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Created page with "== Isaac Sim Automator == The Isaac Sim Automator tool automates the deployment of Isaac Sim to public clouds, enabling efficient and scalable simulations. === Installation =..."
== Isaac Sim Automator ==
The Isaac Sim Automator tool automates the deployment of Isaac Sim to public clouds, enabling efficient and scalable simulations.
=== Installation ===
==== Installing Docker ====
Ensure Docker is installed on your system for container management. For installation guidance, see [https://docs.docker.com/engine/install/ Docker Installation].
==== Obtaining NGC API Key ====
Obtain an NGC API Key to download Docker images from NVIDIA NGC. This can be done at [https://ngc.nvidia.com/setup/api-key NGC API Key Setup].
==== Building the Container ====
To build the Automator container, use the following command in your project root directory:
<source lang="bash">
./build
</source>
This builds and tags the Isaac Sim Automator container as 'isa'.
=== Usage ===
==== Running Automator Commands ====
You can run the Automator commands in two ways:
* Enter the automator container and run commands inside:
<source lang="bash">
./run
./somecommand
</source>
* Or, prepend the command with ./run:
<source lang="bash">
./run ./somecommand <parameters>
</source>
Examples include:
<source lang="bash">
./run ./deploy-aws
./run ./destroy my-deployment
</source>
==== Deploying Isaac Sim ====
Choose the appropriate cloud provider (AWS, GCP, Azure, Alibaba Cloud) and follow the provided steps to deploy Isaac Sim using the automator container.
== K-Scale Sim Library ==
The K-Scale Sim Library is built atop Isaac Gym for simulating Stompy, providing interfaces for defined tasks like walking and getting up.
=== Getting Started with K-Scale Sim Library ===
==== Initial Setup ====
First, clone the K-Scale Sim Library repository and set up the environment:
<source lang="bash">
git clone https://github.com/kscalelabs/sim.git
cd sim
conda create --name kscale-sim-library python=3.8.19
conda activate kscale-sim-library
make install-dev
</source>
==== Installing Dependencies ====
After setting up the base environment, download and install necessary third-party packages:
<source lang="bash">
wget https://developer.nvidia.com/isaac-gym/IsaacGym_Preview_4_Package.tar.gz
tar -xvf IsaacGym_Preview_4_Package.tar.gz
conda env config vars set ISAACGYM_PATH=`pwd`/isaacgym
conda deactivate
conda activate kscale-sim-library
make install-third-party-external
</source>
=== Running Experiments ===
==== Setting Up Experiments ====
Download and prepare the Stompy model for experiments:
<source lang="bash">
wget https://media.kscale.dev/stompy.tar.gz
tar -xzvf stompy.tar.gz
python sim/scripts/create_fixed_torso.py
export MODEL_DIR=stompy
</source>
==== Training and Evaluation ====
* For leg-specific tasks:
<source lang="bash">
python sim/humanoid_gym/train.py --task=legs_ppo --num_envs=4096 --headless
</source>
* For full-body tasks:
<source lang="bash">
python sim/humanoid_gym/train.py --task=stompy_ppo --num_envs=4096 --headless
</source>
Evaluate the models on CPU:
<source lang="bash">
python sim/humanoid_gym/play.py --task=legs_ppo --sim_device=cpu
</source>
=== Troubleshooting ===
Common issues and solutions for setting up and running Isaac Gym and K-Scale simulations.
<source lang="bash">
git submodule update --init --recursive
export LD_LIBRARY_PATH=PATH_TO_YOUR_ENV/lib:$LD_LIBRARY_PATH
sudo apt-get install vulkan1
</source>
The Isaac Sim Automator tool automates the deployment of Isaac Sim to public clouds, enabling efficient and scalable simulations.
=== Installation ===
==== Installing Docker ====
Ensure Docker is installed on your system for container management. For installation guidance, see [https://docs.docker.com/engine/install/ Docker Installation].
==== Obtaining NGC API Key ====
Obtain an NGC API Key to download Docker images from NVIDIA NGC. This can be done at [https://ngc.nvidia.com/setup/api-key NGC API Key Setup].
==== Building the Container ====
To build the Automator container, use the following command in your project root directory:
<source lang="bash">
./build
</source>
This builds and tags the Isaac Sim Automator container as 'isa'.
=== Usage ===
==== Running Automator Commands ====
You can run the Automator commands in two ways:
* Enter the automator container and run commands inside:
<source lang="bash">
./run
./somecommand
</source>
* Or, prepend the command with ./run:
<source lang="bash">
./run ./somecommand <parameters>
</source>
Examples include:
<source lang="bash">
./run ./deploy-aws
./run ./destroy my-deployment
</source>
==== Deploying Isaac Sim ====
Choose the appropriate cloud provider (AWS, GCP, Azure, Alibaba Cloud) and follow the provided steps to deploy Isaac Sim using the automator container.
== K-Scale Sim Library ==
The K-Scale Sim Library is built atop Isaac Gym for simulating Stompy, providing interfaces for defined tasks like walking and getting up.
=== Getting Started with K-Scale Sim Library ===
==== Initial Setup ====
First, clone the K-Scale Sim Library repository and set up the environment:
<source lang="bash">
git clone https://github.com/kscalelabs/sim.git
cd sim
conda create --name kscale-sim-library python=3.8.19
conda activate kscale-sim-library
make install-dev
</source>
==== Installing Dependencies ====
After setting up the base environment, download and install necessary third-party packages:
<source lang="bash">
wget https://developer.nvidia.com/isaac-gym/IsaacGym_Preview_4_Package.tar.gz
tar -xvf IsaacGym_Preview_4_Package.tar.gz
conda env config vars set ISAACGYM_PATH=`pwd`/isaacgym
conda deactivate
conda activate kscale-sim-library
make install-third-party-external
</source>
=== Running Experiments ===
==== Setting Up Experiments ====
Download and prepare the Stompy model for experiments:
<source lang="bash">
wget https://media.kscale.dev/stompy.tar.gz
tar -xzvf stompy.tar.gz
python sim/scripts/create_fixed_torso.py
export MODEL_DIR=stompy
</source>
==== Training and Evaluation ====
* For leg-specific tasks:
<source lang="bash">
python sim/humanoid_gym/train.py --task=legs_ppo --num_envs=4096 --headless
</source>
* For full-body tasks:
<source lang="bash">
python sim/humanoid_gym/train.py --task=stompy_ppo --num_envs=4096 --headless
</source>
Evaluate the models on CPU:
<source lang="bash">
python sim/humanoid_gym/play.py --task=legs_ppo --sim_device=cpu
</source>
=== Troubleshooting ===
Common issues and solutions for setting up and running Isaac Gym and K-Scale simulations.
<source lang="bash">
git submodule update --init --recursive
export LD_LIBRARY_PATH=PATH_TO_YOUR_ENV/lib:$LD_LIBRARY_PATH
sudo apt-get install vulkan1
</source>