Synchronize batch size for cpu test

examples/uci/tutorial.ipynb

@@ -0,0 +1,37 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    ""
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

tests/gpu_tests/cpu_test.py

@@ -34,7 +34,9 @@ def setUpClass(cls) -> None:
         cls.model = cls.model.double()
 
         cls.train_dataset = get_mnist_dataset(split="train", data_path="data")
+        cls.train_dataset = data.Subset(cls.train_dataset, indices=list(range(TRAIN_INDICES)))
         cls.eval_dataset = get_mnist_dataset(split="valid", data_path="data")
+        cls.eval_dataset = data.Subset(cls.eval_dataset, indices=list(range(QUERY_INDICES)))
 
         cls.task = ClassificationTask()
         cls.model = prepare_model(cls.model, cls.task)
@@ -122,7 +124,6 @@ def test_pairwise_scores(self) -> None:
         )
         new_pairwise_scores = self.analyzer.load_pairwise_scores(scores_name=NEW_SCORE_NAME)
 
-        torch.set_printoptions(threshold=30_000)
         print(f"Previous score: {pairwise_scores[ALL_MODULE_NAME][10]}")
         print(f"Previous shape: {pairwise_scores[ALL_MODULE_NAME].shape}")
         print(f"New score: {new_pairwise_scores[ALL_MODULE_NAME][10]}")

tests/gpu_tests/prepare_tests.py

@@ -99,7 +99,6 @@ def run_analysis() -> None:
         activation_covariance_dtype=torch.float64,
         gradient_covariance_dtype=torch.float64,
         lambda_dtype=torch.float64,
-        lambda_iterative_aggregate=False,
     )
     analyzer.fit_all_factors(
         factors_name="single_gpu",