Merge pull request #279 from czoido/do-ai-conan

Yes, You Can Do AI with C++ blogpost

_posts/2024-12-23-You-can-do-AI-with-cpp.markdown

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+---
+layout: post
+comments: false
+title: "Yes, You Can Do AI with C++!"
+meta_title: "C++ AI Libraries Available in Conan Center Index"
+description: "Learn how to implement AI and Machine Learning in C++ using libraries like TensorFlow Lite, Dlib, and ONNX Runtime, all available in Conan Center Index. Discover why C++ is a powerful choice for AI development."
+keywords: "C++, AI, Machine Learning, TensorFlow Lite, Dlib, ONNX Runtime, Conan Center Index"
+---
+
+When thinking about Artificial Intelligence and Machine Learning, languages like Python
+often come to mind. However, **C++ is a powerful choice for developing AI and ML
+applications**, especially when performance and resource efficiency are critical. At
+[Conan Center](https://conan.io/center), you can find a variety of libraries that enable
+AI and ML development in C++. In this post, we will briefly highlight some of the most
+relevant ones available, helping you get started with AI development in C++ easily.
+
+### Why Use C++ for AI and Machine Learning?
+
+C++ offers several advantages for AI and ML development:
+
+- **Performance**: C++ provides high execution speed and efficient resource management,
+  making it ideal for computationally intensive tasks.
+- **Low-Level Optimizations**: C++ enables developers to utilize multiple compilation
+  options and optimize libraries directly from the source code. This provides precise
+  control over memory usage, inference processes, and hardware features like SIMD and
+  CUDA, allowing custom optimizations for specific hardware capabilities.
+
+In summary, C++ can be an excellent choice for working with AI. Let's explore some of the
+most representative AI libraries available in Conan Center Index.
+
+### An Overview of Some AI and ML Libraries Available in Conan Center
+
+Below are some notable libraries available in Conan Center Index. These libraries range
+from running large language models locally to optimizing model inference on edge devices
+or using specialized toolkits for tasks like computer vision and numerical optimization.
+
+#### [LLaMA.cpp](https://conan.io/center/recipes/llama-cpp)
+
+**LLaMA.cpp** is a C/C++ implementation of [Meta’s LLaMA models](https://www.llama.com/)
+and others, enabling local inference with minimal dependencies and high performance. It
+works on CPUs and GPUs, supports diverse architectures, and accommodates a variety of text
+models like [LLaMA 3](https://huggingface.co/models?search=llama),
+[Mistral](https://mistral.ai/), or [Phi](https://azure.microsoft.com/en-us/products/phi),
+as well as multimodal models like [LLaVA](https://github.com/haotian-liu/LLaVA).
+
+One of the most interesting aspects of this library is that it includes a collection of
+CLI tools as examples, making it easy to run your own LLMs straight out of the box. 
+
+Let's try one of those tools. First, install the library with Conan and ensure that you
+enable building the examples and activate the network options (which require `libcurl`).
+Then, use a [Conan deployer](https://docs.conan.io/2/reference/extensions/deployers.html)
+to move the installed files from the Conan cache to the user space. To accomplish this,
+simply run the following command:
+
+```shell
+# Install llama-cpp using Conan and deploy to the local folder
+$ conan install --requires=llama-cpp/b4079 --build=missing \
+                -o="llama-cpp/*:with_examples=True" \
+                -o="llama-cpp/*:with_curl=True" \
+                --deployer=direct_deploy
+```
+
+You can run your chatbot locally by invoking the packaged `llama-cli` application with a
+model from a Hugging Face repository. In this example, we will use a Llama 3.2 model with
+1 billion parameters and 6-bit quantization from the [unsloth
+repository](https://huggingface.co/unsloth). 
+
+Now, simply run the following command to start asking questions:
+
+```shell
+# Run llama-cli downloading a Hugging Face model
+$ ./direct_deploy/llama-cpp/bin/llama-cli \
+   --hf-repo unsloth/Llama-3.2-1B-Instruct-GGUF \
+   --hf-file Llama-3.2-1B-Instruct-Q6_K.gguf \
+   -p "What is the meaning to life and the universe?\n"
+```
+
+Let’s check out our LLM’s perspective:
+
+```text
+What is the meaning to life and the universe?
+
+The meaning to life and the universe is a subject of endless 
+debate among philosophers, theologians, scientists, and everyday 
+people. But what if I told you that there is a simple 
+yet profound truth that can help you find meaning and purpose 
+in life? It's not a complex theory or a scientific formula. 
+It's something that can be discovered by simply observing the 
+world around us.
+
+Here's the truth: **every moment is a 
+new opportunity to create meaning and purpose.**
+...
+```
+
+As you can see, in just a few minutes, we can have our own LLM running locally, all using
+C++. You can also use the libraries provided by the **llama-cpp** Conan package to
+integrate LLMs into your own applications. For example, here is the code for the
+[llama-cli](https://github.com/ggerganov/llama.cpp/blob/b4079/examples/main/main.cpp) that
+we just executed. For more information on the LLaMA.cpp project, please [check their
+repository on GitHub](https://github.com/ggerganov/llama.cpp).
+
+#### [TensorFlow Lite](https://conan.io/center/recipes/tensorflow-lite)
+
+**TensorFlow Lite** is a specialized version of [TensorFlow](https://www.tensorflow.org/)
+designed for deploying machine learning models on mobile, embedded systems, and other
+resource-constrained devices. It’s ideal for applications that require low-latency
+inference, such as edge computing or IoT devices. TensorFlow Lite focuses on optimizing
+performance while minimizing power consumption.
+
+<figure class="centered">
+    <img src="{{ site.baseurl }}/assets/post_images/2023-05-11/pose-detection-tensorflow.gif" 
+         style="display: block; margin-left: auto; margin-right: auto;" 
+         alt="Pose estimation with TensorFlow Lite"/>
+    <figcaption style="text-align: center; font-size: 0.9em;">
+        TensorFlow Lite in action
+    </figcaption>
+</figure>
+
+If you'd like to learn how to use TensorFlow Lite with a neural network model in C++, we
+previously published a [blog
+post](https://blog.conan.io/2023/05/11/tensorflow-lite-cpp-mobile-ml-guide.html)
+showcasing how to build a real-time human pose detection application using TensorFlow Lite
+and OpenCV. Check it out if you haven't read it yet.
+
+One of the interesting aspects of using the library is the availability of numerous models
+on platforms like [Kaggle Models](https://www.kaggle.com/models) for various tasks, which
+can be easily integrated into your code. For more information on Tensorflow Lite, please
+[check their documentation](https://www.tensorflow.org/lite/guide).
+
+#### [ONNX Runtime](https://conan.io/center/recipes/onnxruntime)
+
+**ONNX Runtime** is a high-performance inference engine designed to run models in the
+[ONNX](https://onnx.ai/) format, an open standard for representing network models across
+various AI frameworks such as PyTorch, TensorFlow, and scikit-learn.
+
+Thanks to this interoperability, ONNX Runtime allows you to use models trained in
+different frameworks with a single unified runtime. Here’s the general workflow:
+
+1. **Get a model**: Train a model using your preferred framework and export or convert it
+   to the ONNX format. There are [tutorials](https://onnxruntime.ai/docs/tutorials/)
+   available for popular frameworks and libraries.
+
+2. **Load and run the model with ONNX Runtime**: Check out these [C++ inference
+   examples](https://github.com/microsoft/onnxruntime-inference-examples/tree/main/c_cxx)
+   to get started quickly.
+
+Additionally, ONNX Runtime offers multiple options for tuning performance using various
+runtime configurations or hardware accelerators. Explore [the Performance section in the
+documentation](https://onnxruntime.ai/docs/performance/) for more details. For more
+information, visit the [ONNX Runtime documentation](https://onnxruntime.ai/docs/).
+
+Check all available versions in the Conan Center Index by running:
+
+```shell
+conan search onnxruntime
+```
+
+#### [OpenVINO](https://conan.io/center/recipes/openvino)
+
+**OpenVINO** (Open Visual Inference and Neural Network Optimization) is an
+[Intel-developed toolkit](https://docs.openvino.ai/) that accelerates deep learning
+inference on a wide range of devices. It supports models from frameworks like PyTorch,
+TensorFlow, and ONNX, offering tools to optimize, deploy, and scale AI applications
+efficiently.
+
+The [OpenVINO C++
+examples](https://docs.openvino.ai/2024/learn-openvino/openvino-samples.html) demonstrate
+tasks such as model loading, inference, and performance benchmarking. Explore these
+examples to see how you can integrate OpenVINO into your projects.
+
+For more details, visit the [OpenVINO documentation](https://docs.openvino.ai/2024/).
+
+Check all available versions in the Conan Center Index by running:
+
+```shell
+conan search openvino
+```
+
+#### [mlpack](https://conan.io/center/recipes/mlpack)
+
+**mlpack** is a fast, flexible, and lightweight header-only C++ library for machine
+learning. It is ideal for lightweight deployments and prototyping. It offers a broad range
+of machine learning algorithms for classification, regression, clustering, and more, along
+with preprocessing utilities and data transformations.
+
+Explore [mlpack’s examples
+repository](https://github.com/mlpack/examples/tree/master/cpp), where you’ll find C++
+applications such as training neural networks for digit recognition, decision tree models
+for predicting loan defaults, and clustering algorithms for identifying patterns in
+healthcare data.
+
+For further details, visit the [mlpack documentation](https://www.mlpack.org/).
+
+Check all available versions in the Conan Center Index by running:
+
+```shell
+conan search mlpack
+```
+
+#### [Dlib](https://conan.io/center/recipes/dlib)
+
+**Dlib** is a modern C++ library widely used in research and industry for advanced machine
+learning algorithms and computer vision tasks. Its comprehensive documentation and
+well-designed API make it straightforward to integrate into existing projects.
+
+Dlib provides a variety of algorithms, including facial detection, landmark recognition,
+object classification, and tracking. Examples of these functionalities can be found in
+[their GitHub repository](https://github.com/davisking/dlib/tree/master/examples). 
+
+For more information, visit the [Dlib official site](http://dlib.net/).
+
+Check all available versions in the Conan Center Index by running:
+
+```shell
+conan search dlib
+```
+
+## Conclusion
+
+C++ offers high-performance AI libraries and the flexibility to optimize for your
+hardware. With Conan, integrating these tools is straightforward, enabling efficient,
+scalable AI workflows.
+
+Now, give these tools a go and see your AI ideas come to life in C++!