{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "view-in-github" }, "source": [ "\"Open" ] }, { "cell_type": "markdown", "metadata": { "id": "PO76KS1i-MwA" }, "source": [ "# **šŸ¤– CartPole Tutorial with mjlab**\n", "\n", "This notebook demonstrates how to create a custom reinforcement learning task using mjlab. We'll build a CartPole environment from scratch, including:\n", "\n", "1. **Robot Definition** - Define the CartPole model in MuJoCo XML\n", "2. **Task Configuration** - Set up observations, actions, rewards, and terminations\n", "3. **Training** - Train a policy using PPO\n", "4. **Evaluation** - Visualize the simulation with the trained policy\n", "\n", "> **Note**: This tutorial demonstrates how to create a new task in mjlab. For more context, see the [official documentation](https://mujocolab.github.io/mjlab/)." ] }, { "cell_type": "markdown", "metadata": { "id": "3ywZTgfR3C_w" }, "source": [ "## **šŸ“¦ Setup and Installation**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "id": "dtLMJHzy3Nee" }, "outputs": [], "source": "# Clone the mjlab repository\n!if [ ! -d \"mjlab\" ]; then git clone -q https://github.com/mujocolab/mjlab.git; fi\n%cd /content/mjlab\n\n# Install mjlab in editable mode\n!pip install -e . -q\n\nprint(\"āœ“ Installation complete!\")" }, { "cell_type": "markdown", "metadata": { "id": "SSf2943z3b0s" }, "source": [ "### **šŸ”‘ WandB Setup**\n", "\n", "Configure Weights & Biases for experiment tracking. Add your WandB API key to Colab Secrets:\n", "- `WANDB_API_KEY`: from [wandb.ai/authorize](https://wandb.ai/authorize)\n", "- `WANDB_ENTITY`: your wandb entity name" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "KC9ywCnm3dGg" }, "outputs": [], "source": [ "import os\n", "\n", "from google.colab import userdata\n", "\n", "try:\n", " # Set this to use wandb logger\n", " os.environ[\"WANDB_API_KEY\"] = userdata.get(\"WANDB_API_KEY\")\n", " os.environ[\"WANDB_ENTITY\"] = userdata.get(\"WANDB_ENTITY\")\n", "\n", " print(\"āœ“ WandB configured successfully!\")\n", "except (AttributeError, KeyError):\n", " # Set this to disable wandb logger\n", " os.environ[\"WANDB_MODE\"] = \"disabled\"\n", "\n", " print(\"āš  WandB secrets not found. Training will proceed without WandB logging.\")" ] }, { "cell_type": "markdown", "metadata": { "id": "mispfmy73lmq" }, "source": [ "---\n", "\n", "## **šŸ¤– Step 1: Define the Robot**\n", "\n", "We'll create a simple CartPole robot with:\n", "- A sliding cart (1 DOF)\n", "- A hinged pole (1 DOF)\n", "- A velocity actuator to control the cart" ] }, { "cell_type": "markdown", "metadata": { "id": "-FvJYPWD3scd" }, "source": [ "### **šŸ“ Structure Directories**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "OP-yET-R3ofN" }, "outputs": [], "source": [ "# Create the cartpole robot directory structure\n", "!mkdir -p /content/mjlab/src/mjlab/asset_zoo/robots/cartpole/\n", "!mkdir -p /content/mjlab/src/mjlab/asset_zoo/robots/cartpole/xmls\n", "\n", "print(\"āœ“ Directory structure created\")" ] }, { "cell_type": "markdown", "metadata": { "id": "MRyN1Pok3u25" }, "source": [ "### **šŸ“ Create MuJoCo XML Model**\n", "\n", "This XML defines the CartPole physics:\n", "- **Ground plane** for visualization\n", "- **Cart body** with a sliding joint (Ā±2m range)\n", "- **Pole body** with a hinge joint (Ā±90Ā° range)\n", "- **Velocity actuator** for cart control" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "gWGyFX5V3yWc" }, "outputs": [], "source": [ "%%writefile /content/mjlab/src/mjlab/asset_zoo/robots/cartpole/xmls/cartpole.xml\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "" ] }, { "cell_type": "markdown", "metadata": { "id": "MpYCG9jI31dZ" }, "source": [ "### **āš™ļø Create Robot Configuration**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "HDhiyDTn4AVa" }, "outputs": [], "source": "%%writefile /content/mjlab/src/mjlab/asset_zoo/robots/cartpole/cartpole_constants.py\nfrom pathlib import Path\nimport mujoco\n\nfrom mjlab import MJLAB_SRC_PATH\nfrom mjlab.entity import Entity, EntityCfg, EntityArticulationInfoCfg\nfrom mjlab.actuator import XmlActuatorCfg\n\nCARTPOLE_XML: Path = (\n MJLAB_SRC_PATH / \"asset_zoo\" / \"robots\" / \"cartpole\" / \"xmls\" / \"cartpole.xml\"\n)\nassert CARTPOLE_XML.exists(), f\"XML not found: {CARTPOLE_XML}\"\n\ndef get_spec() -> mujoco.MjSpec:\n return mujoco.MjSpec.from_file(str(CARTPOLE_XML))\n\ndef get_cartpole_robot_cfg() -> EntityCfg:\n \"\"\"Get a fresh CartPole robot configuration instance.\"\"\"\n actuators = (\n XmlActuatorCfg(\n target_names_expr=(\"slide\",),\n ),\n )\n articulation = EntityArticulationInfoCfg(actuators=actuators)\n return EntityCfg(\n spec_fn=get_spec,\n articulation=articulation\n )\n\n# if __name__ == \"__main__\":\n# import mujoco.viewer as viewer\n# robot = Entity(get_cartpole_robot_cfg())\n# viewer.launch(robot.spec.compile())" }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "-WSaDod04FwN" }, "outputs": [], "source": [ "# Create __init__.py for the cartpole robot package\n", "%%writefile /content/mjlab/src/mjlab/asset_zoo/robots/cartpole/__init__.py\n", "# Empty __init__.py to mark the directory as a Python package" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "W1tiBPfp_oVP" }, "outputs": [], "source": [ "import sys\n", "\n", "# Append src dir to python path\n", "mjlab_src = \"/content/mjlab/src\"\n", "if mjlab_src not in sys.path:\n", " sys.path.insert(0, mjlab_src)\n", " print(f\"āœ“ Added {mjlab_src} to Python path\")" ] }, { "cell_type": "markdown", "metadata": { "id": "ToWF84qC4Hfg" }, "source": [ "### **āœ… Verify Robot Setup**\n", "\n", "Let's test that the robot can be loaded correctly." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "5tVsvqzQ4J9h" }, "outputs": [], "source": [ "from mjlab.asset_zoo.robots.cartpole.cartpole_constants import get_cartpole_robot_cfg\n", "\n", "from mjlab.entity import Entity\n", "\n", "# Load the robot\n", "robot = Entity(get_cartpole_robot_cfg())\n", "model = robot.spec.compile()\n", "\n", "# Display robot information\n", "print(\"āœ“ CartPole robot loaded successfully!\")\n", "print(f\" ā€¢ Degrees of Freedom (DOF): {model.nv}\")\n", "print(f\" ā€¢ Number of Actuators: {model.nu}\")\n", "print(f\" ā€¢ Bodies: {model.nbody}\")\n", "print(f\" ā€¢ Joints: {model.njnt}\")" ] }, { "cell_type": "markdown", "metadata": { "id": "e2_9dixlHON1" }, "source": [ "### **šŸ“‹ Register the Robot**\n", "\n", "Add the CartPole robot to the asset zoo registry." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "8qDIF__lHPcb" }, "outputs": [], "source": [ "# Add CartPole import to robots __init__.py\n", "with open(\"/content/mjlab/src/mjlab/asset_zoo/robots/__init__.py\", \"a\") as f:\n", " f.write(\"\\n# CartPole robot\\n\")\n", " f.write(\"from mjlab.asset_zoo.robots.cartpole.cartpole_constants import \")\n", " f.write(\"get_cartpole_robot_cfg as get_cartpole_robot_cfg\\n\")\n", "\n", "print(\"āœ“ CartPole robot registered in asset zoo\")" ] }, { "cell_type": "markdown", "metadata": { "id": "6lVD_L6PHWNm" }, "source": [ "---\n", "\n", "## **šŸŽÆ Step 2: Define the Task (MDP)**\n", "\n", "Now we'll define the Markov Decision Process:\n", "- **Observations**: pole angle, angular velocity, cart position, cart velocity\n", "- **Actions**: cart velocity commands\n", "- **Rewards**: upright reward + effort penalty\n", "- **Terminations**: pole tips over or timeout\n", "- **Events**: random pushes for robustness" ] }, { "cell_type": "markdown", "metadata": { "id": "RQxe4TBrHb-I" }, "source": [ "### **šŸ“ Create Task Directory**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "nWBqdkziHc2G" }, "outputs": [], "source": [ "!mkdir -p /content/mjlab/src/mjlab/tasks/cartpole\n", "\n", "print(\"āœ“ Task directory created\")" ] }, { "cell_type": "markdown", "metadata": { "id": "GJfjPpm0Hhj1" }, "source": [ "### **šŸ“ Create Environment Configuration**\n", "\n", "This file contains the MDP (Markov Decision Process) components:\n", "1. **Scene Config**: 64 parallel environments\n", "2. **Actions**: Joint velocity control with 20.0 scale\n", "3. **Observations**: Normalized state variables\n", "4. **Rewards**: Upright reward (5.0) + effort penalty (-0.01)\n", "5. **Events**: Joint resets + random pushes\n", "6. **Terminations**: Pole tipped (>30Ā°) or timeout (10s)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "javx9XDIHkFI" }, "outputs": [], "source": [ "%%writefile /content/mjlab/src/mjlab/tasks/cartpole/env_cfg.py\n", "\"\"\"CartPole task environment configuration.\"\"\"\n", "\n", "import math\n", "import torch\n", "\n", "from mjlab.envs import ManagerBasedRlEnvCfg\n", "from mjlab.envs.mdp.actions import JointVelocityActionCfg\n", "from mjlab.managers.observation_manager import ObservationGroupCfg, ObservationTermCfg\n", "from mjlab.managers.reward_manager import RewardTermCfg\n", "from mjlab.managers.termination_manager import TerminationTermCfg\n", "from mjlab.managers.event_manager import EventTermCfg\n", "from mjlab.managers.scene_entity_config import SceneEntityCfg\n", "from mjlab.scene import SceneCfg\n", "from mjlab.sim import MujocoCfg, SimulationCfg\n", "from mjlab.viewer import ViewerConfig\n", "from mjlab.asset_zoo.robots.cartpole.cartpole_constants import get_cartpole_robot_cfg\n", "from mjlab.envs import mdp\n", "\n", "\n", "def cartpole_env_cfg(play: bool = False) -> ManagerBasedRlEnvCfg:\n", " \"\"\"Create CartPole environment configuration.\n", "\n", " Args:\n", " play: If True, disables corruption and extends episode length for evaluation.\n", " \"\"\"\n", "\n", " # ==============================================================================\n", " # Scene Configuration\n", " # ==============================================================================\n", "\n", " scene_cfg = SceneCfg(\n", " num_envs=64 if not play else 16, # Fewer envs for play mode\n", " extent=1.0, # Spacing between environments\n", " entities={\"robot\": get_cartpole_robot_cfg()},\n", " )\n", "\n", " viewer_cfg = ViewerConfig(\n", " origin_type=ViewerConfig.OriginType.ASSET_BODY,\n", " entity_name=\"robot\",\n", " body_name=\"pole\",\n", " distance=3.0,\n", " elevation=10.0,\n", " azimuth=90.0,\n", " )\n", "\n", " sim_cfg = SimulationCfg(\n", " mujoco=MujocoCfg(\n", " timestep=0.02, # 50 Hz control\n", " iterations=1,\n", " ),\n", " )\n", "\n", " # ==============================================================================\n", " # Actions\n", " # ==============================================================================\n", "\n", " actions = {\n", " \"joint_pos\": JointVelocityActionCfg(\n", " entity_name=\"robot\",\n", " actuator_names=(\".*\",),\n", " scale=20.0,\n", " use_default_offset=False,\n", " ),\n", " }\n", "\n", " # ==============================================================================\n", " # Observations\n", " # ==============================================================================\n", "\n", " actor_terms = {\n", " \"angle\": ObservationTermCfg(\n", " func=lambda env: env.sim.data.qpos[:, 1:2] / math.pi\n", " ),\n", " \"ang_vel\": ObservationTermCfg(\n", " func=lambda env: env.sim.data.qvel[:, 1:2] / 5.0\n", " ),\n", " \"cart_pos\": ObservationTermCfg(\n", " func=lambda env: env.sim.data.qpos[:, 0:1] / 2.0\n", " ),\n", " \"cart_vel\": ObservationTermCfg(\n", " func=lambda env: env.sim.data.qvel[:, 0:1] / 20.0\n", " ),\n", " }\n", "\n", " observations = {\n", " \"actor\": ObservationGroupCfg(\n", " terms=actor_terms,\n", " concatenate_terms=True,\n", " enable_corruption=not play, # Disable corruption in play mode\n", " ),\n", " \"critic\": ObservationGroupCfg(\n", " terms=actor_terms, # Critic uses same observations\n", " concatenate_terms=True,\n", " enable_corruption=False,\n", " ),\n", " }\n", "\n", " # ==============================================================================\n", " # Rewards\n", " # ==============================================================================\n", "\n", " def compute_upright_reward(env):\n", " \"\"\"Reward for keeping pole upright (cosine of angle).\"\"\"\n", " return env.sim.data.qpos[:, 1].cos()\n", "\n", " def compute_effort_penalty(env):\n", " \"\"\"Penalty for control effort.\"\"\"\n", " return -0.01 * (env.sim.data.ctrl[:, 0] ** 2)\n", "\n", " rewards = {\n", " \"upright\": RewardTermCfg(func=compute_upright_reward, weight=5.0),\n", " \"effort\": RewardTermCfg(func=compute_effort_penalty, weight=1.0),\n", " }\n", "\n", " # ==============================================================================\n", " # Events\n", " # ==============================================================================\n", "\n", " def random_push_cart(env, env_ids, force_range=(-5, 5)):\n", " \"\"\"Apply random force to cart for robustness training.\"\"\"\n", " n = len(env_ids)\n", " random_forces = (\n", " torch.rand(n, device=env.device) *\n", " (force_range[1] - force_range[0]) +\n", " force_range[0]\n", " )\n", " env.sim.data.qfrc_applied[env_ids, 0] = random_forces\n", "\n", " events = {\n", " \"reset_robot_joints\": EventTermCfg(\n", " func=mdp.reset_joints_by_offset,\n", " mode=\"reset\",\n", " params={\n", " \"asset_cfg\": SceneEntityCfg(\"robot\"),\n", " \"position_range\": (-0.1, 0.1),\n", " \"velocity_range\": (-0.1, 0.1),\n", " },\n", " ),\n", " }\n", "\n", " # Add random pushes only in training mode\n", " if not play:\n", " events[\"random_push\"] = EventTermCfg(\n", " func=random_push_cart,\n", " mode=\"interval\",\n", " interval_range_s=(1.0, 2.0),\n", " params={\"force_range\": (-20.0, 20.0)},\n", " )\n", "\n", " # ==============================================================================\n", " # Terminations\n", " # ==============================================================================\n", "\n", " def check_pole_tipped(env):\n", " \"\"\"Check if pole has tipped beyond 30 degrees.\"\"\"\n", " return env.sim.data.qpos[:, 1].abs() > math.radians(30)\n", "\n", " terminations = {\n", " \"timeout\": TerminationTermCfg(func=mdp.time_out, time_out=True),\n", " \"tipped\": TerminationTermCfg(func=check_pole_tipped, time_out=False),\n", " }\n", "\n", " # ==============================================================================\n", " # Environment Configuration\n", " # ==============================================================================\n", "\n", " return ManagerBasedRlEnvCfg(\n", " scene=scene_cfg,\n", " observations=observations,\n", " actions=actions,\n", " rewards=rewards,\n", " events=events,\n", " terminations=terminations,\n", " sim=sim_cfg,\n", " viewer=viewer_cfg,\n", " decimation=1, # No action repeat\n", " episode_length_s=int(1e9) if play else 10.0, # Infinite for play, 10s for training\n", " )" ] }, { "cell_type": "markdown", "metadata": { "id": "fC5maMjzSj_X" }, "source": [ "### **āš™ļø Create RL Configuration**\n", "\n", "This file defines the PPO (Proximal Policy Optimization) training parameters." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "C81zZm6mSj_X" }, "outputs": [], "source": "%%writefile /content/mjlab/src/mjlab/tasks/cartpole/rl_cfg.py\n\"\"\"RL configuration for CartPole task.\"\"\"\n\nfrom mjlab.rl.config import (\n RslRlOnPolicyRunnerCfg,\n RslRlModelCfg,\n RslRlPpoAlgorithmCfg,\n)\n\n\ndef cartpole_ppo_runner_cfg() -> RslRlOnPolicyRunnerCfg:\n \"\"\"Create RL runner configuration for CartPole task.\"\"\"\n return RslRlOnPolicyRunnerCfg(\n actor=RslRlModelCfg(\n hidden_dims=(256, 128, 64), # Smaller network for simpler task\n activation=\"elu\",\n obs_normalization=True,\n distribution_cfg={\n \"class_name\": \"GaussianDistribution\",\n \"init_std\": 1.0,\n \"std_type\": \"scalar\",\n },\n ),\n critic=RslRlModelCfg(\n hidden_dims=(256, 128, 64),\n activation=\"elu\",\n obs_normalization=True,\n ),\n algorithm=RslRlPpoAlgorithmCfg(\n value_loss_coef=1.0,\n use_clipped_value_loss=True,\n clip_param=0.2,\n entropy_coef=0.01,\n num_learning_epochs=5,\n num_mini_batches=4,\n learning_rate=1.0e-3,\n schedule=\"adaptive\",\n gamma=0.99,\n lam=0.95,\n desired_kl=0.01,\n max_grad_norm=1.0,\n ),\n experiment_name=\"cartpole\",\n save_interval=50,\n num_steps_per_env=24,\n max_iterations=5_000, # Fewer iterations for simpler task\n )" }, { "cell_type": "markdown", "metadata": { "id": "Oc8-AHGcHt78" }, "source": [ "### **šŸ“‹ Register the Task Environment**\n", "\n", "Register the CartPole task with mjlab registry." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "YitUGUBRHxD4" }, "outputs": [], "source": [ "%%writefile /content/mjlab/src/mjlab/tasks/cartpole/__init__.py\n", "\"\"\"CartPole task registration.\"\"\"\n", "\n", "from mjlab.tasks.registry import register_mjlab_task\n", "from mjlab.rl.runner import MjlabOnPolicyRunner\n", "\n", "from .env_cfg import cartpole_env_cfg\n", "from .rl_cfg import cartpole_ppo_runner_cfg\n", "\n", "register_mjlab_task(\n", " task_id=\"Mjlab-Cartpole\",\n", " env_cfg=cartpole_env_cfg(),\n", " play_env_cfg=cartpole_env_cfg(play=True),\n", " rl_cfg=cartpole_ppo_runner_cfg(),\n", " runner_cls=MjlabOnPolicyRunner,\n", ")" ] }, { "cell_type": "markdown", "metadata": { "id": "K7wqLZR1rnGn" }, "source": [ "---\n", "\n", "## **šŸš€ Step 3: Train the Agent**\n", "\n", "Now let's train a PPO policy to balance the CartPole!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "Hht_hF4trqP2" }, "outputs": [], "source": [ "!python -m mjlab.scripts.train Mjlab-Cartpole --agent.max-iterations 201 --agent.save-interval 20" ] }, { "cell_type": "markdown", "metadata": { "id": "xCaqPznGrx8H" }, "source": [ "### **šŸ“ Locate Training Checkpoints**\n", "\n", "After training, checkpoints are saved locally." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "uPnmHYu8r0uY" }, "outputs": [], "source": [ "import os\n", "import re\n", "from pathlib import Path\n", "\n", "# Find the most recent training run\n", "log_dir = Path(\"/content/mjlab/logs/rsl_rl/cartpole\")\n", "if log_dir.exists():\n", " runs = sorted(log_dir.glob(\"*\"), key=os.path.getmtime, reverse=True)\n", " if runs:\n", " latest_run = runs[0]\n", " print(f\"āœ“ Latest training run: {latest_run.name}\\n\")\n", "\n", " # List checkpoints - sorted by iteration number\n", " checkpoints = list(latest_run.glob(\"model_*.pt\"))\n", " if checkpoints:\n", " # Extract iteration number and sort numerically\n", " def get_iteration(ckpt):\n", " match = re.search(r\"model_(\\d+)\\.pt\", ckpt.name)\n", " return int(match.group(1)) if match else 0\n", "\n", " checkpoints = sorted(checkpoints, key=get_iteration)\n", "\n", " print(f\"Found {len(checkpoints)} checkpoints:\")\n", " for ckpt in checkpoints[-5:]: # Show last 5\n", " size_mb = ckpt.stat().st_size / (1024 * 1024)\n", " iteration = get_iteration(ckpt)\n", " print(f\" ā€¢ {ckpt.name} (iteration {iteration}, {size_mb:.2f} MB)\")\n", "\n", " # Store the last checkpoint path\n", " last_checkpoint = str(checkpoints[-1])\n", " print(f\"\\nšŸ’¾ Last checkpoint: {last_checkpoint}\")\n", " else:\n", " print(\"āš  No checkpoints found yet\")\n", " else:\n", " print(\"āš  No training runs found\")\n", "else:\n", " print(\"āš  Log directory not found. Have you run training yet?\")" ] }, { "cell_type": "markdown", "metadata": { "id": "eWFS9Pw7r2uH" }, "source": [ "---\n", "\n", "## **šŸŽ® Step 4: Visualize the Trained Policy**\n", "\n", "Let's see the trained policy in action!" ] }, { "cell_type": "markdown", "metadata": { "id": "78PgHtpfr5sb" }, "source": [ "### **šŸŒ Launch Viser API**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "_9tGiFyBr2bW" }, "outputs": [], "source": [ "import subprocess\n", "import sys\n", "\n", "process = subprocess.Popen(\n", " [\n", " \"python\",\n", " \"-m\",\n", " \"mjlab.scripts.play\",\n", " \"Mjlab-Cartpole\",\n", " \"--checkpoint_file\",\n", " last_checkpoint,\n", " \"--num_envs\",\n", " \"4\",\n", " ],\n", " stdout=subprocess.PIPE,\n", " stderr=subprocess.STDOUT,\n", " universal_newlines=True,\n", " bufsize=1,\n", ")\n", "\n", "detected_port = None\n", "\n", "for line in process.stdout:\n", " print(line, end=\"\")\n", " sys.stdout.flush()\n", "\n", " # Extract port number from viser output\n", " port_match = re.search(r\":(\\d{4})\", line)\n", " if port_match and \"viser\" in line.lower():\n", " detected_port = int(port_match.group(1))\n", " print(\"\\n\" + \"=\" * 34)\n", " print(f\"āœ… Server is running on port {detected_port}!\")\n", " print(\"=\" * 34)\n", " break" ] }, { "cell_type": "markdown", "metadata": { "id": "XgzJXyBXssZS" }, "source": [ "### **šŸ–„ļø Embed Client as iframe**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ll89QnuSuUxx" }, "outputs": [], "source": [ "from google.colab import output\n", "\n", "port = detected_port if detected_port else 8081\n", "output.serve_kernel_port_as_iframe(port=port, height=700)" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "include_colab_link": true, "provenance": [], "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" } }, "nbformat": 4, "nbformat_minor": 0 }