Deep learning works quite well on a standard single PC setup with a CPU. However, once your datasets start increasing in size and your model architectures start getting more complex, you need to start thinking about investing in a robust deep learning environment. The major expectations being the system can build and train models efficiently, take less time to train models, and is fault tolerant. Most deep learning computations are essentially millions of matrix operations (data is represented as matrices) and enable fast computation in parallel; GPUs have been proven to work really well in this aspect. You can consider setting up a robust cloud-based deep learning environment or even an in-house environment. Let's look at how we can set up a robust cloud-based deep learning environment in this section.

The major components involved are as follows:

• Choosing a cloud provider 
• Setting up your virtual server 
• Configuring your virtual server
• Installing and updating deep learning dependencies 
• Accessing your deep learning cloud environment
• Validating GPU-enablement on your deep learning environment

Let's look at each of these components in further detail and take a step-by-step process that can help you to set up your own deep learning environment.