn as nn import paddle.nn.functional as F from visualdl import LogWriter class MyNet(nn.Layer): def __init__(self): super(MyNet, self).__init__() self.conv1 = nn.Conv2D( in_channels=1, out_channels=20, kernel_size=5, stride=1, padding=2) self.max_pool1 = nn.MaxPool2D(kernel_size=2, stride=2) self.conv2 = nn.Conv2D( in_channels=20, out_channels=20, kernel_size=5, stride=1, padding=2) self.max_pool2 = nn.MaxPool2D(kernel_size=2, stride=2) self.fc = nn.Linear(in_features=980, out_features=10) def forward(self, inputs): x = self.conv1(inputs) x = F.relu(x) x = self.max_pool1(x) x = self.conv2(x) x = F.relu(x) x = self.max_pool2(x) x = paddle.reshape(x, [x.shape[0], -1]) x = self.fc(x) return x net = MyNet() with LogWriter(logdir="./log/graph_test/") as writer: writer.add_graph( model=net, input_spec=[paddle.static.InputSpec([-1, 1, 28, 28], 'float32')], verbose=True) ``` After running the above program, developers can launch the panel by: ```shell visualdl --logdir ./log/graph_test/ --port 8080 ``` Then, open the browser and enter the address`http://127.0.0.1:8080` to view:

**Note** We provide option --model to specify model structure file in previous versions, and this option is still supported now. You can specify model exported by `add_graph` interface ("vdlgraph" contained in filename), which will be shown in dynamic graph page, and we use string "manual_input_model" in the page to denote the model you specify by this option. Other supported file formats are presented in static graph page. For example ```shell visualdl --model ./log/model.pdmodel --port 8080 ``` which will be shown in static graph page. And ```shell visualdl --model ./log/vdlgraph.1655783158.log --port 8080 ``` shown in dynamic graph page. ### Functional Instructions Graph page is divided into dynamic and static version currently. Dynamic version is used to visualize dynamic model of paddle, which is exported by add_graph interface. The other is used to visualize static model of paddle, which is exported by [paddle.jit.save](https://www.paddlepaddle.org.cn/documentation/docs/en/api/paddle/jit/save_en.html) interface and other supported formats.

**Common functions** - Developers are allowed to drag the model up and down，left and right，zoom in and zoom out.

- Search to locate the specific node

- Click to view the model properties

- Display the model information by selecting corresponded attributes

- Files can be ex as PNG or SVG format

- Click nodes to view attribute information

- Switch the model by one-click

**Specific feature in dynamic version** - Fold and unfold one node

- Fold and unfold all nodes

- Link api specification of paddle If you use paddle.nn components to construct your network model, you can use alt+click mouse to direct to corresponding api specification.

**Specific feature in static version** - Upload the model file by one-click - Supported model：PaddlePaddle、ONNX、Keras、Core ML、Caffe、Caffe2、Darknet、MXNet、ncnn、TensorFlow Lite - Experimental supported model：TorchScript、PyTorch、Torch、 ArmNN、BigDL、Chainer、CNTK、Deeplearning4j、MediaPipe、ML.NET、MNN、OpenVINO、Scikit-learn、Tengine、TensorFlow.js、TensorFlow

## Histogram--Distribution of Tensors ### Introduction Histogram displays how the trend of tensors (weight, bias, gradient, etc.) changes during the training process in the form of histogram. Developers can adjust the model structures accurately by having an in-depth understanding of the effect of each layer. ### Record Interface The interface of the Histogram is shown as follows: ```python add_histogram(tag, values, step, walltime=None, buckets=10) ``` The interface parameters are described as follows: | parameter | format | meaning | | --------- | --------------------- | ------------------------------------------------------------ | | tag | string | Record the name of the image data，e.g.train/loss. Notice that the name cannot contain `%` | | values | numpy.ndarray or list | Data is in ndarray or list format, which shape is (N, ) | | step | int | Record the training steps | | walltime | int | Record the time-stamp of the data, and the default is the current time-stamp | | buckets | int | The number of segments to generate the histogram and the default value is 10 | ### Demo The following shows an example of using Histogram to record data, and the script can be found in [Histogram Demo](https://github.com/PaddlePaddle/VisualDL/blob/develop/demo/components/histogram_test.py) ```python from visualdl import LogWriter import numpy as np if __name__ == '__main__': values = np.arange(0, 1000) with LogWriter(logdir="./log/histogram_test/train") as writer: for index in range(1, 101): interval_start = 1 + 2 * index / 100.0 interval_end = 6 - 2 * index / 100.0 data = np.random.uniform(interval_start, interval_end, size=(10000)) writer.add_histogram(tag='default tag', values=data, step=index, buckets=10) ``` After running the above program, developers can launch the panel by: ```shell visualdl --logdir ./log --port 8080 ``` Then, open the browser and enter the address: `http://127.0.0.1:8080`to view the histogram. ### Functional Instructions - Developers are allowed to zoom in and download the histogram.

- Provide two modes: Offset and Overlay.

- Offset mode

- Overlay mode

- Display the parameters、training steps and frequency by hovering on specific data points. - In the 240th training step, the weight is -0.0031and the frequency is 2734

- Developers can find target histogram by searching corresponded tags.

- Search tags to show the histograms generated by corresponded experiments.

## PR Curve ### Introduction PR Curve presents precision-recall curves in line charts, describing the tradeoff relationship between precision and recall in order to choose a best threshold. ### Record Interface The interface of the PR Curve is shown as follows: ```python add_pr_curve(tag, labels, predictions, step=None, num_thresholds=10) ``` The interface parameters are described as follows: | parameter | format | meaning | | -------------- | --------------------- | ------------------------------------------- | | tag | string | Record the name of the image data，e.g.train/loss. Notice that the name cannot contain `%` | | labels | numpy.ndarray or list | Data is in ndarray or list format, which shape should be (N, ) and value should be 0 or 1 | | predictions | numpy.ndarray or list | Prediction data is in ndarray or list format, which shape should be (N, ) and value should in [0, 1] | | step | int | Record the training steps | | num_thresholds | int | Set the number of thresholds, default as 10, maximum as 127 | | weights | float | Set the weights of TN/FN/TP/FP to calculate precision and recall | | walltime | int | Record the time-stamp of the data, and the default is the current time-stamp | ### Demo The following shows an example of how to use PR Curve component, and script can be found in [PR Curve Demo](../../demo/components/pr_curve_test.py) ```python from visualdl import LogWriter import numpy as np with LogWriter("./log/pr_curve_test/train") as writer: for step in range(3): labels = np.random.randint(2, size=100) predictions = np.random.rand(100) writer.add_pr_curve(tag='pr_curve', labels=labels, predictions=predictions, step=step, num_thresholds=5) ``` After running the above program, developers can launch the panel by: ```shell visualdl --logdir ./log --port 8080 ``` Then, open the browser and enter the address`http://127.0.0.1:8080` to view:

### Functional Instrucions - Developers can zoom in, restore, and download PR Curves

- Developers hover on the specific data point to learn about the detailed information: TP, TN, FP, FN and the corresponded thresholds

- The targeted PR Curves can be displayed by searching tags

- Developers can find specific labels by searching tags or view the all labels

- Developers is able to observe the changes of PR Curves across training steps

- There are three measurement scales of X axis 1. Step: number of iterations 2. Walltime: absolute training time 3. Relative: training time

## ROC Curve ### Introduction ROC Curve shows the performance of a classification model at all classification thresholds; the larger the area under the curve, the better the model performs, aiding developers to evaluate the model performance and choose an appropriate threshold. ### Record Interface The interface of the PR Curve is shown as follows: ```python add_roc_curve(tag, labels, predictions, step=None, num_thresholds=10) ``` The interface parameters are described as follows: | parameter | format | meaning | | -------------- | --------------------- | ------------------------------------------- | | tag | string | Record the name of the image data，e.g.train/loss. Notice that the name cannot contain `%` | | values | numpy.ndarray or list | Data is in ndarray or list format, which shape should be (N, ) and value should be 0 or 1 | | predictions | numpy.ndarray or list | Prediction data is in ndarray or list format, which shape should be (N, ) and value should in [0, 1] | | step | int | Record the training steps | | num_thresholds | int | Set the number of thresholds, default as 10, maximum as 127 | | weights | float | Set the weights of TN/FN/TP/FP to calculate precision and recall | | walltime | int | Record the time-stamp of the data, and the default is the current time-stamp | ### Demo The following shows an example of how to use ROC curve component, and script can be found in [ROC Curve Demo](../../demo/components/roc_curve_test.py) ```python from visualdl import LogWriter import numpy as np with LogWriter("./log/roc_curve_test/train") as writer: for step in range(3): labels = np.random.randint(2, size=100) predictions = np.random.rand(100) writer.add_roc_curve(tag='roc_curve', labels=labels, predictions=predictions, step=step, num_thresholds=5) ``` After running the above program, developers can launch the panel by: ```shell visualdl --logdir ./log --port 8080 ``` Then, open the browser and enter the address`http://127.0.0.1:8080` to view:

*Note: the use of ROC Curve in the frontend is the same as that of PR Curve, please refer to the instructions in PR Curve section if needed. ## High Dimensional--Data Dimensionality Reduction ### Introduction High Dimensional projects high-dimensional data into a low dimensional space, aiding users to have an in-depth analysis of the relationship between high-dimensional data. Three dimensionality reduction algorithms are supported: - PCA : Principle Component Analysis - t-SNE : t-distributed Stochastic Neighbor Embedding - umap: Uniform Manifold Approximation and Projection ### Record Interface The interface of the High Dimensional is shown as follows: ```python add_embeddings(tag, labels, hot_vectors, walltime=None) ``` The interface parameters are described as follows: | parameter | format | meaning | | ----------- | ------------------- | ------------------------------------------------------------ | | tag | string | Record the name of the high dimensional data, e.g.`default`. Notice that the name cannot contain `%` | | labels | numpy.array or list | Represents the label of hot_vectors. The shape of `labels` should be (N, ) if only one dimension, and should be (M, N) if dimension of `labels` more than one, where each element is a one-dimensional label array. Each element is string type. | | hot_vectors | numpy.array or list | Each element can be seen as a feature of the tag corresponding to the label. | | labels_meta | numpy.array or list | The labels of parameter `labels` correspond to `labels` one-to-one. If not specified, the default value `__metadata__` will be used. When parameter `labels` is a one-dimensional array, there is no need to specify this parameter | | walltime | int | Record the time stamp of the data, the default is the current time stamp. | ### Demo The following shows an example of how to use High Dimensional component, and script can be found in [High Dimensional Demo](../../demo/components/high_dimensional_test.py) ```python from visualdl import LogWriter if __name__ == '__main__': hot_vectors = [ [1.3561076367500755, 1.3116267195134017, 1.6785401875616097], [1.1039614644440658, 1.8891609992484688, 1.32030488587171], [1.9924524852447711, 1.9358920727142739, 1.2124401279391606], [1.4129542689796446, 1.7372166387197474, 1.7317806077076527], [1.3913371800587777, 1.4684674577930312, 1.5214136352476377]] labels = ["label_1", "label_2", "label_3", "label_4", "label_5"] # initialize a recorder with LogWriter(logdir="./log/high_dimensional_test/train") as writer: # recorde a set of labels and corresponding hot_vectors to the recorder writer.add_embeddings(tag='default', labels=labels, hot_vectors=hot_vectors) ``` After running the above program, developers can launch the panel by: ```shell visualdl --logdir ./log --port 8080 ``` Then, open the browser and enter the address`http://127.0.0.1:8080` to view:

### Functional Instrucions * Developers are allowed to select specific runs of data or certain labels of data to display

* TSNE

* PCA

* UMAP

## HyperParameters--HyperParameter Visualization ### Introduction HyperParameters visualize the relationship between hyperparameters and model metrics (such as accuracy and loss) in a rich view, helping you identify the best hyperparameters in an efficient way. ### Record Interface The interface of the HyperParameters is slightly different from other components'. Firstly, you need to use the `add_hparams` to record the hyperparameter data(`hparams_dict`) and specify the name of the metrics(`metrics_list`). Then, for the metrics you just added, you need to record those metrics values by using `add_scalar`. In this way you can get all data for HpyerParameters Visualization. ```python add_hparams(hparam_dict, metric_list, walltime=None): ``` The interface parameters are described as follows: | parameter | format | meaning | | ----------- | ------------------- | ---------------------------------- | | hparam_dict | dict | name and data of hparams. | | metric_list | list | The metrics name to be recorded later corresponds to the `tag` parameter in the `add_scalar` interface, and VisualDL corresponds to the indicator data through the `tag`. | | walltime | int | Record the time stamp of the data, the default is the current time stamp. | ### Demo The following shows an example of how to use HyperParameters component, and script can be found in [HyperParameters Demo](https://github.com/PaddlePaddle/VisualDL/blob/develop/demo/components/hparams_test.py) ```python from visualdl import LogWriter # This demo demonstrates the hyperparameter records of two experiments. Take the first # experiment data as an example, First, record the data of the hyperparameter `hparams` # in the `add_hparams` interface. Then specify the name of `metrics` to be recorded later. # Finally, use `add_scalar` to specifically record the data of `metrics`. Note that the # `metrics_list` parameter in the `add_hparams` interface needs to include the `tag` # parameter of the `add_scalar` interface. if __name__ == '__main__': # Record the data of the first experiment with LogWriter('./log/hparams_test/train/run1') as writer: # Record the value of `hparams` and the name of `metrics` writer.add_hparams(hparams_dict={'lr': 0.1, 'bsize': 1, 'opt': 'sgd'}, metrics_list=['hparam/accuracy', 'hparam/loss']) # Record the metrics values ​​of different steps in an experiment by matching # the `tag` in the `add_scalar` interface with `metrics_list` in `add_hparams` interface. for i in range(10): writer.add_scalar(tag='hparam/accuracy', value=i, step=i) writer.add_scalar(tag='hparam/loss', value=2*i, step=i) # Record the data of the second experiment with LogWriter('./log/hparams_test/train/run2') as writer: # Record the value of `hparams` and the name of `metrics` writer.add_hparams(hparams_dict={'lr': 0.2, 'bsize': 2, 'opt': 'relu'}, metrics_list=['hparam/accuracy', 'hparam/loss']) # Record the metrics values ​​of different steps in an experiment by matching # the `tag` in the `add_scalar` interface with `metrics_list` in `add_hparams` interface. for i in range(10): writer.add_scalar(tag='hparam/accuracy', value=1.0/(i+1), step=i) writer.add_scalar(tag='hparam/loss', value=5*i, step=i) ``` After running the above program, developers can launch the panel by: ```shell visualdl --logdir ./log --port 8080 ``` Then, open the browser and enter the address`http://127.0.0.1:8080` to view:

### Functional Instrucions * Table View - The table view can be displayed in a sorted order. - Trial ID represents a specific experiment name, the column name displayed in other normal fonts is the hyperparameter name, and the column displayed in bold font is the metric name. - The position of hyperparameters and metrics can be customized by dragging. - The column width of the table view can be adjusted by dragging. - You can click to expand to view the scalar of the metrics.

* Parallel Coordinates View - The specific values ​​of hyperparameters and metrics in a certain set of experiments can be displayed by hovering. - Scalar of the metrics in this group of experiments can be displayed by selecting a certain curve.

* Scatter Plot Matrix View - The specific values ​​of hyperparameters and metrics in a certain set of experiments can be displayed by hovering. - Scalar of the metrics in this group of experiments can be displayed by selecting a certain point.

* Scalar of Metrics - Can be viewed in table view, parallel coordinates view and scatter plot matrix view. - Scalar of the metrics viewed here can also be viewed under the `SCALARS` board.

* Hyperparameter/metric range selection - Display part of the data by selecting the range of hyperparameters or metrics.

* download data - Two formats can be selected, CSV or TSV.

## Profiler--profiling data visualization ### Introduction VisualDL supports to visualize profiling data exported by paddle and helps you identify program bottlenecks and optimize performance. Please refer to [VisualDL Profiler Guide](./components/profiler/README.md).

## X2Paddle--model format transformation ### Introduction The X2Paddle component is used to read the onnx model, display the network structure of the onnx model, and help users convert the onnx model into a paddle model. Users can compare the original onnx model and the converted paddle model network, and obtain the converted model for use. ### Usage Launch the panel by: ```shell visualdl --port 8080 ``` Then, open the browser and enter the address`http://127.0.0.1:8080` to use X2Paddle component. ### Functional Instrucions - Convert onnx model and download

- Reload a new model

- Compare model network between conversions

**Note**: If failed to convert an onnx model to paddle, you can copy the error message of the model conversion to [X2Paddle](https://github.com/PaddlePaddle/X2Paddle/issues) issue to help us improve this tool. ## FastDeployServer--fastdeploy serving deployment visualization ### Introduction The FastDeployServer component assists users to use fastdeployserver to deploy service conveniently based on [FastDeploy project](https://github.com/PaddlePaddle/FastDeploy). It mainly provides the functions of loading and editing the model repository, service management and monitoring, and providing the client to test service. Please refer to [use VisualDL for fastdeploy serving deployment management](./fastdeploy_server/README.md).

## FastDeployClient--fastdeploy client for request visualization ### Introduction The FastDeployClient component is mainly used to quickly access the fastdeployserver service based on [FastDeploy project](https://github.com/PaddlePaddle/FastDeploy), to help users visualize prediction requests and results, and make quick verification of deployed services. Please refer to [use VisualDL as fastdeploy client for request visualization](./fastdeploy_client/README.md).

## VDL.service ### Introduction VDL.service enables developers to easily save, track and share visualization results with anyone for free. ### Usage Steps 1. Make sure that your get the lastest version of VisualDL, if not, please update by: ``` pip install visualdl --upgrade ``` 2. Upload log/model to save, track and share the visualization results. ``` visualdl service upload --logdir ./log \ --model ./__model__ ``` 3. An unique URL will be given. Then you can view the visualization results by simply copying and pasting the URL to the browser.