doc: update W&B related doc (#10086)

* doc: update W&B related doc

* doc(wandb): mention report_to

* doc(wandb): commit suggestion
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* doc(wandb): fix typo

* doc(wandb): remove WANDB_DISABLED
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

docs/source/community.md

@@ -18,8 +18,8 @@ This page regroups resources around 🤗 Transformers developed by the community
 | [Fine-tune DialoGPT on New Datasets and Languages](https://github.com/ncoop57/i-am-a-nerd/blob/master/_notebooks/2020-05-12-chatbot-part-1.ipynb)  | How to fine-tune the DialoGPT model on a new dataset for open-dialog conversational chatbots |  [Nathan Cooper](https://github.com/ncoop57) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ncoop57/i-am-a-nerd/blob/master/_notebooks/2020-05-12-chatbot-part-1.ipynb) |
 | [Long Sequence Modeling with Reformer](https://github.com/patrickvonplaten/notebooks/blob/master/PyTorch_Reformer.ipynb)  | How to train on sequences as long as 500,000 tokens with Reformer |  [Patrick von Platen](https://github.com/patrickvonplaten) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/PyTorch_Reformer.ipynb)  |
 | [Fine-tune BART for Summarization](https://github.com/ohmeow/ohmeow_website/blob/master/_notebooks/2020-05-23-text-generation-with-blurr.ipynb) | How to fine-tune BART for summarization with fastai using blurr | [Wayde Gilliam](https://ohmeow.com/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ohmeow/ohmeow_website/blob/master/_notebooks/2020-05-23-text-generation-with-blurr.ipynb) |
-| [Fine-tune a pre-trained Transformer on anyone's tweets](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)  | How to generate tweets in the style of your favorite Twitter account by fine-tune a GPT-2 model |  [Boris Dayma](https://github.com/borisdayma) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb) |
-| [A Step by Step Guide to Tracking Hugging Face Model Performance](https://colab.research.google.com/drive/1NEiqNPhiouu2pPwDAVeFoN4-vTYMz9F8)  | A quick tutorial for training NLP models with HuggingFace and & visualizing their performance with Weights & Biases |  [Jack Morris](https://github.com/jxmorris12) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1NEiqNPhiouu2pPwDAVeFoN4-vTYMz9F8) |
+| [Fine-tune a pre-trained Transformer on anyone's tweets](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb) | How to generate tweets in the style of your favorite Twitter account by fine-tuning a GPT-2 model |  [Boris Dayma](https://github.com/borisdayma) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb) |
+| [Optimize 🤗 Hugging Face models with Weights & Biases](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/huggingface/Optimize_Hugging_Face_models_with_Weights_%26_Biases.ipynb) | A complete tutorial showcasing W&B integration with Hugging Face | [Boris Dayma](https://github.com/borisdayma) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/wandb/examples/blob/master/colabs/huggingface/Optimize_Hugging_Face_models_with_Weights_%26_Biases.ipynb) |
 | [Pretrain Longformer](https://github.com/allenai/longformer/blob/master/scripts/convert_model_to_long.ipynb)  | How to build a "long" version of existing pretrained models |  [Iz Beltagy](https://beltagy.net) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/allenai/longformer/blob/master/scripts/convert_model_to_long.ipynb) |
 | [Fine-tune Longformer for QA](https://github.com/patil-suraj/Notebooks/blob/master/longformer_qa_training.ipynb) | How to fine-tune longformer model for QA task | [Suraj Patil](https://github.com/patil-suraj) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/patil-suraj/Notebooks/blob/master/longformer_qa_training.ipynb) |
 | [Evaluate Model with 🤗nlp](https://github.com/patrickvonplaten/notebooks/blob/master/How_to_evaluate_Longformer_on_TriviaQA_using_NLP.ipynb) | How to evaluate longformer on TriviaQA with `nlp` | [Patrick von Platen](https://github.com/patrickvonplaten) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1m7eTGlPmLRgoPkkA7rkhQdZ9ydpmsdLE?usp=sharing) |

examples/README.md

 <!---
 Copyright 2020 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
@@ -147,7 +146,7 @@ python xla_spawn.py --num_cores 8 \
 You can easily log and monitor your runs code. The following are currently supported:
 
 * [TensorBoard](https://www.tensorflow.org/tensorboard)
-* [Weights & Biases](https://docs.wandb.com/library/integrations/huggingface)
+* [Weights & Biases](https://docs.wandb.ai/integrations/huggingface)
 * [Comet ML](https://www.comet.ml/docs/python-sdk/huggingface/)
 
 ### Weights & Biases
@@ -171,9 +170,46 @@ import wandb
 wandb.login()
 ```
 
+To enable logging to W&B, include `"wandb"` in the `report_to` of your `TrainingArguments` or script. Or just pass along `--report_to all` if you have `wandb` installed.
+
 Whenever you use `Trainer` or `TFTrainer` classes, your losses, evaluation metrics, model topology and gradients (for `Trainer` only) will automatically be logged.
 
-When using 🤗 Transformers with PyTorch Lightning, runs can be tracked through `WandbLogger`. Refer to related [documentation & examples](https://docs.wandb.com/library/integrations/lightning).
+Advanced configuration is possible by setting environment variables:
+
+<table>
+  <thead>
+    <tr>
+      <th style="text-align:left">Environment Variables</th>
+      <th style="text-align:left">Options</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td style="text-align:left">WANDB_LOG_MODEL</td>
+      <td style="text-align:left">Log the model as artifact at the end of training (<b>false</b> by default)</td>
+    </tr>
+    <tr>
+      <td style="text-align:left">WANDB_WATCH</td>
+      <td style="text-align:left">
+        <ul>
+          <li><b>gradients</b> (default): Log histograms of the gradients</li>
+          <li><b>all</b>: Log histograms of gradients and parameters</li>
+          <li><b>false</b>: No gradient or parameter logging</li>
+        </ul>
+      </td>
+    </tr>
+    <tr>
+      <td style="text-align:left">WANDB_PROJECT</td>
+      <td style="text-align:left">Organize runs by project</td>
+    </tr>
+  </tbody>
+</table>
+
+Set run names with `run_name` argument present in scripts or as part of `TrainingArguments`.
+
+Additional configuration options are available through generic [wandb environment variables](https://docs.wandb.com/library/environment-variables).
+
+Refer to related [documentation & examples](https://docs.wandb.ai/integrations/huggingface).
 
 ### Comet.ml
 

src/transformers/integrations.py

@@ -529,7 +529,7 @@ class WandbCallback(TrainerCallback):
         Setup the optional Weights & Biases (`wandb`) integration.
 
         One can subclass and override this method to customize the setup if needed. Find more information `here
-        <https://docs.wandb.com/huggingface>`__. You can also override the following environment variables:
+        <https://docs.wandb.ai/integrations/huggingface>`__. You can also override the following environment variables:
 
         Environment:
             WANDB_LOG_MODEL (:obj:`bool`, `optional`, defaults to :obj:`False`):