<!--
SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

SPDX-License-Identifier: Apache-2.0
-->

# Cookbook

OSMO cookbook is a comprehensive suite of AI workflows to perform a wide variety of robotics tasks.

## Tutorials

> [Collection of workflows](./tutorials/) from the [tutorial documentation](https://nvidia.github.io/OSMO/main/user_guide/tutorials/overview.html)

## How-To Guides

| Workflow Name                                                          | Link                                         |
|------------------------------------------------------------------------|----------------------------------------------|
| Isaac Sim: Generating Synthetic Data                                   | [README](./synthetic_data_generation/isaac_sim/README.md) |
| Isaac Lab: Training Robot Policy with Reinforcement Learning           | [README](./reinforcement_learning/single_gpu/README.md) |
| ROS2: Multi-Node Communication                                         | [README](./ros/comm/README.md)                            |
| TorchRun: Training on a Single Node                                    | [README](./dnn_training/single_node/README.md) |
| TorchRun: Training on Multiple Nodes                                   | [README](./dnn_training/torchrun_multinode/README.md) |
| TorchRun: Training with Rescheduling                                   | [README](./dnn_training/torchrun_reschedule/README.md) |
| Hardware-in-the-Loop: Deploying Policy on Jetson                        | [README](./hil/README.md)                      |

## Physical AI End-to-End Pipeline: Nut Pouring

End-to-end pipeline for fine-tuning the GR00T VLA model using the Nut Pouring task: from teleop data through MimicGen, Cosmos Transfer, and LeRobot to a trained policy. Check out the [README](./nut_pouring/README.md) for more information about this workflow series.

## Remote Development

| Workflow Name                                                          | Link                                         |
|------------------------------------------------------------------------|----------------------------------------------|
| JupyterLab: Host a Notebook                                            | [README](./integration_and_tools/jupyterlab/README.md)  |
| Filebrowser: Launching a Workspace                                     | [README](./integration_and_tools/filebrowser/README.md)  |
| VSCode: Spin up a Remote Server                                        | [README](./integration_and_tools/vscode/README.md)      |
| Github: Cloning a Private Repository                                   | [README](./integration_and_tools/github/README.md)  |
| Ray: Create a Cluster                                                  | [README](./integration_and_tools/ray/README.md)         |
| Weights & Biases: Neural Network Training                              | [README](./integration_and_tools/wandb/README.md)       |
| Isaac Sim: Livestreaming Client                                        | [README](./integration_and_tools/isaacsim/README.md)    |

## Additional Applications

| Workflow Name                                                          | Link                                         |
|------------------------------------------------------------------------|----------------------------------------------|
| Isaac Lab: Multi-GPU Reinforcement Learning Training                    | [README](./reinforcement_learning/multi_gpu/README.md)          |
| Isaac Lab: Multi-Node Reinforcement Learning Training                   | [README](./reinforcement_learning/multi_node/README.md)         |
| Isaac Groot: Interactive Notebook for Inference and Fine-tuning        | [README](./groot/groot_notebook/README.md)                    |
| Isaac Groot: Finetuning a Model                                        | [README](./groot/groot_finetune/README.md)                       |
| Isaac Groot: Imitation Learning using Groot Mimic                      | [README](./groot/groot_mimic/README.md)                       |
| ROS2: Running Simulation with Foxglove Visualization                   | [README](./ros/turtlebot/README.md)                     |
| Gazebo: Generating Synthetic Data                                      | [README](./synthetic_data_generation/gazebo/README.md)  |
| Cosmos Predict: Video2World Generation                                 | [README](./cosmos/predict/README.md) |
| Cosmos: Transfer2.5 with Isaac Sim Integration                         | [README](./cosmos/transfer/README.md)               |
| Cosmos: Video Reasoning and Analysis                                   | [README](./cosmos/reason/README.md)                 |
| NIMs: Using NVIDIA NIMs in a workflow                                  | [README](./nims/README.md) |
| TorchRun: Elastic Training on Multiple Nodes                           | [README](./dnn_training/torchrun_elastic/README.md) |
| DeepSpeed: Training on Multiple Nodes                                  | [README](./dnn_training/deepspeed_multinode/README.md) |