chore: lint code and text in integration tutorials

content/tutorials/integration-tutorials/huggingface.md

@@ -6,79 +6,99 @@ menu:
 title: Hugging Face
 weight: 3
 ---
+
 {{< img src="/images/tutorials/huggingface.png" alt="" >}}
 
 {{< cta-button colabLink="https://colab.research.google.com/github/wandb/examples/blob/master/colabs/huggingface/Huggingface_wandb.ipynb" >}}
-Visualize your [Hugging Face](https://github.com/huggingface/transformers) model's performance quickly with a seamless [W&B](https://wandb.ai/site) integration.
+Visualize your [Hugging Face](https://github.com/huggingface/transformers)
+model's performance quickly with a seamless [W&B](https://wandb.ai/site)
+integration.
 
-Compare hyperparameters, output metrics, and system stats like GPU utilization across your models. 
+Compare hyperparameters, output metrics, and system stats like GPU utilization
+across your models.
 
 ## Why should I use W&B?
+
 {.skipvale}
 
 {{< img src="/images/tutorials/huggingface-why.png" alt="" >}}
 
-- **Unified dashboard**: Central repository for all your model metrics and predictions
+- **Unified dashboard**: Central repository for all your model metrics and
+  predictions
 - **Lightweight**: No code changes required to integrate with Hugging Face
 - **Accessible**: Free for individuals and academic teams
 - **Secure**: All projects are private by default
 - **Trusted**: Used by machine learning teams at OpenAI, Toyota, Lyft and more
 
-Think of W&B like GitHub for machine learning models— save machine learning experiments to your private, hosted dashboard. Experiment quickly with the confidence that all the versions of your models are saved for you, no matter where you're running your scripts.
+Think of W&B like GitHub for machine learning models— save machine learning
+experiments to your private, hosted dashboard. Experiment quickly with the
+confidence that all the versions of your models are saved for you, no matter
+where you're running your scripts.
 
-W&B lightweight integrations works with any Python script, and all you need to do is sign up for a free W&B account to start tracking and visualizing your models.
+W&B lightweight integrations works with any Python script, and all you need to
+do is sign up for a free W&B account to start tracking and visualizing your
+models.
 
-In the Hugging Face Transformers repo, we've instrumented the Trainer to automatically log training and evaluation metrics to W&B at each logging step.
+In the Hugging Face Transformers repo, we've instrumented the Trainer to
+automatically log training and evaluation metrics to W&B at each logging step.
 
-Here's an in depth look at how the integration works: [Hugging Face + W&B Report](https://app.wandb.ai/jxmorris12/huggingface-demo/reports/Train-a-model-with-Hugging-Face-and-Weights-%26-Biases--VmlldzoxMDE2MTU).
+Here's an in depth look at how the integration works:
+[Hugging Face + W&B Report](https://app.wandb.ai/jxmorris12/huggingface-demo/reports/Train-a-model-with-Hugging-Face-and-Weights-%26-Biases--VmlldzoxMDE2MTU).
 
 ## Install, import, and log in
 
+Install the Hugging Face and Weights & Biases libraries, and the GLUE dataset
+and training script for this tutorial.
 
-
-Install the Hugging Face and Weights & Biases libraries, and the GLUE dataset and training script for this tutorial.
-- [Hugging Face Transformers](https://github.com/huggingface/transformers): Natural language models and datasets
+- [Hugging Face Transformers](https://github.com/huggingface/transformers):
+  Natural language models and datasets
 - [Weights & Biases]({{< relref "/" >}}): Experiment tracking and visualization
-- [GLUE dataset](https://gluebenchmark.com/): A language understanding benchmark dataset
-- [GLUE script](https://raw.githubusercontent.com/huggingface/transformers/refs/heads/main/examples/pytorch/text-classification/run_glue.py): Model training script for sequence classification
-
+- [GLUE dataset](https://gluebenchmark.com/): A language understanding benchmark
+  dataset
+- [GLUE script](https://raw.githubusercontent.com/huggingface/transformers/refs/heads/main/examples/pytorch/text-classification/run_glue.py):
+  Model training script for sequence classification
 
 ```notebook
 !pip install datasets wandb evaluate accelerate -qU
 !wget https://raw.githubusercontent.com/huggingface/transformers/refs/heads/main/examples/pytorch/text-classification/run_glue.py
 ```
 
-
 ```notebook
 # the run_glue.py script requires transformers dev
 !pip install -q git+https://github.com/huggingface/transformers
 ```
 
-Before continuing, [sign up for a free account](https://app.wandb.ai/login?signup=true).
+Before continuing,
+[sign up for a free account](https://app.wandb.ai/login?signup=true).
 
 ## Put in your API key
 
-Once you've signed up, run the next cell and click on the link to get your API key and authenticate this notebook.
-
+Once you've signed up, run the next cell and click on the link to get your API
+key and authenticate this notebook.
 
 ```python
 import wandb
 wandb.login()
 ```
 
-Optionally, we can set environment variables to customize W&B logging. See [documentation]({{< relref "/guides/integrations/huggingface/" >}}).
-
+Optionally, we can set environment variables to customize W&B logging. See
+[documentation]({{< relref "/guides/integrations/huggingface/" >}}).
 
-```python
+```jupyter
 # Optional: log both gradients and parameters
 %env WANDB_WATCH=all
 ```
 
 ## Train the model
-Next, call the downloaded training script [run_glue.py](https://huggingface.co/transformers/examples.html#glue) and see training automatically get tracked to the Weights & Biases dashboard. This script fine-tunes BERT on the Microsoft Research Paraphrase Corpus— pairs of sentences with human annotations indicating whether they are semantically equivalent.
 
+Next, call the downloaded training script
+[run_glue.py](https://huggingface.co/transformers/examples.html#glue) and see
+training automatically get tracked to the Weights & Biases dashboard. This
+script fine-tunes BERT on the Microsoft Research Paraphrase Corpus— pairs of
+sentences with human annotations indicating whether they are semantically
+equivalent.
 
-```python
+```jupyter
 %env WANDB_PROJECT=huggingface-demo
 %env TASK_NAME=MRPC
 
@@ -96,27 +116,39 @@ Next, call the downloaded training script [run_glue.py](https://huggingface.co/t
   --logging_steps 50
 ```
 
-##  Visualize results in dashboard
-Click the link printed out above, or go to [wandb.ai](https://app.wandb.ai) to see your results stream in live. The link to see your run in the browser will appear after all the dependencies are loaded. Look for the following output: "**wandb**: 🚀 View run at [URL to your unique run]"
+## Visualize results in dashboard
+
+Click the link printed out above, or go to [wandb.ai](https://app.wandb.ai) to
+see your results stream in live. The link to see your run in the browser will
+appear after all the dependencies are loaded. Look for the following output:
+"**wandb**: 🚀 View run at [URL to your unique run]"
 
-**Visualize Model Performance**
-It's easy to look across dozens of experiments, zoom in on interesting findings, and visualize highly dimensional data.
+**Visualize Model Performance** It's easy to look across dozens of experiments,
+zoom in on interesting findings, and visualize highly dimensional data.
 
 {{< img src="/images/tutorials/huggingface-visualize.gif" alt="" >}}
 
-**Compare Architectures**
-Here's an example comparing [BERT vs DistilBERT](https://app.wandb.ai/jack-morris/david-vs-goliath/reports/Does-model-size-matter%3F-Comparing-BERT-and-DistilBERT-using-Sweeps--VmlldzoxMDUxNzU). It's easy to see how different architectures effect the evaluation accuracy throughout training with automatic line plot visualizations.
+**Compare Architectures** Here's an example comparing
+[BERT vs DistilBERT](https://app.wandb.ai/jack-morris/david-vs-goliath/reports/Does-model-size-matter%3F-Comparing-BERT-and-DistilBERT-using-Sweeps--VmlldzoxMDUxNzU).
+It's easy to see how different architectures effect the evaluation accuracy
+throughout training with automatic line plot visualizations.
 
 {{< img src="/images/tutorials/huggingface-comparearchitectures.gif" alt="" >}}
 
 ## Track key information effortlessly by default
-Weights & Biases saves a new run for each experiment. Here's the information that gets saved by default:
+
+Weights & Biases saves a new run for each experiment. Here's the information
+that gets saved by default:
+
 - **Hyperparameters**: Settings for your model are saved in Config
 - **Model Metrics**: Time series data of metrics streaming in are saved in Log
 - **Terminal Logs**: Command line outputs are saved and available in a tab
 - **System Metrics**: GPU and CPU utilization, memory, temperature etc.
 
 ## Learn more
-- [Documentation]({{< relref "/guides/integrations/huggingface" >}}): docs on the Weights & Biases and Hugging Face integration
-- [Videos](http://wandb.me/youtube): tutorials, interviews with practitioners, and more on our YouTube channel
-- Contact: Message us at [email protected] with questions
+
+- [Documentation]({{< relref "/guides/integrations/huggingface" >}}): docs on
+  the Weights & Biases and Hugging Face integration
+- [Videos](http://wandb.me/youtube): tutorials, interviews with practitioners,
+  and more on our YouTube channel
+- Contact: Message us at [email protected] with questions

content/tutorials/integration-tutorials/keras.md

@@ -7,23 +7,25 @@ title: Keras
 ---
 
 {{< cta-button colabLink="https://colab.research.google.com/github/wandb/examples/blob/master/colabs/keras/Use_WandbMetricLogger_in_your_Keras_workflow.ipynb" >}}
-Use Weights & Biases for machine learning experiment tracking, dataset versioning, and project collaboration.
+Use Weights & Biases for machine learning experiment tracking, dataset
+versioning, and project collaboration.
 
 {{< img src="/images/tutorials/huggingface-why.png" alt="" >}}
 
-This Colab notebook introduces the `WandbMetricsLogger` callback. Use this callback for [Experiment Tracking]({{< relref "/guides/models/track" >}}). It will log your training and validation metrics along with system metrics to Weights and Biases.
-
+This notebook introduces the `WandbMetricsLogger` callback. Use this callback
+for [Experiment Tracking]({{< relref "/guides/models/track" >}}). It will log
+your training and validation metrics along with system metrics to Weights and
+Biases.
 
 ## Setup and Installation
 
-First, let us install the latest version of Weights and Biases. We will then authenticate this colab instance to use W&B.
-
+First, let us install the latest version of Weights and Biases. We will then
+authenticate this colab instance to use W&B.
 
 ```shell
 pip install -qq -U wandb
 ```
 
-
 ```python
 import os
 import tensorflow as tf
@@ -36,17 +38,20 @@ import wandb
 from wandb.integration.keras import WandbMetricsLogger
 ```
 
-If this is your first time using W&B or you are not logged in, the link that appears after running `wandb.login()` will take you to sign-up/login page. Signing up for a [free account](https://wandb.ai/signup) is as easy as a few clicks.
-
+If this is your first time using W&B or you are not logged in, the link that
+appears after running `wandb.login()` will take you to sign-up/login page.
+Signing up for a [free account](https://wandb.ai/signup) is as easy as a few
+clicks.
 
 ```python
 wandb.login()
 ```
 
 ## Hyperparameters
 
-Use of proper config system is a recommended best practice for reproducible machine learning. We can track the hyperparameters for every experiment using W&B. In this colab we will be using simple Python `dict` as our config system.
-
+Use of proper config system is a recommended best practice for reproducible
+machine learning. We can track the hyperparameters for every experiment using
+W&B. In this colab we will be using simple Python `dict` as our config system.
 
 ```python
 configs = dict(
@@ -63,14 +68,15 @@ configs = dict(
 
 ## Dataset
 
-In this colab, we will be using [CIFAR100](https://www.tensorflow.org/datasets/catalog/cifar100) dataset from TensorFlow Dataset catalog. We aim to build a simple image classification pipeline using TensorFlow/Keras.
-
+In this colab, we will be using
+[CIFAR100](https://www.tensorflow.org/datasets/catalog/cifar100) dataset from
+TensorFlow Dataset catalog. We aim to build a simple image classification
+pipeline using TensorFlow/Keras.
 
 ```python
 train_ds, valid_ds = tfds.load("fashion_mnist", split=["train", "test"])
 ```
 
-
 ```python
 AUTOTUNE = tf.data.AUTOTUNE
 
@@ -98,15 +104,13 @@ def get_dataloader(ds, configs, dataloader_type="train"):
     return dataloader
 ```
 
-
 ```python
 trainloader = get_dataloader(train_ds, configs)
 validloader = get_dataloader(valid_ds, configs, dataloader_type="valid")
 ```
 
 ## Model
 
-
 ```python
 def get_model(configs):
     backbone = tf.keras.applications.mobilenet_v2.MobileNetV2(
@@ -127,7 +131,6 @@ def get_model(configs):
     return models.Model(inputs=inputs, outputs=outputs)
 ```
 
-
 ```python
 tf.keras.backend.clear_session()
 model = get_model(configs)
@@ -136,7 +139,6 @@ model.summary()
 
 ## Compile Model
 
-
 ```python
 model.compile(
     optimizer="adam",
@@ -150,7 +152,6 @@ model.compile(
 
 ## Train
 
-
 ```python
 # Initialize a W&B run
 run = wandb.init(project="intro-keras", config=configs)
@@ -167,4 +168,4 @@ model.fit(
 
 # Close the W&B run
 run.finish()
-```
+```