Scaling GNNs: 7 Proven Ways to Fix Out-Of-Memory Errors [PyTorch Geometric] скачать в хорошем качестве

Scaling GNNs: 7 Proven Ways to Fix Out-Of-Memory Errors [PyTorch Geometric]

Is your Graph Neural Network running out of memory? In this video, we debate the best strategies for scaling GNN training. Dealing with large datasets that overwhelm processors is a major challenge in deep learning. We dive into the "GeoGrid" case studies to debate the trade-offs between buying better hardware, changing your data representation, or switching algorithms entirely. Drawing from expert insights on learning and inference at scale, we cover the 7 key techniques to handle data overload. 📌 Key Topics Covered: 1. The Root Causes of Scale Problems We analyze the three critical metrics you must track: Memory usage, Time per epoch, and Time to convergence. We discuss why graph size matters and how node features can often be larger than the structural information itself. 2. The Hardware Debate: CPU vs. GPU vs. TPU Should you upgrade your single machine or move to a cluster? We break down the pros and cons of different processors, including why IPUs are becoming a specialized choice for GNNs. 3. Data Representation: Sparse vs. Dense We debate the memory savings of sparse tensors versus the computational speed of dense representations. Learn how to use ToSparseTensor in PyTorch Geometric to optimize your footprint. 4. Algorithm Choice: GCN vs. GraphSAGE We compare the time and space complexity of popular architectures. See why GraphSAGE’s O(Lbdk) complexity makes it a better candidate for scalability compared to the standard GCN. 5. Sampling Strategies & Mini-batching Instead of processing the whole graph, we look at sampling subsets. We compare NeighborSampler (memory efficient) vs. GraphSAINTSampler (better gradient estimates) vs. ShaDowKHopSampler (deeper neighborhood capture). 6. Distributed Data Parallel (DDP) Is multi-GPU training worth the synchronization overhead? We explore how to use dist.init_process_group to split data across GPUs and the decision-making process behind implementing DDP. 7. Advanced Techniques: Remote Backends & Graph Coarsening We discuss storing data in graph databases (like Neo4J) using FeatureStores, and the trade-offs of "shrinking" your graph through clustering and pooling (Graph Coarsening). 👨‍💻 Code & Tools Mentioned: • PyTorch Geometric (PyG) • torch_geometric.loader.NeighborLoader • torch.nn.parallel.DistributedDataParallel • NVIDIA Management Library (pynvml) for memory profiling Timestamps: 0:00 - Introduction: The "Out of Memory" Nightmare 1:30 - 3 Metrics to Measure Scale (Memory, FLOPs, Convergence) 3:45 - Hardware: When to switch from CPU to GPU or TPU 6:10 - Sparse vs. Dense Matrices: Which is better? 8:50 - Algorithm Showdown: GCN vs. GraphSAGE 12:15 - Sampling Techniques (NeighborLoader explained) 15:30 - Distributed Data Parallel (DDP) Training 18:45 - Using Remote Backends (Neo4J/RocksDB) 21:20 - Graph Coarsening: Reducing Graph Size 23:00 - Summary & GeoGrid Case Study Results #GNN #MachineLearning #PyTorchGeometric #DeepLearning #DataScience #Scalability #GraphNeuralNetworks #Python