Push agent to the Hub

README.md

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+---
+tags:
+- LunarLander-v2
+- ppo
+- deep-reinforcement-learning
+- reinforcement-learning
+- custom-implementation
+- deep-rl-course
+model-index:
+- name: PPO
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: -138.99 +/- 59.23
+      name: mean_reward
+      verified: false
+---
+
+  # PPO Agent Playing LunarLander-v2
+
+  This is a trained model of a PPO agent playing LunarLander-v2.
+
+  # Hyperparameters
+  

logs/events.out.tfevents.1679286687.hanbk-robotmecha.1078677.0

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+size 111205

model.pt

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+oid sha256:4f59c8f9da66f962233a9a894cdc61ae834a79ac56cddc7ffcc6239da74a3500
+size 42817

ppo.py

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+import argparse
+import os
+import random
+import time
+from distutils.util import strtobool
+
+import gym
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.distributions.categorical import Categorical
+from torch.utils.tensorboard import SummaryWriter
+
+from huggingface_hub import HfApi, upload_folder
+from huggingface_hub.repocard import metadata_eval_result, metadata_save
+
+from pathlib import Path
+import datetime
+import tempfile
+import json
+import shutil
+import imageio
+
+from wasabi import Printer
+
+msg = Printer()
+
+
+def parse_args():
+    # fmt: off
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--exp-name", type=str, default=os.path.basename(__file__).rstrip(".py"),
+        help="the name of this experiment")
+    parser.add_argument("--seed", type=int, default=1,
+        help="seed of the experiment")
+    parser.add_argument("--torch-deterministic", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
+        help="if toggled, `torch.backends.cudnn.deterministic=False`")
+    parser.add_argument("--cuda", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
+        help="if toggled, cuda will be enabled by default")
+    parser.add_argument("--track", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
+        help="if toggled, this experiment will be tracked with Weights and Biases")
+    parser.add_argument("--wandb-project-name", type=str, default="cleanRL",
+        help="the wandb's project name")
+    parser.add_argument("--wandb-entity", type=str, default=None,
+        help="the entity (team) of wandb's project")
+    parser.add_argument("--capture-video", type=lambda x: bool(strtobool(x)), default=False, nargs="?", const=True,
+        help="weather to capture videos of the agent performances (check out `videos` folder)")
+
+    # Algorithm specific arguments
+    parser.add_argument("--env-id", type=str, default="CartPole-v1",
+        help="the id of the environment")
+    parser.add_argument("--total-timesteps", type=int, default=50000,
+        help="total timesteps of the experiments")
+    parser.add_argument("--learning-rate", type=float, default=2.5e-4,
+        help="the learning rate of the optimizer")
+    parser.add_argument("--num-envs", type=int, default=4,
+        help="the number of parallel game environments")
+    parser.add_argument("--num-steps", type=int, default=128,
+        help="the number of steps to run in each environment per policy rollout")
+    parser.add_argument("--anneal-lr", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
+        help="Toggle learning rate annealing for policy and value networks")
+    parser.add_argument("--gae", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
+        help="Use GAE for advantage computation")
+    parser.add_argument("--gamma", type=float, default=0.99,
+        help="the discount factor gamma")
+    parser.add_argument("--gae-lambda", type=float, default=0.95,
+        help="the lambda for the general advantage estimation")
+    parser.add_argument("--num-minibatches", type=int, default=4,
+        help="the number of mini-batches")
+    parser.add_argument("--update-epochs", type=int, default=4,
+        help="the K epochs to update the policy")
+    parser.add_argument("--norm-adv", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
+        help="Toggles advantages normalization")
+    parser.add_argument("--clip-coef", type=float, default=0.2,
+        help="the surrogate clipping coefficient")
+    parser.add_argument("--clip-vloss", type=lambda x: bool(strtobool(x)), default=True, nargs="?", const=True,
+        help="Toggles whether or not to use a clipped loss for the value function, as per the paper.")
+    parser.add_argument("--ent-coef", type=float, default=0.01,
+        help="coefficient of the entropy")
+    parser.add_argument("--vf-coef", type=float, default=0.5,
+        help="coefficient of the value function")
+    parser.add_argument("--max-grad-norm", type=float, default=0.5,
+        help="the maximum norm for the gradient clipping")
+    parser.add_argument("--target-kl", type=float, default=None,
+        help="the target KL divergence threshold")
+
+    # Adding HuggingFace argument
+    parser.add_argument("--repo-id", type=str, default="bkhan2000/LunaLander-v2", help="id of the model repository from the Hugging Face Hub {username/repo_name}")
+
+    args = parser.parse_args()
+    args.batch_size = int(args.num_envs * args.num_steps)
+    args.minibatch_size = int(args.batch_size // args.num_minibatches)
+    # fmt: on
+    return args
+
+def package_to_hub(
+    repo_id,
+    model,
+    hyperparameters,
+    eval_env,
+    video_fps=30,
+    commit_message="Push agent to the Hub",
+    token=None,
+    logs=None,
+):
+    """
+    Evaluate, Generate a video and Upload a model to Hugging Face Hub.
+    This method does the complete pipeline:
+    - It evaluates the model
+    - It generates the model card
+    - It generates a replay video of the agent
+    - It pushes everything to the hub
+    :param repo_id: id of the model repository from the Hugging Face Hub
+    :param model: trained model
+    :param eval_env: environment used to evaluate the agent
+    :param fps: number of fps for rendering the video
+    :param commit_message: commit message
+    :param logs: directory on local machine of tensorboard logs you'd like to upload
+    """
+    msg.info(
+        "This function will save, evaluate, generate a video of your agent, "
+        "create a model card and push everything to the hub. "
+        "It might take up to 1min. \n "
+        "This is a work in progress: if you encounter a bug, please open an issue."
+    )
+    # Step 1: Clone or create the repo
+    repo_url = HfApi().create_repo(
+        repo_id=repo_id,
+        token=token,
+        private=False,
+        exist_ok=True,
+    )
+
+    with tempfile.TemporaryDirectory() as tmpdirname:
+        tmpdirname = Path("./")
+
+        # Step 2: Save the model
+        torch.save(model.state_dict(), tmpdirname / "model.pt")
+
+        # Step 3: Evaluate the model and build JSON
+        mean_reward, std_reward = _evaluate_agent(eval_env, 10, model)
+
+        # First get datetime
+        eval_datetime = datetime.datetime.now()
+        eval_form_datetime = eval_datetime.isoformat()
+
+        evaluate_data = {
+            "env_id": hyperparameters.env_id,
+            "mean_reward": mean_reward,
+            "std_reward": std_reward,
+            "n_evaluation_episodes": 10,
+            "eval_datetime": eval_form_datetime,
+        }
+
+        # Write a JSON file
+        with open(tmpdirname / "results.json", "w") as outfile:
+            json.dump(evaluate_data, outfile)
+
+        # Step 4: Generate a video
+        video_path = tmpdirname / "replay.mp4"
+        record_video(eval_env, model, video_path, video_fps)
+
+        # Step 5: Generate the model card
+        generated_model_card, metadata = _generate_model_card(
+            "PPO", hyperparameters.env_id, mean_reward, std_reward, hyperparameters
+        )
+        _save_model_card(tmpdirname, generated_model_card, metadata)
+
+        # Step 6: Add logs if needed
+        if logs:
+            _add_logdir(tmpdirname, Path(logs))
+
+        msg.info(f"Pushing repo {repo_id} to the Hugging Face Hub")
+
+        repo_url = upload_folder(
+            repo_id=repo_id,
+            folder_path=tmpdirname,
+            path_in_repo="",
+            commit_message=commit_message,
+            token=token,
+        )
+
+        msg.info(f"Your model is pushed to the Hub. You can view your model here: {repo_url}")
+    return repo_url
+
+def _evaluate_agent(env, n_eval_episodes, policy):
+    """
+    Evaluate the agent for ``n_eval_episodes`` episodes and returns average reward and std of reward.
+    :param env: The evaluation environment
+    :param n_eval_episodes: Number of episode to evaluate the agent
+    :param policy: The agent
+    """
+    episode_rewards = []
+    for episode in range(n_eval_episodes):
+        state = env.reset()
+        step = 0
+        done = False
+        total_rewards_ep = 0
+
+        while done is False:
+            state = torch.Tensor(state).to(device)
+            action, _, _, _ = policy.get_action_and_value(state)
+            new_state, reward, done, info = env.step(action.cpu().numpy())
+            total_rewards_ep += reward
+            if done:
+                break
+            state = new_state
+        episode_rewards.append(total_rewards_ep)
+    mean_reward = np.mean(episode_rewards)
+    std_reward = np.std(episode_rewards)
+
+    return mean_reward, std_reward
+
+
+def record_video(env, policy, out_directory, fps=30):
+    images = []
+    done = False
+    state = env.reset()
+    img = env.render(mode="rgb_array")
+    images.append(img)
+    while not done:
+        state = torch.Tensor(state).to(device)
+        # Take the action (index) that have the maximum expected future reward given that state
+        action, _, _, _ = policy.get_action_and_value(state)
+        state, reward, done, info = env.step(
+            action.cpu().numpy()
+        )  # We directly put next_state = state for recording logic
+        img = env.render(mode="rgb_array")
+        images.append(img)
+    imageio.mimsave(out_directory, [np.array(img) for i, img in enumerate(images)], fps=fps)
+
+
+def _generate_model_card(model_name, env_id, mean_reward, std_reward, hyperparameters):
+    """
+    Generate the model card for the Hub
+    :param model_name: name of the model
+    :env_id: name of the environment
+    :mean_reward: mean reward of the agent
+    :std_reward: standard deviation of the mean reward of the agent
+    :hyperparameters: training arguments
+    """
+    # Step 1: Select the tags
+    metadata = generate_metadata(model_name, env_id, mean_reward, std_reward)
+
+    # Transform the hyperparams namespace to string
+    converted_dict = vars(hyperparameters)
+    converted_str = str(converted_dict)
+    converted_str = converted_str.split(", ")
+    converted_str = "\n".join(converted_str)
+
+    # Step 2: Generate the model card
+    model_card = f"""
+  # PPO Agent Playing {env_id}
+
+  This is a trained model of a PPO agent playing {env_id}.
+
+  # Hyperparameters
+  """
+    return model_card, metadata
+
+
+def generate_metadata(model_name, env_id, mean_reward, std_reward):
+    """
+    Define the tags for the model card
+    :param model_name: name of the model
+    :param env_id: name of the environment
+    :mean_reward: mean reward of the agent
+    :std_reward: standard deviation of the mean reward of the agent
+    """
+    metadata = {}
+    metadata["tags"] = [
+        env_id,
+        "ppo",
+        "deep-reinforcement-learning",
+        "reinforcement-learning",
+        "custom-implementation",
+        "deep-rl-course",
+    ]
+
+    # Add metrics
+    eval = metadata_eval_result(
+        model_pretty_name=model_name,
+        task_pretty_name="reinforcement-learning",
+        task_id="reinforcement-learning",
+        metrics_pretty_name="mean_reward",
+        metrics_id="mean_reward",
+        metrics_value=f"{mean_reward:.2f} +/- {std_reward:.2f}",
+        dataset_pretty_name=env_id,
+        dataset_id=env_id,
+    )
+
+    # Merges both dictionaries
+    metadata = {**metadata, **eval}
+
+    return metadata
+
+
+def _save_model_card(local_path, generated_model_card, metadata):
+    """Saves a model card for the repository.
+    :param local_path: repository directory
+    :param generated_model_card: model card generated by _generate_model_card()
+    :param metadata: metadata
+    """
+    readme_path = local_path / "README.md"
+    readme = ""
+    if readme_path.exists():
+        with readme_path.open("r", encoding="utf8") as f:
+            readme = f.read()
+    else:
+        readme = generated_model_card
+
+    with readme_path.open("w", encoding="utf-8") as f:
+        f.write(readme)
+
+    # Save our metrics to Readme metadata
+    metadata_save(readme_path, metadata)
+
+
+def _add_logdir(local_path: Path, logdir: Path):
+    """Adds a logdir to the repository.
+    :param local_path: repository directory
+    :param logdir: logdir directory
+    """
+    if logdir.exists() and logdir.is_dir():
+        # Add the logdir to the repository under new dir called logs
+        repo_logdir = local_path / "logs"
+
+        # Delete current logs if they exist
+        if repo_logdir.exists():
+            shutil.rmtree(repo_logdir)
+
+        # Copy logdir into repo logdir
+        shutil.copytree(logdir, repo_logdir)
+
+
+def make_env(env_id, seed, idx, capture_video, run_name):
+    def thunk():
+        env = gym.make(env_id)
+        env = gym.wrappers.RecordEpisodeStatistics(env)
+        if capture_video:
+            if idx == 0:
+                env = gym.wrappers.RecordVideo(env, f"videos/{run_name}")
+        env.seed(seed)
+        env.action_space.seed(seed)
+        env.observation_space.seed(seed)
+        return env
+
+    return thunk
+
+def layer_init(layer, std=np.sqrt(2), bias_const=0.0):
+    torch.nn.init.orthogonal_(layer.weight, std)
+    torch.nn.init.constant_(layer.bias, bias_const)
+    return layer
+
+class Agent(nn.Module):
+    def __init__(self, envs):
+        super().__init__()
+        self.critic = nn.Sequential(
+            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(), 64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64, 64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64, 1), std=1.0),
+        )
+        self.actor = nn.Sequential(
+            layer_init(nn.Linear(np.array(envs.single_observation_space.shape).prod(), 64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64, 64)),
+            nn.Tanh(),
+            layer_init(nn.Linear(64, envs.single_action_space.n), std=0.01),
+        )
+
+    def get_value(self, x):
+        return self.critic(x)
+
+    def get_action_and_value(self, x, action=None):
+        logits = self.actor(x)
+        probs = Categorical(logits=logits)
+        if action is None:
+            action = probs.sample()
+        return action, probs.log_prob(action), probs.entropy(), self.critic(x)
+
+if __name__ == "__main__":
+    args = parse_args()
+    run_name = f"{args.env_id}__{args.exp_name}__{args.seed}__{int(time.time())}"
+    if args.track:
+        import wandb
+
+        wandb.init(
+            project=args.wandb_project_name,
+            entity=args.wandb_entity,
+            sync_tensorboard=True,
+            config=vars(args),
+            name=run_name,
+            monitor_gym=True,
+            save_code=True,
+        )
+    writer = SummaryWriter(f"runs/{run_name}")
+    writer.add_text(
+        "hyperparameters",
+        "|param|value|\n|-|-|\n%s" % ("\n".join([f"|{key}|{value}|" for key, value in vars(args).items()])),
+    )
+
+    # TRY NOT TO MODIFY: seeding
+    random.seed(args.seed)
+    np.random.seed(args.seed)
+    torch.manual_seed(args.seed)
+    torch.backends.cudnn.deterministic = args.torch_deterministic
+
+    device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
+
+    # env setup
+    envs = gym.vector.SyncVectorEnv(
+        [make_env(args.env_id, args.seed + i, i, args.capture_video, run_name) for i in range(args.num_envs)]
+    )
+    assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
+
+    agent = Agent(envs).to(device)
+    optimizer = optim.Adam(agent.parameters(), lr=args.learning_rate, eps=1e-5)
+
+    # ALGO Logic: Storage setup
+    obs = torch.zeros((args.num_steps, args.num_envs) + envs.single_observation_space.shape).to(device)
+    actions = torch.zeros((args.num_steps, args.num_envs) + envs.single_action_space.shape).to(device)
+    logprobs = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    rewards = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    dones = torch.zeros((args.num_steps, args.num_envs)).to(device)
+    values = torch.zeros((args.num_steps, args.num_envs)).to(device)
+
+    # TRY NOT TO MODIFY: start the game
+    global_step = 0
+    start_time = time.time()
+    next_obs = torch.Tensor(envs.reset()).to(device)
+    next_done = torch.zeros(args.num_envs).to(device)
+    num_updates = args.total_timesteps // args.batch_size
+
+    for update in range(1, num_updates + 1):
+        # Annealing the rate if instructed to do so.
+        if args.anneal_lr:
+            frac = 1.0 - (update - 1.0) / num_updates
+            lrnow = frac * args.learning_rate
+            optimizer.param_groups[0]["lr"] = lrnow
+
+        for step in range(0, args.num_steps):
+            global_step += 1 * args.num_envs
+            obs[step] = next_obs
+            dones[step] = next_done
+
+            # ALGO LOGIC: action logic
+            with torch.no_grad():
+                action, logprob, _, value = agent.get_action_and_value(next_obs)
+                values[step] = value.flatten()
+            actions[step] = action
+            logprobs[step] = logprob
+
+            # TRY NOT TO MODIFY: execute the game and log data.
+            next_obs, reward, done, info = envs.step(action.cpu().numpy())
+            rewards[step] = torch.tensor(reward).to(device).view(-1)
+            next_obs, next_done = torch.Tensor(next_obs).to(device), torch.Tensor(done).to(device)
+
+            for item in info:
+                if "episode" in item.keys():
+                    print(f"global_step={global_step}, episodic_return={item['episode']['r']}")
+                    writer.add_scalar("charts/episodic_return", item["episode"]["r"], global_step)
+                    writer.add_scalar("charts/episodic_length", item["episode"]["l"], global_step)
+                    break
+
+        # bootstrap value if not done
+        with torch.no_grad():
+            next_value = agent.get_value(next_obs).reshape(1, -1)
+            if args.gae:
+                advantages = torch.zeros_like(rewards).to(device)
+                lastgaelam = 0
+                for t in reversed(range(args.num_steps)):
+                    if t == args.num_steps - 1:
+                        nextnonterminal = 1.0 - next_done
+                        nextvalues = next_value
+                    else:
+                        nextnonterminal = 1.0 - dones[t + 1]
+                        nextvalues = values[t + 1]
+                    delta = rewards[t] + args.gamma * nextvalues * nextnonterminal - values[t]
+                    advantages[t] = lastgaelam = delta + args.gamma * args.gae_lambda * nextnonterminal * lastgaelam
+                returns = advantages + values
+            else:
+                returns = torch.zeros_like(rewards).to(device)
+                for t in reversed(range(args.num_steps)):
+                    if t == args.num_steps - 1:
+                        nextnonterminal = 1.0 - next_done
+                        next_return = next_value
+                    else:
+                        nextnonterminal = 1.0 - dones[t + 1]
+                        next_return = returns[t + 1]
+                    returns[t] = rewards[t] + args.gamma * nextnonterminal * next_return
+                advantages = returns - values
+
+        # flatten the batch
+        b_obs = obs.reshape((-1,) + envs.single_observation_space.shape)
+        b_logprobs = logprobs.reshape(-1)
+        b_actions = actions.reshape((-1,) + envs.single_action_space.shape)
+        b_advantages = advantages.reshape(-1)
+        b_returns = returns.reshape(-1)
+        b_values = values.reshape(-1)
+
+        # Optimizing the policy and value network
+        b_inds = np.arange(args.batch_size)
+        clipfracs = []
+        for epoch in range(args.update_epochs):
+            np.random.shuffle(b_inds)
+            for start in range(0, args.batch_size, args.minibatch_size):
+                end = start + args.minibatch_size
+                mb_inds = b_inds[start:end]
+
+                _, newlogprob, entropy, newvalue = agent.get_action_and_value(
+                    b_obs[mb_inds], b_actions.long()[mb_inds]
+                )
+                logratio = newlogprob - b_logprobs[mb_inds]
+                ratio = logratio.exp()
+
+                with torch.no_grad():
+                    # calculate approx_kl http://joschu.net/blog/kl-approx.html
+                    old_approx_kl = (-logratio).mean()
+                    approx_kl = ((ratio - 1) - logratio).mean()
+                    clipfracs += [((ratio - 1.0).abs() > args.clip_coef).float().mean().item()]
+
+                mb_advantages = b_advantages[mb_inds]
+                if args.norm_adv:
+                    mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)
+
+                # Policy loss
+                pg_loss1 = -mb_advantages * ratio
+                pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - args.clip_coef, 1 + args.clip_coef)
+                pg_loss = torch.max(pg_loss1, pg_loss2).mean()
+
+                # Value loss
+                newvalue = newvalue.view(-1)
+                if args.clip_vloss:
+                    v_loss_unclipped = (newvalue - b_returns[mb_inds]) ** 2
+                    v_clipped = b_values[mb_inds] + torch.clamp(
+                        newvalue - b_values[mb_inds],
+                        -args.clip_coef,
+                        args.clip_coef,
+                    )
+                    v_loss_clipped = (v_clipped - b_returns[mb_inds]) ** 2
+                    v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)
+                    v_loss = 0.5 * v_loss_max.mean()
+                else:
+                    v_loss = 0.5 * ((newvalue - b_returns[mb_inds]) ** 2).mean()
+
+                entropy_loss = entropy.mean()
+                loss = pg_loss - args.ent_coef * entropy_loss + v_loss * args.vf_coef
+
+                optimizer.zero_grad()
+                loss.backward()
+                nn.utils.clip_grad_norm_(agent.parameters(), args.max_grad_norm)
+                optimizer.step()
+
+            if args.target_kl is not None:
+                if approx_kl > args.target_kl:
+                    break
+
+        y_pred, y_true = b_values.cpu().numpy(), b_returns.cpu().numpy()
+        var_y = np.var(y_true)
+        explained_var = np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y
+
+        # TRY NOT TO MODIFY: record rewards for plotting purposes
+        writer.add_scalar("charts/learning_rate", optimizer.param_groups[0]["lr"], global_step)
+        writer.add_scalar("losses/value_loss", v_loss.item(), global_step)
+        writer.add_scalar("losses/policy_loss", pg_loss.item(), global_step)
+        writer.add_scalar("losses/entropy", entropy_loss.item(), global_step)
+        writer.add_scalar("losses/old_approx_kl", old_approx_kl.item(), global_step)
+        writer.add_scalar("losses/approx_kl", approx_kl.item(), global_step)
+        writer.add_scalar("losses/clipfrac", np.mean(clipfracs), global_step)
+        writer.add_scalar("losses/explained_variance", explained_var, global_step)
+        print("SPS:", int(global_step / (time.time() - start_time)))
+        writer.add_scalar("charts/SPS", int(global_step / (time.time() - start_time)), global_step)
+
+    envs.close()
+    writer.close()
+
+    # Create the evaluation environment
+    eval_env = gym.make(args.env_id)
+
+    package_to_hub(
+        repo_id=args.repo_id,
+        model=agent,  # The model we want to save
+        hyperparameters=args,
+        eval_env=gym.make(args.env_id),
+        logs=f"runs/{run_name}",
+    )

ppo_cleanRL.ipynb

@@ -0,0 +1,272 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/hanbk/torch_venv/lib/python3.8/site-packages/IPython/core/display.py:419: UserWarning: Consider using IPython.display.IFrame instead\n",
+      "  warnings.warn(\"Consider using IPython.display.IFrame instead\")\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/MEt6rrxH8W4\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import HTML\n",
+    "\n",
+    "HTML(\n",
+    "    '<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/MEt6rrxH8W4\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe>'\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import argparse\n",
+    "import os\n",
+    "import random\n",
+    "import time\n",
+    "from distutils.util import strtobool\n",
+    "\n",
+    "import gym\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "from torch.distributions.categorical import Categorical\n",
+    "from torch.utils.tensorboard import SummaryWriter\n",
+    "\n",
+    "from huggingface_hub import HfApi, upload_folder\n",
+    "from huggingface_hub.repocard import metadata_eval_result, metadata_save\n",
+    "\n",
+    "from pathlib import Path\n",
+    "import datetime\n",
+    "import tempfile\n",
+    "import json\n",
+    "import shutil\n",
+    "import imageio\n",
+    "\n",
+    "from wasabi import Printer\n",
+    "\n",
+    "msg = Printer()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def parse_args():\n",
+    "    # fmt: off\n",
+    "    parser = argparse.ArgumentParser()\n",
+    "    parser.add_argument(\"--exp-name\", type=str, default=os.path.basename(__file__).rstrip(\".py\"),\n",
+    "        help=\"the name of this experiment\")\n",
+    "    parser.add_argument(\"--seed\", type=int, default=1,\n",
+    "        help=\"seed of the experiment\")\n",
+    "    parser.add_argument(\"--torch-deterministic\", type=lambda x: bool(strtobool(x)), default=True, nargs=\"?\", const=True,\n",
+    "        help=\"if toggled, `torch.backends.cudnn.deterministic=False`\")\n",
+    "    parser.add_argument(\"--cuda\", type=lambda x: bool(strtobool(x)), default=True, nargs=\"?\", const=True,\n",
+    "        help=\"if toggled, cuda will be enabled by default\")\n",
+    "    parser.add_argument(\"--track\", type=lambda x: bool(strtobool(x)), default=False, nargs=\"?\", const=True,\n",
+    "        help=\"if toggled, this experiment will be tracked with Weights and Biases\")\n",
+    "    parser.add_argument(\"--wandb-project-name\", type=str, default=\"cleanRL\",\n",
+    "        help=\"the wandb's project name\")\n",
+    "    parser.add_argument(\"--wandb-entity\", type=str, default=None,\n",
+    "        help=\"the entity (team) of wandb's project\")\n",
+    "    parser.add_argument(\"--capture-video\", type=lambda x: bool(strtobool(x)), default=False, nargs=\"?\", const=True,\n",
+    "        help=\"weather to capture videos of the agent performances (check out `videos` folder)\")\n",
+    "\n",
+    "    # Algorithm specific arguments\n",
+    "    parser.add_argument(\"--env-id\", type=str, default=\"CartPole-v1\",\n",
+    "        help=\"the id of the environment\")\n",
+    "    parser.add_argument(\"--total-timesteps\", type=int, default=50000,\n",
+    "        help=\"total timesteps of the experiments\")\n",
+    "    parser.add_argument(\"--learning-rate\", type=float, default=2.5e-4,\n",
+    "        help=\"the learning rate of the optimizer\")\n",
+    "    parser.add_argument(\"--num-envs\", type=int, default=4,\n",
+    "        help=\"the number of parallel game environments\")\n",
+    "    parser.add_argument(\"--num-steps\", type=int, default=128,\n",
+    "        help=\"the number of steps to run in each environment per policy rollout\")\n",
+    "    parser.add_argument(\"--anneal-lr\", type=lambda x: bool(strtobool(x)), default=True, nargs=\"?\", const=True,\n",
+    "        help=\"Toggle learning rate annealing for policy and value networks\")\n",
+    "    parser.add_argument(\"--gae\", type=lambda x: bool(strtobool(x)), default=True, nargs=\"?\", const=True,\n",
+    "        help=\"Use GAE for advantage computation\")\n",
+    "    parser.add_argument(\"--gamma\", type=float, default=0.99,\n",
+    "        help=\"the discount factor gamma\")\n",
+    "    parser.add_argument(\"--gae-lambda\", type=float, default=0.95,\n",
+    "        help=\"the lambda for the general advantage estimation\")\n",
+    "    parser.add_argument(\"--num-minibatches\", type=int, default=4,\n",
+    "        help=\"the number of mini-batches\")\n",
+    "    parser.add_argument(\"--update-epochs\", type=int, default=4,\n",
+    "        help=\"the K epochs to update the policy\")\n",
+    "    parser.add_argument(\"--norm-adv\", type=lambda x: bool(strtobool(x)), default=True, nargs=\"?\", const=True,\n",
+    "        help=\"Toggles advantages normalization\")\n",
+    "    parser.add_argument(\"--clip-coef\", type=float, default=0.2,\n",
+    "        help=\"the surrogate clipping coefficient\")\n",
+    "    parser.add_argument(\"--clip-vloss\", type=lambda x: bool(strtobool(x)), default=True, nargs=\"?\", const=True,\n",
+    "        help=\"Toggles whether or not to use a clipped loss for the value function, as per the paper.\")\n",
+    "    parser.add_argument(\"--ent-coef\", type=float, default=0.01,\n",
+    "        help=\"coefficient of the entropy\")\n",
+    "    parser.add_argument(\"--vf-coef\", type=float, default=0.5,\n",
+    "        help=\"coefficient of the value function\")\n",
+    "    parser.add_argument(\"--max-grad-norm\", type=float, default=0.5,\n",
+    "        help=\"the maximum norm for the gradient clipping\")\n",
+    "    parser.add_argument(\"--target-kl\", type=float, default=None,\n",
+    "        help=\"the target KL divergence threshold\")\n",
+    "\n",
+    "    # Adding HuggingFace argument\n",
+    "    parser.add_argument(\"--repo-id\", type=str, default=\"ThomasSimonini/ppo-CartPole-v1\", help=\"id of the model repository from the Hugging Face Hub {username/repo_name}\")\n",
+    "\n",
+    "    args = parser.parse_args()\n",
+    "    args.batch_size = int(args.num_envs * args.num_steps)\n",
+    "    args.minibatch_size = int(args.batch_size // args.num_minibatches)\n",
+    "    # fmt: on\n",
+    "    return args"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def package_to_hub(\n",
+    "    repo_id,\n",
+    "    model,\n",
+    "    hyperparameters,\n",
+    "    eval_env,\n",
+    "    video_fps=30,\n",
+    "    commit_message=\"Push agent to the Hub\",\n",
+    "    token=None,\n",
+    "    logs=None,\n",
+    "):\n",
+    "    \"\"\"\n",
+    "    Evaluate, Generate a video and Upload a model to Hugging Face Hub.\n",
+    "    This method does the complete pipeline:\n",
+    "    - It evaluates the model\n",
+    "    - It generates the model card\n",
+    "    - It generates a replay video of the agent\n",
+    "    - It pushes everything to the hub\n",
+    "    :param repo_id: id of the model repository from the Hugging Face Hub\n",
+    "    :param model: trained model\n",
+    "    :param eval_env: environment used to evaluate the agent\n",
+    "    :param fps: number of fps for rendering the video\n",
+    "    :param commit_message: commit message\n",
+    "    :param logs: directory on local machine of tensorboard logs you'd like to upload\n",
+    "    \"\"\"\n",
+    "    msg.info(\n",
+    "        \"This function will save, evaluate, generate a video of your agent, \"\n",
+    "        \"create a model card and push everything to the hub. \"\n",
+    "        \"It might take up to 1min. \\n \"\n",
+    "        \"This is a work in progress: if you encounter a bug, please open an issue.\"\n",
+    "    )\n",
+    "    # Step 1: Clone or create the repo\n",
+    "    repo_url = HfApi().create_repo(\n",
+    "        repo_id=repo_id,\n",
+    "        token=token,\n",
+    "        private=False,\n",
+    "        exist_ok=True,\n",
+    "    )\n",
+    "\n",
+    "    with tempfile.TemporaryDirectory() as tmpdirname:\n",
+    "        tmpdirname = Path(\"./\")\n",
+    "\n",
+    "        # Step 2: Save the model\n",
+    "        torch.save(model.state_dict(), tmpdirname / \"model.pt\")\n",
+    "\n",
+    "        # Step 3: Evaluate the model and build JSON\n",
+    "        mean_reward, std_reward = _evaluate_agent(eval_env, 10, model)\n",
+    "\n",
+    "        # First get datetime\n",
+    "        eval_datetime = datetime.datetime.now()\n",
+    "        eval_form_datetime = eval_datetime.isoformat()\n",
+    "\n",
+    "        evaluate_data = {\n",
+    "            \"env_id\": hyperparameters.env_id,\n",
+    "            \"mean_reward\": mean_reward,\n",
+    "            \"std_reward\": std_reward,\n",
+    "            \"n_evaluation_episodes\": 10,\n",
+    "            \"eval_datetime\": eval_form_datetime,\n",
+    "        }\n",
+    "\n",
+    "        # Write a JSON file\n",
+    "        with open(tmpdirname / \"results.json\", \"w\") as outfile:\n",
+    "            json.dump(evaluate_data, outfile)\n",
+    "\n",
+    "        # Step 4: Generate a video\n",
+    "        video_path = tmpdirname / \"replay.mp4\"\n",
+    "        record_video(eval_env, model, video_path, video_fps)\n",
+    "\n",
+    "        # Step 5: Generate the model card\n",
+    "        generated_model_card, metadata = _generate_model_card(\n",
+    "            \"PPO\", hyperparameters.env_id, mean_reward, std_reward, hyperparameters\n",
+    "        )\n",
+    "        _save_model_card(tmpdirname, generated_model_card, metadata)\n",
+    "\n",
+    "        # Step 6: Add logs if needed\n",
+    "        if logs:\n",
+    "            _add_logdir(tmpdirname, Path(logs))\n",
+    "\n",
+    "        msg.info(f\"Pushing repo {repo_id} to the Hugging Face Hub\")\n",
+    "\n",
+    "        repo_url = upload_folder(\n",
+    "            repo_id=repo_id,\n",
+    "            folder_path=tmpdirname,\n",
+    "            path_in_repo=\"\",\n",
+    "            commit_message=commit_message,\n",
+    "            token=token,\n",
+    "        )\n",
+    "\n",
+    "        msg.info(f\"Your model is pushed to the Hub. You can view your model here: {repo_url}\")\n",
+    "    return repo_url"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.8.10 ('torch_venv')",
+   "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": "3.8.10"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "745a3b3e3fb7ac09f0ebb6d5eb47d006584e16db5d9df6f9a8b654baa561b29f"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

results.json

@@ -0,0 +1 @@
+{"env_id": "LunarLander-v2", "mean_reward": -138.9859847395379, "std_reward": 59.22793304996013, "n_evaluation_episodes": 10, "eval_datetime": "2023-03-20T13:32:08.124907"}

runs/LunarLander-v2__ppo__1__1679286470/events.out.tfevents.1679286470.hanbk-robotmecha.1077606.0

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