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{ |
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"cell_type": "markdown", |
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"metadata": { |
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- "id": "view-in-github", |
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- "colab_type": "text" |
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+ "colab_type": "text", |
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+ "id": "view-in-github" |
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}, |
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"source": [ |
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"<a href=\"https://colab.research.google.com/github/huggingface/deep-rl-class/blob/EdBeeching%2FPPOPart2/notebooks/unit8/unit8_part2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>" |
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@@ -202,11 +202,26 @@ |
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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": 1, |
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"metadata": { |
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"id": "RJMxkaldwIVx" |
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}, |
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- "outputs": [], |
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+ "outputs": [ |
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+ { |
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+ "ename": "CalledProcessError", |
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+ "evalue": "Command 'b'# Install ViZDoom deps from \\n# https://github.com/mwydmuch/ViZDoom/blob/master/doc/Building.md#-linux\\n\\napt-get install build-essential zlib1g-dev libsdl2-dev libjpeg-dev \\\\\\nnasm tar libbz2-dev libgtk2.0-dev cmake git libfluidsynth-dev libgme-dev \\\\\\nlibopenal-dev timidity libwildmidi-dev unzip ffmpeg\\n\\n# Boost libraries\\napt-get install libboost-all-dev\\n\\n# Lua binding dependencies\\napt-get install liblua5.1-dev\\n'' returned non-zero exit status 100.", |
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+ "output_type": "error", |
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+ "traceback": [ |
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+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", |
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+ "\u001b[0;31mCalledProcessError\u001b[0m Traceback (most recent call last)", |
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+ "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m get_ipython()\u001b[39m.\u001b[39;49mrun_cell_magic(\u001b[39m'\u001b[39;49m\u001b[39mbash\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39m# Install ViZDoom deps from \u001b[39;49m\u001b[39m\\n\u001b[39;49;00m\u001b[39m# https://github.com/mwydmuch/ViZDoom/blob/master/doc/Building.md#-linux\u001b[39;49m\u001b[39m\\n\u001b[39;49;00m\u001b[39m\\n\u001b[39;49;00m\u001b[39mapt-get install build-essential zlib1g-dev libsdl2-dev libjpeg-dev \u001b[39;49m\u001b[39m\\\\\u001b[39;49;00m\u001b[39m\\n\u001b[39;49;00m\u001b[39mnasm tar libbz2-dev libgtk2.0-dev cmake git libfluidsynth-dev libgme-dev \u001b[39;49m\u001b[39m\\\\\u001b[39;49;00m\u001b[39m\\n\u001b[39;49;00m\u001b[39mlibopenal-dev timidity libwildmidi-dev unzip ffmpeg\u001b[39;49m\u001b[39m\\n\u001b[39;49;00m\u001b[39m\\n\u001b[39;49;00m\u001b[39m# Boost libraries\u001b[39;49m\u001b[39m\\n\u001b[39;49;00m\u001b[39mapt-get install libboost-all-dev\u001b[39;49m\u001b[39m\\n\u001b[39;49;00m\u001b[39m\\n\u001b[39;49;00m\u001b[39m# Lua binding dependencies\u001b[39;49m\u001b[39m\\n\u001b[39;49;00m\u001b[39mapt-get install liblua5.1-dev\u001b[39;49m\u001b[39m\\n\u001b[39;49;00m\u001b[39m'\u001b[39;49m)\n", |
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+ "File \u001b[0;32m~/miniconda3/envs/deep-rl-class/lib/python3.10/site-packages/IPython/core/interactiveshell.py:2430\u001b[0m, in \u001b[0;36mInteractiveShell.run_cell_magic\u001b[0;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[1;32m 2428\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mbuiltin_trap:\n\u001b[1;32m 2429\u001b[0m args \u001b[39m=\u001b[39m (magic_arg_s, cell)\n\u001b[0;32m-> 2430\u001b[0m result \u001b[39m=\u001b[39m fn(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 2432\u001b[0m \u001b[39m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m 2433\u001b[0m \u001b[39m# when using magics with decodator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m 2434\u001b[0m \u001b[39m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m 2435\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mgetattr\u001b[39m(fn, magic\u001b[39m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[39mFalse\u001b[39;00m):\n", |
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+ "File \u001b[0;32m~/miniconda3/envs/deep-rl-class/lib/python3.10/site-packages/IPython/core/magics/script.py:153\u001b[0m, in \u001b[0;36mScriptMagics._make_script_magic.<locals>.named_script_magic\u001b[0;34m(line, cell)\u001b[0m\n\u001b[1;32m 151\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 152\u001b[0m line \u001b[39m=\u001b[39m script\n\u001b[0;32m--> 153\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mshebang(line, cell)\n", |
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+ "File \u001b[0;32m~/miniconda3/envs/deep-rl-class/lib/python3.10/site-packages/IPython/core/magics/script.py:305\u001b[0m, in \u001b[0;36mScriptMagics.shebang\u001b[0;34m(self, line, cell)\u001b[0m\n\u001b[1;32m 300\u001b[0m \u001b[39mif\u001b[39;00m args\u001b[39m.\u001b[39mraise_error \u001b[39mand\u001b[39;00m p\u001b[39m.\u001b[39mreturncode \u001b[39m!=\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[1;32m 301\u001b[0m \u001b[39m# If we get here and p.returncode is still None, we must have\u001b[39;00m\n\u001b[1;32m 302\u001b[0m \u001b[39m# killed it but not yet seen its return code. We don't wait for it,\u001b[39;00m\n\u001b[1;32m 303\u001b[0m \u001b[39m# in case it's stuck in uninterruptible sleep. -9 = SIGKILL\u001b[39;00m\n\u001b[1;32m 304\u001b[0m rc \u001b[39m=\u001b[39m p\u001b[39m.\u001b[39mreturncode \u001b[39mor\u001b[39;00m \u001b[39m-\u001b[39m\u001b[39m9\u001b[39m\n\u001b[0;32m--> 305\u001b[0m \u001b[39mraise\u001b[39;00m CalledProcessError(rc, cell)\n", |
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+ "\u001b[0;31mCalledProcessError\u001b[0m: Command 'b'# Install ViZDoom deps from \\n# https://github.com/mwydmuch/ViZDoom/blob/master/doc/Building.md#-linux\\n\\napt-get install build-essential zlib1g-dev libsdl2-dev libjpeg-dev \\\\\\nnasm tar libbz2-dev libgtk2.0-dev cmake git libfluidsynth-dev libgme-dev \\\\\\nlibopenal-dev timidity libwildmidi-dev unzip ffmpeg\\n\\n# Boost libraries\\napt-get install libboost-all-dev\\n\\n# Lua binding dependencies\\napt-get install liblua5.1-dev\\n'' returned non-zero exit status 100." |
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+ ] |
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+ } |
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+ ], |
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"source": [ |
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"%%capture\n", |
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"%%bash\n", |
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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": 2, |
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"metadata": { |
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"id": "bbqfPZnIsvA6" |
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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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+ "Collecting sample-factory\n", |
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+ " Downloading sample_factory-2.0.3-py3-none-any.whl (9.0 MB)\n", |
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+ "\u001b[?25h Installing build dependencies ... \u001b[?25ldone\n", |
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+ "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", |
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+ "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", |
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+ "Collecting filelock\n", |
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+ " Using cached filelock-3.9.0-py3-none-any.whl (9.7 kB)\n", |
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+ "Requirement already satisfied: psutil>=5.7.0 in /home/chqma/miniconda3/envs/deep-rl-class/lib/python3.10/site-packages (from sample-factory) (5.9.0)\n", |
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+ "\u001b[?25h Installing build dependencies ... \u001b[?25ldone\n", |
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+ "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", |
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+ "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", |
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+ " Using cached setproctitle-1.3.2-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (30 kB)\n", |
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+ "Requirement already satisfied: idna<4,>=2.5 in /home/chqma/miniconda3/envs/deep-rl-class/lib/python3.10/site-packages (from requests->huggingface-hub<1.0,>=0.10.0->sample-factory) (3.4)\n", |
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+ "Collecting MarkupSafe>=2.1.1\n", |
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+ "Building wheels for collected packages: faster-fifo, gym\n", |
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+ " Building wheel for faster-fifo (pyproject.toml) ... \u001b[?25ldone\n", |
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+ "\u001b[?25h Created wheel for faster-fifo: filename=faster_fifo-1.4.2-cp310-cp310-linux_x86_64.whl size=78864 sha256=db802db0bb9e4e639957e0cc929a5055871d73dac2a78aab10dd613331d4e086\n", |
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+ " Stored in directory: /home/chqma/.cache/pip/wheels/46/57/35/44590621055121fe1a2f1ae60846e531621498f6d6e48c8975\n", |
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+ " Building wheel for gym (pyproject.toml) ... \u001b[?25ldone\n", |
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+ "\u001b[?25h Created wheel for gym: filename=gym-0.26.2-py3-none-any.whl size=827634 sha256=2ecaeda2a512edc3ad62eb999df1ba486447df53cdcd38d3cd5acc3b0b013d44\n", |
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+ " Stored in directory: /home/chqma/.cache/pip/wheels/ae/5f/67/64914473eb34e9ba89dbc7eefe7e9be8f6673fbc6f0273b29f\n", |
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+ "Successfully built faster-fifo gym\n", |
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+ "Installing collected packages: tensorboard-plugin-wit, pyglet, pyasn1, pathtools, gym-notices, appdirs, tqdm, threadpoolctl, tensorboard-data-server, smmap, setproctitle, sentry-sdk, rsa, pyyaml, pyasn1-modules, protobuf, opencv-python, oauthlib, MarkupSafe, markdown, grpcio, filelock, docker-pycreds, cython, colorlog, cloudpickle, Click, cachetools, absl-py, werkzeug, tensorboardx, requests-oauthlib, huggingface-hub, gym, google-auth, gitdb, faster-fifo, signal-slot-mp, google-auth-oauthlib, GitPython, wandb, tensorboard, sample-factory\n", |
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+ "Successfully installed Click-8.1.3 GitPython-3.1.31 MarkupSafe-2.1.2 absl-py-1.4.0 appdirs-1.4.4 cachetools-5.3.0 cloudpickle-2.2.1 colorlog-6.7.0 cython-0.29.33 docker-pycreds-0.4.0 faster-fifo-1.4.2 filelock-3.9.0 gitdb-4.0.10 google-auth-2.16.1 google-auth-oauthlib-0.4.6 grpcio-1.51.3 gym-0.26.2 gym-notices-0.0.8 huggingface-hub-0.12.1 markdown-3.4.1 oauthlib-3.2.2 opencv-python-4.7.0.72 pathtools-0.1.2 protobuf-3.20.3 pyasn1-0.4.8 pyasn1-modules-0.2.8 pyglet-2.0.4 pyyaml-6.0 requests-oauthlib-1.3.1 rsa-4.9 sample-factory-2.0.3 sentry-sdk-1.15.0 setproctitle-1.3.2 signal-slot-mp-1.0.3 smmap-5.0.0 tensorboard-2.12.0 tensorboard-data-server-0.7.0 tensorboard-plugin-wit-1.8.1 tensorboardx-2.6 threadpoolctl-3.1.0 tqdm-4.64.1 wandb-0.13.10 werkzeug-2.2.3\n", |
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+ "Collecting vizdoom\n", |
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+ " Downloading vizdoom-1.1.14.tar.gz (15.7 MB)\n", |
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+ "\u001b[?25h Installing build dependencies ... \u001b[?25ldone\n", |
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+ "\u001b[?25h Getting requirements to build wheel ... \u001b[?25ldone\n", |
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+ "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n", |
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+ "\u001b[?25hRequirement already satisfied: numpy in /home/chqma/miniconda3/envs/deep-rl-class/lib/python3.10/site-packages (from vizdoom) (1.23.5)\n", |
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+ "Building wheels for collected packages: vizdoom\n", |
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+ " Building wheel for vizdoom (pyproject.toml) ... \u001b[?25ldone\n", |
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+ "\u001b[?25h Created wheel for vizdoom: filename=vizdoom-1.1.14-cp310-cp310-linux_x86_64.whl size=14192416 sha256=732a3631973e8da574807abc6da03d63e48238fdf6024c6f34c11e5c4dcf2056\n", |
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+ " Stored in directory: /home/chqma/.cache/pip/wheels/a4/13/80/6927dae582137aef0836f48491051c797a5de184891b8ca6c5\n", |
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+ "Successfully built vizdoom\n", |
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+ "Installing collected packages: vizdoom\n", |
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+ "Successfully installed vizdoom-1.1.14\n" |
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+ ] |
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+ } |
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+ ], |
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"source": [ |
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"# install python libraries\n", |
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"!pip install sample-factory\n", |
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@@ -258,24 +424,24 @@ |
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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": 6, |
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"metadata": { |
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"id": "bCgZbeiavcDU" |
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}, |
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"outputs": [], |
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"source": [ |
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"import functools\n", |
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- "\n", |
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+ "from encoder import make_vizdoom_encoder\n", |
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"from sample_factory.algo.utils.context import global_model_factory\n", |
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"from sample_factory.cfg.arguments import parse_full_cfg, parse_sf_args\n", |
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"from sample_factory.envs.env_utils import register_env\n", |
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"from sample_factory.train import run_rl\n", |
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"\n", |
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- "from sf_examples.vizdoom.doom.doom_model import make_vizdoom_encoder\n", |
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"from sf_examples.vizdoom.doom.doom_params import add_doom_env_args, doom_override_defaults\n", |
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"from sf_examples.vizdoom.doom.doom_utils import DOOM_ENVS, make_doom_env_from_spec\n", |
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"\n", |
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"\n", |
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+ "\n", |
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"# Registers all the ViZDoom environments\n", |
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"def register_vizdoom_envs():\n", |
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" for env_spec in DOOM_ENVS:\n", |
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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": 1, |
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+ "metadata": {}, |
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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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+ "env: CUDA_VISIBLE_DEVICES=1\n" |
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+ ] |
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+ } |
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+ ], |
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+ "source": [ |
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+ "%env CUDA_VISIBLE_DEVICES=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": 5, |
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"metadata": { |
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"id": "y_TeicMvyKHP" |
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}, |
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- "outputs": [], |
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+ "outputs": [ |
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+ { |
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+ "name": "stderr", |
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+ "output_type": "stream", |
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+ "text": [ |
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+ "\u001b[33m[2023-02-24 08:05:26,614][795538] Environment doom_basic already registered, overwriting...\u001b[0m\n", |
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+ "\u001b[33m[2023-02-24 08:05:26,616][795538] Environment doom_two_colors_easy already registered, overwriting...\u001b[0m\n", |
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+ "\u001b[33m[2023-02-24 08:05:26,617][795538] Environment doom_two_colors_hard already registered, overwriting...\u001b[0m\n", |
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+ "\u001b[33m[2023-02-24 08:05:26,619][795538] Environment doom_dm already registered, overwriting...\u001b[0m\n", |
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+ "\u001b[33m[2023-02-24 08:05:26,619][795538] Environment doom_dwango5 already registered, overwriting...\u001b[0m\n", |
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+ "\u001b[33m[2023-02-24 08:05:26,620][795538] Environment doom_my_way_home_flat_actions already registered, overwriting...\u001b[0m\n", |
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+ "\u001b[33m[2023-02-24 08:05:26,621][795538] Environment doom_defend_the_center_flat_actions already registered, overwriting...\u001b[0m\n", |
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+ "\u001b[33m[2023-02-24 08:05:26,621][795538] Environment doom_my_way_home already registered, overwriting...\u001b[0m\n", |
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+ "\u001b[33m[2023-02-24 08:05:26,623][795538] Environment doom_deadly_corridor already registered, overwriting...\u001b[0m\n", |
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+ "\u001b[33m[2023-02-24 08:05:26,623][795538] Environment doom_defend_the_center already registered, overwriting...\u001b[0m\n", |
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+ "\u001b[33m[2023-02-24 08:05:26,624][795538] Environment doom_defend_the_line already registered, overwriting...\u001b[0m\n", |
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+ "\u001b[33m[2023-02-24 08:05:26,624][795538] Environment doom_health_gathering already registered, overwriting...\u001b[0m\n", |
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+ "\u001b[33m[2023-02-24 08:05:26,625][795538] Environment doom_health_gathering_supreme already registered, overwriting...\u001b[0m\n", |
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+ "\u001b[33m[2023-02-24 08:05:26,625][795538] Environment doom_battle already registered, overwriting...\u001b[0m\n", |
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+ "\u001b[33m[2023-02-24 08:05:26,626][795538] Environment doom_battle2 already registered, overwriting...\u001b[0m\n", |
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+ "\u001b[33m[2023-02-24 08:05:26,626][795538] Environment doom_duel_bots already registered, overwriting...\u001b[0m\n", |
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+ "\u001b[33m[2023-02-24 08:05:26,627][795538] Environment doom_deathmatch_bots already registered, overwriting...\u001b[0m\n", |
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+ "\u001b[33m[2023-02-24 08:05:26,628][795538] Environment doom_duel already registered, overwriting...\u001b[0m\n", |
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+ "\u001b[33m[2023-02-24 08:05:26,628][795538] Environment doom_deathmatch_full already registered, overwriting...\u001b[0m\n", |
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+ "\u001b[33m[2023-02-24 08:05:26,629][795538] Environment doom_benchmark already registered, overwriting...\u001b[0m\n", |
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+ "\u001b[36m[2023-02-24 08:05:26,629][795538] register_encoder_factory: <function make_vizdoom_encoder at 0x7efc41caae60>\u001b[0m\n", |
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+ "\u001b[33m[2023-02-24 08:05:26,640][795538] Loading existing experiment configuration from /mnt/chqma/data-ssd-01/dataset/oss/RWKV-LM/deep-rl-class/notebooks/unit8/train_dir/default_experiment/config.json\u001b[0m\n", |
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+ "\u001b[36m[2023-02-24 08:05:26,640][795538] Overriding arg 'train_for_env_steps' with value 40000000 passed from command line\u001b[0m\n", |
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+ "\u001b[36m[2023-02-24 08:05:26,645][795538] Experiment dir /mnt/chqma/data-ssd-01/dataset/oss/RWKV-LM/deep-rl-class/notebooks/unit8/train_dir/default_experiment already exists!\u001b[0m\n", |
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+ "\u001b[36m[2023-02-24 08:05:26,646][795538] Resuming existing experiment from /mnt/chqma/data-ssd-01/dataset/oss/RWKV-LM/deep-rl-class/notebooks/unit8/train_dir/default_experiment...\u001b[0m\n", |
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+ "\u001b[36m[2023-02-24 08:05:26,646][795538] Weights and Biases integration disabled\u001b[0m\n", |
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+ "\u001b[37m\u001b[1m[2023-02-24 08:05:26,649][795538] Environment var CUDA_VISIBLE_DEVICES is 1\u001b[0m\n", |
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+ "Traceback (most recent call last):\n", |
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+ " File \"<string>\", line 1, in <module>\n", |
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+ " File \"/home/chqma/miniconda3/envs/deep-rl-class/lib/python3.10/multiprocessing/spawn.py\", line 116, in spawn_main\n", |
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+ " exitcode = _main(fd, parent_sentinel)\n", |
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+ " File \"/home/chqma/miniconda3/envs/deep-rl-class/lib/python3.10/multiprocessing/spawn.py\", line 126, in _main\n", |
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+ " self = reduction.pickle.load(from_parent)\n", |
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+ "AttributeError: Can't get attribute 'make_vizdoom_encoder' on <module '__main__' (built-in)>\n" |
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+ ] |
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+ }, |
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+ { |
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+ "ename": "KeyboardInterrupt", |
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+ "evalue": "", |
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+ "output_type": "error", |
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+ "traceback": [ |
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+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", |
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+ "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", |
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+ "Cell \u001b[0;32mIn[5], line 9\u001b[0m\n\u001b[1;32m 6\u001b[0m env \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mdoom_health_gathering_supreme\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 7\u001b[0m cfg \u001b[39m=\u001b[39m parse_vizdoom_cfg(argv\u001b[39m=\u001b[39m[\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m--env=\u001b[39m\u001b[39m{\u001b[39;00menv\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m--num_workers=8\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m--num_envs_per_worker=4\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m--train_for_env_steps=40000000\u001b[39m\u001b[39m\"\u001b[39m])\n\u001b[0;32m----> 9\u001b[0m status \u001b[39m=\u001b[39m run_rl(cfg)\n", |
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+ "File \u001b[0;32m~/miniconda3/envs/deep-rl-class/lib/python3.10/site-packages/sample_factory/train.py:37\u001b[0m, in \u001b[0;36mrun_rl\u001b[0;34m(cfg)\u001b[0m\n\u001b[1;32m 32\u001b[0m cfg, runner \u001b[39m=\u001b[39m make_runner(cfg)\n\u001b[1;32m 34\u001b[0m \u001b[39m# here we can register additional message or summary handlers\u001b[39;00m\n\u001b[1;32m 35\u001b[0m \u001b[39m# see sf_examples/dmlab/train_dmlab.py for example\u001b[39;00m\n\u001b[0;32m---> 37\u001b[0m status \u001b[39m=\u001b[39m runner\u001b[39m.\u001b[39;49minit()\n\u001b[1;32m 38\u001b[0m \u001b[39mif\u001b[39;00m status \u001b[39m==\u001b[39m ExperimentStatus\u001b[39m.\u001b[39mSUCCESS:\n\u001b[1;32m 39\u001b[0m status \u001b[39m=\u001b[39m runner\u001b[39m.\u001b[39mrun()\n", |
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+ "File \u001b[0;32m~/miniconda3/envs/deep-rl-class/lib/python3.10/site-packages/sample_factory/algo/runners/runner_parallel.py:21\u001b[0m, in \u001b[0;36mParallelRunner.init\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39minit\u001b[39m(\u001b[39mself\u001b[39m) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m StatusCode:\n\u001b[0;32m---> 21\u001b[0m status \u001b[39m=\u001b[39m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49minit()\n\u001b[1;32m 22\u001b[0m \u001b[39mif\u001b[39;00m status \u001b[39m!=\u001b[39m ExperimentStatus\u001b[39m.\u001b[39mSUCCESS:\n\u001b[1;32m 23\u001b[0m \u001b[39mreturn\u001b[39;00m status\n", |
|
+ "File \u001b[0;32m~/miniconda3/envs/deep-rl-class/lib/python3.10/site-packages/sample_factory/algo/runners/runner.py:542\u001b[0m, in \u001b[0;36mRunner.init\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 540\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39minit\u001b[39m(\u001b[39mself\u001b[39m) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m StatusCode:\n\u001b[1;32m 541\u001b[0m set_global_cuda_envvars(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcfg)\n\u001b[0;32m--> 542\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39menv_info \u001b[39m=\u001b[39m obtain_env_info_in_a_separate_process(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcfg)\n\u001b[1;32m 544\u001b[0m \u001b[39mfor\u001b[39;00m policy_id \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcfg\u001b[39m.\u001b[39mnum_policies):\n\u001b[1;32m 545\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mreward_shaping[policy_id] \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39menv_info\u001b[39m.\u001b[39mreward_shaping_scheme\n", |
|
+ "File \u001b[0;32m~/miniconda3/envs/deep-rl-class/lib/python3.10/site-packages/sample_factory/algo/utils/env_info.py:127\u001b[0m, in \u001b[0;36mobtain_env_info_in_a_separate_process\u001b[0;34m(cfg)\u001b[0m\n\u001b[1;32m 124\u001b[0m p \u001b[39m=\u001b[39m ctx\u001b[39m.\u001b[39mProcess(target\u001b[39m=\u001b[39mspawn_tmp_env_and_get_info, args\u001b[39m=\u001b[39m(sf_context, q, cfg))\n\u001b[1;32m 125\u001b[0m p\u001b[39m.\u001b[39mstart()\n\u001b[0;32m--> 127\u001b[0m env_info \u001b[39m=\u001b[39m q\u001b[39m.\u001b[39;49mget()\n\u001b[1;32m 128\u001b[0m p\u001b[39m.\u001b[39mjoin()\n\u001b[1;32m 130\u001b[0m \u001b[39mif\u001b[39;00m cfg\u001b[39m.\u001b[39muse_env_info_cache:\n", |
|
+ "File \u001b[0;32m~/miniconda3/envs/deep-rl-class/lib/python3.10/multiprocessing/queues.py:103\u001b[0m, in \u001b[0;36mQueue.get\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m 101\u001b[0m \u001b[39mif\u001b[39;00m block \u001b[39mand\u001b[39;00m timeout \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 102\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_rlock:\n\u001b[0;32m--> 103\u001b[0m res \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_recv_bytes()\n\u001b[1;32m 104\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_sem\u001b[39m.\u001b[39mrelease()\n\u001b[1;32m 105\u001b[0m \u001b[39melse\u001b[39;00m:\n", |
|
+ "File \u001b[0;32m~/miniconda3/envs/deep-rl-class/lib/python3.10/multiprocessing/connection.py:216\u001b[0m, in \u001b[0;36m_ConnectionBase.recv_bytes\u001b[0;34m(self, maxlength)\u001b[0m\n\u001b[1;32m 214\u001b[0m \u001b[39mif\u001b[39;00m maxlength \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m maxlength \u001b[39m<\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[1;32m 215\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mnegative maxlength\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m--> 216\u001b[0m buf \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_recv_bytes(maxlength)\n\u001b[1;32m 217\u001b[0m \u001b[39mif\u001b[39;00m buf \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 218\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_bad_message_length()\n", |
|
+ "File \u001b[0;32m~/miniconda3/envs/deep-rl-class/lib/python3.10/multiprocessing/connection.py:414\u001b[0m, in \u001b[0;36mConnection._recv_bytes\u001b[0;34m(self, maxsize)\u001b[0m\n\u001b[1;32m 413\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_recv_bytes\u001b[39m(\u001b[39mself\u001b[39m, maxsize\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[0;32m--> 414\u001b[0m buf \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_recv(\u001b[39m4\u001b[39;49m)\n\u001b[1;32m 415\u001b[0m size, \u001b[39m=\u001b[39m struct\u001b[39m.\u001b[39munpack(\u001b[39m\"\u001b[39m\u001b[39m!i\u001b[39m\u001b[39m\"\u001b[39m, buf\u001b[39m.\u001b[39mgetvalue())\n\u001b[1;32m 416\u001b[0m \u001b[39mif\u001b[39;00m size \u001b[39m==\u001b[39m \u001b[39m-\u001b[39m\u001b[39m1\u001b[39m:\n", |
|
+ "File \u001b[0;32m~/miniconda3/envs/deep-rl-class/lib/python3.10/multiprocessing/connection.py:379\u001b[0m, in \u001b[0;36mConnection._recv\u001b[0;34m(self, size, read)\u001b[0m\n\u001b[1;32m 377\u001b[0m remaining \u001b[39m=\u001b[39m size\n\u001b[1;32m 378\u001b[0m \u001b[39mwhile\u001b[39;00m remaining \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[0;32m--> 379\u001b[0m chunk \u001b[39m=\u001b[39m read(handle, remaining)\n\u001b[1;32m 380\u001b[0m n \u001b[39m=\u001b[39m \u001b[39mlen\u001b[39m(chunk)\n\u001b[1;32m 381\u001b[0m \u001b[39mif\u001b[39;00m n \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m:\n", |
|
+ "\u001b[0;31mKeyboardInterrupt\u001b[0m: " |
|
+ ] |
|
+ } |
|
+ ], |
|
"source": [ |
|
"## Start the training, this should take around 15 minutes\n", |
|
"register_vizdoom_components()\n", |
|
@@ -358,7 +601,7 @@ |
|
"# The scenario we train on today is health gathering\n", |
|
"# other scenarios include \"doom_basic\", \"doom_two_colors_easy\", \"doom_dm\", \"doom_dwango5\", \"doom_my_way_home\", \"doom_deadly_corridor\", \"doom_defend_the_center\", \"doom_defend_the_line\"\n", |
|
"env = \"doom_health_gathering_supreme\"\n", |
|
- "cfg = parse_vizdoom_cfg(argv=[f\"--env={env}\", \"--num_workers=8\", \"--num_envs_per_worker=4\", \"--train_for_env_steps=4000000\"])\n", |
|
+ "cfg = parse_vizdoom_cfg(argv=[f\"--env={env}\", \"--num_workers=8\", \"--num_envs_per_worker=4\", \"--train_for_env_steps=40000000\"])\n", |
|
"\n", |
|
"status = run_rl(cfg)" |
|
] |
|
@@ -416,12 +659,12 @@ |
|
}, |
|
{ |
|
"cell_type": "markdown", |
|
- "source": [ |
|
- "The agent has learned something, but its performance could be better. We would clearly need to train for longer. But let's upload this model to the Hub." |
|
- ], |
|
"metadata": { |
|
"id": "2A4pf_1VwPqR" |
|
- } |
|
+ }, |
|
+ "source": [ |
|
+ "The agent has learned something, but its performance could be better. We would clearly need to train for longer. But let's upload this model to the Hub." |
|
+ ] |
|
}, |
|
{ |
|
"cell_type": "markdown", |
|
@@ -464,11 +707,28 @@ |
|
}, |
|
{ |
|
"cell_type": "code", |
|
- "execution_count": null, |
|
+ "execution_count": 6, |
|
"metadata": { |
|
"id": "GoQm_jYSOts0" |
|
}, |
|
- "outputs": [], |
|
+ "outputs": [ |
|
+ { |
|
+ "ename": "ImportError", |
|
+ "evalue": "The `notebook_login` function can only be used in a notebook (Jupyter or Colab) and you need the `ipywidgets` module: `pip install ipywidgets`.", |
|
+ "output_type": "error", |
|
+ "traceback": [ |
|
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", |
|
+ "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", |
|
+ "File \u001b[0;32m~/miniconda3/envs/deep-rl-class/lib/python3.10/site-packages/huggingface_hub/_login.py:188\u001b[0m, in \u001b[0;36mnotebook_login\u001b[0;34m()\u001b[0m\n\u001b[1;32m 187\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 188\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mipywidgets\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mwidgets\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mwidgets\u001b[39;00m \u001b[39m# type: ignore\u001b[39;00m\n\u001b[1;32m 189\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mIPython\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mdisplay\u001b[39;00m \u001b[39mimport\u001b[39;00m clear_output, display \u001b[39m# type: ignore\u001b[39;00m\n", |
|
+ "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'ipywidgets'", |
|
+ "\nDuring handling of the above exception, another exception occurred:\n", |
|
+ "\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)", |
|
+ "Cell \u001b[0;32mIn[6], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mhuggingface_hub\u001b[39;00m \u001b[39mimport\u001b[39;00m notebook_login\n\u001b[0;32m----> 2\u001b[0m notebook_login()\n\u001b[1;32m 3\u001b[0m get_ipython()\u001b[39m.\u001b[39msystem(\u001b[39m'\u001b[39m\u001b[39mgit config --global credential.helper store\u001b[39m\u001b[39m'\u001b[39m)\n", |
|
+ "File \u001b[0;32m~/miniconda3/envs/deep-rl-class/lib/python3.10/site-packages/huggingface_hub/_login.py:191\u001b[0m, in \u001b[0;36mnotebook_login\u001b[0;34m()\u001b[0m\n\u001b[1;32m 189\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mIPython\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mdisplay\u001b[39;00m \u001b[39mimport\u001b[39;00m clear_output, display \u001b[39m# type: ignore\u001b[39;00m\n\u001b[1;32m 190\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mImportError\u001b[39;00m:\n\u001b[0;32m--> 191\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mImportError\u001b[39;00m(\n\u001b[1;32m 192\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mThe `notebook_login` function can only be used in a notebook (Jupyter or\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 193\u001b[0m \u001b[39m\"\u001b[39m\u001b[39m Colab) and you need the `ipywidgets` module: `pip install ipywidgets`.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 194\u001b[0m )\n\u001b[1;32m 196\u001b[0m box_layout \u001b[39m=\u001b[39m widgets\u001b[39m.\u001b[39mLayout(\n\u001b[1;32m 197\u001b[0m display\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mflex\u001b[39m\u001b[39m\"\u001b[39m, flex_flow\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mcolumn\u001b[39m\u001b[39m\"\u001b[39m, align_items\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mcenter\u001b[39m\u001b[39m\"\u001b[39m, width\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m50\u001b[39m\u001b[39m%\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 198\u001b[0m )\n\u001b[1;32m 200\u001b[0m token_widget \u001b[39m=\u001b[39m widgets\u001b[39m.\u001b[39mPassword(description\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mToken:\u001b[39m\u001b[39m\"\u001b[39m)\n", |
|
+ "\u001b[0;31mImportError\u001b[0m: The `notebook_login` function can only be used in a notebook (Jupyter or Colab) and you need the `ipywidgets` module: `pip install ipywidgets`." |
|
+ ] |
|
+ } |
|
+ ], |
|
"source": [ |
|
"from huggingface_hub import notebook_login\n", |
|
"notebook_login()\n", |
|
@@ -477,15 +737,231 @@ |
|
}, |
|
{ |
|
"cell_type": "code", |
|
- "execution_count": null, |
|
+ "execution_count": 8, |
|
"metadata": { |
|
"id": "sEawW_i0OvJV" |
|
}, |
|
- "outputs": [], |
|
+ "outputs": [ |
|
+ { |
|
+ "name": "stderr", |
|
+ "output_type": "stream", |
|
+ "text": [ |
|
+ "\u001b[33m[2023-02-24 07:58:39,896][784615] Loading existing experiment configuration from /mnt/chqma/data-ssd-01/dataset/oss/RWKV-LM/deep-rl-class/notebooks/unit8/train_dir/default_experiment/config.json\u001b[0m\n", |
|
+ "\u001b[36m[2023-02-24 07:58:39,896][784615] Overriding arg 'num_workers' with value 1 passed from command line\u001b[0m\n", |
|
+ "\u001b[36m[2023-02-24 07:58:39,897][784615] Adding new argument 'no_render'=True that is not in the saved config file!\u001b[0m\n", |
|
+ "\u001b[36m[2023-02-24 07:58:39,897][784615] Adding new argument 'save_video'=True that is not in the saved config file!\u001b[0m\n", |
|
+ "\u001b[36m[2023-02-24 07:58:39,898][784615] Adding new argument 'video_frames'=1000000000.0 that is not in the saved config file!\u001b[0m\n", |
|
+ "\u001b[36m[2023-02-24 07:58:39,898][784615] Adding new argument 'video_name'=None that is not in the saved config file!\u001b[0m\n", |
|
+ "\u001b[36m[2023-02-24 07:58:39,899][784615] Adding new argument 'max_num_frames'=100000 that is not in the saved config file!\u001b[0m\n", |
|
+ "\u001b[36m[2023-02-24 07:58:39,899][784615] Adding new argument 'max_num_episodes'=10 that is not in the saved config file!\u001b[0m\n", |
|
+ "\u001b[36m[2023-02-24 07:58:39,900][784615] Adding new argument 'push_to_hub'=True that is not in the saved config file!\u001b[0m\n", |
|
+ "\u001b[36m[2023-02-24 07:58:39,900][784615] Adding new argument 'hf_repository'='chqmatteo/rl_course_vizdoom_health_gathering_supreme' that is not in the saved config file!\u001b[0m\n", |
|
+ "\u001b[36m[2023-02-24 07:58:39,900][784615] Adding new argument 'policy_index'=0 that is not in the saved config file!\u001b[0m\n", |
|
+ "\u001b[36m[2023-02-24 07:58:39,901][784615] Adding new argument 'eval_deterministic'=False that is not in the saved config file!\u001b[0m\n", |
|
+ "\u001b[36m[2023-02-24 07:58:39,901][784615] Adding new argument 'train_script'=None that is not in the saved config file!\u001b[0m\n", |
|
+ "\u001b[36m[2023-02-24 07:58:39,902][784615] Adding new argument 'enjoy_script'=None that is not in the saved config file!\u001b[0m\n", |
|
+ "\u001b[36m[2023-02-24 07:58:39,902][784615] Using frameskip 1 and render_action_repeat=4 for evaluation\u001b[0m\n", |
|
+ "\u001b[36m[2023-02-24 07:58:39,911][784615] RunningMeanStd input shape: (3, 72, 128)\u001b[0m\n", |
|
+ "\u001b[36m[2023-02-24 07:58:39,912][784615] RunningMeanStd input shape: (1,)\u001b[0m\n", |
|
+ "\u001b[36m[2023-02-24 07:58:39,919][784615] ConvEncoder: input_channels=3\u001b[0m\n", |
|
+ "\u001b[36m[2023-02-24 07:58:39,943][784615] Conv encoder output size: 512\u001b[0m\n", |
|
+ "\u001b[36m[2023-02-24 07:58:39,944][784615] Policy head output size: 512\u001b[0m\n", |
|
+ "\u001b[33m[2023-02-24 07:58:39,980][784615] Loading state from checkpoint /mnt/chqma/data-ssd-01/dataset/oss/RWKV-LM/deep-rl-class/notebooks/unit8/train_dir/default_experiment/checkpoint_p0/checkpoint_000000268_1097728.pth...\u001b[0m\n", |
|
+ "\u001b[36m[2023-02-24 07:58:40,400][784615] Num frames 100...\u001b[0m\n", |
|
+ "\u001b[36m[2023-02-24 07:58:40,470][784615] Num frames 200...\u001b[0m\n", |
|
+ "\u001b[36m[2023-02-24 07:58:40,530][784615] Num frames 300...\u001b[0m\n", |
|
+ "\u001b[36m[2023-02-24 07:58:40,596][784615] Num frames 400...\u001b[0m\n", |
|
+ "\u001b[37m\u001b[1m[2023-02-24 07:58:40,684][784615] Avg episode rewards: #0: 5.480, true rewards: #0: 4.480\u001b[0m\n", |
|
+ "\u001b[37m\u001b[1m[2023-02-24 07:58:40,685][784615] Avg episode reward: 5.480, avg true_objective: 4.480\u001b[0m\n", |
|
+ "\u001b[36m[2023-02-24 07:58:40,719][784615] Num frames 500...\u001b[0m\n", |
|
+ "\u001b[36m[2023-02-24 07:58:40,787][784615] Num frames 600...\u001b[0m\n", |
|
+ "\u001b[36m[2023-02-24 07:58:40,850][784615] Num frames 700...\u001b[0m\n", |
|
+ "\u001b[36m[2023-02-24 07:58:40,918][784615] Num frames 800...\u001b[0m\n", |
|
+ "\u001b[36m[2023-02-24 07:58:41,005][784615] Num frames 900...\u001b[0m\n", |
|
+ "\u001b[37m\u001b[1m[2023-02-24 07:58:41,078][784615] Avg episode rewards: #0: 6.640, true rewards: #0: 4.640\u001b[0m\n", |
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+ "\u001b[36m[2023-02-24 07:58:43,498][784615] Num frames 4100...\u001b[0m\n", |
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+ "\u001b[37m\u001b[1m[2023-02-24 07:58:43,891][784615] Avg episode rewards: #0: 7.413, true rewards: #0: 5.191\u001b[0m\n", |
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+ "\u001b[37m\u001b[1m[2023-02-24 07:58:43,891][784615] Avg episode reward: 7.413, avg true_objective: 5.191\u001b[0m\n", |
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+ "\u001b[36m[2023-02-24 07:58:43,912][784615] Num frames 4700...\u001b[0m\n", |
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+ "\u001b[37m\u001b[1m[2023-02-24 07:58:44,250][784615] Avg episode rewards: #0: 7.220, true rewards: #0: 5.120\u001b[0m\n", |
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+ "\u001b[37m\u001b[1m[2023-02-24 07:58:44,250][784615] Avg episode reward: 7.220, avg true_objective: 5.120\u001b[0m\n", |
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+ "ffmpeg version 4.3 Copyright (c) 2000-2020 the FFmpeg developers\n", |
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+ " built with gcc 7.3.0 (crosstool-NG 1.23.0.449-a04d0)\n", |
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+ " configuration: --prefix=/opt/conda/conda-bld/ffmpeg_1597178665428/_h_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placeh --cc=/opt/conda/conda-bld/ffmpeg_1597178665428/_build_env/bin/x86_64-conda_cos6-linux-gnu-cc --disable-doc --disable-openssl --enable-avresample --enable-gnutls --enable-hardcoded-tables --enable-libfreetype --enable-libopenh264 --enable-pic --enable-pthreads --enable-shared --disable-static --enable-version3 --enable-zlib --enable-libmp3lame\n", |
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+ " libavutil 56. 51.100 / 56. 51.100\n", |
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+ " libavcodec 58. 91.100 / 58. 91.100\n", |
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+ " libavformat 58. 45.100 / 58. 45.100\n", |
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+ " libavdevice 58. 10.100 / 58. 10.100\n", |
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+ " libavfilter 7. 85.100 / 7. 85.100\n", |
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+ " libavresample 4. 0. 0 / 4. 0. 0\n", |
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+ " libswscale 5. 7.100 / 5. 7.100\n", |
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+ " libswresample 3. 7.100 / 3. 7.100\n", |
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+ "Input #0, mov,mp4,m4a,3gp,3g2,mj2, from '/tmp/sf2_chqma/replay.mp4':\n", |
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+ " Metadata:\n", |
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+ " major_brand : isom\n", |
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+ " minor_version : 512\n", |
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+ " compatible_brands: isomiso2mp41\n", |
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+ " encoder : Lavf59.27.100\n", |
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+ " Duration: 00:02:26.57, start: 0.000000, bitrate: 1521 kb/s\n", |
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+ " Stream #0:0(und): Video: mpeg4 (Simple Profile) (mp4v / 0x7634706D), yuv420p, 240x180 [SAR 1:1 DAR 4:3], 1519 kb/s, 35 fps, 35 tbr, 17920 tbn, 35 tbc (default)\n", |
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+ " Metadata:\n", |
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+ " handler_name : VideoHandler\n", |
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+ "Unknown encoder 'libx264'\n", |
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+ "\u001b[36m[2023-02-24 07:58:46,584][784615] Replay video saved to /mnt/chqma/data-ssd-01/dataset/oss/RWKV-LM/deep-rl-class/notebooks/unit8/train_dir/default_experiment/replay.mp4!\u001b[0m\n", |
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+ "/home/chqma/miniconda3/envs/deep-rl-class/lib/python3.10/site-packages/huggingface_hub/_commit_api.py:493: UserWarning: About to commit an empty file: 'git.diff'. Are you sure this is intended?\n", |
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+ " warnings.warn(\n", |
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+ "\n", |
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+ "\n", |
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+ "\n", |
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+ "\u001b[A\n", |
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+ "\u001b[A\n", |
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+ "\u001b[A\n", |
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+ "\u001b[A\n", |
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+ "\u001b[A\n", |
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+ "\u001b[A\n", |
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+ "\u001b[A\n", |
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+ "\u001b[A\n", |
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+ "\u001b[A\n", |
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+ "\u001b[A\n", |
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+ "\u001b[A\n", |
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+ "\u001b[A\n", |
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+ "\u001b[A\n", |
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+ "\u001b[A\n", |
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+ "\u001b[A\n", |
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+ "\u001b[A\n", |
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+ "checkpoint_000000268_1097728.pth: 100%|██████████| 34.9M/34.9M [00:23<00:00, 1.49MB/s]\n", |
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+ "\n", |
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+ "\n", |
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+ "Upload 3 LFS files: 100%|██████████| 3/3 [00:23<00:00, 7.81s/it]\n", |
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+ "\u001b[37m\u001b[1m[2023-02-24 07:59:12,402][784615] The model has been pushed to https://huggingface.co/chqmatteo/rl_course_vizdoom_health_gathering_supreme\u001b[0m\n" |
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+ ] |
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+ } |
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+ ], |
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"source": [ |
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"from sample_factory.enjoy import enjoy\n", |
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"\n", |
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- "hf_username = \"ThomasSimonini\" # insert your HuggingFace username here\n", |
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+ "hf_username = \"chqmatteo\" # insert your HuggingFace username here\n", |
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"\n", |
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"cfg = parse_vizdoom_cfg(argv=[f\"--env={env}\", \"--num_workers=1\", \"--save_video\", \"--no_render\", \"--max_num_episodes=10\", \"--max_num_frames=100000\", \"--push_to_hub\", f\"--hf_repository={hf_username}/rl_course_vizdoom_health_gathering_supreme\"], evaluation=True)\n", |
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"status = enjoy(cfg)" |
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@@ -493,14 +969,14 @@ |
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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": "9PzeXx-qxVvw" |
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+ }, |
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"source": [ |
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"## Let's load another model\n", |
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"\n", |
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"\n" |
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- ], |
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- "metadata": { |
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- "id": "9PzeXx-qxVvw" |
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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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@@ -566,16 +1042,16 @@ |
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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": "ie5YWC3NyKO8" |
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+ }, |
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"source": [ |
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"## Some additional challenges 🏆: Doom Deathmatch\n", |
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"\n", |
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"Training an agent to play a Doom deathmatch **takes many hours on a more beefy machine than is available in Colab**. \n", |
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"\n", |
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"Fortunately, we have have **already trained an agent in this scenario and it is available in the 🤗 Hub!** Let’s download the model and visualize the agent’s performance." |
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- ], |
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- "metadata": { |
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- "id": "ie5YWC3NyKO8" |
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- } |
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+ ] |
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}, |
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{ |
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"cell_type": "code", |
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@@ -591,12 +1067,12 @@ |
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}, |
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{ |
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"cell_type": "markdown", |
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- "source": [ |
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- "Given the agent plays for a long time the video generation can take **10 minutes**." |
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- ], |
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"metadata": { |
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"id": "7AX_LwxR2FQ0" |
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- } |
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+ }, |
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+ "source": [ |
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+ "Given the agent plays for a long time the video generation can take **10 minutes**." |
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+ ] |
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}, |
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{ |
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"cell_type": "code", |
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@@ -623,17 +1099,20 @@ |
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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": "N6mEC-4zyihx" |
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+ }, |
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"source": [ |
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"\n", |
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"You **can try to train your agent in this environment** using the code above, but not on colab.\n", |
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"**Good luck 🤞**" |
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- ], |
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- "metadata": { |
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- "id": "N6mEC-4zyihx" |
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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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+ "id": "YnDAngN6zeeI" |
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+ }, |
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"source": [ |
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"If you prefer an easier scenario, **why not try training in another ViZDoom scenario such as `doom_deadly_corridor` or `doom_defend_the_center`.**\n", |
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"\n", |
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@@ -645,34 +1124,46 @@ |
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"This concludes the last unit. But we are not finished yet! 🤗 The following **bonus section include some of the most interesting, advanced and cutting edge work in Deep Reinforcement Learning**.\n", |
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"\n", |
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"## Keep learning, stay awesome 🤗" |
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- ], |
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- "metadata": { |
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- "id": "YnDAngN6zeeI" |
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- } |
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+ ] |
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} |
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], |
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"metadata": { |
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"accelerator": "GPU", |
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"colab": { |
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- "provenance": [], |
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"collapsed_sections": [ |
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"PU4FVzaoM6fC", |
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"nB68Eb9UgC94", |
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"ez5UhUtYcWXF", |
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"sgRy6wnrgnij" |
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], |
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+ "include_colab_link": true, |
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"private_outputs": true, |
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- "include_colab_link": true |
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+ "provenance": [] |
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}, |
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"gpuClass": "standard", |
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"kernelspec": { |
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- "display_name": "Python 3", |
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+ "display_name": "deep-rl-class", |
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+ "language": "python", |
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"name": "python3" |
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}, |
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"language_info": { |
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- "name": "python" |
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+ "codemirror_mode": { |
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+ "name": "ipython", |
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+ "version": 3 |
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+ }, |
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+ "file_extension": ".py", |
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+ "mimetype": "text/x-python", |
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+ "name": "python", |
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+ "nbconvert_exporter": "python", |
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+ "pygments_lexer": "ipython3", |
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+ "version": "3.10.9" |
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+ }, |
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+ "vscode": { |
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+ "interpreter": { |
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+ "hash": "da4ecdf31b09708386948f91c5b725d7113689587e88c28098219103c44ec57b" |
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+ } |
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} |
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}, |
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"nbformat": 4, |
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"nbformat_minor": 0 |
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-} |
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\ No newline at end of file |
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+} |
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