Update benchmark results

README.md

@@ -7,7 +7,7 @@ PyCave allows you to run traditional machine learning models on CPU, GPU, and ev
 nodes. All models are implemented in [PyTorch](https://pytorch.org/) and provide an `Estimator` API
 that is fully compatible with [scikit-learn](https://scikit-learn.org/stable/).
 
-For Gaussian mixture model, PyCave allows for 50x speed ups when using a GPU and enables to train
+For Gaussian mixture model, PyCave allows for 100x speed ups when using a GPU and enables to train
 on markedly larger datasets via mini-batch training. The full suite of benchmarks run to compare
 PyCave models against scikit-learn models is available on the
 [documentation website](https://pycave.borchero.com/sites/benchmark.html).

docs/sites/benchmark.rst

@@ -36,23 +36,23 @@ Results
       - PyCave GPU (full)
       - PyCave GPU (batches)
     * - ``[10k, 8] -> 4``
-      - 352 ms
-      - 466 ms
-      - 3.7 s
-      - 787 ms
-      - 9.8 s
+      - **352 ms**
+      - 649 ms
+      - 3.9 s
+      - 358 ms
+      - 3.6 s
     * - ``[100k, 32] -> 16``
       - 18.4 s
-      - 4.1 s
-      - 9.3 s
-      - 1.6 s
-      - 14.2 s
+      - 4.3 s
+      - 10.0 s
+      - **527 ms**
+      - 3.9 s
     * - ``[1M, 64] -> 64``
       - 730 s
-      - 190 s
-      - 272 s
-      - 14.7 s
-      - 31.6 s
+      - 196 s
+      - 284 s
+      - **7.7 s**
+      - 15.3 s
 
 .. list-table:: Training Duration for Tied Covariance (``[num_datapoints, num_features] -> num_components``)
     :header-rows: 1
@@ -67,22 +67,22 @@ Results
       - PyCave GPU (batches)
     * - ``[10k, 8] -> 4``
       - 699 ms
-      - <tbd>
-      - <tbd>
-      - <tbd>
-      - <tbd>
+      - 570 ms
+      - 3.6 s
+      - **356 ms**
+      - 3.3 s
     * - ``[100k, 32] -> 16``
       - 72.2 s
-      - <tbd>
-      - <tbd>
-      - <tbd>
-      - <tbd>
+      - 12.1 s
+      - 16.1 s
+      - **919 ms**
+      - 3.8 s
     * - ``[1M, 64] -> 64``
       - --
       - --
       - --
-      - <tbd>
       - --
+      - **63.4 s**
 
 .. list-table:: Training Duration for Full Covariance (``[num_datapoints, num_features] -> num_components``)
     :header-rows: 1
@@ -97,24 +97,24 @@ Results
       - PyCave GPU (batches)
     * - ``[10k, 8] -> 4``
       - 1.1 s
-      - 574 ms
-      - 3.9 s
-      - 2.1 s
-      - 6.6 s
+      - 679 ms
+      - 4.1 s
+      - **648 ms**
+      - 4.4 s
     * - ``[100k, 32] -> 16``
       - 110 s
-      - 12.6 s
-      - 20.1 s
-      - 6.8 s
-      - 19.1 s
+      - 13.5 s
+      - 21.2 s
+      - **2.4 s**
+      - 7.8 s
 
 Summary
 ^^^^^^^
 
 PyCave's implementation of the Gaussian mixture model is markedly more efficient than the one found
 in scikit-learn. Even on the CPU, PyCave outperforms scikit-learn significantly at a 100k
 datapoints already. When moving to the GPU, however, PyCave unfolds its full potential and yields
-speed ups at around 50x. For larger datasets, mini-batch training is the only alternative. PyCave
+speed ups at around 100x. For larger datasets, mini-batch training is the only alternative. PyCave
 fully supports that while the training is approximately twice as large as when training using the
 full data. The reason for this is that the M-step of the EM algorithm needs to be split across
 epochs, which, in turn, requires to replay the E-step.