![]() T23:31:06,302 writing manifest file '/tmp/pip-pip-egg-info-qjj68kst/torch_quiver.egg-info/SOURCES.txt' T23:31:06,299 warning: no files found matching 'README' T23:31:06,299 /home/user/miniconda3/envs/env/lib/python3.8/distutils/extension.py:131: UserWarning: Unknown Extension options: 'with_cuda' T23:31:06,299 reading manifest template 'MANIFEST.in' T23:31:06,298 reading manifest file '/tmp/pip-pip-egg-info-qjj68kst/torch_quiver.egg-info/SOURCES.txt' T23:31:06,280 writing manifest file '/tmp/pip-pip-egg-info-qjj68kst/torch_quiver.egg-info/SOURCES.txt' T23:31:06,280 writing top-level names to /tmp/pip-pip-egg-info-qjj68kst/torch_quiver.egg-info/top_level.txt T23:31:06,280 writing dependency_links to /tmp/pip-pip-egg-info-qjj68kst/torch_quiver.egg-info/dependency_links.txt T23:31:05,344 Running command python setup.py egg_info T23:31:05,344 Created temporary directory: /tmp/pip-pip-egg-info-qjj68kst T23:31:05,344 Running setup.py (path:/tmp/pip-req-build-wc0b161h/setup.py) egg_info for package from file:///home/user/torch-quiver T23:31:05,344 Added file:///home/user/torch-quiver to build tracker '/tmp/pip-req-tracker-jt_5iad6' T23:31:05,329 Created temporary directory: /tmp/pip-req-build-wc0b161h T23:31:05,322 Created temporary directory: /tmp/pip-install-evkuymo2 T23:31:05,322 Initialized build tracking at /tmp/pip-req-tracker-jt_5iad6 T23:31:05,322 Created temporary directory: /tmp/pip-req-tracker-jt_5iad6 T23:31:05,321 Created temporary directory: /tmp/pip-ephem-wheel-cache-ssko_7ys T23:31:05,188 Non-user install because site-packages writeable Hello, I tried both pip install and source install but the build fails. To use Quiver in multi-GPU PyG scripts, we can simply pass quiver.Feature and quiver.Sampler as arguments to the child processes launched in PyTorch's DDP training, as shown below: sample( seeds) # Use Quiver graph sampler batch_feature = quiver_feature # Use Quiver feature collector # Step 3: Train PyG models with Quiver # for batch_size, n_id, adjs in train_loader: # Comment out PyG training loop for seeds in train_loader: # Step 2: Replace PyG feature collectors # feature = data.x.to(device) # Comment out PyG feature collector quiver_feature = quiver. edge_index), sizes =) # Quiver: Graph sampler DataLoader( train_idx) # Quiver: PyTorch Dataloader quiver_sampler = quiver. ) # Comment out PyG sampler train_loader = torch. ![]() # Step 1: Replace PyG graph sampler # train_loader = NeighborSampler(data.edge_index. ![]() Quiver is thus easy to be adopted by PyG users and integrated into production clusters.īelow is a chart that describes a benchmark that evaluates the performance of Quiver, PyG (2.0.1) and DGL (0.7.0) on a 4-GPU server that runs the Open Graph Benchmark.įor system design details, see Quiver's design overview (Chinese version: 设计简介). Easy to use: Quiver requires only a few new lines of code in existing PyG programs and it has no external heavy dependency.This is contributed by Quiver's novel communication-efficient data/processor management techniques and effective usage of fast networking technologies (e.g., NVLink and RDMA). High scalability: Quiver can achieve (super) linear scalability in distributed graph learning. Quiver thus often out-performs PyG and DGL even with a single GPU. High performance: Quiver enables GPUs to be efficiently used in accelerating graph sampling, feature construction and data-parallel training, which usually become bottlenecks in large-scale graph learning. To make scaling efficient, Quiver has several features: A typical scenario is: Quiver users can leverage the high-level APIs and rich examples of PyG to design graph learning algorithms, and then use Quiver to scale PyG algorithms to run at large scale. The primary motivation for this project is to make it easy to take a PyG script and scale it across many GPUs and CPUs. The goal of Quiver is to make large-scale distributed graph learning fast and easy to use. Quiver is a distributed graph learning library for PyTorch Geometric (PyG). ![]()
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