* Initial implementation of Distributed data processing step in the Distributed Training pipeline
Implemented the following:
1) Read the output of parmetis (node-id to partition-id mappings)
2) Read the original graph files
3) Shuffle the node/edge metadata and features
4) output the partition specific files in DGL format using convert_partition.py functionality
5) Graph meta data is serialized in json format on rank-0 machine.
* Bug Fixes identified during verification of the dataset
1. When sending out global-id lookups for non-local nodes, in the msg_alltoall.py, conditional filter was used to identify the indices in node_data which is incorrect. Replaced the conditional filter with intersect1d to find out the common node ids and appropriate indices which are later used to identify the needed information to communicate.
2. When writing the graph level json file in distributed processing, the edge_offset on non-rank-0 machines was starting from 0 instead of the appropriate offset. Now added code to start the edge(s) from correct starting offset instead of 0 always.
* Restructuring and consolidation of code
1) Fixed issue when running verify_mag_dataset.py, Now we read xxx_removed_edges.txt and add these edges to `edge_data`. This will ensure that the self-loops and duplicate edges are handling appropriately when compared to the original dataset.
2) Consolidated code into a fewer files and changed code to following the python naming convention.
* Code changes addressing code review comments
Following changes are made in this commit.
1) Naming convention is defined and code is changed accordingly. Definition of various global_ids are defined and how to read them is mentioned.
2) All the code review comments are addressed
3)Files are moved to a new directory with dgl/tools directory as per suggestion
4) README.md file is include and it contains detailed information about the Naming convention adopted by the code, high level overview of the algorithm used in data-shuffling, example command-line to use on a single machine.
* addressing github review comments
Made code changes addressing all the review comments from GitHub.
* Addressing latest code review comments
Addressed all the latest code reviewing comments. One of the major changes is treating the node and edge metadata as dictionary objects and removing all the python lists with numpy arrays.
* Update README.md
Text rendering corrections
* Addressed code review comments
Addressed code review comments for the latest code review
Co-authored-by: xiang song(charlie.song) <classicxsong@gmail.com>
* Added code for Rectifying (TypeError: unhashable type: 'slice') when copying file
* 1) added distributed preprocessing code to create ParMetis Input from CSV files
2) add code to run pm_dglpart on multiple machines
3) added support for recreating heteregenous graph from homo geneous graph based on dropped edges, as ParMetis currently only supports homogeneous graphs
* move to pandas
* Added comments and remove drop_duplicates as it was redundant
* Addressed Pr Comments
* Rename variable
* Added comment
* Added comment
* updated ReadMe
Co-authored-by: Ankit Garg <gaank@amazon.com>
Co-authored-by: Da Zheng <zhengda1936@gmail.com>
* rename get_data_size.
* remove g from DistTensor.
* remove g from DistEmbedding.
* clean up API of graph partition book.
* fix DistGraph
* fix lint.
* collect all part policies.
* fix.
* fix.
* support distributed sampler.
* remove partition.py
* temp fix omp.
* set server threads.
* add CAPI to set up OMP threads.
* fix.
* fix.
* update namesapce.
* set cpi properly.
* allow to config num worker threads.
* set #threads.
* fix.
* initial version from distributed training.
This is copied from multiprocessing training.
* modify for distributed training.
* it's runnable now.
* measure time in neighbor sampling.
* simplify neighbor sampling.
* fix a bug in distributed neighbor sampling.
* allow single-machine training.
* fix a bug.
* fix a bug.
* fix openmp.
* make some improvement.
* fix.
* add prepare in the sampler.
* prepare nodeflow async.
* fix a bug.
* get id.
* simplify the code.
* improve.
* fix partition.py
* fix the example.
* add more features.
* fix the example.
* allow one partition
* use distributed kvstore.
* do g2l map manually.
* fix commandline.
* a temp script to save reddit.
* fix pull_handler.
* add pytorch version.
* estimate the time for copying data.
* delete unused code.
* fix a bug.
* print id.
* fix a bug
* fix a bug
* fix a bug.
* remove redundent code.
* revert modify in sampler.
* fix temp script.
* remove pytorch version.
* fix.
* distributed training with pytorch.
* add distributed graph store.
* fix.
* add metis_partition_assignment.
* fix a few bugs in distributed graph store.
* fix test.
* fix bugs in distributed graph store.
* fix tests.
* remove code of defining DistGraphStore.
* fix partition.
* fix example.
* update run.sh.
* only read necessary node data.
* batching data fetch of multiple NodeFlows.
* simplify gcn.
* remove unnecessary code.
* use the new copy_from_kvstore.
* update training script.
* print time in graphsage.
* make distributed training runnable.
* use val_nid.
* fix train_sampling.
* add distributed training.
* add run.sh
* add more timing.
* fix a bug.
* save graph metadata when partition.
* create ndata and edata in distributed graph store.
* add timing in minibatch training of GraphSage.
* use pytorch distributed.
* add checks.
* fix a bug in global vs. local ids.
* remove fast pull
* fix a compile error.
* update and add new APIs.
* implement more methods in DistGraphStore.
* update more APIs.
* rename it to DistGraph.
* rename to DistTensor
* remove some unnecessary API.
* remove unnecessary files.
* revert changes in sampler.
* Revert "simplify gcn."
This reverts commit 0ed3a34ca714203a5b45240af71555d4227ce452.
* Revert "simplify neighbor sampling."
This reverts commit 551c72d20f05a029360ba97f312c7a7a578aacec.
* Revert "measure time in neighbor sampling."
This reverts commit 63ae80c7b402bb626e24acbbc8fdfe9fffd0bc64.
* Revert "add timing in minibatch training of GraphSage."
This reverts commit e59dc8957a414c7df5c316f51d78bce822bdef5e.
* Revert "fix train_sampling."
This reverts commit ea6aea9a4aabb8ba0ff63070aa51e7ca81536ad9.
* fix lint.
* add comments and small update.
* add more comments.
* add more unit tests and fix bugs.
* check the existence of shared-mem graph index.
* use new partitioned graph storage.
* fix bugs.
* print error in fast pull.
* fix lint
* fix a compile error.
* save absolute path after partitioning.
* small fixes in the example
* Revert "[kvstore] support any data type for init_data() (#1465)"
This reverts commit 87b6997bf2.
* fix a bug.
* disable evaluation.
* Revert "Revert "[kvstore] support any data type for init_data() (#1465)""
This reverts commit f5b8039c6326eb73bad8287db3d30d93175e5bee.
* support set and init data.
* support set and init data.
* Revert "Revert "[kvstore] support any data type for init_data() (#1465)""
This reverts commit f5b8039c6326eb73bad8287db3d30d93175e5bee.
* fix bugs.
* fix unit test.
* move to dgl.distributed.
* fix lint.
* fix lint.
* remove local_nids.
* fix lint.
* fix test.
* remove train_dist.
* revert train_sampling.
* rename funcs.
* address comments.
* address comments.
Use NodeDataView/EdgeDataView to keep track of data.
* address comments.
* address comments.
* revert.
* save data with DGL serializer.
* use the right way of getting shape.
* fix lint.
* address comments.
* address comments.
* fix an error in mxnet.
* address comments.
* add edge_map.
* add more test and fix bugs.
Co-authored-by: Zheng <dzzhen@186590dc80ff.ant.amazon.com>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-6-131.us-east-2.compute.internal>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-26-167.us-east-2.compute.internal>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-16-150.us-west-2.compute.internal>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-16-250.us-west-2.compute.internal>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-30-135.us-west-2.compute.internal>
* add metis.
* add test.
* construct partition id.
* link to METIS github repo.
* update metis.
* add a tool for partitioning a graph.
* update metis.
* update.
* update.
* fix metis.
* fix lint
* fix indent.
* another way of building metis.
* disable metis in windows.
* test windows
* fix.
* disable metis for windows properly.
* fix for tensorflow.
* skip test for gpu.
* make graph symmetric
* address comments.
* more comments.
* fix compile
* fix a bug.
* add test.
* change the default #hops of HALO nodes.
Co-authored-by: Ubuntu <ubuntu@ip-172-31-26-167.us-east-2.compute.internal>
Da Zheng