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ConfidentialMind Container GPU Monitor (CM PurplePill)

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# ConfidentialMind Container GPU Monitor (CM PurplePill)

A GPU metrics exporter, with Kubernetes pod-level GPU usage tracking, not relying on explicit `<vendor.com>/gpu` resource declarations.

## Features

- Collects GPU metrics using NVML
- Maps GPU processes to Kubernetes pods
- Exposes metrics in Prometheus format
- No dependencies beyond Python 3.7+ and NVML
- Compatible with all K8s distributions
- Tracks GPU usage by pods **without** explicit `nvidia.com/gpu` resource requests

## Metrics

The exporter provides the following metrics:

- `CM_PURPLEPILL_GPU_MEMORY_TOTAL_MIB` - Total GPU memory in MiB
- `CM_PURPLEPILL_GPU_MEMORY_USED_TOTAL_MIB` - Total used GPU memory in MiB
- `CM_PURPLEPILL_GPU_MEMORY_FREE_MIB` - Free GPU memory in MiB
- `CM_PURPLEPILL_GPU_UTILIZATION` - GPU utilization percentage
- `CM_PURPLEPILL_GPU_MEMORY_USED_POD_MIB` - Pod GPU memory usage in MiB

## Deployment Methods

### 1. Kubernetes Deployment (Recommended)

This is the primary deployment method for CM PurplePill, designed to monitor GPU usage across your Kubernetes cluster.

#### Prerequisites

- Kubernetes cluster with NVIDIA GPUs (K3s, AKS, GKE, or other K8s distributions)
- NVIDIA drivers installed on worker nodes
- Prometheus monitoring setup (preferably using Prometheus Operator)

#### DaemonSet Deployment

Deploy CM PurplePill as a DaemonSet that runs on every node with an NVIDIA GPU:

```yaml
apiVersion: apps/v1
kind: DaemonSet
metadata:
  name: cm-purplepill
  namespace: monitoring
  labels:
    app: cm-purplepill
spec:
  selector:
    matchLabels:
      app: cm-purplepill
  template:
    metadata:
      labels:
        app: cm-purplepill
    spec:
      hostPID: true
      containers:
      - name: cm-purplepill
        image: confidentialmindpub.azurecr.io/cm-purplepill:x.y.z # <-- set the actual tag here
        imagePullPolicy: IfNotPresent
        ports:
        - name: metrics
          containerPort: 9531
          protocol: TCP
        resources:
          requests:
            memory: "128Mi"
            cpu: "100m"
        volumeMounts:
        - name: log
          mountPath: /var/log/cm-purplepill
        env:
        - name: CM_PURPLEPILL_HOSTNAME_OVERRIDE
          valueFrom:
            fieldRef:
              fieldPath: spec.nodeName
        securityContext:
          privileged: true
      volumes:
      - name: log
        emptyDir: {}
      nodeSelector:
        feature.node.kubernetes.io/pci-10de.present: "true"  # Target NVIDIA GPU nodes
      tolerations:
      - effect: NoSchedule
        operator: Exists
```

#### Prometheus Configuration

**IMPORTANT! All the nodes should have unique names for Prometheus scraper to be able to collect GPU metrics from them.**

For Prometheus Operator (preferred approach), add this ScrapeConfig:

```yaml
apiVersion: monitoring.coreos.com/v1alpha1
kind: ScrapeConfig
metadata:
  labels:
    release: prometheus #IMPORTANT! check your Prometheus 
                        #"scrapeConfigSelector" and "scrapeConfigNamespaceSelector" current values
  name: cm-purplepill
  namespace: monitoring
spec:
  scrapeInterval: 15s
  kubernetesSDConfigs:
    - role: Pod
  relabelings:
    - sourceLabels: [__meta_kubernetes_pod_label_app]
      regex: 'cm-purplepill'
      action: keep
    - sourceLabels: [__meta_kubernetes_namespace]
      regex: 'monitoring'
      action: keep
    - sourceLabels: [__meta_kubernetes_pod_container_port_name]
      regex: 'metrics'
      action: keep
    - sourceLabels: [__meta_kubernetes_pod_node_name]
      targetLabel: node
    - sourceLabels: [__meta_kubernetes_pod_name]
      targetLabel: pod
    - action: labelmap
      regex: __meta_kubernetes_pod_label_(.+)
```

See [ScrapeConfig CRD](https://prometheus-operator.dev/docs/developer/scrapeconfig/) for more details and options.

### 2. Docker or another OCI-compatible container

For environments where you want to run CM PurplePill directly on a host using Docker or another OCI-compatible container runtime.

#### Environment Variables

The Docker container can be configured using the following environment variables:

| Environment Variable           | Default     | Description |
|--------------------------------|-------------|-------------|
| `CM_PURPLEPILL_PORT`              | `9531`      | Port to expose |
| `CM_PURPLEPILL_INTERVAL`          | `15`        | Interval between metric collections in seconds |
| `CM_PURPLEPILL_HOSTNAME_OVERRIDE` | OS hostname | Override the Hostname used in metrics labels. |

#### Example Usage

```bash
# Run with default settings
docker run -d --gpus all -p 9531:9531 confidentialmind/cm-purplepill

# Run with custom settings
docker run -d --gpus all \
  -p 8080:8080 \
  -e CM_PURPLEPILL_PORT=8080 \
  -e CM_PURPLEPILL_INTERVAL=30 \
  -e CM_PURPLEPILL_HOSTNAME_OVERRIDE="gpu-host-01" \
  confidentialmind/cm-purplepill
```

### 3. Direct Host Installation

If you prefer to install CM PurplePill directly on the host without containerization:

#### Prerequisites

- Python 3.7 or higher
- NVIDIA drivers with `nvidia-smi` tool

#### Pip Installation
- directly from GitHub
```bash
# Install directly from git repository
pip install git+https://github.com/ConfidentialMind/cm-purplepill.git

# Install systemd service (may need to download separately)
curl -o /etc/systemd/system/cm-purplepill.service https://raw.githubusercontent.com/ConfidentialMind/cm-purplepill/main/systemd/cm-purplepill.service
```

- by manual cloning from GitHub
```bash
# Clone repository
git clone https://github.com/ConfidentialMind/cm-purplepill.git
cd cm-purplepill

# Install dependencies
pip install .

# Install systemd service
cp systemd/cm-purplepill.service /etc/systemd/system/
systemctl daemon-reload
systemctl enable cm-purplepill
systemctl start cm-purplepill
```

#### Verification

Check if the exporter is running:

```bash
systemctl status cm-purplepill
curl http://localhost:9531/metrics
```

#### Uninstallation

##### Automatic Uninstallation
```bash
# Stop and disable the systemd service
sudo systemctl stop cm-purplepill
sudo systemctl disable cm-purplepill

# Remove the systemd service file
sudo rm /etc/systemd/system/cm-purplepill.service
sudo systemctl daemon-reload

# Uninstall the package
pip uninstall cm-purplepill
```

##### Manual Uninstallation (Cleanup)
```bash
# Stop and disable the systemd service
sudo systemctl stop cm-purplepill
sudo systemctl disable cm-purplepill

# Remove the systemd service file
sudo rm /etc/systemd/system/cm-purplepill.service
sudo systemctl daemon-reload

# Find and remove Python package files
# The location depends on your Python environment
# Common locations:
sudo rm -rf /usr/local/lib/python*/dist-packages/cm_purplepill*
# OR
sudo rm -rf ~/.local/lib/python*/site-packages/cm_purplepill*

# Remove the cmpp entry point
sudo rm /usr/local/bin/cmpp
# OR
sudo rm ~/.local/bin/cmpp

# Clean up log files and temporary data
sudo rm -f /var/log/cm-purplepill.log
sudo rm -f /tmp/cmpp_metrics.prom
sudo rm -f /tmp/cmpp-exporter.pid
```

### 4. LXC or Other Non-OCI Container Environments

For LXC or other non-OCI container environments, you have two options:

**Note:** Similar to Docker deployment, the code would need modifications to correctly identify and label GPU processes for non-Kubernetes environments. Specifically, the container identification logic in `pod_info.py` would need to be extended to properly recognize LXC containers.

#### Option A: Direct Host Installation

Install CM PurplePill directly on the host as described in the "Direct Host Installation" section above. The service will be able to monitor GPUs across all containers.

#### Option B: Dockerfile-Based Approach

Create a container environment similar to the Dockerfile provided with CM PurplePill:

1. Start with a base image that includes CUDA libraries
2. Install Python 3.7+
3. Install CM PurplePill
4. Ensure the container has access to NVIDIA devices and can run `nvidia-smi`

## Docker build (multiarch)

```bash
docker run --privileged --rm tonistiigi/binfmt --install all
docker buildx create --use --name multi-arch-builder
docker buildx build --platform linux/amd64,linux/arm64 .
# or combined with "push"
docker buildx build --platform linux/amd64,linux/arm64 . --push --tag <registry>/cm-purplepill:x.x.x
```

## Configuration

The exporter can be configured using command-line arguments:

```
Usage:
    cmpp [options]

Options:
    --port PORT             Port to expose HTTP metrics server [default: 9531]
    --interval SECONDS      Interval between metric collections [default: 15]
    --log-file FILE         Log file path [default: /var/log/cm-purplepill.log]
    --metrics-file FILE     File to store metrics [default: /tmp/cmpp_metrics.prom]
    --help                  Show this help message and exit
    --version               Show version and exit
```

## Example PromQL Queries

```
# Total GPU memory per node
sum by (instance) (CM_PURPLEPILL_GPU_MEMORY_TOTAL_MIB)

# GPU utilization per pod
CM_PURPLEPILL_GPU_MEMORY_USED_POD_MIB

# Percentage of GPU memory used by pod
CM_PURPLEPILL_GPU_MEMORY_USED_POD_MIB / on (gpu, UUID) CM_PURPLEPILL_GPU_MEMORY_TOTAL_MIB * 100
```

## Troubleshooting

Check the logs:

```bash
# For Kubernetes deployment
kubectl logs -n monitoring daemonset/cm-purplepill

# For systemd service
journalctl -u cm-purplepill
# or
cat /var/log/cm-purplepill.log

# For Docker container
docker logs <container_id>
```

## Comparing CM PurplePill vs. NVIDIA DCGM Exporter

See [CMPurplePill-vs-DCGM.md](CMPurplePill-vs-DCGM.md) for a detailed comparison between CM PurplePill and NVIDIA's DCGM Exporter.

Key advantages of CM PurplePill:
1. Tracks GPU usage by containers (Kubernetes pods, Docker containers) that **don't explicitly request GPU resources**
2. Works across multiple container platforms and orchestration systems
3. Lightweight implementation with minimal dependencies
4. Flexibility to extend with additional metrics from nvidia-smi
5. Can be adapted for non-NVIDIA GPUs by modifying the data collection layer

## Future Enhancements (Coming Soon)

- Enable stdout in docker
- Add ConfidentialMind logo to stdout and logs
- Add CM PurplePill name to stdout and logs
- Additional GPU metrics from nvidia-smi

## License

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

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