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docs/services/individual/jellyfin-olares.md
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docs/services/individual/jellyfin-olares.md
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# Jellyfin on Olares
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## Service Overview
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| Property | Value |
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|----------|-------|
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| **Host** | olares (192.168.0.145) |
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| **Platform** | Olares Marketplace (K3s) |
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| **Namespace** | `jellyfin-vishinator` |
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| **Image** | `docker.io/beclab/jellyfin-jellyfin:10.11.6` |
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| **LAN Access** | `http://192.168.0.145:30096` |
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| **Olares Proxy** | `https://7e89d2a1.vishinator.olares.com` |
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| **GPU** | NVIDIA RTX 5090 Max-Q (24GB) — hardware transcoding |
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## Purpose
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Jellyfin media server on Olares with NVIDIA GPU hardware transcoding and NFS media from Atlantis. Replaces a previous Plex attempt (Plex had issues with Olares proxy auth and indirect connections from desktop apps).
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## Architecture
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```
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Atlantis NAS (192.168.0.200)
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└─ NFS: /volume1/data/media
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└─ mounted on olares at /mnt/atlantis_media (fstab)
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└─ hostPath volume in Jellyfin pod at /media (read-only)
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Olares K3s cluster
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└─ jellyfin-vishinator namespace
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└─ Deployment: jellyfin (2 containers)
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├─ jellyfin (main app, port 8096)
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└─ olares-envoy-sidecar (Olares proxy)
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```
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## Deployment Patches
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The Jellyfin app was installed from the Olares marketplace, then patched with `kubectl patch` for:
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### 1. NFS Media Mount
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```bash
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kubectl patch deployment jellyfin -n jellyfin-vishinator --type=json -p '[
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{"op":"add","path":"/spec/template/spec/volumes/-","value":{"name":"atlantis-media","hostPath":{"path":"/mnt/atlantis_media","type":"Directory"}}},
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{"op":"add","path":"/spec/template/spec/containers/0/volumeMounts/-","value":{"name":"atlantis-media","mountPath":"/media","readOnly":true}}
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]'
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```
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### 2. GPU Access (NVIDIA runtime + env vars)
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```bash
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kubectl patch deployment jellyfin -n jellyfin-vishinator --type=json -p '[
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{"op":"add","path":"/spec/template/spec/REDACTED_APP_PASSWORD","value":"nvidia"},
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{"op":"add","path":"/spec/template/metadata/annotations","value":{"applications.app.bytetrade.io/gpu-inject":"true"}},
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{"op":"replace","path":"/spec/template/spec/containers/0/resources/limits","value":{"cpu":"4","memory":"8Gi"}},
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{"op":"add","path":"/spec/template/spec/containers/0/env/-","value":{"name":"NVIDIA_VISIBLE_DEVICES","value":"all"}},
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{"op":"add","path":"/spec/template/spec/containers/0/env/-","value":{"name":"NVIDIA_DRIVER_CAPABILITIES","value":"all"}}
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]'
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```
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**Important**: Do NOT request `nvidia.com/gpu` or `nvidia.com/gpumem` resources. HAMI's vGPU interceptor (`libvgpu.so` injected via `/etc/ld.so.preload`) causes ffmpeg to segfault (exit code 139) during CUDA transcode operations (especially `tonemap_cuda`). By omitting GPU resource requests, HAMI doesn't inject its interceptor, and Jellyfin gets direct GPU access via the nvidia runtime class.
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### 3. HAMI Memory Override (if GPU resources are requested)
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If you do need HAMI GPU scheduling (e.g., to share GPU fairly with LLM workloads), override the memory limit:
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```bash
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kubectl patch deployment jellyfin -n jellyfin-vishinator --type=json -p '[
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{"op":"add","path":"/spec/template/spec/containers/0/env/-","value":{"name":"CUDA_DEVICE_MEMORY_LIMIT_0","value":"8192m"}}
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]'
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```
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Note: This alone does NOT fix the segfault — `libvgpu.so` in `/etc/ld.so.preload` is the root cause.
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## LAN Access
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Olares's envoy proxy adds ~100ms per request, causing buffering on high-bitrate streams. Direct LAN access bypasses this.
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### NodePort Service
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```yaml
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apiVersion: v1
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kind: Service
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metadata:
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name: jellyfin-lan
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namespace: jellyfin-vishinator
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spec:
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type: NodePort
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externalIPs:
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- 192.168.0.145
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selector:
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app: jellyfin
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ports:
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- port: 8096
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targetPort: 8096
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nodePort: 30096
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name: jellyfin-web
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```
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### Calico GlobalNetworkPolicy
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Olares auto-creates restrictive NetworkPolicies (`app-np`) that block external LAN traffic and cannot be modified (admission webhook reverts changes). A Calico GlobalNetworkPolicy bypasses this:
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```yaml
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apiVersion: crd.projectcalico.org/v1
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kind: GlobalNetworkPolicy
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metadata:
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name: allow-lan-to-jellyfin
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spec:
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order: 100
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selector: app == 'jellyfin'
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types:
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- Ingress
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ingress:
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- action: Allow
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source:
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nets:
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- 192.168.0.0/24
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```
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This is the **correct** approach for LAN access on Olares. Alternatives that don't work:
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- Patching `app-np` NetworkPolicy — webhook reverts it
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- Adding custom NetworkPolicy — webhook deletes it
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- iptables rules on Calico chains — Calico reconciles and removes them
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## Jellyfin Settings
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### Hardware Transcoding
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In Dashboard > Playback > Transcoding:
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- **Hardware acceleration**: NVIDIA NVENC
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- **Hardware decoding**: All codecs enabled (H264, HEVC, VP9, AV1, etc.)
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- **Enhanced NVDEC**: Enabled
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- **Hardware encoding**: Enabled
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- **HEVC encoding**: Allowed
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- **AV1 encoding**: Allowed (RTX 5090 supports AV1 encode)
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- **Tone mapping**: Enabled (bt2390, HDR→SDR on GPU)
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### Library Paths
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| Library | Path |
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|---------|------|
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| Movies | `/media/movies` |
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| TV Shows | `/media/tv` |
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| Anime | `/media/anime` |
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| Music | `/media/music` |
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| Audiobooks | `/media/audiobooks` |
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## NFS Mount
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```
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# /etc/fstab on olares
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192.168.0.200:/volume1/data/media /mnt/atlantis_media nfs rw,async,hard,intr,rsize=131072,wsize=131072 0 0
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```
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### Performance
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- Sequential read: 180-420 MB/s (varies by cache state)
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- More than sufficient for multiple 4K remux streams (~100 Mbps each)
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## Known Issues
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- **Patches lost on Olares app update** — if Jellyfin is updated via the marketplace, the NFS mount and GPU patches need to be re-applied
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- **HAMI vGPU causes ffmpeg segfaults** — do NOT request `nvidia.com/gpu` resources; use nvidia runtime class without HAMI resource limits
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- **Olares proxy buffering** — use direct LAN access (`http://192.168.0.145:30096`) for streaming, not the Olares proxy URL
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- **GPU shared with Ollama** — both Jellyfin and Ollama access the full 24GB VRAM without HAMI partitioning; heavy concurrent use (4K transcode + large model inference) may cause OOM
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## Maintenance
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### Check status
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```bash
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kubectl get pods -n jellyfin-vishinator
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kubectl exec -n jellyfin-vishinator deploy/jellyfin -c jellyfin -- nvidia-smi
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```
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### Re-apply patches after update
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Run the kubectl patch commands from the Deployment Patches section above.
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### Check transcoding
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```bash
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# Is ffmpeg using GPU?
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kubectl exec -n jellyfin-vishinator deploy/jellyfin -c jellyfin -- nvidia-smi
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# Look for ffmpeg process with GPU memory usage
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# Check transcode logs
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kubectl logs -n jellyfin-vishinator deploy/jellyfin -c jellyfin | grep ffmpeg | tail -5
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```
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---
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**Last Updated**: 2026-04-03
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