Updating dockerfile for learning.

Dockerfile

@@ -40,6 +40,25 @@ RUN apt-get update && \
     libxrender1 \
     libsm6 \
     libfontconfig1 \
+    cmake \
+    libglvnd-dev \
+    libgl1-mesa-dev \
+    libegl1-mesa-dev \
+    libgles2-mesa-dev \
+    libgbm-dev \
+    build-essential \
+    git \
+    cmake \
+    build-essential \
+    libgl1 \
+    libglib2.0-0 \
+    libsm6 \
+    libxext6 \
+    libxrender1 \
+    ffmpeg \
+    libx264-dev \
+    libvulkan-dev \
+    vulkan-tools \
     && rm -rf /var/lib/apt/lists/*
 
 # Download and install Miniconda
@@ -61,41 +80,52 @@ RUN echo "source activate roble" >> ~/.bashrc
 ENV PATH /opt/conda/envs/roble/bin:$PATH
 RUN source activate roble
 
-# Set the working directory for your application
-WORKDIR /playground
+WORKDIR /app
 
-RUN apt-get update && apt-get install -y --no-install-recommends git cmake build-essential libgl1 libglib2.0-0 libsm6 libxext6 libxrender1 ffmpeg libx264-dev
-## Install the requirements for your learning code.
-COPY requirements.txt requirements.txt
-RUN pip install -r requirements.txt
-# RUN pip freeze
+## Install Libero
+# RUN pip install cmake==3.24.3
+RUN git clone https://github.com/montrealrobotics/LIBERO.git
+# COPY --link ./LIBERO /playground/LIBERO
+RUN pip install -r ./LIBERO/requirements.txt
+RUN pip install -e ./LIBERO 
+# ENV PYTHONPATH=/playground/LIBERO:$PYTHONPATH
 # RUN python -c "import libero"
 
-## Install pytorch and cuda
-RUN pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
+# Create a default config file to avoid an input prompt from LIBERO's init script.
+# https://github.com/Lifelong-Robot-Learning/LIBERO/blob/master/libero/libero/__init__.py
+ENV LIBERO_CONFIG_PATH=/tmp/libero
+RUN mkdir -p /tmp/libero && cat <<'EOF' > /tmp/libero/config.yaml
+benchmark_root: /app/LIBERO/libero/libero
+bddl_files: /app/LIBERO/libero/libero/bddl_files
+init_states: /app/LIBERO/libero/libero/init_files
+datasets: /app/LIBERO/libero/datasets
+assets: /app/LIBERO/libero/libero/assets
+EOF
+
 
 ## Install simulators simpleEnv
 # RUN pip install cmake==3.24.3
-RUN conda install -c conda-forge cmake
-RUN git clone https://github.com/milarobotlearningcourse/SimplerEnv --recurse-submodules
-## Change directory to SimplerEnv and install ManiSkill2 and ManiSkill2_real2sim
-# RUN cd SimplerEnv/ManiSkill2
-# RUN cd SimplerEnv/ManiSkill2_real2sim
-RUN pip install -e ./SimplerEnv/ManiSkill2_real2sim
-# RUN cd ../
-RUN pip install -e ./SimplerEnv
+# RUN conda install -c conda-forge cmake
+# RUN git clone https://github.com/milarobotlearningcourse/SimplerEnv --recurse-submodules
+# ## Change directory to SimplerEnv and install ManiSkill2 and ManiSkill2_real2sim
+# # RUN cd SimplerEnv/ManiSkill2
+# # RUN cd SimplerEnv/ManiSkill2_real2sim
+# RUN pip install -e ./SimplerEnv/ManiSkill2_real2sim
+# # RUN cd ../
+# RUN pip install -e ./SimplerEnv
 # RUN cd ../
-RUN apt-get install -yqq --no-install-recommends libvulkan-dev vulkan-tools
+# RUN apt-get update && \
+#     apt-get install -y --no-install-recommends \
+#     libvulkan-dev \
+#     vulkan-tools \
+#     && rm -rf /var/lib/apt/lists/*
 RUN conda install conda-forge::vulkan-tools conda-forge::vulkan-headers
 
-# 2. MANUALLY Generate the NVIDIA Vulkan ICD (The critical fix)
 # This tells Vulkan to use the NVIDIA driver instead of looking for a display
-RUN mkdir -p /etc/vulkan/icd.d && \
-    echo '{ "file_format_version" : "1.0.0", "ICD": { "library_path": "libGLX_nvidia.so.0", "api_version" : "1.3.0" } }' > /etc/vulkan/icd.d/nvidia_icd.json
+RUN mkdir -p /etc/vulkan/icd.d && echo '{ "file_format_version" : "1.0.0", "ICD": { "library_path": "libGLX_nvidia.so.0", "api_version" : "1.3.0" } }' > /etc/vulkan/icd.d/nvidia_icd.json
 
 # 3. Setup EGL (Required for headless SAPIEN/PyRender)
-RUN mkdir -p /usr/share/glvnd/egl_vendor.d && \
-    echo '{ "file_format_version" : "1.0.0", "ICD" : { "library_path" : "libEGL_nvidia.so.0" } }' > /usr/share/glvnd/egl_vendor.d/10_nvidia.json
+RUN mkdir -p /usr/share/glvnd/egl_vendor.d && echo '{ "file_format_version" : "1.0.0", "ICD" : { "library_path" : "libEGL_nvidia.so.0" } }' > /usr/share/glvnd/egl_vendor.d/10_nvidia.json
 
 # 4. Set Environment Variables permanently in the image
 ENV NVIDIA_VISIBLE_DEVICES=all
@@ -105,29 +135,22 @@ ENV SAP_NO_GUI=1
 ENV DISPLAY=:0
 
 # Add LIBERO to Python path
-# ENV PYTHONPATH=/playground/LIBERO:$PYTHONPATH
+ENV PYTHONPATH=/app/LIBERO/:$PYTHONPATH
 
-## Install Libero
-# RUN pip install cmake==3.24.3
-RUN apt-get update && apt-get install -y --no-install-recommends \
-cmake \
-libglvnd-dev \
-libgl1-mesa-dev \
-libegl1-mesa-dev \
-libgles2-mesa-dev \
-libgbm-dev \
-build-essential \
-&& rm -rf /var/lib/apt/lists/*
+# Set the working directory for your application
+WORKDIR /playground
+
+# RUN apt-get update && apt-get install -y --no-install-recommends git cmake build-essential libgl1 libglib2.0-0 libsm6 libxext6 libxrender1 ffmpeg libx264-dev
+## Install the requirements for your learning code.
+COPY requirements.txt requirements.txt
+RUN pip install -r requirements.txt
 
+## Install pytorch and cuda
+RUN pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu121
 
-RUN git clone https://github.com/montrealrobotics/LIBERO.git
-# COPY --link ./LIBERO /playground/LIBERO
-RUN pip install -r ./LIBERO/requirements.txt
-RUN pip install -e ./LIBERO 
-# ENV PYTHONPATH=/playground/LIBERO:$PYTHONPATH
-RUN python -c "import libero"
+COPY requirements-app.txt requirements-app.txt
+RUN pip install -r requirements-app.txt
 
-# COPY --link . /playground
+COPY --link . /playground
 
 # ENTRYPOINT [ "python" ]
-CMD

README.md

@@ -21,5 +21,11 @@ grp_model.py
 ## Build the Docker File
 
 ```
-docker build -t ghcr.io/neo-x/mini-grp/roble:latest .
+docker build -t ghcr.io/neo-x/mini-grp/roble-eval:latest .
+```
+
+## Run Evaluation in Docker
+
+```
+docker run --gpus=all ghcr.io/neo-x/mini-grp/roble-eval:latest python main.py 
 ```

app.py

@@ -55,6 +55,7 @@ def evaluate_policy(model_id):
         cfg = OmegaConf.load(hydra_config_file_path)
         cfg.dataset.load_dataset = "skip"
         cfg.testing = True
+        cfg.sim.task_set = "libero_spatial"
         ## load the GRP model from the file downloaded in the snapshot
         # Dynamically load the module
         import importlib.util, sys
@@ -142,7 +143,7 @@ def run_evaluation_loop():
     
     # 4. Update the Dataframes
     # Update Requests (Mark as Done or Failed)
-    requests_df.loc[row_index, "status"] = "Done" if score is not None else "Failed"
+    # requests_df.loc[row_index, "status"] = "Done" if score is not None else "Failed"
 
     # Prepare Results Row
     if score is not None:
@@ -151,14 +152,15 @@ def run_evaluation_loop():
             "mean_reward": score['rewards'],
             "run_time": score["time"],
             "status": "Success",
-            "completed_at": time.time()
+            "completed_at": time.time(),
+            "video_url": score.get("video_url", "") # Optional: if you upload videos to the Hub or elsewhere, include the URL here.
         }
         
         # Load Results Dataset
         try:
             results_df = pd.read_csv(f"hf://datasets/{RESULTS_DATASET}/results.csv")
         except:
-            results_df = pd.DataFrame(columns=["model_id", "mean_reward", "run_time", "status", "completed_at"])
+            results_df = pd.DataFrame(columns=["model_id", "mean_reward", "run_time", "status", "completed_at", "video_url"])
 
         # Append new result
         results_df = pd.concat([results_df, pd.DataFrame([new_result])], ignore_index=True)

requirements-app.txt

@@ -0,0 +1,2 @@
+gradio==6.2.0
+gradio_client==2.0.2

requirements.txt

@@ -40,7 +40,9 @@ google-auth-oauthlib==1.2.2
 google-pasta==0.2.0
 greenlet==3.2.4
 grpcio==1.75.0
-gymnasium===0.28.1
+gym==0.25.2
+gym-notices==0.1.0
+gymnasium==1.0.0
 h5py==3.14.0
 hf-xet==1.1.10
 huggingface==0.0.1
@@ -90,6 +92,7 @@ Pympler==1.1
 pyparsing==3.2.4
 pyperclip==1.10.0
 python-box==7.3.2
+python-dotenv==1.2.1
 pytz==2025.2
 PyYAML==6.0.2
 regex==2025.9.18
@@ -134,3 +137,5 @@ Werkzeug==3.1.3
 wrapt==1.14.2
 xxhash==3.5.0
 yarl==1.20.1
+# git+https://github.com/montrealrobotics/LIBERO.git@master#egg=libero
+# libero @ https://github.com/montrealrobotics/LIBERO.git

sim_eval.py

@@ -108,6 +108,7 @@ def eval_model_in_sim(cfg, model, device, log_dir, env, env_unwrapped,
     path_ = os.path.join(log_dir, f"simple-env-{iter_}.mp4")
     import imageio
     imageio.mimsave(path_, frames, fps=20)
+    episode_stats['video_url'] = path_
 
     if not cfg.testing:
         try:
@@ -167,8 +168,6 @@ def eval_libero(model, device, cfg, iter_=0, log_dir="./",
     from libero.libero.utils import get_libero_path
     from gymnasium.wrappers import FrameStackObservation
     from einops import rearrange
-    from collections import deque
-
 
     benchmark_dict = benchmark.get_benchmark_dict()
     task_suite_name = cfg.sim.task_set # can also choose libero_spatial, libero_object, etc.
@@ -353,6 +352,7 @@ def eval_libero(model, device, cfg, iter_=0, log_dir="./",
             imageio.mimsave(path_, frames, fps=20)
     episode_stats = info.get('episode_stats', {})
     episode_stats['rewards'] = np.mean(rewards) 
+    episode_stats['video_url'] = path_
     print(f"avg reward {np.mean(rewards):.8f}")
     if not cfg.testing:
         wandb.log({"avg reward_"+str(task_id): np.mean(rewards)})