> For the complete documentation index, see [llms.txt](https://docs.roboflow.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.roboflow.com/reference/inference/inference-sdk/configuration.md).

# Configuration

## Configuration options

### Configuring with context managers

The methods `use_configuration(...)` and `use_model(...)` are designed to work in context managers. **Once the context manager is left, old config values are restored.**

```python
from inference_sdk import InferenceHTTPClient, InferenceConfiguration

image_url = "https://source.roboflow.com/pwYAXv9BTpqLyFfgQoPZ/u48G0UpWfk8giSw7wrU8/original.jpg"

custom_configuration = InferenceConfiguration(confidence_threshold=0.8)
# Replace ROBOFLOW_API_KEY with your Roboflow API Key
CLIENT = InferenceHTTPClient(
    api_url="http://localhost:9001",
    api_key="ROBOFLOW_API_KEY"
)

with CLIENT.use_configuration(custom_configuration):
    _ = CLIENT.infer(image_url, model_id="soccer-players-5fuqs/1")

with CLIENT.use_model("soccer-players-5fuqs/1"):
    _ = CLIENT.infer(image_url)

# after leaving context manager - changes are reverted and `model_id` is still required
_ = CLIENT.infer(image_url, model_id="soccer-players-5fuqs/1")
```

As you can see, `model_id` is required for a prediction method only when a default model is not configured.

{% hint style="info" %}
The model ID is composed of the string `<project_id>/<version_id>`. See [Workspace and Project IDs](/reference/authentication/authentication/workspace-and-project-ids.md) to find these pieces of information.
{% endhint %}

### Setting the configuration once and using it until the next change

The methods `configure(...)` and `select_model(...)` alter the client state and the change is preserved until the next change.

```python
from inference_sdk import InferenceHTTPClient, InferenceConfiguration

image_url = "https://source.roboflow.com/pwYAXv9BTpqLyFfgQoPZ/u48G0UpWfk8giSw7wrU8/original.jpg"

custom_configuration = InferenceConfiguration(confidence_threshold=0.8)
# Replace ROBOFLOW_API_KEY with your Roboflow API Key
CLIENT = InferenceHTTPClient(
    api_url="http://localhost:9001",
    api_key="ROBOFLOW_API_KEY"
)

CLIENT.configure(custom_configuration)
CLIENT.infer(image_url, model_id="soccer-players-5fuqs/1")

# custom configuration still holds
CLIENT.select_model(model_id="soccer-players-5fuqs/1")
_ = CLIENT.infer(image_url)

# custom configuration and selected model - still holds
_ = CLIENT.infer(image_url)
```

You may also initialise in `chain` mode:

```python
from inference_sdk import InferenceHTTPClient, InferenceConfiguration

# Replace ROBOFLOW_API_KEY with your Roboflow API Key
CLIENT = InferenceHTTPClient(api_url="http://localhost:9001", api_key="ROBOFLOW_API_KEY") \
    .select_model("soccer-players-5fuqs/1")
```

### Overriding `model_id` for a specific call

`model_id` can be overridden for a specific call:

```python
from inference_sdk import InferenceHTTPClient

image_url = "https://source.roboflow.com/pwYAXv9BTpqLyFfgQoPZ/u48G0UpWfk8giSw7wrU8/original.jpg"

# Replace ROBOFLOW_API_KEY with your Roboflow API Key
CLIENT = InferenceHTTPClient(api_url="http://localhost:9001", api_key="ROBOFLOW_API_KEY") \
    .select_model("soccer-players-5fuqs/1")

_ = CLIENT.infer(image_url, model_id="another-model/1")
```

## Details about client configuration

`InferenceHTTPClient` provides the `InferenceConfiguration` dataclass to hold the full configuration.

```python
from inference_sdk import InferenceConfiguration
```

Overriding fields in this config changes the behaviour of the client (and of the API serving the model). Specific fields are used in specific contexts. In particular:

### Classification model

* `visualize_predictions`: flag to enable / disable visualisation
* `confidence_threshold` as `confidence`
* `stroke_width`: width of stroke in visualisation
* `disable_preproc_auto_orientation`, `disable_preproc_contrast`, `disable_preproc_grayscale`, `disable_preproc_static_crop` to alter server-side pre-processing
* `disable_active_learning` to prevent the [Active Learning](https://docs.roboflow.com/deployment/monitoring-and-analytics/active-learning) feature from registering the datapoint (can be useful, for instance, while testing a model)
* `active_learning_target_dataset` - when making inference from a specific model (let's say `project_a/1`) and you want to save data in another project `project_b`, the latter should be pointed to by this parameter. **Note that you cannot use different types of models in `project_a` and `project_b`; if that is the case, data will not be registered.**
* `source`: optional string that sets a "source" attribute on the inference call. If using [model monitoring](https://docs.roboflow.com/deployment/monitoring-and-analytics/model-monitoring), this is logged with the inference request so you can filter or query inference requests coming from a particular source, for example to identify which application, system, or deployment is making the request.
* `source_info`: optional string that sets an additional "source\_info" attribute on the inference call, for example to identify a sub-component in an app.

### Object detection model

* `visualize_predictions`: flag to enable / disable visualisation
* `visualize_labels`: flag to enable / disable label visualisation if visualisation is enabled
* `confidence_threshold` as `confidence`
* `class_filter` to filter out a list of classes
* `class_agnostic_nms`: flag to control whether NMS is class-agnostic
* `fix_batch_size`
* `iou_threshold`: to dictate the NMS IoU threshold
* `stroke_width`: width of stroke in visualisation
* `max_detections`: max detections to return from the model
* `max_candidates`: max candidates for post-processing from the model
* `disable_preproc_auto_orientation`, `disable_preproc_contrast`, `disable_preproc_grayscale`, `disable_preproc_static_crop` to alter server-side pre-processing
* `disable_active_learning`, `active_learning_target_dataset`, `source`, `source_info` - as described above

### Keypoint detection model

* `visualize_predictions`: flag to enable / disable visualisation
* `visualize_labels`: flag to enable / disable label visualisation if visualisation is enabled
* `confidence_threshold` as `confidence`
* `keypoint_confidence_threshold` (as `keypoint_confidence`) to filter out detected keypoints based on model confidence
* `class_filter` to filter out a list of object classes
* `class_agnostic_nms`: flag to control whether NMS is class-agnostic
* `fix_batch_size`
* `iou_threshold`: to dictate the NMS IoU threshold
* `stroke_width`: width of stroke in visualisation
* `max_detections`: max detections to return from the model
* `max_candidates`: max candidates for post-processing from the model
* `disable_preproc_auto_orientation`, `disable_preproc_contrast`, `disable_preproc_grayscale`, `disable_preproc_static_crop` to alter server-side pre-processing
* `disable_active_learning`, `active_learning_target_dataset`, `source`, `source_info` - as described above

### Instance segmentation model

* `visualize_predictions`: flag to enable / disable visualisation
* `visualize_labels`: flag to enable / disable label visualisation if visualisation is enabled
* `confidence_threshold` as `confidence`
* `class_filter` to filter out a list of classes
* `class_agnostic_nms`: flag to control whether NMS is class-agnostic
* `fix_batch_size`
* `iou_threshold`: to dictate the NMS IoU threshold
* `stroke_width`: width of stroke in visualisation
* `max_detections`: max detections to return from the model
* `max_candidates`: max candidates for post-processing from the model
* `disable_preproc_auto_orientation`, `disable_preproc_contrast`, `disable_preproc_grayscale`, `disable_preproc_static_crop` to alter server-side pre-processing
* `mask_decode_mode`
* `tradeoff_factor`
* `disable_active_learning`, `active_learning_target_dataset`, `source`, `source_info` - as described above

### Configuration of the client

* `output_visualisation_format`: one of `VisualisationResponseFormat.BASE64`, `VisualisationResponseFormat.NUMPY`, `VisualisationResponseFormat.PILLOW`. Given that server-side visualisation is enabled, you may choose which format should be used in the output.
* `client_downsizing_disabled`: set to `False` if you want to perform client-side downsizing. Default `True`. Client-side scaling is only supposed to down-scale (keeping aspect ratio) the input for inference, to utilise the internet connection more efficiently (at the price of image manipulation / transcoding). Model input size information is used to determine the target size; if not available, `default_max_input_size` is used.
* `max_concurrent_requests`: max number of concurrent requests that can be started
* `max_batch_size`: max number of elements that can be injected into a single request
* `workflow_run_retries_enabled`: flag that decides if transient errors in Workflows executions should be retried. Defaults to `true` and the default can be altered with the environment variable `WORKFLOW_RUN_RETRIES_ENABLED`.

### Configuration of Workflows execution

* `profiling_directory`: specifies the location where Workflows profiler traces are saved. By default, it is the `./inference_profiling` directory.