👉 running on a GPU
- - To enhance model performance in GPU-accelerated environments, install CUDA-compatible dependencies instead: +## Video Tutorials + +
+
+
-)
-with client.use_model(model_id="soccer-players-5fuqs/1"):
- predictions = client.infer("https://media.roboflow.com/inference/soccer.jpg")
-```
-
-If you're using the hosted API, change the local API URL to `https://detect.roboflow.com`. Accessing the hosted inference server and/or using any of the fine-tuned models require a `ROBOFLOW_API_KEY`. For further information, visit the 🔑 keys section.
-
-## 🎥 inference pipeline
-
-The inference pipeline is an efficient method for processing static video files and streams. Select a model, define the video source, and set a callback action. You can choose from predefined callbacks that allow you to [display results](/reference/inference/core/interfaces/stream/sinks.md#inference.core.interfaces.stream.sinks.render_boxes) on the screen or [save them to a file](/reference/inference/core/interfaces/stream/sinks.md#inference.core.interfaces.stream.sinks.VideoFileSink).
-
-```python
-from inference import InferencePipeline
-from inference.core.interfaces.stream.sinks import render_boxes
-
-pipeline = InferencePipeline.init(
- model_id="yolov8x-1280",
- video_reference="https://media.roboflow.com/inference/people-walking.mp4",
- on_prediction=render_boxes
-)
-
-pipeline.start()
-pipeline.join()
-```
-
-## 🔑 keys
+
+
-Inference enables the deployment of a wide range of pre-trained and foundational models without an API key. To access thousands of fine-tuned models shared by the [Roboflow Universe](https://universe.roboflow.com/) community, [configure your](https://app.roboflow.com/settings/api) API key.
+
\ No newline at end of file
diff --git a/docs/scripts/gen_ref_pages.py b/docs/scripts/gen_ref_pages.py
index 63063780d2..2250b9675e 100644
--- a/docs/scripts/gen_ref_pages.py
+++ b/docs/scripts/gen_ref_pages.py
@@ -3,39 +3,41 @@
from pathlib import Path
import mkdocs_gen_files
+import os
SKIP_MODULES = [
"inference.enterprise.device_manager.command_handler",
"inference.enterprise.parallel.celeryconfig",
]
-for package in ["inference", "inference_sdk", "inference_cli"]:
- nav = mkdocs_gen_files.Nav()
- src = Path(__file__).parent.parent.parent / package
-
- for path in sorted(p for p in src.rglob("*.py") if "landing" not in p.parts):
- module_path = path.relative_to(src.parent).with_suffix("")
- doc_path = path.relative_to(src.parent).with_suffix(".md")
- full_doc_path = Path("reference", doc_path)
-
- parts = list(module_path.parts)
- identifier = ".".join(parts)
- if parts[-1] == "__main__" or parts[-1] == "__init__" or identifier in SKIP_MODULES:
- # print("SKIPPING", identifier)
- continue
-
- nav[parts] = f"/reference/{module_path.as_posix()}.md"
-
- with mkdocs_gen_files.open(full_doc_path, "w") as fd:
- fd.write(f"::: {identifier}")
-
- edit_path = f"https://github.com/roboflow/inference/tree/main/{module_path.as_posix()}.py"
- # print("Edit path:", edit_path)
- mkdocs_gen_files.set_edit_path(full_doc_path, edit_path)
-
- with mkdocs_gen_files.open(f"reference/{package}/index.md", "w") as nav_file:
- generator = nav.build_literate_nav()
- lines = list(generator)
- # print("GENERATING NAVIGATION")
- # print("\n".join(lines))
- nav_file.writelines(lines)
+if not os.environ.get("SKIP_CODEGEN"):
+ for package in ["inference", "inference_sdk", "inference_cli"]:
+ nav = mkdocs_gen_files.Nav()
+ src = Path(__file__).parent.parent.parent / package
+
+ for path in sorted(p for p in src.rglob("*.py") if "landing" not in p.parts):
+ module_path = path.relative_to(src.parent).with_suffix("")
+ doc_path = path.relative_to(src.parent).with_suffix(".md")
+ full_doc_path = Path("reference", doc_path)
+
+ parts = list(module_path.parts)
+ identifier = ".".join(parts)
+ if parts[-1] == "__main__" or parts[-1] == "__init__" or identifier in SKIP_MODULES:
+ # print("SKIPPING", identifier)
+ continue
+
+ nav[parts] = f"/reference/{module_path.as_posix()}.md"
+
+ with mkdocs_gen_files.open(full_doc_path, "w") as fd:
+ fd.write(f"::: {identifier}")
+
+ edit_path = f"https://github.com/roboflow/inference/tree/main/{module_path.as_posix()}.py"
+ # print("Edit path:", edit_path)
+ mkdocs_gen_files.set_edit_path(full_doc_path, edit_path)
+
+ with mkdocs_gen_files.open(f"reference/{package}/index.md", "w") as nav_file:
+ generator = nav.build_literate_nav()
+ lines = list(generator)
+ # print("GENERATING NAVIGATION")
+ # print("\n".join(lines))
+ nav_file.writelines(lines)
diff --git a/docs/styles.css b/docs/styles.css
index e0c1d42cbc..1090ae1b64 100644
--- a/docs/styles.css
+++ b/docs/styles.css
@@ -18,6 +18,10 @@
justify-content: space-between;
}
+.grecaptcha-badge {
+ box-shadow: none !important;
+}
+
@media screen and (min-width: 76.25em) {
.md-nav__item--section>.md-nav__link {
font-weight: 700;
+
+ 
+
+
+
+
+
+
+
+
+ Tutorial: Build a Smart Parking System
+
+
+ Created: 27 Nov 2024
+ + Build a smart parking lot management system using Roboflow Workflows! + This tutorial covers license plate detection with YOLOv8, object tracking + with ByteTrack, and real-time notifications with a Telegram bot. +
+
+
+ 
+
+
- ```bash
- pip install inference-gpu
- ```
-
-
-
+
+
+
+ Tutorial: Build a Traffic Monitoring Application with Workflows
+
+
+ Created: 22 Oct 2024
+ + Learn how to build and deploy Workflows for common use-cases like detecting + vehicles, filtering detections, visualizing results, and calculating dwell + time on a live video stream. +
+
-
- - Note that
- - The
-
-## 🔥 quickstart
-
-Use Inference SDK to run models locally with just a few lines of code. The image input can be a URL, a numpy array, or a PIL image.
-
-```python
-from inference import get_model
-
-model = get_model(model_id="yolov8n-640")
-
-results = model.infer("https://media.roboflow.com/inference/people-walking.jpg")
-```
-
-👉 advanced models
- - Inference supports multiple model types for specialized tasks. From Grounding DINO for identifying objects with a text prompt, to DocTR for OCR, to CogVLM for asking questions about images - you can find out more in the Foundation Models page. - -- - Note that
inference and inference-gpu packages install only the minimal shared dependencies. Instead, install model-specific dependencies to ensure code compatibility and license compliance.
-
- - - The
inference and inference-gpu packages install only the minimal shared dependencies. Install model-specific dependencies to ensure code compatibility and license compliance. Learn more about the models supported by Inference.
-
- ```bash
- pip install inference[yolo-world]
- ```
-
-
-
- -Set up your
-
-👉 roboflow models
- -- -Set up your
ROBOFLOW_API_KEY to access thousands of fine-tuned models shared by the Roboflow Universe community and your custom model. Navigate to 🔑 keys section to learn more.
-
-```python
-from inference import get_model
-
-model = get_model(model_id="soccer-players-5fuqs/1")
-
-results = model.infer(
- image="https://media.roboflow.com/inference/soccer.jpg",
- confidence=0.5,
- iou_threshold=0.5
-)
-```
-
-
-
-
-## 📟 inference server
-
-You can also run Inference as a microservice with Docker.
-
-### deploy server
-
-The inference server is distributed via Docker. Behind the scenes, inference will download and run the image that is appropriate for your hardware. [Here](/quickstart/docker.md#advanced-build-a-docker-container-from-scratch), you can learn more about the supported images.
-
-```bash
-inference server start
-```
-
-### run client
-
-Consume inference server predictions using the HTTP client available in the Inference SDK.
-
-```python
-from inference_sdk import InferenceHTTPClient
-
-client = InferenceHTTPClient(
- api_url="http://localhost:9001",
- api_key=