I was testing the helpdesk-assistant from https://github.com/RasaHQ/helpdesk-assistant.
- Set up colab in GPU runtime
- Run following in cells
!git clone https://github.com/RasaHQ/helpdesk-assistant.git
!git checkout 4c6d31c # move down to rasa 2 since forum post says rasa 3 and gpu don't work
!pip install -U pip==20.2
!pip install -r requirements.txt
!pip install -U ipython
#manual restart runtime
!pip install colab-xterm
%load_ext colabxterm
%xterm
Run watch nvidia-smi
inside xterm to see usage every 2 seconds.
By the way, xterm (non-blocking) seems to be useful for running action server too, compared to the blocking !bash
.
%cd helpdesk-assistant/
!rasa train
- On Mac it took 4mins to train.
- On sagemaker studio lab CPU it’s 3.5min, GPU it’s 3min
- On colab CPU runtime 8min.
- On colab GPU runtime and
pip install tensorflow-gpu
, its 5min (DIET took 1:16min) - GPU-Util peaked at 40% during DietClassifier and 13% during a section about
Processed trackers
(not sure what this trains, later ends withCore model training completed.
)
- Why is colab’s GPU training time even longer than CPU on my Mac and sagemaker studio lab?
- Is GPU really being used?
watch nvidia-smi
shows a maximum of about930MiB / 15109MiB
duringrasa train
(specifically the NLU part on DIET). - Why goes Rasa 3 version of helpdesk-assistant also take 8min on GPU, is it really being used? This post seems to say GPU was used on 2 but not 3 (How to train Rasa3 nlu on gpu), however his 2,3 timing difference is way bigger than my 5min vs 8min, and i doubt the GPU memory allocation or whatever other overhead takes so long?
- Any guidelines to workflows when developing in rasa when each tiny edit requires an entire rasa train again before we can verify results? I can’t imagine waiting 10mins of
rasa train
per change. Even if we break it down torasa train nlu
andrasa train core
, it is still unlikely unscalable. Even 1 min training onrasa init
feels slow. - Any resources/tutorials on people using GPU? I know there’s an article here but he uses docker, but sagemaker and colab cannot run docker. How do we work with docker on colab, given that helpdesk-assistant needs docker for
docker run -p 8000:8000 rasa/duckling
. (yes i know we can install duckling to avoid docker, but is there any more direct solution that allows following the readme to use docker?)