[Doc] Clarify headless MuJoCo EGL dependencies

knowledge_base/DM_CONTROL_INSTALLATION.md

@@ -72,19 +72,22 @@ multi-GPU machine, all rendering contends on a **single GPU** — even if the
 host has 8 GPUs. This inflates per-worker render time by ~3x (e.g. 17ms serial
 → 54ms with 8 workers sharing one GPU's EGL queue).
 
-**Root cause:** Inside Docker or SLURM containers, the NVIDIA container runtime
-only exposes the GPU(s) assigned to the job to EGL. `eglQueryDevicesEXT()`
-returns 1 device regardless of how many physical GPUs the host has.
-Setting `MUJOCO_EGL_DEVICE_ID` or `EGL_DEVICE_ID` to anything other than 0
-raises:
+**Common root causes:** Inside Docker or SLURM containers, the NVIDIA
+container runtime may expose only a subset of devices to EGL, or a minimal CUDA
+image may omit the NVIDIA graphics userspace libraries entirely. In those
+cases, `eglQueryDevicesEXT()` can return fewer devices than the node has, or
+EGL initialization can fail even though CUDA and `nvidia-smi` work. Setting
+`MUJOCO_EGL_DEVICE_ID` or `EGL_DEVICE_ID` to an unavailable EGL device raises:
 
 ```
 RuntimeError: MUJOCO_EGL_DEVICE_ID must be an integer between 0 and 0 (inclusive), got 1.
 ```
 
-Unsetting `CUDA_VISIBLE_DEVICES` in the worker does **not** help — the
-container isolation happens at the NVIDIA driver/runtime level, below the
-environment variable.
+Unsetting `CUDA_VISIBLE_DEVICES` in the worker does **not** help once the
+container runtime has hidden devices from the driver. Conversely,
+`NVIDIA_DRIVER_CAPABILITIES=compute,utility` by itself does not prove EGL is
+impossible: if matching NVIDIA EGL/GLVND userspace libraries are installed
+inside the container, EGL may still work.
 
 **Note on variable naming:** dm_control uses `MUJOCO_EGL_DEVICE_ID` internally
 (which maps to the same thing as MuJoCo's variable). Historically there was
@@ -98,17 +101,42 @@ for the unification discussion.
 
 **Workarounds:**
 
-1. **Configure container for full GPU access.** If you control the container
+1. **Verify the graphics userspace stack first.** Minimal CUDA containers often
+   omit the EGL/GLVND loader packages and NVIDIA graphics libraries. On
+   Debian/Ubuntu images, install the generic runtime packages:
+
+   ```bash
+   sudo apt-get update
+   sudo apt-get install -y libegl-dev libglvnd0 libglx0 libgles2
+   ```
+
+   Then verify the NVIDIA pieces are visible and match the host driver:
+
+   ```bash
+   nvidia-smi --query-gpu=driver_version,name --format=csv,noheader | head
+   ldconfig -p | grep -E 'libEGL_nvidia|libnvidia-eglcore|libGLX_nvidia'
+   ls /usr/share/glvnd/egl_vendor.d/10_nvidia.json
+   ```
+
+   If the NVIDIA libraries are missing, install a matching
+   `libnvidia-gl-<driver-version>` package or provide a matching userspace
+   bundle and point `LD_LIBRARY_PATH` / `ldconfig` at it. The GLVND vendor JSON
+   should point EGL at `libEGL_nvidia.so.0`.
+
+2. **Configure container for full GPU access.** If you control the container
    runtime, set `NVIDIA_VISIBLE_DEVICES=all` and
-   `NVIDIA_DRIVER_CAPABILITIES=all` so EGL can see all GPUs. Then assign
+   include `graphics` in `NVIDIA_DRIVER_CAPABILITIES` (or use `all`) so the
+   driver stack and all intended GPUs are mounted. Then assign
    `MUJOCO_EGL_DEVICE_ID=<worker_idx % num_gpus>` per worker process
    **before** dm_control is imported (the EGL display is created at import
-   time).
+   time). For LIBERO / robosuite environments, prefer passing
+   `render_gpu_device_id=<worker_idx % num_gpus>` to the environment
+   constructor.
 
-2. **Run outside containers.** On bare metal, `eglQueryDevicesEXT()` correctly
+3. **Run outside containers.** On bare metal, `eglQueryDevicesEXT()` correctly
    returns all GPUs (plus the X server display, if any).
 
-3. **Reduce rendering overhead.** If multi-GPU rendering is not possible:
+4. **Reduce rendering overhead.** If multi-GPU rendering is not possible:
    - Lower the rendering resolution (e.g. 64x64 instead of 84x84)
    - Render at a lower frequency than the simulation step (frame-skip)
    - Use state-only observations where possible — the IPC overhead is small

knowledge_base/MUJOCO_INSTALLATION.md

@@ -23,11 +23,25 @@ To do so, MuJoCo will use one of the following backends: glfw, osmesa or egl.
 Of these, glfw will not work in headless environments. On the other hand, osmesa 
 will not run on GPU. Therefore, our advice is to use the egl backend.
 
-If you have a sudo access on your machine, you can install the following dependencies
-to enable fast rendering:
+If you have sudo access on your machine, install the generic OpenGL/EGL
+dependencies needed by MuJoCo:
 ```shell
-$ sudo apt-get install libglfw3 libglew2.0 libgl1-mesa-glx libosmesa6
+$ sudo apt-get install libglfw3 libglew2.0 libglx-mesa0 libosmesa6 \
+    libegl-dev libglvnd0 libglx0 libgles2
 ```
+For NVIDIA EGL rendering, the NVIDIA graphics userspace libraries must also
+be visible and compatible with the host driver. In minimal CUDA containers,
+`nvidia-smi` can work even when the graphics stack is missing. Check for:
+
+```shell
+$ ldconfig -p | grep -E 'libEGL_nvidia|libnvidia-eglcore|libGLX_nvidia'
+$ ls /usr/share/glvnd/egl_vendor.d/10_nvidia.json
+```
+
+If these are absent, install a `libnvidia-gl-<driver-version>` package matching
+the host driver, or provide a matching NVIDIA userspace bundle and add its
+library directory to `LD_LIBRARY_PATH` / `ldconfig`. The GLVND vendor file
+should point EGL at `libEGL_nvidia.so.0`.
 If you don't, these libraries can be installed via conda but be aware of the fact
 that this is not the intended workflow and things may not work as expected:
 ```shell
@@ -39,7 +53,8 @@ $ conda install -c menpo glfw3
 ```
 
 In both cases, when running your code, you will want to tell mujoco which backend to use.
-This can be done by setting the appropriate environment variables.
+This can be done by setting the appropriate environment variables. The
+variables must be set before MuJoCo / dm_control / robosuite is imported.
 ```shell
 $ conda env config vars set MUJOCO_GL=egl PYOPENGL_PLATFORM=egl
 $ conda deactivate && conda activate mujoco_env
@@ -227,6 +242,10 @@ RuntimeError: Failed to initialize OpenGL
     _Solution_: Make sure you have installed mujoco and all its dependencies (see instructions above).
     Make sure you have set the `MUJOCO_GL=egl`.
     Make sure you have a GPU accessible on your machine.
+    In containers, also verify the NVIDIA EGL/GLVND userspace libraries:
+    `libEGL_nvidia`, `libnvidia-eglcore`, `libGLX_nvidia`, and
+    `/usr/share/glvnd/egl_vendor.d/10_nvidia.json`. A CUDA-capable container
+    without these files can still run `nvidia-smi` but fail headless EGL.
 
 12. `cannot find -lGL: No such file or directory`
 
@@ -236,9 +255,9 @@ RuntimeError: Failed to initialize OpenGL
     RuntimeError: Failed to initialize OpenGL
     ```
 
-    _Solution_: Install libEGL:
+    _Solution_: Install libEGL and the GLVND runtime:
 
-    - Ubuntu: `sudo apt install libegl-dev libegl`
+    - Ubuntu: `sudo apt install libegl-dev libegl1 libglvnd0 libglx0 libgles2`
     - CentOS: `sudo yum install mesa-libEGL mesa-libEGL-devel`
     - Conda: `conda install -c anaconda mesa-libegl-cos6-x86_64`