List all tutorials and syllabi in the top-level README and document Brev instance and providers requirements.

brycelelbach · brycelelbach · commit 76d275bbe18b · 2025-11-13T13:44:19.000-05:00
diff --git a/README.md b/README.md
@@ -4,6 +4,17 @@ This repository is a home for open learning materials related to GPU computing.
 
 The intention is to create a living project where documents, examples, best practices, optimizations, and new features can become both visible and accessible to users quickly and easily.
 
+## Tutorials and Syllabi
+
+| Content | Docker Compose | Brev Instance | Brev Provider |
+|---------|----------------|---------------|---------------|
+| [CUDA C++ Tutorial](tutorials/cuda-cpp/README.md) | [docker-compose.yml](tutorials/cuda-cpp/brev/docker-compose.yml) | L40S, L4, or T4 | Crusoe or any other with Flexible Ports |
+| [Standard Parallelism Tutorial](tutorials/stdpar/README.md) | [docker-compose.yml](tutorials/stdpar/brev/docker-compose.yml) | 4xL4, 2xL4, 2xL40S, or 1x L40S | GCP, AWS, or any other with Flexible Ports and Linux 6.1.24+, 6.2.11+, or 6.3+ (for HMM) |
+| [Accelerated Python Tutorial](tutorials/accelerated-python/README.md) | [docker-compose.yml](tutorials/accelerated-python/brev/docker-compose.yml) | L40S, L4, or T4; 4xL4 or 2xL4 for distributed | Crusoe or any other with Flexible Ports |
+| [nvmath-python Tutorial](tutorials/nvmath-python/README.md) | [docker-compose.yml](tutorials/nvmath-python/brev/docker-compose.yml) | 4xL4, 2xL4, 2xL40S, or 1x L40S | Crusoe or any other with Flexible Ports |
+| [CUDA Python Tutorial - CuPy, cuDF, CCCL, & Kernels - 8 Hours](tutorials/accelerated-python/notebooks/syllabi/cuda_python__cupy_cudf_cccl_kernels__8_hours.ipynb) | [docker-compose.yml](https://github.com/NVIDIA/accelerated-computing-hub/blob/generated/main/tutorials/accelerated-python/notebooks/syllabi/cuda_python__cupy_cudf_cccl_kernels__8_hours__docker_compose.yml) | L40S, L4, or T4 | Crusoe or any other with Flexible Ports |
+| [CUDA Python Tutorial - cuda.core & CCCL - 2 Hours](tutorials/accelerated-python/notebooks/syllabi/cuda_python__cuda_core_cccl__2_hours.ipynb) | [docker-compose.yml](https://github.com/NVIDIA/accelerated-computing-hub/blob/generated/main/tutorials/accelerated-python/notebooks/syllabi/cuda_python__cuda_core_cccl__2_hours__docker_compose.yml) | L40S, L4, or T4 | Crusoe or any other with Flexible Ports |
+
 ## License
 
 All written materials (user guides, documentation, presentations) are subject to [Creative Commons CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).
diff --git a/tutorials/accelerated-python/README.md b/tutorials/accelerated-python/README.md
@@ -5,7 +5,12 @@ This modular tutorial contains content on all things related to accelerated Pyth
 - [Notebooks](./notebooks) containing lessons and exercises, intended for self-paced or instructor-led learning, which can be run on [NVIDIA Brev](https://brev.nvidia.com) or [Google Colab](https://colab.research.google.com).
 - [Slides](./slides) containing the lecture content for the lessons.
 - [Syllabi](./notebooks/syllabi) that select a subset of the notebooks for a particular learning objective.
-- [Docker Images](https://github.com/NVIDIA/accelerated-computing-hub/pkgs/container/accelerated-python-tutorial) and [Docker Compose files](./brev/docker-compose.yml) for creating Brev Launchables.
+- [Docker Images](https://github.com/NVIDIA/accelerated-computing-hub/pkgs/container/accelerated-python-tutorial) and [Docker Compose files](./brev/docker-compose.yml) for creating Brev Launchables or running locally.
+
+Brev Launchables of this tutorial should use:
+- L40S, L4, or T4 instances (for non-distributed notebooks).
+- 4xL4 or 2xL4 instances (for distributed notebooks).
+- Crusoe or any other provider with Flexible Ports.
 
 ## Syllabi
 
diff --git a/tutorials/accelerated-python/notebooks/syllabi/cuda_python__cuda_core_cccl__2_hours.ipynb b/tutorials/accelerated-python/notebooks/syllabi/cuda_python__cuda_core_cccl__2_hours.ipynb
@@ -17,9 +17,9 @@
     "\n",
     "### Materials\n",
     "\n",
-    "<table><tr>\n",
-    "<td><a href=\"https://github.com/NVIDIA/accelerated-computing-hub/blob/generated/main/tutorials/accelerated-python/notebooks/syllabi/cuda_python__cuda_core_cccl__2_hours__docker_compose.yml\">Docker Compose for Brev Launchable</a></td>\n",
-    "</tr></table>\n",
+    "| Docker Compose | Brev Instance | Brev Provider |\n",
+    "|----------------|---------------|---------------|\n",
+    "| [Link](https://github.com/NVIDIA/accelerated-computing-hub/blob/generated/main/tutorials/accelerated-python/notebooks/syllabi/cuda_python__cuda_core_cccl__2_hours__docker_compose.yml) | L40S, L4, or T4 | Crusoe or any other with Flexible Ports |\n",
     "\n",
     "### Introduction\n",
     "\n",
diff --git a/tutorials/accelerated-python/notebooks/syllabi/cuda_python__cupy_cudf_cccl_kernels__8_hours.ipynb b/tutorials/accelerated-python/notebooks/syllabi/cuda_python__cupy_cudf_cccl_kernels__8_hours.ipynb
@@ -22,11 +22,9 @@
     "\n",
     "### Materials\n",
     "\n",
-    "<table><tr>\n",
-    "<td><a href=\"https://github.com/NVIDIA/accelerated-computing-hub/raw/refs/heads/main/tutorials/accelerated-python/slides/cuda_python__cupy_cudf_kernels__8_hours__r5.pptx\">Slides (PPTX)</a></td>\n",
-    "<td><a href=\"https://github.com/NVIDIA/accelerated-computing-hub/raw/refs/heads/main/tutorials/accelerated-python/slides/cuda_python__cupy_cudf_kernels__8_hours__r5.pdf\">Slides (PDF)</a></td>\n",
-    "<td><a href=\"https://github.com/NVIDIA/accelerated-computing-hub/blob/generated/main/tutorials/accelerated-python/notebooks/syllabi/cuda_python__cupy_cudf_cccl_kernels__8_hours__docker_compose.yml\">Docker Compose for Brev Launchable</a></td>\n",
-    "</tr></table>\n",
+    "| Slides | Docker Compose | Brev Instance | Brev Provider |\n",
+    "|--------|----------------|---------------|---------------|\n",
+    "| [PPTX](https://github.com/NVIDIA/accelerated-computing-hub/raw/refs/heads/main/tutorials/accelerated-python/slides/cuda_python__cupy_cudf_kernels__8_hours__r5.pptx), [PDF](https://github.com/NVIDIA/accelerated-computing-hub/raw/refs/heads/main/tutorials/accelerated-python/slides/cuda_python__cupy_cudf_kernels__8_hours__r5.pdf) | [Link](https://github.com/NVIDIA/accelerated-computing-hub/blob/generated/main/tutorials/accelerated-python/notebooks/syllabi/cuda_python__cupy_cudf_cccl_kernels__8_hours__docker_compose.yml) | L40S, L4, or T4 | Crusoe or any other with Flexible Ports |\n",
     "\n",
     "### Introduction\n",
     "\n",
diff --git a/tutorials/cuda-cpp/README.md b/tutorials/cuda-cpp/README.md
@@ -5,7 +5,11 @@ following content:
 
 - [Notebooks](./notebooks) containing lessons and exercises, intended for self-paced or instructor-led learning, which can be run on [NVIDIA Brev](https://brev.nvidia.com) or [Google Colab](https://colab.research.google.com).
 - [Slides](./slides) containing the lecture content for the lessons.
-- [Docker Images](https://github.com/NVIDIA/accelerated-computing-hub/pkgs/container/cuda-cpp-tutorial) and [Docker Compose files](./brev/docker-compose.yml) for creating Brev Launchables.
+- [Docker Images](https://github.com/NVIDIA/accelerated-computing-hub/pkgs/container/cuda-cpp-tutorial) and [Docker Compose files](./brev/docker-compose.yml) for creating Brev Launchables or running locally.
+
+Brev Launchables of this tutorial should use:
+- L40S, L4, or T4 instances.
+- Crusoe or any other provider with Flexible Ports.
 
 ## Notebooks
 
diff --git a/tutorials/nvmath-python/README.md b/tutorials/nvmath-python/README.md
@@ -10,6 +10,12 @@ nvmath-python is designed to provide high-performance mathematical operations th
 
 These notebooks can be run on [NVIDIA Brev](https://brev.nvidia.com) or [Google Colab](https://colab.research.google.com).
 
+There are [Docker Images](https://github.com/NVIDIA/accelerated-computing-hub/pkgs/container/nvmath-python-tutorial) and [Docker Compose files](./brev/docker-compose.yml) for creating Brev Launchables or running locally.
+
+Brev Launchables of this tutorial should use:
+- 4xL4, 2xL4, 2xL40S, or 1x L40S instances.
+- Crusoe or any other provider with Flexible Ports.
+
 ## Prerequisites
 
 To use these notebooks, you will need:
@@ -53,13 +59,13 @@ Please refer to the [nvmath-python documentation](https://docs.nvidia.com/cuda/n
 **Key Topics:**
 - Understanding memory and execution spaces in nvmath-python
 - Flexibility of working with both CPU and GPU memory
-- Difference between specialized APIs and generic APIs 
+- Difference between specialized APIs and generic APIs
 - Performance implications of data transfers between memory spaces
 - Using nvmath-python's logging mechanism to understand internal operations
 
 **Key Insights:**
 - Memory space (where data is stored) and execution space (where computation happens) may differ
-- Specialized APIs like `matmul` only support GPU execution, triggering expensive automatic data transfers for CPU inputs 
+- Specialized APIs like `matmul` only support GPU execution, triggering expensive automatic data transfers for CPU inputs
 - Generic APIs like FFT adapt to input location, executing on CPU for CPU inputs and GPU for GPU inputs
 - Logging provides visibility into specification, planning, and execution phases
 
@@ -76,8 +82,8 @@ Please refer to the [nvmath-python documentation](https://docs.nvidia.com/cuda/n
 
 **Key Insights:**
 - Stateless API is convenient for single operations but repeats specification/planning for each call
-- Stateful API allows specification and planning once, then multiple executions 
-- Autotuning finds optimal kernels when built-in heuristics are suboptimal 
+- Stateful API allows specification and planning once, then multiple executions
+- Autotuning finds optimal kernels when built-in heuristics are suboptimal
 - Critical for scenarios with repeated operations on similar-shaped data
 
 ---
@@ -96,7 +102,7 @@ Please refer to the [nvmath-python documentation](https://docs.nvidia.com/cuda/n
 - Callbacks enable custom element-wise operations fused with FFT kernels
 - JIT compilation overhead is one-time cost that can be amortized
 - For single images, CuPy may be faster due to nvmath-python's compilation overhead
-- For large enough batches amortization makes stateful API a preferred choice 
+- For large enough batches amortization makes stateful API a preferred choice
 
 ---
 
diff --git a/tutorials/stdpar/README.md b/tutorials/stdpar/README.md
@@ -4,7 +4,11 @@ This tutorial teaches you how to accelerate portable HPC applications with CPUs
 
 - [Notebooks](./notebooks) containing lessons and exercises, intended for self-paced or instructor-led learning, which can be run on [NVIDIA Brev](https://brev.nvidia.com) or [Google Colab](https://colab.research.google.com).
 - [Slides](./slides) containing the lecture content for the lessons.
-- [Docker Images](https://github.com/NVIDIA/accelerated-computing-hub/pkgs/container/stdpar-tutorial) and [Docker Compose files](./brev/docker-compose.yml) for creating Brev Launchables.
+- [Docker Images](https://github.com/NVIDIA/accelerated-computing-hub/pkgs/container/stdpar-tutorial) and [Docker Compose files](./brev/docker-compose.yml) for creating Brev Launchables or running locally.
+
+Brev Launchables of this tutorial should use:
+- 4xL4, 2xL4, 2xL40S, or 1x L40S instances.
+- GCP, AWS, or any other with Flexible Ports and Linux 6.1.24+, 6.2.11+, or 6.3+ (for HMM).
 
 ## Notebooks
 
