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update docs for xgboost1.7.1 and add python notebooks #252
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Signed-off-by: liyuan <[email protected]>
Signed-off-by: liyuan <[email protected]>
Signed-off-by: liyuan <[email protected]>
Signed-off-by: liyuan <[email protected]>
Signed-off-by: liyuan <[email protected]>
Signed-off-by: liyuan <[email protected]>
Signed-off-by: liyuan <[email protected]>
Signed-off-by: liyuan <[email protected]>
Signed-off-by: liyuan <[email protected]>
docs/get-started/xgboost-examples/on-prem-cluster/standalone-python.md
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Signed-off-by: liyuan <[email protected]>
Signed-off-by: liyuan <[email protected]>
examples/XGBoost-Examples/agaricus/notebooks/python/agaricus-gpu.ipynb
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…dsPerBatch default value Signed-off-by: liyuan <[email protected]>
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CI job against the notebooks got PASS, +1
…rk-rapids-examples into xgb170-2212-updates
3. Install the XGBoost, cudf-cu11, numpy libraries on all nodes before running XGBoost application. | ||
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``` bash | ||
pip install xgboost |
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Do we need to install scikit-learn?
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yes, add 'pip install scikit-learn '
``` bash | ||
pip install xgboost | ||
pip install cudf-cu11 --extra-index-url=https://pypi.ngc.nvidia.com | ||
pip install numpy |
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do we still install numpy?
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``` bash | ||
pip install xgboost | ||
pip install cudf-cu11 --extra-index-url=https://pypi.ngc.nvidia.com | ||
pip install numpy |
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same with the previous comment
Most data scientists spend a lot of time not only on | ||
Training models but also processing the large amounts of data needed to train these models. | ||
As you can see below, XGBoost training on GPUs can be up to 10X and data processing using | ||
RAPIDS Accelerator can also be accelerated with an end-to-end speed-up of 7X on GPU compared to CPU. | ||
As you can see below, Pyspark+XGBoost training on GPUs can be up to 13X and data processing using |
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Can we also have a benchmark testing for xgboost-jvm-gpu?
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no, but I think we can add it in another PR.
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Overall, it LGTM.
Signed-off-by: liyuan <[email protected]>
Signed-off-by: liyuan [email protected]
this pr is to: