mirror of
https://github.com/huggingface/deep-rl-class.git
synced 2026-04-14 10:22:37 +08:00
Add package to hub
This commit is contained in:
SuperSecureHuman
@@ -10,12 +10,12 @@
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 7,
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"id": "2e020e33",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-05-07T16:36:51.138888Z",
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"start_time": "2022-05-07T16:36:49.643694Z"
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"end_time": "2022-05-08T01:11:29.835785Z",
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"start_time": "2022-05-08T01:11:29.831878Z"
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}
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},
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"outputs": [],
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@@ -28,7 +28,7 @@
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"from stable_baselines3.common.evaluation import evaluate_policy\n",
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"from stable_baselines3.common.env_util import make_vec_env\n",
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"\n",
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"from stable_baselines3.common.vec_env import VecVideoRecorder\n",
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"from stable_baselines3.common.vec_env import VecVideoRecorder , DummyVecEnv\n",
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"\n",
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"# Import wandb stuff\n",
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"import wandb\n",
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@@ -81,8 +81,8 @@
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"id": "ae0fd212",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-05-07T16:36:59.329343Z",
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"start_time": "2022-05-07T16:36:53.808583Z"
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"end_time": "2022-05-08T00:55:11.285850Z",
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"start_time": "2022-05-08T00:55:05.640088Z"
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}
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},
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"outputs": [
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@@ -108,7 +108,7 @@
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{
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"data": {
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"text/html": [
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"Run data is saved locally in <code>/home/venom/Desktop/deep-rl-class/unit1/unit1_bonus/wandb/run-20220507_220654-8czogswz</code>"
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"Run data is saved locally in <code>/home/venom/Desktop/deep-rl-class/unit1/unit1_bonus/wandb/run-20220508_062506-2ovixu73</code>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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@@ -120,7 +120,7 @@
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{
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"data": {
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"text/html": [
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"Syncing run <strong><a href=\"https://wandb.ai/supersecurehuman/LunarLander-v2/runs/8czogswz\" target=\"_blank\">brisk-fire-2</a></strong> to <a href=\"https://wandb.ai/supersecurehuman/LunarLander-v2\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>"
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||||
"Syncing run <strong><a href=\"https://wandb.ai/supersecurehuman/LunarLander-v2/runs/2ovixu73\" target=\"_blank\">warm-gorge-3</a></strong> to <a href=\"https://wandb.ai/supersecurehuman/LunarLander-v2\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://wandb.me/run\" target=\"_blank\">docs</a>)<br/>"
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],
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"text/plain": [
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"<IPython.core.display.HTML object>"
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@@ -138,6 +138,7 @@
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" \"policy_type\": \"MlpPolicy\",\n",
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" \"total_timesteps\": 100000,\n",
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" \"env_name\": \"LunarLander-v2\",\n",
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" \"learning_rate\" : 0.0002,\n",
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"}\n",
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"\n",
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"run = wandb.init(\n",
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@@ -166,8 +167,8 @@
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"id": "0b8046ae",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-05-07T16:37:34.011591Z",
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"start_time": "2022-05-07T16:37:29.500555Z"
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"end_time": "2022-05-08T00:55:59.965683Z",
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"start_time": "2022-05-08T00:55:55.396867Z"
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}
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},
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"outputs": [
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@@ -185,29 +186,126 @@
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"# be pretty resource intensive. \n",
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"# env = VecVideoRecorder(env, f\"videos/{run.id}\", record_video_trigger=lambda x: x % 2000 == 0, video_length=200) # Set the video recorder, to record our agent during training\n",
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"\n",
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"# I would suggest you to add all your hyperparameters in the config dictionary defined before the wandb init step. This would help you to visualize the effect those hyper parameters\n",
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"# have on your model, via the wandb dashboard\n",
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"model = PPO(\n",
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" policy = config[\"policy_type\"],\n",
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" env = env,\n",
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" learning_rate=config[\"learning_rate\"],\n",
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" tensorboard_log=\"logs\",\n",
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" verbose=1)"
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]
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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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"execution_count": 4,
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"id": "f772cc3f",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2022-05-07T16:39:54.654747Z",
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"start_time": "2022-05-07T16:37:35.052748Z"
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"end_time": "2022-05-08T00:59:14.354142Z",
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"start_time": "2022-05-08T00:56:32.259913Z"
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},
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"scrolled": true
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},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Logging to logs/PPO_1\n",
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"---------------------------------\n",
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"| rollout/ | |\n",
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"| ep_len_mean | 91.1 |\n",
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"| ep_rew_mean | -170 |\n",
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"| time/ | |\n",
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"| fps | 5432 |\n",
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"| iterations | 1 |\n",
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"| time_elapsed | 6 |\n",
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"| total_timesteps | 32768 |\n",
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"---------------------------------\n",
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"-----------------------------------------\n",
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"| rollout/ | |\n",
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"| ep_len_mean | 98.9 |\n",
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"| ep_rew_mean | -132 |\n",
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"| time/ | |\n",
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"| fps | 1480 |\n",
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"| iterations | 2 |\n",
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"| time_elapsed | 44 |\n",
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"| total_timesteps | 65536 |\n",
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"| train/ | |\n",
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"| approx_kl | 0.009069282 |\n",
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"| clip_fraction | 0.0836 |\n",
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"| clip_range | 0.2 |\n",
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"| entropy_loss | -1.38 |\n",
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"| explained_variance | 0.00211 |\n",
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"| learning_rate | 0.0002 |\n",
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"| loss | 345 |\n",
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"| n_updates | 10 |\n",
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"| policy_gradient_loss | -0.00783 |\n",
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"| value_loss | 810 |\n",
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"-----------------------------------------\n",
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"-----------------------------------------\n",
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"| rollout/ | |\n",
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"| ep_len_mean | 103 |\n",
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"| ep_rew_mean | -104 |\n",
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"| time/ | |\n",
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"| fps | 1135 |\n",
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"| iterations | 3 |\n",
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"| time_elapsed | 86 |\n",
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"| total_timesteps | 98304 |\n",
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"| train/ | |\n",
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"| approx_kl | 0.012206479 |\n",
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"| clip_fraction | 0.12 |\n",
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"| clip_range | 0.2 |\n",
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"| entropy_loss | -1.35 |\n",
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"| explained_variance | 0.436 |\n",
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"| learning_rate | 0.0002 |\n",
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"| loss | 77.5 |\n",
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"| n_updates | 20 |\n",
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"| policy_gradient_loss | -0.0125 |\n",
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"| value_loss | 335 |\n",
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"-----------------------------------------\n",
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"-----------------------------------------\n",
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"| rollout/ | |\n",
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"| ep_len_mean | 113 |\n",
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"| ep_rew_mean | -87.2 |\n",
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"| time/ | |\n",
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"| fps | 1031 |\n",
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"| iterations | 4 |\n",
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"| time_elapsed | 127 |\n",
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"| total_timesteps | 131072 |\n",
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"| train/ | |\n",
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"| approx_kl | 0.012416394 |\n",
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"| clip_fraction | 0.167 |\n",
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"| clip_range | 0.2 |\n",
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"| entropy_loss | -1.31 |\n",
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"| explained_variance | 0.513 |\n",
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"| learning_rate | 0.0002 |\n",
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"| loss | 66.1 |\n",
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"| n_updates | 30 |\n",
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"| policy_gradient_loss | -0.015 |\n",
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"| value_loss | 257 |\n",
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||||
"-----------------------------------------\n"
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||||
]
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||||
},
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{
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"data": {
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||||
"text/plain": [
|
||||
"<stable_baselines3.ppo.ppo.PPO at 0x7fb0d70ccd50>"
|
||||
]
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||||
},
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||||
"execution_count": 4,
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||||
"metadata": {},
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"output_type": "execute_result"
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||||
}
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||||
],
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"source": [
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||||
"# Now we do the magical stuff of logging to wandb. All you have to do is add the wandb callback to the model's callback like this\n",
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"\n",
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"model.learn(total_timesteps=config[\"total_timesteps\"], callback=[WandbCallback()])"
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"model.learn(total_timesteps=config[\"total_timesteps\"], \n",
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||||
" callback=[WandbCallback(\n",
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||||
" gradient_save_freq=100\n",
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||||
" )])"
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||||
]
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||||
},
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{
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@@ -216,8 +314,8 @@
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"id": "d2a6341c",
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"metadata": {
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"ExecuteTime": {
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||||
"end_time": "2022-05-07T16:40:06.824067Z",
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||||
"start_time": "2022-05-07T16:39:54.696684Z"
|
||||
"end_time": "2022-05-08T00:59:27.663456Z",
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||||
"start_time": "2022-05-08T00:59:14.382414Z"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
@@ -255,7 +353,7 @@
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||||
" .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
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||||
" .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
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||||
" </style>\n",
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||||
"<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>global_step</td><td>▁▃▆█</td></tr><tr><td>rollout/ep_len_mean</td><td>▁▃▄█</td></tr><tr><td>rollout/ep_rew_mean</td><td>▁▄▆█</td></tr><tr><td>time/fps</td><td>█▁▁▁</td></tr><tr><td>train/approx_kl</td><td>▁▇█</td></tr><tr><td>train/clip_fraction</td><td>▁▅█</td></tr><tr><td>train/clip_range</td><td>▁▁▁</td></tr><tr><td>train/entropy_loss</td><td>▁▄█</td></tr><tr><td>train/explained_variance</td><td>▁▇█</td></tr><tr><td>train/learning_rate</td><td>▁▁▁</td></tr><tr><td>train/loss</td><td>█▅▁</td></tr><tr><td>train/policy_gradient_loss</td><td>█▄▁</td></tr><tr><td>train/value_loss</td><td>█▂▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>global_step</td><td>131072</td></tr><tr><td>rollout/ep_len_mean</td><td>107.59</td></tr><tr><td>rollout/ep_rew_mean</td><td>-75.28564</td></tr><tr><td>time/fps</td><td>1176.0</td></tr><tr><td>train/approx_kl</td><td>0.01353</td></tr><tr><td>train/clip_fraction</td><td>0.20614</td></tr><tr><td>train/clip_range</td><td>0.2</td></tr><tr><td>train/entropy_loss</td><td>-1.30999</td></tr><tr><td>train/explained_variance</td><td>0.65612</td></tr><tr><td>train/learning_rate</td><td>0.0003</td></tr><tr><td>train/loss</td><td>44.55813</td></tr><tr><td>train/policy_gradient_loss</td><td>-0.01972</td></tr><tr><td>train/value_loss</td><td>212.05472</td></tr></table><br/></div></div>"
|
||||
"<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>global_step</td><td>▁▃▆█</td></tr><tr><td>rollout/ep_len_mean</td><td>▁▃▅█</td></tr><tr><td>rollout/ep_rew_mean</td><td>▁▄▇█</td></tr><tr><td>time/fps</td><td>█▂▁▁</td></tr><tr><td>train/approx_kl</td><td>▁██</td></tr><tr><td>train/clip_fraction</td><td>▁▄█</td></tr><tr><td>train/clip_range</td><td>▁▁▁</td></tr><tr><td>train/entropy_loss</td><td>▁▄█</td></tr><tr><td>train/explained_variance</td><td>▁▇█</td></tr><tr><td>train/learning_rate</td><td>▁▁▁</td></tr><tr><td>train/loss</td><td>█▁▁</td></tr><tr><td>train/policy_gradient_loss</td><td>█▃▁</td></tr><tr><td>train/value_loss</td><td>█▂▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>global_step</td><td>131072</td></tr><tr><td>rollout/ep_len_mean</td><td>113.48</td></tr><tr><td>rollout/ep_rew_mean</td><td>-87.24881</td></tr><tr><td>time/fps</td><td>1031.0</td></tr><tr><td>train/approx_kl</td><td>0.01242</td></tr><tr><td>train/clip_fraction</td><td>0.16663</td></tr><tr><td>train/clip_range</td><td>0.2</td></tr><tr><td>train/entropy_loss</td><td>-1.30755</td></tr><tr><td>train/explained_variance</td><td>0.51275</td></tr><tr><td>train/learning_rate</td><td>0.0002</td></tr><tr><td>train/loss</td><td>66.06168</td></tr><tr><td>train/policy_gradient_loss</td><td>-0.01496</td></tr><tr><td>train/value_loss</td><td>257.38614</td></tr></table><br/></div></div>"
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.HTML object>"
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@@ -267,7 +365,7 @@
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{
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||||
"data": {
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"text/html": [
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||||
"Synced <strong style=\"color:#cdcd00\">brisk-fire-2</strong>: <a href=\"https://wandb.ai/supersecurehuman/LunarLander-v2/runs/8czogswz\" target=\"_blank\">https://wandb.ai/supersecurehuman/LunarLander-v2/runs/8czogswz</a><br/>Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)"
|
||||
"Synced <strong style=\"color:#cdcd00\">warm-gorge-3</strong>: <a href=\"https://wandb.ai/supersecurehuman/LunarLander-v2/runs/2ovixu73\" target=\"_blank\">https://wandb.ai/supersecurehuman/LunarLander-v2/runs/2ovixu73</a><br/>Synced 6 W&B file(s), 0 media file(s), 0 artifact file(s) and 1 other file(s)"
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.HTML object>"
|
||||
@@ -279,7 +377,7 @@
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||||
{
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||||
"data": {
|
||||
"text/html": [
|
||||
"Find logs at: <code>./wandb/run-20220507_220654-8czogswz/logs</code>"
|
||||
"Find logs at: <code>./wandb/run-20220508_062506-2ovixu73/logs</code>"
|
||||
],
|
||||
"text/plain": [
|
||||
"<IPython.core.display.HTML object>"
|
||||
@@ -298,7 +396,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "3ffa0dae",
|
||||
"id": "e9f1e6a5",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Note\n",
|
||||
@@ -308,7 +406,288 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "a03b8746",
|
||||
"id": "4f448887",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Package to 🤗 hub"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"id": "f8d1b517",
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2022-05-08T01:13:41.386029Z",
|
||||
"start_time": "2022-05-08T01:13:41.335337Z"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": []
|
||||
},
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# You have to disable wandb while packaging it to hub, because it seems to be interfering with package to hub function.\n",
|
||||
"wandb.init(mode=\"disabled\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "5f93f140",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from huggingface_hub import notebook_login # To log to our Hugging Face account to be able to upload models to the Hub."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "f0461e29",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Note: You just need to run notebook_login() once in any machine you are trying to login. The token is saved in you machine, making future access to your account easier\n",
|
||||
"notebook_login()\n",
|
||||
"!git config --global credential.helper store"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"id": "7d7ea1b3",
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2022-05-08T01:14:35.254796Z",
|
||||
"start_time": "2022-05-08T01:13:43.366135Z"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\u001b[38;5;4mℹ This function will save, evaluate, generate a video of your agent,\n",
|
||||
"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 and use\n",
|
||||
"push_to_hub instead.\u001b[0m\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/home/venom/miniconda3/envs/RL/lib/python3.7/site-packages/huggingface_hub/hf_api.py:82: FutureWarning: `name` and `organization` input arguments are deprecated and will be removed in v0.7. Pass `repo_id` instead.\n",
|
||||
" FutureWarning,\n",
|
||||
"/home/venom/Desktop/deep-rl-class/unit1/unit1_bonus/hub/LunarLander_v2_PPO_wandb is already a clone of https://huggingface.co/SuperSecureHuman/LunarLander_v2_PPO_wandb. Make sure you pull the latest changes with `repo.git_pull()`.\n",
|
||||
"/home/venom/miniconda3/envs/RL/lib/python3.7/site-packages/stable_baselines3/common/evaluation.py:69: UserWarning: Evaluation environment is not wrapped with a ``Monitor`` wrapper. This may result in reporting modified episode lengths and rewards, if other wrappers happen to modify these. Consider wrapping environment first with ``Monitor`` wrapper.\n",
|
||||
" UserWarning,\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Saving video to /home/venom/Desktop/deep-rl-class/unit1/unit1_bonus/-step-0-to-step-1000.mp4\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
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|
||||
" built with gcc 7.3.0 (crosstool-NG 1.23.0.449-a04d0)\n",
|
||||
" configuration: --prefix=/home/venom/miniconda3/envs/RL --cc=/tmp/build/80754af9/ffmpeg_1587154242452/_build_env/bin/x86_64-conda_cos6-linux-gnu-cc --disable-doc --enable-avresample --enable-gmp --enable-hardcoded-tables --enable-libfreetype --enable-libvpx --enable-pthreads --enable-libopus --enable-postproc --enable-pic --enable-pthreads --enable-shared --enable-static --enable-version3 --enable-zlib --enable-libmp3lame --disable-nonfree --enable-gpl --enable-gnutls --disable-openssl --enable-libopenh264 --enable-libx264\n",
|
||||
" libavutil 56. 31.100 / 56. 31.100\n",
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" libavcodec 58. 54.100 / 58. 54.100\n",
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" libavformat 58. 29.100 / 58. 29.100\n",
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" libavdevice 58. 8.100 / 58. 8.100\n",
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" libavfilter 7. 57.100 / 7. 57.100\n",
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" libavresample 4. 0. 0 / 4. 0. 0\n",
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" libswscale 5. 5.100 / 5. 5.100\n",
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" libswresample 3. 5.100 / 3. 5.100\n",
|
||||
" libpostproc 55. 5.100 / 55. 5.100\n",
|
||||
"Input #0, mov,mp4,m4a,3gp,3g2,mj2, from './test.mp4':\n",
|
||||
" Metadata:\n",
|
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" major_brand : isom\n",
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" minor_version : 512\n",
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|
||||
" encoder : Lavf58.29.100\n",
|
||||
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||||
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|
||||
" Metadata:\n",
|
||||
" handler_name : VideoHandler\n",
|
||||
"Stream mapping:\n",
|
||||
" Stream #0:0 -> #0:0 (h264 (native) -> h264 (libx264))\n",
|
||||
"Press [q] to stop, [?] for help\n",
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"[libx264 @ 0x560e05024ec0] using cpu capabilities: MMX2 SSE2Fast SSSE3 SSE4.2 AVX FMA3 BMI2 AVX2 AVX512\n",
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"[libx264 @ 0x560e05024ec0] profile High, level 3.1, 4:2:0, 8-bit\n",
|
||||
"[libx264 @ 0x560e05024ec0] 264 - core 157 - H.264/MPEG-4 AVC codec - Copyleft 2003-2018 - http://www.videolan.org/x264.html - options: cabac=1 ref=3 deblock=1:0:0 analyse=0x3:0x113 me=hex subme=7 psy=1 psy_rd=1.00:0.00 mixed_ref=1 me_range=16 chroma_me=1 trellis=1 8x8dct=1 cqm=0 deadzone=21,11 fast_pskip=1 chroma_qp_offset=-2 threads=12 lookahead_threads=2 sliced_threads=0 nr=0 decimate=1 interlaced=0 bluray_compat=0 constrained_intra=0 bframes=3 b_pyramid=2 b_adapt=1 b_bias=0 direct=1 weightb=1 open_gop=0 weightp=2 keyint=250 keyint_min=25 scenecut=40 intra_refresh=0 rc_lookahead=40 rc=crf mbtree=1 crf=23.0 qcomp=0.60 qpmin=0 qpmax=69 qpstep=4 ip_ratio=1.40 aq=1:1.00\n",
|
||||
"Output #0, mp4, to 'replay.mp4':\n",
|
||||
" Metadata:\n",
|
||||
" major_brand : isom\n",
|
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" minor_version : 512\n",
|
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" Metadata:\n",
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" handler_name : VideoHandler\n",
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" encoder : Lavc58.54.100 libx264\n",
|
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" Side data:\n",
|
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"video:82kB audio:0kB subtitle:0kB other streams:0kB global headers:0kB muxing overhead: 15.259376%\n",
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"[libx264 @ 0x560e05024ec0] frame I:5 Avg QP:10.02 size: 1676\n",
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"[libx264 @ 0x560e05024ec0] frame P:259 Avg QP:17.41 size: 99\n",
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"[libx264 @ 0x560e05024ec0] frame B:737 Avg QP:20.72 size: 67\n",
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||||
"[libx264 @ 0x560e05024ec0] consecutive B-frames: 1.0% 1.8% 2.1% 95.1%\n",
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"[libx264 @ 0x560e05024ec0] mb I I16..4: 87.6% 7.3% 5.0%\n",
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||||
"[libx264 @ 0x560e05024ec0] mb P I16..4: 0.1% 0.3% 0.1% P16..4: 0.9% 0.1% 0.0% 0.0% 0.0% skip:98.5%\n",
|
||||
"[libx264 @ 0x560e05024ec0] mb B I16..4: 0.0% 0.0% 0.1% B16..8: 1.2% 0.1% 0.0% direct: 0.0% skip:98.6% L0:57.8% L1:41.6% BI: 0.5%\n",
|
||||
"[libx264 @ 0x560e05024ec0] 8x8 transform intra:17.5% inter:12.1%\n",
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||||
"[libx264 @ 0x560e05024ec0] coded y,uvDC,uvAC intra: 6.9% 9.7% 9.3% inter: 0.1% 0.1% 0.0%\n",
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"[libx264 @ 0x560e05024ec0] i16 v,h,dc,p: 89% 5% 6% 0%\n",
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"[libx264 @ 0x560e05024ec0] i8 v,h,dc,ddl,ddr,vr,hd,vl,hu: 26% 8% 65% 0% 0% 0% 0% 0% 0%\n",
|
||||
"[libx264 @ 0x560e05024ec0] i4 v,h,dc,ddl,ddr,vr,hd,vl,hu: 13% 14% 60% 2% 2% 2% 3% 2% 3%\n",
|
||||
"[libx264 @ 0x560e05024ec0] i8c dc,h,v,p: 96% 3% 2% 0%\n",
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||||
"[libx264 @ 0x560e05024ec0] Weighted P-Frames: Y:0.0% UV:0.0%\n",
|
||||
"[libx264 @ 0x560e05024ec0] ref P L0: 76.2% 0.8% 16.4% 6.6%\n",
|
||||
"[libx264 @ 0x560e05024ec0] ref B L0: 62.0% 34.4% 3.7%\n",
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"[libx264 @ 0x560e05024ec0] ref B L1: 94.1% 5.9%\n",
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"[libx264 @ 0x560e05024ec0] kb/s:33.21\n"
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]
|
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},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\u001b[38;5;4mℹ Pushing repo LunarLander_v2_PPO_wandb to the Hugging Face Hub\u001b[0m\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "ee4b1a1441da4bff8a0a30cbfaa79d54",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"Upload file PPO-LunarLander-v2.zip: 23%|##2 | 32.0k/141k [00:00<?, ?B/s]"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "ea5e56decd7142dea38707f5b56eede1",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"Upload file PPO-LunarLander-v2/policy.optimizer.pth: 39%|###8 | 32.0k/82.8k [00:00<?, ?B/s]"
|
||||
]
|
||||
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|
||||
"metadata": {},
|
||||
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|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "e716bdab138e4b9cb10d30387ba16953",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"Upload file PPO-LunarLander-v2/policy.pth: 76%|#######5 | 32.0k/42.2k [00:00<?, ?B/s]"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "b966e0bc19604640b3eb0cea62265631",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"Upload file replay.mp4: 34%|###3 | 32.0k/94.3k [00:00<?, ?B/s]"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "d357e49f80d94c569864c2daca4974e5",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"Upload file PPO-LunarLander-v2/pytorch_variables.pth: 100%|##########| 431/431 [00:00<?, ?B/s]"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"remote: Enforcing permissions... \n",
|
||||
"remote: Allowed refs: all \n",
|
||||
"To https://huggingface.co/SuperSecureHuman/LunarLander_v2_PPO_wandb\n",
|
||||
" b5f588e..67fd722 main -> main\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"\u001b[38;5;4mℹ Your model is pushed to the hub. You can view your model here:\n",
|
||||
"https://huggingface.co/SuperSecureHuman/LunarLander_v2_PPO_wandb\u001b[0m\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from huggingface_sb3 import package_to_hub\n",
|
||||
"\n",
|
||||
"env_id = config[\"env_name\"]\n",
|
||||
"\n",
|
||||
"model_architecture = \"PPO\"\n",
|
||||
"model_name = \"PPO-LunarLander-v2\"\n",
|
||||
"\n",
|
||||
"repo_id = \"SuperSecureHuman/LunarLander_v2_PPO_wandb\"\n",
|
||||
"\n",
|
||||
"commit_message = \"Initial Commit\"\n",
|
||||
"\n",
|
||||
"eval_env = DummyVecEnv([lambda: gym.make(env_id)])\n",
|
||||
"\n",
|
||||
"package_to_hub(model=model, # Our trained model\n",
|
||||
" model_name=model_name, # The name of our trained model \n",
|
||||
" model_architecture=model_architecture, # The model architecture we used: in our case PPO\n",
|
||||
" env_id=env_id, # Name of the environment\n",
|
||||
" eval_env=eval_env, # Evaluation Environment\n",
|
||||
" repo_id=repo_id, # id of the model repository from the Hugging Face Hub\n",
|
||||
" commit_message=commit_message)\n",
|
||||
"eval_env.close()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "9368a9f9",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Congarts!\n",
|
||||
|
||||
Reference in New Issue
Block a user