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Running Pandas on GPU, Taking It To The Moon🚀 - Analytics Vidhya


Pandas library comes in handy while performing data-related operations. Everyone starting with their Data Science journey has to get a good understanding of this library. Pandas can handle a significant amount of data and process it most efficiently. But at the core, it is still running on CPUs. Parallel processing can be achieved to speed up the process but it is still not efficient to handle large amounts of data.

Xilinx Kria Platform Brings Adaptive AI Acceleration To The Masses At The Edge


Silicon Valley adaptive computing bellwether Xilinx announced its entrance into the growing system-on-module (SOM) market today, with a portfolio of palm-sized compute modules for embedded applications that accelerate AI, machine learning and vision at the edge. Xilinx Kria will eventually expand into a family of single board computers based on reconfigurable FPGA (Field Programmable Gate Array) technology, coupled to Arm core CPU engines and a full software stack with an app store, the first of which is specifically is targeted at AI machine vision and inference applications. The Xilinx Kria K26 SOM employs the company's UltraScale multi-processor system on a chip (MPSoC) architecture, which sports a quad-core Arm Cortex A53 CPU, along with over 250 thousand logic cells and an H.264/265 video compression / decompression engine (CODEC). This may sound like alphabet soup as I spit out acronyms, however, the underlying solution is a compelling offering for developers and engineers looking to give new intelligent systems, in industries like security, smart cities, retail analytics, autonomous machines and robotics, the ability to see, infer information and adapt to their deployments in the field. Also on board the Xilinx Kria K26 SOM is 4GB of DDR4 memory and 245 general purpose IO, along with the ability to support 15 cameras, up to 40 Gbps of combined Ethernet throughput, and four USB 2/3 compatible ports.

How I built my ML workstation 🔬


Kaggle Kernels and Google Colab are great. I would drop my mic at this point if this article was not about building a custom ML workstation. There are always some "buts" that make our lives harder. When you start to approach nearly real-life problems and you see hundreds of gigabytes of large datasets, your gut feeling starts to tell you that your CPU or AMD GPU devices are not going to be enough to do meaningful things. This is how I came here. I was taking part in Human Protein Atlas (HPA) -- Single Cell Classification competition on Kaggle. I thought I would be able to prototype locally and then execute notebooks on the cloud GPU. As it turned out, there are a lot of frictions in the mentioned workflow. First of all, my solution source code quickly became an entire project with a lot of source code and dependencies. I used poetry as a package manager and decided to generate an installable package every time I made meaningful changes to the project in order to test them in the cloud. These installable packages I was uploading into a private Kaggle dataset which in turn was mounted to a notebook.

This Raspberry Pi Guitar Pedal Uses Machine Learning for Effects


NeuralPi is a Raspberry Pi-based guitar pedal that uses machine learning to create custom effects. We've always insisted the best Raspberry Pi …

The perfect JupyterLab environment with tensorflow_gpu installation


Note:- MLEnv is the name of the virtual environment. You can name it according to your choice. This will activate your virtual environment. It will open a new tab in your default browser and now you can create a new notebook in the location of your choice in that particular accessed drive and start coding. Hope you understand every point… If there is any doubt regarding the installation process, then please comment down below.

Does using Nvidia's DLSS require a better CPU? Ask an expert


Q: If you want to use DLSS to make a game run faster, does it need a better CPU since the GPU is utilized less? To bring everyone on the same page, let's recap first what DLSS is. Deep Learning Super Sampling, or DLSS, is Nvidia's proprietary technology that uses machine learning and dedicated hardware on the company's RTX cards to internally render games at a lower resolution, then upscale and output it to the desired resolution. The first version of this tech didn't look great, but version 2.0 brought massive improvements. Now, not only does the final result look virtually the same as native rendering in most circumstances, but you also get higher performance--meaning you can either enjoy those higher framerates at higher resolutions, or switch on a resource-intensive setting like raytracing without suffering for it.

Cloud GPU Instances: What Are the Options? - DATAVERSITY


Click here to learn more about Gilad David Maayan. If you're running demanding machine learning and deep learning models on your laptop or on GPU-equipped machines owned by your organization, there is a new and compelling alternative. All major cloud providers offer cloud GPUs – compute instances with powerful hardware acceleration, which you can rent per hour, letting you run deep learning workloads on the cloud. Let's review the concept of cloud GPUs and the offerings by the big three cloud providers – Amazon, Azure, and Google Cloud. A cloud graphics processing unit (GPU) provides hardware acceleration for an application, without requiring that a GPU is deployed on the user's local device.

Google plans to build a practical quantum computer by 2029 at new center


Google's new Quantum AI Campus in Santa Barbara, California, will employ hundreds of researchers, engineers and other staff. Google has begun building a new and larger quantum computing research center that will employ hundreds of people to design and build a broadly useful quantum computer by 2029. It's the latest sign that the competition to turn these radical new machines into practical tools is growing more intense as established players like IBM and Honeywell vie with quantum computing startups. The new Google Quantum AI campus is in Santa Barbara, California, where Google's first quantum computing lab already employs dozens of researchers and engineers, Google said at its annual I/O developer conference on Tuesday. A few initial researchers already are working there. One top job at Google's new quantum computing center is making the fundamental data processing elements, called qubits, more reliable, said Jeff Dean, senior vice president of Google Research and Health, who helped build some of Google's most important technologies like search, advertising and AI.

Google: We'll build this 'useful' quantum computer by the end of the decade


Google has unveiled its new Quantum AI campus in Santa Barbara, California, where engineers and scientists will be working on its first commercial quantum computer – but that will probably be a decade way. The new campus has a focus on both software and hardware. On the latter front, these include its first quantum data center, quantum hardware research labs, and Google's own quantum processor chip fabrication facilities, says Erik Lucero, lead engineer for Google Quantum AI in a blogpost. Quantum computers offer great promise for cryptography and optimization problems. ZDNet explores what quantum computers will and won't be able to do, and the challenges we still face.

Optimal training of variational quantum algorithms without barren plateaus Machine Learning

Variational quantum algorithms (VQAs) promise efficient use of near-term quantum computers. However, training VQAs often requires an extensive amount of time and suffers from the barren plateau problem where the magnitude of the gradients vanishes with increasing number of qubits. Here, we show how to optimally train a VQA for learning quantum states. Parameterized quantum circuits can form Gaussian kernels, which we use to derive optimal adaptive learning rates for gradient ascent. We introduce the generalized quantum natural gradient that features stability and optimized movement in parameter space. Both methods together outperform other optimization routines and can enhance VQAs as well as quantum control techniques. The gradients of the VQA do not vanish when the fidelity between the initial state and the state to be learned is bounded from below. We identify a VQA for quantum simulation with such a constraint that thus can be trained free of barren plateaus. Finally, we propose the application of Gaussian kernels for quantum machine learning.