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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
} |