OSError: libcusparse.so.11: cannot open shared object file: No such file or directory
  • 10-May-2023
Lightrun Team
Author Lightrun Team
OSError: libcusparse.so.11: cannot open shared object file: No such file or directory

OSError: libcusparse.so.11: cannot open shared object file: No such file or directory

Lightrun Team
Lightrun Team

Explanation of the problem

When running the line of code from torch_geometric.data import Data, an OSError is encountered, specifically a libcusparse.so.11 error. This error indicates that the shared object file libcusparse.so.11 cannot be found or accessed. The traceback reveals that the error occurs within the torch_sparse module, more specifically during the loading of the library. The issue seems to be related to the CUDA installation and the missing libcusparse.so.11 file.

The environment in which the error occurs is an Ubuntu 18.04 operating system with Python version 3.7.6. PyTorch version 1.7.1 and CUDA/cuDNN version 11.0 are also installed. The installation of PyTorch Geometric and its extensions was done using binaries, following the provided installation guide. It’s important to note that the CUDA setup is mentioned as a potential source of the problem, particularly if multiple CUDA versions are present on the system. The user is unsure about how to check the installed CUDA versions but reports successful execution of PyTorch on the GPU previously.

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Problem solution for OSError: libcusparse.so.11: cannot open shared object file: No such file or directory

To resolve the issue, it’s recommended to ensure that the libcusparse.so.11 file is accessible. This can be achieved by verifying the CUDA installation and making sure that the necessary CUDA dependencies are properly installed. Additionally, it’s advisable to check the CUDA version compatibility with PyTorch Geometric and its extensions, as mismatched versions can lead to such errors. The user can try to explicitly specify the CUDA version during installation or ensure that the correct CUDA version is being used when running the code. Verifying the CUDA setup and ensuring that the appropriate CUDA libraries are accessible should help resolve the OSError related to libcusparse.so.11.

Other popular problems with PyG Team PyTorch Geometric

Problem: Memory Usage

One of the most common problems encountered by users of PyG is its high memory usage. When working with large graph datasets, memory usage can quickly become a bottleneck, leading to out-of-memory errors and decreased performance. This is particularly true when using GPU acceleration, as the memory constraints of GPUs can be more strict than those of CPUs.


To mitigate this problem, users can employ a number of strategies, such as using more efficient data structures for storing graph data, reducing the size of the graph by removing unimportant nodes or edges, and increasing the batch size to reduce the frequency of data transfers between the CPU and GPU. Additionally, users can also try to use memory-efficient versions of PyTorch, such as torch.Size([]) or torch.Tensor.new_zeros(), to minimize memory overhead.

Problem: Lack of Built-in Visualization Tools

Another issue faced by users of PyG is the lack of built-in visualization tools. Unlike other graph libraries, PyG does not provide users with any easy-to-use visualization tools for graph data. This can make it difficult for users to understand the structure and properties of their graph data, and can lead to difficulties in debugging and troubleshooting.


To address this issue, users can use third-party libraries such as NetworkX or Gephi, which provide a variety of visualization tools for graph data. These libraries can be integrated with PyG using the NetworkX or Gephi Python APIs, allowing users to easily visualize and manipulate their graph data.

Problem: Incomplete Documentation

The third most common problem faced by users of PyG is the incompleteness of its documentation. The PyG documentation is often missing important information, making it difficult for users to understand how to use the library effectively. Additionally, the examples provided in the documentation are often not well-explained, making it difficult for users to understand how to apply the concepts presented in the examples to their own use cases.


To address this issue, users can turn to the PyG community for help, as there are many experienced users who can offer guidance and support. Additionally, users can contribute to the development of the PyG documentation by writing examples, explanations, and tutorials, which can help make the documentation more comprehensive and accessible for other users.

A brief introduction to PyG Team PyTorch Geometric

PyTorch Geometric (PyG) is a high-performance geometric deep learning library for PyTorch. It provides a set of tools and algorithms for building, training, and evaluating graph neural networks (GNNs) on graph and point cloud data. PyG is designed to work seamlessly with the PyTorch framework, allowing users to easily implement and experiment with different GNN models. It also includes a large number of popular datasets and benchmark tasks, making it an ideal platform for research and development in the field of geometric deep learning.

PyG implements a number of state-of-the-art algorithms for graph classification, graph regression, graph representation learning, and graph generation. It also provides a number of utilities for graph preprocessing, such as graph normalization, graph splitting, and graph pooling, making it easier for users to prepare their data for use with GNNs. Furthermore, PyG is optimized for GPU acceleration, making it possible to train large-scale GNNs on graph data in a timely manner. PyG is an open-source library and is actively maintained and developed by a large community of contributors, making it a reliable and widely-used tool for geometric deep learning research and development.

Most popular use cases for PyG Team PyTorch Geometric

  1. Graph Classification and Regression: PyG can be used for graph-based machine learning tasks, such as graph classification and graph regression. These tasks involve learning a function that maps a graph to a scalar label or real-valued target. An example of a graph classification task using PyG is shown below:
import torch
import torch.nn as nn
import torch.optim as optim
from torch_geometric.nn import GCNConv

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = GCNConv(dataset.num_node_features, 16)
        self.conv2 = GCNConv(16, dataset.num_classes)

    def forward(self, data):
        x, edge_index = data.x, data.edge_index
        x = self.conv1(x, edge_index)
        x = torch.relu(x)
        x = self.conv2(x, edge_index)
        return x

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = Net().to(device)
optimizer = optim.Adam(model.parameters(), lr=0.01, weight_decay=5e-4)

for epoch in range(200):
    out = model(data)
    loss = F.cross_entropy(out[data.train_mask], data.y[data.train_mask])
  1. Graph Representation Learning: PyG can also be used for graph representation learning, which involves learning low-dimensional embeddings for the nodes in a graph. These embeddings can be used for a variety of downstream tasks, such as graph visualization and graph classification. An example of graph representation learning using PyG is shown below:
import torch
import torch_geometric.transforms as T
from torch_geometric.datasets import Planetoid

dataset = Planetoid(root='/tmp/Cora', name='Cora')
dataset = dataset.shuffle()

  1. Graph Generation: PyG can also be used for graph generation, which involves learning a generative model that can generate new graphs that are similar to a given set of training graphs. This can be useful for a variety of applications, such as generating graphs for use in simulation or testing. An example of graph generation using PyG is shown below:
import torch
import torch.nn.functional as F
import torch_geometric.transforms as T
from torch_geometric.datasets import QM9
from torch_geometric.nn import GCNConv, GAE, VGAE

dataset = QM9(root='/tmp/QM9').shuffle()

conv = GCNConv(dataset.num_features, 16)
enc1 = GAE(16, 32)
enc2 = GAE(32, 64)
dec1 = GAE(64, 32)
dec2 = GAE(32, 16)

for data in dataset:
    x, edge_index = data

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