2020-08-17 Paperspace benchmarks

Using the XGBoost benchmark

python tests/benchmark/benchmark_tree.py --tree_method=gpu_hist
python tests/benchmark/benchmark_tree.py --tree_method=hist

my own CPU Ryzen 3900X: 15.4sec

my own el-cheapo GPU (GT1030): 872.6sec . Clearly I have saved up money on this.

Paperspace free-P5000 CPU:48.12 sec (2.4GHz CPU) ($0.78/hr)

Paperspace free-P5000 GPU: 8.47sec ($0.78usd/hour)

Paperspace V100 CPU: 58sec (it has 2.2GHz x4 CPU) ($2.4/hr)

Paperspace V100 GPU: 4.432 sec – not bad! ($2.4/hr)

I think I will be happy with my own CPU for prototyping, and then Free-5000 instance for running the experiments. I can also develop my code in such way that it can be ran on multiple GPU+ instances ($0.45/hr each). The V100 will come handy when it comes to my upcoming FDTD code

Also, note that V100 and GPU+ are not available in Europe; P5000 is available in Europe. That’s OK.

$ gradient machines availability --region AMS1 --machineType V100
# Machine available: False

$ gradient machines availability --region AMS1 --machineType GPU+
# Machine available: False

$ gradient machines availability --region AMS1 --machineType P5000
# Machine available: True

2020-08-17 I get a top 3% score in a Kaggle competition

competition success - top 3 percent - screenshot

my score vs other submissions - histogram

Techniques used:

The source notebooks (it’s a pipeline) available on request to demonstrate that I am a genuine author of the solution.

2020-08-08 Complete a Kaggle tutorial

I’d say it was extremely basic, but still, for purposes of demonstration and job finding:

George - Intro to Machine Learning