If you are writing an AI application that handles text in Natural Language Processing (NLP) models, you will be pleased to hear that OpenVINO Model Server now supports sending and receiving text in string format.
Now you can combine optimized inference execution with a simple method for sending text data to the model server and reading text responses.
Introduction
Deep Learning models do not deal with text content directly. Instead, they require a numerical representation of text to process it.
Figure 1. Shows Text Input (yellow), Tokenization (blue) and Encoding (green)
The conversion from human readable text to a machine-readable format is done via a process of tokenization and encoding. Without going into the specifics of tokenization and encoding, these operations are not trivial. Many algorithms exist for these tasks and most often the operation is run by dedicated software libraries.
Generally, during the inference operation, a client application must reproduce the same method for text tokenization and encoding, similar to what is used during the model training phase.
For reference, below are two examples showing how this can be implemented on the application side as pre- and post-processing steps:
In TensorFlow it’s also possible to embed the tokenization operation inside the model by adding a dedicated neuron model layer SentencePieceTokenizer.
Tokenization and Encoding with OpenVINO Model Server
Starting with the 2023.0 release, OpenVINO Model Server can greatly simplify writing applications that leverage LLM and NLP models. We addressed both using models that require tokens and models with an embedded tokenization layer. Both use cases are demonstrated below with a simple client application that sends and receives text in a string format. The complexity of text conversion is fully delegated to the remote serving endpoint.
GPT-J Pipeline
In this demo we deploy the tokenizer as a custom node in OpenVINO Model Server. As a result, we get a pipeline with seed strings as input and generated texts as the output.
Text generation can be executed iteratively in a loop. An example of the client application generating text output is shown below.
Multilingual Universal Sentence Encoder (MUSE)
The next demonstration includes serving the MUSE model from TensorFlow Hub. The demo shows how OpenVINO Model Server can be used to servethe MUSE model and with 2x better performance without any changes on the client side.
The calls to the model server are simple using a REST API. Below is an example of a call with a batch size 3.
curl -X POST http://localhost:8000/v1/models/usem:predict \
-H 'Content-Type:application/json' \
-d'{"instances": ["dog", "Puppies are nice.","I enjoy taking long walks along the beach with my dog."]}'
A similar call can be made over gRPC interface using the ovmsclient library which is compatible with the TensorFlow Serving (TFS) API.
from ovmsclientimport make_grpc_client
client = make_grpc_client("localhost:9000")
data = ["dog","Puppies are nice.", "I enjoy taking long walks along the beachwith my dog."]
inputs = {"inputs":data}
results = client.predict(inputs=inputs,model_name="usem")
In addition to the TFS API, it is also possible to run inference calls using the KServe v2 API. Check the code snippets for more details.
Conclusion
OpenVINO Model Server can simplify writing AI applications that handle text. It can execute a complete text analysis pipeline with just few[TA4] lines of code on the client side without compromising performance by using a tokenizer in C++ and high performance OpenVINO backend to run the AI models. Together with widely used, standard APIs, OpenVINO Model Server is a great solution for deploying effective and efficient AI applications.
In the ever-evolving landscape of biopharmaceutical technology and drug development, a recent effort in the field of Cell Analytics for Monoclonal Antibody Production has shed light on the crucial role of Edge AI Technology in navigating complex challenges of scaling and producing solutions.
In this 2-part blog series, we will explore the use of Intel Edge AI Technology in biopharma and drug development, addressing challenges and providing insights into the development of AI pipelines for cell segmentation and analysis.
Intel has been involved in this process with a variety of partners. One of Intel’s contributions to the cell image project centers around processing brightfield1 images using an AI pipeline containing multiple deep learning models. The pipeline's purpose is to identify cells and other biological components and provide feedback on dynamic biological characteristics such as cell morphology, viability, and phenotypic changes, among others. Throughout this process, working on cell-AI projects usually brings a unique set of challenges to the forefront.
First, it is an interdisciplinary field and the knowledge gap between data scientists and biopharma experts requires more back-and-forth clear communications for planning and validity checks. Frequently when attempting to implement AI solutions in the laboratory, data scientists and bench scientists struggle to fully grasp the nature and needs of each other’s role. This lack of mutual understanding can also hinder the usability and scalability of an AI solution needing to be integrated into diverse lab environments.
The secondchallenge is instrument variability. Different plate reader2 microscopes have different hardware, optics, and apertures which cause their produced images not to be consistent. This adds an extra layer of work to assess and address these inconsistencies along the way (like regular tracked calibration and adjustment). Additionally, equipment vendor-to-vendor differences, culture temperature, medium conditions, and genetic modifications can all affect the variability of data and the inherent transferability of the deep learning pipeline. This would drive the need to monitor the performance of DL models at the edge and cloud ML ops components.
The third challenge is obtaining peer-review labels because the process is based on supervised Machine Learning and obtaining clean accurate labels is very costly and time-consuming.
And the last challenge is about the model deployment. In most cases, cloud deployment is not an option due to data size and data privacy. Produced images from plate reader microscopes are huge and transferring data to the cloud and sending the results back would create high latency because a huge amount of data must be streamed (30Gb per hour). And more importantly, laboratories are usually not willing to share the data. Due to these two constraints, cloud deployments are not usually an option, and the pipeline must be deployed at the edge.
Now, let’s talk about a specific application of this technology: the CHO Cell Segmentation Use Case.
CHO Cell Segmentation Use Case
CHO cells, or Chinese Hamster Ovary cells, are a cornerstone in the production of complex protein molecules such as monoclonal antibodies, fusion proteins, hormones, and coagulation factors. Unlike stem cells or CAR-T cells, where the cells themselves are the therapeutic product, in CHO cells, it is the proteins they produce that are of paramount importance. Monitoring the health, viability, and production capability of these cells is a critical step in commercial protein production.
Traditionally, assessing the condition of CHO cells involves a multi-step process that is not only time-consuming but also requires the use of expensive reagents and chemicals. Depending on the process, the workflow can be something like below.
Culture cells
Fix cells – wash in expensive reagents to remove the culture medium.
Permeabilization – wash in more expensive chemicals to permeabilize the cell membrane (to stain for intercellular proteins).
Blocking – incubate cells in another expensive reagent to prevent binding of no specific antibodies.
Primary Antibody Incubation – antibody specifically to bind to a protein that is being produced.
Washing – removing unbound Primary Antibodies using more expensive chemicals.
Nuclear staining – use nuclear stain like DAPI to visualize cell nuclei then wash with the same chemicals from the washing step
Mounting – get ready to read in the microscope (plate reader1)
Imaging – Stained cells …. count them up and determine the state in the protein production cycle and relative cell health (eventually they peter out and stop producing and the batch needs to be flushed. (Cell count, viability number, etc. are the output not the image)
From culturing to imaging, each step plays a vital role in ensuring the quality of the protein product. However, with the advent of AI and deep learning, there is an opportunity to streamline this workflow significantly. Using an AI pipeline including multiple Deep Learning models and data pre and post-processing, we can go from Step 1 directly to Step 9, removing the majority of the labor and latency in getting actionable results out of a staining workflow and bypassing expensive specialty chemicals requirement. Intel has put together a reference implementation for deploying said pipeline and inferencing of these images on the edge as part of the Cell Image project https://www.cellimage.ie/. OpenVINO Toolkit, OpenVINO Model Server, and AI Connect for Scientific Data are used in this design.Let’s briefly talk about each of these wonderful SW packages in part 2 of this article series. Stay tuned!
Conclusion
In conclusion, the integration of Intel Edge AI Technology into the biopharmaceutical sector represents a transformative step towards more efficient and scalable drug development processes. As we have seen in this first installment of our blog series, the deployment of AI pipelines for cell segmentation and analysis in monoclonal antibody production is not without its challenges. These include bridging the interdisciplinary knowledge gap, managing instrument variability, acquiring peer-reviewed labels, and overcoming the hurdles associated with model deployment.
Despite these challenges, the potential benefits of Edge AI in biopharma are substantial. By leveraging Intel's advanced AI technologies, we can significantly reduce the time and cost associated with traditional cell analysis methods, while also enhancing the accuracy and reliability of the results. The use of edge computing addresses the concerns of data size and privacy, allowing for real-time processing and analysis without the need for cloud transfer.
As we move forward in this blog series, we will delve deeper into the specifics of Intel's Edge AI solutions, including the OpenVINO toolkit, OpenVINO Model Server, and AI Connect for Scientific Data. We will explore how these tools are being applied in real-world scenarios to drive innovation and improve outcomes in the realm of biopharma and drug development in the next part of this series.
We'd like to hear from you! Let us know in the comments or discuss – which AI use cases in health and life sciences do you think will have the greatest impact on global health?
If you enjoyed hearing from the Health and Life Sciences team and want to hear more, give this post a like and ensure you subscribe to get the latest updates from the team.
About the Author
Nooshin Nabizadeh has Ph.D. in Electrical and Computer Engineering from the University of Miami and works at Intel Corporation as AI Solutions Architect. She enjoys photography, writing poetry, reading about psychology and philosophy, and optimizing solutions to run as fast as possible on a given piece of hardware. Connect with her on LinkedIn https://www.linkedin.com/in/nooshin-nabizadeh/ by mentioning this blog.
Brightfield microscopy is a widely used technique for observing the morphology of cells and tissues.
A plate reader is a laboratory instrument used to obtain images from samples in microtiter plates. The reader shines a specific calibrated frequency of light (UV, visible, fluorescence, etc.) through the samples in the wells of the plate. Plate reader microscopy data sets have inherent variability which drives the requirement of regular tracked calibration and adjustment.
GroundingDINO introduces a language-guided query selection module to enhance object detection using input text. This module selects relevant features from image and text inputs and uses them as decoder queries. In this blog, we provide the OpenVINO™ optimization for GroundingDINO on Intel® platforms.
The public GroundingDINO project is referenced from: GroundingDINO
The GroundingDINO refer the model structure in below picture:
Figure 1. The framework of Grounding DINO. We present the overall framework, a feature enhancer layer, and a decoder layer in block 1,block 2, and block 3,respectively.
OpenVINO™ backend on GroundingDINO
In this project, you do not require to download OpenVINO™ and build the library with GroundingDINO project manually. It’s already fully integrated with OpenVINO™ runtime library for downloading, program compiling and linking.
At present, this repository already optimized and validated by OpenVINO™ 2023.1.0.dev20230811 version. Check the operating system which can support OpenVINO™ runtime library directly:
Ubuntu 22.04 long-term support (LTS), 64-bit (Kernel 5.15+)
Ubuntu 20.04 long-term support (LTS), 64-bit (Kernel 5.15+)
Ubuntu 18.04 long-term support (LTS) with limitations, 64-bit (Kernel 5.4+)
Windows* 10
Windows* 11
macOS* 10.15 and above, 64-bit
Red Hat Enterprise Linux* 8, 64-bit
Step 1: Install system dependency and setup environment
OpenVINO is an open-source toolkit for optimization and deployment of AI inference. OpenVINO results in more efficient inference of deep learning models at the edge or in data centers. OpenVINO compiles models to run on many different devices, meaning you will have the flexibility to write code once and deploy your model across CPUs, GPUs, VPUs and other accelerators.
The new family of Intel discrete GPUs are not just for gaming, they can also run AI at the edge or on servers. Use this guide to install drivers and setup your system before using OpenVINO for GPU-based inference.
OpenVINO and GPU Compatibility
To get the best possible performance, it’s important to properly set up and install the current GPU drivers on your system. Below, I provide some recommendations for installing drivers on Windows and Ubuntu. This article was tested on Intel® Arc™ graphics and Intel® Data Center GPU Flex Series on systems with Ubuntu 22.04 LTS and Windows 11. To use the OpenVINO™ GPU plugin and offload inference to Intel® GPU, the Intel® Graphics Driver must be properly configured on your system.
Recommended Configuration for Ubuntu 22.04 LTS
The driver for Ubuntu 22.04 works out of the box with Kernel 5.15.0-57. However, if you upgraded/downgraded your kernel or upgraded from Ubuntu 20.04 LTS to 22.04, I suggest updating the kernel version to linux-image-5.19.0-43-generic.
After updating the kernel, check for the latest driver release. I updated my Ubuntu machine to version 23.13.26032.30, which was the latest version at the time of publishing this article, however OpenVINO could be run on discrete GPU with older or newer driver versions.
NOTE: If you upgraded Ubuntu 20.04 to 22.04, please verify your kernel version `uname –r` before updating the driver.
Even if you are using the latest available driver, you should always check if your AI models are running properly and generating the expected results. If you discover a bug for a particular model or failure to run a specific model, please file an issue on GitHub. Before reporting an issue, please check whether using the latest Beta version of the driver and latest version of OpenVINO solves the issue.
NOTE: Always refer to the official GPU driver documentation when setting up your system. This blog provides additional recommendations for the best results when using OpenVINO but it is not a replacement for documentation.
Conclusion
Checking the system requirements in Ubuntu 22.04 LTS and Windows 11 resolves some issues running Generative AI models like Stable Diffusion with OpenVINO on discrete GPUs. These updates prevent crashes and compilation errors or poor performance with Stable Diffusion. I suggest testing your AI models with the new driver installation, as it will likely improve the performance of your application. Try out this Stable Diffusion notebook for testing purposes.
