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github.com/NVIDIA/gbm-bench @main

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162 symbols 383 edges 16 files 46 documented · 28%
What it actually does AI analysis from the code graph — generated when you open this
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Core symbols most depended-on inside this repo

fit
called by 10
algorithms.py
_get_datapath
called by 9
3rdparty/fast_retraining/experiments/libs/loaders.py
configure
called by 8
algorithms.py
read_from_dict
called by 5
json2csv.py
retrieve
called by 5
datasets.py
get_wins
called by 4
3rdparty/fast_retraining/experiments/libs/football.py
evaluate_metrics
called by 3
metrics.py
get_file_count
called by 3
3rdparty/fast_retraining/experiments/libs/planet_kaggle.py

Shape

Function 85
Method 57
Class 20

Languages

Python100%

Modules by API surface

algorithms.py68 symbols
datasets.py16 symbols
3rdparty/fast_retraining/experiments/libs/football.py14 symbols
3rdparty/codebase/python/machine_learning/metrics.py10 symbols
3rdparty/fast_retraining/experiments/libs/planet_kaggle.py9 symbols
json2csv.py8 symbols
3rdparty/fast_retraining/experiments/libs/loaders.py8 symbols
3rdparty/fast_retraining/experiments/libs/timer.py6 symbols
runme.py5 symbols
metrics.py5 symbols
3rdparty/fast_retraining/experiments/libs/notebook_memory_management.py4 symbols
3rdparty/fast_retraining/experiments/libs/metrics.py4 symbols

For agents

$ claude mcp add gbm-bench \
  -- python -m otcore.mcp_server <graph>

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