Accelerate Your Machine Learning Applications With Zero Code Changes
Data Scientists and machine learning Engineers Can Now Speedup Their ML Applications Instantly Using the Power of Hardware Accelerators on the Cloud or on-Prem.
InAccel, a pioneer on FPGA-based acceleration, has released an accelerated machine learning platform that allows instant acceleration of ML applications and neural network models.
Data scientists and ML engineers can now speedup by more than 10x computationally intensive workloads and reduce the total cost of ownership with zero code changes. It fully supports widely used frameworks like Keras, Scikit-learn, Jupyter Notebooks and Spark.
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FPGAs are adaptable hardware platforms that can offer great performance, low-latency and reduced OpEx for applications like machine learning, video processing, quantitative finance, genomics, etc. However, the easy and efficient deployment from users with no prior knowledge on FPGA was challenging.
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 and data processing applications. Users can deploy their applications from Python, Jupyter notebooks or even terminals instantly.
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 any other 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 utilizing the aws f1 instances. That way, users can enjoy the simplicity of the Jupyter notebooks and at the same time experience significant speedups on their applications like regression, clustering or classification.
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