Docs onnx tutorial (#373)

* docs: add onnx tutorial

* docs: add documentation for onnx encoder

* docs: fix some syntax errors

* docs: fix some syntax errors

* docs: add annotations

* docs:remove unused import

Co-authored-by: George Mastrapas <georgios.mastrapas@jina.ai>

Author: jemmyshin

docs/get-started/onnx/index.md

@@ -0,0 +1,251 @@
+# Using ONNX to deploy a finetuned model
+
+Imagine a scenario where you successfully used Finetuner to finetune a model on your search problem using data from the respective domain. What comes next?
+
+Naturally, you would want to deploy your {term}`embedding model` in a service and use it to
+encode data as part of a bigger search application. [Jina](https://docs.jina.ai/)
+provides the infrastructure layer to help you build and deploy neural search components.
+By implementing custom `Executor`s or using existing ones from [Jina Hub](https://hub.jina.ai/)
+you can define the building blocks of your search application and glue everything
+together in a fully-fledged search pipeline.
+
+To use your model with Jina, you would have to build a new `Executor` tailored to your model and upload it to Jina Hub. You can then use a Jina `Flow` to deploy your search
+application locally or in the cloud using kubernetes.
+
+It is difficult to provide a unified `Executor` that can support all models trained with Finetuner,
+since Finetuner supports different computational frameworks and each model differs in terms
+of pre-processing required, input type etc.
+
+There is an alternative though, that can be useful for standardizing your deployment
+procedure and unifying different models. Finetuner allows the user to use ONNX for converting
+finetuned models to the ONNX format, whether these models have been trained with PyTorch,
+TensorFlow or PaddlePaddle. The ONNX format is an open standard for defining
+neural network architectures. You can find out more in the [ONNX webpage](https://onnx.ai/).
+
+After converting a trained model to the ONNX format, you can use the
+[ONNXEncoder](https://hub.jina.ai/executor/2cuinbko) from Jina Hub to deploy your {term}`embedding
+model`. The encoder simply loads your {term}`embedding model` in ONNX format and uses the ONNX
+runtime for inference. The `Document` tensors are fed to the ONNX model as input
+and the output NumPy vectors are assigned to the `Document` as embeddings.
+
+The `ONNXEncoder` takes care of the embedding part and the only thing left is to add a custom
+`Executor` for pre-processing in case there is the need for one. Additionally, you gain
+a boost in efficiency, since the conversion to ONNX already optimizes the model based on the platform/hardware device.
+
+This tutorial will walk you through a simple model finetuning and how you can convert to
+ONNX and use the `ONNXEncoder` to deploy a Jina `Flow`.
+
+````{info}
+Jina, onnx, torch and torchvision are required to run this example. You can install the packages using:
+
+```
+pip install 'jina>=3.0' onnx torch torchvision
+```
+````
+
+
+## Exporting to ONNX
+
+Let's fine-tune a ResNet18 on a customized CelebA dataset.
+[Download the dataset](https://static.jina.ai/celeba/celeba-img.zip) and decompress it to
+`'./img_align_celeba'`.
+
+```python
+import torchvision
+from docarray import DocumentArray
+
+import finetuner as ft
+
+data = DocumentArray.from_files('img_align_celeba/*.jpg')
+
+
+def preprocess(doc):
+    return (
+        doc.load_uri_to_image_tensor(224, 224)
+        .set_image_tensor_normalization()
+        .set_image_tensor_channel_axis(-1, 0)
+    )
+
+
+data.apply(preprocess)
+
+resnet = torchvision.models.resnet18(pretrained=True)
+
+tuned_model = ft.fit(
+    model=resnet,
+    train_data=data,
+    loss='TripletLoss',
+    epochs=20,
+    batch_size=128,
+    to_embedding_model=True,
+    input_size=(3, 224, 224),
+    layer_name='adaptiveavgpool2d_67',
+    freeze=False,
+)
+
+```
+
+We can now export the model to the ONNX format, using the `to_onnx` method provided
+by Finetuner. The `onnx` package is required to use this functionality. Also, if
+you are using TensorFlow or PaddlePaddle you will need to install additional packages
+for the ONNX conversion. Specifically you will need: 
+[tf2onnx](https://github.com/onnx/tensorflow-onnx) for TensorFlow and
+[paddle2onnx](https://github.com/PaddlePaddle/Paddle2ONNX) for PaddlePaddle.
+
+```bash
+pip install onnx
+pip install tf2onnx
+pip install paddle2onnx
+```
+
+Back to the ONNX conversion:
+
+```python
+from finetuner.tuner.onnx import to_onnx
+
+to_onnx(tuned_model, 'tuned-model.onnx', input_shape=[3, 224, 224], opset_version=13)
+```
+
+The ONNX exported model should be stored now at `tuned-model.onnx`.
+
+A helper function for validating the ONNX export is also provided:
+```python
+from finetuner.tuner.onnx import validate_onnx_export
+
+validate_onnx_export(tuned_model, 'tuned-model.onnx', input_shape=[3, 224, 224])
+```
+
+This function compares the outputs of the native model and its ONNX counterpart against
+the same random input. If the outputs differ an assertion error is raised.
+
+Now that we have our model exported and have verified that it has the same behavior as the
+original, it's time to deploy it 🚀.
+
+
+## Deploying using the `ONNXEncoder`
+
+You have already finetuned your own model and transferred it to ONNX format. Let's start deploying in the Jina `Flow`. If you are not familiar with Jina Hub or Jina `Flow`, check this:
+[Use Hub Executor](https://docs.jina.ai/advanced/hub/use-hub-executor/)
+[Use Jina Flow](https://docs.jina.ai/fundamentals/flow/)
+
+Here are the steps you can follow:
+
+### Add `ONNXEncoder` to Jina `Flow`
+
+We already have `ONNXEncoder` on Jina Hub, let's add `ONNXEncoder` to Jina `Flow`:
+
+using docker image:
+
+```python
+from jina import Flow
+
+f = Flow().add(uses='jinahub+docker://ONNXEncoder',
+                uses_with={'model_path': 'tuned-model.onnx'})
+```
+
+or using source code:
+
+```python
+from jina import Flow
+
+f = Flow().add(uses='jinahub://ONNXEncoder',
+                uses_with={'model_path': 'tuned-model.onnx'})
+```
+
+`model_path` is the path of the ONNX model you exported.
+
+### Complete the `Flow` by adding indexer and starting the service
+
+```python
+import numpy as np
+from docarray import DocumentArray
+
+from jina import Executor, Flow, requests
+
+
+class SimpleIndexer(Executor):
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self._da = DocumentArray()
+
+    @requests(on='/index')
+    def index(self, docs: DocumentArray, **kwargs):
+        self._da.extend(docs)
+
+    @requests(on='/search')
+    def search(self, docs: DocumentArray, **kwargs):
+        docs.match(self._da)
+        
+
+f = Flow(port_expose=12345, 
+         protocol='http').add(uses='jinahub://ONNXEncoder', 
+                              uses_with={'model_path': 'tuned-model.onnx'},
+                              name='Encoder').add(uses=SimpleIndexer, 
+                                                  name='Indexer')
+        
+with f:
+    f.post('/index', data, show_progress=True)
+    f.block()
+```
+
+You will see something like this:
+
+```bash
+    Flow@6260[I]:🎉 Flow is ready to use!                                           
+    🔗 Protocol:            HTTP
+    🏠 Local access:        0.0.0.0:12345
+    🔒 Private network:     192.168.31.213:12345
+    💬 Swagger UI:          http://localhost:12345/docs
+    📚 Redoc:               http://localhost:12345/redoc
+⠼       DONE ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╸ 0:00:14 100% ETA: 0 seconds 80 steps done in 14 seconds
+```
+
+which means that the service has been successfully started.
+
+### Start to query!
+
+Finally it's time to see what we can get from our service. Start a client and try to query using the service we have just built. 
+
+```python
+from docarray import DocumentArray
+
+from jina import Client
+from jina.types.request.data import Response
+
+# the callback function invoked when task is done
+def print_matches(resp: Response):
+   
+    # print top-3 matches for first doc
+    for idx, d in enumerate(resp.docs[0].matches[:3]):
+        print(f'[{idx}]{d.scores["cosine"].value:2f}')
+
+
+data = DocumentArray.from_files('img_align_celeba/*.jpg')
+
+def preprocess(doc):
+    return (
+        doc.load_uri_to_image_tensor(224, 224)
+        .set_image_tensor_normalization()
+        .set_image_tensor_channel_axis(-1, 0)
+    )
+
+
+data.apply(preprocess)
+
+# connect to localhost:12345
+c = Client(protocol='http', port=12345)
+c.post('/search', inputs=data, on_done=print_matches)
+```
+
+And outputs will be like this:
+
+```bash
+[0]0.000001
+[1]0.080417
+[2]0.115125
+```
+
+The first column is the index of images and the second column is the cosine distance.
+
+Congratulations! You have implemented the pipeline which includes finetuning the model, converting to ONNX and deploying in Jina `Flow`.

docs/index.md

@@ -93,6 +93,7 @@ get-started/swiss-roll/index
 get-started/totally-looks-like/index
 get-started/clinc150/index
 get-started/3d-mesh/index
+get-started/onnx/index
 ```