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Description
I am reaching out to report an issue I am encountering with the marius_trian, specifically when using the disk mode. Despite the segmentation fault being addressed in this PR (#147), I am still experiencing the same problem after updating to the latest version of the project.
It is worth noting that the operation of Marius in memory mode is completely normal. Below are my configurations for Marius preprocessing.

The following are my disk YAML configurations for marius_train.
# examples/configuration/ogbn_paper100m_disk.yaml
model:
learning_task: NODE_CLASSIFICATION
encoder:
train_neighbor_sampling:
- type: UNIFORM
options:
max_neighbors: 10
- type: UNIFORM
options:
max_neighbors: 10
- type: UNIFORM
options:
max_neighbors: 10
layers:
- - type: FEATURE
output_dim: 100
- - type: GNN
options:
type: GRAPH_SAGE
aggregator: MEAN
input_dim: 100
output_dim: 256
bias: true
- - type: GNN
options:
type: GRAPH_SAGE
aggregator: MEAN
input_dim: 256
output_dim: 256
bias: true
- - type: GNN
options:
type: GRAPH_SAGE
aggregator: MEAN
input_dim: 256
output_dim: 150
bias: true
decoder:
type: NODE
loss:
type: CROSS_ENTROPY
options:
reduction: SUM
dense_optimizer:
type: ADAM
options:
learning_rate: 0.01
storage:
device_type: cuda
dataset:
dataset_dir: /data/wb2001/
edges:
type: FLAT_FILE
nodes:
type: HOST_MEMORY
features:
type: PARTITION_BUFFER
options:
num_partitions: 16
buffer_capacity: 5
prefetching: true
fine_to_coarse_ratio: 1
num_cache_partitions: 0
node_partition_ordering: DISPERSED
# prefetch: false
# shuffle_input: true
# full_graph_evaluation: true
training:
batch_size: 1000
num_epochs: 3
pipeline:
sync: true
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