poedator

"Но я уже умею ставить дрова видеокарты себе на комп": на машинах с 8x A100 используется хитрая виртуализация GPU, простые способы установить на них nvidia-driver обычно не работают или работают нестабильно.

Часть 1. Подготовка

до установки важно поставить сделать sudo apt update и чтобы update закончился без ошибок. Если видишь ошибки - исправляй ошибки, пока update не завершится успешно.

Проверьте sudo apt list nvidia-driver* nvidia-fabricmanager* nvidia-fabricmanager-dev* libnvidia-nscq* Эта команда должна вывести в списке свежие версии nvidia-driver и nvidia-fabricmanager. Нужная вам версия должна совпадать у driver и fabricmanager хотя бы до первых трёх чисел. Например, эти версии совпадают:

nvidia-driver-530/unknown,now 530.30.02-0ubuntu1 amd64

Abusagit

#!/usr/bin/env python

import io
import os
import pandas as pd


def read_vcf(path):
    with open(path, 'r') as f:
        lines = [l for l in f if not l.startswith('##')]

sidneyarcidiacono

# Import everything we need to build our network:
from torch.nn import Linear
import torch.nn.functional as F
from torch_geometric.nn import GCNConv
from torch_geometric.nn import global_mean_pool

# Define our GCN class as a pytorch Module
class GCN(torch.nn.Module):
    def __init__(self, hidden_channels):
        super(GCN, self).__init__()

sidneyarcidiacono

# Import DataLoader for batching
from torch_geometric.data import DataLoader

# our DataLoader creates diagonal adjacency matrices, and concatenates features
# and target matrices in the node dimension. This allows differing numbers of nodes and edges 
# over examples in one batch. (from pytorch geometric docs)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)

sidneyarcidiacono

# Now, we need to perform our train/test split.
# We create a seed, and then shuffle our data
torch.manual_seed(12345)
dataset = dataset.shuffle()

# Once it's shuffled, we slice the data to split
train_dataset = dataset[150:-150]
test_dataset = dataset[0:150]

# Take a look at the training versus test graphs

standaloneSA

List of desktop Nvidia GPUS ordered by CUDA core count

Compute Capability from (https://developer.nvidia.com/cuda-gpus) Check the card / architecture / gencode info: (https://arnon.dk/matching-sm-architectures-arch-and-gencode-for-various-nvidia-cards/)

GPU	CUDA cores	Memory	Processor frequency	Compute Capability	CUDA Support
GeForce GTX TITAN Z	5760	12 GB	705 / 876	3.5	until CUDA 11
NVIDIA TITAN Xp	3840	12 GB	1582	6.1	CUDA 8+

f0nzie

def normalizeRows(x):
    """
    Implement a function that normalizes each row of the matrix x (to have unit length).
    
    Argument:
    x -- A numpy matrix of shape (n, m)
    
    Returns:
    x -- The normalized (by row) numpy matrix. You are allowed to modify x.
    """

jihunchoi

def _sequence_mask(sequence_length, max_len=None):
    if max_len is None:
        max_len = sequence_length.data.max()
    batch_size = sequence_length.size(0)
    seq_range = torch.range(0, max_len - 1).long()
    seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len)
    seq_range_expand = Variable(seq_range_expand)
    if sequence_length.is_cuda:
        seq_range_expand = seq_range_expand.cuda()
    seq_length_expand = (sequence_length.unsqueeze(1)

vladimirtsyupko

git fetch --all
git reset --hard origin/master
git pull origin master