docs: document containerRegistryAuthId for private Flash images

flash/configuration/parameters.mdx

@@ -1,4 +1,4 @@
 ---
 title: "Endpoint parameters"
 sidebarTitle: "Parameters"
 description: "Complete reference for all Endpoint class parameters. Review setup, configuration, deployment, and usage guidance for Runpod Flash."
@@ -10,34 +10,34 @@
 
 | Parameter | Type | Description | Default |
 |-----------|------|-------------|---------|
 | `name` | `str` | Endpoint name (required unless `id=` is used) | - |
 | `id` | `str` | Connect to existing endpoint by ID | `None` |
 | `gpu` | `GpuGroup`, `GpuType`, or list | GPU type(s) for the endpoint | `GpuGroup.ANY` |
 | `cpu` | `str` or `CpuInstanceType` | CPU instance type (mutually exclusive with `gpu`) | `None` |
 | `workers` | `int` or `(min, max)` | Worker scaling configuration | `(0, 1)` |
 | `idle_timeout` | `int` | Seconds before scaling down idle workers | `60` |
 | `dependencies` | `list[str]` | Python packages to install | `None` |
 | `system_dependencies` | `list[str]` | System packages to install (apt) | `None` |
 | `accelerate_downloads` | `bool` | Enable download acceleration | `True` |
 | `volume` | `NetworkVolume` or list | Network volume(s) for persistent storage | `None` |
 | `datacenter` | `DataCenter`, list, or `None` | Datacenter(s) for deployment | `None` (all DCs) |
 | `env` | `dict[str, str]` | Environment variables | `None` |
 | `gpu_count` | `int` | GPUs per worker | `1` |
 | `execution_timeout_ms` | `int` | Max execution time in milliseconds | `0` (no limit) |
 | `flashboot` | `bool` | Enable Flashboot fast startup | `True` |
 | `image` | `str` | Custom Docker image to deploy | `None` |
 | `scaler_type` | `ServerlessScalerType` | Scaling strategy | auto |
 | `scaler_value` | `int` | Scaling threshold | `4` |
 | `template` | `PodTemplate` | Pod template overrides | `None` |
 | `min_cuda_version` | `str` or `CudaVersion` | Minimum CUDA version for GPU host selection | `"12.8"` (GPU) / `None` (CPU) |
 | `python_version` | `str` | Python version for the worker image | Local Python |
 
 ## Parameter details
 
 ### name
 
 **Type**: `str`
 **Required**: Yes (unless `id=` is specified)
 
 The endpoint name visible in the [Runpod console](https://console.runpod.io/serverless). Use descriptive names to easily identify endpoints.
 
@@ -50,12 +50,12 @@
 Use naming conventions like `image-generation-prod` or `batch-processor-dev` to organize your endpoints.
 </Tip>
 
 ### id
 
 **Type**: `str`
 **Default**: `None`
 
 Connect to an existing deployed endpoint by its ID. When `id` is specified, `name` is not required.
 
 ```python
 # Connect to existing endpoint
@@ -66,10 +66,10 @@
 result = await ep.post("/inference", {"data": "..."})
 ```
 
 ### gpu
 
 **Type**: `GpuGroup`, `GpuType`, or `list[GpuGroup | GpuType]`
 **Default**: `GpuGroup.ANY` (if neither `gpu` nor `cpu` is specified)
 
 Specifies GPU hardware for the endpoint. Accepts a single GPU type/group or a list for fallback strategies.
 
@@ -91,7 +91,7 @@
 
 See [GPU types](/flash/configuration/gpu-types) for all available options.
 
 ### cpu
 
 **Type**: `str` or `CpuInstanceType`
 **Default**: `None`
@@ -112,12 +112,12 @@
 
 See [CPU types](/flash/configuration/cpu-types) for all available options.
 
 ### workers
 
 **Type**: `int` or `tuple[int, int]`
 **Default**: `(0, 1)`
 
 Controls worker scaling. Accepts either a single integer (max workers with min=0) or a tuple of (min, max).
 
 ```python
 # Just max: scales from 0 to 5
@@ -135,13 +135,13 @@
 - `workers=(1, N)`: Avoid cold starts by keeping at least one worker warm
 - `workers=(N, N)`: Fixed worker count for consistent performance
 
 ### idle_timeout
 
 **Type**: `int`
 **Default**: `60`
 **Valid range**: 1-3600 seconds
 
 Number of seconds workers will stay active (running) after completing a request, waiting for additional requests before scaling down (to minimum workers).
 
 ```python
 # Quick scale-down for cost savings
@@ -156,7 +156,7 @@
 - `60-120 seconds`: Balanced, variable traffic patterns
 - `120-300 seconds`: Latency-optimized, consistent traffic
 
 ### dependencies
 
 **Type**: `list[str]`
 **Default**: `None`
@@ -176,7 +176,7 @@
 Packages must be imported **inside** the function body, not at the top of your file.
 </Warning>
 
 ### system_dependencies
 
 **Type**: `list[str]`
 **Default**: `None`
@@ -193,12 +193,12 @@
 async def process_video(data): ...
 ```
 
 ### accelerate_downloads
 
 **Type**: `bool`
 **Default**: `True`
 
 Enables faster downloads for dependencies, models, and large files. Disable if you encounter compatibility issues.
 
 ```python
 @Endpoint(
@@ -209,12 +209,12 @@
 async def process(data): ...
 ```
 
 ### volume
 
 **Type**: `NetworkVolume` or `list[NetworkVolume]`
 **Default**: `None`
 
 Attaches network volume(s) for persistent storage. Volumes are mounted at `/runpod-volume/`. Flash uses the volume `name` to find an existing volume or create a new one. Each volume is tied to a specific datacenter.
 
 ```python
 from runpod_flash import Endpoint, GpuGroup, DataCenter, NetworkVolume
@@ -234,7 +234,7 @@
     ...
 ```
 
 For multi-datacenter deployments, pass a list of volumes (one per datacenter):
 
 ```python
 from runpod_flash import Endpoint, GpuGroup, DataCenter, NetworkVolume
@@ -265,12 +265,12 @@
 
 See [Storage](/flash/configuration/storage) for setup instructions.
 
 ### datacenter
 
 **Type**: `DataCenter`, `list[DataCenter]`, `str`, `list[str]`, or `None`
 **Default**: `None` (all available datacenters)
 
 Specifies the datacenter(s) for worker deployment. When set to `None`, the endpoint is available in all datacenters.
 
 ```python
 from runpod_flash import Endpoint, GpuGroup, DataCenter
@@ -308,12 +308,12 @@
 
 | Value | Location |
 |-------|----------|
 | `DataCenter.US_CA_2` | US - California |
 | `DataCenter.US_GA_2` | US - Georgia |
 | `DataCenter.US_IL_1` | US - Illinois |
 | `DataCenter.US_KS_2` | US - Kansas |
 | `DataCenter.US_MD_1` | US - Maryland |
 | `DataCenter.US_MO_1` | US - Missouri |
 | `DataCenter.US_MO_2` | US - Missouri |
 | `DataCenter.US_NC_1` | US - North Carolina |
 | `DataCenter.US_NC_2` | US - North Carolina |
@@ -328,7 +328,7 @@
 CPU endpoints are restricted to `CPU_DATACENTERS`, which currently only includes `EU_RO_1`.
 </Note>
 
 ### env
 
 **Type**: `dict[str, str]`
 **Default**: `None`
@@ -374,7 +374,7 @@
 Environment variables are excluded from configuration hashing. Changing environment values won't trigger endpoint recreation, making it easy to rotate API keys.
 </Note>
 
 ### gpu_count
 
 **Type**: `int`
 **Default**: `1`
@@ -391,12 +391,12 @@
 async def train(data): ...
 ```
 
 ### execution_timeout_ms
 
 **Type**: `int`
 **Default**: `0` (no limit)
 
 Maximum execution time for a single job in milliseconds. Jobs exceeding this timeout are terminated.
 
 ```python
 # 5 minute timeout
@@ -420,7 +420,7 @@
 The Flash SDK's `runsync()` method uses your `execution_timeout_ms` value as the client-side HTTP timeout. If set to a positive value, the SDK waits that duration for the job to complete. If unset or set to `0`, the SDK defaults to a 60-second timeout. For long-running inference jobs, set `execution_timeout_ms` to prevent premature timeouts.
 </Note>
 
 ### flashboot
 
 **Type**: `bool`
 **Default**: `True`
@@ -438,7 +438,7 @@
 
 Set to `False` for debugging or compatibility reasons.
 
 ### image
 
 **Type**: `str`
 **Default**: `None`
@@ -461,14 +461,14 @@
 
 See [Custom Docker images](/flash/custom-docker-images) for complete documentation.
 
 ### scaler_type
 
 **Type**: `ServerlessScalerType`
 **Default**: Auto-selected based on endpoint type
 
 Scaling algorithm strategy. Defaults are automatically set:
 - Queue-based: `QUEUE_DELAY` (scales based on queue depth)
 - Load-balanced: `REQUEST_COUNT` (scales based on active requests)
 
 ```python
 from runpod_flash import Endpoint, ServerlessScalerType
@@ -481,7 +481,7 @@
 async def process(data): ...
 ```
 
 ### scaler_value
 
 **Type**: `int`
 **Default**: `4`
@@ -498,7 +498,7 @@
 async def process(data): ...
 ```
 
 ### template
 
 **Type**: `PodTemplate`
 **Default**: `None`
@@ -519,37 +519,41 @@
 async def process(data): ...
 ```
 
 ## PodTemplate
 
 `PodTemplate` provides advanced pod configuration options:
 
 | Parameter | Type | Description | Default |
 |-----------|------|-------------|---------|
 | `containerDiskInGb` | `int` | Container disk size in GB | 64 |
+| `containerRegistryAuthId` | `str` | ID of saved registry credentials for pulling private container images | `None` |
 | `env` | `list[dict]` | Environment variables as list of `{"key": "...", "value": "..."}` | `None` |
 
 ```python
 from runpod_flash import PodTemplate
 
 template = PodTemplate(
     containerDiskInGb=100,
+    containerRegistryAuthId="REGISTRY_CREDENTIAL_ID",
     env=[
         {"key": "PYTHONPATH", "value": "/workspace"},
         {"key": "CUDA_VISIBLE_DEVICES", "value": "0"}
     ]
 )
 ```
 
+To find the registry credential ID for `containerRegistryAuthId`, list your saved credentials with `runpodctl registry list` or check **Container Registry Authentication** in the [Runpod console settings](https://console.runpod.io/user/settings). See [Private images](/flash/custom-docker-images#private-images) for a complete example.
+
 <Tip>
 For simple environment variables, use the `env` parameter on `Endpoint` instead of `PodTemplate.env`.
 </Tip>
 
 ### min_cuda_version
 
 **Type**: `str` or `CudaVersion`
 **Default**: `"12.8"` for GPU endpoints, `None` for CPU endpoints
 
 Specifies the minimum CUDA driver version required on the host machine. GPU endpoints default to `"12.8"` to ensure workers run on hosts with recent CUDA drivers.
 
 ```python
 from runpod_flash import Endpoint, GpuType, CudaVersion
@@ -581,18 +585,18 @@
 Valid CUDA versions: `CudaVersion.V11_1`, `V11_4`, `V11_7`, `V11_8`, `V12_0`, `V12_1`, `V12_2`, `V12_3`, `V12_4`, `V12_6`, `V12_8` (or equivalent strings like `"12.4"`). Invalid values raise a `ValueError`.
 </Note>
 
 ### python_version
 
 **Type**: `str`
 **Default**: Local Python version
 
 Sets the Python version for the worker image. Supported values: `"3.10"`, `"3.11"`, `"3.12"`, and `"3.13"`.
 
 When you don't specify a Python version, Flash matches your local interpreter (the Python version you run Flash from). The resolution order is:
 
 1. `--python-version` CLI flag (highest priority)
 2. `python_version` declared on resource configs
 3. Your local Python version (`sys.version_info`)
 
 ```python
 from runpod_flash import Endpoint, GpuGroup
@@ -624,11 +628,11 @@
 Python 3.10 reaches end-of-life on 2026-10-31. Consider migrating to Python 3.11 or later.
 </Note>
 
 If your local Python version is not supported (for example, 3.9 or 3.14), the build fails with an actionable error message listing the supported versions.
 
 The `--python-version` CLI flag on `flash build` and `flash deploy` overrides both per-resource declarations and local interpreter detection.
 
 ## EndpointJob
 
 When using `Endpoint(id=...)` or `Endpoint(image=...)`, the `.run()` method returns an `EndpointJob` object for async operations:
 
@@ -661,25 +665,25 @@
 ### Changes that recreate workers
 
 These changes restart all workers:
 - GPU configuration (`gpu`, `gpu_count`)
 - CPU instance type (`cpu`)
 - Docker image (`image`)
 - Storage (`volume`)
 - Datacenter (`datacenter`)
 - Flashboot setting (`flashboot`)
 - CUDA version requirement (`min_cuda_version`)
 - Python version (`python_version`)
 
 Workers are temporarily unavailable during recreation (typically 30-90 seconds).
 
 ### Changes that update settings only
 
 These changes apply immediately with no downtime:
 - Worker scaling (`workers`)
 - Timeouts (`idle_timeout`, `execution_timeout_ms`)
 - Scaler settings (`scaler_type`, `scaler_value`)
 - Environment variables (`env`)
 - Endpoint name (`name`)
 
 ```python
 # First deployment

flash/custom-docker-images.mdx

@@ -1,4 +1,4 @@
 ---
 title: "Use custom containers with Flash"
 sidebarTitle: "Custom containers"
 description: "Deploy pre-built Docker images with Flash using Endpoint. Review setup, configuration, deployment, and usage guidance for Runpod Flash."
@@ -12,8 +12,8 @@
 
 Use custom Docker images when you need:
 
 - **Pre-built inference servers**: vLLM, TensorRT-LLM, or other specialized serving frameworks.
 - **System-level dependencies**: Custom CUDA versions, cuDNN, or system libraries not installable via `pip`.
 - **Baked-in models**: Large models pre-downloaded in the image to avoid runtime downloads.
 - **Existing Serverless workers**: You already have a working Runpod Serverless Docker image.
 
@@ -23,7 +23,7 @@
 
 ## Available Docker images
 
 ### Official Runpod workers
 
 Runpod provides pre-built worker images for common frameworks:
 
@@ -108,7 +108,37 @@
 </Step>
 </Steps>
 
+## Private images
+
+To deploy an image from a private registry, first save your registry credentials in the Runpod console, then reference the credential ID in your endpoint's `PodTemplate` using the `containerRegistryAuthId` field:
+
+```python
+from runpod_flash import Endpoint, GpuType, PodTemplate
+
+vllm = Endpoint(
+    name="private-vllm",
+    image="ghcr.io/your-org/private-worker:latest",
+    gpu=GpuType.NVIDIA_GEFORCE_RTX_4090,
+    template=PodTemplate(
+        containerDiskInGb=64,
+        containerRegistryAuthId="REGISTRY_CREDENTIAL_ID"
+    )
+)
+```
+
+<Warning>
+Without `containerRegistryAuthId`, Runpod cannot pull a private image, and workers fail to start.
+</Warning>
+
+To find the credential ID, list your saved registry credentials with the Runpod CLI:
+
+```bash
+runpodctl registry list
+```
+
+To create a new credential, use [`runpodctl registry create`](/runpodctl/reference/runpodctl-registry), or navigate to [Settings](https://console.runpod.io/user/settings) in the Runpod console and scroll down to **Container Registry Authentication**. For GitHub Container Registry, use a personal access token scoped to `read:packages`.
+
 ## Complete example: vLLM inference
 
 This example deploys vLLM and makes inference requests:
 
@@ -176,7 +206,7 @@
 )
 ```
 
 ### CPU endpoints
 
 For CPU workloads, use the `cpu` parameter:
 
@@ -209,7 +239,7 @@
 print(job.error)   # Error message if failed
 ```
 
 ### HTTP requests
 
 Use `.get()`, `.post()`, `.put()`, `.delete()` for direct HTTP calls:
 
@@ -228,7 +258,7 @@
 )
 ```
 
 ## EndpointJob reference
 
 The `.run()` method returns an `EndpointJob` for async operations:
 
@@ -250,7 +280,7 @@
 ## Limitations
 
 - **Input format**: Queue-based calls require `{"input": {...}}` format.
 - **Code execution**: Cannot execute arbitrary Python code remotely. Your Docker image must include all logic.
 - **@Endpoint decorator**: The decorator pattern doesn't work with `image=`. Use the instance pattern instead.
 - **Handler required**: Your Docker image must implement a Runpod Serverless [handler function](/serverless/workers/handler-functions).
 
@@ -258,7 +288,7 @@
 
 ### Endpoint fails to initialize
 
 **Problem**: Workers fail to start or crash immediately.
 
 **Solutions**:
 - Verify your Docker image is compatible with [Runpod Serverless](/serverless/overview).
@@ -268,7 +298,7 @@
 
 ### Out of memory errors
 
 **Problem**: Workers crash with CUDA OOM or RAM errors.
 
 **Solutions**:
 - Use a larger GPU: `gpu=GpuType.NVIDIA_A100_80GB_PCIe`
@@ -278,11 +308,11 @@
 
 ### Authentication errors
 
 **Problem**: Cannot download gated models or private images.
 
 **Solutions**:
 - Add `HF_TOKEN` to `env` for Hugging Face gated models.
-- Configure Docker registry authentication in [Runpod console](https://console.runpod.io/user/settings) for private images.
+- For private images, save registry credentials in the [Runpod console](https://console.runpod.io/user/settings) and pass the credential ID to your endpoint with `PodTemplate(containerRegistryAuthId=...)`. See [Private images](#private-images).
 
 ## Next steps