Ci-dessous, les différences entre deux révisions de la page.
Les deux révisions précédentes Révision précédente Prochaine révision | Révision précédente | ||
ressources:ressources:tensorflow [2019/09/28 05:42] equemene |
ressources:ressources:tensorflow [2022/08/20 16:42] (Version actuelle) equemene |
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- | ====== Tensorflow ====== | + | <note warning>Documentation obsolète : elle correspond à un ancien système et ne doit pas être exploitée en ce moment. Pour exploiter le Machine Learning au CBP, suivez ce lien [[ressources:miniconda4cbp|Exploitation ou Installation de Miniconda au Centre Blaise Pascal]]</note> |
- | Un nouvel environnement [[https://www.tensorflow.org/|Tensorflow]] a été installé ce printemps au Centre Blaise Pascal. | ||
- | Il exploite l'environnement Anaconda3 installé dans le dossier ''/opt/anaconda3'' | + | ====== Tensorflow ====== |
+ | |||
+ | Un premier environnement [[https://www.tensorflow.org/|Tensorflow]] a été installé au printemps au Centre Blaise Pascal. Il exploite l'environnement Anaconda3-2019.3 installé dans le dossier ''/opt/anaconda3'' | ||
+ | Un second environnement [[https://www.tensorflow.org/|Tensorflow]] a été installé cet automne au Centre Blaise Pascal. Il exploite l'environnement Anaconda3-2019.10 installé dans le dossier ''/opt/anaconda3-2019.10'' | ||
===== Configuration d'exploitation ===== | ===== Configuration d'exploitation ===== | ||
- | L'environnement Tensorflow installé dans Anaconda3 exploite une version de TensorFlow 1.12 | + | Deux environnements Tensorflow sont installés dans deux Anaconda3 différents : |
+ | * l'environnement Tensorflow installé dans Anaconda3-2019.03 exploite une version de TensorFlow 1.12 | ||
+ | * l'environnement Tensorflow installé dans Anaconda3-2019.10 exploite une version de TensorFlow 2.0 | ||
- | Pour la charge dans le SIDUS standard du CBP:<code>source /etc/conda.init | + | Pour charger l'environnement Tensorflow 1.12 |
+ | Pour la charge dans le SIDUS standard du CBP:<code>source /etc/tensorflow.init | ||
</code> | </code> | ||
+ | |||
+ | Pour charger l'environnement Tensorflow 2.0 | ||
+ | Pour la charge dans le SIDUS standard du CBP:<code>source /etc/tensorflow2.init | ||
+ | </code> | ||
+ | |||
+ | |||
Lorsqu'il est activé l'invite de commande est alors préfixée de ''(base)''. Par exemple, l'utilisateur ''einstein'' sur la machine ''ascenseur'' aura comme invite de commande :<code>(base) einstein@ascenseur:~$</code> | Lorsqu'il est activé l'invite de commande est alors préfixée de ''(base)''. Par exemple, l'utilisateur ''einstein'' sur la machine ''ascenseur'' aura comme invite de commande :<code>(base) einstein@ascenseur:~$</code> | ||
- | ===== Exemple ===== | + | ===== Exemple pour Tensorflow 1.12 ===== |
L'exemple suivant, fourni par le [[https://www.tensorflow.org/|site officiel]], permet de rapidement juger du fonctionnement de l'environnement. Il nécessite le lancement de l'interpréteur python : | L'exemple suivant, fourni par le [[https://www.tensorflow.org/|site officiel]], permet de rapidement juger du fonctionnement de l'environnement. Il nécessite le lancement de l'interpréteur python : | ||
Ligne 59: | Ligne 70: | ||
Une grande variété de [[https://www.tensorflow.org/tutorials/|tutoriels]] en ligne permettent de vérifier le bon fonctionnement. | Une grande variété de [[https://www.tensorflow.org/tutorials/|tutoriels]] en ligne permettent de vérifier le bon fonctionnement. | ||
+ | ===== Exemple pour Tensorflow 2.0 ===== | ||
+ | |||
+ | L'exemple précédent ne fonctionne pas : voici un petit exemple qui fonctionne pour tester son tensorflow 2.0 | ||
+ | <code> | ||
+ | # Python | ||
+ | from __future__ import absolute_import, division, print_function, unicode_literals | ||
+ | import tensorflow as tf | ||
+ | # Create some tensors | ||
+ | a = tf.constant([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]) | ||
+ | b = tf.constant([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]]) | ||
+ | c = tf.matmul(a, b) | ||
+ | print(c) | ||
+ | </code> | ||
+ | |||
+ | A l'exécution de la troisième ligne, l'environnement détecte les cartes graphiques susceptibles d'être exploitées :<code> | ||
+ | 2019-11-04 17:58:31.722039: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcuda.so.1 | ||
+ | 2019-11-04 17:58:31.749152: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero | ||
+ | 2019-11-04 17:58:31.749646: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: | ||
+ | name: Tesla V100-PCIE-16GB major: 7 minor: 0 memoryClockRate(GHz): 1.38 | ||
+ | pciBusID: 0000:07:00.0 | ||
+ | 2019-11-04 17:58:31.750304: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0 | ||
+ | 2019-11-04 17:58:31.752157: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0 | ||
+ | 2019-11-04 17:58:31.753851: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0 | ||
+ | 2019-11-04 17:58:31.755073: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0 | ||
+ | 2019-11-04 17:58:31.757047: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0 | ||
+ | 2019-11-04 17:58:31.758713: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0 | ||
+ | 2019-11-04 17:58:31.762588: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 | ||
+ | 2019-11-04 17:58:31.762748: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero | ||
+ | 2019-11-04 17:58:31.763286: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero | ||
+ | 2019-11-04 17:58:31.763714: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0 | ||
+ | 2019-11-04 17:58:31.763967: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 AVX512F FMA | ||
+ | 2019-11-04 17:58:31.768995: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3192495000 Hz | ||
+ | 2019-11-04 17:58:31.769414: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55a533b0ac90 executing computations on platform Host. Devices: | ||
+ | 2019-11-04 17:58:31.769443: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): Host, Default Version | ||
+ | 2019-11-04 17:58:31.769643: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero | ||
+ | 2019-11-04 17:58:31.770095: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties: | ||
+ | name: Tesla V100-PCIE-16GB major: 7 minor: 0 memoryClockRate(GHz): 1.38 | ||
+ | pciBusID: 0000:07:00.0 | ||
+ | 2019-11-04 17:58:31.770124: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0 | ||
+ | 2019-11-04 17:58:31.770139: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0 | ||
+ | 2019-11-04 17:58:31.770150: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcufft.so.10.0 | ||
+ | 2019-11-04 17:58:31.770164: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcurand.so.10.0 | ||
+ | 2019-11-04 17:58:31.770178: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusolver.so.10.0 | ||
+ | 2019-11-04 17:58:31.770190: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcusparse.so.10.0 | ||
+ | 2019-11-04 17:58:31.770202: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudnn.so.7 | ||
+ | 2019-11-04 17:58:31.770292: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero | ||
+ | 2019-11-04 17:58:31.770799: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero | ||
+ | 2019-11-04 17:58:31.771228: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1746] Adding visible gpu devices: 0 | ||
+ | 2019-11-04 17:58:31.771260: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.0 | ||
+ | 2019-11-04 17:58:31.859125: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1159] Device interconnect StreamExecutor with strength 1 edge matrix: | ||
+ | 2019-11-04 17:58:31.859173: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1165] 0 | ||
+ | 2019-11-04 17:58:31.859183: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1178] 0: N | ||
+ | 2019-11-04 17:58:31.859388: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero | ||
+ | 2019-11-04 17:58:31.859930: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero | ||
+ | 2019-11-04 17:58:31.860424: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1006] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero | ||
+ | 2019-11-04 17:58:31.860871: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1304] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 14961 MB memory) -> physical GPU (device: 0, name: Tesla V100-PCIE-16GB, pci bus id: 0000:07:00.0, compute capability: 7.0) | ||
+ | 2019-11-04 17:58:31.862849: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55a534edf7d0 executing computations on platform CUDA. Devices: | ||
+ | 2019-11-04 17:58:31.862884: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): Tesla V100-PCIE-16GB, Compute Capability 7.0 | ||
+ | 2019-11-04 17:58:31.863918: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10.0 | ||
+ | tf.Tensor( | ||
+ | [[22. 28.] | ||
+ | [49. 64.]], shape=(2, 2), dtype=float32) | ||
+ | </code> | ||
+ | |||
+ | On reconnait la carte **Tesla V100-PCIE-16GB** détectée. | ||
+ | |||
+ | ===== Utilisation de TensorFlow2 pour du TensorFlow1 ===== | ||
+ | |||
+ | Il est possible, comme le précise la [[https://www.tensorflow.org/guide/migrate|documentation de TensorFlow]], d'exécuter un code de l'ancienne version sur la nouvelle avec le chargement suivant : | ||
+ | <code> | ||
+ | import tensorflow.compat.v1 as tf | ||
+ | tf.disable_v2_behavior() | ||
+ | </code> | ||
===== Astuces en cas de plantage incompréhensible ===== | ===== Astuces en cas de plantage incompréhensible ===== | ||
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* circuits Maxwell : GTX 980Ti | * circuits Maxwell : GTX 980Ti | ||
* circuits Pascal : GTX 1060, GTX 1070, GTX 1080, GTX 1080Ti, Tesla P100 | * circuits Pascal : GTX 1060, GTX 1070, GTX 1080, GTX 1080Ti, Tesla P100 | ||
+ | * circuits Turing : RTX Titan, RTX 2080 Ti, RTX 2080 Super | ||
+ | * circuits Volta : Tesla V100 | ||
===== GPU du CBP invalidés pour TensorFlow ===== | ===== GPU du CBP invalidés pour TensorFlow ===== |