Cleanup notebooks

libs/computer-server/computer_server/handlers/macos.py

@@ -386,7 +386,7 @@ class MacOSAccessibilityHandler(BaseAccessibilityHandler):
         # From NSWorkspace.runningApplications docs: https://developer.apple.com/documentation/appkit/nsworkspace/runningapplications
         # "Similar to the NSRunningApplication class’s properties, this property will only change when the main run loop runs in a common mode"
         # So we need to run the main run loop to get the latest running applications
-        Foundation.CFRunLoopRunInMode(Foundation.kCFRunLoopDefaultMode, 0.1, False)
+        Foundation.CFRunLoopRunInMode(Foundation.kCFRunLoopDefaultMode, 0.1, False)  # type: ignore
         return NSWorkspace.sharedWorkspace().runningApplications()
 
     def get_ax_attribute(self, element, attribute):

libs/lume/src/ContainerRegistry/ImageContainerRegistry.swift

@@ -638,6 +638,9 @@ class ImageContainerRegistry: @unchecked Sendable {
         try FileManager.default.moveItem(at: tempVMDir, to: URL(fileURLWithPath: vmDir.dir.path))
 
         Logger.info("Download complete: Files extracted to \(vmDir.dir.path)")
+        Logger.info(
+            "Run 'lume run \(vmName)' to reduce the disk image file size by using macOS sparse file system"
+        )
     }
 
     private func copyFromCache(manifest: Manifest, manifestId: String, to destination: URL)

notebooks/agent_nb.ipynb

@@ -71,7 +71,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -88,13 +88,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
     "import os\n",
-    "os.environ[\"ANTHROPIC_API_KEY\"] = \"your-anthropic-api-key\"\n",
-    "os.environ[\"OPENAI_API_KEY\"] = \"your-openai-api-key\""
+    "\n",
+    "# Get API keys from environment or prompt user\n",
+    "anthropic_key = os.getenv(\"ANTHROPIC_API_KEY\") or input(\"Enter your Anthropic API key: \")\n",
+    "openai_key = os.getenv(\"OPENAI_API_KEY\") or input(\"Enter your OpenAI API key: \")\n",
+    "\n",
+    "os.environ[\"ANTHROPIC_API_KEY\"] = anthropic_key\n",
+    "os.environ[\"OPENAI_API_KEY\"] = openai_key"
    ]
   },
   {
@@ -118,8 +123,8 @@
     "# Create agent with Anthropic loop and provider\n",
     "agent = ComputerAgent(\n",
     "        computer=computer,\n",
-    "        loop=AgentLoop.ANTHROPIC,\n",
-    "        model=LLM(provider=LLMProvider.ANTHROPIC, name=\"claude-3-7-sonnet-20250219\"),\n",
+    "        loop=AgentLoop.OPENAI,\n",
+    "        model=LLM(provider=LLMProvider.OPENAI),\n",
     "        save_trajectory=True,\n",
     "        trajectory_dir=str(Path(\"trajectories\")),\n",
     "        only_n_most_recent_images=3,\n",
@@ -196,7 +201,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "cua",
    "language": "python",
    "name": "python3"
   },
@@ -210,7 +215,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.11.11"
   }
  },
  "nbformat": 4,

notebooks/computer_nb.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "## Computer\n",
     "\n",
-    "This notebook demonstrates how to use Computer to operate a Lume sandbox programmatically on Apple Silicon macOS systems."
+    "This notebook demonstrates how to use Computer to operate a Lume sandbox VMs programmatically on Apple Silicon macOS systems."
    ]
   },
   {
@@ -86,7 +86,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Pull the latest pre-built macos-sequoia-cua image. This image, based on macOS Sequoia, contains all dependencies needed to be controlled from the Computer interface."
+    "Pull the latest pre-built macos-sequoia-cua image. This image, based on macOS Sequoia, contains all dependencies needed to be controlled from the cua-computer interface."
    ]
   },
   {
@@ -95,16 +95,23 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!lume pull macos-sequoia-cua:latest"
+    "!lume pull macos-sequoia-cua:latest --no-cache"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The initial image download of thew macos-sequoia-cua image requires 80GB of storage space. However, after the first run, the image size reduces to around 20GB. Thanks to macOS's sparse file system, VM disk space is allocated dynamically - while VMs may show a total size of 50GB, they typically only consume about 20GB of physical disk space.\n",
+    "Initial download requires 80GB storage, but reduces to ~30GB after first run due to macOS's sparse file system.\n",
     "\n",
-    "Sandbox are stored in `~/.lume`, and locally cached images are stored in `~/.lume/cache`.\n"
+    "VMs are stored in `~/.lume`, and locally cached images are stored in `~/.lume/cache`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "You can remove the `--no-cache` flag to also save the image to your local cache during pull (requires double the storage space). This is useful if you plan to use the same image multiple times to create other VMs."
    ]
   },
   {
@@ -215,9 +222,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Equivalent curl command:\n",
+      "curl -X POST \\\n",
+      "  'http://localhost:3000/lume/vms/macos-sequoia-cua_latest/run' \\\n",
+      "  -H 'Content-Type: application/json' \\\n",
+      "  -d '{\"noDisplay\": false, \"sharedDirectories\": []}'\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "computer = Computer(\n",
     "    display=\"1024x768\",\n",
@@ -261,7 +282,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -374,12 +395,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Get accessibility tree\n",
-    "accessibility_tree = await computer.interface.get_accessibility_tree()"
+    "accessibility_tree = await computer.interface.get_accessibility_tree()\n",
+    "print(accessibility_tree)"
    ]
   },
   {
@@ -488,7 +510,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "cua",
    "language": "python",
    "name": "python3"
   },
@@ -502,7 +524,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.2"
+   "version": "3.11.11"
   }
  },
  "nbformat": 4,

notebooks/pylume_nb.ipynb

@@ -27,6 +27,15 @@
     "!pip install pylume"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install pydantic"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -41,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -326,7 +335,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv",
+   "display_name": "cua",
    "language": "python",
    "name": "python3"
   },
@@ -340,7 +349,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.11.11"
   }
  },
  "nbformat": 4,