[Docs] Add a image for neptune (#1312)

open-mmlab · Aug 18, 2023 · 03ad86c · 03ad86c
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diff --git a/docs/en/common_usage/visualize_training_log.md b/docs/en/common_usage/visualize_training_log.md
@@ -6,7 +6,7 @@ Below, we'll show you how to configure an experiment management tool in just one
 
 ## TensorBoard
 
-Configure the `visualizer` in the initialization parameters of the Runner, and set `vis_backends` to `TensorboardVisBackend`.
+Configure the `visualizer` in the initialization parameters of the Runner, and set `vis_backends` to [TensorboardVisBackend](mmengine.visualization.TensorboardVisBackend).
 
 ```python
 runner = Runner(
@@ -32,7 +32,7 @@ pip install wandb
 wandb login
 ```
 
-Configure the `visualizer` in the initialization parameters of the Runner, and set `vis_backends` to `WandbVisBackend`.
+Configure the `visualizer` in the initialization parameters of the Runner, and set `vis_backends` to [WandbVisBackend](mmengine.visualization.WandbVisBackend).
 
 ```python
 runner = Runner(
@@ -81,7 +81,7 @@ pip install clearml
 clearml-init
 ```
 
-Configure the `visualizer` in the initialization parameters of the Runner, and set `vis_backends` to `ClearMLVisBackend`.
+Configure the `visualizer` in the initialization parameters of the Runner, and set `vis_backends` to [ClearMLVisBackend](mmengine.visualization.ClearMLVisBackend).
 
 ```python
 runner = Runner(
@@ -108,7 +108,7 @@ Before using Neptune, you need to install `neptune` dependency library and refer
 pip install neptune
 ```
 
-Configure the `Runner` in the initialization parameters of the Runner, and set `vis_backends` to `NeptuneVisBackend`.
+Configure the `Runner` in the initialization parameters of the Runner, and set `vis_backends` to [NeptuneVisBackend](mmengine.visualization.NeptuneVisBackend).
 
 ```python
 runner = Runner(
@@ -125,6 +125,8 @@ runner = Runner(
 runner.train()
 ```
 
+![image](https://github.com/open-mmlab/mmengine/assets/58739961/9122e2ac-cc4f-43b2-bad3-ae33faa64043)
+
 Please note: If the `project` and `api_token` are not specified, neptune will be set to offline mode and the generated files will be saved to the local `.neptune` file.
 It is recommended to specify the `project` and `api_token` during initialization as shown below.
 

diff --git a/docs/zh_cn/common_usage/visualize_training_log.md b/docs/zh_cn/common_usage/visualize_training_log.md
@@ -1,12 +1,12 @@
 # 可视化训练日志
 
-MMEngine 集成了 [TensorBoard](https://www.tensorflow.org/tensorboard?hl=zh-cn)、[Weights & Biases (WandB)](https://docs.wandb.ai/)、[MLflow](https://mlflow.org/docs/latest/index.html) 、 [ClearML](https://clear.ml/docs/latest/docs) 和 [Neptune](https://docs.neptune.ai/)实验管理工具，你可以很方便地跟踪和可视化损失及准确率等指标。
+MMEngine 集成了 [TensorBoard](https://www.tensorflow.org/tensorboard?hl=zh-cn)、[Weights & Biases (WandB)](https://docs.wandb.ai/)、[MLflow](https://mlflow.org/docs/latest/index.html) 、[ClearML](https://clear.ml/docs/latest/docs) 和 [Neptune](https://docs.neptune.ai/) 实验管理工具，你可以很方便地跟踪和可视化损失及准确率等指标。
 
 下面基于[15 分钟上手 MMENGINE](../get_started/15_minutes.md)中的例子介绍如何一行配置实验管理工具。
 
 ## TensorBoard
 
-设置 `Runner` 初始化参数中的 `visualizer`，并将 `vis_backends` 设置为 `TensorboardVisBackend`。
+设置 `Runner` 初始化参数中的 `visualizer`，并将 `vis_backends` 设置为 [TensorboardVisBackend](mmengine.visualization.TensorboardVisBackend)。
 
 ```python
 runner = Runner(
@@ -32,7 +32,7 @@ pip install wandb
 wandb login
 ```
 
-设置 `Runner` 初始化参数中的 `visualizer`，并将 `vis_backends` 设置为 `WandbVisBackend`。
+设置 `Runner` 初始化参数中的 `visualizer`，并将 `vis_backends` 设置为 [WandbVisBackend](mmengine.visualization.WandbVisBackend)。
 
 ```python
 runner = Runner(
@@ -81,7 +81,7 @@ pip install clearml
 clearml-init
 ```
 
-设置 `Runner` 初始化参数中的 `visualizer`，并将 `vis_backends` 设置为 `ClearMLVisBackend`。
+设置 `Runner` 初始化参数中的 `visualizer`，并将 `vis_backends` 设置为 [ClearMLVisBackend](mmengine.visualization.ClearMLVisBackend)。
 
 ```python
 runner = Runner(
@@ -108,7 +108,7 @@ runner.train()
 pip install neptune
 ```
 
-设置 `Runner` 初始化参数中的 `visualizer`，并将 `vis_backends` 设置为 `NeptuneVisBackend`。
+设置 `Runner` 初始化参数中的 `visualizer`，并将 `vis_backends` 设置为 [NeptuneVisBackend](mmengine.visualization.NeptuneVisBackend)。
 
 ```python
 runner = Runner(
@@ -125,7 +125,9 @@ runner = Runner(
 runner.train()
 ```
 
-请注意：若未提供 `project` 和 `api_token` ，neptune 将被设置成离线模式，产生的文件将保存到本地`.neptune`文件下。
+![image](https://github.com/open-mmlab/mmengine/assets/58739961/9122e2ac-cc4f-43b2-bad3-ae33faa64043)
+
+请注意：若未提供 `project` 和 `api_token` ，neptune 将被设置成离线模式，产生的文件将保存到本地 `.neptune` 文件下。
 推荐在初始化时提供 `project` 和 `api_token` ，具体方法如下所示：
 
 ```python

diff --git a/mmengine/visualization/vis_backend.py b/mmengine/visualization/vis_backend.py
@@ -1005,6 +1005,9 @@ class NeptuneVisBackend(BaseVisBackend):
         >>> cfg = Config(dict(a=1, b=dict(b1=[0, 1])))
         >>> neptune_vis_backend.add_config(cfg)
 
+    Note:
+        `New in version 0.8.5.`
+
     Args:
         save_dir (str, optional): The root directory to save the files
             produced by the visualizer. NeptuneVisBackend does
@@ -1016,7 +1019,7 @@ class NeptuneVisBackend(BaseVisBackend):
               `namespace/project_name`. If `project` is not specified,
               the value of `NEPTUNE_PROJECT` environment variable
               will be taken.
-            - api_token (str): User’s API token. If api_token is not api_token,
+            - api_token (str): User's API token. If api_token is not api_token,
               the value of `NEPTUNE_API_TOKEN` environment variable will
               be taken. Note: It is strongly recommended to use
               `NEPTUNE_API_TOKEN` environment variable rather than