feat: upload torch executor

.github/README-exec/onnx.readme.md

@@ -0,0 +1,177 @@
+# CLIPOnnxEncoder
+
+**CLIPOnnxEncoder** is the executor implemented in [clip-as-service](https://github.com/jina-ai/clip-as-service). 
+It serves OpenAI released [CLIP](https://github.com/openai/CLIP) models with ONNX runtime (🚀 **3x** speed up). 
+The introduction of the CLIP model [can be found here](https://openai.com/blog/clip/).
+
+- 🔀 **Automatic**: Auto-detect image and text documents depending on their content.
+- ⚡ **Efficiency**: Faster CLIP model inference on CPU and GPU via ONNX runtime. 
+- 📈 **Observability**: Monitoring the serving via Prometheus and Grafana (see [Usage Guide](https://docs.jina.ai/how-to/monitoring/#deploying-locally)).
+
+
+## Model support
+
+Open AI has released 9 models so far. `ViT-B/32` is used as default model. Please also note that different model give **different size of output dimensions**. 
+
+| Model          | ONNX   | Output dimension | 
+|----------------|-----| --- |
+| RN50           | ✅   | 1024 | 
+| RN101          | ✅   | 512 | 
+| RN50x4         | ✅   | 640 |
+| RN50x16        | ✅   | 768 |
+| RN50x64        | ✅   | 1024 |
+| ViT-B/32       | ✅   | 512 |
+| ViT-B/16       | ✅   | 512 |
+| ViT-L/14       | ✅   | 768 |
+| ViT-L/14@336px | ✅   | 768 |
+
+## Usage
+
+### Use in Jina Flow 
+
+- **via Docker image (recommended)**
+
+```python
+from jina import Flow
+from docarray import Document
+import numpy as np
+
+f = Flow().add(
+    uses='jinahub+docker://CLIPOnnxEncoder',
+)
+```
+
+- **via source code**
+
+```python
+from jina import Flow
+from docarray import Document
+import numpy as np
+
+f = Flow().add(
+    uses='jinahub://CLIPOnnxEncoder',
+)
+```
+
+You can set the following parameters via `with`:
+
+| Parameter | Description                                                                                                                   |
+|-----------|-------------------------------------------------------------------------------------------------------------------------------|
+| `name`    | Model weights, default is `ViT-B/32`. Support all OpenAI released pretrained models.                                          |
+| `num_worker_preprocess` | The number of CPU workers for image & text prerpocessing, default 4.                                                          | 
+| `minibatch_size` | The size of a minibatch for CPU preprocessing and GPU encoding, default 16. Reduce the size of it if you encounter OOM on GPU. |
+| `device`  | `cuda` or `cpu`. Default is `None` means auto-detect.                                                                         |
+
+### Encoding
+
+Encoding here means getting the fixed-length vector representation of a sentence or image.
+
+```python
+from jina import Flow
+from docarray import Document, DocumentArray
+
+da = DocumentArray(
+    [
+        Document(text='she smiled, with pain'),
+        Document(uri='apple.png'),
+        Document(uri='apple.png').load_uri_to_image_tensor(),
+        Document(blob=open('apple.png', 'rb').read()),
+        Document(uri='https://clip-as-service.jina.ai/_static/favicon.png'),
+        Document(
+            uri='data:image/gif;base64,R0lGODlhEAAQAMQAAORHHOVSKudfOulrSOp3WOyDZu6QdvCchPGolfO0o/XBs/fNwfjZ0frl3/zy7////wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACH5BAkAABAALAAAAAAQABAAAAVVICSOZGlCQAosJ6mu7fiyZeKqNKToQGDsM8hBADgUXoGAiqhSvp5QAnQKGIgUhwFUYLCVDFCrKUE1lBavAViFIDlTImbKC5Gm2hB0SlBCBMQiB0UjIQA7'
+        ),
+    ]
+)
+
+f = Flow().add(
+    uses='jinahub+docker://CLIPTorchEncoder',
+)
+with f:
+    f.post(on='/', inputs=da)
+    da.summary()
+```
+
+From the output, you will see all the text and image docs have `embedding` attached.
+
+```text
+╭──────────────────────────── Documents Summary ─────────────────────────────╮
+│                                                                            │
+│   Length                        6                                          │
+│   Homogenous Documents          False                                      │
+│   4 Documents have attributes   ('id', 'mime_type', 'uri', 'embedding')    │
+│   1 Document has attributes     ('id', 'mime_type', 'text', 'embedding')   │
+│   1 Document has attributes     ('id', 'embedding')                        │
+│                                                                            │
+╰────────────────────────────────────────────────────────────────────────────╯
+╭────────────────────── Attributes Summary ───────────────────────╮
+│                                                                 │
+│   Attribute   Data type      #Unique values   Has empty value   │
+│  ─────────────────────────────────────────────────────────────  │
+│   embedding   ('ndarray',)   6                False             │
+│   id          ('str',)       6                False             │
+│   mime_type   ('str',)       5                False             │
+│   text        ('str',)       2                False             │
+│   uri         ('str',)       4                False             │
+│                                                                 │
+╰─────────────────────────────────────────────────────────────────╯
+```
+
+👉 Access the embedding playground in **clip-as-service** [doc](https://clip-as-service.jina.ai/playground/embedding), type sentence or image URL and see **live embedding**!
+
+### Ranking
+
+One can also rank cross-modal matches via `/rank` endpoint. 
+First construct a *cross-modal* Document where the root contains an image and `.matches` contain sentences to rerank. 
+
+```python
+from docarray import Document
+
+d = Document(
+    uri='rerank.png',
+    matches=[
+        Document(text=f'a photo of a {p}')
+        for p in (
+            'control room',
+            'lecture room',
+            'conference room',
+            'podium indoor',
+            'television studio',
+        )
+    ],
+)
+```
+
+Then send the request via `/rank` endpoint:
+
+```python
+f = Flow().add(
+    uses='jinahub+docker://CLIPTorchEncoder',
+)
+with f:
+    r = f.post(on='/rank', inputs=da)
+    print(r['@m', ['text', 'scores__clip_score__value']])
+```
+
+Finally, in the return you can observe the matches are re-ranked according to `.scores['clip_score']`:
+
+```bash
+[['a photo of a television studio', 'a photo of a conference room', 'a photo of a lecture room', 'a photo of a control room', 'a photo of a podium indoor'], 
+[0.9920725226402283, 0.006038925610482693, 0.0009973491542041302, 0.00078492151806131, 0.00010626466246321797]]
+```
+
+One can also construct `text-to-image` rerank as below:
+
+```python
+from docarray import Document
+
+d = Document(
+    text='a photo of conference room',
+    matches=[
+        Document(uri='https://picsum.photos/300'),
+        Document(uri='https://picsum.photos/id/331/50'),
+        Document(uri='https://clip-as-service.jina.ai/_static/favicon.png'),
+    ],
+)
+```
+
+👉 Access the ranking playground in **clip-as-service** [doc](https://clip-as-service.jina.ai/playground/reasoning/). Just input the reasoning texts as prompts, the server will rank the prompts and return sorted prompts with scores.

.github/README-exec/torch.readme.md

@@ -0,0 +1,179 @@
+# CLIPTorchEncoder
+
+**CLIPTorchEncoder** is the executor implemented in [clip-as-service](https://github.com/jina-ai/clip-as-service). 
+It serves OpenAI released [CLIP](https://github.com/openai/CLIP) models with PyTorch runtime. 
+The introduction of the CLIP model [can be found here](https://openai.com/blog/clip/).
+
+- 🔀 **Automatic**: Auto-detect image and text documents depending on their content.
+- ⚡ **Efficiency**: Faster CLIP model inference on CPU and GPU via leveraging the best practices. 
+- 📈 **Observability**: Monitoring the serving via Prometheus and Grafana (see [Usage Guide](https://docs.jina.ai/how-to/monitoring/#deploying-locally)).
+
+With advances of ONNX runtime, you can use `CLIPOnnxExecutor` (see [link](https://hub.jina.ai/executor/2a7auwg2)) instead to achieve **3x** model inference speed up.   
+
+## Model support
+
+Open AI has released **9 models** so far. `ViT-B/32` is used as default model. Please also note that different models give **the different sizes of output dimensions**. 
+
+| Model          | PyTorch | Output dimension | 
+|----------------|---------|------------------|
+| RN50           | ✅       | 1024             | 
+| RN101          | ✅       | 512              | 
+| RN50x4         | ✅       | 640              |
+| RN50x16        | ✅       | 768              |
+| RN50x64        | ✅       | 1024             |
+| ViT-B/32       | ✅       | 512              |
+| ViT-B/16       | ✅       | 512              |
+| ViT-L/14       | ✅       | 768              |
+| ViT-L/14@336px | ✅       | 768              |
+
+## Usage
+
+### Use in Jina Flow 
+
+- **via Docker image (recommended)**
+
+```python
+from jina import Flow
+from docarray import Document
+import numpy as np
+
+f = Flow().add(
+    uses='jinahub+docker://CLIPTorchEncoder',
+)
+```
+
+- **via source code**
+
+```python
+from jina import Flow
+from docarray import Document
+import numpy as np
+
+f = Flow().add(
+    uses='jinahub://CLIPTorchEncoder',
+)
+```
+
+You can set the following parameters via `with`:
+
+| Parameter               | Description                                                                                                                    |
+|-------------------------|--------------------------------------------------------------------------------------------------------------------------------|
+| `name`                  | Model weights, default is `ViT-B/32`. Support all OpenAI released pretrained models.                                           |
+| `num_worker_preprocess` | The number of CPU workers for image & text prerpocessing, default 4.                                                           | 
+| `minibatch_size`        | The size of a minibatch for CPU preprocessing and GPU encoding, default 32. Reduce the size of it if you encounter OOM on GPU. |
+| `device`                | `cuda` or `cpu`. Default is `None` means auto-detect.                                                                          |
+| `jit`                   | If to enable Torchscript JIT, default is `False`.                                                                              |
+
+### Encoding
+
+Encoding here means getting the fixed-length vector representation of a sentence or image.
+
+```python
+from jina import Flow
+from docarray import Document, DocumentArray
+
+da = DocumentArray(
+    [
+        Document(text='she smiled, with pain'),
+        Document(uri='apple.png'),
+        Document(uri='apple.png').load_uri_to_image_tensor(),
+        Document(blob=open('apple.png', 'rb').read()),
+        Document(uri='https://clip-as-service.jina.ai/_static/favicon.png'),
+        Document(
+            uri='data:image/gif;base64,R0lGODlhEAAQAMQAAORHHOVSKudfOulrSOp3WOyDZu6QdvCchPGolfO0o/XBs/fNwfjZ0frl3/zy7////wAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACH5BAkAABAALAAAAAAQABAAAAVVICSOZGlCQAosJ6mu7fiyZeKqNKToQGDsM8hBADgUXoGAiqhSvp5QAnQKGIgUhwFUYLCVDFCrKUE1lBavAViFIDlTImbKC5Gm2hB0SlBCBMQiB0UjIQA7'
+        ),
+    ]
+)
+
+f = Flow().add(
+    uses='jinahub+docker://CLIPTorchEncoder',
+)
+with f:
+    f.post(on='/', inputs=da)
+    da.summary()
+```
+
+From the output, you will see all the text and image docs have `embedding` attached.
+
+```text
+╭──────────────────────────── Documents Summary ─────────────────────────────╮
+│                                                                            │
+│   Length                        6                                          │
+│   Homogenous Documents          False                                      │
+│   4 Documents have attributes   ('id', 'mime_type', 'uri', 'embedding')    │
+│   1 Document has attributes     ('id', 'mime_type', 'text', 'embedding')   │
+│   1 Document has attributes     ('id', 'embedding')                        │
+│                                                                            │
+╰────────────────────────────────────────────────────────────────────────────╯
+╭────────────────────── Attributes Summary ───────────────────────╮
+│                                                                 │
+│   Attribute   Data type      #Unique values   Has empty value   │
+│  ─────────────────────────────────────────────────────────────  │
+│   embedding   ('ndarray',)   6                False             │
+│   id          ('str',)       6                False             │
+│   mime_type   ('str',)       5                False             │
+│   text        ('str',)       2                False             │
+│   uri         ('str',)       4                False             │
+│                                                                 │
+╰─────────────────────────────────────────────────────────────────╯
+```
+
+👉 Access the embedding playground in **clip-as-service** [doc](https://clip-as-service.jina.ai/playground/embedding), type sentence or image URL and see **live embedding**!
+
+### Ranking
+
+One can also rank cross-modal matches via `/rank` endpoint. 
+First construct a *cross-modal* Document where the root contains an image and `.matches` contain sentences to rerank. 
+
+```python
+from docarray import Document
+
+d = Document(
+    uri='rerank.png',
+    matches=[
+        Document(text=f'a photo of a {p}')
+        for p in (
+            'control room',
+            'lecture room',
+            'conference room',
+            'podium indoor',
+            'television studio',
+        )
+    ],
+)
+```
+
+Then send the request via `/rank` endpoint:
+
+```python
+f = Flow().add(
+    uses='jinahub+docker://CLIPTorchEncoder',
+)
+with f:
+    r = f.post(on='/rank', inputs=da)
+    print(r['@m', ['text', 'scores__clip_score__value']])
+```
+
+Finally, you can observe the matches are re-ranked based on `.scores['clip_score']`:
+
+```bash
+[['a photo of a television studio', 'a photo of a conference room', 'a photo of a lecture room', 'a photo of a control room', 'a photo of a podium indoor'], 
+[0.9920725226402283, 0.006038925610482693, 0.0009973491542041302, 0.00078492151806131, 0.00010626466246321797]]
+```
+
+One can also construct `text-to-image` rerank as below:
+
+```python
+from docarray import Document
+
+d = Document(
+    text='a photo of conference room',
+    matches=[
+        Document(uri='https://picsum.photos/300'),
+        Document(uri='https://picsum.photos/id/331/50'),
+        Document(uri='https://clip-as-service.jina.ai/_static/favicon.png'),
+    ],
+)
+```
+
+👉 Access the ranking playground in **clip-as-service** [doc](https://clip-as-service.jina.ai/playground/reasoning/). Just input the reasoning texts as prompts, the server will rank the prompts and return sorted prompts with scores.

.github/workflows/force-docker-build.yml

@@ -21,7 +21,7 @@ jobs:
         env:
           release_token: ${{ secrets.CAS_RELEASE_TOKEN }}
 
-  regular-release:
+  docker-release:
     needs: token-check
     runs-on: ubuntu-latest
     strategy:
@@ -104,7 +104,7 @@ jobs:
         if: ${{ matrix.engine_tag == '' && matrix.pip_tag != 'tensorrt' }}
         uses: docker/build-push-action@v2
         with:
-          context: .
+          context: server
           file: Dockerfiles/base.Dockerfile
           platforms: linux/amd64
           cache-from: type=registry,ref=jinaai/clip_executor:latest
@@ -116,13 +116,12 @@ jobs:
             CAS_VERSION=${{env.CAS_VERSION}}
             VCS_REF=${{env.VCS_REF}}
             BACKEND_TAG=${{env.BACKEND_TAG}}
-            PIP_TAG=${{matrix.pip_tag}}
       - name: CUDA Build and push
         id: cuda_docker_build
         if: ${{ matrix.engine_tag == 'cuda' }}
         uses: docker/build-push-action@v2
         with:
-          context: .
+          context: server
           file: Dockerfiles/cuda.Dockerfile
           platforms: linux/amd64
           cache-from: type=registry,ref=jinaai/clip_executor:latest-cuda
@@ -134,4 +133,3 @@ jobs:
             CAS_VERSION=${{env.CAS_VERSION}}
             VCS_REF=${{env.VCS_REF}}
             BACKEND_TAG=${{env.BACKEND_TAG}}
-            PIP_TAG=${{matrix.pip_tag}}