rapidsai-community · jayavenkatesh19 · Jul 10, 2026 · Jul 10, 2026 · Jul 10, 2026
diff --git a/setup.md → 1-setup.md b/setup.md → 1-setup.md
diff --git a/environments.md → 2-environments.md b/environments.md → 2-environments.md
diff --git a/rapids-cli-verify-environment.md → 3-rapids-cli-verify-env.md b/rapids-cli-verify-environment.md → 3-rapids-cli-verify-env.md
diff --git a/accel-examples-profiling.md → 4-acceleration-examples.md b/accel-examples-profiling.md → 4-acceleration-examples.md
diff --git a/monitoring-and-debugging.md → 5-monitoring-and-debugging.md b/monitoring-and-debugging.md → 5-monitoring-and-debugging.md
diff --git a/README.md b/README.md
@@ -9,7 +9,53 @@ This repository contains materials for the tutorial:
 
 For the in-person version of this tutorial we will use [NVIDIA Brev](https://brev.nvidia.com/)
 
-#### Content
+### Content
 
-| module | Link |
+<!-- TODO: update links to full GitHub URLs once the repo is public -->
+| Module | Link |
 | --- | --- |
+| 1 - Setup Brev | [1-setup.md](1-setup.md) |
+| 2 - Setting Up Your GPU Python Environment | [2-environments.md](2-environments.md) |
+| 3 - Verifying Your Environment with the RAPIDS CLI | [3-rapids-cli-verify-env.md](3-rapids-cli-verify-env.md) |
+| 4 - Easy GPU Acceleration Wins with RAPIDS Accelerators | [4-acceleration-examples.md](4-acceleration-examples.md) |
+| 5 - Monitoring and Debugging GPU Python Workloads | [5-monitoring-and-debugging.md](5-monitoring-and-debugging.md) |
+
+### 1-Setup Brev
+
+- Covers how to get access to a cloud GPU using [NVIDIA Brev](https://brev.nvidia.com).
+- Walks through launching a VM (choosing GPU type, provider, and deploying).
+- Explains how to connect to the VM via SSH using the `brev` CLI on macOS, Linux, and Windows.
+
+### 2-Setting Up Your GPU Python Environment
+
+- Guides users through installing Python packages for GPU workloads.
+- Supports three package managers: `uv`/`pip`, `conda`, and `pixi` via config files (`pyproject.toml`, `environment.yaml`, `pixi.toml`) to manage dependencies— pick one and follow that path.
+- Covers checking for core CUDA libraries and creating isolated, reproducible environments.
+
+### 3-Verifying Your Environment with the RAPIDS CLI
+
+- Introduces two commands to validate a GPU environment after setup: `rapids doctor` and `rapids debug`.
+- `rapids doctor` runs health checks on the driver, CUDA toolkit, GPU compute capability, and NVLink.
+- Failed checks return actionable messages explaining how to fix the issue.
+- Showcases how libraries can register their own checks, using `cuml` as an example.
+- `rapids debug` produces a full snapshot of driver versions, installed packages, and build tools.
+- `rapids debug --json` outputs a machine-readable version, useful for bug reports and sharing environment state.
+
+### 4-Easy GPU Acceleration Wins with RAPIDS Accelerators
+
+- Shows how to get GPU speedups on existing Python code with zero rewrites.
+- Shows how to use the accelerators via command line and in notebooks.
+- Discusses built-in `--profile` and `--line-profile` flags show which operations ran on the GPU vs. CPU.
+- Introduces JupyterLab NVDashboard GPU Accelerators toggle that enables the same zero-code acceleration interactively in notebooks.
+
+### 5-Monitoring and Debugging GPU Python Workloads
+
+- A hands-on diagnostic guide for when GPU code runs but doesn't deliver the expected speedup.
+- Covers a progression of tools, each answering a different question about what the GPU is doing.
+- `nvidia-smi` and `nvtop` confirm driver-level visibility and live GPU utilization.
+- `cProfile` + `SnakeViz` identify CPU-side bottlenecks before any GPU work begins.
+- Nsight Systems (`nsys`) produces a full CPU/GPU timeline showing transfers, kernel launches, and idle gaps.
+- Uses a real climate data workload (`xarray` EOF analysis) to walk through two common pitfalls:
+  - Moving data back to the CPU before the expensive operation.
+  - Naively porting a Python loop to the GPU, causing thousands of tiny kernel launches.
+- Shows how to identify and fix both issues using profiler output and CUDA timeline traces.