Add pretrained notebook

.gitignore

@@ -1,3 +1,7 @@
+# Apple
+.DS_Store
+._.DS_Store
+
 # Logging
 logs
 tmp

.pre-commit-config.yaml

@@ -23,6 +23,13 @@ repos:
       - id: ruff
         args: [--fix]
       - id: ruff-format
+  - repo: https://github.com/nbQA-dev/nbQA
+    rev: 1.8.4
+    hooks:
+      - id: nbqa-ruff
+      - id: nbqa-pyupgrade
+        args: ["--py310-plus"]
+      - id: nbqa-isort
   - repo: https://github.com/python-poetry/poetry
     rev: 1.8.0
     hooks:

examples/notebook_utils.py

@@ -0,0 +1,31 @@
+from pprint import pprint
+
+from hydra import compose, initialize
+from hydra.core.global_hydra import GlobalHydra
+from omegaconf import OmegaConf
+from omegaconf.dictconfig import DictConfig
+
+CONFIG_DIR = "../lerobot/configs"
+DEFAULT_CONFIG = "default"
+
+
+def config_notebook(
+    policy: str = "diffusion",
+    env: str = "pusht",
+    device: str = "cpu",
+    config_name=DEFAULT_CONFIG,
+    config_path=CONFIG_DIR,
+    print_config: bool = False,
+) -> DictConfig:
+    GlobalHydra.instance().clear()
+    initialize(config_path=config_path)
+    overrides = [
+        f"env={env}",
+        f"policy={policy}",
+        f"device={device}",
+    ]
+    cfg = compose(config_name=config_name, overrides=overrides)
+    if print_config:
+        pprint(OmegaConf.to_container(cfg))
+
+    return cfg

examples/pretrained.ipynb

@@ -0,0 +1,56 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "\n",
+    "from IPython.display import Video\n",
+    "\n",
+    "from examples.notebook_utils import config_notebook\n",
+    "from lerobot.scripts.eval import eval\n",
+    "\n",
+    "OUT_DIR = Path(\"./outputs\")\n",
+    "POLICY = \"act\"\n",
+    "ENV = \"aloha\"\n",
+    "\n",
+    "# setup config\n",
+    "cfg = config_notebook(policy=POLICY, env=ENV, device=\"cpu\", print_config=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eval(cfg, out_dir=OUT_DIR)\n",
+    "Video(OUT_DIR / \"eval\" / \"eval_episode_0.mp4\", embed=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "lerobot",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

lerobot/scripts/eval.py

@@ -145,6 +145,7 @@ def eval(cfg: dict, out_dir=None):
     logging.info("make_env")
     env = make_env(cfg, transform=offline_buffer.transform)
 
+    # TODO(aliberts, Cadene): fetch pretrained model from HF hub
     if cfg.policy.pretrained_model_path:
         policy = make_policy(cfg)
         policy = TensorDictModule(

lerobot/scripts/train.py

@@ -25,15 +25,6 @@ def train_cli(cfg: dict):
     )
 
 
-def train_notebook(out_dir=None, job_name=None, config_name="default", config_path="../configs"):
-    from hydra import compose, initialize
-
-    hydra.core.global_hydra.GlobalHydra.instance().clear()
-    initialize(config_path=config_path)
-    cfg = compose(config_name=config_name)
-    train(cfg, out_dir=out_dir, job_name=job_name)
-
-
 def log_train_info(logger, info, step, cfg, offline_buffer, is_offline):
     loss = info["loss"]
     grad_norm = info["grad_norm"]

pyproject.toml

@@ -60,6 +60,13 @@ debugpy = "^1.8.1"
 pytest = "^8.1.0"
 
 
+[tool.poetry.group.examples]
+optional = true
+
+
+[tool.poetry.group.examples.dependencies]
+jupyter = "^1.0.0"
+
 [tool.ruff]
 line-length = 110
 target-version = "py310"