Asked 5th Oct, 2021

A cheap/free high memory server for machine learning algorithms research (open source)?

I'm looking for a cheap server with 2 TB or more RAM memory to test a novel machine learning algorithm that gives auspicious results when running with 64 GB of RAM. By interpolating current results, it seems that the machine learning algorithm would start to give good results after 1 TB or more memory.
If the algorithm works, the plan is to publish the results in a scientific journal (low impact factor thought to get the paper accepted) and develop C++ implementation in my Dinrhiw2 machine learning library (open source). Amazon AWS sells such a server at 5 USD/hour level meaning approx 840 USD/week (too much) (one month of computational time should be enough).

All Answers (3)

6th Oct, 2021
Khurram Hameed
Edith Cowan University
I have used google cloud based colab and other services but that are really slow. I am not sure about Kaggle, but used weights and bias. The best is Amazon Aws known to me which is really expensive.
From our experience at OroraTech, Hetzner ( have a number of much cheaper solutions that AWS/GCP. You pay much lower fees for traffic and their team is really helpful in discussing custom solutions.
Hope it helps, good luck!
7th Oct, 2021
Shima Baniadamdizaj
Friedrich Schiller University Jena
You can try "". 2 TB RAM 130 USD monthly. Also, as recommended before, "" works as well. Although personally prefer to use Google colab. Good luck!

Similar questions and discussions

How can I compare multiple time series in R?
4 answers
  • Lino SanchezLino Sanchez
I was hoping to reach out to this research community to seek some perspective and advice on a statistical task I am working on through R. The objective is very simple and straightforward, but I am taken back by how complex the process may actually be.
The objective is outlined as follows: I have three boxes in a single room. One is blue, one is green, and one is red. In each box is a temperature reader recording a single temperature reading per day over a 30-day period. So now for each box, I have data that contains a single temperature reading for each day for 30 days.
To my understanding, this would be time-series data. Now I want to address the question: Is there a significant difference in temperature between the three boxes? Phrased differently: Does the choice of color dictate the overall temperature in the box?
Of course I can just take the mean temperature for the 30-day period for each box and just compare that, but this doesn't seem complete. Since I am working with categorical data (color of box) and continuous data (temperature), I was thinking I need to perform an ANOVA test. Then I would perform the Tukey HSD post-hoc test to look for individual comparisons, such that in addition to comparing all the boxes together, I would compare the blue box to the red box and the blue box to the green box, and compare the green box to the red box. This however, would just be looking at the affect of color on temperature, ignoring the whole time-series component. How can I add the time component to this?
I know R has the time series function: ts(), so would it work to just make each data set for each box a time-series abject, and then just run the ANOVA and Tukey HSD post-hoc test on these time-series objects? How should I best proceed with my objective? I know there are factors such as seasonality and auto-correlation here, but I am not sure how to incorporate these considerations. Is there a simple way to do all of this? Could you perhaps provide some R code examples?
Thank you so much!

Related Publications

Reading this volume (see record 1995-97230-000 ) prompted in the reviewer an unintended comparison with one written by J. A. McGeoch nearly a half century ago. Geoch's book was based on empirical evidence and interpretations of research on learning available at the time. Conceptualizations, threaded throughout the present volume, contrast dramatica...
Got a technical question?
Get high-quality answers from experts.