I'm just wondering if anyone is encountering the same error…
\n# Detect hardware, return appropriate distribution strategy\ntry:\n TPU = tf.distribute.cluster_resolver.TPUClusterResolver() # TPU detection. No parameters necessary if TPU_NAME environment variable is set. On Kaggle this is always the case.\n print('Running on TPU ', TPU.master())\nexcept ValueError:\n print('Running on GPU')\n TPU = None\n\nif TPU:\n tf.config.experimental_connect_to_cluster(TPU)\n tf.tpu.experimental.initialize_tpu_system(TPU)\n strategy = tf.distribute.experimental.TPUStrategy(TPU)\nelse:\n strategy = tf.distribute.get_strategy() # default distribution strategy in Tensorflow. Works on CPU and single GPU.\n\nREPLICAS = strategy.num_replicas_in_sync\nprint(f'REPLICAS: {REPLICAS}')\n2021-09-20 12:22:10.919681: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set\n2021-09-20 12:22:10.922381: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /opt/conda/lib\n2021-09-20 12:22:10.922415: W tensorflow/stream_executor/cuda/cuda_driver.cc:326] failed call to cuInit: UNKNOWN ERROR (303)\n2021-09-20 12:22:10.922441: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (18c20b5fff42): /proc/driver/nvidia/version does not exist\nRunning on TPU grpc://10.0.0.2:8470\n2021-09-20 12:22:10.924971: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n2021-09-20 12:22:10.926428: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n2021-09-20 12:22:10.932359: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\nI'm just wondering if anyone is encountering the same error…
\n# Detect hardware, return appropriate distribution strategy\ntry:\n TPU = tf.distribute.cluster_resolver.TPUClusterResolver() # TPU detection. No parameters necessary if TPU_NAME environment variable is set. On Kaggle this is always the case.\n print('Running on TPU ', TPU.master())\nexcept ValueError:\n print('Running on GPU')\n TPU = None\n\nif TPU:\n tf.config.experimental_connect_to_cluster(TPU)\n tf.tpu.experimental.initialize_tpu_system(TPU)\n strategy = tf.distribute.experimental.TPUStrategy(TPU)\nelse:\n strategy = tf.distribute.get_strategy() # default distribution strategy in Tensorflow. Works on CPU and single GPU.\n\nREPLICAS = strategy.num_replicas_in_sync\nprint(f'REPLICAS: {REPLICAS}')\n2021-09-20 12:22:10.919681: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set\n2021-09-20 12:22:10.922381: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /opt/conda/lib\n2021-09-20 12:22:10.922415: W tensorflow/stream_executor/cuda/cuda_driver.cc:326] failed call to cuInit: UNKNOWN ERROR (303)\n2021-09-20 12:22:10.922441: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (18c20b5fff42): /proc/driver/nvidia/version does not exist\nRunning on TPU grpc://10.0.0.2:8470\n2021-09-20 12:22:10.924971: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n2021-09-20 12:22:10.926428: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n2021-09-20 12:22:10.932359: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\nYou can check out this solution. I hope it will solve your error.
", "rawMarkdown": "You can check out this [solution](https://stackoverflow.com/questions/65907365/tensorflow-not-creating-xla-devices-tf-xla-enable-xla-devices-not-set). I hope it will solve your error.", "votes": null }, { "id": "1519438", "postDate": "09/21/2021 15:54:02", "content": "Thanks, but it seems that the solution didn't solve 'missing cuda error'
", "rawMarkdown": "Thanks, but it seems that the solution didn't solve 'missing cuda error'", "votes": null } ], "comments": [ { "id": 1518533, "author_name": "towhidultonmoy", "author_url": "", "post_date": "09/20/2021 18:05:21", "content": "You can check out this solution. I hope it will solve your error.
", "votes": null, "replies": [ { "id": 1519438, "author_name": "gdoong", "author_url": "", "post_date": "09/21/2021 15:54:02", "content": "Thanks, but it seems that the solution didn't solve 'missing cuda error'
", "votes": null, "replies": [] } ] } ], "raw_markdown_by_id": { "1518123": "I'm just wondering if anyone is encountering the same error...\n```python\n# Detect hardware, return appropriate distribution strategy\ntry:\n TPU = tf.distribute.cluster_resolver.TPUClusterResolver() # TPU detection. No parameters necessary if TPU_NAME environment variable is set. On Kaggle this is always the case.\n print('Running on TPU ', TPU.master())\nexcept ValueError:\n print('Running on GPU')\n TPU = None\n\nif TPU:\n tf.config.experimental_connect_to_cluster(TPU)\n tf.tpu.experimental.initialize_tpu_system(TPU)\n strategy = tf.distribute.experimental.TPUStrategy(TPU)\nelse:\n strategy = tf.distribute.get_strategy() # default distribution strategy in Tensorflow. Works on CPU and single GPU.\n\nREPLICAS = strategy.num_replicas_in_sync\nprint(f'REPLICAS: {REPLICAS}')\n```\n\n```bash\n2021-09-20 12:22:10.919681: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set\n2021-09-20 12:22:10.922381: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /opt/conda/lib\n2021-09-20 12:22:10.922415: W tensorflow/stream_executor/cuda/cuda_driver.cc:326] failed call to cuInit: UNKNOWN ERROR (303)\n2021-09-20 12:22:10.922441: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (18c20b5fff42): /proc/driver/nvidia/version does not exist\nRunning on TPU grpc://10.0.0.2:8470\n2021-09-20 12:22:10.924971: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\nTo enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n2021-09-20 12:22:10.926428: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n2021-09-20 12:22:10.932359: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n```", "1518533": "You can check out this [solution](https://stackoverflow.com/questions/65907365/tensorflow-not-creating-xla-devices-tf-xla-enable-xla-devices-not-set). I hope it will solve your error.", "1519438": "Thanks, but it seems that the solution didn't solve 'missing cuda error'" }, "source": "meta" }