{"id":19175044,"url":"https://github.com/losttech/gradient-perf","last_synced_at":"2026-06-23T14:31:12.025Z","repository":{"id":124319749,"uuid":"248642483","full_name":"losttech/Gradient-Perf","owner":"losttech","description":"Gradient performance 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TensorFlow .NET\n\n```\ntensorflow/core/platform/cpu_feature_guard.cc:142 Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2\nTraining epoch: 1\niter 000: Loss=2.3022, Training Accuracy=14.00% 221ms\niter 100: Loss=0.5189, Training Accuracy=88.00% 3041ms\niter 200: Loss=0.1782, Training Accuracy=95.00% 3021ms\niter 300: Loss=0.1938, Training Accuracy=91.00% 3016ms\niter 400: Loss=0.0924, Training Accuracy=96.00% 3037ms\niter 500: Loss=0.1010, Training Accuracy=98.00% 3017ms\n---------------------------------------------------------\nEpoch: 1, validation loss: 0.1122, validation accuracy: 96.74%\n---------------------------------------------------------\nTraining epoch: 2\niter 000: Loss=0.1888, Training Accuracy=95.00% 1777ms\niter 100: Loss=0.0528, Training Accuracy=99.00% 3010ms\niter 200: Loss=0.0763, Training Accuracy=98.00% 3045ms\niter 300: Loss=0.0360, Training Accuracy=99.00% 3040ms\n```\n\n### Gradient\n\n```\nWARNING:tensorflow:From C:\\Users\\lost\\.conda\\envs\\tf-1.x-gpu\\lib\\site-packages\\tensorflow_core\\python\\util\\deprecation.py:503: calling argmax (from tensorflow.python.ops.math_ops) with dimension is deprecated and will be removed in a future version.\nInstructions for updating:\nUse the `axis` argument instead\n2020-03-19 18:32:46.419558: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library nvcuda.dll\n2020-03-19 18:32:46.444918: E tensorflow/stream_executor/cuda/cuda_driver.cc:318] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n2020-03-19 18:32:46.448919: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: lost-pc\n2020-03-19 18:32:46.452414: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: lost-pc\n2020-03-19 18:32:46.455298: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2\nTraining epoch: 1\niter 000: Loss=2.3023, Training Accuracy=14.00% 271ms\niter 100: Loss=0.4832, Training Accuracy=89.00% 2479ms\niter 200: Loss=0.3046, Training Accuracy=95.00% 2482ms\niter 300: Loss=0.1041, Training Accuracy=95.00% 2464ms\niter 400: Loss=0.0930, Training Accuracy=96.00% 2471ms\niter 500: Loss=0.1119, Training Accuracy=96.00% 2483ms\n---------------------------------------------------------\nEpoch: 1, validation loss: 0.1042, validation accuracy: 96.76%\n---------------------------------------------------------\nTraining epoch: 2\niter 000: Loss=0.1296, Training Accuracy=96.00% 1659ms\niter 100: Loss=0.1223, Training Accuracy=98.00% 2546ms\niter 200: Loss=0.0770, Training Accuracy=96.00% 2469ms\niter 300: Loss=0.0419, Training Accuracy=100.00% 2517ms\n```\n\n### Instructions to run (tested on Windows only)\n\n- clone --recursive\n- create a Conda environment `tf-1.x-cpu` with Python 3.7\n- in the Conda environment install `tensorflow-cpu==1.15.0`\n- Launch the solution, switch config to Release\n- In `Debug` section of `Bench.Gradient` project properties add `GRADIENT_PYTHON_ENVIRONMENT` = `conda:tf-1.x-cpu` environment variable\n- launch `Bench.Gradient` or `Bench.TF.NET` without debugging (e.g. Ctrl-F5)\n\n### Remarks\n\nThis uses SciSharp's own sample code, a version, that works with their latest NuGet packages.\nGradient adaptation is in `src\\Bench.Gradient\\DigitRecognitionCNN.cs`. It is basically\na copy-paste with a few shims and Gradient-specific edits (see file history).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flosttech%2Fgradient-perf","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flosttech%2Fgradient-perf","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flosttech%2Fgradient-perf/lists"}