Instructions from scratch in Docker

[kylemcdonald]

I started with a Docker image that had conda, cuda and pytorch preinstalled. I had to downgrade the pytorch version, so it might be faster to just start with an Anaconda-only Docker image. I also had to make one small change to the benchmark script.

$ git clone https://github.com/bkj/auction-lap.git && cd auction-lap
$ docker run --runtime=nvidia -it --rm --mount type=bind,src="$(pwd)",dst=/mnt anibali/pytorch:cuda-9.1 bash
# conda install pytorch==0.3.1 -c pytorch -y
# cd /mnt
# sudo apt update && sudo apt install -y build-essential
# pip install -r requirements.txt
# python benchmark.py --max-entry 1000 --eps 100 | tee results-1000-approx.jl
{"max_entry": 1000, "dim": 1000, "eps": 100.0, "gat_score": 997918, "src_score": 997918, "auc_gpu_score": 995778, "gat_time": 0.015303611755371094, "src_time": 0.015269994735717773, "auc_gpu_time": 0.009377479553222656}
{"max_entry": 1000, "dim": 2000, "eps": 100.0, "gat_score": 1997270, "src_score": 1997270, "auc_gpu_score": 1994676, "gat_time": 0.07352685928344727, "src_time": 0.07250046730041504, "auc_gpu_time": 0.01680469512939453}
{"max_entry": 1000, "dim": 3000, "eps": 100.0, "gat_score": 2996631, "src_score": 2996631, "auc_gpu_score": 2993748, "gat_time": 0.20714282989501953, "src_time": 0.227341890335083, "auc_gpu_time": 0.019696950912475586}
{"max_entry": 1000, "dim": 4000, "eps": 100.0, "gat_score": 3995860, "src_score": 3995860, "auc_gpu_score": 3992263, "gat_time": 0.36600685119628906, "src_time": 0.4125962257385254, "auc_gpu_time": 0.036045074462890625}
{"max_entry": 1000, "dim": 5000, "eps": 100.0, "gat_score": 4994927, "src_score": 4994927, "auc_gpu_score": 4991027, "gat_time": 0.5070977210998535, "src_time": 0.5707781314849854, "auc_gpu_time": 0.057653188705444336}
{"max_entry": 1000, "dim": 6000, "eps": 100.0, "gat_score": 5993973, "src_score": 5993973, "auc_gpu_score": 5989808, "gat_time": 0.6040315628051758, "src_time": 0.7091362476348877, "auc_gpu_time": 0.07340669631958008}
{"max_entry": 1000, "dim": 7000, "eps": 100.0, "gat_score": 6992986, "src_score": 6992986, "auc_gpu_score": 6989354, "gat_time": 0.7249176502227783, "src_time": 0.8651707172393799, "auc_gpu_time": 0.0954732894897461}
{"max_entry": 1000, "dim": 8000, "eps": 100.0, "gat_score": 7991996, "src_score": 7991996, "auc_gpu_score": 7988295, "gat_time": 0.8422048091888428, "src_time": 1.1722643375396729, "auc_gpu_time": 0.10980439186096191}
{"max_entry": 1000, "dim": 9000, "eps": 100.0, "gat_score": 8990999, "src_score": 8990999, "auc_gpu_score": 8987232, "gat_time": 1.0558438301086426, "src_time": 1.3648254871368408, "auc_gpu_time": 0.13532614707946777}
{"max_entry": 1000, "dim": 10000, "eps": 100.0, "gat_score": 9990000, "src_score": 9990000, "auc_gpu_score": 9986504, "gat_time": 1.2002484798431396, "src_time": 1.620683193206787, "auc_gpu_time": 0.1632983684539795}

If you don't downgrade pytorch, there seem to be some problems with the latest version:

# python benchmark.py --max-entry 1000 --eps 100 | tee results-1000-approx.jl
Traceback (most recent call last):
  File "benchmark.py", line 57, in <module>
    auc_cpu_score, auc_cpu_ass, _ = auction_lap(Xt_cpu, eps=None) # Score is accurate to within n * eps
  File "/mnt/auction-lap/auction_lap.py", line 54, in auction_lap
    src=bid_increments.view(-1, 1)
RuntimeError: dimension out of range (expected to be in range of [-1, 0], but got 1)

I think this bug can be fixed by changing this line to unassigned = (curr_ass == -1).nonzero().squeeze(dim=1) (i.e., adding dim=1).