add aws readme

README.md

@@ -39,6 +39,7 @@ Published in [ACM Transactions on Graphics (TOG)](https://dl.acm.org/doi/abs/10.
     - [📦 Deploying on vast.ai](#-deploying-on-vastai)
     - [📦 Deploying on RunPod](#-deploying-on-runpod)
     - [📦 Deploying on Scaleway](#-deploying-on-scaleway)
+    - [📦 Deploying on Amazon Web Services](#-deploying-on-amazon-web-services)
     - [📦 Deploying on Google Compute Engine](#-deploying-on-google-compute-engine)
 - [🧑‍💻 Setting Up Your Development Environment](#-setting-up-your-development-environment)
 - [📃 License](#-license)
@@ -85,7 +86,7 @@ Our Python interface is designed with the following principles in mind:
 
 - **🚫 No Mesh Data**: Preparing mesh data using external tools can be cumbersome. Our frontend minimizes this effort by allowing meshes to be created on the fly or downloaded when needed.
 
-- **🔗 Method Chaining**: We adopt the method chaining style from JavaScript, making the API intuitive and easy to use.
+- **🔗 Method Chaining**: We adopt the method chaining style from JavaScript, making the API intuitive and easy to understand.
 
 - **📦 Single Import for Everything**: All frontend features are accessible by simply importing with `from frontend import App`.
 
@@ -399,9 +400,17 @@ You can deploy our solver on a RunPod instance. To do this, we need to select an
 - Select type `L4-1-24G` or `GPU-3070-S`
 - Choose `Ubuntu Jammy GPU OS 12`
 - *Do not skip* the Docker container creation in the installation process; it is required.
+- This setup costs approximately €0.76 per hour.
 
 - CLI instructions are described in [[Markdown]](./articles/cloud.md#-scaleway).
 
+### 📦 Deploying on [Amazon Web Services](https://aws.amazon.com/en/)
+
+- Amazon Machine Image (AMI): `Deep Learning Base OSS Nvidia Driver GPU AMI (Ubuntu 22.04)`
+- Instance Type: `g6.2xlarge` (Recommended)
+- This setup costs around $1 per hour.
+- *Do not skip* the Docker container creation in the installation process; it is required.
+
 ### 📦 Deploying on [Google Compute Engine](https://cloud.google.com/products/compute)
 
 - Select `GPUs`. We recommend the GPU type `NVIDIA L4` because it's affordable and accessible, as it does not require a high quota. You may select `T4` instead for testing purposes.

articles/cloud.md

@@ -225,7 +225,7 @@ rm -rf .runpod
 Set up your Scaleway CLI by following 📚 [this guide](https://www.scaleway.com/en/cli/).
 Also, register your public SSH key.
 Here’s how to create a GPU instance and SSH into the instance with port forwarding.
-As of late 2024, this setup costs approximately €0.76 per hour.
+This setup costs approximately €0.76 per hour.
 
 ```bash
 # set zone

examples/hang.ipynb

@@ -33,8 +33,16 @@
     "param = app.session.param().set(\"dt\",0.001)\n",
     "param.set(\"strain-limit-eps\", 0.005).set(\"strain-limit-tau\", 0.005)\n",
     "param.set(\"frames\",200)\n",
-    "param.dyn(\"gravity\").time(1).hold().time(1.1).change(9.8).time(2.0).change(-9.8)\n",
-    "\n",
+    "param.dyn(\"gravity\").time(1).hold().time(1.1).change(9.8).time(2.0).change(-9.8);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7284cf76-4110-41a0-88d2-6e028fe45aba",
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "session = app.session.create(\"two-pins-hang\").init(fixed)\n",
     "session.start(param).preview();\n",
     "session.stream();"
@@ -72,6 +80,42 @@
     "# export all simulated frames\n",
     "session.export_animation(f\"export/{session.info.name}\")"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b318af59-58be-4ad4-ae4b-63af01e225fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "param.set(\"dt\",0.1).set(\"enable-retry\",True)\n",
+    "\n",
+    "session = app.session.create(\"two-pins-hang-dt-01\").init(fixed)\n",
+    "session.start(param).preview();\n",
+    "session.stream();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2887e9da-aa29-4468-9ace-62c93a7c25ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run this cell after sufficnt frames are simulated\n",
+    "session.animate();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9105a519-6aaa-4b59-9c5c-0bf96c081fc3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this is for CI\n",
+    "assert session.finished()"
+   ]
   }
  ],
  "metadata": {

src/backend.rs

@@ -115,6 +115,10 @@ impl Backend {
     }
 
     pub fn run(&mut self, args: &Args, dataset: DataSet, mut param: ParamSet, scene: Scene) {
+        let finished_path = std::path::Path::new(args.output.as_str()).join("finished.txt");
+        if finished_path.exists() {
+            std::fs::remove_file(finished_path.clone()).unwrap();
+        }
         unsafe {
             initialize(&dataset, &param);
         }
@@ -232,7 +236,6 @@ impl Backend {
             }
         }
         let _ = result_receiver.try_recv();
-        let finished_path = std::path::Path::new(args.output.as_str()).join("finished.txt");
         write_current_time_to_file(finished_path.to_str().unwrap()).unwrap();
     }
 }