[BUG] Missing cuvs_bench/config/algos/ggnn.yaml
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Description
Describe the bug
I want to run cuvs_bench for ggnn algorithm, but cuvs_bench does not start ggnn.
I found that no cuvs_bench/config/algos/ggnn.yaml was provided.
Steps/Code to reproduce bug
python3 -m cuvs_bench.run --algorithms=ggnn --dataset-path=/usr3/jiangyinzuo_data/cuvs-bench-dataset/
Enter the number of neighbors to search for [10]:
Enter the batch size [10000]:
Enter the name of dataset [glove-100-inner]:
Enter the comma separated groups of parameters [base]:
Enter the search mode ("latency" or "throughput") [latency]:
Enter the log level [info]:
/home/jiangyinzuo/micromamba/envs/rapids_raft3/lib/python3.11/site-packages/cuvs_bench/run/run.py:188: UserWarning: Could not load YAML config /home/jiangyinzuo/micromamba/envs/rapids_raft3/lib/python3.11/site-packages/cuvs_bench/run/../config/algos/__pycache__ due to [Errno 21] Is a directory: '/home/jiangyinzuo/micromamba/envs/rapids_raft3/lib/python3.11/site-packages/cuvs_bench/run/../config/algos/__pycache__'
warnings.warn(f"Could not load YAML config {algo_f} due to {e}")
Expected behavior
A clear and concise description of what you expected to happen.
Environment details (please complete the following information):
- Environment location: Bare-metal, Ubuntu 20.04
- Method of RAFT install: from source
Additional context
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with the python3 -m cuvs_bench.run entry point and inspect the configuration under cuvs_bench/config/algos, focusing on the missing cuvs_bench/config/algos/ggnn.yaml file. Compare the available algorithm configuration patterns, then run the reported command with --algorithms=ggnn; done means cuvs_bench starts without the missing-config warning.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- cli, tooling
- Issue type
- Bug
- Difficulty
- 2/5
- Estimated time
- 1-3 hours
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 45/100