File size: 3,801 Bytes
9ed1ff1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 | import os
import random
from datetime import datetime
import pandas as pd
from torch.utils.data import Dataset
import torchvision.transforms as T
import torch
import numpy as np
def split_datalist_for_gpu(df, gpu_id, gpu_ids, node_id, node_ids):
node_index = node_ids.index(node_id) # Position of the current node in the node list
gpu_index = gpu_ids.index(gpu_id) # Position of the current GPU in the GPU list
# first split the dataframe for different nodes
total_nodes = len(node_ids)
rows_per_split = len(df) // total_nodes
start_index = node_index * rows_per_split
end_index = start_index + rows_per_split if node_index < total_nodes - 1 else len(df)
df = df[start_index:end_index]
# then split the dataframe for different gpus
total_gpus = len(gpu_ids)
rows_per_split = len(df) // total_gpus
start_index = gpu_index * rows_per_split
end_index = start_index + rows_per_split if gpu_index < total_gpus - 1 else len(df)
return df[start_index:end_index]
def split_dataframe_for_gpu(df, gpu_id, gpu_ids, node_id, node_ids):
"""
Splits the dataframe for a specific GPU on a specific node, supporting arbitrary GPU and node identifiers.
Args:
df (pd.DataFrame): The dataframe to split.
gpu_id (int): The identifier of the GPU for which the split is intended.
gpu_ids (list): List of all GPU IDs across all nodes, which can be non-sequential.
node_id (int): The identifier of the node on which the GPU is located.
node_ids (list): List of all node IDs, which can be non-sequential.
Returns:
pd.DataFrame: A subset of the original dataframe intended for the specific GPU on a specific node.
"""
# Calculate the unique index for this GPU on this node by finding its position in the global list of GPUs
node_index = node_ids.index(node_id) # Position of the current node in the node list
gpu_index = gpu_ids.index(gpu_id) # Position of the current GPU in the GPU list
# first split the dataframe for different nodes
total_nodes = len(node_ids)
rows_per_split = len(df) // total_nodes
start_index = node_index * rows_per_split
end_index = start_index + rows_per_split if node_index < total_nodes - 1 else len(df)
df = df.iloc[start_index:end_index]
# then split the dataframe for different gpus
total_gpus = len(gpu_ids)
rows_per_split = len(df) // total_gpus
start_index = gpu_index * rows_per_split
end_index = start_index + rows_per_split if gpu_index < total_gpus - 1 else len(df)
return df.iloc[start_index:end_index]
def split_dataframe_for_node(df, node_id, node_ids):
"""
Splits the dataframe for a specific node, supporting arbitrary node identifiers.
Args:
df (pd.DataFrame): The dataframe to split.
node_id (int): The identifier of the node
node_ids (list): List of all node IDs, which can be non-sequential.
Returns:
pd.DataFrame: A subset of the original dataframe intended for the specific node.
"""
# Calculate the unique index for this GPU on this node by finding its position in the global list of GPUs
node_index = node_ids.index(node_id) # Position of the current node in the node list
global_index = node_index # Unique index across all GPUs on all nodes
# Calculate the total number of splits needed
total_nodes = len(node_ids)
# Calculate the number of rows per split
rows_per_split = len(df) // total_nodes
# Calculate the start and end indices of the rows for this particular split
start_index = global_index * rows_per_split
end_index = start_index + rows_per_split if global_index < total_nodes - 1 else len(df)
# Get the subset of the dataframe
return df.iloc[start_index:end_index] |