Can't search for images and face recognition does not work

[ScribbleGhost]

I have searched the existing issues, both open and closed, to make sure this is not a duplicate report.

• Yes

The bug

Since I upgraded to version 2.0.1 Immich fails to recognize faces and fails to search...

Let's start with the failed search:

No matter what I am searching for I get this error:

On mobile:

Here is the log from Immich:

The OS that Immich Server is running on

Unraid 7.1.4

Version of Immich Server

v2.0.1

Version of Immich Mobile App

v2.0.1 build.3021

Platform with the issue

• Server
• Web
• Mobile

Device make and model

Sansung Galaxy S23

Your docker-compose.yml content

<?xml version="1.0"?>
<Container version="2">
  <Name>immich</Name>
  <Repository>ghcr.io/imagegenius/immich:openvino</Repository>
  <Registry>https://github.com/orgs/imagegenius/packages/container/package/immich</Registry>
  <Network>bridge</Network>
  <MyIP/>
  <Shell>bash</Shell>
  <Privileged>false</Privileged>
  <Support>https://github.com/imagegenius/docker-immich/issues/new/choose</Support>
  <Project>https://immich.app/</Project>
  <Overview>Immich is a high performance self-hosted photo and video backup solution.</Overview>
  <Category>Backup: Cloud: MediaApp:Photos spotlight:</Category>
  <WebUI>http://[IP]:[PORT:8080]</WebUI>
  <TemplateURL>https://raw.githubusercontent.com/imagegenius/templates/master/unraid/immich.xml</TemplateURL>
  <Icon>https://raw.githubusercontent.com/imagegenius/templates/main/unraid/img/immich.png</Icon>
  <ExtraParams/>
  <PostArgs/>
  <CPUset/>
  <DateInstalled>1755410405</DateInstalled>
  <DonateText/>
  <DonateLink/>
  <Requires>brPostgreSQL 14/15/16/17 with (https://github.com/tensorchord/VectorChord), RedisbrThis container requires an external application to be run separately.brYou can also use a docker mods to start redis within the container,brSet `DOCKER_MODS=imagegenius/mods:universal-redis`, and `REDIS_HOSTNAME` to `localhost`.br&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;&amp;amp;nbsp;</Requires>
  <Config Name="WebUI" Target="8080" Default="8080" Mode="tcp" Description="WebUI Port" Type="Port" Display="always" Required="true" Mask="false">3030</Config>
  <Config Name="Path: /photos" Target="/photos" Default="" Mode="rw" Description="Contains all the photos uploaded to Immich" Type="Path" Display="always" Required="true" Mask="false">/mnt/user/immich/</Config>
  <Config Name="Path: /libraries" Target="/libraries" Default="" Mode="rw" Description="External libraries to track assets stored outside of Immich" Type="Path" Display="always" Required="false" Mask="false"/>
  <Config Name="DB_HOSTNAME" Target="DB_HOSTNAME" Default="192.168.1.x" Mode="{3}" Description="PostgreSQL Host" Type="Variable" Display="always" Required="true" Mask="false">192.168.1.110</Config>
  <Config Name="DB_USERNAME" Target="DB_USERNAME" Default="postgres" Mode="{3}" Description="PostgreSQL Username" Type="Variable" Display="always" Required="true" Mask="false">postgres</Config>
  <Config Name="DB_PASSWORD" Target="DB_PASSWORD" Default="postgres" Mode="{3}" Description="PostgreSQL Password" Type="Variable" Display="always" Required="true" Mask="true">postgres</Config>
  <Config Name="DB_DATABASE_NAME" Target="DB_DATABASE_NAME" Default="immich" Mode="{3}" Description="PostgreSQL Database Name" Type="Variable" Display="always" Required="true" Mask="false">immich</Config>
  <Config Name="REDIS_HOSTNAME" Target="REDIS_HOSTNAME" Default="192.168.1.x" Mode="{3}" Description="Valkey/Redis Hostname" Type="Variable" Display="always" Required="true" Mask="false">localhost</Config>
  <Config Name="DB_PORT" Target="DB_PORT" Default="5432" Mode="{3}" Description="PostgreSQL Port" Type="Variable" Display="always" Required="false" Mask="false">5433</Config>
  <Config Name="REDIS_PORT" Target="REDIS_PORT" Default="6379" Mode="{3}" Description="Valkey/Redis Port" Type="Variable" Display="always" Required="false" Mask="false">6379</Config>
  <Config Name="REDIS_PASSWORD" Target="REDIS_PASSWORD" Default="" Mode="{3}" Description="Valkey/Redis password" Type="Variable" Display="always" Required="false" Mask="true">shes_sur_GROR_max_buy</Config>
  <Config Name="SERVER_HOST" Target="SERVER_HOST" Default="0.0.0.0" Mode="{3}" Description="Immich server host" Type="Variable" Display="always" Required="false" Mask="false">0.0.0.0</Config>
  <Config Name="SERVER_PORT" Target="SERVER_PORT" Default="8080" Mode="{3}" Description="Immich server port" Type="Variable" Display="always" Required="false" Mask="false">8080</Config>
  <Config Name="MACHINE_LEARNING_HOST" Target="MACHINE_LEARNING_HOST" Default="0.0.0.0" Mode="{3}" Description="Immich machine-learning host" Type="Variable" Display="always" Required="false" Mask="false">0.0.0.0</Config>
  <Config Name="MACHINE_LEARNING_PORT" Target="MACHINE_LEARNING_PORT" Default="3003" Mode="{3}" Description="Immich machine-learning port" Type="Variable" Display="always" Required="false" Mask="false">3003</Config>
  <Config Name="MACHINE_LEARNING_WORKERS" Target="MACHINE_LEARNING_WORKERS" Default="1" Mode="{3}" Description="Machine learning workers" Type="Variable" Display="always" Required="false" Mask="false">1</Config>
  <Config Name="MACHINE_LEARNING_WORKER_TIMEOUT" Target="MACHINE_LEARNING_WORKER_TIMEOUT" Default="120" Mode="{3}" Description="Machine learning worker timeout" Type="Variable" Display="always" Required="false" Mask="false">120</Config>
  <Config Name="Redis" Target="DOCKER_MODS" Default="" Mode="" Description="" Type="Variable" Display="always" Required="false" Mask="false">imagegenius/mods:universal-redis</Config>
  <Config Name="Direct Rendering Infrastructure (Intel Quicksync)" Target="/dev/dri" Default="" Mode="" Description="" Type="Device" Display="always" Required="false" Mask="false">/dev/dri</Config>
  <Config Name="Appdata" Target="/config" Default="/mnt/user/appdata/immich" Mode="rw" Description="Contains machine learning models (~1.5GB with default models)" Type="Path" Display="advanced" Required="true" Mask="false">/mnt/user/appdata/immich</Config>
  <Config Name="PUID" Target="PUID" Default="99" Mode="{3}" Description="UID for permissions. Do not change unless you know what you're doing." Type="Variable" Display="advanced-hide" Required="true" Mask="false">99</Config>
  <Config Name="PGID" Target="PGID" Default="100" Mode="{3}" Description="GID for permissions. Do not change unless you know what you're doing." Type="Variable" Display="advanced-hide" Required="true" Mask="false">100</Config>
  <Config Name="UMASK" Target="UMASK" Default="022" Mode="{3}" Description="UMASK for newly created files. Do not change unless you know what you're doing." Type="Variable" Display="advanced-hide" Required="false" Mask="false">022</Config>
  <TailscaleEnabled>true</TailscaleEnabled>
  <TailscaleIsExitNode>false</TailscaleIsExitNode>
  <TailscaleHostname>immich</TailscaleHostname>
  <TailscaleExitNodeIP/>
  <TailscaleSSH>true</TailscaleSSH>
  <TailscaleUserspaceNetworking>false</TailscaleUserspaceNetworking>
  <TailscaleLANAccess>false</TailscaleLANAccess>
  <TailscaleServe>serve</TailscaleServe>
  <TailscaleWebUI>https://[hostname][magicdns]</TailscaleWebUI>
  <TailscaleServePort>8080</TailscaleServePort>
  <TailscaleServeTarget/>
  <TailscaleServeLocalPath/>
  <TailscaleServeProtocol/>
  <TailscaleServeProtocolPort/>
  <TailscaleServePath/>
  <TailscaleDParams/>
  <TailscaleParams/>
  <TailscaleRoutes/>
  <TailscaleAcceptRoutes>false</TailscaleAcceptRoutes>
  <TailscaleStateDir/>
</Container>

Your .env content

Unraid doesn’t use docker-compose internally. Instead, it uses Docker templates stored as XML files. Each app’s template describes the container configuration (image, ports, volumes, environment variables, etc.) that Unraid then uses to create the container via the Docker API — not docker-compose.

Reproduction steps

1. Start Immich 2.0.1 on Unraid.
2. Search for 2025 given that you actually have files beginning with the string.
3. Get an error saying "Error Failed to search smart (Immich Server Error)

Relevant log output

[Nest] 1046  - 10/05/2025, 9:43:19 AM     LOG [Api:EventRepository] Websocket Disconnect: zj_O5IFGj32KUXExAAAP
[10/05/25 09:43:24] INFO     Loading textual model 'ViT-B-32__openai' to memory 
[10/05/25 09:43:24] INFO     Setting execution providers to                     
                             ['OpenVINOExecutionProvider',                      
                             'CPUExecutionProvider'], in descending order of    
                             preference                                         
2025-10-05 09:43:25.868967168 [E:onnxruntime:, sequential_executor.cc:516 ExecuteKernel] Non-zero status code returned while running OpenVINO-EP-subgraph_8 node. Name:'OpenVINOExecutionProvider_OpenVINO-EP-subgraph_8_0' Status Message: /onnxruntime/onnxruntime/core/providers/openvino/ov_interface.cc:243 void onnxruntime::openvino_ep::OVInferRequest::WaitRequest() [OpenVINO-EP]  Wait Model Failed: Exception from src/inference/src/cpp/infer_request.cpp:245:
Exception from src/plugins/intel_gpu/src/runtime/ocl/ocl_stream.cpp:381:
[GPU] clFlush, error code: -5



[10/05/25 09:43:25] ERROR    Exception in ASGI application                      
                                                                                
                             ╭─────── Traceback (most recent call last) ───────╮
                             │ /app/immich/machine-learning/immich_ml/main.py: │
                             │ 177 in predict                                  │
                             │                                                 │
                             │   174 │   │   inputs = text                     │
                             │   175 │   else:                                 │
                             │   176 │   │   raise HTTPException(400, "Either  │
                             │ ❱ 177 │   response = await run_inference(inputs │
                             │   178 │   return ORJSONResponse(response)       │
                             │   179                                           │
                             │   180                                           │
                             │                                                 │
                             │ /app/immich/machine-learning/immich_ml/main.py: │
                             │ 200 in run_inference                            │
                             │                                                 │
                             │   197 │   │   response[entry["task"]] = output  │
                             │   198 │                                         │
                             │   199 │   without_deps, with_deps = entries     │
                             │ ❱ 200 │   await asyncio.gather(*[_run_inference │
                             │   201 │   if with_deps:                         │
                             │   202 │   │   await asyncio.gather(*[_run_infer │
                             │   203 │   if isinstance(payload, Image):        │[Nest] 1046  - 10/05/2025, 9:43:25 AM   ERROR [Api:ErrorInterceptor~2x3gvrzo] Unknown error: Error: Machine learning request '{"clip":{"textual":{"modelName":"ViT-B-32__openai","options":{"language":"en-US"}}}}' failed for all URLs
Error: Machine learning request '{"clip":{"textual":{"modelName":"ViT-B-32__openai","options":{"language":"en-US"}}}}' failed for all URLs

    at MachineLearningRepository.predict (/app/immich/server/dist/repositories/machine-learning.repository.js:115:15)
    at process.processTicksAndRejections (node:internal/process/task_queues:105:5)
                             │                                                 │    at async MachineLearningRepository.encodeText (/app/immich/server/dist/repositories/machine-learning.repository.js:138:26)

    at async SearchService.searchSmart (/app/immich/server/dist/services/search.service.js:94:29)
                             │ /app/immich/machine-learning/immich_ml/main.py: │
                             │ 195 in _run_inference                           │
                             │                                                 │
                             │   192 │   │   │   │   message = f"Task {entry[' │
                             │       output of {dep}"                          │
                             │   193 │   │   │   │   raise HTTPException(400,  │
                             │   194 │   │   model = await load(model)         │
                             │ ❱ 195 │   │   output = await run(model.predict, │
                             │   196 │   │   outputs[model.identity] = output  │
                             │   197 │   │   response[entry["task"]] = output  │
                             │   198                                           │
                             │                                                 │
                             │ /app/immich/machine-learning/immich_ml/main.py: │
                             │ 213 in run                                      │
                             │                                                 │
                             │   210 │   if thread_pool is None:               │
                             │   211 │   │   return func(*args, **kwargs)      │
                             │   212 │   partial_func = partial(func, *args, * │
                             │ ❱ 213 │   return await asyncio.get_running_loop │
                             │   214                                           │
                             │   215                                           │
                             │   216 async def load(model: InferenceModel) ->  │
                             │                                                 │
                             │ /usr/lib/python3.11/concurrent/futures/thread.p │
                             │ y:58 in run                                     │
                             │                                                 │
                             │ /app/immich/machine-learning/immich_ml/models/b │
                             │ ase.py:61 in predict                            │
                             │                                                 │
                             │    58 │   │   self.load()                       │
                             │    59 │   │   if model_kwargs:                  │
                             │    60 │   │   │   self.configure(**model_kwargs │
                             │ ❱  61 │   │   return self._predict(*inputs, **m │
                             │    62 │                                         │
                             │    63 │   @abstractmethod                       │
                             │    64 │   def _predict(self, *inputs: Any, **mo │
                             │                                                 │
                             │ /app/immich/machine-learning/immich_ml/models/c │
                             │ lip/textual.py:24 in _predict                   │
                             │                                                 │
                             │    21 │                                         │
                             │    22 │   def _predict(self, inputs: str, langu │
                             │    23 │   │   tokens = self.tokenize(inputs, la │
                             │ ❱  24 │   │   res: NDArray[np.float32] = self.s │
                             │    25 │   │   return serialize_np_array(res)    │
                             │    26 │                                         │
                             │    27 │   def _load(self) -> ModelSession:      │
                             │                                                 │
                             │ /app/immich/machine-learning/immich_ml/sessions │
                             │ /ort.py:49 in run                               │
                             │                                                 │
                             │    46 │   │   input_feed: dict[str, NDArray[np. │
                             │    47 │   │   run_options: Any = None,          │
                             │    48 │   ) -> list[NDArray[np.float32]]:       │
                             │ ❱  49 │   │   outputs: list[NDArray[np.float32] │
                             │       run_options)                              │
                             │    50 │   │   return outputs                    │
                             │    51 │                                         │
                             │    52 │   @property                             │
                             │                                                 │
                             │ /lsiopy/lib/python3.11/site-packages/onnxruntim │
                             │ e/capi/onnxruntime_inference_collection.py:220  │
                             │ in run                                          │
                             │                                                 │
                             │    217 │   │   if not output_names:             │
                             │    218 │   │   │   output_names = [output.name  │
                             │    219 │   │   try:                             │
                             │ ❱  220 │   │   │   return self._sess.run(output │
                             │    221 │   │   except C.EPFail as err:          │
                             │    222 │   │   │   if self._enable_fallback:    │
                             │    223 │   │   │   │   print(f"EP Error: {err!s │
                             ╰─────────────────────────────────────────────────╯
                             Fail: [ONNXRuntimeError] : 1 : FAIL : Non-zero     
                             status code returned while running                 
                             OpenVINO-EP-subgraph_8 node.                       
                             Name:'OpenVINOExecutionProvider_OpenVINO-EP-subgrap
                             h_8_0' Status Message:                             
                             /onnxruntime/onnxruntime/core/providers/openvino/ov
                             _interface.cc:243 void                             
                             onnxruntime::openvino_ep::OVInferRequest::WaitReque
                             st() [OpenVINO-EP]  Wait Model Failed: Exception   
                             from src/inference/src/cpp/infer_request.cpp:245:  
                             Exception from                                     
                             src/plugins/intel_gpu/src/runtime/ocl/ocl_stream.cp
                             p:381:                                             
                             [GPU] clFlush, error code: -5                      
                                                                                
                                                                                
                                                                                
[Nest] 1046  - 10/05/2025, 9:43:25 AM    WARN [Api:MachineLearningRepository~2x3gvrzo] Machine learning request to "http://127.0.0.1:3003" failed with status 500: Internal Server Error
[Nest] 1046  - 10/05/2025, 9:43:25 AM     LOG [Api:MachineLearningRepository~2x3gvrzo] Machine learning server became unhealthy (http://127.0.0.1:3003).

Additional information

No response

[ScribbleGhost]

Uhm... Ok. Why is this issue moved to a discussion without any explanation? Isn't it an issue? I am confused...

[ScribbleGhost]

This issue has suddenly disappeared. Not sure why. Immich runs fine again, but I am not sure if I should trust Immich. Especially after getting no response on this.