Update Lightning Examples

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Thanks for contributing to wandb/examples! We appreciate your efforts in opening a PR for the examples repository. Our goal is to ensure a smooth and enjoyable experience for you 😎.

Guidelines

The examples repo is regularly tested against the ever-evolving ML stack. To facilitate our work, please adhere to the following guidelines:

• Notebook naming: You can use a combination of snakecase and CamelCase for your notebook name. Avoid using spaces (replace them with `) and special characters (&%$?`). For example:

  Cool_Keras_integration_example_with_weights_and_biases.ipynb 

  is acceptable, but

  Cool Keras Example with W&B.ipynb

  is not. Avoid spaces and the & character. To refer to W&B, you can use: weights_and_biases or just wandb (it's our library, after all!)

• Managing dependencies within the notebook: You may need to set up dependencies to ensure that your code works. Please avoid the following practices:

  • Docker-related activities. If Docker installation is required, consider adding a full example with the corresponding Dockerfile to the wandb/examples/examples folder (where non-Colab examples reside).
  • Using pip install as the primary method to install packages. When calling pip in a cell, avoid performing other tasks. We automatically filter these types of cells, and executing other actions might break the automatic testing of the notebooks. For example,

    pip install -qU wandb transformers gpt4

    is acceptable, but

    pip install -qU wandb
    import wandb

    is not.

  • Installing packages from a GitHub branch. Although it's acceptable 😎 to directly obtain the latest bleeding-edge libraries from GitHub, did you know that you can install them like this:

  !pip install -q git+https://github.com/huggingface/transformers

  You don't need to clone, then cd into the repo and install it in editable mode.

  • Avoid referencing specific Colab directories. Google Colab has a /content directory where everything resides. Avoid explicitly referencing this directory because we test our notebooks with pure Jupyter (without Colab). Instead, use relative paths to make the notebook reproducible.

• The Jupyter notebook file .ipynb is nothing more than a JSON file with primarily two types of cells: markdown and code. There is also a bunch of other metadata specific to Google Colab. We have a set of tools to ensure proper notebook formatting. These tools can be found at wandb/nb_helpers.

Before merging, wait for a maintainer to clean and format the notebooks you're adding. You can tag @tcapelle.

Before marking the PR as ready for review, please run your notebook one more time. Restart the Colab and run all. We will provide you with links to open the Colabs below

The following colabs were changed -colabs/pytorch-lightning/Fine_tuning_a_Transformer_with_Pytorch_Lightning.ipynb -colabs/pytorch-lightning/Image_Classification_using_PyTorch_Lightning.ipynb -colabs/pytorch-lightning/Optimize_Pytorch_Lightning_models_withWeights&_Biases.ipynb -colabs/pytorch-lightning/Profile_PyTorch_Code.ipynb -colabs/pytorch-lightning/Supercharge_your_Training_with_Pytorch_Lightning_and_Weights_and_Biases.ipynb -colabs/pytorch-lightning/Transfer_Learning_Using_PyTorch_Lightning.ipynb -colabs/pytorch-lightning/Wandb_End_to_End_with_PyTorch_Lightning.ipynb

[ash0ts]

Transfer_Learning_Using_PyTorch_Lightning.ipynb is broken due to an issue with the dataset no longer being easily available. It would require some workaround to download that data into the proper form from kaggle