diff --git a/_doc/examples/plot_onnx_tree_ensemble_parallel.py b/_doc/examples/plot_onnx_tree_ensemble_parallel.py
@@ -128,7 +128,7 @@ def ti(r, n):
     for r in sorted(res1):
         r1 = res1[r]
         r2 = res2[r]
-        ratio = r2['total'] / r1['total']
+        ratio = r2['ttime'] / r1['ttime']
         res[r] = ratio
     return res
 

diff --git a/_doc/examples/plot_op_add.py b/_doc/examples/plot_op_add.py
@@ -24,7 +24,7 @@
 from onnxruntime import InferenceSession
 from skl2onnx.common.data_types import FloatTensorType
 from skl2onnx.algebra.onnx_ops import OnnxAdd
-from mlprodict.tools import measure_time
+from cpyquickhelper.numbers import measure_time
 from tqdm import tqdm
 from mlprodict.testing.experimental_c import code_optimisation
 print(code_optimisation())

diff --git a/_doc/examples/plot_op_einsum.py b/_doc/examples/plot_op_einsum.py
@@ -30,7 +30,7 @@
 from onnxruntime import InferenceSession
 from skl2onnx.common.data_types import FloatTensorType
 from skl2onnx.algebra.onnx_ops import OnnxEinsum
-from mlprodict.tools import measure_time
+from cpyquickhelper.numbers import measure_time
 from tqdm import tqdm
 from opt_einsum import contract
 from mlprodict.testing.experimental_c import (

diff --git a/_doc/examples/plot_op_onnx_topk.py b/_doc/examples/plot_op_onnx_topk.py
@@ -23,7 +23,7 @@
 from pandas import DataFrame
 from onnxruntime import InferenceSession, __version__ as ort_version
 from tqdm import tqdm
-from mlprodict.tools import measure_time
+from cpyquickhelper.numbers import measure_time
 from pyquickhelper.pycode.profiling import profile
 from skl2onnx.algebra.onnx_ops import OnnxTopK_11
 from skl2onnx.common.data_types import FloatTensorType
@@ -139,7 +139,7 @@ def ti(n):
     for r in sorted(res1):
         r1 = res1[r]
         r2 = res2[r]
-        ratio = r2['total'] / r1['total']
+        ratio = r2['ttime'] / r1['ttime']
         res[r] = ratio
     return res
 

diff --git a/_doc/examples/plot_op_reducemax.py b/_doc/examples/plot_op_reducemax.py
@@ -23,7 +23,7 @@
 from onnxruntime import InferenceSession
 from skl2onnx.common.data_types import FloatTensorType
 from skl2onnx.algebra.onnx_ops import OnnxReduceMax
-from mlprodict.tools import measure_time
+from cpyquickhelper.numbers import measure_time
 from tqdm import tqdm
 from mlprodict.testing.experimental_c import code_optimisation
 print(code_optimisation())

diff --git a/_doc/examples/plot_op_reducemean.py b/_doc/examples/plot_op_reducemean.py
@@ -23,7 +23,7 @@
 from onnxruntime import InferenceSession
 from skl2onnx.common.data_types import FloatTensorType
 from skl2onnx.algebra.onnx_ops import OnnxReduceMean
-from mlprodict.tools import measure_time
+from cpyquickhelper.numbers import measure_time
 from tqdm import tqdm
 from mlprodict.testing.experimental_c import code_optimisation
 print(code_optimisation())

diff --git a/_doc/examples/plot_op_reducesum.py b/_doc/examples/plot_op_reducesum.py
@@ -23,7 +23,7 @@
 from onnxruntime import InferenceSession
 from skl2onnx.common.data_types import FloatTensorType
 from skl2onnx.algebra.onnx_ops import OnnxReduceSumApi11
-from mlprodict.tools import measure_time
+from cpyquickhelper.numbers import measure_time
 from tqdm import tqdm
 from mlprodict.testing.experimental_c import (
     code_optimisation, custom_reducesum_rk_float)

diff --git a/_doc/examples/plot_op_reducesumsquare.py b/_doc/examples/plot_op_reducesumsquare.py
@@ -23,7 +23,7 @@
 from onnxruntime import InferenceSession
 from skl2onnx.common.data_types import FloatTensorType
 from skl2onnx.algebra.onnx_ops import OnnxReduceSumSquare
-from mlprodict.tools import measure_time
+from cpyquickhelper.numbers import measure_time
 from tqdm import tqdm
 from mlprodict.testing.experimental_c import code_optimisation
 print(code_optimisation())

diff --git a/_doc/examples/plot_op_transpose.py b/_doc/examples/plot_op_transpose.py
@@ -26,7 +26,7 @@
 from onnxruntime import InferenceSession
 from skl2onnx.common.data_types import FloatTensorType
 from skl2onnx.algebra.onnx_ops import OnnxTranspose
-from mlprodict.tools import measure_time
+from cpyquickhelper.numbers import measure_time
 from tqdm import tqdm
 from mlprodict.testing.experimental_c import code_optimisation
 print(code_optimisation())

diff --git a/_doc/examples/plot_op_where.py b/_doc/examples/plot_op_where.py
@@ -23,7 +23,7 @@
 from onnxruntime import InferenceSession
 from skl2onnx.common.data_types import FloatTensorType, BooleanTensorType
 from skl2onnx.algebra.onnx_ops import OnnxWhere, OnnxSub, OnnxMul
-from mlprodict.tools import measure_time
+from cpyquickhelper.numbers import measure_time
 from tqdm import tqdm
 from mlprodict.testing.experimental_c import code_optimisation
 print(code_optimisation())

diff --git a/_doc/examples/plot_parallelism.py b/_doc/examples/plot_parallelism.py
@@ -24,7 +24,7 @@
 from sklearn.datasets import make_regression
 from sklearn.ensemble import HistGradientBoostingRegressor
 from sklearn.model_selection import train_test_split
-from mlprodict.tools import measure_time
+from cpyquickhelper.numbers import measure_time
 from pyquickhelper.pycode.profiling import profile
 from mlprodict.onnx_conv import to_onnx, register_rewritten_operators
 from mlprodict.onnxrt import OnnxInference

diff --git a/_doc/examples/plot_speedup_pca.py b/_doc/examples/plot_speedup_pca.py
@@ -35,7 +35,7 @@
 from sklearn.decomposition import PCA
 from pyquickhelper.pycode.profiling import profile
 from mlprodict.sklapi import OnnxSpeedupTransformer
-from mlprodict.tools.speed_measure import measure_time
+from cpyquickhelper.numbers.speed_measure import measure_time
 from tqdm import tqdm
 
 ################################

diff --git a/_doc/notebooks/topk_cpp.ipynb b/_doc/notebooks/topk_cpp.ipynb
diff --git a/_doc/sphinxdoc/source/api/tools.rst b/_doc/sphinxdoc/source/api/tools.rst
@@ -129,11 +129,6 @@ the possibility later to only show a part of a graph.
 Others
 ======
 
-Benchmark
-+++++++++
-
-.. autosignature:: mlprodict.tools.speed_measure.measure_time
-
 Plotting
 ++++++++
 

diff --git a/_doc/sphinxdoc/source/index.rst b/_doc/sphinxdoc/source/index.rst
@@ -79,15 +79,10 @@ mlprodict
     all_notebooks
     HISTORY
 
-*mlprodict* explores couple of ways to compute predictions faster
-than the library used to build the machine learned model,
-mostly :epkg:`scikit-learn` which is optimized for training,
-which is equivalent to batch predictions.
-One way is to use :epkg:`ONNX`.
-:epkg:`onnxruntime` provides an efficient way
-to compute predictions. *mlprodict* implements
-a *python/numpy* runtime for :epkg:`ONNX` which
-does not have any dependency on :epkg:`scikit-learn`.
+*mlprodict* was initially started to help implementing converters
+to :epkg:`ONNX`. The main feature is a python runtime for
+:epkg:`ONNX`. It gives more feedback than :epkg:`onnxruntime`
+when the execution fails.
 
 .. runpython::
     :showcode:
@@ -112,7 +107,8 @@ does not have any dependency on :epkg:`scikit-learn`.
 
     # Conversion into ONNX.
     from mlprodict.onnx_conv import to_onnx
-    model_onnx = to_onnx(lr, X.astype(numpy.float32))
+    model_onnx = to_onnx(lr, X.astype(numpy.float32),
+                         black_op={'LinearRegressor'})
     print("ONNX:", str(model_onnx)[:200] + "\n...")
 
     # Predictions with onnxruntime
@@ -124,6 +120,12 @@ does not have any dependency on :epkg:`scikit-learn`.
     # Measuring the maximum difference.
     print("max abs diff:", measure_relative_difference(expected, ypred['variable']))
 
+    # And the python runtime
+    oinf = OnnxInference(model_onnx, runtime='python')
+    ypred = oinf.run({'X': X[:5].astype(numpy.float32)},
+                     verbose=1, fLOG=print)
+    print("ONNX output:", ypred)
+
 These predictions are obtained with the
 following :epkg:`ONNX` graph.
 

diff --git a/_unittests/ut_onnxrt/test_onnxrt_simple.py b/_unittests/ut_onnxrt/test_onnxrt_simple.py
@@ -22,7 +22,8 @@
 from pyquickhelper.pycode import ExtTestCase, get_temp_folder, ignore_warnings
 from skl2onnx.algebra.onnx_ops import (  # pylint: disable=E0611
     OnnxAdd, OnnxLinearRegressor, OnnxLinearClassifier,
-    OnnxConstantOfShape, OnnxShape, OnnxIdentity)
+    OnnxConstantOfShape, OnnxShape, OnnxIdentity,
+    OnnxIf, OnnxSub, OnnxGreater, OnnxReduceSum)
 from skl2onnx.common.data_types import FloatTensorType, Int64TensorType
 from skl2onnx import __version__ as skl2onnx_version
 from mlprodict.onnx_conv import to_onnx
@@ -462,6 +463,38 @@ def test_blofat16(self):
             [1, -5, -6], dtype=numpy.float16))
         self.assertIn('n0_min(X, Y)', str(oinf))
 
+    @ignore_warnings(DeprecationWarning)
+    def test_onnx_if_to_dot(self):
+        opv = 15
+        x1 = numpy.array([[0, 3], [7, 0]], dtype=numpy.float32)
+        x2 = numpy.array([[1, 0], [2, 0]], dtype=numpy.float32)
+
+        node = OnnxAdd(
+            'x1', 'x2', output_names=['absxythen'], op_version=opv)
+        then_body = node.to_onnx(
+            {'x1': x1, 'x2': x2}, target_opset=opv,
+            outputs=[('absxythen', FloatTensorType())])
+        node = OnnxSub(
+            'x1', 'x2', output_names=['absxyelse'], op_version=opv)
+        else_body = node.to_onnx(
+            {'x1': x1, 'x2': x2}, target_opset=opv,
+            outputs=[('absxyelse', FloatTensorType())])
+        del else_body.graph.input[:]
+        del then_body.graph.input[:]
+
+        cond = OnnxGreater(
+            OnnxReduceSum('x1', op_version=opv),
+            OnnxReduceSum('x2', op_version=opv),
+            op_version=opv)
+        ifnode = OnnxIf(cond, then_branch=then_body.graph,
+                        else_branch=else_body.graph,
+                        op_version=opv, output_names=['y'])
+        model_def = ifnode.to_onnx(
+            {'x1': x1, 'x2': x2}, target_opset=opv,
+            outputs=[('y', FloatTensorType())])
+        dot = OnnxInference(model_def, skip_run=True).to_dot(recursive=True)
+        self.assertIn("subgraph cluster_If", dot)
+
 
 if __name__ == "__main__":
     unittest.main()
diff --git a/_unittests/ut_tools/test_mesure_time.py b/_unittests/ut_tools/test_mesure_time.py
diff --git a/mlprodict/onnxrt/onnx_inference.py b/mlprodict/onnxrt/onnx_inference.py
@@ -66,6 +66,7 @@ class OnnxInference:
         outputs may share the same name)
     :param static_inputs: Loop can use static variables,
         variables from the graph which runs the loop
+        (enumerate of strings)
     :param new_outputs: if the loading fails, it might worth
         cutting the graph, if not None, the graph will
         be cut to have these new_outputs as the final outputs
@@ -80,6 +81,9 @@ class OnnxInference:
 
     .. versionchanged:: 0.7
         Parameters *new_outputs*, *new_opset* were added.
+
+    .. versionchanged:: 0.8
+        Parameter *static_inputs* was added.
     """
 
     def __init__(self, onnx_or_bytes_or_stream, runtime=None,

diff --git a/mlprodict/onnxrt/onnx_inference_exports.py b/mlprodict/onnxrt/onnx_inference_exports.py
@@ -184,6 +184,8 @@ def dot_label(text):
 
         # nodes
         fill_names = {}
+        static_inputs = [n.name for n in self.oinf.obj.graph.input]
+        static_inputs.extend(n.name for n in self.oinf.obj.graph.initializer)
         for node in self.oinf.obj.graph.node:
             exp.append("")
             for out in node.output:
@@ -195,6 +197,7 @@ def dot_label(text):
                     exp.append(
                         '  {2}{0} [shape=box label="{0}{3}" fontsize={1}];'.format(
                             dot_name(out), fontsize, dot_name(prefix), dot_label(sh)))
+                static_inputs.append(out)
 
             dobj = _var_as_dict(node)
             if dobj['name'].strip() == '':  # pragma: no cover
@@ -231,7 +234,8 @@ def dot_label(text):
                     # creates the subgraph
                     body = dobj['atts'][field]['value']
                     oinf = self.oinf.__class__(
-                        body, runtime=self.oinf.runtime, skip_run=self.oinf.skip_run)
+                        body, runtime=self.oinf.runtime, skip_run=self.oinf.skip_run,
+                        static_inputs=static_inputs)
                     subprefix = prefix + "B_"
                     subdot = oinf.to_dot(recursive=recursive, prefix=subprefix,
                                          add_rt_shapes=add_rt_shapes)

diff --git a/mlprodict/onnxrt/validate/validate_benchmark.py b/mlprodict/onnxrt/validate/validate_benchmark.py
@@ -160,7 +160,7 @@ def allow(N, obs):
                                   number=number, div_by_number=True)
         if (skip_long_test and not node_time and
                 res[N] is not None and
-                res[N].get('total', time_limit) >= time_limit):
+                res[N].get('ttime', time_limit) >= time_limit):
             # too long
             break  # pragma: no cover
     if node_time:

diff --git a/mlprodict/onnxrt/validate/validate_helper.py b/mlprodict/onnxrt/validate/validate_helper.py
@@ -6,13 +6,13 @@
 """
 import math
 import copy
-from timeit import Timer
 import os
 import warnings
 from importlib import import_module
 import pickle
 from time import perf_counter
 import numpy
+from cpyquickhelper.numbers import measure_time as _c_measure_time
 from sklearn.base import BaseEstimator
 from sklearn.linear_model._base import LinearModel
 from sklearn.model_selection import train_test_split
@@ -325,7 +325,8 @@ def default_time_kwargs():
     }
 
 
-def measure_time(stmt, x, repeat=10, number=50, div_by_number=False, first_run=True):
+def measure_time(stmt, x, repeat=10, number=50, div_by_number=False,
+                 first_run=True, max_time=None):
     """
     Measures a statement and returns the results as a dictionary.
 
@@ -335,6 +336,9 @@ def measure_time(stmt, x, repeat=10, number=50, div_by_number=False, first_run=T
     :param number: number of executions in one row
     :param div_by_number: divide by the number of executions
     :param first_run: if True, runs the function once before measuring
+    :param max_time: execute the statement until the total goes
+        beyond this time (approximatively), *repeat* is ignored,
+        *div_by_number* must be set to True
     :return: dictionary
 
     See `Timer.repeat <https://docs.python.org/3/library/timeit.html?timeit.Timer.repeat>`_
@@ -345,28 +349,17 @@ def measure_time(stmt, x, repeat=10, number=50, div_by_number=False, first_run=T
     if x is None:
         raise ValueError("x cannot be None")  # pragma: no cover
 
-    try:
-        stmt(x)
-    except RuntimeError as e:  # pragma: no cover
-        raise RuntimeError("{}-{}".format(type(x), x.dtype)) from e
-
     def fct():
         stmt(x)
 
     if first_run:
-        fct()
-    tim = Timer(fct)
-    res = numpy.array(tim.repeat(repeat=repeat, number=number))
-    total = numpy.sum(res)
-    if div_by_number:
-        res /= number
-    mean = numpy.mean(res)
-    dev = numpy.mean(res ** 2)
-    dev = max(0, (dev - mean**2)) ** 0.5
-    mes = dict(average=mean, deviation=dev, min_exec=numpy.min(res),
-               max_exec=numpy.max(res), repeat=repeat, number=number,
-               total=total)
-    return mes
+        try:
+            fct()
+        except RuntimeError as e:  # pragma: no cover
+            raise RuntimeError("{}-{}".format(type(x), x.dtype)) from e
+
+    return _c_measure_time(fct, context={}, repeat=repeat, number=number,
+                           div_by_number=div_by_number, max_time=max_time)
 
 
 def _multiply_time_kwargs(time_kwargs, time_kwargs_fact, inst):
-Original file line number
+Diff line change
@@ Expand Up / @@ -129,11 +129,6 @@ the possibility later to only show a part of a graph. @@
     Others
     ======
-    Benchmark
-    +++++++++
-    .. autosignature:: mlprodict.tools.speed_measure.measure_time
     Plotting
     ++++++++
@@ Expand Down @@