Possibility to upgrade its capabilities, like adding a bigger SSD, more RAM, or easily replace battery. Make sure your laptop can handle it without melting.Ī SSD of at least 256GB should be enough. You are going to run workloads for at least hours. Remember that you can’t train serious Deep Learning models from scratch in a laptop.Ī good cooling system. It will be orders of magnitude faster than almost any CPU for that task. Only if you need to prototype or fine-tune simple Deep Learning models. It will save you a lot of time while processing data for obvious reasons.Ī NVIDIA GPU of at least 4GB of RAM. Go with 32GB if you can afford it.Ī powerful processor. This is the most important feature as it will limit the amount of data you can easily process in memory (without using tools like Dask or Spark). Most libraries just work out of the box with little extra configuration.Īllows to cover the full spectrum of Data related tasks, from Small to Big Data, and from standard Machine Learning models to Deep Learning prototyping.ĭo not need to break your bank account to buy expensive hardware and software.Īt least 16GB of RAM. Standard Data Science tools like Python, R, and its libraries are easy to install and maintain. Why this guide? Over time, we found many students and fellow Data Scientists looking for a solid environment with some fundamental features: This is the standard setup both Pedro and me use at WhiteBox. Never missed a single feature while using it. I have been using this setup for more than 5 years with little changes (mainly hardware improvements), in many companies, and helped me in the development of dozens of Data projects. In this post I would like to describe in detail our setup and development environment (hardware & software) and how to get it, step by step.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |