Repository: huggingface/transformers
Pull request: add trackio to training notebooks

@stevhliu I also need to remove IDEFICS example (too old) and fix image captioning one (dataset was taken down, although it's hard to find an unbroken image captioning dataset), will do on a follow-up PR

Changed files:
filename: docs/source/en/tasks/image_classification.md
patch: @@ -38,7 +38,7 @@ To see all architectures and checkpoints compatible with this task, we recommend
 Before you begin, make sure you have all the necessary libraries installed:
 
 ```bash
-pip install transformers datasets evaluate accelerate pillow torchvision scikit-learn
+pip install transformers datasets evaluate accelerate pillow torchvision scikit-learn trackio
 ```
 
 We encourage you to log in to your Hugging Face account to upload and share your model with the community. When prompted, enter your token to log in:
@@ -213,6 +213,8 @@ At this point, only three steps remain:
 ...     num_train_epochs=3,
 ...     warmup_steps=0.1,
 ...     logging_steps=10,
+...     report_to="trackio",
+...     run_name="food101",
 ...     load_best_model_at_end=True,
 ...     metric_for_best_model="accuracy",
 ...     push_to_hub=True,

filename: docs/source/en/tasks/knowledge_distillation_for_image_classification.md
patch: @@ -24,7 +24,7 @@ This guide demonstrates how you can distill a [fine-tuned ViT model](https://hug
 Let's install the libraries needed for distillation and evaluating the process.
 
 ```bash
-pip install transformers datasets accelerate tensorboard evaluate --upgrade
+pip install transformers datasets accelerate tensorboard evaluate trackio --upgrade
 ```
 
 In this example, we are using the `merve/beans-vit-224` model as teacher model. It's an image classification model, based on `google/vit-base-patch16-224-in21k` fine-tuned on beans dataset. We will distill this model to a randomly initialized MobileNetV2.
@@ -114,10 +114,11 @@ training_args = TrainingArguments(
     save_strategy="epoch",
     load_best_model_at_end=True,
     metric_for_best_model="accuracy",
-    report_to="tensorboard",
     push_to_hub=True,
     hub_strategy="every_save",
     hub_model_id=repo_name,
+    report_to="trackio",
+    run_name="distillation",
     )
 
 num_labels = len(processed_datasets["train"].features["labels"].names)

filename: docs/source/en/tasks/object_detection.md
patch: @@ -41,7 +41,7 @@ To see all architectures and checkpoints compatible with this task, we recommend
 Before you begin, make sure you have all the necessary libraries installed:
 
 ```bash
-pip install -q datasets transformers accelerate timm
+pip install -q datasets transformers accelerate timm trackio
 pip install -q -U albumentations>=1.4.5 torchmetrics pycocotools
 ```
 
@@ -558,6 +558,8 @@ Face to upload your model).
 ...     save_strategy="epoch",
 ...     save_total_limit=2,
 ...     remove_unused_columns=False,
+...     report_to="trackio",
+...     run_name="cppe",
 ...     eval_do_concat_batches=False,
 ...     push_to_hub=True,
 ... )

filename: docs/source/en/tasks/semantic_segmentation.md
patch: @@ -31,7 +31,7 @@ Before you begin, make sure you have all the necessary libraries installed:
 
 ```py
 # uncomment to install the necessary libraries
-!pip install -q datasets transformers evaluate accelerate
+!pip install -q datasets transformers evaluate accelerate trackio
 ```
 
 We encourage you to log in to your Hugging Face account so you can upload and share your model with the community. When prompted, enter your token to log in:
@@ -436,6 +436,8 @@ At this point, only three steps remain:
 ...     save_steps=20,
 ...     eval_steps=20,
 ...     logging_steps=1,
+...     report_to="trackio",
+...     run_name="scene-parse-150",
 ...     eval_accumulation_steps=5,
 ...     remove_unused_columns=False,
 ...     push_to_hub=True,