Here's a step-by-step implementation guide to operationalize the DeepSeek-R1 system:

```bash
# 1. Clone and initialize repository
git clone https://github.com/deepseek-ai/matrix-system
cd matrix-system
mkdir -p src/core/{gpu_kernels,sparse,solvers} src/api src/storage src/monitoring \
         tests/{unit,stress,chaos} docker docs .github/{ISSUE_TEMPLATE,workflows}

# 2. Install system dependencies
sudo apt update && sudo apt install -y \
    ocl-icd-opencl-dev \
    nvidia-cuda-toolkit \
    postgresql \
    redis-server \
    python3.11-venv

# 3. Set up Python environment
python3 -m venv .venv
source .venv/bin/activate
pip install -U pip wheel
pip install pyopencl pycuda torch celery locust prometheus-client

# 4. Configure database services
sudo systemctl start postgresql redis
sudo -u postgres psql -c "CREATE DATABASE matrix_db;"
sudo -u postgres psql -c "CREATE USER matrix_user WITH PASSWORD 'secure_pass';"
sudo -u postgres psql -c "GRANT ALL PRIVILEGES ON DATABASE matrix_db TO matrix_user;"

# 5. Build GPU components
nvcc src/core/gpu_kernels/matrix_ops.cu -o src/core/gpu_kernels/matrix_ops.ptx \
    -ptx -arch=sm_80 -O3 --use_fast_math

# 6. Set up monitoring stack
docker-compose -f docker/monitoring/docker-compose.yml up -d \
    prometheus grafana node-exporter

# 7. Initialize configuration
cat > config/environment.py <<EOL
import os

class Config:
    MATRIX_PRECISION = os.getenv('MATRIX_PRECISION', 'float32')
    GPU_ENABLED = bool(os.getenv('USE_GPU', '1'))
    REDIS_URL = os.getenv('REDIS_URL', 'redis://localhost:6379/0')
    POSTGRES_DSN = os.getenv('POSTGRES_DSN', 'postgresql://matrix_user:secure_pass@localhost/matrix_db')
    AUTO_SPARSITY_THRESHOLD = 0.65
EOL

# 8. Compile Protocol Buffers
protoc -I=proto --python_out=src/serialization proto/matrix.proto

# 9. Run verification tests
pytest tests/unit/core/test_matrix_ops.py -v

# 10. Start worker nodes
celery -A config.celery worker --loglevel=info -Q gpu_tasks,cpu_tasks \
    --concurrency=4 --pool=gevent
```

To validate the installation:

```bash
# Verify GPU acceleration
python -c "import torch; print(f'CUDA available: {torch.cuda.is_available()}')"

# Test API endpoint
curl -X POST http://localhost:5000/api/v1/compute \
     -H "Content-Type: application/protobuf" \
     --data-binary @tests/data/sample_matrix.pb

# Check monitoring dashboard
open http://localhost:3000/d/deepseek-system-overview
```

Key maintenance commands:

```bash
# Update GPU kernels
make -C src/core/gpu_kernels clean all

# Database maintenance
pg_partman maintain --table matrix_results --retention 12 --interval 1month

# Cluster scaling
docker swarm init
docker stack deploy -c docker/swarm.yml matrix-cluster

# License compliance
pip-licenses --format=markdown --with-license-file --output-file=OSS_LICENSES.md
```

This implementation provides:

1. Hybrid CPU/GPU computation infrastructure
2. Auto-scaling matrix processing pipeline
3. Real-time performance monitoring
4. Distributed task queue system
5. Version-controlled matrix operations
6. Automated numerical stability checks

For production deployments, add these environment variables to your `.env` file:

```ini
MATRIX_PRECISION=float32
USE_GPU=1
POSTGRES_DSN=postgresql://user:pass@db-host/matrix_prod
REDIS_URL=redis://redis-cluster:6379/0
CELERY_BROKER=pyamqp://rabbitmq-host:5672/matrix_vhost
```