Closed 5james closed 5 years ago
The max_mem_size restricts the maximum memory size of RAM memory and GPU memory. If you set max_mem_size to 6000, then both RAM memory and GPU memory will not exceed 6GB. To avoid the lack of RAM memory, I think you can set max_mem_size smaller.
Well then either I'm doing something wrong or it doesn't work:
svm_classifier = thundersvm.SVC(kernel=params['kernel'],
degree=params['degree'],
coef0=params['coef0'],
probability=True,
verbose=True,
max_mem_size=6000,
random_state=np.random.RandomState(0))
When I'm watching it in htop it starts ~7.5GB of total RAM usage (1.6GB other processes and loaded data). After couple minutes it rises to ~10GB. When it's doing fit function it rises above 12-13GB.
Could it be somehow due to wrong thundersvm (through pip) installation?
Hi, @5james
Thundersvm doesn't use 6GB memory at the beginning of the training. The usage of memory will increase in the training process. In your case, I think the other processes already occupied 7.5GB at the beginning. Thundersvm only uses about (13-7.5)=5.5 GB. We have tried max_mem_size and it works. If you still find the parameter not working, please provide the datasets so that we may reproduce it. Thanks.
This issue is not active now, so we like to close it.
Hello, I tried using thundersvm and it was super fast on gpu. Unfortunately after some time my python script gets killed due to lack of RAM memory. I thought about using max_mem_size. Unfortunately my script got killed again :(. When I set max_mem_size=6000 I can see in htop about 7/16 GB ram usage at first. That would check out with other memory allocated. After couple minutes usage rises to ~11GB. And it doesn't drop. When predict was happening script got killed again. Beside usual kernel parameters I'm using: probability=True, verbose=True, random_state=np.random.RandomState(0), max_mem_size=6000.
Now my question is: max_mem_size restricts RAM memory or VRAM (GPU) memory?
Extra question: Is there any way of utilizing my gpu more efficiently? Now I'm using like 2% of its vram.