From 90e48acb4734717fc0f2db3c7520cb73a8e3c609 Mon Sep 17 00:00:00 2001 From: Vector Nguyen Date: Tue, 10 Oct 2023 10:34:10 +0700 Subject: [PATCH 1/8] Add solution improve Image Retrieval --- image-search-engine/README.md | 53 +++++++++++++++++++++++++++++++++-- 1 file changed, 50 insertions(+), 3 deletions(-) diff --git a/image-search-engine/README.md b/image-search-engine/README.md index 11cac16..78cf23c 100644 --- a/image-search-engine/README.md +++ b/image-search-engine/README.md @@ -1,5 +1,22 @@ # Shopee Image Search Engine +## Table of Contents + +1. [About the Solution](#about-the-solution) +2. [Web Framework for ML Services](#web-framework-for-ml-services) + - [FastAPI](#fastapi) +3. [Machine Learning Model Serving Platform](#machine-learning-model-serving-platform) + - [Triton Inference Server](#triton-inference-server) + - [BentoML](#bentoml) +4. [Vector Database - Vector Search](#vector-database---vector-search) + - [Faiss](#faiss) + - [Vector Search in ElasticSearch](#vector-search-in-elasticsearch) + - [Qdrant](#qdrant) +5. [Strategies for Improving Image Retrieval](#strategies-for-improving-image-retrieval) +6. [Development Environment](#development-environment) +7. [Testing and Results](#testing-and-results) +8. [References](#references) + ## About the Solution This project implements an image search engine for Shopee using qdrant as the vector database for efficient similarity search. The choice of qdrant over faiss was made to start with a small server that runs on CPU and scales when there is a significant increase in traffic. @@ -241,9 +258,33 @@ This project implements an image search engine for Shopee using qdrant as the ve - [Batch Vector Search with Qdrant](https://blog.qdrant.tech/batch-vector-search-with-qdrant-8c4d598179d5) - [Qdrant GitHub Repository](https://github.com/qdrant/qdrant/issues/1656) -## Environments +## Strategies for Improving Image Retrieval + +1. **Leveraging Deep Learning Architectures** + + Deep learning architectures, such as Convolutional Neural Networks (CNNs), can play a pivotal role in feature extraction. Utilize pre-trained models like Vision Transformers (ViTs), ResNet, VGG, or EfficientNet to extract meaningful image features. + + [Learn more](https://paperswithcode.com/sota/image-classification-on-imagenet) + +2. **Utilizing Embeddings** + + Consider employing techniques like Siamese networks or triplet loss to generate embeddings for images. These embeddings help create a feature space conducive to similarity-based searches, a core aspect of image retrieval. + +3. **Fine-tuning Models** + + Fine-tune pre-trained models on your dataset. Transfer learning enables you to fine-tune models to your specific task, capitalizing on the knowledge embedded in pre-trained weights. -### Development Environment +4. **Cross-Modal Retrieval** + + Leverage both image and text information if available in your dataset. Cross-modal retrieval techniques enable you to utilize textual descriptions or tags for enhanced retrieval. + + [Learn more](https://www.sbert.net/examples/applications/image-search/README.html) + +5. **Feedback Loops** + + Incorporate feedback loops where user interactions with the retrieval system inform and improve future recommendations. + +## Development Environment 1. **Create Environment and Install Packages** @@ -264,7 +305,7 @@ This project implements an image search engine for Shopee using qdrant as the ve uvicorn app:app ``` -### Results +## Testing and Results

Qdrant Vector Store @@ -274,3 +315,9 @@ This project implements an image search engine for Shopee using qdrant as the ve
- Created and added 100,000 points in 6 minutes in qdrant. - p95: [Provide Performance Data] + +## References + +- [Kaggle Landmark Retrieval Competition Discussion](https://www.kaggle.com/competitions/landmark-retrieval-2021/discussion/277099) +- [System Design for Discovery](https://eugeneyan.com/writing/system-design-for-discovery/) +- [Qdrant Food Discovery Demo](https://github.com/qdrant/demo-food-discovery.git) From 7bc23f0b77b686aaa36f9e13c04a4bf90d782c63 Mon Sep 17 00:00:00 2001 From: Vector Nguyen Date: Tue, 10 Oct 2023 14:08:09 +0700 Subject: [PATCH 2/8] Add locus test --- image-search-engine/locust/locustfile.py | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) create mode 100644 image-search-engine/locust/locustfile.py diff --git a/image-search-engine/locust/locustfile.py b/image-search-engine/locust/locustfile.py new file mode 100644 index 0000000..071e8a7 --- /dev/null +++ b/image-search-engine/locust/locustfile.py @@ -0,0 +1,18 @@ +from locust import HttpUser, task + + +class SearchImageUser(HttpUser): + @task + def search_image(self): + """ + Simulate a user uploading an image and performing a search. + """ + + # Create a random image file + image_file = open("test.jpg", "rb") + + # Construct the request body + request_body = {"file": image_file} + + # Send the request to the API and get the response + self.client.post("/search-image", files=request_body) From e4136b9a002b037b620e8f03ffae4261848c61ad Mon Sep 17 00:00:00 2001 From: Vector Nguyen Date: Tue, 10 Oct 2023 14:09:25 +0700 Subject: [PATCH 3/8] Add locus test --- image-search-engine/README.md | 26 ++++++++++++++++++++++++++ image-search-engine/app.py | 6 ++++++ 2 files changed, 32 insertions(+) diff --git a/image-search-engine/README.md b/image-search-engine/README.md index 78cf23c..dbd1a45 100644 --- a/image-search-engine/README.md +++ b/image-search-engine/README.md @@ -307,6 +307,32 @@ This project implements an image search engine for Shopee using qdrant as the ve ## Testing and Results +### Locust - Load Testing + +1. **Overview** + + Locust is an open-source, Python-based load testing tool that allows you to test the performance and scalability of your web applications or services. It is designed to be user-friendly, highly customizable, and easy to extend. + +2. **How to run** + + ``` + pip install locust + ``` + + ``` + cd locust + locust + ``` + +3. **References** + +- [Locust Documentation](http://docs.locust.io/en/stable/) +- [Writing a Locustfile](http://docs.locust.io/en/stable/writing-a-locustfile.html) +- [Increasing Performance](http://docs.locust.io/en/stable/increase-performance.html) +- [Running Distributed Tests](http://docs.locust.io/en/stable/running-distributed.html) + +### Results +

Qdrant Vector Store
diff --git a/image-search-engine/app.py b/image-search-engine/app.py index cab907a..85ef5c9 100644 --- a/image-search-engine/app.py +++ b/image-search-engine/app.py @@ -30,6 +30,12 @@ ) +@app.get("/") +def healthcheck() -> bool: + """Check the server's status.""" + return True + + @app.post("/search-image", response_model=list[Product]) async def search_image_qdrant(file: UploadFile = File(...)): """ From b677663e2c7778d5792bb1974ac9db43556cef7c Mon Sep 17 00:00:00 2001 From: Vector Nguyen Date: Tue, 10 Oct 2023 14:47:08 +0700 Subject: [PATCH 4/8] Add api health check --- text-search-engine/app.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/text-search-engine/app.py b/text-search-engine/app.py index 7b2a4fe..9390d5a 100644 --- a/text-search-engine/app.py +++ b/text-search-engine/app.py @@ -24,6 +24,12 @@ elastic_search = ElasticSearcher() +@app.get("/") +def healthcheck() -> bool: + """Check the server's status.""" + return True + + @app.get("/full-text-search", response_model=list[Product]) async def full_text_search(query: str, size: int): """ From 62df24c71ee072ba271e09b0dbe8f02dddbf0038 Mon Sep 17 00:00:00 2001 From: Vector Nguyen Date: Tue, 10 Oct 2023 14:47:52 +0700 Subject: [PATCH 5/8] Add triton server --- .gitignore | 3 +++ docker-compose.yaml | 16 ++++++++++++ image-search-engine/README.md | 8 ++++++ .../assets/documents/locust.jpg | Bin 0 -> 63463 bytes image-search-engine/locust/locustfile.py | 4 +-- .../efficientnet_b3/config.pbtxt | 24 ++++++++++++++++++ 6 files changed, 53 insertions(+), 2 deletions(-) create mode 100644 image-search-engine/assets/documents/locust.jpg create mode 100644 image-search-engine/model_repository/efficientnet_b3/config.pbtxt diff --git a/.gitignore b/.gitignore index bcd5985..3aa1880 100644 --- a/.gitignore +++ b/.gitignore @@ -13,6 +13,9 @@ data.csv image-search-engine/assets/uploaded_images/* !image-search-engine/assets/uploaded_images/.gitkeep +# Model +*.pth + # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] diff --git a/docker-compose.yaml b/docker-compose.yaml index 688d83a..f53a388 100644 --- a/docker-compose.yaml +++ b/docker-compose.yaml @@ -1,5 +1,21 @@ version: "3" services: + triton: + image: nvcr.io/nvidia/tritonserver:22.02-py3 + ipc: host + ports: + - 9000:8000 + - 9001:8001 + - 9002:8002 + command: tritonserver --model-repository=/models + volumes: + - ./image-search-engine/model_repository:/models + # deploy: + # resources: + # reservations: + # devices: + # - capabilities: [gpu] + elasticsearch: image: docker.elastic.co/elasticsearch/elasticsearch:7.10.0 container_name: elasticsearch diff --git a/image-search-engine/README.md b/image-search-engine/README.md index dbd1a45..cb707dc 100644 --- a/image-search-engine/README.md +++ b/image-search-engine/README.md @@ -322,8 +322,16 @@ This project implements an image search engine for Shopee using qdrant as the ve ``` cd locust locust + http://localhost:8089/ ``` +

+ Locust Load Test +
+ Locust Load Test +

+
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b/image-search-engine/model_repository/efficientnet_b3/config.pbtxt @@ -0,0 +1,24 @@ +name: "efficientnet_b3" +platform: "pytorch_libtorch" +max_batch_size: 32 +dynamic_batching { } +instance_group [ + { + count: 1 + kind: KIND_CPU + } +] +input [ + { + name: "input__0" + data_type: TYPE_FP32 + dims: [ 3, 320, 320 ] + } +] +output [ + { + name: "output__0" + data_type: TYPE_FP32 + dims: [ 1000 ] + } +] From 5e3a7f49be53601484d8cce1985b236b58f354bc Mon Sep 17 00:00:00 2001 From: Vector Nguyen Date: Tue, 10 Oct 2023 15:38:42 +0700 Subject: [PATCH 6/8] Compare FastAPI with Pytriton --- .pre-commit-config.yaml | 1 - image-search-engine/README.md | 54 +++++++++++++++++- .../assets/documents/fastapi-triton.png | Bin 0 -> 1016927 bytes .../assets/documents/triton.png | Bin 0 -> 73541 bytes 4 files changed, 51 insertions(+), 4 deletions(-) create mode 100644 image-search-engine/assets/documents/fastapi-triton.png create mode 100644 image-search-engine/assets/documents/triton.png diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index b2ba2ee..b96346a 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -6,7 +6,6 @@ repos: - repo: https://github.com/pre-commit/pre-commit-hooks rev: v4.1.0 hooks: - - id: check-added-large-files # prevents giant files from being committed. - id: check-merge-conflict # checks for some markers such as "<<<<<<<", "=======", and ">>>>>>>". - id: detect-private-key # detects the presence of private keys. - id: end-of-file-fixer # ensures that a file is either empty, or ends with one newline. diff --git a/image-search-engine/README.md b/image-search-engine/README.md index cb707dc..3ade4a9 100644 --- a/image-search-engine/README.md +++ b/image-search-engine/README.md @@ -71,7 +71,7 @@ This project implements an image search engine for Shopee using qdrant as the ve 1. **Overview**

- Triton Inference Server Architecture + Triton Inference Server Architecture
Batching Architecture

@@ -90,14 +90,62 @@ This project implements an image search engine for Shopee using qdrant as the ve - **Flexibility**: Triton supports various deployment options and can be integrated with existing infrastructure. - **Ease of Use**: Triton provides tools for deploying, managing, and monitoring AI models in production. -3. **Conclusion** +3. **FastAPI with Triton Inference Server** +

+ FastAPI with Triton Inference Server +
+ FastAPI - Triton Inference Server Architecture +

+ + Using FastAPI with Triton Server is a more flexible approach to deploying and serving AI models with Python. FastAPI is a modern, high-performance web framework that is easy to use and extend. Triton Server is a high-performance inference serving software that can be used to deploy and serve AI models on CPUs, GPUs, and other accelerators. + +4. **PyTriton** + + PyTriton is a Flask/FastAPI-like interface that simplifies Triton's deployment in Python environments. It provides a simple and intuitive way to deploy and serve AI models with Triton Server. + + ```python + import pytriton + + app = pytriton.App() + + # Define the model. + model = pytriton.Model( + "linear_regression", + "linear_regression.model", + [pytriton.Input("data", pytriton.DataType.FLOAT32, (-1, 1))], + [pytriton.Output("prediction", pytriton.DataType.FLOAT32, (1,))], + ) + + # Deploy the model. + app.deploy(model) + + # Start the Triton Inference Server. + app.start_triton() + + # Serve the model. + app.serve() + + ``` + + Once you have started the PyTriton application, you can access the model through its HTTP/gRPC API. For example, to make a prediction using the model, you can send an HTTP POST request to the /predict endpoint with the input data in the request body. + +5. **FastAPI with Triton Server and PyTriton Comparison** + Both FastAPI with Triton Server and PyTriton are good options for deploying and serving AI models with Python. However, there are some key differences between the two approaches: + + - Flexibility: FastAPI with Triton Server is a more flexible approach. You can use FastAPI to create custom APIs that expose your models in a variety of ways. PyTriton is less flexible, but it is easier to use. + - Performance: FastAPI with Triton Server can be more performant than PyTriton, especially if you are using a complex model or a large dataset. + - Ease of use: PyTriton is easier to use than FastAPI with Triton Server. PyTriton provides a simple and intuitive interface for deploying and serving models. + +6. **Conclusion** - Triton Inference Server is a powerful and flexible platform for deploying AI models at scale. It is a popular choice for companies and organizations needing to deploy AI models in production. -4. **References** +7. **References** - [Triton Inference Server GitHub Repository](https://github.com/triton-inference-server/client/blob/main/src/python/examples/image_client.py) - [Triton Inference Server Architecture](https://github.com/triton-inference-server/server/blob/main/docs/user_guide/architecture.md#models-and-schedulers) - [BentoML vs. Triton Inference Server](https://www.bentoml.com/blog/bentoml-or-triton-inference-server-choose-both) + - [FastAPI + gRPC AsyncIO + Triton GitHub Repository](https://github.com/Curt-Park/mnist-fastapi-aio-triton) + - [Best Tools to Do ML Model Serving](https://neptune.ai/blog/ml-model-serving-best-tools) ### BentoML diff --git a/image-search-engine/assets/documents/fastapi-triton.png b/image-search-engine/assets/documents/fastapi-triton.png new file mode 100644 index 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z)2M=C$(AnmYI}_vdUuIvo0FxOFruPRNDZr`dM3%i$CJ8hGn*r;A5+QWVyd~w zNw8F4Of&CnXTw(fXyunT%uk!B)U9L=0eCG=_%YOx*T{nIXwv@xuB!$}7mrbpfog$8h=<_{6_3KXSpn1zXZGCWZkSMqSA0CcaXS;yZ|6>NLLgh~u&)d?H h2mfD}+dj=~3n+iSA#k1=waq()(Ph(1C3-GV{~PqUlO+HE literal 0 HcmV?d00001 From bddbb6ec02908cca93bbf6d128eed000a5597031 Mon Sep 17 00:00:00 2001 From: Vector Nguyen Date: Tue, 10 Oct 2023 15:43:17 +0700 Subject: [PATCH 7/8] Update Compare FastAPI with Pytriton --- image-search-engine/README.md | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/image-search-engine/README.md b/image-search-engine/README.md index 3ade4a9..daea9df 100644 --- a/image-search-engine/README.md +++ b/image-search-engine/README.md @@ -129,12 +129,13 @@ This project implements an image search engine for Shopee using qdrant as the ve Once you have started the PyTriton application, you can access the model through its HTTP/gRPC API. For example, to make a prediction using the model, you can send an HTTP POST request to the /predict endpoint with the input data in the request body. -5. **FastAPI with Triton Server and PyTriton Comparison** - Both FastAPI with Triton Server and PyTriton are good options for deploying and serving AI models with Python. However, there are some key differences between the two approaches: +5. **FastAPI with Triton Server and PyTriton** - - Flexibility: FastAPI with Triton Server is a more flexible approach. You can use FastAPI to create custom APIs that expose your models in a variety of ways. PyTriton is less flexible, but it is easier to use. - - Performance: FastAPI with Triton Server can be more performant than PyTriton, especially if you are using a complex model or a large dataset. - - Ease of use: PyTriton is easier to use than FastAPI with Triton Server. PyTriton provides a simple and intuitive interface for deploying and serving models. + - Both FastAPI with Triton Server and PyTriton are good options for deploying and serving AI models with Python. However, there are some key differences between the two approaches: + + - _Flexibility_: FastAPI with Triton Server is a more flexible approach. You can use FastAPI to create custom APIs that expose your models in a variety of ways. PyTriton is less flexible, but it is easier to use. + - _Performance_: FastAPI with Triton Server can be more performant than PyTriton, especially if you are using a complex model or a large dataset. + - _Ease of use_: PyTriton is easier to use than FastAPI with Triton Server. PyTriton provides a simple and intuitive interface for deploying and serving models. 6. **Conclusion** @@ -146,6 +147,7 @@ This project implements an image search engine for Shopee using qdrant as the ve - [BentoML vs. Triton Inference Server](https://www.bentoml.com/blog/bentoml-or-triton-inference-server-choose-both) - [FastAPI + gRPC AsyncIO + Triton GitHub Repository](https://github.com/Curt-Park/mnist-fastapi-aio-triton) - [Best Tools to Do ML Model Serving](https://neptune.ai/blog/ml-model-serving-best-tools) + - [Pytriton GitHub Repository](https://github.com/triton-inference-server/pytriton) ### BentoML From 3ecc0020caa7ca6ce0458d7196cbaea767f9ae83 Mon Sep 17 00:00:00 2001 From: Vector Nguyen Date: Tue, 10 Oct 2023 21:54:21 +0700 Subject: [PATCH 8/8] Update text search --- frontend/src/app/page.tsx | 2 +- text-search-engine/elastic_ingest.py | 9 +++++++-- 2 files changed, 8 insertions(+), 3 deletions(-) diff --git a/frontend/src/app/page.tsx b/frontend/src/app/page.tsx index 749d0dc..fa2b500 100644 --- a/frontend/src/app/page.tsx +++ b/frontend/src/app/page.tsx @@ -27,7 +27,7 @@ export default function Home() { useEffect(() => { // This effect runs when the component mounts - const apiUrl = `${NEXT_PUBLIC_TEXT_SEARCH}/full-text-search?query=aoso&size=20`; // You can set your initial API URL here + const apiUrl = `${NEXT_PUBLIC_TEXT_SEARCH}/full-text-search?query=Áo&size=20`; // You can set your initial API URL here fetch(apiUrl, { method: "GET" }) .then((response) => response.json()) .then((data) => { diff --git a/text-search-engine/elastic_ingest.py b/text-search-engine/elastic_ingest.py index 1789479..f436ded 100644 --- a/text-search-engine/elastic_ingest.py +++ b/text-search-engine/elastic_ingest.py @@ -171,14 +171,19 @@ def indexing_batch_document(self): print(f"Indexed {successes}/{self.number_of_docs} documents") + def check_index_exists(self): + """Check index name exists""" + return not self.elastic_search.indices.exists(index=self.index_name) + def main(): """ Main function to perform Elasticsearch data ingestion. """ es_ingest = ElasticSeachIngest() - es_ingest.create_index() - es_ingest.indexing_batch_document() + if not es_ingest.check_index_exists(): + es_ingest.create_index() + es_ingest.indexing_batch_document() if __name__ == "__main__":