Month: September 2021

Imagine you’re sitting in Discord chat, telling your buddies about the last heroic round of your favorite game, where you broke through the enemy’s defenses and cinched the victory on your own. Your friends think you’re bluffing and demand proof. With GeForce NOW’s content capture tools running automatically in the cloud, you’ll have all the Read article >

The post How to Use NVIDIA Highlights, Freestyle and Montage in GeForce NOW appeared first on The Official NVIDIA Blog.

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AI is at play on a global stage, and local developers are stealing the show. Grassroot communities are essential to driving AI innovation, according to Kate Kallot, head of emerging areas at NVIDIA. On its opening day, Kallot gave a keynote speech at the largest AI Expo Africa to date, addressing a virtual crowd of Read article >

The post The Bright Continent: AI Fueling a Technological Revolution in Africa appeared first on The Official NVIDIA Blog.

The transportation industry is adding more torque toward realizing autonomy, electrification and sustainability. That was a key takeaway from Germany’s premier auto show, IAA Mobility 2021 (Internationale Automobil-Ausstellung), which took place this week in Munich. The event brought together leading automakers, as well as execs at companies that deliver mobility solutions spanning from electric vehicles Read article >

The post Autonomy, Electrification, Sustainability Take Center Stage at Germany’s IAA Auto Show appeared first on The Official NVIDIA Blog.

Running PyCarert on GPU not only streamline model building but offsets the time cost.

PyCaret is a low-code Python machine learning library based on the popular Caret library for R. It automates the data science process from data preprocessing to insights, such that short lines of code can accomplish each step with minimal manual effort. In addition, the ability to compare and tune many models with simple commands streamlines efficiency and productivity with less time spent in the weeds of creating useful models.

The PyCaret team added NVIDIA GPU support in version 2.2, including all the latest and greatest from RAPIDS. With GPU acceleration, PyCaret modeling times can be between 2 and 200 times faster depending on the workload.

This post will go over how to use PyCaret on GPUs to save both development and computation costs by an order of magnitude.

All benchmarks were run with nearly identical code on a machine with a 32-core CPU and four NVIDIA Tesla T4s. For simplicity, GPU code was written to run on a single GPU.

Getting started with PyCaret

Using PyCaret is as simple as importing the library and executing a set-up statement. The setup() function creates the environment and offers a host of pre-processing features all in one go.

from pycaret.regression import *
exp_reg = setup(data = df, target = ‘Year’, session_id = 123, normalize = True)

After a simple setup, a data scientist can develop the rest of their pipeline, including data preprocessing/preparation, model training, ensembling, analysis, and deployment. After the data is prepared, a great place to start is by comparing models.

True to PyCaret’s ethos of simplicity, we can compare a host of standard models to see which are best for our data with a single line of code. The compare_models command trains all the models in PyCaret’s model library using default hyperparameters and evaluates performance metrics using cross-validation. A data scientist can then select the models they’d like to use, tune, and ensemble based on this info.

top3 = compare_models(exclude = [‘ransac’], n_select=3)

Comparing Models

**Models are sorted best to worst, and PyCaret highlights the top results in each metric category for ease of use.

Accelerating PyCaret with RAPIDS cuML

PyCaret is a great tool for any data scientist to have in their arsenal, as it streamlines model building and makes running many models easy. PyCaret can be made even better with GPUs. Since PyCaret does so much work behind the scenes, seemingly simple commands can take a long time. For example, we ran the commands preceding on a dataset with roughly half a million instances and over 90 attributes (UC Irvine’s Year Prediction MSD dataset). On the CPU, it took over 3 hours. On a GPU, it took less than half that.

In the past, using PyCaret on a GPU would have required many manual coding, but thankfully, the PyCaret team has integrated the RAPIDS machine learning library (cuML), meaning you can use the same simple API that makes PyCaret so effective while also using the computational ability of your GPU.

Running PyCaret on a GPU tends to be much faster-meaning you can make full use of everything PyCaret has to offer without balancing time costs. Using the same dataset just mentioned, we tested PyCaret ML functionality on both a CPU and a GPU, including comparing, creating, tuning, and ensembling models. Performing the switch to GPU is simple; we set use_gpu to True in the setup function:

exp_reg = setup(data = df, target = ‘Year’, session_id = 123, normalize = True, use_gpu = True)

With PyCaret set to run on GPU, it uses cuML to train all of the following models:

• Logistic Regression
• Ridge Classifier
• Random Forest
• K Neighbors Classifier
• K Neighbors Regressor
• Support Vector Machine
• Linear Regression
• Ridge Regression
• Lasso Regression
• K-Means Clustering
• Density-Based Spatial Clustering

Running the same compare_models code solely on GPU was over 2.5 times as fast.

The impact was even greater on a model-by-model basis with popular but computationally expensive models. The K Neighbors Regressor, for example, was 265 times as fast on GPU.

Impact

The simplicity of PyCaret’s API frees up time that would otherwise be spent coding so data scientists can do more experiments and fine-tune their experiments. When paired with GPUs, this impact is even greater, as the computation costs of taking full advantage of PyCaret’s suite of evaluation and comparison tools are significantly lower.

Conclusion

Extensive comparing and evaluating models can help improve the quality of your results, and doing so efficiently is exactly what PyCaret is for. PyCaret on GPU offsets the time costs that go along with so much processing.

The goal of RAPIDS is to accelerate your data science, and PyCaret is among a growing list of libraries whose compatibility with the RAPIDS suite can help bring a new layer of efficiency to your machine learning pursuits.

**Code used for this notebook can be found here.

Combining mentoring, socializing, and specialized training proved key for the virtual 2021 KISTI GPU Hackathon.

Due to the coronavirus, the 2021 Korea Institute of Science and Technology Information (KISTI) GPU Hackathon was held virtually, under the guidance of expert mentors from KISTI, NVIDIA, and the OpenACC Organization. With the goal of inspiring possibilities for scientists to accelerate their AI research or HPC codes, the hackathon provided opportunities for solving research problems and expanding expertise using NVIDIA GPU parallel computing technology. 

Known for being a face-to-face event, a virtual hackathon poses its own challenges for both attendees and hosts. The new format also required juggling a diversity of teams—composed of three HPC and AI teams, four higher education and research teams, and two industry teams. 

The event team found the following recipe helped create a meaningful and successful experience for the participants:

Mentoring 

Based on their expertise in specific domains or programming languages, dedicated mentors were paired with teams for guidance in setting goals, and considering different approaches. The mentors collaboratively worked to solve problems and troubleshoot obstacles the teams encountered. Daily mentor sync-up calls each day kept everyone focused and working toward the best strategy for meeting their goals. 

Socializing 

Everyone knows that all work and no play can actually hinder a team’s productivity. The hackathon provided a TGIF social hour session for participants and mentors. Using the Metaverse Gather Town Space, mentors and teams shared experiences, recharged their batteries, and developed connections that helped them continue forward for the duration of the event.

Resources and Live Seminars

Another important ingredient to success was making specialized training and resources available to attendees. For example, an NVIDIA Deep Learning Institute (DLI) workshop covering CUDA C/C++ topics was presented by a DLI ambassador and mentor. Other mentors provided team-dedicated tech sessions focused on TRT and NVIDIA Triton, OpenACC, and Nsight systems for profiling, parallel computing, and optimization.

Hard Work Pays Off

The PasCal team from Yonsei University is developing a thermal fluid solver that efficiently calculates the thermal motion of turbulence. At this hackathon, the team converted existing code based on CPUs to a multi-GPU environment through OpenACC and cuFFT Library. This resulted in accelerating the calculations by 4.84 times RHS (right-hand side, fraction step) of one of most time consuming subroutines. 

The Amore Opt team from AmorePacific cosmetics company worked on GPU optimization of DeepLabV3 + segmentation model. By applying what they learned about the TensorRT Inference optimizer and NVIDIA Triton inference server, they improved inference speed making it 26 times faster. They did this while maintaining the accuracy in their AI models to detect skin problems for their future large-scale customer service. 

The TFC team from Seoul National University joined a project to accelerate a CPU-based Fortran in-house fluid calculation code. By using NVIDIA GPUs at KISTI, the team accelerated the time-consuming Tri-Diagonal Matrix Algorithm (TDMA) for thermal solver and momentum solver and Fast Fourier Transform (FFT) for pressure solver calculations. They achieved a speed 11.15 times faster on a single V100 GPU.

NVIDIA Inception member Nota and HangYang University teamed-up to optimize the Nota Model compression engine by leveraging the Tensor Core in NVIDIA GPUs for INT4 quantization. Named NOTA-HYU, the team learned to use NVIDIA profiling tools Nsight system and Nsight Compute. They then applied NVIDIA library CUTLASS to achieve an overall speed 1.85 times faster for their residual block with CUDA optimization.

For more information on GPU Hackathons and future events, visit https://www.gpuhackathons.org/

Also don’t miss out on the OpenACC Summit 2021 scheduled from September 14-15, 2021.