{ "cells": [ { "cell_type": "markdown", "id": "udlwdep9gm", "metadata": {}, "source": [ "# LeRobot Data: Download, Convert, Train, and Evaluate\n", "\n", "This notebook walks through the full workflow for using [LeRobot](https://github.com/huggingface/lerobot) datasets with VLA Foundry: downloading a dataset from HuggingFace Hub, converting it to WebDataset format, training a diffusion policy model, and running inference. We set the training scale to a small number to make the tutorial but this is not enough to get a meaningful policy. The user is welcome to modify the tutorial as they see fit.\n", "\n", "We use `lerobot/pusht` as the running example throughout this tutorial. PushT is a simple 2D pushing task that makes a great starting point." ] }, { "cell_type": "markdown", "id": "qo9x6aplrsj", "metadata": {}, "source": [ "## Prerequisites\n", "\n", "- Python 3.12+ with VLA Foundry installed via `uv`\n", "- The **`Python (vla_foundry)`** Jupyter kernel — install it once from the repo root:\n", " ```bash\n", " bash tutorials/install_kernel.sh\n", " ```\n", " This syncs dependencies (preprocessing, gym-pusht, etc.) and registers the kernel.\n", "- Restart your notebook kernel after installing the vla_foundry kernel.\n", "- For training, a machine with one or more GPUs\n", "- (Optional) AWS credentials configured if you want to read/write data on S3" ] }, { "cell_type": "code", "execution_count": 1, "id": "3ezsf6xwp6i", "metadata": { "execution": { "iopub.execute_input": "2026-03-25T21:00:20.488499Z", "iopub.status.busy": "2026-03-25T21:00:20.487866Z", "iopub.status.idle": "2026-03-25T21:00:20.495692Z", "shell.execute_reply": "2026-03-25T21:00:20.494608Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Working directory: /media/jeanmercat/471dbe07-0fd5-429a-b5d9-63c4eb5824b4/Code/lbm2_bis/worktrees/jean/lerobot_tutorial\n" ] } ], "source": [ "import os\n", "import subprocess\n", "\n", "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n", "\n", "# Change to the repo root (works regardless of where the notebook is opened from)\n", "repo_root = subprocess.check_output([\"git\", \"rev-parse\", \"--show-toplevel\"], text=True).strip()\n", "os.chdir(repo_root)\n", "print(f\"Working directory: {os.getcwd()}\")" ] }, { "cell_type": "markdown", "id": "uwpnxdkjm3d", "metadata": {}, "source": [ "**Stop here.** Switch to the **\"Python (vla_foundry)\"** kernel in your notebook UI (Kernel → Change Kernel) and restart it before running the cells below." ] }, { "cell_type": "code", "execution_count": null, "id": "fe80f699", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "169zui94qoi", "metadata": {}, "source": [ "## 1. Download a LeRobot Dataset\n", "\n", "### Browsing Available Datasets\n", "\n", "LeRobot datasets are hosted on HuggingFace Hub. You can browse them at:\n", "- [https://huggingface.co/lerobot](https://huggingface.co/lerobot)\n", "\n", "Some well-known datasets include:\n", "- `lerobot/pusht` -- Push-T task (2D)\n", "- `lerobot/aloha_sim_transfer_cube_human` -- ALOHA bimanual cube transfer (simulation)\n", "- `lerobot/aloha_sim_insertion_human` -- ALOHA peg insertion (simulation)\n", "- `lerobot/xarm_lift_medium_replay` -- xArm lift task\n", "\n", "### Downloading with huggingface-cli\n", "\n", "> **Important -- Use `--revision v2.0`.** The default version on HuggingFace is now v3.0, which uses a different directory layout that the converter does not support yet. Always specify `--revision v2.0` when downloading datasets for use with VLA Foundry." ] }, { "cell_type": "code", "execution_count": 2, "id": "xcwzdxp84oh", "metadata": { "execution": { "iopub.execute_input": "2026-03-25T21:00:21.030419Z", "iopub.status.busy": "2026-03-25T21:00:21.029815Z", "iopub.status.idle": "2026-03-25T21:00:22.591482Z", "shell.execute_reply": "2026-03-25T21:00:22.589543Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fetching 418 files: 100%|████████████████████| 418/418 [00:00<00:00, 598.69it/s]\n", "/media/jeanmercat/471dbe07-0fd5-429a-b5d9-63c4eb5824b4/Code/lbm2_bis/worktrees/jean/lerobot_tutorial/tutorials/data/lerobot/pusht\n" ] } ], "source": [ "!huggingface-cli download lerobot/pusht \\\n", " --repo-type dataset \\\n", " --revision v2.0 \\\n", " --local-dir ./tutorials/data/lerobot/pusht" ] }, { "cell_type": "markdown", "id": "ymjfu6o8uto", "metadata": {}, "source": [ "### LeRobot Data Format (v2.0)\n", "\n", "After downloading, the dataset directory has this structure (v2.0 format):\n", "\n", "```\n", "pusht/\n", " meta/\n", " info.json # Dataset metadata (fps, features, shapes, etc.)\n", " episodes.jsonl # Per-episode metadata (length, task, index)\n", " tasks.jsonl # Task descriptions (language instructions)\n", " data/\n", " chunk-000/\n", " episode_000000.parquet # Episode data (states, actions)\n", " episode_000001.parquet\n", " ...\n", " videos/ # (Optional) Video files for camera observations\n", " chunk-000/\n", " observation.image/\n", " episode_000000.mp4\n", " ...\n", "```\n", "\n", "Key files:\n", "- **`meta/info.json`**: Contains the dataset FPS, feature names, data shapes, and number of episodes\n", "- **`meta/episodes.jsonl`**: One JSON object per line, each describing an episode\n", "- **`data/chunk-XXXX/episode_XXXXXX.parquet`**: Parquet files containing observation and action data\n", "- **`videos/`**: (Optional) Video files organized by camera name and chunk" ] }, { "cell_type": "markdown", "id": "pilpmtfemkf", "metadata": {}, "source": [ "## 2. Convert to WebDataset Format\n", "\n", "VLA Foundry uses WebDataset tar shards for training. The conversion script handles LeRobot datasets with `--type lerobot`.\n", "\n", "### Inspecting Your Dataset\n", "\n", "Before running preprocessing, inspect the dataset to discover the correct column names for observations, actions, and images. Column names vary between datasets, so always check before proceeding." ] }, { "cell_type": "code", "execution_count": 3, "id": "wi3xkjpr52s", "metadata": { "execution": { "iopub.execute_input": "2026-03-25T21:00:22.596695Z", "iopub.status.busy": "2026-03-25T21:00:22.596166Z", "iopub.status.idle": "2026-03-25T21:00:23.729619Z", "shell.execute_reply": "2026-03-25T21:00:23.728607Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Columns: ['observation.state', 'action', 'episode_index', 'frame_index', 'timestamp', 'next.reward', 'next.done', 'next.success', 'index', 'task_index']\n", "\n", "First few rows:\n" ] }, { "data": { "text/html": [ "
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" ], "text/plain": [ " observation.state action episode_index frame_index \\\n", "0 [222.0, 97.0] [233.0, 71.0] 0 0 \n", "1 [225.2524, 89.31253] [229.0, 83.0] 0 1 \n", "2 [227.59233, 84.53438] [229.0, 86.0] 0 2 \n", "3 [228.42017, 84.27986] [230.0, 86.0] 0 3 \n", "4 [229.04222, 84.9571] [239.0, 89.0] 0 4 \n", "\n", " timestamp next.reward next.done next.success index task_index \n", "0 0.0 0.190297 False False 0 0 \n", "1 0.1 0.190297 False False 1 0 \n", "2 0.2 0.190297 False False 2 0 \n", "3 0.3 0.190297 False False 3 0 \n", "4 0.4 0.190297 False False 4 0 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "df = pd.read_parquet(\"./tutorials/data/lerobot/pusht/data/chunk-000/episode_000000.parquet\")\n", "print(\"Columns:\", df.columns.tolist())\n", "print(\"\\nFirst few rows:\")\n", "df.head()" ] }, { "cell_type": "markdown", "id": "pv643o9srw", "metadata": {}, "source": [ "**For video-based datasets** (like `pusht`), image/camera columns will **not** appear in the parquet file. Instead, camera names come from the subdirectories under `videos/`:" ] }, { "cell_type": "code", "execution_count": 4, "id": "pq2zelgogy9", "metadata": { "execution": { "iopub.execute_input": "2026-03-25T21:00:23.732039Z", "iopub.status.busy": "2026-03-25T21:00:23.731900Z", "iopub.status.idle": "2026-03-25T21:00:23.735670Z", "shell.execute_reply": "2026-03-25T21:00:23.734851Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Camera names: ['observation.image']\n" ] } ], "source": [ "import os\n", "\n", "# For video-based datasets, camera names come from the videos/ directory\n", "video_dirs = os.listdir(\"./tutorials/data/lerobot/pusht/videos/chunk-000/\")\n", "print(\"Camera names:\", video_dirs)" ] }, { "cell_type": "markdown", "id": "30vn7cdtuzs", "metadata": {}, "source": [ "### Basic Conversion (Local)\n", "\n", "This example is adapted from `examples/preprocessing/preprocess_robotics_data_lerobot.sh`.\n", "\n", "Arguments that accept lists use Python list literal syntax as a string (e.g., `\"['observation.state']\"`).\n", "\n", "### Key Arguments\n", "\n", "| Argument | Description |\n", "|---|---|\n", "| `--type \"lerobot\"` | Selects the LeRobot converter |\n", "| `--source_episodes` | List of paths to LeRobot dataset root directories (local or S3) |\n", "| `--output_dir` | Where to write the converted WebDataset shards (local or S3) |\n", "| `--camera_names` | List of camera/image names (e.g., `observation.image`) |\n", "| `--observation_keys` | List of observation column names to include (e.g., `observation.state`) |\n", "| `--action_keys` | List of action column names (e.g., `action` or `actions`) |\n", "| `--config_path` | Path to a YAML file with windowing/preprocessing parameters |\n", "| `--samples_per_shard` | Number of training samples per tar shard (default: 128) |\n", "\n", "The preprocessing config at `vla_foundry/config_presets/data/robotics_preprocessing_params_1past_14future.yaml` defines the temporal windowing: each training sample contains 1 past + 14 future low-dimensional steps (states/actions) with 2 images (at timesteps -1 and 0 relative to the anchor)." ] }, { "cell_type": "code", "execution_count": 5, "id": "b056v2jnkba", "metadata": { "execution": { "iopub.execute_input": "2026-03-25T21:00:23.737711Z", "iopub.status.busy": "2026-03-25T21:00:23.737586Z", "iopub.status.idle": "2026-03-25T21:00:23.741595Z", "shell.execute_reply": "2026-03-25T21:00:23.740716Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2026-03-27 00:39:31,945\tINFO worker.py:2004 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32mhttp://127.0.0.1:8265 \u001b[39m\u001b[22m\n", "2026-03-27 00:39:31,951\tWARNING working_dir.py:86 -- Directory '.git' is now ignored by default when packaging the working directory. To disable this behavior, set the `RAY_OVERRIDE_RUNTIME_ENV_DEFAULT_EXCLUDES=''` environment variable.\n", "2026-03-27 00:39:31,953\tWARNING working_dir.py:86 -- Directory '.venv' is now ignored by default when packaging the working directory. To disable this behavior, set the `RAY_OVERRIDE_RUNTIME_ENV_DEFAULT_EXCLUDES=''` environment variable.\n", "2026-03-27 00:39:31,974\tINFO packaging.py:392 -- Ignoring upload to cluster for these files: [PosixPath('/media/jeanmercat/471dbe07-0fd5-429a-b5d9-63c4eb5824b4/Code/lbm2_bis/worktrees/jean/lerobot_tutorial/.gitignore')]\n", "2026-03-27 00:39:32,023\tINFO packaging.py:392 -- Ignoring upload to cluster for these files: [PosixPath('/media/jeanmercat/471dbe07-0fd5-429a-b5d9-63c4eb5824b4/Code/lbm2_bis/worktrees/jean/lerobot_tutorial/.ruff_cache/.gitignore')]\n", "2026-03-27 00:39:32,024\tINFO packaging.py:392 -- Ignoring upload to cluster for these files: [PosixPath('/media/jeanmercat/471dbe07-0fd5-429a-b5d9-63c4eb5824b4/Code/lbm2_bis/worktrees/jean/lerobot_tutorial/.venv/.gitignore')]\n", "2026-03-27 00:39:32,025\tINFO packaging.py:392 -- Ignoring upload to cluster for these files: [PosixPath('/media/jeanmercat/471dbe07-0fd5-429a-b5d9-63c4eb5824b4/Code/lbm2_bis/worktrees/jean/lerobot_tutorial/.pytest_cache/.gitignore')]\n", "2026-03-27 00:39:32,165\tINFO packaging.py:392 -- Ignoring upload to cluster for these files: [PosixPath('/media/jeanmercat/471dbe07-0fd5-429a-b5d9-63c4eb5824b4/Code/lbm2_bis/worktrees/jean/lerobot_tutorial/packages/libero-wrapper/.gitignore')]\n", "2026-03-27 00:39:32,356\tINFO packaging.py:392 -- Ignoring upload to cluster for these files: [PosixPath('/media/jeanmercat/471dbe07-0fd5-429a-b5d9-63c4eb5824b4/Code/lbm2_bis/worktrees/jean/lerobot_tutorial/vla_foundry/tri/lbm_eval/eval_campaigns/.gitignore')]\n", "2026-03-27 00:39:32,993\tINFO packaging.py:691 -- Creating a file package for local module '/media/jeanmercat/471dbe07-0fd5-429a-b5d9-63c4eb5824b4/Code/lbm2_bis/worktrees/jean/lerobot_tutorial'.\n", "2026-03-27 00:39:32,994\tINFO packaging.py:392 -- Ignoring upload to cluster for these files: [PosixPath('/media/jeanmercat/471dbe07-0fd5-429a-b5d9-63c4eb5824b4/Code/lbm2_bis/worktrees/jean/lerobot_tutorial/.gitignore')]\n", "2026-03-27 00:39:33,015\tINFO packaging.py:392 -- Ignoring upload to cluster for these files: [PosixPath('/media/jeanmercat/471dbe07-0fd5-429a-b5d9-63c4eb5824b4/Code/lbm2_bis/worktrees/jean/lerobot_tutorial/.ruff_cache/.gitignore')]\n", "2026-03-27 00:39:33,016\tINFO packaging.py:392 -- Ignoring upload to cluster for these files: [PosixPath('/media/jeanmercat/471dbe07-0fd5-429a-b5d9-63c4eb5824b4/Code/lbm2_bis/worktrees/jean/lerobot_tutorial/.venv/.gitignore')]\n", "2026-03-27 00:39:33,017\tINFO packaging.py:392 -- Ignoring upload to cluster for these files: [PosixPath('/media/jeanmercat/471dbe07-0fd5-429a-b5d9-63c4eb5824b4/Code/lbm2_bis/worktrees/jean/lerobot_tutorial/.pytest_cache/.gitignore')]\n", "2026-03-27 00:39:33,188\tINFO packaging.py:392 -- Ignoring upload to cluster for these files: [PosixPath('/media/jeanmercat/471dbe07-0fd5-429a-b5d9-63c4eb5824b4/Code/lbm2_bis/worktrees/jean/lerobot_tutorial/packages/libero-wrapper/.gitignore')]\n", "2026-03-27 00:39:33,423\tINFO packaging.py:392 -- Ignoring upload to cluster for these files: [PosixPath('/media/jeanmercat/471dbe07-0fd5-429a-b5d9-63c4eb5824b4/Code/lbm2_bis/worktrees/jean/lerobot_tutorial/vla_foundry/tri/lbm_eval/eval_campaigns/.gitignore')]\n", "2026-03-27 00:39:34,111\tWARNING packaging.py:516 -- File /media/jeanmercat/471dbe07-0fd5-429a-b5d9-63c4eb5824b4/Code/lbm2_bis/worktrees/jean/lerobot_tutorial/tests/essential/test_assets/small_lbm_dataset/stats.json is very large (16.06MiB). Consider adding this file to the 'excludes' list to skip uploading it: `ray.init(..., runtime_env={'excludes': ['/media/jeanmercat/471dbe07-0fd5-429a-b5d9-63c4eb5824b4/Code/lbm2_bis/worktrees/jean/lerobot_tutorial/tests/essential/test_assets/small_lbm_dataset/stats.json']})`\n", "2026-03-27 00:39:34,268\tINFO packaging.py:463 -- Pushing file package 'gcs://_ray_pkg_91aa62442888aacf.zip' (78.57MiB) to Ray cluster...\n", "2026-03-27 00:39:34,758\tINFO packaging.py:476 -- Successfully pushed file package 'gcs://_ray_pkg_91aa62442888aacf.zip'.\n", "/media/jeanmercat/471dbe07-0fd5-429a-b5d9-63c4eb5824b4/Code/lbm2_bis/worktrees/jean/lerobot_tutorial/.venv/lib/python3.12/site-packages/ray/_private/worker.py:2052: FutureWarning: Tip: In future versions of Ray, Ray will no longer override accelerator visible devices env var if num_gpus=0 or num_gpus=None (default). To enable this behavior and turn off this error message, set RAY_ACCEL_ENV_VAR_OVERRIDE_ON_ZERO=0\n", " warnings.warn(\n", "Started new local Ray cluster with num_cpus=None\n", "Detected FPS from global setting: 10.0\n", "Building episode lookup dict from 1 chunks...\n", "\u001b[33m(raylet)\u001b[0m \u001b[1m\u001b[33mwarning\u001b[39m\u001b[0m\u001b[1m:\u001b[0m \u001b[1m`VIRTUAL_ENV=/media/jeanmercat/471dbe07-0fd5-429a-b5d9-63c4eb5824b4/Code/lbm2_bis/worktrees/jean/lerobot_tutorial/.venv` does not match the project environment path `.venv` and will be ignored; use `--active` to target the active environment instead\u001b[0m\n", "\u001b[33m(raylet)\u001b[0m Using CPython \u001b[36m3.12.12\u001b[39m\u001b[36m\u001b[39m\n", "\u001b[33m(raylet)\u001b[0m Creating virtual environment at: \u001b[36m.venv\u001b[39m\n", "\u001b[33m(raylet)\u001b[0m \u001b[36m\u001b[1mBuilding\u001b[0m\u001b[39m vla-foundry\u001b[2m @ file:///tmp/ray/session_2026-03-27_00-39-27_396037_2754594/runtime_resources/working_dir_files/_ray_pkg_91aa62442888aacf\u001b[0m\n", "\u001b[33m(raylet)\u001b[0m \u001b[2mInstalled \u001b[1m164 packages\u001b[0m \u001b[2min 254ms\u001b[0m\u001b[0m\n", "Built episode lookup with 206 episodes\n", "Discovered cameras: ['observation.image']\n", "Discovered cameras: ['observation.image']\n", "Building video lookup dict from 1 chunks and 1 cameras...\n", "Built video lookup with 206 video files\n", "Loaded 206 episodes metadata\n", "🔍 Discovering episodes...\n", "Found 206 episodes\n", "🔍 Analyzing Python dependencies...\n", "📋 Found 11 critical dependency files\n", "⚠️ Found uncommitted changes to CRITICAL preprocessing code:\n", " 🔴 data\n", "🏷️ Creating git tag for reproducibility (critical changes detected)...\n", "✅ Created and pushed git tag: preprocessing_robotics-data_v20260327_003946\n", "📌 Use git tag 'preprocessing_robotics-data_v20260327_003946' to reproduce this exact code state\n", "🚀 Processing 206 episodes and uploading to S3...\n", "💻 CPU mode: Processing without point clouds\n", "\u001b[33m(raylet)\u001b[0m \u001b[1m\u001b[33mwarning\u001b[39m\u001b[0m\u001b[1m:\u001b[0m \u001b[1m`VIRTUAL_ENV=/media/jeanmercat/471dbe07-0fd5-429a-b5d9-63c4eb5824b4/Code/lbm2_bis/worktrees/jean/lerobot_tutorial/.venv` does not match the project environment path `.venv` and will be ignored; use `--active` to target the active environment instead\u001b[0m\u001b[32m [repeated 8x across cluster] (Ray deduplicates logs by default. Set RAY_DEDUP_LOGS=0 to disable log deduplication, or see https://docs.ray.io/en/master/ray-observability/user-guides/configure-logging.html#log-deduplication for more options.)\u001b[0m\n", "\u001b[33m(raylet)\u001b[0m \u001b[32m\u001b[1mBuilt\u001b[0m\u001b[39m vla-foundry\u001b[2m @ file:///tmp/ray/session_2026-03-27_00-39-27_396037_2754594/runtime_resources/working_dir_files/_ray_pkg_91aa62442888aacf\u001b[0m\n", "\u001b[36m(streaming_episode_worker pid=2758685)\u001b[0m Saved /media/jeanmercat/471dbe07-0fd5-429a-b5d9-63c4eb5824b4/Code/lbm2_bis/worktrees/jean/lerobot_tutorial/tutorials/data/preprocessed/pusht/frames/e59a7740-a042-5539-b0e2-ea84a9d0632b_episode_000172.parquet_frame_15.tar\n", "\u001b[36m(streaming_episode_worker pid=2758685)\u001b[0m Saved /media/jeanmercat/471dbe07-0fd5-429a-b5d9-63c4eb5824b4/Code/lbm2_bis/worktrees/jean/lerobot_tutorial/tutorials/data/preprocessed/pusht/frames/e59a7740-a042-5539-b0e2-ea84a9d0632b_episode_000172.parquet_frame_11.tar\n", 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Starting sharding phase...\n", "Creating 257 shards with up to 100 samples each\n", "\u001b[36m(streaming_episode_worker pid=2761717)\u001b[0m \n", "\u001b[36m(streaming_episode_worker pid=2761717)\u001b[0m \n", "\u001b[36m(streaming_episode_worker pid=2761717)\u001b[0m \n", "\u001b[36m(create_shard pid=2759410)\u001b[0m Saved shard shard_000026.tar to /media/jeanmercat/471dbe07-0fd5-429a-b5d9-63c4eb5824b4/Code/lbm2_bis/worktrees/jean/lerobot_tutorial/tutorials/data/preprocessed/pusht/shards/shard_000026.tar\n", "✅ Created 257 shards.\n", "Creating 206 episode shards\n", "\u001b[36m(create_episode_shard pid=2758685)\u001b[0m Saved episode shard episode_cdf7e77f-129b-5950-906e-10b97c5e97eb_episode_000056.parquet.tar to /media/jeanmercat/471dbe07-0fd5-429a-b5d9-63c4eb5824b4/Code/lbm2_bis/worktrees/jean/lerobot_tutorial/tutorials/data/preprocessed/pusht/episodes/episode_cdf7e77f-129b-5950-906e-10b97c5e97eb_episode_000056.parquet.tar\n", "✅ Created 206 episode shards.\n", "Saved manifest.jsonl to /media/jeanmercat/471dbe07-0fd5-429a-b5d9-63c4eb5824b4/Code/lbm2_bis/worktrees/jean/lerobot_tutorial/tutorials/data/preprocessed/pusht/episodes/manifest.jsonl\n", "Saved manifest.jsonl to /media/jeanmercat/471dbe07-0fd5-429a-b5d9-63c4eb5824b4/Code/lbm2_bis/worktrees/jean/lerobot_tutorial/tutorials/data/preprocessed/pusht/shards/manifest.jsonl\n", "Saved stats.json to /media/jeanmercat/471dbe07-0fd5-429a-b5d9-63c4eb5824b4/Code/lbm2_bis/worktrees/jean/lerobot_tutorial/tutorials/data/preprocessed/pusht/shards/stats.json\n", "Saved stats.json to /media/jeanmercat/471dbe07-0fd5-429a-b5d9-63c4eb5824b4/Code/lbm2_bis/worktrees/jean/lerobot_tutorial/tutorials/data/preprocessed/pusht/episodes/stats.json\n", "Sample counts: {'total_potential_samples': 25650, 'still_samples_filtered': 0, 'padding_samples_filtered': 0}\n", "Saved processing_metadata.json to /media/jeanmercat/471dbe07-0fd5-429a-b5d9-63c4eb5824b4/Code/lbm2_bis/worktrees/jean/lerobot_tutorial/tutorials/data/preprocessed/pusht/shards/processing_metadata.json\n", "Saved preprocessing_config.yaml to /media/jeanmercat/471dbe07-0fd5-429a-b5d9-63c4eb5824b4/Code/lbm2_bis/worktrees/jean/lerobot_tutorial/tutorials/data/preprocessed/pusht/shards/preprocessing_config.yaml\n", "⚠️ Skipping fixed-path copy (only enabled for S3 -> S3): /media/jeanmercat/471dbe07-0fd5-429a-b5d9-63c4eb5824b4/Code/lbm2_bis/worktrees/jean/lerobot_tutorial/tutorials/data/preprocessed/pusht -> s3://tri-ml-datasets-uw2/vla_foundry_datasets_fixed/716097bc-ce22-4427-9a98-b18b382d2e3e\n", "\u001b[36m(streaming_episode_worker pid=2761717)\u001b[0m Saved /media/jeanmercat/471dbe07-0fd5-429a-b5d9-63c4eb5824b4/Code/lbm2_bis/worktrees/jean/lerobot_tutorial/tutorials/data/preprocessed/pusht/frames/f8c55650-f24e-5724-98ad-a2b94e9c7e7c_episode_000070.parquet_frame_185.tar\u001b[32m [repeated 2201x across cluster]\u001b[0m\n", "\u001b[36m(create_shard pid=2762535)\u001b[0m Saved shard shard_000252.tar to /media/jeanmercat/471dbe07-0fd5-429a-b5d9-63c4eb5824b4/Code/lbm2_bis/worktrees/jean/lerobot_tutorial/tutorials/data/preprocessed/pusht/shards/shard_000252.tar\u001b[32m [repeated 256x across cluster]\u001b[0m\n", "\u001b[36m(create_episode_shard pid=2763362)\u001b[0m Saved episode shard episode_cf5282bd-5e1f-5a0e-9999-e42413e35a46_episode_000129.parquet.tar to /media/jeanmercat/471dbe07-0fd5-429a-b5d9-63c4eb5824b4/Code/lbm2_bis/worktrees/jean/lerobot_tutorial/tutorials/data/preprocessed/pusht/episodes/episode_cf5282bd-5e1f-5a0e-9999-e42413e35a46_episode_000129.parquet.tar\u001b[32m [repeated 205x across cluster]\u001b[0m\n", "\u001b[33m(raylet)\u001b[0m \u001b[1m\u001b[33mwarning\u001b[39m\u001b[0m\u001b[1m:\u001b[0m \u001b[1m`VIRTUAL_ENV=/media/jeanmercat/471dbe07-0fd5-429a-b5d9-63c4eb5824b4/Code/lbm2_bis/worktrees/jean/lerobot_tutorial/.venv` does not match the project environment path `.venv` and will be ignored; use `--active` to target the active environment instead\u001b[0m\u001b[32m [repeated 9x across cluster]\u001b[0m\n", "🎉 Complete! All samples uploaded and sharded.\n", "\u001b[0m" ] } ], "source": [ "import os\n", "\n", "output_dir = os.path.abspath(\"./tutorials/data/preprocessed/pusht/\")\n", "!MPLBACKEND= uv run --group preprocessing python vla_foundry/data/preprocessing/preprocess_robotics_to_tar.py \\\n", " --type \"lerobot\" \\\n", " --source_episodes \"['./tutorials/data/lerobot/pusht/']\" \\\n", " --output_dir {output_dir} \\\n", " --camera_names \"['observation.image']\" \\\n", " --observation_keys \"['observation.state']\" \\\n", " --action_keys \"['action']\" \\\n", " --samples_per_shard 100 \\\n", " --config_path \"vla_foundry/config_presets/data/robotics_preprocessing_params_1past_14future.yaml\"" ] }, { "cell_type": "markdown", "id": "44ggmymx13", "metadata": {}, "source": [ "### Output Structure\n", "\n", "After conversion, the output directory contains:\n", "\n", "```\n", "tutorials/data/preprocessed/pusht/\n", " shards/\n", " manifest.jsonl # Manifest listing all shards and their sample counts\n", " stats.json # Dataset statistics (mean, std, min, max per field)\n", " shard_000000.tar # WebDataset tar shards\n", " shard_000001.tar\n", " ...\n", " episodes/\n", " manifest.jsonl # Episode-based manifest\n", " stats.json # Same statistics (copy)\n", " episode_*.tar # Episode-based tar shards\n", "```\n", "\n", "The key files for training are:\n", "- **`shards/manifest.jsonl`** -- referenced by `dataset_manifest` in the training config\n", "- **`shards/stats.json`** -- referenced by `dataset_statistics` in the training config\n", "\n", "### Additional Options\n", "\n", "**Ray Parallelization** -- For large datasets, add `--ray_address local --ray_num_cpus 16` to parallelize episode processing across multiple CPU cores.\n", "\n", "**Writing to S3** -- Replace `--output_dir ./tutorials/data/preprocessed/pusht/` with an S3 path like `--output_dir s3://my-bucket/vla_foundry_datasets/lerobot/pusht/`.\n", "\n", "**Multi-Camera** -- For datasets with multiple cameras, list all camera names: `--camera_names \"['cam_left', 'cam_right', 'cam_wrist']\"`." ] }, { "cell_type": "markdown", "id": "cq91i9thw64", "metadata": {}, "source": [ "## 3. Train a Model\n", "\n", "After preprocessing, you can train a model using the converted data. VLA Foundry uses a YAML config system powered by draccus.\n", "\n", "### Creating a Training Config\n", "\n", "Create a YAML config file that references your converted data. Here is an example for training a diffusion policy on the PushT dataset.\n", "\n", "**Note:** The `<<: !include` syntax is a draccus feature that merges in fields from another YAML file. This lets you reuse common presets for models, data pipelines, and hyperparameters.\n", "\n", "> **Important -- Temporal config must match between preprocessing and training.**\n", "> The `past_lowdim_steps` and `future_lowdim_steps` values in your preprocessing config must match the `lowdim_past_timesteps` and `lowdim_future_timesteps` in the training data config.\n", "\n", "### Available Model Presets\n", "\n", "Model presets are located in `vla_foundry/config_presets/models/`. Some commonly used ones:\n", "\n", "| Preset | Description |\n", "|---|---|\n", "| `diffusion_policy.yaml` | Diffusion/flow matching policy with CLIP vision backbone |\n", "| `transformer_tiny.yaml` | Tiny transformer (for testing/debugging) |\n", "| `transformer_100m.yaml` | 100M parameter transformer |\n", "| `vlm_3b.yaml` | 3B vision-language model (PaliGemma) |\n", "| `vla_diffusion_paligemma2.yaml` | VLA diffusion policy with PaliGemma2 backbone |" ] }, { "cell_type": "code", "execution_count": 6, "id": "5r84eq1a9g", "metadata": { "execution": { "iopub.execute_input": "2026-03-25T21:00:23.743664Z", "iopub.status.busy": "2026-03-25T21:00:23.743550Z", "iopub.status.idle": "2026-03-25T21:00:23.747986Z", "shell.execute_reply": "2026-03-25T21:00:23.747085Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting pusht_training_config.yaml\n" ] } ], "source": [ "%%writefile pusht_training_config.yaml\n", "# Training config for PushT diffusion policy with a small ViT backbone (no pretrained weights).\n", "# Uses 96x96 images directly (native PushT resolution), no augmentation.\n", "\n", "model:\n", " <<: !include vla_foundry/config_presets/models/diffusion_policy.yaml\n", " vision_language_backbone:\n", " type: vit_backbone\n", " img_size: 96\n", " hidden_dim: 64\n", " inter_dim: 256\n", " n_heads: 4\n", " n_layers: 4\n", " patch_size: 8\n", " # Minimal denoising head (this IS the diffusion head, not an LLM)\n", " transformer:\n", " type: transformer\n", " hidden_dim: 64\n", " n_heads: 4\n", " n_layers: 2\n", " is_causal: True\n", "\n", "data:\n", " <<: !include vla_foundry/config_presets/data/diffusion_policy.yaml\n", " processor: \"none\"\n", " image_size: 96\n", " dataset_manifest:\n", " - ./tutorials/data/preprocessed/pusht/shards/manifest.jsonl\n", " dataset_statistics:\n", " - ./tutorials/data/preprocessed/pusht/shards/stats.json\n", " dataset_modality:\n", " - robotics\n", " dataset_weighting:\n", " - 1.0\n", " camera_names:\n", " - observation.image\n", " image_indices:\n", " - -1\n", " - 0\n", " action_fields:\n", " - action\n", " proprioception_fields:\n", " - observation.state\n", " num_workers: 4\n", "\n", "distributed:\n", " fsdp: True\n", "\n", "hparams:\n", " <<: !include vla_foundry/config_presets/hparams/diffusion_policy.yaml\n", " per_gpu_batch_size: 32\n", " global_batch_size: 512" ] }, { "cell_type": "markdown", "id": "ebtvunyyj26", "metadata": {}, "source": [ "### Running Training Locally\n", "\n", "Launch training with `torchrun`. Key training arguments:\n", "\n", "| Argument | Description |\n", "|---|---|\n", "| `--config_path` | Path to training config YAML |\n", "| `--total_train_samples` | Total number of training samples to process |\n", "| `--num_checkpoints` | Number of checkpoints to save during training |\n", "| `--remote_sync` | S3 path to sync checkpoints to (optional) |\n", "| `--wandb False` | Disable Weights & Biases logging |\n", "| `--name` | Experiment name (used for logging) |\n", "\n", "For this tutorial, we run a small training job (5000 samples, 1 checkpoint) with wandb disabled." ] }, { "cell_type": "code", "execution_count": 7, "id": "mkl29rh9ilp", "metadata": { "execution": { "iopub.execute_input": "2026-03-25T21:00:23.750018Z", "iopub.status.busy": "2026-03-25T21:00:23.749908Z", "iopub.status.idle": "2026-03-25T21:01:22.085662Z", "shell.execute_reply": "2026-03-25T21:01:22.083934Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WARNING:root:No pose groups specified. Relative coordinate transformations will not be available. Please add pose_groups to your data configuration if you need relative coordinates.\n", "INFO:root:Loaded processing metadata from ./tutorials/data/preprocessed/pusht/shards/processing_metadata.json\n", "2026-03-27,00:40:19 | INFO | Loaded processing metadata from ./tutorials/data/preprocessed/pusht/shards/processing_metadata.json\n", "INFO:root:Running with a single process. Device cuda:0.\n", "2026-03-27,00:40:19 | INFO | Running with a single process. Device cuda:0.\n", "Total parameters: 6,877,314\n", "Trainable parameters: 6,877,314\n", "INFO:botocore.tokens:Loading cached SSO token for sso\n", "2026-03-27,00:40:20 | INFO | Loading cached SSO token for sso\n", "INFO:root:Logged training job start to DynamoDB: 9002ef24-88fc-4cb5-9ef7-23f5154a8190\n", "2026-03-27,00:40:21 | INFO | Logged training job start to DynamoDB: 9002ef24-88fc-4cb5-9ef7-23f5154a8190\n", "INFO:root:Start checkpoint 0\n", "2026-03-27,00:40:21 | INFO | Start checkpoint 0\n", "INFO:root:Now training on: [./tutorials/data/preprocessed/pusht/shards/{shard_000003, shard_000007, shard_000026, shard_000038, shard_000046, shard_000050, shard_000051, shard_000054, shard_000062, shard_000070, shard_000075, shard_000080, shard_000084, shard_000088, shard_000089, shard_000092, shard_000094, shard_000095, shard_000100, shard_000101, shard_000104, shard_000112, shard_000118, shard_000130, shard_000131, shard_000135, shard_000145, shard_000166, shard_000169, shard_000180, shard_000184, shard_000187, shard_000191, shard_000192, shard_000210, shard_000211, shard_000225, shard_000226, shard_000227, shard_000228, shard_000232, shard_000240, shard_000246, shard_000248, shard_000250, shard_000252, shard_000255, shard_000256}.tar]\n", "2026-03-27,00:40:21 | INFO | Now training on: [./tutorials/data/preprocessed/pusht/shards/{shard_000003, shard_000007, shard_000026, shard_000038, shard_000046, shard_000050, shard_000051, shard_000054, shard_000062, shard_000070, shard_000075, shard_000080, shard_000084, shard_000088, shard_000089, shard_000092, shard_000094, shard_000095, shard_000100, shard_000101, shard_000104, shard_000112, shard_000118, shard_000130, shard_000131, shard_000135, shard_000145, shard_000166, shard_000169, shard_000180, shard_000184, shard_000187, shard_000191, shard_000192, shard_000210, shard_000211, shard_000225, shard_000226, shard_000227, shard_000228, shard_000232, shard_000240, shard_000246, shard_000248, shard_000250, shard_000252, shard_000255, shard_000256}.tar]\n", "INFO:root:Samples: 0 / 5000\n", "2026-03-27,00:40:21 | INFO | Samples: 0 / 5000\n", "INFO:root:Samples in this checkpoint (per dataset): [4750]\n", "2026-03-27,00:40:21 | INFO | Samples in this checkpoint (per dataset): [4750]\n", "INFO:root:RoboticsNormalizer initialized: method=std, scope=global\n", "2026-03-27,00:40:21 | INFO | RoboticsNormalizer initialized: method=std, scope=global\n", "Checkpoint 0: 78%|██▎| 7/9 [00:07<00:01, 1.34step/s, loss=3.1104, lr=0.000003]INFO:root:Train Checkpoint: 0 [4096/4096 (100%)] Loss: 3.168 Data (t): 0.404 Batch (t): 1.046, 830.756/s, 830.756/s/gpu LR: 0.000004 \n", "2026-03-27,00:40:30 | INFO | Train Checkpoint: 0 [4096/4096 (100%)] Loss: 3.168 Data (t): 0.404 Batch (t): 1.046, 830.756/s, 830.756/s/gpu LR: 0.000004 \n", "Checkpoint 0: 89%|██▋| 8/9 [00:08<00:01, 1.05s/step, loss=3.1662, lr=0.000004]\n", "Saving checkpoint_1 in experiments/2026_03_27-00_40_19-model_diffusion_policy-lr_0.0005-bsz_512/checkpoints/checkpoint_1.pt...\n", "Saving optimizer_1 in experiments/2026_03_27-00_40_19-model_diffusion_policy-lr_0.0005-bsz_512/checkpoints/optimizer_1.pt...\n", "INFO:root:Start checkpoint 1\n", "2026-03-27,00:40:30 | INFO | Start checkpoint 1\n", "INFO:root:Now training on: [./tutorials/data/preprocessed/pusht/shards/{shard_000004, shard_000008, shard_000009, shard_000020, shard_000024, shard_000025, shard_000029, shard_000033, shard_000037, shard_000059, shard_000067, shard_000072, shard_000079, shard_000082, shard_000096, shard_000098, shard_000107, shard_000108, shard_000124, shard_000126, shard_000133, shard_000138, shard_000143, shard_000144, shard_000147, shard_000149, shard_000152, shard_000155, shard_000156, shard_000158, shard_000161, shard_000165, shard_000173, shard_000177, shard_000183, shard_000186, shard_000188, shard_000196, shard_000213, shard_000215, shard_000222, shard_000229, shard_000233, shard_000241, shard_000242, shard_000243, shard_000251, shard_000253}.tar]\n", "2026-03-27,00:40:30 | INFO | Now training on: [./tutorials/data/preprocessed/pusht/shards/{shard_000004, shard_000008, shard_000009, shard_000020, shard_000024, shard_000025, shard_000029, shard_000033, shard_000037, shard_000059, shard_000067, shard_000072, shard_000079, shard_000082, shard_000096, shard_000098, shard_000107, shard_000108, shard_000124, shard_000126, shard_000133, shard_000138, shard_000143, shard_000144, shard_000147, shard_000149, shard_000152, shard_000155, shard_000156, shard_000158, shard_000161, shard_000165, shard_000173, shard_000177, shard_000183, shard_000186, shard_000188, shard_000196, shard_000213, shard_000215, shard_000222, shard_000229, shard_000233, shard_000241, shard_000242, shard_000243, shard_000251, shard_000253}.tar]\n", "INFO:root:Samples: 4096 / 5000\n", "2026-03-27,00:40:30 | INFO | Samples: 4096 / 5000\n", "INFO:root:Samples in this checkpoint (per dataset): [4800]\n", "2026-03-27,00:40:30 | INFO | Samples in this checkpoint (per dataset): [4800]\n", "INFO:root:RoboticsNormalizer initialized: method=std, scope=global\n", "2026-03-27,00:40:30 | INFO | RoboticsNormalizer initialized: method=std, scope=global\n", "Checkpoint 1: 100%|███| 9/9 [00:03<00:00, 3.03s/step, loss=3.0570, lr=0.000005]WARNING:root:step: 9 has reached/exceeded total_steps: 9. ending training.\n", "2026-03-27,00:40:33 | WARNING | step: 9 has reached/exceeded total_steps: 9. ending training.\n", "Checkpoint 1: 100%|███| 9/9 [00:03<00:00, 3.03s/step, loss=3.0570, lr=0.000005]\n", "Saving checkpoint_2 in experiments/2026_03_27-00_40_19-model_diffusion_policy-lr_0.0005-bsz_512/checkpoints/checkpoint_2.pt...\n", "Saving optimizer_2 in experiments/2026_03_27-00_40_19-model_diffusion_policy-lr_0.0005-bsz_512/checkpoints/optimizer_2.pt...\n", "INFO:root:Model has seen the desired number of samples. Ending training.\n", "2026-03-27,00:40:33 | INFO | Model has seen the desired number of samples. Ending training.\n", "INFO:root:Logged training completion to DynamoDB: 9002ef24-88fc-4cb5-9ef7-23f5154a8190\n", "2026-03-27,00:40:34 | INFO | Logged training completion to DynamoDB: 9002ef24-88fc-4cb5-9ef7-23f5154a8190\n" ] } ], "source": [ "!uv run torchrun --nproc_per_node=1 --nnodes=1 vla_foundry/main.py \\\n", " --config_path pusht_training_config.yaml \\\n", " --total_train_samples 5000 \\\n", " --num_checkpoints 1 \\\n", " --wandb False" ] }, { "cell_type": "markdown", "id": "njt032hu3d", "metadata": {}, "source": [ "### Additional Training Options\n", "\n", "**Previewing the resolved config** -- Before training, preview the fully resolved configuration with all `!include` directives expanded:\n", "```bash\n", "uv run torchrun --nproc_per_node=1 --nnodes=1 vla_foundry/main.py \\\n", " --config_path pusht_training_config.yaml \\\n", " --total_train_samples 100000 \\\n", " --num_checkpoints 5 \\\n", " --resolve_configs True \\\n", " --resolve_configs_path ./\n", "```\n", "\n", "**Syncing checkpoints to S3** -- Add `--remote_sync s3://my-bucket/model_checkpoints/pusht_diffusion_policy` to save checkpoints to S3 during training.\n", "\n", "**Finetuning from pretrained weights** -- Use `--model.resume_from_checkpoint ` and `--model.resume_weights_only True` to load only model weights without resuming optimizer state." ] }, { "cell_type": "markdown", "id": "c2jg1tq1lm", "metadata": {}, "source": [ "## 4. Verify Inference\n", "\n", "Now let's verify the trained model works by loading the checkpoint, feeding it a sample from the preprocessed data, and running a forward pass to generate action predictions." ] }, { "cell_type": "code", "execution_count": 8, "id": "70rcndmbrs3", "metadata": { "execution": { "iopub.execute_input": "2026-03-25T21:01:22.091547Z", "iopub.status.busy": "2026-03-25T21:01:22.090984Z", "iopub.status.idle": "2026-03-25T21:01:22.104960Z", "shell.execute_reply": "2026-03-25T21:01:22.103648Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using experiment directory: experiments/2026_03_27-00_40_19-model_diffusion_policy-lr_0.0005-bsz_512\n", "Using checkpoint: experiments/2026_03_27-00_40_19-model_diffusion_policy-lr_0.0005-bsz_512/checkpoints/checkpoint_2.pt\n", "Using config: experiments/2026_03_27-00_40_19-model_diffusion_policy-lr_0.0005-bsz_512/config.yaml\n", "Using data shard: tutorials/data/preprocessed/pusht/shards/shard_000000.tar\n" ] } ], "source": [ "import glob\n", "import os\n", "\n", "# Find the experiment directory (most recent one)\n", "experiment_dirs = sorted(glob.glob(\"experiments/*/\"), key=os.path.getmtime)\n", "assert len(experiment_dirs) > 0, \"No experiment directories found. Did training complete?\"\n", "experiment_dir = experiment_dirs[-1].rstrip(\"/\")\n", "print(f\"Using experiment directory: {experiment_dir}\")\n", "\n", "# Find the checkpoint\n", "checkpoint_files = sorted(glob.glob(os.path.join(experiment_dir, \"checkpoints\", \"checkpoint_*.pt\")))\n", "assert len(checkpoint_files) > 0, \"No checkpoints found.\"\n", "checkpoint_path = checkpoint_files[-1]\n", "print(f\"Using checkpoint: {checkpoint_path}\")\n", "\n", "# Config path\n", "config_path = os.path.join(experiment_dir, \"config.yaml\")\n", "print(f\"Using config: {config_path}\")\n", "\n", "# Data shard\n", "shard_path = \"tutorials/data/preprocessed/pusht/shards/shard_000000.tar\"\n", "print(f\"Using data shard: {shard_path}\")" ] }, { "cell_type": "code", "execution_count": 9, "id": "i8i7uoqffd", "metadata": { "execution": { "iopub.execute_input": "2026-03-25T21:01:22.109843Z", "iopub.status.busy": "2026-03-25T21:01:22.109359Z", "iopub.status.idle": "2026-03-25T21:01:30.960491Z", "shell.execute_reply": "2026-03-25T21:01:30.959311Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:root:No pose groups specified. Relative coordinate transformations will not be available. Please add pose_groups to your data configuration if you need relative coordinates.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Device: cuda\n", "Model type: diffusion_policy\n", "Action dim: 2\n", "Proprioception dim: 2\n", "Checkpoint loaded: checkpoint_num=2, global_step=9\n", "Model on cuda, eval mode\n" ] } ], "source": [ "import io\n", "import json\n", "import tarfile\n", "\n", "import numpy as np\n", "import torch\n", "from PIL import Image\n", "\n", "from vla_foundry.data.processor.robotics_processor import RoboticsProcessor\n", "from vla_foundry.file_utils import load_model_checkpoint\n", "from vla_foundry.models import create_model\n", "from vla_foundry.params.train_experiment_params import load_experiment_params_from_yaml\n", "\n", "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "print(f\"Device: {DEVICE}\")\n", "\n", "# Load experiment config\n", "cfg = load_experiment_params_from_yaml(config_path, localize_params=True)\n", "print(f\"Model type: {cfg.model.type}\")\n", "print(f\"Action dim: {cfg.model.action_dim}\")\n", "print(f\"Proprioception dim: {cfg.model.proprioception_dim}\")\n", "\n", "# Create model and load checkpoint\n", "model = create_model(cfg.model, load_pretrained=False)\n", "start_ckpt, global_step, _ = load_model_checkpoint(model, checkpoint_path)\n", "print(f\"Checkpoint loaded: checkpoint_num={start_ckpt}, global_step={global_step}\")\n", "\n", "model.to(DEVICE)\n", "model.eval()\n", "print(f\"Model on {DEVICE}, eval mode\")" ] }, { "cell_type": "code", "execution_count": 10, "id": "e6y1umeeu8u", "metadata": { "execution": { "iopub.execute_input": "2026-03-25T21:01:30.963887Z", "iopub.status.busy": "2026-03-25T21:01:30.963732Z", "iopub.status.idle": "2026-03-25T21:01:35.509167Z", "shell.execute_reply": "2026-03-25T21:01:35.508590Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:root:No pose groups specified. Relative coordinate transformations will not be available. Please add pose_groups to your data configuration if you need relative coordinates.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "RoboticsProcessor loaded\n", " Normalizer enabled: True\n", "\n", "Sample key: 5cacc60d-417e-4ec7-bacd-408b16b7d73a\n", "Files in sample: ['5cacc60d-417e-4ec7-bacd-408b16b7d73a.observation.image_t-1.jpg', '5cacc60d-417e-4ec7-bacd-408b16b7d73a.observation.image_t0.jpg', '5cacc60d-417e-4ec7-bacd-408b16b7d73a.lowdim.npz', '5cacc60d-417e-4ec7-bacd-408b16b7d73a.metadata.json', '5cacc60d-417e-4ec7-bacd-408b16b7d73a.language_instructions.json']\n", " Image: 5cacc60d-417e-4ec7-bacd-408b16b7d73a.observation.image_t-1.jpg -> size=(384, 384)\n", " Image: 5cacc60d-417e-4ec7-bacd-408b16b7d73a.observation.image_t0.jpg -> size=(384, 384)\n", " Lowdim fields: ['observation.state', 'action', 'past_mask', 'future_mask']\n", " observation.state: shape=(16, 2), dtype=float32\n", " action: shape=(16, 2), dtype=float32\n", " past_mask: shape=(16,), dtype=bool\n", " future_mask: shape=(16,), dtype=bool\n", " Metadata: {'camera_names': ['observation.image'], 'anchor_relative_idx': 1, 'episode_index': '13', 'frame_index': '76', 'timestamp': '7.6', 'next.reward': '0.4309188', 'next.done': 'False', 'next.success': 'False', 'index': '1900', 'task_index': '0', 'original_image_sizes': {'observation.image': [96, 96]}}\n", " Language instructions: {'original': 'Push the T-shaped block onto the T-shaped target.'}\n" ] } ], "source": [ "# Load RoboticsProcessor for normalization\n", "robotics_processor = RoboticsProcessor.from_pretrained(experiment_dir)\n", "print(\"RoboticsProcessor loaded\")\n", "print(f\" Normalizer enabled: {robotics_processor.normalizer is not None}\")\n", "\n", "# Extract one sample from the tar shard\n", "sample_files = {}\n", "first_sample_key = None\n", "\n", "with tarfile.open(shard_path, \"r\") as tar:\n", " for member in tar.getmembers():\n", " name = member.name\n", " sample_key = name.split(\".\", 1)[0]\n", " if first_sample_key is None:\n", " first_sample_key = sample_key\n", " if sample_key != first_sample_key:\n", " if len(sample_files) >= 4:\n", " break\n", " continue\n", " f = tar.extractfile(member)\n", " if f is not None:\n", " sample_files[name] = f.read()\n", "\n", "print(f\"\\nSample key: {first_sample_key}\")\n", "print(f\"Files in sample: {list(sample_files.keys())}\")\n", "\n", "# Parse the sample data\n", "images = {}\n", "lowdim_data = None\n", "metadata = None\n", "language_instructions = None\n", "\n", "for filename, data in sample_files.items():\n", " if filename.endswith(\".jpg\") or filename.endswith(\".png\"):\n", " img = Image.open(io.BytesIO(data)).convert(\"RGB\")\n", " images[filename] = img\n", " print(f\" Image: {filename} -> size={img.size}\")\n", " elif filename.endswith(\".npz\"):\n", " lowdim_data = dict(np.load(io.BytesIO(data)))\n", " print(f\" Lowdim fields: {list(lowdim_data.keys())}\")\n", " for k, v in lowdim_data.items():\n", " print(f\" {k}: shape={v.shape}, dtype={v.dtype}\")\n", " elif filename.endswith(\"metadata.json\"):\n", " metadata = json.loads(data.decode())\n", " print(f\" Metadata: {metadata}\")\n", " elif filename.endswith(\"language_instructions.json\"):\n", " language_instructions = json.loads(data.decode())\n", " print(f\" Language instructions: {language_instructions}\")" ] }, { "cell_type": "code", "execution_count": 11, "id": "w0pdynqmpp", "metadata": { "execution": { "iopub.execute_input": "2026-03-25T21:01:35.512330Z", "iopub.status.busy": "2026-03-25T21:01:35.512182Z", "iopub.status.idle": "2026-03-25T21:01:35.760923Z", "shell.execute_reply": "2026-03-25T21:01:35.759817Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Data config:\n", " past_timesteps: 1\n", " future_timesteps: 14\n", " total_timesteps: 16\n", " action_dim: 2\n", " image_names: ['observation.image_t-1', 'observation.image_t0']\n", "\n", "Number of images for model: 2\n", " pixel_values: torch.Size([1, 2, 3, 96, 96])\n", "\n", "Running generate_actions...\n", "\n", "Predicted actions shape: torch.Size([1, 16, 2])\n", "Predicted actions dtype: torch.float32\n", "\n", "Predicted actions stats (normalized):\n", " Min: -1.563396\n", " Max: 1.651834\n", " Mean: -0.243074\n", " Std: 0.815102\n", "\n", "First 5 predicted action timesteps (normalized):\n", " t=0: [-0.9466377 -0.69293565]\n", " t=1: [-0.9565578 -0.57671404]\n", " t=2: [-0.77475303 0.25668603]\n", " t=3: [-1.4104786 0.76262146]\n", " t=4: [0.78147554 1.6518345 ]\n", "\n", "Denormalized actions (first 5 timesteps):\n", " t=0: [131. 230.]\n", " t=1: [130. 241.]\n", " t=2: [148.32681 319.87866]\n", " t=3: [ 84.242584 367.76382 ]\n", " t=4: [305.2022 451.925 ]\n", "\n", " Denormalized range: [68.83, 451.92]\n", "\n", "Inference verification complete!\n" ] } ], "source": [ "import torchvision.transforms as T\n", "\n", "# Build model input and run inference\n", "lowdim_past_timesteps = cfg.data.lowdim_past_timesteps\n", "lowdim_future_timesteps = cfg.data.lowdim_future_timesteps\n", "total_timesteps = lowdim_past_timesteps + 1 + lowdim_future_timesteps\n", "action_dim = cfg.data.action_dim\n", "image_names = cfg.data.image_names\n", "\n", "print(\"Data config:\")\n", "print(f\" past_timesteps: {lowdim_past_timesteps}\")\n", "print(f\" future_timesteps: {lowdim_future_timesteps}\")\n", "print(f\" total_timesteps: {total_timesteps}\")\n", "print(f\" action_dim: {action_dim}\")\n", "print(f\" image_names: {image_names}\")\n", "\n", "# Collect images in order and convert to tensors\n", "img_to_tensor = T.ToTensor()\n", "\n", "ordered_image_tensors = []\n", "for img_name in image_names:\n", " for filename, img in images.items():\n", " if img_name in filename:\n", " # Resize to model's expected size (96x96)\n", " img_resized = img.resize((96, 96))\n", " ordered_image_tensors.append(img_to_tensor(img_resized))\n", " break\n", "\n", "print(f\"\\nNumber of images for model: {len(ordered_image_tensors)}\")\n", "\n", "# Stack into [1, N, C, H, W]\n", "pixel_values = torch.stack(ordered_image_tensors).unsqueeze(0)\n", "# Dummy text inputs (ViT backbone ignores them)\n", "input_ids = torch.zeros(1, 1, dtype=torch.long)\n", "attention_mask = torch.ones(1, 1, dtype=torch.long)\n", "\n", "print(f\" pixel_values: {pixel_values.shape}\")\n", "\n", "# Build action and proprioception tensors\n", "raw_actions = torch.tensor(lowdim_data[\"action\"], dtype=torch.float32)\n", "if robotics_processor.normalizer is not None:\n", " actions_normalized = robotics_processor.normalizer.normalize_tensor(raw_actions.unsqueeze(0), \"action\")\n", "else:\n", " actions_normalized = raw_actions.unsqueeze(0)\n", "\n", "raw_proprio = torch.tensor(lowdim_data[\"observation.state\"], dtype=torch.float32)\n", "if robotics_processor.normalizer is not None:\n", " proprio_normalized = robotics_processor.normalizer.normalize_tensor(raw_proprio.unsqueeze(0), \"observation.state\")\n", "else:\n", " proprio_normalized = raw_proprio.unsqueeze(0)\n", "proprio_normalized = proprio_normalized[:, : lowdim_past_timesteps + 1]\n", "\n", "# Build past_mask\n", "past_mask = torch.zeros(1, total_timesteps, dtype=torch.bool)\n", "past_mask[:, : lowdim_past_timesteps + 1] = True\n", "\n", "# Move to device\n", "input_ids = input_ids.to(DEVICE)\n", "attention_mask = attention_mask.to(DEVICE)\n", "pixel_values = pixel_values.to(DEVICE, dtype=torch.float32)\n", "actions_normalized = actions_normalized.to(DEVICE, dtype=torch.float32)\n", "past_mask = past_mask.to(DEVICE)\n", "proprio_normalized = proprio_normalized.to(DEVICE, dtype=torch.float32)\n", "\n", "# Run inference\n", "print(\"\\nRunning generate_actions...\")\n", "NUM_FLOW_STEPS = 10\n", "\n", "with torch.no_grad():\n", " predicted_actions = model.generate_actions(\n", " input_ids=input_ids,\n", " pixel_values=pixel_values,\n", " actions=actions_normalized,\n", " attention_mask=attention_mask,\n", " num_inference_steps=NUM_FLOW_STEPS,\n", " past_mask=past_mask,\n", " proprioception=proprio_normalized,\n", " )\n", "\n", "print(f\"\\nPredicted actions shape: {predicted_actions.shape}\")\n", "print(f\"Predicted actions dtype: {predicted_actions.dtype}\")\n", "\n", "pred_cpu = predicted_actions.cpu().float().numpy()\n", "print(\"\\nPredicted actions stats (normalized):\")\n", "print(f\" Min: {pred_cpu.min():.6f}\")\n", "print(f\" Max: {pred_cpu.max():.6f}\")\n", "print(f\" Mean: {pred_cpu.mean():.6f}\")\n", "print(f\" Std: {pred_cpu.std():.6f}\")\n", "\n", "print(\"\\nFirst 5 predicted action timesteps (normalized):\")\n", "for t in range(min(5, pred_cpu.shape[1])):\n", " print(f\" t={t}: {pred_cpu[0, t]}\")\n", "\n", "# Denormalize to get actual action values\n", "if robotics_processor.normalizer is not None:\n", " denorm_actions = robotics_processor.normalizer.denormalize_tensor(predicted_actions.cpu(), \"action\").numpy()\n", " print(\"\\nDenormalized actions (first 5 timesteps):\")\n", " for t in range(min(5, denorm_actions.shape[1])):\n", " print(f\" t={t}: {denorm_actions[0, t]}\")\n", " print(f\"\\n Denormalized range: [{denorm_actions.min():.2f}, {denorm_actions.max():.2f}]\")\n", "\n", "print(\"\\nInference verification complete!\")" ] }, { "cell_type": "markdown", "id": "zf4zjrteaj9", "metadata": {}, "source": [ "## 5. Interactive PushT Evaluation\n", "\n", "Now let's close the loop: run the trained model inside the actual PushT simulation environment. This requires the `gym-pusht` package (a Gymnasium environment for the 2D Push-T task).\n", "\n", "The eval loop:\n", "1. Resets the environment and gets the initial observation (96×96 image + 2D agent position)\n", "2. At each step, builds the model input from the live observation (two images, proprioception, text instruction)\n", "3. Runs `generate_actions` to get an action chunk (16 timesteps)\n", "4. Executes the **first predicted future action** in the environment\n", "5. Records frames for visualization\n", "\n", "> **Note:** The model was only trained for 5000 samples in this tutorial, so don't expect great performance. For a well-performing policy, train for 100k+ samples." ] }, { "cell_type": "code", "execution_count": 12, "id": "2okicqm1hht", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Environment created. Running eval for up to 300 steps...\n", " Image size: (96, 96, 3) (native, no resizing)\n", " Agent pos: [ 85. 359.]\n", "\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " Step 50/300 reward=0.0598 coverage=0.0568\n", " Step 100/300 reward=0.0598 coverage=0.0568\n", " Step 150/300 reward=0.0598 coverage=0.0568\n", " Step 200/300 reward=0.0598 coverage=0.0568\n", " Step 250/300 reward=0.0598 coverage=0.0568\n", "Episode ended at step 300\n", "\n", "Eval complete: 300 steps\n", " Final reward: 0.0598\n", " Max reward: 0.0598\n", " Final coverage: 0.0568\n", " Success: False\n", " Frames captured: 301\n" ] } ], "source": [ "import gymnasium as gym\n", "import numpy as np\n", "import torch\n", "from PIL import Image\n", "from torchvision import transforms\n", "\n", "# --- Configuration ---\n", "MAX_STEPS = 300 # Maximum environment steps per episode\n", "NUM_FLOW_STEPS = 10 # Denoising steps for action generation\n", "ACTION_EXEC_HORIZON = 8 # How many actions to execute before re-planning\n", "IMG_SIZE = 96 # Native PushT resolution, matches training\n", "\n", "# Data config from training\n", "lowdim_past_timesteps = cfg.data.lowdim_past_timesteps # 1\n", "lowdim_future_timesteps = cfg.data.lowdim_future_timesteps # 14\n", "total_timesteps = lowdim_past_timesteps + 1 + lowdim_future_timesteps # 16\n", "anchor_idx = lowdim_past_timesteps # index of \"current\" timestep in the window\n", "\n", "# Pre-compute static tensors\n", "past_mask = torch.zeros(1, total_timesteps, dtype=torch.bool, device=DEVICE)\n", "past_mask[:, : lowdim_past_timesteps + 1] = True\n", "dummy_actions = torch.zeros(1, total_timesteps, cfg.data.action_dim, device=DEVICE)\n", "\n", "# Image transform: uint8 HWC -> float32 CHW [0, 1]\n", "img_transform = transforms.ToTensor() # handles both conversion and normalization to [0,1]\n", "\n", "\n", "def obs_to_pixel_values(prev_pixels: np.ndarray, curr_pixels: np.ndarray) -> torch.Tensor:\n", " \"\"\"Convert two observation images to model input [1, 2, C, H, W].\"\"\"\n", " t_prev = img_transform(prev_pixels) # [C, H, W]\n", " t_curr = img_transform(curr_pixels) # [C, H, W]\n", " return torch.stack([t_prev, t_curr]).unsqueeze(0) # [1, 2, C, H, W]\n", "\n", "\n", "# --- Create environment ---\n", "env = gym.make(\"gym_pusht/PushT-v0\", obs_type=\"pixels_agent_pos\", render_mode=\"rgb_array\")\n", "obs, info = env.reset(seed=42)\n", "\n", "prev_pixels = obs[\"pixels\"]\n", "curr_pixels = obs[\"pixels\"]\n", "prev_agent_pos = obs[\"agent_pos\"].copy()\n", "curr_agent_pos = obs[\"agent_pos\"].copy()\n", "\n", "frames = [env.render()]\n", "rewards = []\n", "action_queue = []\n", "\n", "print(f\"Environment created. Running eval for up to {MAX_STEPS} steps...\")\n", "print(f\" Image size: {obs['pixels'].shape} (native, no resizing)\")\n", "print(f\" Agent pos: {obs['agent_pos']}\")\n", "print()\n", "\n", "for step in range(MAX_STEPS):\n", " if len(action_queue) == 0:\n", " # Build proprioception: [t-1, t0] normalized\n", " proprio_raw = torch.tensor(\n", " np.stack([prev_agent_pos, curr_agent_pos]),\n", " dtype=torch.float32,\n", " ).unsqueeze(0) # [1, 2, 2]\n", " if robotics_processor.normalizer is not None:\n", " proprio_norm = robotics_processor.normalizer.normalize_tensor(proprio_raw, \"observation.state\")\n", " else:\n", " proprio_norm = proprio_raw\n", "\n", " # Build pixel values directly (no CLIP processor needed)\n", " pixel_values = obs_to_pixel_values(prev_pixels, curr_pixels).to(DEVICE, dtype=torch.float32)\n", "\n", " # Generate actions (no input_ids/attention_mask — ViT backbone ignores them)\n", " with torch.no_grad():\n", " predicted_actions = model.generate_actions(\n", " input_ids=torch.zeros(1, 1, dtype=torch.long, device=DEVICE),\n", " pixel_values=pixel_values,\n", " actions=dummy_actions,\n", " attention_mask=torch.ones(1, 1, dtype=torch.long, device=DEVICE),\n", " num_inference_steps=NUM_FLOW_STEPS,\n", " past_mask=past_mask,\n", " proprioception=proprio_norm.to(DEVICE, dtype=torch.float32),\n", " )\n", "\n", " # Denormalize and extract future actions\n", " if robotics_processor.normalizer is not None:\n", " denorm = robotics_processor.normalizer.denormalize_tensor(predicted_actions.cpu(), \"action\").numpy()[0]\n", " else:\n", " denorm = predicted_actions.cpu().numpy()[0]\n", "\n", " future_actions = denorm[anchor_idx : anchor_idx + ACTION_EXEC_HORIZON]\n", " action_queue = list(future_actions)\n", "\n", " # Execute next action\n", " action = action_queue.pop(0)\n", " action = np.clip(action, 0.0, 512.0).astype(np.float32)\n", "\n", " obs, reward, terminated, truncated, info = env.step(action)\n", " rewards.append(reward)\n", " frames.append(env.render())\n", "\n", " # Update observation history\n", " prev_pixels = curr_pixels\n", " prev_agent_pos = curr_agent_pos.copy()\n", " curr_pixels = obs[\"pixels\"]\n", " curr_agent_pos = obs[\"agent_pos\"].copy()\n", "\n", " if terminated or truncated:\n", " print(f\"Episode ended at step {step + 1}\")\n", " break\n", "\n", " if (step + 1) % 50 == 0:\n", " print(f\" Step {step + 1}/{MAX_STEPS} reward={reward:.4f} coverage={info.get('coverage', 0):.4f}\")\n", "\n", "env.close()\n", "\n", "print(f\"\\nEval complete: {len(rewards)} steps\")\n", "print(f\" Final reward: {rewards[-1]:.4f}\")\n", "print(f\" Max reward: {max(rewards):.4f}\")\n", "print(f\" Final coverage: {info.get('coverage', 0):.4f}\")\n", "print(f\" Success: {info.get('is_success', False)}\")\n", "print(f\" Frames captured: {len(frames)}\")" ] }, { "cell_type": "code", "execution_count": 13, "id": "0wc18ir4n7me", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:imageio_ffmpeg:IMAGEIO FFMPEG_WRITER WARNING: input image is not divisible by macro_block_size=16, resizing from (680, 680) to (688, 688) to ensure video compatibility with most codecs and players. To prevent resizing, make your input image divisible by the macro_block_size or set the macro_block_size to 1 (risking incompatibility).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Video saved to tutorials/data/pusht_eval_rollout.mp4\n" ] }, { "data": { "text/html": [ "\n", "\n" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Render the rollout as an inline video in the notebook\n", "import base64\n", "\n", "import imageio.v3 as iio\n", "from IPython.display import HTML, display\n", "\n", "video_path = \"tutorials/data/pusht_eval_rollout.mp4\"\n", "iio.imwrite(video_path, np.stack(frames), fps=10, codec=\"h264\")\n", "print(f\"Video saved to {video_path}\")\n", "\n", "# Display inline\n", "with open(video_path, \"rb\") as f:\n", " video_data = f.read()\n", "b64 = base64.b64encode(video_data).decode()\n", "display(\n", " HTML(f\"\"\"\n", "\n", "\"\"\")\n", ")" ] }, { "cell_type": "markdown", "id": "5u7a12vgsig", "metadata": {}, "source": [ "## 6. Other Evaluation Environments (Reference)\n", "\n", "VLA Foundry includes evaluation runners for several simulation environments. To evaluate a trained checkpoint:\n", "\n", "```bash\n", "uv run python vla_foundry/eval/run_eval.py \\\n", " --env libero_spatial \\\n", " --task \\\n", " --model_path ./checkpoints/checkpoint_4.pt \\\n", " --num_rollouts 10 \\\n", " --num_steps 50 \\\n", " --action_window 8 \\\n", " --video_path ./eval_video.mp4\n", "```\n", "\n", "Supported environments include:\n", "- **LIBERO** (`libero_spatial`, `libero_object`, `libero_goal`, `libero_10`, `libero_90`) -- requires `uv sync --group libero`\n", "- **RoboCasa** (`robocasa`) -- Kitchen manipulation tasks\n", "- **RoboSuite** (`robosuite`) -- Standard manipulation benchmarks\n", "- **CALVIN** (`calvin`) -- Language-conditioned manipulation\n", "\n", "Each environment has its own dependencies. Install the appropriate dependency group before running evaluation.\n", "\n", "For deploying a trained model as a policy server (e.g., for real robot control), see `vla_foundry/inference/robotics/inference_policy.py`.\n", "\n", "## Tips and Common Issues\n", "\n", "- **Camera name mismatch**: The `--camera_names` passed during preprocessing must match the column names in the LeRobot parquet files\n", "- **Action key names vary**: Some LeRobot datasets use `action` (singular) while others use `actions` (plural)\n", "- **Image format**: The converter handles both inline image bytes in parquet files and video-based storage (MP4 files). It auto-detects which format is used\n", "- **Memory usage**: For very large datasets, use Ray parallelization to distribute processing across cores\n", "- **Statistics file**: The `stats.json` file is critical for normalization during training. If preprocessing is interrupted before statistics are saved, re-run\n", "\n", "**See also**:\n", "- `tutorials/lbm.md` -- LBM (Spartan) data workflow\n", "- `tutorials/adding_new_models.md` -- Adding custom models\n", "- `vla_foundry/data/preprocessing/README.md` -- Ray cluster setup for preprocessing\n", "- `examples/preprocessing/preprocess_robotics_data_lerobot.sh` -- LeRobot preprocessing example script" ] } ], "metadata": { "kernelspec": { "display_name": "Python (vla_foundry)", "language": "python", "name": "vla_foundry" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.12" } }, "nbformat": 4, "nbformat_minor": 5 }