Common Errors

This section documents errors commonly encountered when installing and running MOSAIC. Errors shared across platforms are listed below. For platform-specific issues, see the sub-pages:

Virtual Environment

python -m env – “No module named env”

Error:

/usr/bin/python3: No module named env

Cause: The module is called venv (with a v), not env.

Fix:

python3.11 -m venv .venv

Wrong Python version (< 3.10)

Error:

ERROR: Package 'mosaic' requires a different Python: 3.9.13 not in '>=3.10,<3.13'

Cause: MOSAIC requires Python 3.10, 3.11, or 3.12. Your system default python3 may point to an older version.

Fix:

# Check what you have
python3 --version

# Install Python 3.11 if needed (Ubuntu)
sudo add-apt-repository ppa:deadsnakes/ppa
sudo apt-get update
sudo apt-get install -y python3.11 python3.11-venv python3.11-dev

# Create venv with the correct version
python3.11 -m venv .venv

System Dependencies (Build Failures)

Several MOSAIC optional packages compile native C/C++ code and require system-level build tools. If you see Failed building wheel for ..., the fix is usually installing the missing system package.

Install all build dependencies at once:

sudo apt-get update
sudo apt-get install -y \
    build-essential \
    cmake \
    swig \
    flex \
    bison \
    libbz2-dev \
    libopenmpi-dev

box2d-py: “command ‘swig’ failed”

Error:

swig -python -c++ -IBox2D -small -O -includeall ...
error: command 'swig' failed: No such file or directory
ERROR: Failed building wheel for box2d-py

Cause: The box2d extra (LunarLander, BipedalWalker, CarRacing) requires box2d-py, which wraps C++ using SWIG. SWIG is not installed by default.

Fix:

sudo apt-get install -y swig build-essential
pip install -e ".[box2d]"

nle (NetHack): build failure

Error:

ERROR: Failed building wheel for nle

Cause: The NetHack Learning Environment (nle) compiles the NetHack game engine from C source. It requires cmake, flex, bison, and libbz2-dev.

Fix:

sudo apt-get install -y build-essential cmake flex bison libbz2-dev
pip install -e ".[nethack]"

mpi4py: “Cannot compile MPI programs”

Error:

error: Cannot compile MPI programs. Check your configuration!

or:

mpicc: No such file or directory

Cause: The XuanCe worker depends on mpi4py, which compiles against MPI libraries. The MPI compiler wrapper mpicc is not installed by default.

Fix:

# Ubuntu/Debian/WSL
sudo apt-get install -y libopenmpi-dev

# macOS
brew install open-mpi

# Then retry
pip install mpi4py

Tip

MOSAIC sets MPI4PY_RC_INITIALIZE=0 in the .env file to prevent MPI_Init() from blocking when XuanCe is imported outside of mpirun. Make sure your .env file includes this setting.

TensorBoard: “No module named ‘pkg_resources’”

Error:

ModuleNotFoundError: No module named 'pkg_resources'

This error may appear when launching TensorBoard (either from the command line or via the embedded TensorBoard viewer in the Render View).

Cause: setuptools version 78 and later removed the pkg_resources package, which was previously bundled with setuptools. TensorBoard (and several other packages) still imports pkg_resources at startup. If your environment has setuptools>=78, the import fails.

Fix: Downgrade setuptools to a version that still includes pkg_resources:

pip install "setuptools<78"

# Verify
python -c "import pkg_resources; print('pkg_resources OK')"
tensorboard --version

MOSAIC’s requirements/base.txt now pins setuptools<78 to prevent this on fresh installs.

Dependency Conflicts

smac / smacv2: protobuf version conflict

Error:

ERROR: Cannot install mosaic[smac] because these package versions have
conflicting dependencies.

The conflict is caused by:
    mosaic 0.1.0 depends on protobuf>=4.25.0
    smac 1.0.0 depends on protobuf<3.21

Cause: This is a fundamental version conflict between two parts of the stack:

  • MOSAIC uses gRPC (grpcio) for trainer <-> GUI communication. The gRPC protocol buffers are compiled using grpcio-tools and require protobuf>=4.25. The generated stubs live in gym_gui/services/trainer/proto/ and can be regenerated with:

    bash tools/generate_protos.sh

  • SMAC / SMACv2 (StarCraft Multi-Agent Challenge) uses the StarCraft II client protocol (s2clientprotocol), which declares a dependency on protobuf<3.21.

These two version ranges (>=4.25 vs <3.21) are mutually exclusive, so pip refuses to install both.

Fix: Install SMAC with --no-deps to bypass the declared constraint:

# Install SMAC without its dependency constraints
pip install --no-deps "smac @ git+https://github.com/oxwhirl/smac.git"
pip install --no-deps "smacv2 @ git+https://github.com/oxwhirl/smacv2.git"

# Manually install SMAC's other dependencies (excluding protobuf)
pip install pysc2 s2clientprotocol pygame

Note

Why does this work at runtime? The protobuf<3.21 pin in SMAC is overly conservative. The s2clientprotocol package works with protobuf>=4.25 in practice – the protobuf library maintains backwards compatibility for compiled proto messages. Hamid’s development machine runs both MOSAIC (gRPC) and SMAC successfully with protobuf>=4.25.

Proto regeneration: If you modify trainer.proto or encounter stale proto stubs, regenerate them:

source .venv/bin/activate
bash tools/generate_protos.sh

This runs grpc_tools.protoc on gym_gui/services/trainer/proto/trainer.proto and produces:

  • trainer_pb2.py: message classes

  • trainer_pb2_grpc.py: gRPC service stubs

  • trainer_pb2.pyi: type stubs

grpcio-tools: “grpcio-tools not installed”

Error (when running ``tools/generate_protos.sh``):

(.venv) $ bash ./tools/generate_protos.sh
[protos] Root: /path/to/mosaic
[protos] grpcio-tools not installed. Run: pip install grpcio-tools

Cause: The proto generation script requires grpcio-tools to compile .proto files into Python stubs. This package is listed in MOSAIC’s core dependencies (grpcio-tools>=1.60.0), but may be missing if you installed with --no-deps or if the initial pip install -e . was interrupted.

Fix:

pip install grpcio-tools

Then re-run the proto generation:

(.venv) $ bash ./tools/generate_protos.sh
[protos] Root: /path/to/mosaic
[protos] Generating trainer stubs
[protos] Done

Note

Proto stubs are checked into the repository, so you only need to regenerate them if you modify trainer.proto or if the stubs become stale after a protobuf version upgrade.

Startup Errors

Trainer daemon failed to start: “VersionError: Detected incompatible Protobuf Gencode/Runtime versions”

Error (when running ``./run.sh``):

Checking for existing trainer processes...
Starting trainer daemon...
Waiting for trainer daemon...
ERROR: Trainer daemon failed to start. Check var/logs/trainer_daemon.log

Check the log (var/logs/trainer_daemon.log):

File ".../google/protobuf/runtime_version.py", line 100, in ValidateProtobufRuntimeVersion
  _ReportVersionError(
File ".../google/protobuf/runtime_version.py", line 50, in _ReportVersionError
  raise VersionError(msg)
google.protobuf.runtime_version.VersionError: Detected incompatible Protobuf Gencode/Runtime versions when loading trainer.proto: gencode 6.31.1 runtime 5.29.6. Runtime version cannot be older than the linked gencode version.

Cause: The generated Protobuf files (trainer_pb2.py) checked into the repository were compiled with a newer version of Protobuf (e.g., 6.31.1) than the one installed in your Python environment (e.g., 5.29.6). Protobuf requires that the runtime library be at least as new as the compiler used to generate the code.

Fix: Regenerate the Protobuf files using your local environment’s tools:

source .venv/bin/activate
bash tools/generate_protos.sh

Then restart the application:

./run.sh

Trainer daemon failed to start: “No module named ‘dotenv’”

Error (when running ``./run.sh``):

Checking for existing trainer processes...
Starting trainer daemon...
Waiting for trainer daemon...
ERROR: Trainer daemon failed to start. Check var/logs/trainer_daemon.log

Check the log (var/logs/trainer_daemon.log):

google.protobuf.runtime_version.VersionError: Detected incompatible Protobuf
Gencode/Runtime versions when loading trainer.proto: gencode 6.31.1 runtime 5.29.6.

Cause: The generated Protobuf files (trainer_pb2.py) checked into the repository were compiled with a newer version of Protobuf than the one installed in your environment.

Fix: Regenerate the Protobuf files using your local environment’s tools:

source .venv/bin/activate
bash tools/generate_protos.sh
./run.sh

Trainer daemon: “No module named ‘dotenv’”

Error (in ``var/logs/trainer_daemon.log``):

ModuleNotFoundError: No module named 'dotenv'

Cause: The core MOSAIC package was not installed.

Fix:

source .venv/bin/activate
pip install -e .
./run.sh

Missing .env file

Symptom: The GUI launches but uses incorrect defaults, or the trainer daemon fails to connect with unexpected settings.

Cause: MOSAIC reads configuration from a .env file in the project root. This file is gitignored (never committed) and must be created locally from the provided template.

Fix:

cp .env.example .env

The .env.example file is fully documented with all available settings. Key variables you should review:

Variable

Purpose

QT_API=PyQt6

Required. Must be PyQt6 with exact capitalization for qasync

PLATFORM=ubuntu

Platform identifier (ubuntu for both native Ubuntu and WSL)

MUJOCO_GL=egl

MuJoCo rendering backend (egl for headless, glfw for display)

MPI4PY_RC_INITIALIZE=0

Required. Prevents MPI deadlock when XuanCe is imported

PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python

Required for SMAC. Allows protobuf 5.x to read SMAC’s 3.x pb2 files

GRPC_VERBOSITY=ERROR

gRPC log level (ERROR recommended; DEBUG is very noisy)

WANDB_API_KEY

Weights & Biases API key (get from https://wandb.ai/authorize)

OPENROUTER_API_KEY

OpenRouter LLM API key (get from https://openrouter.ai/keys)

HF_TOKEN

HuggingFace token for gated models

Warning

The .env file contains API keys and secrets. It is gitignored by default. Never commit it to version control.

Startup crash: “No module named ‘torch’” / “No module named ‘psutil’”

Error:

ModuleNotFoundError: No module named 'torch'

Or:

ModuleNotFoundError: No module named 'psutil'

Cause: app.py imports torch, psutil, and pynvml unconditionally at startup for GPU detection and system info display.

Fix:

pip install torch psutil pynvml

Note

For CUDA GPU support, install PyTorch with the CUDA index:

pip install torch --index-url https://download.pytorch.org/whl/cu121

Startup crash: “No module named ‘stockfish’” (misleading Qt error)

Error:

[gym_gui] Qt bindings not available. Install qtpy and a Qt backend
(PyQt5/PyQt6/PySide2/PySide6): No module named 'stockfish'

Cause: The stockfish Python package was imported at module level. The missing import was caught by a generic handler, producing a misleading “Qt bindings not available” message.

Fix: This was a bug (fixed). The import was moved to a lazy location. If you encounter this on an older version:

pip install stockfish

Startup crash: “No module named ‘syllabus’”

Error:

ModuleNotFoundError: No module named 'syllabus'

Cause: The CleanRL worker uses Syllabus-RL for curriculum learning. Syllabus is a local 3rd-party package.

Fix:

# If the directory is empty, initialize the submodule
git submodule update --init 3rd_party/Syllabus

# Install in editable mode
pip install -e 3rd_party/Syllabus

Worker load warnings (non-fatal)

During startup, MOSAIC discovers workers via entry points. Missing optional workers produce warnings but do not prevent the GUI from launching:

Failed to load worker 'cleanrl' from entry point: No module named 'scipy'
Failed to load worker 'ray' from entry point: No module named 'tree'

These are informational – install the relevant extras to enable each worker:

# CleanRL worker
pip install -e 3rd_party/Syllabus
pip install matplotlib scipy

# Ray/RLlib worker
pip install -e ".[ray-rllib]"

Runtime Errors

CleanRL training crash: “No module named ‘tyro’”

Error (in ``var/logs/trainer_daemon.log``):

ModuleNotFoundError: No module named 'tyro'

Cause: The CleanRL worker requires tyro, wandb, tensorboard, tenacity, and moviepy.

Fix: Install the full CleanRL extras:

pip install -e ".[cleanrl]"

pynvml deprecation warning

Warning (at startup):

FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead.

Fix: Uninstall the deprecated wrapper:

pip uninstall -y pynvml
pip install nvidia-ml-py

gRPC: “Log level DEBUG is not suitable for production”

Warning (when running ``./run.sh``):

WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
W0000 00:00:1771284312.764300  84092 log.cc:110] Log level DEBUG is not suitable
for production.

This is harmless. Set GRPC_VERBOSITY=ERROR in .env to suppress it.

resource_tracker – “leaked shared_memory objects”

Warning (on shutdown):

resource_tracker: There appear to be 1 leaked shared_memory objects to clean up at shutdown

This is harmless. The tracker cleans up the leaked segments automatically.

Syllabus / CleanRL: missing transitive dependencies

Error:

ModuleNotFoundError: No module named 'matplotlib'

or:

Failed to load worker 'cleanrl' from entry point: No module named 'scipy'

Fix:

pip install matplotlib scipy

Stockfish not found (Chess Engine)

Error:

stockfish.StockfishException: Stockfish binary not found

Fix:

# Ubuntu/Debian/WSL
sudo apt-get install -y stockfish

# macOS
brew install stockfish

CleanRL PPO crash: “mat1 and mat2 shapes cannot be multiplied” (Discrete obs)

Error (when training PPO on FrozenLake-v1, Taxi-v3, CliffWalking-v0, etc.):

RuntimeError: mat1 and mat2 shapes cannot be multiplied (1x4 and 1x64)

Cause: Environments with Discrete observation spaces (e.g. FrozenLake-v1 has Discrete(16)) return a single integer as the observation. CleanRL’s MLPAgent computes the input dimension as prod(obs_space.shape). For Discrete(n) the shape is () so prod(()) == 1, causing a shape mismatch.

Fix: This is now handled automatically. The make_env factory detects Discrete observation spaces and wraps them with one-hot encoding. For example, FrozenLake-v1’s Discrete(16) becomes Box(0, 1, (16,), float32).

Affected environments: Any environment with a Discrete observation space, including FrozenLake-v1, Taxi-v3, and CliffWalking-v0. Environments with Box observation spaces (CartPole-v1, LunarLander-v2, etc.) are unaffected.

Quick Reference: All System Dependencies

# Install everything at once
sudo apt-get update
sudo apt-get install -y \
    build-essential \
    cmake \
    swig \
    flex \
    bison \
    libbz2-dev \
    libopenmpi-dev \
    libegl1 libgl1 libopengl0 libxkbcommon0 \
    libnss3 libnspr4 libasound2 \
    libxcomposite1 libxdamage1 libxkbfile1 \
    libxcb-cursor0 \
    fontconfig fonts-dejavu-extra fonts-liberation fonts-noto \
    stockfish \
    xvfb

System Package

Required By

Error Without It

build-essential

Most native extensions

gcc: command not found

cmake

nle (NetHack)

Failed building wheel for nle

swig

box2d-py

command 'swig' failed

flex, bison

nle (NetHack)

Failed building wheel for nle

libbz2-dev

nle (NetHack)

Failed building wheel for nle

libopenmpi-dev

mpi4py (XuanCe)

Cannot compile MPI programs

libegl1, libgl1, libopengl0

PyQt6 (OpenGL/EGL)

libGL.so.1: cannot open shared object file

libxkbcommon0

PyQt6 (keyboard handling)

libxkbcommon.so.0: cannot open shared object file

libnss3, libnspr4

PyQt6-WebEngine (TensorBoard viewer)

libsmime3.so: cannot open shared object file

libasound2

PyQt6-WebEngine (audio backend)

libasound.so.2: cannot open shared object file

libxcomposite1

PyQt6-WebEngine (X11 compositing)

libXcomposite.so.1: cannot open shared object file

libxdamage1

PyQt6-WebEngine (X11 damage tracking)

libXdamage.so.1: cannot open shared object file

libxkbfile1

PyQt6-WebEngine (keyboard layout files)

libxkbfile.so.1: cannot open shared object file

libxcb-cursor0

PyQt6 (X11 cursor support, required since Qt 6.5)

Could not load the Qt platform plugin "xcb"

fontconfig

PyQt6 (font discovery)

GUI launches but all text is invisible

fonts-dejavu-extra, fonts-liberation

System fonts for Qt rendering

GUI launches but all text is invisible

stockfish

Chess engine

Stockfish binary not found

xvfb

Headless display (optional)

Could not find platform plugin "xcb"