example output from torch_minst example

Helmholtz-AI-Energy · Aug 22, 2023 · ba35219 · ba35219
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diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -28,6 +28,8 @@ repos:
   rev: v2.2.0
   hooks:
   - id: seed-isort-config
+    args: 
+    - --exclude=examples/
 - repo: https://github.com/pycqa/isort
   rev: 5.12.0
   hooks:

diff --git a/examples/torch_mnist/README.md b/examples/torch_mnist/README.md
@@ -50,33 +50,80 @@ The text report from running the MNIST example should look like this:
 PERUN REPORT
 
 App name: torch_mnist
-First run: 2023-08-22T17:10:52.864155
-Last run: 2023-08-22T17:10:52.864155
+First run: 2023-08-22T17:44:34.927402
+Last run: 2023-08-22T17:44:34.927402
 
 
-RUN ID: 2023-08-22T17:10:52.864155
+RUN ID: 2023-08-22T17:44:34.927402
 
-|   Round # | Host            | RUNTIME   | CPU_UTIL   | MEM_UTIL   |
-|----------:|:----------------|:----------|:-----------|:-----------|
-|         0 | juan-20w000p2ge | 330.841 s | 63.367 %   | 0.559 %    |
-|         0 | All             | 330.841 s | 63.367 %   | 0.559 %    |
+|   Round # | Host                | RUNTIME   | ENERGY    | CPU_POWER   | CPU_UTIL   | GPU_POWER   | GPU_MEM   | DRAM_POWER   | MEM_UTIL   |
+|----------:|:--------------------|:----------|:----------|:------------|:-----------|:------------|:----------|:-------------|:-----------|
+|         0 | hkn0402.localdomain | 61.954 s  | 28.440 kJ | 203.619 W   | 0.867 %    | 232.448 W   | 4.037 GB  | 22.923 W     | 0.033 %    |
+|         0 | All                 | 61.954 s  | 28.440 kJ | 203.619 W   | 0.867 %    | 232.448 W   | 4.037 GB  | 22.923 W     | 0.033 %    |
 
 Monitored Functions
 
-|   Round # | Function    |   Avg Calls / Rank | Avg Runtime     | Avg Power     | Avg CPU Util   | Avg GPU Mem Util   |
-|----------:|:------------|-------------------:|:----------------|:--------------|:---------------|:-------------------|
-|         0 | train       |                  1 | 329.300±0.000 s | 0.000±0.000 W | 63.594±0.000 % | 0.000±0.000 %      |
-|         0 | train_epoch |                  5 | 61.563±2.827 s  | 0.000±0.000 W | 64.669±2.130 % | 0.000±0.000 %      |
-|         0 | test        |                  5 | 4.297±0.069 s   | 0.000±0.000 W | 46.278±1.119 % | 0.000±0.000 %      |
+|   Round # | Function    |   Avg Calls / Rank | Avg Runtime    | Avg Power        | Avg CPU Util   | Avg GPU Mem Util   |
+|----------:|:------------|-------------------:|:---------------|:-----------------|:---------------|:-------------------|
+|         0 | train       |                  1 | 50.390±0.000 s | 456.993±0.000 W  | 0.869±0.000 %  | 2.731±0.000 %      |
+|         0 | train_epoch |                  5 | 8.980±1.055 s  | 433.082±11.012 W | 0.874±0.007 %  | 2.746±0.148 %      |
+|         0 | test        |                  5 | 1.098±0.003 s  | 274.947±83.746 W | 0.804±0.030 %  | 2.808±0.025 %      |
+
+The application has run been run 1 times. Throught its runtime, it has used 0.012 kWh, released a total of 0.005 kgCO2e into the atmosphere, and you paid 0.00 € in electricity for it.
 
-The application has run been run 1 times.
 ```
 
 ## Benchmarking
 
-Benchmarking mode can help users obtain more indept data about the runtime and energy consumption of your scripts, by running the application multiple times and providing statistics like mean, std, min and max values for the usual measurements. To use benchmarking mode, add the `--bench` option to the perun command:
+If you need to run your code multiple times to gather statistics, perun includes an option called ```--rounds```. The application will be run multiple times, with each run added to similar tables as the one generated for a single run.
 
 
 ```console
 perun monitor --rounds 5 torch_mnist.py
 ```
+
+```text
+PERUN REPORT
+
+App name: torch_mnist
+First run: 2023-08-22T17:44:34.927402
+Last run: 2023-08-22T17:45:46.992693
+
+
+RUN ID: 2023-08-22T17:45:46.992693
+
+|   Round # | Host                | RUNTIME   | ENERGY    | CPU_POWER   | CPU_UTIL   | GPU_POWER   | GPU_MEM   | DRAM_POWER   | MEM_UTIL   |
+|----------:|:--------------------|:----------|:----------|:------------|:-----------|:------------|:----------|:-------------|:-----------|
+|         0 | hkn0402.localdomain | 52.988 s  | 24.379 kJ | 202.854 W   | 0.865 %    | 234.184 W   | 4.281 GB  | 22.858 W     | 0.034 %    |
+|         0 | All                 | 52.988 s  | 24.379 kJ | 202.854 W   | 0.865 %    | 234.184 W   | 4.281 GB  | 22.858 W     | 0.034 %    |
+|         1 | hkn0402.localdomain | 48.401 s  | 22.319 kJ | 203.366 W   | 0.886 %    | 234.821 W   | 4.513 GB  | 22.798 W     | 0.034 %    |
+|         1 | All                 | 48.401 s  | 22.319 kJ | 203.366 W   | 0.886 %    | 234.821 W   | 4.513 GB  | 22.798 W     | 0.034 %    |
+|         2 | hkn0402.localdomain | 48.258 s  | 22.248 kJ | 203.339 W   | 0.884 %    | 234.720 W   | 4.513 GB  | 22.850 W     | 0.034 %    |
+|         2 | All                 | 48.258 s  | 22.248 kJ | 203.339 W   | 0.884 %    | 234.720 W   | 4.513 GB  | 22.850 W     | 0.034 %    |
+|         3 | hkn0402.localdomain | 48.537 s  | 22.393 kJ | 203.269 W   | 0.884 %    | 234.984 W   | 4.513 GB  | 22.968 W     | 0.034 %    |
+|         3 | All                 | 48.537 s  | 22.393 kJ | 203.269 W   | 0.884 %    | 234.984 W   | 4.513 GB  | 22.968 W     | 0.034 %    |
+|         4 | hkn0402.localdomain | 48.416 s  | 22.323 kJ | 203.408 W   | 0.888 %    | 234.626 W   | 4.513 GB  | 22.928 W     | 0.034 %    |
+|         4 | All                 | 48.416 s  | 22.323 kJ | 203.408 W   | 0.888 %    | 234.626 W   | 4.513 GB  | 22.928 W     | 0.034 %    |
+
+Monitored Functions
+
+|   Round # | Function    |   Avg Calls / Rank | Avg Runtime    | Avg Power        | Avg CPU Util   | Avg GPU Mem Util   |
+|----------:|:------------|-------------------:|:---------------|:-----------------|:---------------|:-------------------|
+|         0 | train       |                  1 | 50.169±0.000 s | 458.380±0.000 W  | 0.875±0.000 %  | 2.727±0.000 %      |
+|         0 | train_epoch |                  5 | 8.930±0.903 s  | 439.707±12.743 W | 0.875±0.008 %  | 2.743±0.154 %      |
+|         0 | test        |                  5 | 1.103±0.004 s  | 232.750±1.219 W  | 0.805±0.030 %  | 2.809±0.023 %      |
+|         1 | train       |                  1 | 48.354±0.000 s | 453.376±0.000 W  | 0.886±0.000 %  | 2.820±0.000 %      |
+|         1 | train_epoch |                  5 | 8.556±0.008 s  | 428.418±11.199 W | 0.890±0.018 %  | 2.820±0.000 %      |
+|         1 | test        |                  5 | 1.115±0.002 s  | 272.918±80.330 W | 0.798±0.018 %  | 2.820±0.000 %      |
+|         2 | train       |                  1 | 48.210±0.000 s | 453.867±0.000 W  | 0.884±0.000 %  | 2.820±0.000 %      |
+|         2 | train_epoch |                  5 | 8.525±0.022 s  | 423.647±1.049 W  | 0.888±0.013 %  | 2.820±0.000 %      |
+|         2 | test        |                  5 | 1.117±0.005 s  | 312.983±97.688 W | 0.806±0.012 %  | 2.820±0.000 %      |
+|         3 | train       |                  1 | 48.486±0.000 s | 452.940±0.000 W  | 0.884±0.000 %  | 2.820±0.000 %      |
+|         3 | train_epoch |                  5 | 8.577±0.012 s  | 433.627±13.812 W | 0.888±0.017 %  | 2.820±0.000 %      |
+|         3 | test        |                  5 | 1.120±0.003 s  | 233.973±3.516 W  | 0.789±0.022 %  | 2.820±0.000 %      |
+|         4 | train       |                  1 | 48.367±0.000 s | 453.256±0.000 W  | 0.888±0.000 %  | 2.820±0.000 %      |
+|         4 | train_epoch |                  5 | 8.555±0.011 s  | 433.582±12.606 W | 0.899±0.029 %  | 2.820±0.000 %      |
+|         4 | test        |                  5 | 1.118±0.002 s  | 233.367±2.238 W  | 0.818±0.045 %  | 2.820±0.000 %      |
+
+The application has run been run 2 times. Throught its runtime, it has used 0.062 kWh, released a total of 0.026 kgCO2e into the atmosphere, and you paid 0.02 € in electricity for it.
+```
diff --git a/perun/__init__.py b/perun/__init__.py
@@ -1,6 +1,6 @@
 """perun module."""
 # flake8: noqa
-__version__ = "0.3.2"
+__version__ = "0.4.0"
 from perun.configuration import config
 from perun.logging import init_logging
 

diff --git a/pyproject.toml b/pyproject.toml
@@ -66,7 +66,7 @@ testpaths = ["tests"]
 
 [tool.isort]
 skip = ["perun/__init__.py"]
-known_third_party = ["click", "cpuinfo", "h5py", "numpy", "pandas", "psutil", "pynvml", "pytest", "torch", "torchvision"]
+known_third_party = ["click", "cpuinfo", "h5py", "numpy", "pandas", "psutil", "pynvml", "pytest"]
 profile = "black"
 
 [tool.pydocstyle]