OSError: /lib/x86_64-linux-gnu/libm.so.6: version `GLIBC_2.27′ not found (required by /home/user/anaconda3/envs/hbk_gcn/lib/python3.9/site-packages/torch_spline_conv/_basis_cuda.so) in PyG Team PyTorch Geometric
Explanation of the problem
The operating system used for installation is Ubuntu 16.04 and the Python version is 3.9. The PyTorch version is 1.10 and the CUDA/cuDNN version is 10.2. The GCC version is also specified. The user followed the installation guide and checked the FAQ but could not find the error message. The user confirms that they have set up CUDA correctly and are able to compile CUDA code via nvcc. However, there is the possibility of multiple CUDA versions present on the machine.
This problem is being encountered while trying to install PyTorch Geometric and its extensions, either via wheel or source. The user has followed the installation guide and tried to find the error message in the FAQ but was unable to do so. The user has confirmed that CUDA is set up correctly and can be used to compile CUDA code via nvcc. The presence of multiple CUDA versions on the machine may also be contributing to the problem.
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Problem solution for OSError: /lib/x86_64-linux-gnu/libm.so.6: version `GLIBC_2.27′ not found (required by /home/user/anaconda3/envs/hbk_gcn/lib/python3.9/site-packages/torch_spline_conv/_basis_cuda.so) in PyG Team PyTorch Geometric
OSError: /lib/x86_64-linux-gnu/libm.so.6: version `GLIBC_2.27′ not found (required by /home/user/anaconda3/envs/hbk_gcn/lib/python3.9/site-packages/torch_spline_conv/_basis_cuda.so) is a common error encountered when installing PyG Team’s PyTorch Geometric package. The error occurs because the required version of the GLIBC library is not found on the system. The following is a solution to this problem:
To resolve this issue, you need to update your GLIBC library to the required version or higher. You can do this by updating your system packages or by installing a newer version of the library manually. In some cases, it may be necessary to rebuild the package using the updated library.
Another solution is to install the package in a new conda environment with a different version of the GLIBC library that is compatible with PyTorch Geometric. This can be done by creating a new environment with the desired version of the library, installing the required packages, and then installing PyTorch Geometric. This will ensure that the required version of the library is present and prevent any conflicts with other packages on the system.
In conclusion, resolving the OSError: /lib/x86_64-linux-gnu/libm.so.6: version `GLIBC_2.27′ not found error requires updating the GLIBC library or installing the package in a new environment with a compatible version of the library.
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
- 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) model.train() for epoch in range(200): optimizer.zero_grad() out = model(data) loss = F.cross_entropy(out[data.train_mask], data.y[data.train_mask]) loss.backward() optimizer.step()
- 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() print(dataset)
- 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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