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add google compute deploy guide
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@ryichsecondary
ryichsecondary committed Dec 19, 2024
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Expand Up @@ -18,6 +18,27 @@ Published in [ACM Transactions on Graphics (TOG)](https://dl.acm.org/doi/abs/10.
- **✨ Stay Clean**: You can remove all traces after use.
- **👌 Open**: We have opted the Apache v2.0 license.

## 🔖 Table of Contents

- [🎓 Technical Materials](#-technical-materials)
- [⚡️ Requirements](#️-requirements)
- [📝 Change History](#-change-history)
- [🐍 How To Use](#-how-to-use)
- [🖼️ Catalogue](#️-catalogue)
- [💨 Getting Started](#-getting-started)
- [🎥 Installation Videos](#-installation-videos)
- [🐳 Installing Docker](#-installing-docker)
- [🪟 Windows](#-windows)
- [🐧 Linux](#-linux)
- [🧹 Cleaning Up](#-cleaning-up)
- [📡 Deploying on Cloud Services](#-deploying-on-cloud-services)
- [📦 Deploying on vast.ai](#-deploying-on-vastai)
- [📦 Deploying on RunPod](#-deploying-on-runpod)
- [📦 Deploying on Google Compute Engine](#-deploying-on-google-compute-engine)
- [📃 License](#-license)
- [🙏 Acknowledgements](#-acknowledgements)
- [🖋 Citation](#-citation)

## 🎓 Technical Materials

- 🎥 Main video [[Video]](https://drive.google.com/file/d/1OzPbUoqddUYDvXMvRnUHH7kz0nZhmt7K/view?usp=drive_link)
Expand All @@ -40,6 +61,7 @@ Published in [ACM Transactions on Graphics (TOG)](https://dl.acm.org/doi/abs/10.
- (2024.12.16) Removed thrust dependencies to fix runtime errors for the driver version `560.94` [[Issue Link]](https://github.com/st-tech/ppf-contact-solver/issues/1).

## 🐍 How To Use

Our frontend is accessible through 🌐 a browser using our built-in JupyterLab 🐍 interface.
All is set up when you open it for the first time.
Results can be interactively viewed through the browser and exported as needed.
Expand Down Expand Up @@ -147,7 +169,7 @@ This is not a self-hosted runner, meaning that each time the runner launches, al

### 🎥 Installation Videos

We provide uninterrupted recorded installation videos (🪟 Windows [[Video]](https://drive.google.com/file/d/1Np3MwUtSlppQPMrawtobzoGtZZWrmFgG/view?usp=sharing), 🐧 Linux [[Video]](https://drive.google.com/file/d/1ZDnzsn46E1I6xNzyg0S8Q6xvgXw_Lw7M/view?usp=sharing) and ☁ [vast.ai](https://vast.ai) [[Video]](https://drive.google.com/file/d/1k0LnkPKXuEwZZvElaKohWZeDd6M3ONe1/view?usp=sharing))
We provide uninterrupted recorded installation videos (🪟 Windows [[Video]](https://drive.google.com/file/d/1Np3MwUtSlppQPMrawtobzoGtZZWrmFgG/view?usp=sharing), 🐧 Linux [[Video]](https://drive.google.com/file/d/1ZDnzsn46E1I6xNzyg0S8Q6xvgXw_Lw7M/view?usp=sharing), ☁ [vast.ai](https://vast.ai) [[Video]](https://drive.google.com/file/d/1k0LnkPKXuEwZZvElaKohWZeDd6M3ONe1/view?usp=sharing), and ☁️ [Google Cloud](https://cloud.google.com/products/compute) [[Video]](https://drive.google.com/file/d/1dj6TvR2IVLKLFXtO8QRrRl-8xQ7G547A/view?usp=sharing))
to reduce stress 😣 during the installation process. We encourage you to 👀 check them out to get a sense of how things go ⏳ and how long ⏱️ each step takes.

### 🐳 Installing Docker
Expand Down Expand Up @@ -304,7 +326,7 @@ and now you can access our JupyterLab frontend from http://localhost:8080 on you
The port number `8080` is the one we set for `$MY_WEB_PORT`.
Enjoy! 😄
## 🧹 Cleaning Up
### 🧹 Cleaning Up
To remove all traces, simply stop 🛑 the container and ❌ delete it.
Be aware that all simulation data will be also lost. Back up any important data if needed.
Expand All @@ -321,7 +343,9 @@ docker rm $MY_CONTAINER_NAME
> ```
> but don't do this if you still need it.
## ☁ Running on [vast.ai](https://vast.ai)
## 📡 Deploying on Cloud Services
### 📦 Deploying on [vast.ai](https://vast.ai)
The exact same steps above should work (see `.github/workflows/getting-started-vast.yml`), except that you'll need to create a Docker template. Here's one:
Expand All @@ -332,10 +356,12 @@ The exact same steps above should work (see `.github/workflows/getting-started-v
- For a better experience, choose a geographically nearby server with a high connection speed.
- Also, make sure to allocate a large disk space, such as 64GB.
**Important**: Don't forget to ❌ delete the instance after use, or you’ll be 💸 charged for nothing.
<img src="./asset/image/vast-template.png" alt="vast template">
<img src="./asset/image/vast-diskspace.png" alt="vast diskspace">
## 📦 Running on [RunPod](https://runpod.io)
### 📦 Deploying on [RunPod](https://runpod.io)
You can deploy our solver on a RunPod instance. To do this, we need to select an official RunPod Docker image instead.
Here's how
Expand All @@ -349,9 +375,64 @@ Here's how
- ✅ Make sure to select `SSH Terminal Access`
- ❌ Deselect `Start Jupyter Notebook`
**Important**: Don't forget to ❌ delete the instance after use, or you’ll be 💸 charged for nothing.
<img src="./asset/image/runpod-template.png" alt="runpod template">
<img src="./asset/image/runpod-deploy.png" alt="runpod deploy">
### 📦 Deploying on [Google Compute Engine](https://cloud.google.com/products/compute)
First, set up your `gcloud` CLI by following 📚 [this guide](https://cloud.google.com/sdk/docs/install?hl=en) Next, run the command below to provision an ⚡ NVIDIA L4 GPU instance.
As of late 2024, this setup costs approximately 💵 $1 per hour.
```bash
export IMAGE="projects/ml-images/global/images/c0-deeplearning-common-gpu-v20241118-debian-11-py310"
export ZONE="asia-east1-c"
export INSTANCE_NAME="ppf-contact-solver"
export INSTANCE_TYPE="g2-standard-8"
gcloud compute instances create $INSTANCE_NAME \
--zone=$ZONE \
--machine-type=$INSTANCE_TYPE \
--network-interface=network-tier=PREMIUM,stack-type=IPV4_ONLY,subnet=default \
--maintenance-policy=TERMINATE \
--provisioning-model=STANDARD \
--accelerator=count=1,type=nvidia-l4 \
--create-disk=auto-delete=yes,boot=yes,image=$IMAGE,mode=rw,size=50,type=pd-balanced \
--no-shielded-secure-boot \
--shielded-vtpm
```

After creating the instance, try connecting to it via `ssh` using the 🌐 `gcloud` interface. Since port `8080` is taken by the deployed image, make sure to select a different port on the host side.
Here, we set the host-side port to `8888`.
Note that the local port remains `8080` so that the JupyterLab interface can be accessed at `http://localhost:8080`.
I know this might be confusing, so just copy and paste if you're okay with it.

```bash
# Port number 8080 is taken, so let's use 8888
gcloud compute ssh --zone=$ZONE $INSTANCE_NAME -- -L 8080:localhost:8888
```

As shown in this [[Video]](https://drive.google.com/file/d/1dj6TvR2IVLKLFXtO8QRrRl-8xQ7G547A/view?usp=sharing), the instance may take a few minutes to load, so early SSH access fails.
Keep trying at intervals; it should connect once the host is ready.

Next, run the same Docker 🐧 [Linux](#-linux) command in the instance to set up a 🐳 Docker environment. Be sure to change `$MY_WEB_PORT` to `8888` in this case.

```
MY_WEB_PORT=8888 # Make sure to set the port to 8888
MY_TIME_ZONE=Asia/Tokyo # Your time zone
MY_CONTAINER_NAME=ppf-contact-solver # Container name
```

The rest of the installation process is identical. After use, don't forget to ❌ delete the instance, or you will continue to be 💸 charged.

```bash
gcloud compute instances stop --zone=$ZONE $INSTANCE_NAME
gcloud compute instances delete --zone=$ZONE $INSTANCE_NAME
```

Just to be sure, double-check from the 🖥️ web console to confirm that the instance has been successfully ✅ deleted.

## 📃 License

📝 This project is licensed under Apache v2.0 license.
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