diff --git a/tensorflow_privacy/privacy/logistic_regression/BUILD b/tensorflow_privacy/privacy/logistic_regression/BUILD
index a90b871..c1bc1ce 100644
--- a/tensorflow_privacy/privacy/logistic_regression/BUILD
+++ b/tensorflow_privacy/privacy/logistic_regression/BUILD
@@ -11,9 +11,10 @@ py_library(
     deps = [
         ":datasets",
         ":single_layer_softmax",
-        "//tensorflow_privacy/privacy/analysis:compute_dp_sgd_privacy_lib",
         "//tensorflow_privacy/privacy/optimizers:dp_optimizer_keras",
-        "@com_google_differential_py//python/dp_accounting:common",
+        "@com_google_differential_py//python/dp_accounting:dp_event",
+        "@com_google_differential_py//python/dp_accounting:mechanism_calibration",
+        "@com_google_differential_py//python/dp_accounting/rdp:rdp_privacy_accountant",
     ],
 )
 
diff --git a/tensorflow_privacy/privacy/logistic_regression/multinomial_logistic.py b/tensorflow_privacy/privacy/logistic_regression/multinomial_logistic.py
index 36fd7b0..a4ab2a0 100644
--- a/tensorflow_privacy/privacy/logistic_regression/multinomial_logistic.py
+++ b/tensorflow_privacy/privacy/logistic_regression/multinomial_logistic.py
@@ -30,12 +30,13 @@ from typing import List, Optional, Tuple
 
 import numpy as np
 import tensorflow as tf
-from tensorflow_privacy.privacy.analysis import compute_dp_sgd_privacy_lib
 from tensorflow_privacy.privacy.logistic_regression import datasets
 from tensorflow_privacy.privacy.logistic_regression import single_layer_softmax
 from tensorflow_privacy.privacy.optimizers import dp_optimizer_keras
 
-from com_google_differential_py.python.dp_accounting import common
+from com_google_differential_py.python.dp_accounting import dp_event
+from com_google_differential_py.python.dp_accounting import mechanism_calibration
+from com_google_differential_py.python.dp_accounting.rdp import rdp_privacy_accountant
 
 
 @tf.keras.utils.register_keras_serializable(package='Custom', name='Kifer')
@@ -170,15 +171,23 @@ def compute_dpsgd_noise_multiplier(num_train: int,
     the given tolerance) for which using DPKerasAdamOptimizer will result in an
     (epsilon, delta)-differentially private trained model.
   """
-  search_parameters = common.BinarySearchParameters(
-      lower_bound=0, upper_bound=math.inf, initial_guess=1, tolerance=tolerance)
+  orders = ([1.25, 1.5, 1.75, 2., 2.25, 2.5, 3., 3.5, 4., 4.5] +
+            list(range(5, 64)) + [128, 256, 512])
+  steps = int(math.ceil(epochs * num_train / batch_size))
 
-  def _func(x):
-    result = compute_dp_sgd_privacy_lib.compute_dp_sgd_privacy(
-        num_train, batch_size, x, epochs, delta)
-    return result[0]
+  def make_event_from_param(noise_multiplier):
+    return dp_event.SelfComposedDpEvent(
+        dp_event.PoissonSampledDpEvent(
+            sampling_probability=batch_size / num_train,
+            event=dp_event.GaussianDpEvent(noise_multiplier)), steps)
 
-  return common.inverse_monotone_function(_func, epsilon, search_parameters)
+  return mechanism_calibration.calibrate_dp_mechanism(
+      lambda: rdp_privacy_accountant.RdpAccountant(orders),
+      make_event_from_param,
+      epsilon,
+      delta,
+      mechanism_calibration.LowerEndpointAndGuess(0, 1),
+      tol=tolerance)
 
 
 def logistic_dpsgd(train_dataset: datasets.RegressionDataset,