Add docs assets for 0.0.9 release (#284)

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## Summary

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solves. -->

## Related issues

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## Checks

- [ ] `make lint`: I've run `make lint` to lint the changes in this PR.
- [ ] `make test`: I've made sure the tests (`make test-cpu` or `make
test`) are passing.
- Additional tests:
   - [ ] Benchmark tests (when contributing new models)
   - [ ] GPU/HW tests

docs/running-inference.md

@@ -0,0 +1,68 @@
+!!! note
+    In this section, we expect that you have already installed NOS and have already [started the server](/docs/starting-the-server).
+
+```python
+import nos
+
+nos.init(runtime="auto", utilization=1.0)
+```
+
+### Connecting to the NOS Server
+
+You can now send inference requests using the NOS client.
+Let's start by importing the NOS client and creating an `InferenceClient` instance. The client instance is used to send inference requests to the NOS server via gRPC.
+
+```python
+from nos.client import InferenceClient, TaskType
+
+# Create a client that connects to the server via gRPC (over 50051)
+client = InferenceClient("[::]:50051")
+
+# We provide helper functions to wait for the server to be ready
+# if the server is simultaneously spun up in a separate process.
+client.WaitForServer()
+
+# Finally, we can check if the server is healthy.
+client.IsHealthy()
+```
+
+## Running Inference with the `client.Module` Interface
+
+NOS provides a `client.Module` interface to get model handles for remote-model execution. Let's see an example of running `yolox/nano` to run 2D object detection on a sample image.
+
+```python
+# Get a model handle for yolox/nano
+detect2d = client.Module(TaskType.OBJECT_DETECTION_2D, "yolox/nano")
+
+# Run inference on a sample image
+img = Image.open("sample.jpg")
+predictions = detect2d(images=[img])
+```
+
+!!!note In essense, the `client.Module` is an [`InferenceModule`](/docs/api/client.md#nosclientgrpcinferencemodule)  that provides a *logical* handle for the model on the remote server. The model handle could contain multiple replicas, or live in a specialized runtime (GPU, ASICs), however, the user does not need to be aware of these abstractions. Instead, you can simply call the model as a regular Python function where the task gets dispatched to the associated set of remote workers.
+
+## More examples
+
+### Text-to-image generation with [StableDiffusionV2](https://huggingface.co/stabilityai/stable-diffusion-2)
+
+```python
+prompts = ["fox jumped over the moon", "fox jumped over the sun"]
+sdv2 = client.Module(TaskType.IMAGE_GENERATION, "stabilityai/stable-diffusion-2")
+images = sdv2(prompts=prompts, width=512, height=512, num_images=1)
+images[0]
+```
+
+### Image-embedding with [OpenAI CLIP](https://huggingface.co/openai/clip-vit-base-patch32)
+
+```python
+clip_img2vec = client.Module(TaskType.IMAGE_EMBEDDING, "openai/clip")
+predictions = clip_img2vec(images=[img])
+predictions["embedding"].shape
+```
+
+### Text-embedding with [OpenAI CLIP](https://huggingface.co/openai/clip-vit-base-patch32)
+```python
+clip_txt2vec = client.Run(TaskType.TEXT_EMBEDDING, "openai/clip")
+predictions = clip_txt2vec(texts=["fox jumped over the mooon", "fox jumped over the sun"])
+predictions["embedding"].shape
+```

mkdocs.yml

@@ -26,14 +26,13 @@ nav:
     - nos.hub: docs/api/hub.md
     - nos.managers: docs/api/managers.md
     - nos.executors: docs/api/executors.md
-  - FAQ: docs/faq.md
-
-- ✍️ Blog:
-  - docs/blog/index.md
-  - 2023:
-    - docs/blog/posts/01-hello-world.md
-- 🏁 Roadmap: docs/roadmap.md
-- 🧑‍💻 Contributing: docs/CONTRIBUTING.md
+#   - FAQ: docs/faq.md
+# - ✍️ Blog:
+#   - docs/blog/index.md
+#   - 2023:
+#     - docs/blog/posts/01-hello-world.md
+# - 🏁 Roadmap: docs/roadmap.md
+# - 🧑‍💻 Contributing: docs/CONTRIBUTING.md
 - ❓ Support: docs/support.md