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Accelerated Machine Learning

Accelerated Machine Learning from Your Browser in 2020

Data scientists and ML engineers can now speedup their applications using the power of FPGA accelerators from their browser.

FPGAs are adaptable hardware platforms that can offer great performance, low-latency and reduced OpEx for applications like machine learning, video processing, quantitative finance, etc. However, the easy and efficient deployment from users with no prior knowledge on FPGA was challenging.

InAccel, a pioneer on application acceleration, makes accessible the power of FPGA acceleration from your browser. Data scientists and ML engineers can now easily deploy and manage FPGAs, speeding up compute-intense workloads and reduce total cost of ownership with zero code changes.

InAccel provides an FPGA resource manager that allows the instant deployment, scaling and resource management of FPGAs making easier than ever the utilization of FPGAs for applications like machine learning, data processing, data analytics and many more applications. Users can deploy their application from Python, Spark, Jupyter notebooks or even terminals.

Through the JupyterHub integration, users can now enjoy all the benefits that JupyterHub provide such as easy access to computational environment for instant execution of Jupyter notebooks. At the same time, users can now enjoy the benefits of FPGAs such as lower-latency, lower execution time and much higher performance without any prior-knowledge of FPGAs. InAccel’s framework allows the use of Xilinx’s Vitis Open-Source optimized libraries or 3rd party IP cores (for machine learning, data analytics, genomics, compression, encryption and computer vision applications.)

The Accelerated Machine Learning Platform provided by InAccel’s FPGA orchestrator can be used either on-prem or on cloud. That way, users can enjoy the simplicity of the Jupyter notebooks and at the same time experience significant speedups on their applications.

Users can test for free the available libraries on the InAccel cluster using the following link: https://inaccel.com/accelerated-data-science/

The platform is available for demonstration purposes. Multiple users may access the available cluster with the 2 Alveo cards.

Source: Inside Big Data

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