From 599cf838a007d6121929ce84f117de0e9f371e2c Mon Sep 17 00:00:00 2001 From: GitLab CI Date: Wed, 29 Apr 2026 18:42:01 +0000 Subject: [PATCH] chore: Regenerate all playbooks --- README.md | 2 - nvidia/hermes-agent/README.md | 262 ------------------ nvidia/i4h-so-arm/README.md | 488 ---------------------------------- 3 files changed, 752 deletions(-) delete mode 100644 nvidia/hermes-agent/README.md delete mode 100644 nvidia/i4h-so-arm/README.md diff --git a/README.md b/README.md index bf07497..5458d39 100644 --- a/README.md +++ b/README.md @@ -28,8 +28,6 @@ Each playbook includes prerequisites, step-by-step instructions, troubleshooting - [CUDA-X Data Science](nvidia/cuda-x-data-science/) - [DGX Dashboard](nvidia/dgx-dashboard/) - [FLUX.1 Dreambooth LoRA Fine-tuning](nvidia/flux-finetuning/) -- [Hermes-agent with Local Models](nvidia/hermes-agent/) -- [Develop and Deploy Healthcare Robots with Isaac For Healthcare](nvidia/i4h-so-arm/) - [Install and Use Isaac Sim and Isaac Lab](nvidia/isaac/) - [Optimized JAX](nvidia/jax/) - [Live VLM WebUI](nvidia/live-vlm-webui/) diff --git a/nvidia/hermes-agent/README.md b/nvidia/hermes-agent/README.md deleted file mode 100644 index 1adc24a..0000000 --- a/nvidia/hermes-agent/README.md +++ /dev/null @@ -1,262 +0,0 @@ -# Hermes-agent with Local Models - -> Install and run the Hermes self-improving AI agent on DGX Spark. - -## Table of Contents - -- [Overview](#overview) -- [Instructions](#instructions) -- [Troubleshooting](#troubleshooting) - ---- - -## Overview - -## Basic idea - -[Hermes Agent](https://github.com/NousResearch/hermes-agent) is a **self-improving** AI agent built by [Nous Research](https://nousresearch.com). It runs as a terminal TUI on your machine and, through a built-in gateway, can also be reached from messaging platforms like Telegram, Discord, and Slack. It creates skills from experience, improves them during use, persists memory across sessions, and can run scheduled tasks via its built-in cron. - -Running Hermes and its LLM **fully on your DGX Spark** keeps your conversations and data private and avoids ongoing cloud API costs. DGX Spark is well suited for this: it runs Linux, is designed to stay on, and has **128GB memory**, so you can serve large local models for better reasoning quality and connect to the agent from your phone over Telegram while the heavy work runs locally. - -## What you'll accomplish - -You will have Hermes installed on your DGX Spark and connected to a local LLM served by Ollama. You can chat with the agent from the DGX Spark terminal and from Telegram on your phone or laptop. The gateway runs as a system service, so the agent stays reachable across reboots without anyone logging in. - -- Install Ollama and pull a local model -- Install Hermes and configure it against the local Ollama endpoint -- Set up a Telegram bot so you can message Hermes from any Telegram client -- Resume past sessions, switch models, update, and uninstall using the `hermes` CLI - -## Popular use cases - -- **Personal assistant from your phone**: Chat with Hermes via Telegram while the model runs on your Spark — manage email drafts, summarize docs, or answer questions on the go. -- **Multi-step task automation**: Ask the agent to walk you through configurations (e.g., setting up email); on non-trivial tasks Hermes can autonomously persist a reusable skill for next time. -- **Scheduled checks**: Use the built-in cron to watch a product price online or run a daily check, and have results delivered to your Telegram home channel. -- **Reasoning-visible problem solving**: Use `/reasoning show` in the TUI to follow the agent's intermediate reasoning on complex problems. - -## What to know before starting - -- Basic use of the Linux terminal and a text editor -- Familiarity with Ollama or willingness to follow the [Ollama playbook](../ollama/) first -- A Telegram account if you want to use the messaging gateway -- Awareness of the security considerations below - -## Important: security and risks - -AI agents that can execute commands and reach external services introduce real risks. Read the upstream guidance: [Hermes documentation](https://hermes-agent.nousresearch.com/docs/). - -Main risks: - -1. **Data exposure**: Personal information or files on your DGX Spark may be leaked through agent actions or messaging channels. -2. **Unauthorized access**: A Telegram bot left open to anyone who finds it can be misused; a model endpoint exposed beyond `localhost` can be abused. - -You cannot eliminate all risk; proceed at your own risk. **Recommended security measures:** - -- **Restrict the Telegram bot** by entering one or more numeric Telegram user IDs at the *"Allowed user IDs"* prompt during install. Leaving this blank allows anyone who finds the bot to use it. -- Keep the Ollama endpoint bound to **`localhost` only**; do not expose `http://:11434` to your LAN or the public internet without strong authentication. -- Run Hermes on a Spark dedicated to this purpose where possible, and only place files on it that the agent is allowed to access. -- **Monitor activity**: Periodically review the gateway service logs (`journalctl -u `) and the Hermes session history. - -## Prerequisites - -- DGX Spark running Linux, connected to your network -- Terminal (SSH or local) access to the Spark -- `curl` and `git` installed (verified in Step 1 of the instructions) -- Enough disk and GPU memory for the Ollama model you plan to serve (the playbook uses `qwen3.6:27b` as the example; pick a smaller model if you want a faster first install) -- A Telegram account and the ability to create a bot via [@BotFather](https://t.me/BotFather) if you plan to use the messaging gateway - -## Time and risk - -- **Duration**: About 30 minutes for install and first-time setup; model download time depends on size and network speed. -- **Risk level**: **Medium** — the agent can execute commands, persist skills, and is reachable from Telegram. Risk increases if you skip the allowed-user-IDs restriction or expose the local model endpoint beyond `localhost`. Always follow the security measures above. -- **Rollback**: Run `hermes uninstall` to remove Hermes, the gateway service, and the shell-profile entry. Uninstall Ollama separately if desired. -- **Last Updated**: 2026-04-26 - - First Publication - -## Instructions - -## Step 1. Verify your environment - -Before installing Hermes, confirm that your DGX Spark is running DGX OS, has network access, and exposes the basic command-line tools used during install. - -```bash -uname -a -curl --version -git --version -``` - -Expected output should show DGX OS and working `curl` / `git` binaries. - -## Step 2. Install Ollama and pull a model - -Hermes will be configured against a local Ollama endpoint, so Ollama must be installed and serving at least one model before you run the Hermes installer. If you have already completed the [Ollama playbook](../ollama/), you can skip this step. - -Install Ollama: - -```bash -curl -fsSL https://ollama.com/install.sh | sh -``` - -Verify the Ollama service is running and reachable on the default port: - -```bash -curl http://localhost:11434/api/tags -``` - -Pull the model you intend to use with Hermes (this playbook uses `qwen3.6:27b` as the example): - -```bash -ollama pull qwen3.6:27b -``` - -## Step 3. Install Hermes - -```bash -curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash -``` - -The installer will walk you through an interactive setup. Respond to each prompt in the order they appear: - -1. **"Install ripgrep for faster file search ffmpeg for TTS voice messages? [Y/n]"** — Press **Enter** to accept the default and install both helpers. - -2. **"How would you like to set up Hermes?"** — Choose **Quick setup** to proceed with the recommended defaults. - -3. **"Select Provider"** — Choose **Custom endpoint (enter URL manually)** so Hermes can be pointed at the model endpoint running on your DGX Spark. - -4. **"API base URL [e.g. https://api.example.com/v1]:"** — Enter the URL of your local model server. For a local Ollama endpoint, use `http://localhost:11434/v1`. - -5. **"API key [optional]"** — Leave blank and press **Enter**; no key is required for a local model. - -6. **Model selection** — The installer lists the models available from your local Ollama instance. Select one to use with Hermes (for example, `qwen3.6:27b`). - -7. **"Context length in tokens [leave blank for auto-detect]:"** — Press **Enter** to let Hermes auto-detect the context length from the selected model. - -8. **"Display name [Local (localhost:11434)]"** — Press **Enter** to accept the suggested label, or type a custom name to identify this endpoint in the Hermes UI. - -9. **"Connect a messaging platform? (Telegram, Discord, etc.)"** — Choose **Set up messaging now (recommended)** to configure a gateway during installation. - -10. **"Select platforms to configure:"** — Choose **Telegram**. The remaining steps in this playbook use Telegram as the example; the same flow applies to the other supported gateways. - -11. **"Telegram bot token:"** — Open Telegram and start a chat with [@BotFather](https://t.me/BotFather), follow its guided flow to create a new bot, then paste the token BotFather returns into this prompt. The terminal will not echo any characters as the token is pasted — this is expected. Press **Enter** to submit; the installer should respond with `Telegram token saved`. - -12. **"Allowed user IDs (comma-separated, leave empty for open access):"** — To restrict the bot to specific Telegram accounts, follow the on-screen instructions to look up your numeric Telegram user ID, then enter one or more IDs separated by commas. Leaving this field blank allows anyone who can reach the bot to use it, which is generally not recommended. - -13. **"Use your user ID (\) as the home channel? [Y/n]:"** — Press **Enter** to accept. This designates your own Telegram account as the default channel Hermes will use for proactive messages and scheduled deliveries. - -14. **"Install the gateway as a systemd service? (runs in background, starts on boot) [Y/n]:"** — Press **Enter** to accept. The gateway will run as a background service and start automatically whenever your DGX Spark boots. - -15. **"Choose how the gateway should run in the background:"** — Choose **System service**. The DGX Spark is typically an always-on machine, and a system service starts on boot without requiring an interactive login or the `linger` workaround that user services need. The service will still run under your user account so it can read your Hermes configuration; only installation requires `sudo`. - -16. **"Launch hermes chat now? [Y/n]:"** — Press **Enter** to launch the Hermes TUI immediately and verify the installation end-to-end. Once the TUI is open, type `hello` and press **Enter**; the agent should respond, confirming that the model endpoint and Hermes are wired up correctly. When you're done, type `/exit` to leave the chat and return to your shell. On exit, Hermes prints the exact command needed to resume this conversation later — `hermes --resume `. Save it if you want to pick up where you left off. - -17. **"Would you like to install the gateway as a background service? [Y/n]:"** — Press **Enter** to accept. This finalizes the gateway as a background service so it stays available for messaging-platform traffic outside of an interactive Hermes session. - -18. **Reload your shell** to make the `hermes` command available: - - ```bash - source ~/.bashrc - ``` - -## Step 4. Switch to a different Ollama model (optional) - -You configured an initial model during the Hermes install. To switch to a different one later, pull the new model with Ollama and then re-point Hermes at the same local endpoint. - -1. Pull the new model with Ollama (replace `` with the model you want): - - ```bash - ollama pull - ``` - -2. Launch the Hermes model picker: - - ```bash - hermes model - ``` - -3. At the **"Select Provider"** prompt, choose **Custom endpoint (enter URL manually)**. - -4. At the **"API base URL"** prompt, enter the same local Ollama endpoint as before: - - ``` - http://localhost:11434/v1 - ``` - -5. When the installer lists the models served by Ollama, choose the one you just pulled. Hermes will use it for subsequent sessions. - -## Step 5. Resume a previous Hermes session - -To pick up a past conversation, launch Hermes with the `--resume` flag and the session ID printed when you exited that chat: - -```bash -hermes --resume -``` - -The TUI will reopen with the prior conversation history restored, ready for follow-up prompts. - -## Step 6. Talk to Hermes from Telegram - -The Telegram gateway you configured during install is already running as a background service, so you can reach Hermes from any Telegram client without a terminal session. - -1. Open Telegram (mobile or desktop) and search for your bot by the username you assigned through @BotFather. - -2. Open the chat with the bot and tap **Start** (or send `/start`) on first contact. - -3. Send the message `hello`. Hermes will reply through the bot, confirming the gateway is wired to your DGX Spark and the underlying model. - -From here you can send any prompt you would normally type in the TUI — Hermes will run on your DGX Spark and stream the response back to Telegram. - -## Step 7. Update Hermes - -To upgrade an existing Hermes installation to the latest release, run: - -```bash -hermes update -``` - -The command pulls the latest Hermes version, applies any required dependency changes, and restarts the gateway service so the new version takes effect. - -## Step 8. Cleanup - -> [!WARNING] -> This removes the Hermes installation and the gateway service. By default, `~/.hermes/` (configuration, conversation history, and skills) is preserved unless you opt into a full uninstall at the on-screen prompt. - -Because the gateway was installed as a **System service** in Step 15, run the uninstall with `sudo` so it has permission to remove the system-scope systemd unit: - -```bash -sudo hermes uninstall -``` - -Follow the on-screen prompts to confirm removal. `sudo hermes uninstall` automatically: - -- Stops and removes the systemd gateway service. -- Removes the `hermes` wrapper script and the PATH entries added to your shell profile. -- Deletes the Hermes installation directory. - -## Step 9. Next steps - -1. **Inspect the agent's reasoning.** Inside the TUI, run `/reasoning show` to surface the model's intermediate reasoning alongside its responses. This is especially useful for following the agent's progress on multi-step or complex problems and for debugging unexpected answers. -2. **Try a multi-step task to trigger skill creation.** For example, ask the agent how to set up email — Hermes will walk through the configuration with you and, on completing a non-trivial task like this, may autonomously persist a reusable skill so the next email-related request is faster. -3. **Configure scheduled automations via the built-in cron.** For example, ask Hermes to check the price of a product online once a day and notify you on Telegram when it drops below a threshold. Hermes will schedule the task with its built-in cron and deliver each result through the messaging gateway you set up. - -## Troubleshooting - -| Symptom | Cause | Fix | -|---------|-------|-----| -| `hermes: command not found` after install | Shell profile not reloaded in the current session | Run `source ~/.bashrc` (or `source ~/.zshrc`) and retry. Open a new terminal if the issue persists. | -| Hermes installer can't list any models at the model-selection prompt | Ollama is not running or has no models pulled | Sanity-check Ollama in another terminal: list installed models with `ollama list`, hit the API with `curl http://localhost:11434/api/tags`, and confirm a model can actually serve requests by running `ollama run ` (e.g. `ollama run qwen3.6:27b`) and sending a test prompt. If the list is empty or the API is unreachable, start Ollama and pull a model with `ollama pull `, then re-run the Hermes installer. | -| `Connection refused` to `http://localhost:11434/v1` from Hermes | Ollama service not running on the default port | Start the Ollama service and confirm it is listening on `11434`. On systemd hosts: `systemctl status ollama` and `systemctl start ollama`. | -| Pasting the Telegram bot token shows nothing on the screen | Expected — the installer hides token characters as a security measure | Paste the token, then press **Enter**. The installer should respond with `Telegram token saved`. | -| Telegram bot does not reply when you send `hello` | Gateway service not running, or your account is not in the allowed user IDs list | Check the gateway service status with `systemctl status` (look for the Hermes gateway unit installed in Step 14). If your Telegram user ID was not added during install, re-run `hermes` setup or update the gateway config to include it. | -| Out-of-memory or very slow inference | Selected Ollama model is too large for available GPU memory, or other GPU workloads are competing | Check usage with `nvidia-smi`, free GPU memory by closing other workloads, or pull a smaller model with `ollama pull ` and switch to it via `hermes model`. | -| `hermes update` fails or the gateway does not restart | Gateway service still bound to the previous version, or insufficient permissions on a system-service install | Re-run `hermes update` with `sudo` if the gateway was installed as a **System service**. If the service is stuck, restart it manually: `sudo systemctl restart `. | -| Cannot resume a previous session | The `` value is missing or wrong | Launch `hermes` without `--resume` to start fresh; past session IDs are printed to the terminal each time you `/exit` a chat. | - -> [!NOTE] -> DGX Spark uses a Unified Memory Architecture (UMA), which enables dynamic memory sharing between the GPU and CPU. -> With many applications still updating to take advantage of UMA, you may encounter memory issues even when within -> the memory capacity of DGX Spark. If that happens, manually flush the buffer cache with: -```bash -sudo sh -c 'sync; echo 3 > /proc/sys/vm/drop_caches' -``` - -For latest known issues, please review the [DGX Spark User Guide](https://docs.nvidia.com/dgx/dgx-spark/known-issues.html). diff --git a/nvidia/i4h-so-arm/README.md b/nvidia/i4h-so-arm/README.md deleted file mode 100644 index 9267d34..0000000 --- a/nvidia/i4h-so-arm/README.md +++ /dev/null @@ -1,488 +0,0 @@ -# Develop and Deploy Healthcare Robots with Isaac For Healthcare - -> End-to-end development and deployment of healthcare robots on DGX Spark - -## Table of Contents - -- [Overview](#overview) -- [Part 1: Preparation](#part-1-preparation) - - [Set Up Conda Environment](#set-up-conda-environment) - - [Set Up Docker Environment](#set-up-docker-environment) - - [Set Up the Scene](#set-up-the-scene) - - [Calibrate the Robot](#calibrate-the-robot) - - [Test Teleoperation](#test-teleoperation) -- [Part 2: Synthetic Data Generation](#part-2-synthetic-data-generation) -- [Part 3: Real-World Data Collection](#part-3-real-world-data-collection) -- [Part 4: GR00T N1.5 Fine-Tuning](#part-4-gr00t-n15-fine-tuning) -- [Part 5: Deploying Trained Robotic Policy](#part-5-deploying-trained-robotic-policy) - ---- - -## Overview - -## Basic idea - -Robotics and physical AI are driving the next wave of AI breakthroughs. Developing physical AI requires [3 computers](https://blogs.nvidia.com/blog/three-computers-robotics/) — 1. A simulation computer to generate synthetic data and digital twins, bridging the data gap. 2. A training computer to build the necessary foundation and world models. 3. A runtime computer to handle real-time robotic inference and intelligent interactions. - -This tutorial demonstrates the development and deployment of an autonomous healthcare robot using [NVIDIA Isaac For Healthcare](https://developer.nvidia.com/blog/introducing-nvidia-isaac-for-healthcare-an-ai-powered-medical-robotics-development-platform/) on a single [DGX Spark](https://www.nvidia.com/en-us/products/workstations/dgx-spark/), consolidating the 3-computers developer workflow onto one hardware platform. The example focuses on the [SO-101 robot](https://github.com/TheRobotStudio/SO-ARM100?tab=readme-ov-file) acting as a scrub nurse—a specialized nursing professional working directly in the sterile field during surgical procedures—to perform a crucial pick-and-place task — autonomously picking up a pair of surgical scissors and placing them into a surgical tray. - -## What you'll accomplish - -You'll complete the full development lifecycle of an autonomous healthcare robot on DGX Spark, covering the following stages: - -- **Part 1 — Preparation.** Set up the hardware, software environments, and task environment. -- **Part 2 — Generating synthetic data with Isaac Sim.** Collect synthetic pick-and-place demonstrations using teleoperation in a simulated environment. -- **Part 3 — Collecting real-world data.** Collect real-world teleoperation data with the physical SO-101 robot. -- **Part 4 — Fine-tuning the GR00T N1.5 model.** Fine-tune a pretrained GR00T N1.5 model using the collected data. -- **Part 5 — Deploying trained robotic policy.** Deploy the fine-tuned model in both simulated and real-world environments. - -## What to know before starting - -- Experience with Linux command line -- Basic understanding of Docker containers -- Familiarity with Python and conda environments -- Basic knowledge of robotics concepts (teleoperation, calibration) -- Familiarity with machine learning concepts (helpful but not required) - -## Prerequisites - -**Hardware Requirements:** -- [NVIDIA DGX Spark](https://www.nvidia.com/en-us/products/workstations/dgx-spark/) with FastOS version 1.91.+ (verify with `cat /etc/fastos-release`; upgrade if necessary following [steps here](https://docs.nvidia.com/dgx/dgx-spark/system-recovery.html#recovery-process-steps)) -- [SO-101 Robot](https://github.com/TheRobotStudio/SO-ARM100?tab=readme-ov-file) with both leader & follower arms and wrist camera module (ensure mounting/fixation tools are included or acquired separately) -- USB-C splitter (needed since 4 USB connections are required and DGX Spark has only 3 available USB-C ports; use a high-quality splitter to minimize latency) -- OpenCV compatible USB web camera (for the room camera) -- Surgical tray (dimensions 24cm x 16cm x 5cm) -- Surgical scissors (length 18cm) -- Scene setup accessories — table, table cloth, and a camera stand/holder for the room camera - -**Software Requirements:** -- NVIDIA DGX OS -- Miniconda: [installation guidelines](https://www.anaconda.com/docs/getting-started/miniconda/install#aws-graviton2%2Farm64) -- Docker (pre-installed on DGX OS) - -## Ancillary files - -All required assets can be found in the [NVIDIA Isaac-For-Healthcare-Workflows repository](https://github.com/isaac-for-healthcare/i4h-workflows). - -- `workflows/so_arm_starter/` - Source code for the robotic scrub nurse example workflow -- `tools/env_setup_so_arm_starter.sh` - Environment setup script for the conda environment -- `workflows/so_arm_starter/docker/dgx.Dockerfile` - Dockerfile for the Docker environment - -## Time & risk - -* **Estimated time:** Approximately 2 days (GR00T N1.5 fine-tuning at 30,000 steps takes around 24 hours on DGX Spark; data collection and other setup steps require several additional hours) -* **Risk level:** Medium - * Robot calibration must remain consistent throughout the tutorial; re-calibrating after data collection or training may require restarting the entire process - * Large downloads and Docker builds may take significant time - * Leader and follower arm power cords have different voltages—do not mix them up -* **Rollback:** Conda environment and Docker image can be removed to revert software changes. Collected datasets can be deleted from `~/.cache/huggingface/lerobot/`. - -## Part 1: Preparation - -## Step 1. Prepare Hardware and Accessories - -Required components: - -* [**NVIDIA DGX Spark**](https://www.nvidia.com/en-us/products/workstations/dgx-spark/) — Verify that FastOS version is 1.91.+ with `cat /etc/fastos-release`; upgrade if necessary following [steps here](https://docs.nvidia.com/dgx/dgx-spark/system-recovery.html#recovery-process-steps). -* [**SO-101 Robot**](https://github.com/TheRobotStudio/SO-ARM100?tab=readme-ov-file) — Requires both leader & follower arms with wrist camera module. Ensure mounting/fixation tools are included or acquired separately. -* **USB-C Splitter** — Needed since 4 USB connections (2 USB-C for arms, 2 USB-A for cameras) are required and DGX Spark has only 3 available USB-C ports. Use a high-quality splitter to minimize latency. -* **OpenCV compatible USB web camera** — For the room camera. -* **Surgical Tray** — Dimensions 24cm x 16cm x 5cm. -* **Surgical Scissors** — Length 18cm. -* **Scene Setup Accessories** — Table, table cloth, and a camera stand/holder for the room camera. - -## Step 2. Set Up Software Environments - -Power on DGX Spark and open a terminal window. - -Create a folder named `workspace` under your home directory, and clone the NVIDIA Isaac-For-Healthcare-Workflows repository `i4h-workflows` from GitHub: - -```shell -mkdir ~/workspace -cd ~/workspace && git clone https://github.com/isaac-for-healthcare/i4h-workflows.git -``` - -The source code for several Isaac For Healthcare example workflows is in this repository, including the robotic scrub nurse example at `/workflows/so_arm_starter`. - -This tutorial requires two separate software environments on DGX Spark: - -1. A conda environment for most of the tasks. -2. A docker environment for all tasks that require Isaac-GR00T. - -A separate docker environment was needed primarily because of the complexity in installing certain Isaac-GR00T dependencies, like `flash_attn`, on the DGX Spark's native arm64 OS. - -### Set Up Conda Environment - -First, ensure Miniconda is installed on DGX Spark. If not, follow the [installation guidelines here](https://www.anaconda.com/docs/getting-started/miniconda/install#aws-graviton2%2Farm64). Then, create a new conda environment and install the necessary dependencies for this tutorial: - -```shell -conda create -n so_arm_starter python=3.11 -y -conda activate so_arm_starter -cd && bash tools/env_setup_so_arm_starter.sh -``` - -Installation takes about 20 minutes and, when complete, prints a success message to the terminal. - -```shell -========================================== -Environment setup script finished. -========================================== -``` - -After installation, **deactivate and reactivate the `so_arm_starter` environment** to apply configurations: - -```shell -conda deactivate -conda activate so_arm_starter -``` - -After reactivating the conda environment, set the following environment variable: - -```shell -export PYTHONPATH=/workflows/so_arm_starter/scripts -``` - -To avoid manually setting the environment variable each time you activate `so_arm_starter`, optionally add the command to `~/.bashrc`. Source the file immediately after adding it to activate it in the current session. - -### Set Up Docker Environment - -To set up the docker environment, build a docker image using the `dgx.Dockerfile` provided under `/workflows/so_arm_starter/docker`: - -```shell -cd /workflows/so_arm_starter/docker -docker build -t soarm-dgx -f dgx.Dockerfile . -``` - -The build takes about 20 minutes, creating a docker image named `soarm-dgx`. - -## Step 3. Set Up the Task Environment - -### Set Up the Scene - -To set up the scrub nurse pick-and-place scene: - -1. **Mount Arms:** Firmly mount the follower arm on the table and the leader arm nearby for comfortable teleoperation. -2. **Set Scene:** Place the table cloth, surgical tray, and scissors on the table. Use a non-reflective, dark table cloth to minimize reflections and maintain consistent background color. Fixate the table cloth to the table to prevent movement when the follower's gripper touches it. Ensure the tray and scissors are within easy reach of the follower arm's gripper. -3. **Mount Camera:** Mount the room camera above the table for a top-down view. While other positions (like a side-view) might offer better object localization, the top-down view minimizes environmental elements, focusing only on task-relevant objects for a more robust setup. - -To finally adjust the table and room camera stand for optimal wrist and room camera views, power on the robot and cameras. Connect the following to the DGX Spark: - -* Leader and follower arms (2x USB-C) -* Wrist camera (1x USB-A) -* Room camera (1x USB-A or USB-C) - -Due to limited DGX Spark USB-C ports, a USB-C splitter (and optional USB-A/C converters) is needed. Power the leader and follower arms, **taking care not to mix up the power cords as voltages differ.** Use a camera tool (e.g., Cheese on DGX Spark) to check live feeds and finalize positioning. - -### Calibrate the Robot - -First, identify the device IDs for the two robot arms and the two cameras. - -Open a new terminal on DGX Spark. Activate the `so_arm_starter` conda environment: - -```shell -conda activate so_arm_starter -``` - -Execute the following command and follow the on-screen instructions to identify the device IDs of the leader arm and the follower arm: - -```shell -python -m lerobot.find_port -``` - -On a Linux-based system, the device IDs are usually `/dev/ttyACM0` and `/dev/ttyACM1`. - -Execute the following command to identify the wrist and room camera indices: - -```shell -python -m lerobot.find_cameras -``` - -The console should list 2 cameras with their indices (e.g., `/dev/video0` and `/dev/video2`). This command also captures and saves the current camera frames as distinct PNG images in `outputs/captured_images/`, using camera indices in the filename for easy identification and verification of feeds. - -Set access permissions for the robot arms before calibration by running: - -```shell -sudo chmod 666 /dev/ttyACM0 -sudo chmod 666 /dev/ttyACM1 -``` - -Adjust device IDs as needed. **Execute these commands every time the robot disconnects from and reconnects to DGX Spark.** - -Run the following commands in the terminal to calibrate the leader arm and the follower arm: - -```shell -## Leader arm: -python -m lerobot.calibrate --teleop.type=so101_leader --teleop.port=/dev/ttyACM0 --teleop.id=so101_leader - -## Follower arm: -python -m lerobot.calibrate --robot.type=so101_follower --robot.port=/dev/ttyACM1 --robot.id=so101_follower -``` - -Adjust device IDs and customize `--teleop.id` and `--robot.id` to set different device names if needed. Then, follow on-screen instructions and refer to the [video here](https://huggingface.co/docs/lerobot/so101#calibration-video) for proper calibration. - -> [!WARNING] -> Maintain *one* single follower arm calibration for this tutorial. Re-calibrating after collecting data or training the GR00T model risks needing to restart everything, as subsequent steps rely on the initial calibration. - -### Test Teleoperation - -To complete the preparation, teleoperate the follower arm using the leader arm. - -Run the following command to teleoperate without camera feeds: - -```shell -python -m lerobot.teleoperate \ ---robot.type=so101_follower \ ---robot.port=/dev/ttyACM1 \ ---robot.id=so101_follower \ ---teleop.type=so101_leader \ ---teleop.port=/dev/ttyACM0 \ ---teleop.id=so101_leader -``` - -Adjust the `--robot.port`, `--teleop.port`, `--robot.id` and `--teleop.id` arguments if needed. - -Run the following command to teleoperate with camera feeds: - -```shell -python -m lerobot.teleoperate \ ---robot.type=so101_follower \ ---robot.port=/dev/ttyACM1 \ ---robot.id=so101_follower \ ---robot.cameras="{wrist: {type: opencv, index_or_path: 2, width: 640, height: 480, fps: 30}, room: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \ ---teleop.type=so101_leader \ ---teleop.port=/dev/ttyACM0 \ ---teleop.id=so101_leader \ ---display_data=true -``` - -Adjust device IDs, names and camera indices if needed. - -During teleoperation with camera feeds, the [Rerun viewer](https://rerun.io/) UI appears, showing real-time views from both cameras and the robot's motor action data. - -## Part 2: Synthetic Data Generation - -## Step 1. Launch Isaac Sim for Data Collection - -Ensure the leader arm is powered on and connected to DGX Spark. Open a new terminal on DGX Spark, activate the `so_arm_starter` conda environment and set the `PYTHONPATH`: - -```shell -conda activate so_arm_starter -export PYTHONPATH=/workflows/so_arm_starter/scripts -``` - -Then, run the following command in the terminal: - -```shell -python -m simulation.environments.teleoperation_record \ - --port=/dev/ttyACM0 \ - --enable_cameras \ - --record \ - --dataset_path=./data-collection-sim/dataset.hdf5 -``` - -If needed, adjust the leader arm device ID and modify the `--dataset_path` argument to save data elsewhere. - -The command launches [Isaac Sim](https://developer.nvidia.com/isaac/sim), loading a scene with a follower arm, table, surgical scissors, and a tray. The initial load may take about 2 minutes; if Isaac Sim seems unresponsive, do not force quit—wait for it to load fully. - -To change the simulated follower arm's color to match your physical robot, go to the `Stage` panel (right side of Isaac Sim) → `World` → `envs` → `env_0` → `robot` → `Looks` → `material_a_3d_printed`, then under the `Property` tab, adjust the `Albedo Color`. - -The first command run requires leader arm calibration, even if previously done, due to a different program-specific calibration file. Your existing calibration remains unchanged. - -## Step 2. Collect Synthetic Pick-and-Place Demonstrations - -To teleoperate the robot in Isaac Sim and collect synthetic pick-and-place demonstrations: - -* Press "B" to begin teleoperation; the robot moves to the initial position. -* Use the physical leader arm to control the virtual follower arm for the pick-and-place task. -* Press "N" to save a successful episode. -* Press "R" to restart without saving. -* Scissors position and angle are slightly randomized per new episode. -* Press Ctrl + C to quit. - -Use these shortcuts for Isaac Sim viewport navigation: - -* "F" key after clicking the robot to auto-focus. -* Middle mouse wheel to zoom. -* "ALT" + left mouse drag to change the view angle. -* Middle mouse wheel click + drag to move in the viewport. - -Collecting around 70 synthetic episodes is sufficient for this tutorial. - -## Step 3. Convert Data to LeRobot Format - -After collecting the synthetic data, convert them to the Hugging Face [LeRobot](https://github.com/huggingface/lerobot) dataset format for fine-tuning the Isaac GR00T model: - -```shell -python -m training.hdf5_to_lerobot \ ---repo_id=spark/scrub-nurse-sim \ ---hdf5_path=./data-collection-sim/dataset.hdf5 \ ---task_description="Grip the scissors and put them into the tray." -``` - -Modify `--repo_id` and `--task_description` as needed, but ensure a meaningful task description. The resulting dataset, containing motor actions, wrist camera, and room camera recordings, is stored under `/home/$USER/.cache/huggingface/lerobot/`. - -## Part 3: Real-World Data Collection - -## Step 1. Set Up for Real-World Data Collection - -Ensure the leader arm, follower arm, wrist camera, and room camera are connected to DGX Spark. On DGX Spark, open a new terminal, activate the `so_arm_starter` conda environment: - -```shell -conda activate so_arm_starter -``` - -## Step 2. Collect Real-World Data Episodes - -Run the following command to collect real-world data episodes as LeRobot dataset: - -```shell -python -m lerobot.record \ ---robot.type=so101_follower \ ---robot.port=/dev/ttyACM1 \ ---robot.cameras="{wrist: {type: opencv, index_or_path: 2, width: 640, height: 480, fps: 30}, room: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}}" \ ---robot.id=so101_follower \ ---teleop.type=so101_leader \ ---teleop.port=/dev/ttyACM0 \ ---teleop.id=so101_leader \ ---display_data=true \ ---dataset.repo_id="spark/scrub-nurse-real" \ ---dataset.num_episodes=20 \ ---dataset.single_task="Grip the scissors and put them into the tray." \ ---dataset.push_to_hub=false -``` - -Modify robot device IDs, names and camera indices to match yours. Ensure `--dataset.single_task` matches the task description for synthetic data collection. You can change `--dataset.repo_id` to alter the LeRobot dataset name. The dataset will be saved under `/home/$USER/.cache/huggingface/lerobot/`. - -The command initiates the Rerun viewer and teleoperation for both arms. Follow these steps for pick-and-place demonstration recording: - -* The recording starts immediately upon command execution for the current episode; be prepared or you'll need to re-record. -* Each episode's recording has three sequential states: - 1. **Demonstration recording** (60s) — Record the task. - 2. **Scene Reset** (60s) — Perform randomization, robot/object resets. Rerun displays signals, but no recording occurs. - 3. **Data Saving** (approx. 5s) — Saves recording to a LeRobot dataset. Rerun temporarily freezes; no recording occurs. -* Right Arrow (→) — skips to the next state. Cannot skip State 3 (saving stage); pressing it then could corrupt the episode. -* Left Arrow (←) (during State 1) — cancels the current recording, giving 60 seconds to reset the scene before recording restarts. Use this if you mess up. -* **ESC** — stops recording and saves all currently recorded content. Use after a completed successful episode to avoid including unwanted "garbage" data. -* Collecting multiple small, separate LeRobot datasets might be easier, and they can be combined for GR00T training later. - -## Step 3. Prepare Datasets for Training - -After creating the datasets, copy the `modality.json` file generated during synthetic data creation (e.g., `/home/$USER/.cache/huggingface/lerobot/spark/scrub-nurse-sim/meta/modality.json`) to each dataset's `meta` folder. This file is essential for GR00T model training. - -Collecting 20 real-world episodes should be sufficient for this tutorial. - -## Part 4: GR00T N1.5 Fine-Tuning - -## Step 1. Launch Docker Container - -Run the following command on DGX Spark to start a docker container: - -```shell -docker run -it --gpus all --privileged --rm \ - --ipc=host \ - --network=host \ - --ulimit memlock=-1 \ - --ulimit stack=67108864 \ - --entrypoint=bash \ - -e "NVIDIA_VISIBLE_DEVICES=all" \ - -e "PYTHONPATH=/workflows/so_arm_starter/scripts"\ - -v /dev:/dev \ - -v /home/"$USER"/.cache/huggingface/lerobot:/root/.cache/huggingface/lerobot \ - -v $(pwd):/workspace \ - -w /workspace \ -soarm-dgx -``` - -We mount `/home/"$USER"/.cache/huggingface/lerobot` to the container so previous calibration files and datasets are accessible. - -## Step 2. Download Pretrained Model - -Download our pretrained GR00T N1.5 model [here](https://github.com/isaac-for-healthcare/i4h-workflows/blob/main/workflows/so_arm_starter/README.md#-running-workflows). The model was trained on 70 simulated and 5 real episodes. This model will likely require fine-tuning due to variations in your robot hardware, calibration, and task setup. - -## Step 3. Run GR00T N1.5 Fine-Tuning - -Run the following command to run GR00T N1.5 fine-tuning: - -```shell -PYTHONWARNINGS="ignore::UserWarning" python -m training.gr00t_n1_5.train \ ---dataset_path ... \ ---output_dir /workspace/training-output/ \ ---data_config so100_dualcam \ ---base-model-path \ ---max-steps 30000 \ ---save-steps 2000 -``` - -Change `--base-model-path` to the pretrained model path. Experiment with `--max-steps` and `--save-steps`; we found 30,000 steps typically sufficient for convergence. On DGX Spark, 30,000 steps should take around 24 hours. - -You can use Tensorboard to monitor the training progress. - -## Part 5: Deploying Trained Robotic Policy - -## Step 1. Convert Model to TensorRT Format - -To get the optimal inference performance, let's convert the fine-tuned GR00T N1.5 model to [TensorRT](https://developer.nvidia.com/tensorrt) format. - -Open a terminal window and create the same docker container as in Part 4. Then, run the following commands: - -```shell -python -m policy_runner.gr00tn1_5.trt.export_onnx --ckpt_path -bash /workflows/so_arm_starter/scripts/policy_runner/gr00tn1_5/trt/build_engine.sh -``` - -This generates a `gr00t_engine` folder that contains the converted TensorRT model. Avoid running heavy compute or graphics tasks on DGX Spark during conversion. - -## Step 2. Deploy in Isaac Sim - -To deploy the trained policy model in Isaac Sim, an [RTI DDS](https://www.rti.com/products/dds-standard) license file is required for communication of different modules. Get a professional or evaluation license from [here](https://www.rti.com/get-connext). - -Open a new terminal window and create the same docker container as in Part 4. First, set the `RTI_LICENSE_FILE` environment variable: - -```shell -export RTI_LICENSE_FILE= -``` - -Then, run the following command: - -```shell -python -m policy_runner.run_policy \ ---ckpt_path= \ ---task_description="Grip the scissors and put them into the tray." \ ---trt \ ---trt_engine_path= -``` - -This loads the GR00T model for inference in the background. - -Open another terminal window. Activate the `so_arm_starter` conda environment and set `PYTHONPATH` and `RTI_LICENSE_FILE`: - -```shell -conda activate so_arm_starter -export PYTHONPATH=/workflows/so_arm_starter/scripts -export RTI_LICENSE_FILE= -``` - -Then, run the following command in the terminal: - -```shell -python -m simulation.environments.sim_with_dds --enable_cameras -``` - -Isaac Sim will open up and load the pick-and-place scene, then the simulated robot will execute the task autonomously, driven by the GR00T N1.5 policy model. - -## Step 3. Deploy in Real World - -Ensure the follower arm, wrist camera, and room camera are connected to DGX Spark. - -Launch the same docker container as in Part 4. Find and modify the configuration file under `/workflows/so_arm_starter/scripts/holoscan_apps/soarm_robot_config.yaml` to update the follower arm's device ID, name, camera indices, and the fine-tuned GR00T model path. Then, run the following command: - -```shell -python -m holoscan_apps.gr00t_inference_app \ ---config /workflows/so_arm_starter/scripts/holoscan_apps/soarm_robot_config.yaml -``` - -This command launches an efficient GR00T N1.5 inference application using [NVIDIA Holoscan SDK](https://github.com/nvidia-holoscan/holoscan-sdk). The follower arm will execute the task autonomously shortly after. - -## Conclusion - -This tutorial demonstrated the end-to-end workflow of developing and deploying an autonomous healthcare robot on a single **NVIDIA DGX Spark**. Leveraging **NVIDIA Isaac For Healthcare**, we consolidated the 3-computers workflow of synthetic data generation, GR00T N1.5 training, and robotic policy deployment onto one powerful hardware platform. This workflow highlights the efficiency of the DGX Spark for accelerating the physical AI development pipeline, making the creation and deployment of intelligent healthcare robots more streamlined and accessible.