mirror of
https://github.com/huggingface/deep-rl-class.git
synced 2026-04-01 09:40:26 +08:00
Update colab
This commit is contained in:
Thomas Simonini
@@ -46,12 +46,12 @@
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{
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"cell_type": "markdown",
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"source": [
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"###🎮 Environments: \n",
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"### 🎮 Environments: \n",
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"\n",
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"- [CartPole-v1](https://www.gymlibrary.dev/environments/classic_control/cart_pole/)\n",
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"- [PixelCopter](https://pygame-learning-environment.readthedocs.io/en/latest/user/games/pixelcopter.html)\n",
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"\n",
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"###📚 RL-Library: \n",
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"### 📚 RL-Library: \n",
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"\n",
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"- Python\n",
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"- PyTorch\n",
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@@ -351,15 +351,6 @@
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"### [The environment 🎮](https://www.gymlibrary.dev/environments/classic_control/cart_pole/)\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "vVwcV9LjMzQk"
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},
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"source": [
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""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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@@ -1028,6 +1019,8 @@
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"import json\n",
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"import imageio\n",
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"\n",
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"import tempfile\n",
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"\n",
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"import os"
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],
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"metadata": {
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@@ -1068,18 +1061,12 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "D1ywQFrkf3t8"
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},
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"outputs": [],
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"source": [
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"def push_to_hub(repo_id, \n",
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" model,\n",
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" hyperparameters,\n",
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" eval_env,\n",
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" video_fps=30,\n",
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" local_repo_path=\"hub\"\n",
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" video_fps=30\n",
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" ):\n",
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" \"\"\"\n",
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" Evaluate, Generate a video and Upload a model to Hugging Face Hub.\n",
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@@ -1094,7 +1081,6 @@
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" :param hyperparameters: training hyperparameters\n",
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" :param eval_env: evaluation environment\n",
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" :param video_fps: how many frame per seconds to record our video replay \n",
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" :param local_repo_path: where the local repository is\n",
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" \"\"\"\n",
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"\n",
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" _, repo_name = repo_id.split(\"/\")\n",
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@@ -1106,97 +1092,101 @@
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" exist_ok=True,\n",
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" )\n",
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"\n",
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" # Step 2: Download files\n",
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" repo_local_path = Path(snapshot_download(repo_id=repo_id))\n",
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"\n",
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" # Step 3: Save the model\n",
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" torch.save(model, os.path.join(repo_local_path,\"model.pt\"))\n",
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"\n",
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" # Step 4: Save the hyperparameters to JSON\n",
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" with open(Path(repo_local_path) / \"hyperparameters.json\", \"w\") as outfile:\n",
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" json.dump(hyperparameters, outfile)\n",
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" with tempfile.TemporaryDirectory() as tmpdirname:\n",
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" local_directory = Path(tmpdirname)\n",
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" \n",
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" # Step 5: Evaluate the model and build JSON\n",
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" mean_reward, std_reward = evaluate_agent(eval_env, \n",
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" hyperparameters[\"max_t\"],\n",
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" hyperparameters[\"n_evaluation_episodes\"], \n",
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" model)\n",
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" # Get datetime\n",
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" eval_datetime = datetime.datetime.now()\n",
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" eval_form_datetime = eval_datetime.isoformat()\n",
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" # Step 2: Save the model\n",
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" torch.save(model, local_directory / \"model.pt\")\n",
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"\n",
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" evaluate_data = {\n",
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" \"env_id\": hyperparameters[\"env_id\"], \n",
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" \"mean_reward\": mean_reward,\n",
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" \"n_evaluation_episodes\": hyperparameters[\"n_evaluation_episodes\"],\n",
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" \"eval_datetime\": eval_form_datetime,\n",
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" }\n",
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" # Step 3: Save the hyperparameters to JSON\n",
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" with open(local_directory / \"hyperparameters.json\", \"w\") as outfile:\n",
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" json.dump(hyperparameters, outfile)\n",
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" \n",
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" # Step 4: Evaluate the model and build JSON\n",
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" mean_reward, std_reward = evaluate_agent(eval_env, \n",
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" hyperparameters[\"max_t\"],\n",
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" hyperparameters[\"n_evaluation_episodes\"], \n",
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" model)\n",
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" # Get datetime\n",
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" eval_datetime = datetime.datetime.now()\n",
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" eval_form_datetime = eval_datetime.isoformat()\n",
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"\n",
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" # Write a JSON file\n",
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" with open(Path(repo_local_path) / \"results.json\", \"w\") as outfile:\n",
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" json.dump(evaluate_data, outfile)\n",
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" evaluate_data = {\n",
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" \"env_id\": hyperparameters[\"env_id\"], \n",
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" \"mean_reward\": mean_reward,\n",
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" \"n_evaluation_episodes\": hyperparameters[\"n_evaluation_episodes\"],\n",
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" \"eval_datetime\": eval_form_datetime,\n",
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" }\n",
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"\n",
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" # Step 6: Create the model card\n",
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" # Env id\n",
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" env_name = hyperparameters[\"env_id\"]\n",
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" \n",
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" metadata = {}\n",
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" metadata[\"tags\"] = [\n",
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" env_name,\n",
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" \"reinforce\",\n",
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" \"reinforcement-learning\",\n",
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" \"custom-implementation\",\n",
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" \"deep-rl-class\"\n",
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" ]\n",
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" # Write a JSON file\n",
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" with open(local_directory / \"results.json\", \"w\") as outfile:\n",
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" json.dump(evaluate_data, outfile)\n",
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"\n",
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" # Add metrics\n",
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" eval = metadata_eval_result(\n",
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" model_pretty_name=repo_name,\n",
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" task_pretty_name=\"reinforcement-learning\",\n",
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" task_id=\"reinforcement-learning\",\n",
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" metrics_pretty_name=\"mean_reward\",\n",
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" metrics_id=\"mean_reward\",\n",
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" metrics_value=f\"{mean_reward:.2f} +/- {std_reward:.2f}\",\n",
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" dataset_pretty_name=env_name,\n",
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" dataset_id=env_name,\n",
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" )\n",
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" # Step 5: Create the model card\n",
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" env_name = hyperparameters[\"env_id\"]\n",
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" \n",
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" metadata = {}\n",
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" metadata[\"tags\"] = [\n",
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" env_name,\n",
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" \"reinforce\",\n",
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" \"reinforcement-learning\",\n",
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" \"custom-implementation\",\n",
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" \"deep-rl-class\"\n",
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" ]\n",
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"\n",
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" # Merges both dictionaries\n",
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" metadata = {**metadata, **eval}\n",
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" # Add metrics\n",
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" eval = metadata_eval_result(\n",
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" model_pretty_name=repo_name,\n",
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" task_pretty_name=\"reinforcement-learning\",\n",
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" task_id=\"reinforcement-learning\",\n",
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" metrics_pretty_name=\"mean_reward\",\n",
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" metrics_id=\"mean_reward\",\n",
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" metrics_value=f\"{mean_reward:.2f} +/- {std_reward:.2f}\",\n",
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" dataset_pretty_name=env_name,\n",
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" dataset_id=env_name,\n",
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" )\n",
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"\n",
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" model_card = f\"\"\"\n",
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" # Merges both dictionaries\n",
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" metadata = {**metadata, **eval}\n",
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"\n",
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" model_card = f\"\"\"\n",
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" # **Reinforce** Agent playing **{env_id}**\n",
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" This is a trained model of a **Reinforce** agent playing **{env_id}** .\n",
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" To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction\n",
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" \"\"\"\n",
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"\n",
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" readme_path = repo_local_path / \"README.md\"\n",
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" readme = \"\"\n",
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" if readme_path.exists():\n",
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" with readme_path.open(\"r\", encoding=\"utf8\") as f:\n",
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" readme = f.read()\n",
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" else:\n",
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" readme = model_card\n",
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" readme_path = local_directory / \"README.md\"\n",
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" readme = \"\"\n",
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" if readme_path.exists():\n",
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" with readme_path.open(\"r\", encoding=\"utf8\") as f:\n",
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" readme = f.read()\n",
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" else:\n",
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" readme = model_card\n",
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"\n",
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" with readme_path.open(\"w\", encoding=\"utf-8\") as f:\n",
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" f.write(readme)\n",
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" with readme_path.open(\"w\", encoding=\"utf-8\") as f:\n",
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" f.write(readme)\n",
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"\n",
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" # Save our metrics to Readme metadata\n",
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" metadata_save(readme_path, metadata)\n",
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" # Save our metrics to Readme metadata\n",
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" metadata_save(readme_path, metadata)\n",
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"\n",
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" # Step 7: Record a video\n",
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" video_path = repo_local_path / \"replay.mp4\"\n",
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" record_video(env, model, video_path, video_fps)\n",
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" # Step 6: Record a video\n",
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" video_path = local_directory / \"replay.mp4\"\n",
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" record_video(env, model, video_path, video_fps)\n",
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"\n",
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" # Step 7. Push everything to the Hub\n",
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" api.upload_folder(\n",
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" repo_id=repo_id,\n",
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" folder_path=repo_local_path,\n",
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" path_in_repo=\".\",\n",
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" )\n",
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" # Step 7. Push everything to the Hub\n",
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" api.upload_folder(\n",
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" repo_id=repo_id,\n",
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" folder_path=local_directory,\n",
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" path_in_repo=\".\",\n",
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" )\n",
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"\n",
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" print(f\"Your model is pushed to the hub. You can view your model here: {repo_url}\")"
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]
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" print(f\"Your model is pushed to the Hub. You can view your model here: {repo_url}\")"
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],
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"metadata": {
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"id": "_TPdq47D7_f_"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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@@ -1240,7 +1230,6 @@
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},
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"outputs": [],
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"source": [
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"from huggingface_hub import notebook_login\n",
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"notebook_login()"
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]
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},
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@@ -1275,8 +1264,7 @@
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" cartpole_policy, # The model we want to save\n",
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" cartpole_hyperparameters, # Hyperparameters\n",
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" eval_env, # Evaluation environment\n",
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" video_fps=30,\n",
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" local_repo_path=\"hub\",\n",
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" video_fps=30\n",
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" )"
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]
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},
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@@ -1529,8 +1517,7 @@
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" pixelcopter_policy, # The model we want to save\n",
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" pixelcopter_hyperparameters, # Hyperparameters\n",
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" eval_env, # Evaluation environment\n",
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" video_fps=30,\n",
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" local_repo_path=\"hub\",\n",
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" video_fps=30\n",
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" )"
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],
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"metadata": {
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@@ -1598,7 +1585,6 @@
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"JoTC9o2SczNn",
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"gfGJNZBUP7Vn",
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"YB0Cxrw1StrP",
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"Jmhs1k-cftIq",
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"47iuAFqV8Ws-",
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"x62pP0PHdA-y"
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],
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