Deep learning uses neural networks to exhibit human-like learning capabilities. Training deep learning models is not feasible on traditional CPU servers. Deep learning workloads require fast-processing capabilities GPUs. E2E Deep Learning Cloud delivers the GPU-accelerated computing power needed for your deep learning model training and inferencing.
E2E Deep Learning Cloud offers GPU Cloud Instances based on NVIDIA Tesla v100 and NVIDIA T4 GPU cards. NVIDIA Tesla V100 is the most advanced data center GPU ever built.
You can use NVIDIA Tesla V100 based GPU instances to train your deep learning models to bring down the time to deliver and overall computational costs.
You can deploy the trained models on NVIDIA T4 for inferencing in the production environments. Also, you can train deep learning models on NVIDIA T4 GPU Instances if NVIDIA Tesla V100 instances are overpowering for your workloads.
E2E Deep Learning Cloud seamlessly lets you run deep learning workloads based on various frameworks and libraries like TensorFlow, PyTorch, Theano, Caffe2, MXNet, and many more popular deep learning libraries.
E2E Deep Learning Cloud can save up to 70% costs when compared to other Cloud GPU services. The billing for the E2E Deep Learning Cloud is entirely in INR, insulating your Cloud bills from USD-INR fluctuations.
Another advantage of the E2E Deep Learning Cloud is that the GPU cards NVIDIA T4 and NVIDIA Tesla V100 come attached dedicatedly to the Cloud Instances and are not shared with any other instances; that is, your workloads running on GPU instances will have 100% access to the processing power of the underlying GPU cards, either NVIDIA Tesla V100 or NVIDIA T4.
E2E Deep Learning Cloud offers NVIDIA Tesla V100 Instances with 32GB on-board graphics memory, making it highly-efficient and speedy for handling large data volumes of your deep learning workloads.
Using E2E Deep Learning Cloud, you can efficiently run workloads based on Computer Vision, Speech Recognition, Recommendation Engines, and Natural Language Processing.
Also, the datacenters are located in the Indian region, making it easier for you to achieve regulatory compliance and other financial data regulations mandated by the Indian laws.