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feature: set sparse Dockerfile with Python interpreter only

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This commit is contained in:
Christoph Schranz
2020-06-22 13:26:01 +02:00
parent da9c98689e
commit a4d5573698
10 changed files with 232 additions and 318 deletions
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100
extra/Getting_Started/GPU-processing.ipynb
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@@ -27,15 +27,15 @@
"name": "stdout",
"output_type": "stream",
"text": [
"Wed Mar 11 07:16:17 2020 \n",
"Mon Jun 22 11:24:08 2020 \n",
"+-----------------------------------------------------------------------------+\n",
"| NVIDIA-SMI 440.48.02 Driver Version: 440.48.02 CUDA Version: 10.2 |\n",
"| NVIDIA-SMI 440.82 Driver Version: 440.82 CUDA Version: 10.2 |\n",
"|-------------------------------+----------------------+----------------------+\n",
"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
"|===============================+======================+======================|\n",
"| 0 GeForce RTX 207... Off | 00000000:01:00.0 Off | N/A |\n",
"| 0% 42C P8 1W / 215W | 1788MiB / 7974MiB | 0% Default |\n",
"| 0 GeForce RTX 207... Off | 00000000:01:00.0 On | N/A |\n",
"| 0% 49C P0 38W / 215W | 430MiB / 7974MiB | 5% Default |\n",
"+-------------------------------+----------------------+----------------------+\n",
" \n",
"+-----------------------------------------------------------------------------+\n",
@@ -87,10 +87,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From <ipython-input-3-d1bfbb527297>:3: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use `tf.config.list_physical_devices('GPU')` instead.\n",
"True\n"
"[PhysicalDevice(name='/physical_device:XLA_GPU:0', device_type='XLA_GPU')]\n"
]
},
{
@@ -101,30 +98,20 @@
" memory_limit: 268435456\n",
" locality {\n",
" }\n",
" incarnation: 8034786465358909470,\n",
" incarnation: 12436949185972503812,\n",
" name: \"/device:XLA_CPU:0\"\n",
" device_type: \"XLA_CPU\"\n",
" memory_limit: 17179869184\n",
" locality {\n",
" }\n",
" incarnation: 13772661904993777233\n",
" incarnation: 9674938692146126962\n",
" physical_device_desc: \"device: XLA_CPU device\",\n",
" name: \"/device:GPU:0\"\n",
" device_type: \"GPU\"\n",
" memory_limit: 5480775680\n",
" locality {\n",
" bus_id: 1\n",
" links {\n",
" }\n",
" }\n",
" incarnation: 8336380964433791501\n",
" physical_device_desc: \"device: 0, name: GeForce RTX 2070 SUPER, pci bus id: 0000:01:00.0, compute capability: 7.5\",\n",
" name: \"/device:XLA_GPU:0\"\n",
" device_type: \"XLA_GPU\"\n",
" memory_limit: 17179869184\n",
" locality {\n",
" }\n",
" incarnation: 4817022749254415174\n",
" incarnation: 7870544216044264725\n",
" physical_device_desc: \"device: XLA_GPU device\"]"
]
},
@@ -136,7 +123,7 @@
"source": [
"import tensorflow as tf\n",
"from tensorflow.python.client import device_lib\n",
"print(tf.test.is_gpu_available(cuda_only=True))\n",
"print(tf.config.list_physical_devices('XLA_GPU'))\n",
"device_lib.list_local_devices()"
]
},
@@ -148,11 +135,11 @@
{
"data": {
"text/plain": [
"tensor([[0.1091, 0.0178, 0.2500],\n",
" [0.1409, 0.9612, 0.0325],\n",
" [0.8944, 0.3869, 0.9657],\n",
" [0.8131, 0.5454, 0.2587],\n",
" [0.6570, 0.0147, 0.1361]])"
"tensor([[0.0399, 0.1738, 0.2486],\n",
" [0.7464, 0.1461, 0.8991],\n",
" [0.7264, 0.9835, 0.8844],\n",
" [0.4544, 0.8331, 0.8435],\n",
" [0.0109, 0.0689, 0.2997]])"
]
},
"execution_count": 4,
@@ -202,7 +189,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
"248 ms ± 174 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
"276 ms ± 9.97 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
]
}
],
@@ -236,13 +223,13 @@
"name": "stdout",
"output_type": "stream",
"text": [
"78.2 ms ± 250 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
"82.1 ms ± 1.85 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
]
}
],
"source": [
"%%timeit\n",
"# Calculate the projection matrix of x\n",
"# Calculate the projection matrix of x on the CPU\n",
"H = x.mm( (x.t().mm(x)).inverse() ).mm(x.t())"
]
},
@@ -262,16 +249,16 @@
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[0.0962, 0.3125, 0.7327, 0.5982, 0.4624],\n",
" [0.4655, 0.4890, 0.9603, 0.4339, 0.0524],\n",
" [0.9294, 0.9639, 0.6312, 0.1752, 0.7721],\n",
" [0.5533, 0.3656, 0.9329, 0.8796, 0.9513],\n",
" [0.4949, 0.0972, 0.2892, 0.7570, 0.2847]], device='cuda:0')\n",
"tensor([[0.0962, 0.3125, 0.7327, 0.5982, 0.4624],\n",
" [0.4655, 0.4890, 0.9603, 0.4339, 0.0524],\n",
" [0.9294, 0.9639, 0.6312, 0.1752, 0.7721],\n",
" [0.5533, 0.3656, 0.9329, 0.8796, 0.9513],\n",
" [0.4949, 0.0972, 0.2892, 0.7570, 0.2847]], dtype=torch.float64)\n"
"tensor([[0.2854, 0.3384, 0.6473, 0.0433, 0.5640],\n",
" [0.3960, 0.0449, 0.6597, 0.5347, 0.8402],\n",
" [0.0048, 0.9231, 0.0311, 0.2545, 0.0409],\n",
" [0.6506, 0.8651, 0.7558, 0.1086, 0.8135],\n",
" [0.1083, 0.0039, 0.6049, 0.3596, 0.1359]], device='cuda:0')\n",
"tensor([[0.2854, 0.3384, 0.6473, 0.0433, 0.5640],\n",
" [0.3960, 0.0449, 0.6597, 0.5347, 0.8402],\n",
" [0.0048, 0.9231, 0.0311, 0.2545, 0.0409],\n",
" [0.6506, 0.8651, 0.7558, 0.1086, 0.8135],\n",
" [0.1083, 0.0039, 0.6049, 0.3596, 0.1359]], dtype=torch.float64)\n"
]
}
],
@@ -295,12 +282,13 @@
"name": "stdout",
"output_type": "stream",
"text": [
"11.4 ms ± 60.2 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
"11.4 ms ± 28.8 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
]
}
],
"source": [
"%%timeit\n",
"# Calculate the projection matrix of x on the GPU\n",
"H = x.mm( (x.t().mm(x)).inverse() ).mm(x.t())"
]
},
@@ -341,11 +329,11 @@
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[0.4303, 0.7364, 0.1235, 0.7786, 0.7036],\n",
" [0.3256, 0.4515, 0.7994, 0.9814, 0.7705],\n",
" [0.2292, 0.5194, 0.4354, 0.3964, 0.5804],\n",
" [0.8855, 0.5156, 0.9321, 0.9555, 0.4150],\n",
" [0.0640, 0.0665, 0.1170, 0.9547, 0.2668]], device='cuda:0')\n"
"tensor([[0.1101, 0.7887, 0.0641, 0.1327, 0.1681],\n",
" [0.7914, 0.7248, 0.7731, 0.2662, 0.4908],\n",
" [0.2451, 0.3568, 0.4006, 0.2099, 0.5212],\n",
" [0.6195, 0.5120, 0.5212, 0.7321, 0.2272],\n",
" [0.2374, 0.4540, 0.0868, 0.9393, 0.1561]], device='cuda:0')\n"
]
}
],
@@ -376,11 +364,11 @@
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[1.0966e-03, 3.5866e-04, 4.0044e-04, 3.2466e-04, 2.3044e-04],\n",
" [3.5866e-04, 9.7424e-04, 2.8649e-04, 8.2904e-04, 2.0482e-04],\n",
" [4.0044e-04, 2.8649e-04, 5.4179e-04, 1.2729e-04, 9.4659e-05],\n",
" [3.2466e-04, 8.2904e-04, 1.2729e-04, 1.3005e-03, 6.6951e-06],\n",
" [2.3044e-04, 2.0482e-04, 9.4659e-05, 6.6950e-06, 1.3420e-03]],\n",
"tensor([[ 6.4681e-04, -1.5392e-05, 3.3608e-04, 2.1025e-04, 8.0912e-05],\n",
" [-1.5392e-05, 5.0718e-04, -1.1769e-04, -2.3084e-05, -2.3264e-04],\n",
" [ 3.3608e-04, -1.1769e-04, 6.9678e-04, 2.2663e-04, -1.8900e-04],\n",
" [ 2.1025e-04, -2.3084e-05, 2.2663e-04, 6.0036e-04, 2.7787e-04],\n",
" [ 8.0912e-05, -2.3264e-04, -1.8900e-04, 2.7787e-04, 1.4208e-03]],\n",
" device='cuda:0')\n"
]
}
@@ -399,11 +387,11 @@
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[1.0966e-03, 3.5866e-04, 4.0044e-04, 3.2466e-04, 2.3044e-04],\n",
" [3.5866e-04, 9.7424e-04, 2.8649e-04, 8.2904e-04, 2.0482e-04],\n",
" [4.0044e-04, 2.8649e-04, 5.4179e-04, 1.2729e-04, 9.4659e-05],\n",
" [3.2466e-04, 8.2904e-04, 1.2729e-04, 1.3005e-03, 6.6951e-06],\n",
" [2.3044e-04, 2.0482e-04, 9.4659e-05, 6.6950e-06, 1.3420e-03]],\n",
"tensor([[ 6.4681e-04, -1.5392e-05, 3.3608e-04, 2.1025e-04, 8.0912e-05],\n",
" [-1.5392e-05, 5.0718e-04, -1.1769e-04, -2.3084e-05, -2.3264e-04],\n",
" [ 3.3608e-04, -1.1769e-04, 6.9678e-04, 2.2663e-04, -1.8900e-04],\n",
" [ 2.1025e-04, -2.3084e-05, 2.2663e-04, 6.0036e-04, 2.7787e-04],\n",
" [ 8.0912e-05, -2.3264e-04, -1.8900e-04, 2.7787e-04, 1.4208e-03]],\n",
" dtype=torch.float64)\n"
]
}