importpickleimportosimporttimefromitertoolsimportproductimportmatplotlib.pyplotaspltimportnumpyimportpandasfromlightgbmimportLGBMRegressorfromonnxruntimeimportInferenceSession,SessionOptionsfrompsutilimportcpu_countfromsphinx_runpython.runpythonimportrun_cmdfromskl2onnximportto_onnx,update_registered_converterfromskl2onnx.common.shape_calculatorimportcalculate_linear_regressor_output_shapesfromsklearnimportset_configfromsklearn.ensembleimportRandomForestRegressorfromtqdmimporttqdmfromxgboostimportXGBRegressorfromonnxmltools.convert.xgboost.operator_converters.XGBoostimportconvert_xgboostdefskl2onnx_convert_lightgbm(scope,operator,container):fromonnxmltools.convert.lightgbm.operator_converters.LightGbmimport(convert_lightgbm,)options=scope.get_options(operator.raw_operator)operator.split=options.get("split",None)convert_lightgbm(scope,operator,container)update_registered_converter(LGBMRegressor,"LightGbmLGBMRegressor",calculate_linear_regressor_output_shapes,skl2onnx_convert_lightgbm,options={"split":None},)update_registered_converter(XGBRegressor,"XGBoostXGBRegressor",calculate_linear_regressor_output_shapes,convert_xgboost,)# The following instruction reduces the time spent by scikit-learn# to validate the data.set_config(assume_finite=True)

print(f"Number of cores:{cpu_count()}")

Number of cores: 20

try:out,err=run_cmd("lscpu")print(out)exceptExceptionase:print(f"lscpu not available:{e}")

<Popen: returncode: None args: ['lscpu']>

out,err=run_cmd("cat /proc/cpuinfo")print(out)

<Popen: returncode: None args: ['cat', '/proc/cpuinfo']>

defmeasure_inference(fct,X,repeat,max_time=5,quantile=1):"""    Run *repeat* times the same function on data *X*.    :param fct: fonction to run    :param X: data    :param repeat: number of times to run    :param max_time: maximum time to use to measure the inference    :return: number of runs, sum of the time, average, median    """times=[]for_ninrange(repeat):perf=time.perf_counter()fct(X)delta=time.perf_counter()-perftimes.append(delta)iflen(times)<3:continueifmax_timeisnotNoneandsum(times)>=max_time:breaktimes.sort()quantile=0if(len(times)-quantile*2)<3elsequantileifquantile==0:tt=timeselse:tt=times[quantile:-quantile]return(len(times),sum(times),sum(tt)/len(tt),times[len(times)//2])

small=cpu_count()<25ifsmall:N=1000n_features=10n_jobs=[1,cpu_count()//2,cpu_count()]n_ests=[10,20,30]depth=[4,6,8,10]Regressor=RandomForestRegressorelse:N=100000n_features=50n_jobs=[cpu_count(),cpu_count()//2,1]n_ests=[100,200,400]depth=[6,8,10,12,14]Regressor=RandomForestRegressorlegend=f"parallel-nf-{n_features}-"# avoid duplicates on machine with 1 or 2 cores.n_jobs=list(sorted(set(n_jobs),reverse=True))

repeat=7# repeat n times the same inferencequantile=1# exclude extreme timesmax_time=5# maximum number of seconds to spend on one configuration

X=numpy.random.randn(N,n_features).astype(numpy.float32)noise=(numpy.random.randn(X.shape[0])/(n_features//5)).astype(numpy.float32)y=X.mean(axis=1)+noisen_train=min(N,N//3)data=[]couples=list(product(n_jobs,depth,n_ests))bar=tqdm(couples)cache_dir="_cache"ifnotos.path.exists(cache_dir):os.mkdir(cache_dir)forn_j,max_depth,n_estimatorsinbar:ifn_j==1andn_estimators>n_ests[0]:# skippingcontinue# parallelizationcache_name=os.path.join(cache_dir,f"nf-{X.shape[1]}-rf-J-{n_j}-E-{n_estimators}-D-{max_depth}.pkl")ifos.path.exists(cache_name):withopen(cache_name,"rb")asf:rf=pickle.load(f)else:bar.set_description(f"J={n_j} E={n_estimators} D={max_depth} train rf")ifn_j==1andissubclass(Regressor,RandomForestRegressor):rf=Regressor(max_depth=max_depth,n_estimators=n_estimators,n_jobs=-1)rf.fit(X[:n_train],y[:n_train])rf.n_jobs=1else:rf=Regressor(max_depth=max_depth,n_estimators=n_estimators,n_jobs=n_j)rf.fit(X[:n_train],y[:n_train])withopen(cache_name,"wb")asf:pickle.dump(rf,f)bar.set_description(f"J={n_j} E={n_estimators} D={max_depth} ISession")so=SessionOptions()so.intra_op_num_threads=n_jcache_name=os.path.join(cache_dir,f"nf-{X.shape[1]}-rf-J-{n_j}-E-{n_estimators}-D-{max_depth}.onnx")ifos.path.exists(cache_name):sess=InferenceSession(cache_name,so,providers=["CPUExecutionProvider"])else:bar.set_description(f"J={n_j} E={n_estimators} D={max_depth} cvt onnx")onx=to_onnx(rf,X[:1])withopen(cache_name,"wb")asf:f.write(onx.SerializeToString())sess=InferenceSession(cache_name,so,providers=["CPUExecutionProvider"])onx_size=os.stat(cache_name).st_size# run once to avoid counting the first runbar.set_description(f"J={n_j} E={n_estimators} D={max_depth} predict1")rf.predict(X)sess.run(None,{"X":X})# fixed dataobs=dict(n_jobs=n_j,max_depth=max_depth,n_estimators=n_estimators,repeat=repeat,max_time=max_time,name=rf.__class__.__name__,n_rows=X.shape[0],n_features=X.shape[1],onnx_size=onx_size,)# baselinebar.set_description(f"J={n_j} E={n_estimators} D={max_depth} predictB")r,t,mean,med=measure_inference(rf.predict,X,repeat=repeat,max_time=max_time)o1=obs.copy()o1.update(dict(avg=mean,med=med,n_runs=r,ttime=t,name="base"))data.append(o1)# onnxruntimebar.set_description(f"J={n_j} E={n_estimators} D={max_depth} predictO")r,t,mean,med=measure_inference(lambdax,sess=sess:sess.run(None,{"X":x}),X,repeat=repeat,max_time=max_time,)o2=obs.copy()o2.update(dict(avg=mean,med=med,n_runs=r,ttime=t,name="ort_"))data.append(o2)

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ISession:  56%|█████▌    | 20/36 [00:08<00:04,  3.57it/s]J=10 E=30 D=8 cvt onnx:  56%|█████▌    | 20/36 [00:08<00:04,  3.57it/s]J=10 E=30 D=8 predict1:  56%|█████▌    | 20/36 [00:08<00:04,  3.57it/s]J=10 E=30 D=8 predictB:  56%|█████▌    | 20/36 [00:08<00:04,  3.57it/s]J=10 E=30 D=8 predictO:  56%|█████▌    | 20/36 [00:08<00:04,  3.57it/s]J=10 E=30 D=8 predictO:  58%|█████▊    | 21/36 [00:08<00:04,  3.36it/s]J=10 E=10 D=10 train rf:  58%|█████▊    | 21/36 [00:08<00:04,  3.36it/s]J=10 E=10 D=10 ISession:  58%|█████▊    | 21/36 [00:08<00:04,  3.36it/s]J=10 E=10 D=10 cvt onnx:  58%|█████▊    | 21/36 [00:08<00:04,  3.36it/s]J=10 E=10 D=10 predict1:  58%|█████▊    | 21/36 [00:08<00:04,  3.36it/s]J=10 E=10 D=10 predictB:  58%|█████▊    | 21/36 [00:08<00:04,  3.36it/s]J=10 E=10 D=10 predictO:  58%|█████▊    | 21/36 [00:09<00:04,  3.36it/s]J=10 E=10 D=10 predictO:  61%|██████    | 22/36 [00:09<00:03,  3.69it/s]J=10 E=20 D=10 train rf:  61%|██████    | 22/36 [00:09<00:03,  3.69it/s]J=10 E=20 D=10 ISession:  61%|██████    | 22/36 [00:09<00:03,  3.69it/s]J=10 E=20 D=10 cvt onnx:  61%|██████    | 22/36 [00:09<00:03,  3.69it/s]J=10 E=20 D=10 predict1:  61%|██████    | 22/36 [00:09<00:03,  3.69it/s]J=10 E=20 D=10 predictB:  61%|██████    | 22/36 [00:09<00:03,  3.69it/s]J=10 E=20 D=10 predictO:  61%|██████    | 22/36 [00:09<00:03,  3.69it/s]J=10 E=20 D=10 predictO:  64%|██████▍   | 23/36 [00:09<00:03,  3.85it/s]J=10 E=30 D=10 train rf:  64%|██████▍   | 23/36 [00:09<00:03,  3.85it/s]J=10 E=30 D=10 ISession:  64%|██████▍   | 23/36 [00:09<00:03,  3.85it/s]J=10 E=30 D=10 cvt onnx:  64%|██████▍   | 23/36 [00:09<00:03,  3.85it/s]J=10 E=30 D=10 predict1:  64%|██████▍   | 23/36 [00:09<00:03,  3.85it/s]J=10 E=30 D=10 predictB:  64%|██████▍   | 23/36 [00:09<00:03,  3.85it/s]J=10 E=30 D=10 predictO:  64%|██████▍   | 23/36 [00:09<00:03,  3.85it/s]J=10 E=30 D=10 predictO:  67%|██████▋   | 24/36 [00:09<00:03,  3.43it/s]J=1 E=10 D=4 train rf:  67%|██████▋   | 24/36 [00:09<00:03,  3.43it/s]J=1 E=10 D=4 ISession:  67%|██████▋   | 24/36 [00:09<00:03,  3.43it/s]J=1 E=10 D=4 cvt onnx:  67%|██████▋   | 24/36 [00:09<00:03,  3.43it/s]J=1 E=10 D=4 predict1:  67%|██████▋   | 24/36 [00:09<00:03,  3.43it/s]J=1 E=10 D=4 predictB:  67%|██████▋   | 24/36 [00:09<00:03,  3.43it/s]J=1 E=10 D=4 predictO:  67%|██████▋   | 24/36 [00:09<00:03,  3.43it/s]J=1 E=10 D=6 train rf:  67%|██████▋   | 24/36 [00:09<00:03,  3.43it/s]J=1 E=10 D=6 ISession:  67%|██████▋   | 24/36 [00:09<00:03,  3.43it/s]J=1 E=10 D=6 cvt onnx:  67%|██████▋   | 24/36 [00:09<00:03,  3.43it/s]J=1 E=10 D=6 predict1:  67%|██████▋   | 24/36 [00:09<00:03,  3.43it/s]J=1 E=10 D=6 predictB:  67%|██████▋   | 24/36 [00:09<00:03,  3.43it/s]J=1 E=10 D=6 predictO:  67%|██████▋   | 24/36 [00:09<00:03,  3.43it/s]J=1 E=10 D=6 predictO:  78%|███████▊  | 28/36 [00:09<00:00,  8.12it/s]J=1 E=10 D=8 train rf:  78%|███████▊  | 28/36 [00:09<00:00,  8.12it/s]J=1 E=10 D=8 ISession:  78%|███████▊  | 28/36 [00:09<00:00,  8.12it/s]J=1 E=10 D=8 cvt onnx:  78%|███████▊  | 28/36 [00:09<00:00,  8.12it/s]J=1 E=10 D=8 predict1:  78%|███████▊  | 28/36 [00:09<00:00,  8.12it/s]J=1 E=10 D=8 predictB:  78%|███████▊  | 28/36 [00:09<00:00,  8.12it/s]J=1 E=10 D=8 predictO:  78%|███████▊  | 28/36 [00:09<00:00,  8.12it/s]J=1 E=10 D=10 train rf:  78%|███████▊  | 28/36 [00:09<00:00,  8.12it/s]J=1 E=10 D=10 ISession:  78%|███████▊  | 28/36 [00:09<00:00,  8.12it/s]J=1 E=10 D=10 cvt onnx:  78%|███████▊  | 28/36 [00:09<00:00,  8.12it/s]J=1 E=10 D=10 predict1:  78%|███████▊  | 28/36 [00:09<00:00,  8.12it/s]J=1 E=10 D=10 predictB:  78%|███████▊  | 28/36 [00:09<00:00,  8.12it/s]J=1 E=10 D=10 predictO:  78%|███████▊  | 28/36 [00:09<00:00,  8.12it/s]J=1 E=10 D=10 predictO:  94%|█████████▍| 34/36 [00:09<00:00, 14.98it/s]J=1 E=10 D=10 predictO: 100%|██████████| 36/36 [00:09<00:00,  3.65it/s]

name=os.path.join(cache_dir,"plot_beanchmark_rf")print(f"Saving data into{name!r}")df=pandas.DataFrame(data)df2=df.copy()df2["legend"]=legenddf2.to_csv(f"{name}-{legend}.csv",index=False)

Saving data into '_cache/plot_beanchmark_rf'

df

n_rows=len(n_jobs)n_cols=len(n_ests)fig,axes=plt.subplots(n_rows,n_cols,figsize=(4*n_cols,4*n_rows))fig.suptitle(f"{rf.__class__.__name__}\nX.shape={X.shape}")forn_j,n_estimatorsintqdm(product(n_jobs,n_ests)):i=n_jobs.index(n_j)j=n_ests.index(n_estimators)ax=axes[i,j]subdf=df[(df.n_estimators==n_estimators)&(df.n_jobs==n_j)]ifsubdf.shape[0]==0:continuepiv=subdf.pivot(index="max_depth",columns="name",values=["avg","med"])piv.plot(ax=ax,title=f"jobs={n_j}, trees={n_estimators}")ax.set_ylabel(f"n_jobs={n_j}",fontsize="small")ax.set_xlabel("max_depth",fontsize="small")# ratioax2=ax.twinx()piv1=subdf.pivot(index="max_depth",columns="name",values="avg")piv1["speedup"]=piv1.base/piv1.ort_ax2.plot(piv1.index,piv1.speedup,"b--",label="speedup avg")piv1=subdf.pivot(index="max_depth",columns="name",values="med")piv1["speedup"]=piv1.base/piv1.ort_ax2.plot(piv1.index,piv1.speedup,"y--",label="speedup med")ax2.legend(fontsize="x-small")# 1ax2.plot(piv1.index,[1for_inpiv1.index],"k--",label="no speedup")foriinrange(axes.shape[0]):forjinrange(axes.shape[1]):axes[i,j].legend(fontsize="small")fig.tight_layout()fig.savefig(f"{name}-{legend}.png")# plt.show()

0it [00:00, ?it/s]4it [00:00, 38.92it/s]8it [00:00, 32.62it/s]9it [00:00, 37.54it/s]~/github/onnx-array-api/_doc/examples/plot_benchmark_rf.py:307: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.  axes[i, j].legend(fontsize="small")

DownloadJupyternotebook:plot_benchmark_rf.ipynb

DownloadPythonsourcecode:plot_benchmark_rf.py

Downloadzipped:plot_benchmark_rf.zip