TensorFlow : Docker छवि का उपयोग करें (CPU)2023/09/21 |
TensorFlow स्थापित करें जो मशीन लर्निंग लाइब्रेरी है।
इस उदाहरण पर, GPU समर्थन के बिना TensorFlow आधिकारिक Docker छवि स्थापित करें और इसे कंटेनरों पर चलाएं।
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[2] | TensorFlow Docker (केवल CPU) स्थापित करें। |
root@dlp:~#
root@dlp:~# docker pull tensorflow/tensorflow:latest
docker images REPOSITORY TAG IMAGE ID CREATED SIZE tensorflow/tensorflow latest 976c17ec6daa 34 hours ago 1.46GB # कंटेनर चलाएँ root@dlp:~# docker run --rm tensorflow/tensorflow:latest \ python -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))" 2022-09-08 05:46:54.777140: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2022-09-08 05:46:57.014887: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. tf.Tensor(-654.6173, shape=(), dtype=float32) import tensorflow as tf hello = tf.constant('Hello, TensorFlow World!') tf.print(hello) docker run --rm -v $PWD:/tmp -w /tmp tensorflow/tensorflow:latest python3 ./hello_tensorflow.py 2022-09-08 05:48:16.886674: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2022-09-08 05:48:18.236757: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. Hello, TensorFlow World! |
[3] | TensorFlow Docker Image स्थापित करें जिसमें Jupyter Notebook शामिल है। |
root@dlp:~#
root@dlp:~# docker pull tensorflow/tensorflow:latest-jupyter
docker images REPOSITORY TAG IMAGE ID CREATED SIZE tensorflow/tensorflow latest-jupyter c94342dbd1e8 34 hours ago 1.68GB tensorflow/tensorflow latest 976c17ec6daa 34 hours ago 1.46GB # कंटेनर को डेमॉन के रूप में चलाएँ root@dlp:~# docker run -dt -p 8888:8888 tensorflow/tensorflow:latest-jupyter a2c4cd0c12f8d36b24e54edc257099cba7581c1ed6d14864595879e6e47f49b6root@dlp:~# docker ps CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES a2c4cd0c12f8 tensorflow/tensorflow:latest-jupyter "bash -c 'source /et…" 8 seconds ago Up 7 seconds 0.0.0.0:8888->8888/tcp, :::8888->8888/tcp unruffled_fermat # यूआरएल की पुष्टि करें root@dlp:~# docker exec a2c4cd0c12f8 bash -c "jupyter notebook list" Currently running servers: http://0.0.0.0:8888/?token=d3a7a2bd19e9bf986ff54b7280812d5de75c93d06e4898ce :: /tf |
उपरोक्त URL तक पहुंच, फिर Jupyter Notebook का उपयोग करना संभव है। |
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