Build your own machine and install KNIME, Jupyter-Notebook and Tableau to be fully equipped for all data science and deep learning tasks. By Dennis Ganzaroli, Data Scientist and Head of Report & Data-Management in a big Telco in Switzerland.
Do you want to get started with data science but lack the appropriate infrastructure or are you already a professional but still have knowledge gaps in deep learning? Then you have two options: Rent a virtual machine from a cloud provider like Amazon, Microsoft Azure, Google Cloud or similar. Or build your own physical machine and install the right software.
The author tried both options but in the end the decision to build his own rig was the better one, and these are the reasons why:
- Costs savings
- More power and resources
- Your machine can also be used for other tasks
A study from Bizon-Tech shows that a pre-build using 1 GPU is up to 10 times cheaper and those with 4 GPUs are up to 21 times cheaper within 1 year compared to web-based services. And when it comes to storage capacity, prices for web services go through the roof above a certain size.
The article also covers:
- Choosing the right system and software
- Building the machine
- Installation of the software
We should strongly go for a NVIDIA graphics card since all current state of the art frameworks (be it Keras, TensorFlow, PyTorch or any other library) fully support NVIDIA’s CUDA SDK, a software library for interfacing to GPUs. Another important point are Tensor Cores. Tensor Cores accelerate matrix operations, which are foundational to deep learning, and perform mixed-precision matrix multiply and accumulate calculations in a single operation. Excellent source of information for personal computing builders!
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