Typo error: np.clip() method for numpy refered to Pytorch instead of …

.devcontainer/Dockerfile

@@ -0,0 +1,12 @@
+FROM mcr.microsoft.com/devcontainers/python:0-3.10
+
+COPY requirements.txt /tmp/
+
+RUN echo "(*) Installing tools..." \
+    && su - vscode -c "pip install -r /tmp/requirements.txt" \
+    && apt-get update \
+    && apt-get -y install --no-install-recommends graphviz
+
+ENV PATH="/home/vscode/.local/bin:${PATH}"
+
+COPY welcome-message.txt /usr/local/etc/vscode-dev-containers/first-run-notice.txt

.devcontainer/devcontainer.json

@@ -0,0 +1,21 @@
+// For more details, see https://aka.ms/devcontainer.json.
+{
+    "build": {
+        "context": ".",
+        "dockerfile": "Dockerfile"
+    },
+
+    // Uncomment to install NVIDIA CUDA - required for a GPU-powered codespace. For more details, see https://github.com/fastai/course22/blob/master/getting-started-with-codespaces.md#gpu-powered-codespaces
+    // "features": {
+    //     "ghcr.io/devcontainers/features/nvidia-cuda:1": {
+    //         "installCudnn": true
+    //     }
+    // },
+
+    "hostRequirements": {
+        "storage": "64gb"
+    },
+    "runArgs": [
+        "--shm-size=16g"
+    ]
+}

.devcontainer/requirements.txt

@@ -0,0 +1,17 @@
+duckduckgo_search
+fastai
+fastkaggle
+gradio
+graphviz
+ipywidgets
+jupyterlab
+kaggle
+nbdev
+plotly
+seaborn
+statsmodels
+sympy
+timm
+torch
+torchvision
+transformers

.devcontainer/welcome-message.txt

@@ -0,0 +1,7 @@
+👋 Welcome to "Practical Deep Learning for Coders" in Codespaces!
+
+🛠️  Your environment is fully setup with all the required software and machine learning libraries.
+
+🚀 To get started, either open the notebook file in the VS Code editor,
+   or open this Codespace with "Open in Jupyterlab" at https://github.com/codespaces
+

README.md

@@ -9,3 +9,4 @@ This is where you'll find the notebooks, slides, and spreadsheets for the 2022 e
 - `xl`: Excel spreadsheets
 - `slides`: Jeremy's slide decks
 - `tools`: Ignore (tools for creating this repo)
+- `getting-started-with-codespaces`: A document to help run the notebooks in a GitHub Codespace

getting-started-with-codespaces.md

@@ -0,0 +1,30 @@
+# Using Codespaces to work with the "Practical Deep Learning for Coders" course
+
+
+To get started, create a codespace for this repository by clicking this 👇 
+
+[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new?hide_repo_select=true&ref=master&repo=485606685)
+
+A codespace will open in a web-based version of Visual Studio Code.
+
+**Note**: Dev containers is an open spec which is supported by [GitHub Codespaces](https://github.com/codespaces) and [other supporting tools](https://containers.dev/supporting).
+
+## Opening a notebook
+
+The [dev container](.devcontainer/devcontainer.json) is fully configured with software and [machine learning libraries](.devcontainer/requirements.txt) needed for this course.
+
+In the VS Code editor, open any notebook file and start executing the notebook's cells.
+
+## Opening your codespace in JupyterLab
+
+You can open your codespace in JupyterLab from the "Your codespaces" page at [github.com/codespaces](https://github.com/codespaces), or by using [GitHub CLI](https://docs.github.com/en/codespaces/developing-in-codespaces/opening-an-existing-codespace?tool=cli#opening-an-existing-codespace) with `gh codespace jupyter`. For more information, see "[Opening an existing codespace](https://docs.github.com/en/codespaces/developing-in-codespaces/opening-an-existing-codespace)".
+
+## GPU-powered Codespaces
+
+GPU-powered Codespaces are now available in limited beta. Having access to a GPU from within a codespace allows developers to run complex Machine Learning models much more quickly. 
+
+To request access to the GPU machine types, or any additional machine type, [please complete the sign up form](https://github.surveymonkey.com/r/Y75GX9T).
+
+Once, GPU is enabled and configured for your codespace, uncomment [this section](.devcontainer/devcontainer.json#L9-L13) which installs NVIDIA CUDA.
+
+**Note**: Notebooks [09-small-models-road-to-the-top-part-2](09-small-models-road-to-the-top-part-2.ipynb) and [10-scaling-up-road-to-the-top-part-3](10-scaling-up-road-to-the-top-part-3.ipynb) requires a powerful machine to ensure that the kernel does not crash. Hence, some notebook cells for these two notebooks might not execute without a GPU-powered codespace.