Segmind's Docker first approach for deep learning will drastically speed up the training workflows, while also giving them maximum flexibility for customisations.
Bring your data -> preprocess data -> setup your environment -> install all the required packages ->search and clone the right algorithm from github -> verify that the code works-> write code to log and track metrics ->start training -> finish the training and get your baseline.
You have the flexibility to refine your models further in your choice of IDE (jupyter, VSCode) with Segmind managed Instances. Built on scalable and containerized Kubernetes backend, Instances offer you a full range of cloud GPUs and CPUs that you can choose to build and refine your models further.
Want to build your own custom dockers? Segmind's Docker create feature let's you create your own custom docker through our easy docker build UI. Our Docker repository is a better alternative to Docker hub where you can directly connect your docker to any instance and start developing models.
We have built Segmind's workflow and features to make you more productive and manage it all, on one single platform
No more hassle around setting up your own Tensorflow, PyTorch, CUDA, CuDNN environments. Segmind lets you attach a docker and start your work, within minutes
Want to collaborate with your teammate or have them review/help with your code? With a single click, you can share your instance and start working together.
With Segmind's seamless switch, you can scale up or scale down your CPU/GPU resources seamlessly, within couple of minutes.
Cut down your time to setup Kubernetes enabled scalable CPU or GPU clusters, under 30mins instead of weeks/months.