Description
Demand for GPUs has grown exponentially since the onset of machine learning workloads. However, the cost of an efficient GPU remains very high. For a machine without a GPU, one solution is to send the GPU workload to a dedicated cluster of GPU-enabled instances for processing. However, without the proper knowledge, this method turns out to be very inefficient due to improper load balancing and instance tuning. We propose PyPlover, a serverless GPU framework that allows the user to send kernels and inputs to a serverless provider without needing to worry about set-up costs and load balancing.