CUML：无法在多 GPU Dask 集群上训练随机森林模型

编程问答 2022-05-13

问题描述

基于官方的分布式模型训练示例（https://github.com/rapidsai/cuml/blob/branch-0.18/notebooks/random_forest_mnmg_demo.ipynb），我使用 Iris 数据集在多 GPU dask 集群（一个调度程序节点，三个工作节点）上训练随机森林模型，但是模型可以不训练。结果如下：

CuML accuracy:   0.36666666666666664
Exception ignored in: <object repr() Failed>
Traceback (most recent call last):
  File "cuml/ensemble/randomforestclassifier.pyx",line 334,in cuml.ensemble.randomforestclassifier.RandomForestClassifier.__del__
  File "cuml/ensemble/randomforestclassifier.pyx",line 350,in cuml.ensemble.randomforestclassifier.RandomForestClassifier._reset_forest_data
AttributeError: 'nonetype' object has no attribute 'free_treelite_model'

Process finished with exit code 0

我的环境是通过conda命令构建的：

conda create -n rapids-0.18 -c rapidsai -c nvidia -c conda-forge \
    -c defaults rapids-blazing=0.18 python=3.8 cudatoolkit=10.2

我用于 RAPIDs RandomForestClassifier 的代码是：

import pandas as pd
import cudf
import cuml
from cuml import train_test_split
from cuml.metrics import accuracy_score
from dask.distributed import Client,wait
from dask_cuda import LocalCUDACluster
import dask_cudf
from cuml.dask.ensemble import RandomForestClassifier as cumldaskRF

# start dask cluster
c = Client('node0:8786')

# Query the client for all connected workers
workers = c.has_what().keys()
n_workers = len(workers)
n_streams = 8  # Performance optimization

# Random Forest building parameters
max_depth = 12
n_bins = 16
n_trees = 1000

# Read data
pdf = pd.read_csv('/data/iris.csv',header = 0,delimiter = ',') # Get complete CSV
cdf = cudf.from_pandas(pdf) # Get cuda dataframe
features = cdf.iloc[:,[0,1,2,3]].astype('float32') # Get data columns
labels = cdf.iloc[:,4].astype('category').cat.codes.astype('int32') # Get label column

# Split train and test data
X_train,X_test,y_train,y_test = train_test_split(feature,label,train_size=0.8,shuffle=True)

# distribute data to worker GPUs
n_partitions = n_workers
X_train_dask = dask_cudf.from_cudf(X_train,npartitions=n_partitions)
X_test_dask = dask_cudf.from_cudf(X_test,npartitions=n_partitions)
y_train_dask = dask_cudf.from_cudf(y_train,npartitions=n_partitions)

# Train the distributed cuML model
cuml_model = cumldaskRF(max_depth=max_depth,n_estimators=n_trees,n_bins=n_bins,n_streams=n_streams)
cuml_model.fit(X_train_dask,y_train_dask)

wait(cuml_model.rfs)  # Allow asynchronous training tasks to finish

# Predict and check accuracy
cuml_y_pred = cuml_model.predict(X_test_dask).compute().to_array()
print("CuML accuracy:  ",accuracy_score(y_test.to_array(),cuml_y_pred))

使用 LocalCUDACluster 后结果没有改变。

你能指出我的错误并给我正确的代码吗？如果我想在经过训练的随机森林模型上评估决策树，我怎样才能得到那些经过训练的决策树？

谢谢。

解决方法

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dask-distributedpythonrandom-forestrapids