New Libraries Boost Data Science and AI Model Performance Quickly

NumExpr vs. NumPy: A Performance Comparison Recently, a data scientist revealed that a library called NumExpr can perform certain complex numerical operations up to 15 times faster than NumPy, Python’s cornerstone for numerical computation. Given NumPy's dominant role in data science, widely used for machine learning, data exploration, and model training, this claim has garnered significant attention. To verify these assertions, actual performance tests were conducted. What is NumExpr? NumExpr is a fast numerical expression evaluator specifically designed for NumPy arrays. It optimizes memory usage and supports multi-threading, significantly reducing computation time and memory consumption, particularly on multi-core CPUs. According to its GitHub page, NumExpr excels in scenarios where high-performance numerical processing is required. Setting Up the Development Environment To test NumExpr, the data scientist recommended setting up an independent Python environment using tools like conda or Miniconda. The steps are straightforward: Create a new environment and install necessary libraries: (base) $ conda create -n numexpr_test python=3.12 -y (base) $ conda activate numexpr_test (numexpr_test) $ pip install numexpr (numexpr_test) $ pip install jupyter Start Jupyter Notebook. If the browser does not open automatically, use the URL provided in the command-line output. Performance Benchmarking Example 1: Simple Array Addition The first test involved simple large array addition, run 5000 times: - NumPy version: 12.036807 seconds - NumExpr version: 1.807596 seconds Result: NumExpr achieved a performance gain of approximately 6 times. Example 2: Monte Carlo Simulation to Estimate π Next, a Monte Carlo simulation was used to estimate π, run 1000 times: - NumPy version: 10.642844 seconds - NumExpr version: 8.077501 seconds Result: While not as dramatic, NumExpr still showed a 20% improvement. This was attributed to its suboptimal SUM() function. Example 3: Sobel Image Filter A Sobel filter for edge detection was then implemented: - NumPy version: 8.093792 seconds - NumExpr version: 4.938702 seconds Result: NumExpr almost doubled the performance in this scenario. Example 4: Fourier Series Approximation Finally, a Fourier series approximation for complex periodic functions was tested: - NumPy version: 7.765800 seconds - NumExpr version: 1.553160 seconds Result: NumExpr again demonstrated a significant advantage, achieving a 5 times speed improvement. Summary The performance tests reveal that NumExpr can indeed outperform NumPy in several numerical calculations, especially in multi-core CPU environments. While the official claim of a 15 times speed boost may be a bit exaggerated, the observed improvements ranging from a few times to 10 times are substantial. For data scientists and researchers who require high-performance numerical computations, NumExpr is a valuable tool to consider. Despite limited support for all NumPy operations, there are no significant negative impacts, making it a viable alternative. Industry Insiders' Evaluation and Company Profiles NumExpr is primarily developed and maintained by members of the Python scientific computing community, known for their commitment to improving Python's performance and functionality. The library's optimizations in memory management and multi-threading have won it positive reviews. Experienced data scientists highlight its usefulness in handling large datasets, noting that it provides significant speedups without compromising code readability. This endorsement from the community suggests that NumExpr has strong potential in the numerical computing space. Introducing Qwen3: A Leap Forward in Large Language Models The Qwen team recently announced the release of Qwen3, a new generation of large language models (LLMs) that surpasses previous versions in coding, mathematical, and general capabilities. The flagship model, Qwen3-235B-A22B, performs on par with top models like DeepSeek-R1, Grok-3, and Gemini-2.5-Pro. Even smaller models, like the Qwen3-30B-A3B, exhibit exceptional performance with only a fraction of the parameters. The smallest model, Qwen3-4B, matches the capabilities of the larger Qwen2.5-72B-Instruct. Key Features Dual-Thinking Modes Qwen3 supports two operation modes: - Thinking Mode: Gradual reasoning leading to a final answer, ideal for complex problems. - Non-Thinking Mode: Rapid responses for simple queries, focusing on speed rather than depth. This design allows users to balance cost and performance according to their needs. Multilingual Support Qwen3 supports 119 languages and dialects, covering major linguistic families, enhancing its global application potential. Pre-Training and Post-Training Processes Pre-Training: Qwen3’s data volume almost doubled from its predecessor, totaling about 36 trillion tokens. This includes web content and text extracted from PDFs, optimized by the Qwen2.5 series models. Post-Training: The process involves four stages: Initial Bootstrapping: Fine-tuning with diverse long-chain reasoning data. Reinforcement Learning Based on Reasoning: Expanding computational resources and improving exploration and exploitation. Fusion of Thinking Mode: Combining long-chain reasoning and common instruction fine-tuning data. General Reinforcement Learning: Enhancing overall model capability across various tasks. Usage and Deployment Qwen3 is available on platforms such as Hugging Face, ModelScope, and Kaggle, released under the Apache 2.0 license. Users can deploy it using frameworks like SGLang and vLLM, and for local development, tools like Ollama, LMStudio, lla.cpp, and KTransformers are recommended. Dynamic control over model behavior can be achieved by adding /think and /no_think tags in prompts or system messages. Proxy Functionality and Community Support Qwen3 also excels in tool invocation, with Qwen-Agent simplifying the process by encapsulating tool call templates and parsers. This reduces programming complexity and expands the model’s functional capabilities. The continued support and contributions from the community have been instrumental in the model's development. Future iterations aim to enhance Qwen3 further in data scale, model parameters, context length, modal support, and long-term reasoning through environmental feedback. Future Outlook Qwen3 represents a significant step towards artificial general intelligence (AGI) and artificial superintelligence (ASI). The team plans to improve the model from multiple angles, including increasing data size, optimizing reinforcement learning methods, and expanding modal support. This transformative approach signals a shift from mere model training to training intelligent agents, promising more meaningful assistance to users. Industry experts laud Qwen3 for its innovation and practicality. As a prominent project of Alibaba Cloud, Qwen3 is poised to lead in the competitive landscape of LLMs, driving advancements in the field. AutoRound: Advancing Post-Training Quantization The growing complexity of large language models (LLMs) and visual language models (VLMs) necessitates efficient deployment solutions. Intel's latest tool, AutoRound, addresses this challenge by optimizing weight-only post-training quantization (PTQ), achieving high accuracy and reduced inference latency. AutoRound uses gradient descent methods to optimize weight rounding and clipping ranges, maintaining performance even at low bit precisions such as INT2 to INT8. Key Benefits High Accuracy in Low-Bit Quantization AutoRound demonstrates superior accuracy in various tasks. For instance, at INT2 precision, it outperforms other popular baselines by 2.1 times in relative accuracy. At 4-bit precision, it remains competitive in most benchmarks. Broad Compatibility Support for LLMs: AutoRound works with almost all leading LLM architectures, including Qwen, LLaMA, and DeepSeek. Support for VLMs: It supports over 10 VLMs, such as Mistral-Small-3.1 and Gemma3, though some accuracy loss may occur without fine-tuning. Device Support: AutoRound runs on a variety of devices, including CPUs, Intel GPUs, and CUDA devices. Flexible and Efficient Quantization AutoRound requires only 200 tuning steps and a small calibration dataset of 128 samples to achieve high accuracy. Quantizing a 72 billion parameter model takes just 37 minutes on a Nvidia A100 GPU. Using AutoRound Installation Install AutoRound via pip: pip install auto-round Command-Line Usage AutoRound offers three configuration options: auto-round (default), auto-round-best (highest accuracy), and auto-round-light (fastest quantization). Select the appropriate configuration based on the model size and precision requirements. API Usage For programmatic integration: ```python from transformers import AutoModelForCausalLM, AutoTokenizer from auto_round import AutoRound model_name = "Qwen/Qwen3-0.6B" model = AutoModelForCausalLM.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) bits, group_size, sym = 4, 128, True autoround = AutoRound( model, tokenizer, bits=bits, group_size=group_size, sym=sym, ) output_dir = "./tmp_autoround" autoround.quantize_and_save(output_dir, format='auto_round,auto_awq,auto_gptq') ``` Inference AutoRound automatically selects the best backend for inference, ensuring flexibility across different devices. Converting GPTQ/AWQ Models Most GPTQ/AWQ models can be converted to AutoRound format to enhance compatibility with Intel devices and maintain performance: ```python from transformers import AutoModelForCausalLM, AutoTokenizer, AutoRoundConfig model_name = "ybelkada/opt-125m-gptq-4bit" quantization_config = AutoRoundConfig() model = AutoModelForCausalLM.from_pretrained(model_name, device_map="cpu", torch_dtype="auto", quantization_config=quantization_config) tokenizer = AutoTokenizer.from_pretrained(model_name) ``` Conclusion AutoRound marks a significant breakthrough in post-training quantization, offering high accuracy, efficiency, and broad compatibility. It enables effective deployment of LLMs and VLMs in resource-constrained environments, making AI more accessible and practical. We encourage users to try AutoRound and contribute to the community's efforts in advancing AI deployment. Industry Evaluation and Company Background Experts in the tech industry praise AutoRound for its innovative approach to quantization. They highlight its ability to maintain high accuracy while significantly reducing resource consumption, which is crucial for large-scale AI deployment and edge computing. Intel continues to lead in AI technology, and the release of AutoRound further solidifies its position. AutoRound's community support and flexible design make it a valuable tool for developers and researchers alike.