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Seamlessly Integrated Deep Learning Environment with Terraform, Google cloud, Gitlab and Docker
When you are starting with some serious deep learning projects, you usually have the problem that you need a proper GPU. Buying reasonable workstations which are suitable for deep learning workloads can easily become very expensive. Luckily there are some options in the cloud. One that I tried out was using the wonderful Google Compute Engine. GPUs are available in the GCE as external accelerators of an instance. Currently, there are these GPUs available (prices for us-central1).
- NVIDIA® Tesla® P4: $1267.28 USD per GPU per Month
- NVIDIA® Tesla® V100: $306.60 USD per GPU per Month
- NVIDIA® Tesla® P100: $746.06 USD per GPU per Month
- NVIDIA® Tesla® K80: $229.95 USD per GPU per Month
Manual configuration usually isn’t something you can scale up easily, so I did some investigations on if there are methods with which I can ramp-up my environment as seamless as possible and destroy it the same way. Consequently, I found a solution which uses terraform to set-up the infrastructure in the Google Compute Platform. The Source Code is deployed from Git and a Docker container is automatically started having all necessary dependencies like tensorflow, keras and jupyter installed. In this blog post I…