Upload notebook.ipynb

notebook.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "NQUk3Y0WwYZ4"
+      },
+      "source": [
+        "# 🤗 x 🦾: Training SmolVLA with LeRobot Notebook\n",
+        "\n",
+        "Welcome to the **LeRobot SmolVLA training notebook**! This notebook provides a ready-to-run setup for training imitation learning policies using the [🤗 LeRobot](https://github.com/huggingface/lerobot) library.\n",
+        "\n",
+        "In this example, we train an `SmolVLA` policy using a dataset hosted on the [Hugging Face Hub](https://huggingface.co/), and optionally track training metrics with [Weights & Biases (wandb)](https://wandb.ai/).\n",
+        "\n",
+        "## ⚙️ Requirements\n",
+        "- A Hugging Face dataset repo ID containing your training data (`--dataset.repo_id=YOUR_USERNAME/YOUR_DATASET`)\n",
+        "- Optional: A [wandb](https://wandb.ai/) account if you want to enable training visualization\n",
+        "- Recommended: GPU runtime (e.g., NVIDIA A100) for faster training\n",
+        "\n",
+        "## ⏱️ Expected Training Time\n",
+        "Training with the `SmolVLA` policy for 20,000 steps typically takes **about 5 hours on an NVIDIA A100** GPU. On less powerful GPUs or CPUs, training may take significantly longer!\n",
+        "\n",
+        "## Example Output\n",
+        "Model checkpoints, logs, and training plots will be saved to the specified `--output_dir`. If `wandb` is enabled, progress will also be visualized in your wandb project dashboard.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MOJyX0CnwA5m"
+      },
+      "source": [
+        "## Install conda\n",
+        "This cell uses `condacolab` to bootstrap a full Conda environment inside Google Colab.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "QlKjL1X5t_zM"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -q condacolab\n",
+        "import condacolab\n",
+        "condacolab.install()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DxCc3CARwUjN"
+      },
+      "source": [
+        "## Install LeRobot\n",
+        "This cell clones the `lerobot` repository from Hugging Face, installs FFmpeg (version 7.1.1), and installs the package in editable mode.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "dgLu7QT5tUik"
+      },
+      "outputs": [],
+      "source": [
+        "!git clone https://github.com/huggingface/lerobot.git\n",
+        "!conda install ffmpeg=7.1.1 -c conda-forge\n",
+        "!cd lerobot && pip install -e ."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Q8Sn2wG4wldo"
+      },
+      "source": [
+        "## Weights & Biases login\n",
+        "This cell logs you into Weights & Biases (wandb) to enable experiment tracking and logging."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "PolVM_movEvp"
+      },
+      "outputs": [],
+      "source": [
+        "!wandb login"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zTWQAgX9xseE"
+      },
+      "source": [
+        "## Install SmolVLA dependencies"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "DiHs0BKwxseE"
+      },
+      "outputs": [],
+      "source": [
+        "!cd lerobot && pip install -e \".[smolvla]\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IkzTo4mNwxaC"
+      },
+      "source": [
+        "## Start training SmolVLA with LeRobot\n",
+        "\n",
+        "This cell runs the `train.py` script from the `lerobot` library to train a robot control policy.  \n",
+        "\n",
+        "Make sure to adjust the following arguments to your setup:\n",
+        "\n",
+        "1. `--dataset.repo_id=YOUR_HF_USERNAME/YOUR_DATASET`:  \n",
+        "   Replace this with the Hugging Face Hub repo ID where your dataset is stored, e.g., `pepijn223/il_gym0`.\n",
+        "\n",
+        "2. `--batch_size=64`: means the model processes 64 training samples in parallel before doing one gradient update. Reduce this number if you have a GPU with low memory.\n",
+        "\n",
+        "3. `--output_dir=outputs/train/...`:  \n",
+        "   Directory where training logs and model checkpoints will be saved.\n",
+        "\n",
+        "4. `--job_name=...`:  \n",
+        "   A name for this training job, used for logging and Weights & Biases.\n",
+        "\n",
+        "5. `--policy.device=cuda`:  \n",
+        "   Use `cuda` if training on an NVIDIA GPU. Use `mps` for Apple Silicon, or `cpu` if no GPU is available.\n",
+        "\n",
+        "6. `--wandb.enable=true`:  \n",
+        "   Enables Weights & Biases for visualizing training progress. You must be logged in via `wandb login` before running this."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ZO52lcQtxseE"
+      },
+      "outputs": [],
+      "source": [
+        "!cd lerobot && python lerobot/scripts/train.py \\\n",
+        "  --policy.path=lerobot/smolvla_base \\\n",
+        "  --dataset.repo_id=${HF_USER}/mydataset \\\n",
+        "  --batch_size=64 \\\n",
+        "  --steps=20000 \\\n",
+        "  --output_dir=outputs/train/my_smolvla \\\n",
+        "  --job_name=my_smolvla_training \\\n",
+        "  --policy.device=cuda \\\n",
+        "  --wandb.enable=true"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2PBu7izpxseF"
+      },
+      "source": [
+        "## Login into Hugging Face Hub\n",
+        "Now after training is done login into the Hugging Face hub and upload the last checkpoint"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "8yu5khQGIHi6"
+      },
+      "outputs": [],
+      "source": [
+        "!huggingface-cli login"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "zFMLGuVkH7UN"
+      },
+      "outputs": [],
+      "source": [
+        "!huggingface-cli upload ${HF_USER}/my_smolvla \\\n",
+        "  /content/lerobot/outputs/train/my_smolvla/checkpoints/last/pretrained_model"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "A100",
+      "machine_shape": "hm",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}