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Calculated Shard: 197 (from laksa112)

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curl -X POST \
  'http://laksa197.int.ahrefs:8124/' \
  -H 'Content-Type: text/plain' \
  -H 'X-ClickHouse-Database: crawler3' \
  -H 'Authorization: Basic YXBpOg==' \
  -d 'SELECT getAhrefsURLFromUnparsed(src_unparsed) AS found_url, ifNull(toUnixTimestamp(download_stamp), 0) AS crawl_time, ifNull(toUnixTimestamp(props_url_first_seen), 0) AS first_indexed_time, download_http_code AS http_code, src_unparsed AS src_unparsed, src_root_hash AS src_root_hash, history_drop_reason AS history_drop_reason, meta_title AS meta_title, meta_descriptions AS meta_descriptions, attrs_boilerpipe_text AS attrs_boilerpipe_text, attrs_markdown AS attrs_markdown, attrs_readable_markdown AS attrs_readable_markdown, meta_canonical AS meta_canonical FROM crawler3.page_info_local FINAL PREWHERE (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'https://www.pythonpool.com/solved-runtimeerror-cuda-error-invalid-device-ordinal/\')), getAhrefsUnparsedNoserviceFromURL(\'https://www.pythonpool.com/solved-runtimeerror-cuda-error-invalid-device-ordinal/\'))) FORMAT JSONEachRow'

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{"found_url":"https:\/\/www.pythonpool.com\/solved-runtimeerror-cuda-error-invalid-device-ordinal\/","crawl_time":1775263156,"first_indexed_time":1704361397,"http_code":200,"src_unparsed":"com,pythonpool!www,\/solved-runtimeerror-cuda-error-invalid-device-ordinal\/ s443","src_root_hash":"10768594352196822397","history_drop_reason":null,"meta_title":"[Solved] runtimeerror: cuda error: invalid device ordinal - Python Pool","meta_descriptions":["The runtimeerror: cuda error: invalid device ordinal error happens when the CUDA runtime finds an unrecognizable or unavailable GPU device."],"attrs_boilerpipe_text":"The ‘runtimeerror: cuda error: invalid device ordinal’ error happens when the CUDA runtime finds an unrecognizable, non-existent, or unavailable GPU device. Scroll down to know why this error occurs and how you can prevent this on your system.\nContents\nReasons of error\nMethods of resolving the error\nCheck GPU Availability\nSet Device Ordinal\/ GPU id\nCheck CUDA availability\nGPU Usage check\nruntimeerror: cuda error: invalid device ordinal PyTorch\nruntimeerror: cuda error: invalid device ordinal torchrun\nOther tips while working with CUDA and GPU\nFAQs\nConclusion\nTrending Now\nReasons of error\nYou get the “runtimeerror: cuda error: invalid device ordinal” error because of a couple of reasons.\nCheck this list\nout to know more:\nYour system might not have a GPU, or if it exists, it may not be configured correctly. CUDA-compatible GPUs are necessary for a system, and thus, you need to check first.\nThe GPU ID that you have used for accessing is not correct. In this case, the GPU ID should be valid.\nYour CUDA driver might not be updated. Outdated drivers are not able to recognize the GPU on your device.\nYour CUDA environment may not be properly set. See if the CUDA_HOME directory has the CUDA installation details only.\nIf two conflicting libraries try to access the CUDA device simultaneously, it throws an error.\nIf your GPU usage exceeds what is available to you, you may get this error.\nMethods of resolving the error\nYou can go through the following pointers in order to resolve the “runtimeerror: cuda error: invalid device ordinal” error.\nCheck if GPU actually exists on your system. For this, use the Nvidia-smi command-line tool. It lists the GPU on your system. It provides information about device IDs, status, and temperature. In case no such GPUs exist, you got the reason of your error. You need to have physical GPU hardware to run this command.\nYour CUDA driver may need an update. Go to the NVIDIA website:\nhttps:\/\/www.nvidia.com\/download\/index.aspx\nin order to download the latest driver. You can choose your operating system and GPU model from the site itself.\nThe environment variable should have the correct CUDA installation directory. Use the following command to check this:\necho $CUDA_HOME\nIf you change CUDA configurations, driver installations, or environment variables in any way, consider restarting the system once.\nIf you don’t know which GPU you should point out in your command, you can simply write cuda. Otherwise, the indexing starts from 0, so you can count and verify.\ntorch.device('cuda')\nCheck GPU Availability\nThis code uses the nvidia_smi library to check if gpu device is present on your device or not. If it doesn’t exist, you’ll get an error.\nimport nvidia_smi\n\ndevices = nvidia_smi.nvml.DeviceGetAll()\nif not devices:\n    raise RuntimeError(\"No GPUs detected\")\nSet Device Ordinal\/ GPU id\nYou have the feasibility to set the device ordinal all by yourself. For this purpose,  the torch.cuda.set_device() function is present in the torch library of Python. Check the GPU that you want to use and replace it in the given code.\nimport torch\n\ngpu_id = 0  # Replace with the desired GPU ordinal\ntorch.cuda.set_device(gpu_id)\nOr you can explicitly mention it in this way as well:\nvar = EmotionRecognition(device='gpu', gpu_id=1)\nPopular now\nCheck CUDA availability\nThe presence of the CUDA environment is extremely important. The torch.cuda.is_available() under the torch library enables you to check whether CUDA is available or not.\nimport torch\n\nif not torch.cuda.is_available():\n    print(\"CUDA is not available\")\n    exit()\nGPU Usage check\nIt is suggested to check if you have enough GPU hardware devices or not. The torch.cuda.device_count() of the torch library is an excellent way to check this. It draws a comparison with the desired GPUs.\nimport torch\n\nnum_gpus = torch.cuda.device_count()\nif num_gpus < desired_gpus:\n    print(\"Not enough GPUs available\")\n    exit()\nThe GPU id is counted from 0. Therefore, you should mention the GPU id correctly. If the device has only one GPU, then it is better to state 0. This refers to the first GPU. If you want to avoid the specifications, use torch.device(‘cuda’) directly.\nApart from this, check the\nCUDA_VISIBLE_DEVICES\nenvironment variable to know the details.\nruntimeerror: cuda error: invalid device ordinal torchrun\nWhile using torchrun if you are specifying the local rank using os.environ[“LOCAL_RANK”] and have set it equal to a GPU ID, check if you have done the right assignment\nTrending\nOther tips while working with CUDA and GPU\nSet the device ordinal yourself if more than one GPU exists.\nWork with try-except blocks for exception handling.\nNvidia-semi is an exception tool to monitor your memory usage.\nIf you plan to work on deep learning and scientific computing projects, CUDA libraries like cuDNN (CUDA Deep Neural Networks) and cuSPARSE (CUDA Sparse Matrix Library) are quite efficient.\nFAQs\nHow do I update my CUDA drivers?\nNVIDIA’s website has a list of CUDA drivers.  You can install the one that suits your device.\nConclusion\nThis article covers the runtimeerror: cuda error: invalid device ordinal error in Python.  It explains different reasons for the error and how you can resolve it.\nTrending Now\n[Fixed] typeerror can’t compare datetime.datetime to datetime.date\nby Namrata Gulati\n●\nJanuary 11, 2024\n[Fixed] nameerror: name Unicode is not defined\nby Namrata Gulati\n●\nJanuary 2, 2024\n[Fixed] typeerror: type numpy.ndarray doesn’t define __round__ method\nby Namrata Gulati\n●\nJanuary 2, 2024\nCannot mask with non-boolean array containing NA\/NaN values\nby Namrata Gulati\n●\nJanuary 2, 2024","attrs_markdown":"[Skip to content](https:\/\/www.pythonpool.com\/solved-runtimeerror-cuda-error-invalid-device-ordinal\/#content \"Skip to content\")\n\n[![Python Pool](data:image\/svg+xml,%3Csvg%20xmlns='http:\/\/www.w3.org\/2000\/svg'%20viewBox='0%200%201031%20310'%3E%3C\/svg%3E)![Python Pool](http:\/\/www.pythonpool.com\/wp-content\/uploads\/2020\/10\/White-Blue-Arrow-Icon-Travel-Logo-1.png)](https:\/\/www.pythonpool.com\/ \"Python Pool\")\n\nMenu\n\n- [Blog](https:\/\/www.pythonpool.com\/blog\/)\n- [About Us](https:\/\/www.pythonpool.com\/about-us\/)\n- [Write For Us](https:\/\/www.pythonpool.com\/guest-post\/)\n- [Contact Us](https:\/\/www.pythonpool.com\/contact-us\/)\n- [Privacy Policy](https:\/\/www.pythonpool.com\/privacy-policy\/)\n- [DMCA](https:\/\/www.pythonpool.com\/dmca\/)\n- [Python Interpreter](https:\/\/www.pythonpool.com\/python-compiler\/)\n- [Cookies Policy](https:\/\/www.pythonpool.com\/cookies-policy\/)\n- [Home](https:\/\/www.pythonpool.com\/)\n\nMenu\n\n- [Blog](https:\/\/www.pythonpool.com\/blog\/)\n- [About Us](https:\/\/www.pythonpool.com\/about-us\/)\n- [Write For Us](https:\/\/www.pythonpool.com\/guest-post\/)\n- [Contact Us](https:\/\/www.pythonpool.com\/contact-us\/)\n- [Privacy Policy](https:\/\/www.pythonpool.com\/privacy-policy\/)\n- [DMCA](https:\/\/www.pythonpool.com\/dmca\/)\n- [Python Interpreter](https:\/\/www.pythonpool.com\/python-compiler\/)\n- [Cookies Policy](https:\/\/www.pythonpool.com\/cookies-policy\/)\n- [Home](https:\/\/www.pythonpool.com\/)\n\n# \\[Solved\\] runtimeerror: cuda error: invalid device ordinal\nJanuary 2, 2024\n\n![\\[Solved\\] runtimeerror cuda error invalid device ordinal](data:image\/svg+xml,%3Csvg%20xmlns='http:\/\/www.w3.org\/2000\/svg'%20viewBox='0%200%201200%20628'%3E%3C\/svg%3E)\n\n![\\[Solved\\] runtimeerror cuda error invalid device ordinal](https:\/\/www.pythonpool.com\/wp-content\/uploads\/2024\/01\/Solved-runtimeerror-cuda-error-invalid-device-ordinal.webp)\n\nThe ‘runtimeerror: cuda error: invalid device ordinal’ error happens when the CUDA runtime finds an unrecognizable, non-existent, or unavailable GPU device. Scroll down to know why this error occurs and how you can prevent this on your system.\n\nContents\n\n[Toggle](https:\/\/www.pythonpool.com\/solved-runtimeerror-cuda-error-invalid-device-ordinal\/)\n\n- [Reasons of error](https:\/\/www.pythonpool.com\/solved-runtimeerror-cuda-error-invalid-device-ordinal\/#Reasons_of_error)\n- [Methods of resolving the error](https:\/\/www.pythonpool.com\/solved-runtimeerror-cuda-error-invalid-device-ordinal\/#Methods_of_resolving_the_error)\n- [Check GPU Availability](https:\/\/www.pythonpool.com\/solved-runtimeerror-cuda-error-invalid-device-ordinal\/#Check_GPU_Availability)\n- [Set Device Ordinal\/ GPU id](https:\/\/www.pythonpool.com\/solved-runtimeerror-cuda-error-invalid-device-ordinal\/#Set_Device_Ordinal_GPU_id)\n- [Check CUDA availability](https:\/\/www.pythonpool.com\/solved-runtimeerror-cuda-error-invalid-device-ordinal\/#Check_CUDA_availability)\n- [GPU Usage check](https:\/\/www.pythonpool.com\/solved-runtimeerror-cuda-error-invalid-device-ordinal\/#GPU_Usage_check)\n- [runtimeerror: cuda error: invalid device ordinal PyTorch](https:\/\/www.pythonpool.com\/solved-runtimeerror-cuda-error-invalid-device-ordinal\/#runtimeerror_cuda_error_invalid_device_ordinal_PyTorch)\n- [runtimeerror: cuda error: invalid device ordinal torchrun](https:\/\/www.pythonpool.com\/solved-runtimeerror-cuda-error-invalid-device-ordinal\/#runtimeerror_cuda_error_invalid_device_ordinal_torchrun)\n- [Other tips while working with CUDA and GPU](https:\/\/www.pythonpool.com\/solved-runtimeerror-cuda-error-invalid-device-ordinal\/#Other_tips_while_working_with_CUDA_and_GPU)\n- [FAQs](https:\/\/www.pythonpool.com\/solved-runtimeerror-cuda-error-invalid-device-ordinal\/#FAQs)\n- [Conclusion](https:\/\/www.pythonpool.com\/solved-runtimeerror-cuda-error-invalid-device-ordinal\/#Conclusion)\n- [Trending Now](https:\/\/www.pythonpool.com\/solved-runtimeerror-cuda-error-invalid-device-ordinal\/#Trending_Now)\n## Reasons of error\nYou get the “runtimeerror: cuda error: invalid device ordinal” error because of a couple of reasons. [Check this list](https:\/\/www.pythonpool.com\/python-check-if-list-is-empty\/) out to know more:\n\n- Your system might not have a GPU, or if it exists, it may not be configured correctly. CUDA-compatible GPUs are necessary for a system, and thus, you need to check first.\n- The GPU ID that you have used for accessing is not correct. In this case, the GPU ID should be valid.\n- Your CUDA driver might not be updated. Outdated drivers are not able to recognize the GPU on your device.\n- Your CUDA environment may not be properly set. See if the CUDA\\_HOME directory has the CUDA installation details only.\n- If two conflicting libraries try to access the CUDA device simultaneously, it throws an error.\n- If your GPU usage exceeds what is available to you, you may get this error.\n## Methods of resolving the error\nYou can go through the following pointers in order to resolve the “runtimeerror: cuda error: invalid device ordinal” error.\n\n- Check if GPU actually exists on your system. For this, use the Nvidia-smi command-line tool. It lists the GPU on your system. It provides information about device IDs, status, and temperature. In case no such GPUs exist, you got the reason of your error. You need to have physical GPU hardware to run this command.\n- Your CUDA driver may need an update. Go to the NVIDIA website: <https:\/\/www.nvidia.com\/download\/index.aspx> in order to download the latest driver. You can choose your operating system and GPU model from the site itself.\n- The environment variable should have the correct CUDA installation directory. Use the following command to check this:\n```\necho $CUDA_HOME\n```\n- If you change CUDA configurations, driver installations, or environment variables in any way, consider restarting the system once.\n- If you don’t know which GPU you should point out in your command, you can simply write cuda. Otherwise, the indexing starts from 0, so you can count and verify.\n```\ntorch.device('cuda')\n```\n## Check GPU Availability\nThis code uses the nvidia\\_smi library to check if gpu device is present on your device or not. If it doesn’t exist, you’ll get an error.\n```\nimport nvidia_smi\n\ndevices = nvidia_smi.nvml.DeviceGetAll()\nif not devices:\n    raise RuntimeError(\"No GPUs detected\")\n```\n## Set Device Ordinal\/ GPU id\nYou have the feasibility to set the device ordinal all by yourself. For this purpose, the torch.cuda.set\\_device() function is present in the torch library of Python. Check the GPU that you want to use and replace it in the given code.\n```\nimport torch\n\ngpu_id = 0  # Replace with the desired GPU ordinal\ntorch.cuda.set_device(gpu_id)\n```\nOr you can explicitly mention it in this way as well:\n```\nvar = EmotionRecognition(device='gpu', gpu_id=1)\n```\nPopular now\n\n[\\[Fixed\\] typeerror can’t compare datetime.datetime to datetime.date](https:\/\/www.pythonpool.com\/fixed-typeerror-cant-compare-datetime-datetime-to-datetime-date\/)\n## Check CUDA availability\nThe presence of the CUDA environment is extremely important. The torch.cuda.is\\_available() under the torch library enables you to check whether CUDA is available or not.\n```\nimport torch\n\nif not torch.cuda.is_available():\n    print(\"CUDA is not available\")\n    exit()\n```\n## GPU Usage check\nIt is suggested to check if you have enough GPU hardware devices or not. The torch.cuda.device\\_count() of the torch library is an excellent way to check this. It draws a comparison with the desired GPUs.\n```\nimport torch\n\nnum_gpus = torch.cuda.device_count()\nif num_gpus < desired_gpus:\n    print(\"Not enough GPUs available\")\n    exit()\n```\n## runtimeerror: cuda error: invalid device ordinal PyTorch\nThe GPU id is counted from 0. Therefore, you should mention the GPU id correctly. If the device has only one GPU, then it is better to state 0. This refers to the first GPU. If you want to avoid the specifications, use torch.device(‘cuda’) directly.\n\nApart from this, check the `CUDA_VISIBLE_DEVICES` environment variable to know the details.\n\n## runtimeerror: cuda error: invalid device ordinal torchrun\nWhile using torchrun if you are specifying the local rank using os.environ\\[“LOCAL\\_RANK”\\] and have set it equal to a GPU ID, check if you have done the right assignment\n\n[![\\[Fixed\\] nameerror: name Unicode is not defined](data:image\/svg+xml,%3Csvg%20xmlns='http:\/\/www.w3.org\/2000\/svg'%20viewBox='0%200%200%200'%3E%3C\/svg%3E)![\\[Fixed\\] nameerror: name Unicode is not defined](https:\/\/www.pythonpool.com\/wp-content\/uploads\/2024\/01\/Fixed-nameerror-name-Unicode-is-not-defined-300x157.webp)](https:\/\/www.pythonpool.com\/fixed-nameerror-name-unicode-is-not-defined\/)\n\nTrending\n\n[\\[Fixed\\] nameerror: name Unicode is not defined](https:\/\/www.pythonpool.com\/fixed-nameerror-name-unicode-is-not-defined\/)\n## Other tips while working with CUDA and GPU\n- Set the device ordinal yourself if more than one GPU exists.\n- Work with try-except blocks for exception handling.\n- Nvidia-semi is an exception tool to monitor your memory usage.\n- If you plan to work on deep learning and scientific computing projects, CUDA libraries like cuDNN (CUDA Deep Neural Networks) and cuSPARSE (CUDA Sparse Matrix Library) are quite efficient.\n## FAQs\n****How do I update my CUDA drivers?****\n\nNVIDIA’s website has a list of CUDA drivers. You can install the one that suits your device.\n## Conclusion\nThis article covers the runtimeerror: cuda error: invalid device ordinal error in Python. It explains different reasons for the error and how you can resolve it.\n\n## Trending Now\n- [![\\[Fixed\\] typeerror can’t compare datetime.datetime to datetime.date](data:image\/svg+xml,%3Csvg%20xmlns='http:\/\/www.w3.org\/2000\/svg'%20viewBox='0%200%200%200'%3E%3C\/svg%3E) ![\\[Fixed\\] typeerror can’t compare datetime.datetime to datetime.date](https:\/\/www.pythonpool.com\/wp-content\/uploads\/2024\/01\/typeerror-cant-compare-datetime.datetime-to-datetime.date_-300x157.webp)\\[Fixed\\] typeerror can’t compare datetime.datetime to datetime.date by Namrata Gulati●January 11, 2024](https:\/\/www.pythonpool.com\/fixed-typeerror-cant-compare-datetime-datetime-to-datetime-date\/)\n- [![\\[Fixed\\] nameerror: name Unicode is not defined](data:image\/svg+xml,%3Csvg%20xmlns='http:\/\/www.w3.org\/2000\/svg'%20viewBox='0%200%200%200'%3E%3C\/svg%3E) ![\\[Fixed\\] nameerror: name Unicode is not defined](https:\/\/www.pythonpool.com\/wp-content\/uploads\/2024\/01\/Fixed-nameerror-name-Unicode-is-not-defined-300x157.webp)\\[Fixed\\] nameerror: name Unicode is not defined by Namrata Gulati●January 2, 2024](https:\/\/www.pythonpool.com\/fixed-nameerror-name-unicode-is-not-defined\/)\n- [![\\[Fixed\\] typeerror: type numpy.ndarray doesn’t define \\_\\_round\\_\\_ method](data:image\/svg+xml,%3Csvg%20xmlns='http:\/\/www.w3.org\/2000\/svg'%20viewBox='0%200%200%200'%3E%3C\/svg%3E) ![\\[Fixed\\] typeerror: type numpy.ndarray doesn’t define \\_\\_round\\_\\_ method](https:\/\/www.pythonpool.com\/wp-content\/uploads\/2024\/01\/Fixed-typeerror-type-numpy.ndarray-doesnt-define-__round__-method-300x157.webp)\\[Fixed\\] typeerror: type numpy.ndarray doesn’t define \\_\\_round\\_\\_ method by Namrata Gulati●January 2, 2024](https:\/\/www.pythonpool.com\/fixed-typeerror-type-numpy-ndarray-doesnt-define-__round__-method\/)\n- [![Cannot mask with non-boolean array containing NA\/NaN values](data:image\/svg+xml,%3Csvg%20xmlns='http:\/\/www.w3.org\/2000\/svg'%20viewBox='0%200%200%200'%3E%3C\/svg%3E) ![Cannot mask with non-boolean array containing NA\/NaN values](https:\/\/www.pythonpool.com\/wp-content\/uploads\/2024\/01\/Cannot-mask-with-non-boolean-array-containing-NANaN-values-300x157.webp)Cannot mask with non-boolean array containing NA\/NaN values by Namrata Gulati●January 2, 2024](https:\/\/www.pythonpool.com\/cannot-mask-with-non-boolean-array-containing-na-nan-values\/)\n\n[\\[Fixed\\] typeerror: type numpy.ndarray doesn’t define \\_\\_round\\_\\_ method](https:\/\/www.pythonpool.com\/fixed-typeerror-type-numpy-ndarray-doesnt-define-__round__-method\/)\n\n[\\[Fixed\\] nameerror: name Unicode is not defined](https:\/\/www.pythonpool.com\/fixed-nameerror-name-unicode-is-not-defined\/)\n\nSubscribe\n\n[Login](https:\/\/www.pythonpool.com\/link2access\/?redirect_to=https%3A%2F%2Fwww.pythonpool.com%2Fsolved-runtimeerror-cuda-error-invalid-device-ordinal%2F)\n\n0 Comments\n\nOldest\n\nNewest Most Voted\n\nInline Feedbacks\n\nView all comments\n\n## About us\nPython Pool is a platform where you can learn and become an expert in every aspect of Python programming language as well as in AI, ML, and Data Science.\n\n## Quick Links\n- [AI](https:\/\/www.pythonpool.com\/category\/ai\/)\n- [Algorithm](https:\/\/www.pythonpool.com\/category\/algorithm\/)\n- [Books](https:\/\/www.pythonpool.com\/category\/books\/)\n- [Career](https:\/\/www.pythonpool.com\/category\/career\/)\n- [Comparison](https:\/\/www.pythonpool.com\/category\/comparison\/)\n- [Data Science](https:\/\/www.pythonpool.com\/category\/data-science\/)\n- [Error](https:\/\/www.pythonpool.com\/category\/error\/)\n- [Flask](https:\/\/www.pythonpool.com\/category\/flask\/)\n- [How to](https:\/\/www.pythonpool.com\/category\/how-to\/)\n- [IDE & Editor](https:\/\/www.pythonpool.com\/category\/ide-editor\/)\n- [Jupyter](https:\/\/www.pythonpool.com\/category\/jupyter\/)\n- 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Scroll down to know why this error occurs and how you can prevent this on your system.\n\nContents\n\n- [Reasons of error](https:\/\/www.pythonpool.com\/solved-runtimeerror-cuda-error-invalid-device-ordinal\/#Reasons_of_error)\n- [Methods of resolving the error](https:\/\/www.pythonpool.com\/solved-runtimeerror-cuda-error-invalid-device-ordinal\/#Methods_of_resolving_the_error)\n- [Check GPU Availability](https:\/\/www.pythonpool.com\/solved-runtimeerror-cuda-error-invalid-device-ordinal\/#Check_GPU_Availability)\n- [Set Device Ordinal\/ GPU id](https:\/\/www.pythonpool.com\/solved-runtimeerror-cuda-error-invalid-device-ordinal\/#Set_Device_Ordinal_GPU_id)\n- [Check CUDA availability](https:\/\/www.pythonpool.com\/solved-runtimeerror-cuda-error-invalid-device-ordinal\/#Check_CUDA_availability)\n- [GPU Usage check](https:\/\/www.pythonpool.com\/solved-runtimeerror-cuda-error-invalid-device-ordinal\/#GPU_Usage_check)\n- [runtimeerror: cuda error: invalid device ordinal PyTorch](https:\/\/www.pythonpool.com\/solved-runtimeerror-cuda-error-invalid-device-ordinal\/#runtimeerror_cuda_error_invalid_device_ordinal_PyTorch)\n- [runtimeerror: cuda error: invalid device ordinal torchrun](https:\/\/www.pythonpool.com\/solved-runtimeerror-cuda-error-invalid-device-ordinal\/#runtimeerror_cuda_error_invalid_device_ordinal_torchrun)\n- [Other tips while working with CUDA and GPU](https:\/\/www.pythonpool.com\/solved-runtimeerror-cuda-error-invalid-device-ordinal\/#Other_tips_while_working_with_CUDA_and_GPU)\n- [FAQs](https:\/\/www.pythonpool.com\/solved-runtimeerror-cuda-error-invalid-device-ordinal\/#FAQs)\n- [Conclusion](https:\/\/www.pythonpool.com\/solved-runtimeerror-cuda-error-invalid-device-ordinal\/#Conclusion)\n- [Trending Now](https:\/\/www.pythonpool.com\/solved-runtimeerror-cuda-error-invalid-device-ordinal\/#Trending_Now)\n## Reasons of error\nYou get the “runtimeerror: cuda error: invalid device ordinal” error because of a couple of reasons. [Check this list](https:\/\/www.pythonpool.com\/python-check-if-list-is-empty\/) out to know more:\n\n- Your system might not have a GPU, or if it exists, it may not be configured correctly. CUDA-compatible GPUs are necessary for a system, and thus, you need to check first.\n- The GPU ID that you have used for accessing is not correct. In this case, the GPU ID should be valid.\n- Your CUDA driver might not be updated. Outdated drivers are not able to recognize the GPU on your device.\n- Your CUDA environment may not be properly set. See if the CUDA\\_HOME directory has the CUDA installation details only.\n- If two conflicting libraries try to access the CUDA device simultaneously, it throws an error.\n- If your GPU usage exceeds what is available to you, you may get this error.\n## Methods of resolving the error\nYou can go through the following pointers in order to resolve the “runtimeerror: cuda error: invalid device ordinal” error.\n\n- Check if GPU actually exists on your system. For this, use the Nvidia-smi command-line tool. It lists the GPU on your system. It provides information about device IDs, status, and temperature. In case no such GPUs exist, you got the reason of your error. You need to have physical GPU hardware to run this command.\n- Your CUDA driver may need an update. Go to the NVIDIA website: <https:\/\/www.nvidia.com\/download\/index.aspx> in order to download the latest driver. You can choose your operating system and GPU model from the site itself.\n- The environment variable should have the correct CUDA installation directory. Use the following command to check this:\n```\necho $CUDA_HOME\n```\n- If you change CUDA configurations, driver installations, or environment variables in any way, consider restarting the system once.\n- If you don’t know which GPU you should point out in your command, you can simply write cuda. Otherwise, the indexing starts from 0, so you can count and verify.\n```\ntorch.device('cuda')\n```\n## Check GPU Availability\nThis code uses the nvidia\\_smi library to check if gpu device is present on your device or not. If it doesn’t exist, you’ll get an error.\n```\nimport nvidia_smi\n\ndevices = nvidia_smi.nvml.DeviceGetAll()\nif not devices:\n    raise RuntimeError(\"No GPUs detected\")\n```\n## Set Device Ordinal\/ GPU id\nYou have the feasibility to set the device ordinal all by yourself. For this purpose, the torch.cuda.set\\_device() function is present in the torch library of Python. Check the GPU that you want to use and replace it in the given code.\n```\nimport torch\n\ngpu_id = 0  # Replace with the desired GPU ordinal\ntorch.cuda.set_device(gpu_id)\n```\nOr you can explicitly mention it in this way as well:\n```\nvar = EmotionRecognition(device='gpu', gpu_id=1)\n```\nPopular now\n\n## Check CUDA availability\nThe presence of the CUDA environment is extremely important. The torch.cuda.is\\_available() under the torch library enables you to check whether CUDA is available or not.\n```\nimport torch\n\nif not torch.cuda.is_available():\n    print(\"CUDA is not available\")\n    exit()\n```\n## GPU Usage check\nIt is suggested to check if you have enough GPU hardware devices or not. The torch.cuda.device\\_count() of the torch library is an excellent way to check this. It draws a comparison with the desired GPUs.\n```\nimport torch\n\nnum_gpus = torch.cuda.device_count()\nif num_gpus < desired_gpus:\n    print(\"Not enough GPUs available\")\n    exit()\n```\nThe GPU id is counted from 0. Therefore, you should mention the GPU id correctly. If the device has only one GPU, then it is better to state 0. This refers to the first GPU. If you want to avoid the specifications, use torch.device(‘cuda’) directly.\n\nApart from this, check the `CUDA_VISIBLE_DEVICES` environment variable to know the details.\n\n## runtimeerror: cuda error: invalid device ordinal torchrun\nWhile using torchrun if you are specifying the local rank using os.environ\\[“LOCAL\\_RANK”\\] and have set it equal to a GPU ID, check if you have done the right assignment\n\n[![\\[Fixed\\] nameerror: name Unicode is not defined](https:\/\/www.pythonpool.com\/wp-content\/uploads\/2024\/01\/Fixed-nameerror-name-Unicode-is-not-defined-300x157.webp)](https:\/\/www.pythonpool.com\/fixed-nameerror-name-unicode-is-not-defined\/)\n\nTrending\n## Other tips while working with CUDA and GPU\n- Set the device ordinal yourself if more than one GPU exists.\n- Work with try-except blocks for exception handling.\n- Nvidia-semi is an exception tool to monitor your memory usage.\n- If you plan to work on deep learning and scientific computing projects, CUDA libraries like cuDNN (CUDA Deep Neural Networks) and cuSPARSE (CUDA Sparse Matrix Library) are quite efficient.\n## FAQs\n****How do I update my CUDA drivers?****\n\nNVIDIA’s website has a list of CUDA drivers. You can install the one that suits your device.\n## Conclusion\nThis article covers the runtimeerror: cuda error: invalid device ordinal error in Python. It explains different reasons for the error and how you can resolve it.\n\n## Trending Now\n- [![\\[Fixed\\] typeerror can’t compare datetime.datetime to datetime.date](https:\/\/www.pythonpool.com\/wp-content\/uploads\/2024\/01\/typeerror-cant-compare-datetime.datetime-to-datetime.date_-300x157.webp)\\[Fixed\\] typeerror can’t compare datetime.datetime to datetime.date by Namrata Gulati●January 11, 2024](https:\/\/www.pythonpool.com\/fixed-typeerror-cant-compare-datetime-datetime-to-datetime-date\/)\n- [![\\[Fixed\\] nameerror: name Unicode is not defined](https:\/\/www.pythonpool.com\/wp-content\/uploads\/2024\/01\/Fixed-nameerror-name-Unicode-is-not-defined-300x157.webp)\\[Fixed\\] nameerror: name Unicode is not defined by Namrata Gulati●January 2, 2024](https:\/\/www.pythonpool.com\/fixed-nameerror-name-unicode-is-not-defined\/)\n- [![\\[Fixed\\] typeerror: type numpy.ndarray doesn’t define \\_\\_round\\_\\_ method](https:\/\/www.pythonpool.com\/wp-content\/uploads\/2024\/01\/Fixed-typeerror-type-numpy.ndarray-doesnt-define-__round__-method-300x157.webp)\\[Fixed\\] typeerror: type numpy.ndarray doesn’t define \\_\\_round\\_\\_ method by Namrata Gulati●January 2, 2024](https:\/\/www.pythonpool.com\/fixed-typeerror-type-numpy-ndarray-doesnt-define-__round__-method\/)\n- [![Cannot mask with non-boolean array containing NA\/NaN values](https:\/\/www.pythonpool.com\/wp-content\/uploads\/2024\/01\/Cannot-mask-with-non-boolean-array-containing-NANaN-values-300x157.webp)Cannot mask with non-boolean array containing NA\/NaN values by Namrata Gulati●January 2, 2024](https:\/\/www.pythonpool.com\/cannot-mask-with-non-boolean-array-containing-na-nan-values\/)","meta_canonical":null}

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Boilerpipe Text	The ‘runtimeerror: cuda error: invalid device ordinal’ error happens when the CUDA runtime finds an unrecognizable, non-existent, or unavailable GPU device. Scroll down to know why this error occurs and how you can prevent this on your system. Contents Reasons of error Methods of resolving the error Check GPU Availability Set Device Ordinal/ GPU id Check CUDA availability GPU Usage check runtimeerror: cuda error: invalid device ordinal PyTorch runtimeerror: cuda error: invalid device ordinal torchrun Other tips while working with CUDA and GPU FAQs Conclusion Trending Now Reasons of error You get the “runtimeerror: cuda error: invalid device ordinal” error because of a couple of reasons. Check this list out to know more: Your system might not have a GPU, or if it exists, it may not be configured correctly. CUDA-compatible GPUs are necessary for a system, and thus, you need to check first. The GPU ID that you have used for accessing is not correct. In this case, the GPU ID should be valid. Your CUDA driver might not be updated. Outdated drivers are not able to recognize the GPU on your device. Your CUDA environment may not be properly set. See if the CUDA_HOME directory has the CUDA installation details only. If two conflicting libraries try to access the CUDA device simultaneously, it throws an error. If your GPU usage exceeds what is available to you, you may get this error. Methods of resolving the error You can go through the following pointers in order to resolve the “runtimeerror: cuda error: invalid device ordinal” error. Check if GPU actually exists on your system. For this, use the Nvidia-smi command-line tool. It lists the GPU on your system. It provides information about device IDs, status, and temperature. In case no such GPUs exist, you got the reason of your error. You need to have physical GPU hardware to run this command. Your CUDA driver may need an update. Go to the NVIDIA website: https://www.nvidia.com/download/index.aspx in order to download the latest driver. You can choose your operating system and GPU model from the site itself. The environment variable should have the correct CUDA installation directory. Use the following command to check this: echo $CUDA_HOME If you change CUDA configurations, driver installations, or environment variables in any way, consider restarting the system once. If you don’t know which GPU you should point out in your command, you can simply write cuda. Otherwise, the indexing starts from 0, so you can count and verify. torch.device('cuda') Check GPU Availability This code uses the nvidia_smi library to check if gpu device is present on your device or not. If it doesn’t exist, you’ll get an error. import nvidia_smi devices = nvidia_smi.nvml.DeviceGetAll() if not devices: raise RuntimeError("No GPUs detected") Set Device Ordinal/ GPU id You have the feasibility to set the device ordinal all by yourself. For this purpose, the torch.cuda.set_device() function is present in the torch library of Python. Check the GPU that you want to use and replace it in the given code. import torch gpu_id = 0 # Replace with the desired GPU ordinal torch.cuda.set_device(gpu_id) Or you can explicitly mention it in this way as well: var = EmotionRecognition(device='gpu', gpu_id=1) Popular now Check CUDA availability The presence of the CUDA environment is extremely important. The torch.cuda.is_available() under the torch library enables you to check whether CUDA is available or not. import torch if not torch.cuda.is_available(): print("CUDA is not available") exit() GPU Usage check It is suggested to check if you have enough GPU hardware devices or not. The torch.cuda.device_count() of the torch library is an excellent way to check this. It draws a comparison with the desired GPUs. import torch num_gpus = torch.cuda.device_count() if num_gpus < desired_gpus: print("Not enough GPUs available") exit() The GPU id is counted from 0. Therefore, you should mention the GPU id correctly. If the device has only one GPU, then it is better to state 0. This refers to the first GPU. If you want to avoid the specifications, use torch.device(‘cuda’) directly. Apart from this, check the CUDA_VISIBLE_DEVICES environment variable to know the details. runtimeerror: cuda error: invalid device ordinal torchrun While using torchrun if you are specifying the local rank using os.environ[“LOCAL_RANK”] and have set it equal to a GPU ID, check if you have done the right assignment Trending Other tips while working with CUDA and GPU Set the device ordinal yourself if more than one GPU exists. Work with try-except blocks for exception handling. Nvidia-semi is an exception tool to monitor your memory usage. If you plan to work on deep learning and scientific computing projects, CUDA libraries like cuDNN (CUDA Deep Neural Networks) and cuSPARSE (CUDA Sparse Matrix Library) are quite efficient. FAQs How do I update my CUDA drivers? NVIDIA’s website has a list of CUDA drivers. You can install the one that suits your device. Conclusion This article covers the runtimeerror: cuda error: invalid device ordinal error in Python. It explains different reasons for the error and how you can resolve it. Trending Now [Fixed] typeerror can’t compare datetime.datetime to datetime.date by Namrata Gulati ● January 11, 2024 [Fixed] nameerror: name Unicode is not defined by Namrata Gulati ● January 2, 2024 [Fixed] typeerror: type numpy.ndarray doesn’t define __round__ method by Namrata Gulati ● January 2, 2024 Cannot mask with non-boolean array containing NA/NaN values by Namrata Gulati ● January 2, 2024
Markdown	[Skip to content](https://www.pythonpool.com/solved-runtimeerror-cuda-error-invalid-device-ordinal/#content "Skip to content") [![Python Pool](data:image/svg+xml,%3Csvg%20xmlns='http://www.w3.org/2000/svg'%20viewBox='0%200%201031%20310'%3E%3C/svg%3E)![Python Pool](http://www.pythonpool.com/wp-content/uploads/2020/10/White-Blue-Arrow-Icon-Travel-Logo-1.png)](https://www.pythonpool.com/ "Python Pool") Menu - [Blog](https://www.pythonpool.com/blog/) - [About Us](https://www.pythonpool.com/about-us/) - [Write For Us](https://www.pythonpool.com/guest-post/) - [Contact Us](https://www.pythonpool.com/contact-us/) - [Privacy Policy](https://www.pythonpool.com/privacy-policy/) - [DMCA](https://www.pythonpool.com/dmca/) - [Python Interpreter](https://www.pythonpool.com/python-compiler/) - [Cookies Policy](https://www.pythonpool.com/cookies-policy/) - [Home](https://www.pythonpool.com/) Menu - [Blog](https://www.pythonpool.com/blog/) - [About Us](https://www.pythonpool.com/about-us/) - [Write For Us](https://www.pythonpool.com/guest-post/) - [Contact Us](https://www.pythonpool.com/contact-us/) - [Privacy Policy](https://www.pythonpool.com/privacy-policy/) - [DMCA](https://www.pythonpool.com/dmca/) - [Python Interpreter](https://www.pythonpool.com/python-compiler/) - [Cookies Policy](https://www.pythonpool.com/cookies-policy/) - [Home](https://www.pythonpool.com/) # \[Solved\] runtimeerror: cuda error: invalid device ordinal January 2, 2024 ![\[Solved\] runtimeerror cuda error invalid device ordinal](data:image/svg+xml,%3Csvg%20xmlns='http://www.w3.org/2000/svg'%20viewBox='0%200%201200%20628'%3E%3C/svg%3E) ![\[Solved\] runtimeerror cuda error invalid device ordinal](https://www.pythonpool.com/wp-content/uploads/2024/01/Solved-runtimeerror-cuda-error-invalid-device-ordinal.webp) The ‘runtimeerror: cuda error: invalid device ordinal’ error happens when the CUDA runtime finds an unrecognizable, non-existent, or unavailable GPU device. Scroll down to know why this error occurs and how you can prevent this on your system. Contents [Toggle](https://www.pythonpool.com/solved-runtimeerror-cuda-error-invalid-device-ordinal/) - [Reasons of error](https://www.pythonpool.com/solved-runtimeerror-cuda-error-invalid-device-ordinal/#Reasons_of_error) - [Methods of resolving the error](https://www.pythonpool.com/solved-runtimeerror-cuda-error-invalid-device-ordinal/#Methods_of_resolving_the_error) - [Check GPU Availability](https://www.pythonpool.com/solved-runtimeerror-cuda-error-invalid-device-ordinal/#Check_GPU_Availability) - [Set Device Ordinal/ GPU id](https://www.pythonpool.com/solved-runtimeerror-cuda-error-invalid-device-ordinal/#Set_Device_Ordinal_GPU_id) - [Check CUDA availability](https://www.pythonpool.com/solved-runtimeerror-cuda-error-invalid-device-ordinal/#Check_CUDA_availability) - [GPU Usage check](https://www.pythonpool.com/solved-runtimeerror-cuda-error-invalid-device-ordinal/#GPU_Usage_check) - [runtimeerror: cuda error: invalid device ordinal PyTorch](https://www.pythonpool.com/solved-runtimeerror-cuda-error-invalid-device-ordinal/#runtimeerror_cuda_error_invalid_device_ordinal_PyTorch) - [runtimeerror: cuda error: invalid device ordinal torchrun](https://www.pythonpool.com/solved-runtimeerror-cuda-error-invalid-device-ordinal/#runtimeerror_cuda_error_invalid_device_ordinal_torchrun) - [Other tips while working with CUDA and GPU](https://www.pythonpool.com/solved-runtimeerror-cuda-error-invalid-device-ordinal/#Other_tips_while_working_with_CUDA_and_GPU) - [FAQs](https://www.pythonpool.com/solved-runtimeerror-cuda-error-invalid-device-ordinal/#FAQs) - [Conclusion](https://www.pythonpool.com/solved-runtimeerror-cuda-error-invalid-device-ordinal/#Conclusion) - [Trending Now](https://www.pythonpool.com/solved-runtimeerror-cuda-error-invalid-device-ordinal/#Trending_Now) ## Reasons of error You get the “runtimeerror: cuda error: invalid device ordinal” error because of a couple of reasons. [Check this list](https://www.pythonpool.com/python-check-if-list-is-empty/) out to know more: - Your system might not have a GPU, or if it exists, it may not be configured correctly. CUDA-compatible GPUs are necessary for a system, and thus, you need to check first. - The GPU ID that you have used for accessing is not correct. In this case, the GPU ID should be valid. - Your CUDA driver might not be updated. Outdated drivers are not able to recognize the GPU on your device. - Your CUDA environment may not be properly set. See if the CUDA\_HOME directory has the CUDA installation details only. - If two conflicting libraries try to access the CUDA device simultaneously, it throws an error. - If your GPU usage exceeds what is available to you, you may get this error. ## Methods of resolving the error You can go through the following pointers in order to resolve the “runtimeerror: cuda error: invalid device ordinal” error. - Check if GPU actually exists on your system. For this, use the Nvidia-smi command-line tool. It lists the GPU on your system. It provides information about device IDs, status, and temperature. In case no such GPUs exist, you got the reason of your error. You need to have physical GPU hardware to run this command. - Your CUDA driver may need an update. Go to the NVIDIA website: <https://www.nvidia.com/download/index.aspx> in order to download the latest driver. You can choose your operating system and GPU model from the site itself. - The environment variable should have the correct CUDA installation directory. Use the following command to check this: ``` echo $CUDA_HOME ``` - If you change CUDA configurations, driver installations, or environment variables in any way, consider restarting the system once. - If you don’t know which GPU you should point out in your command, you can simply write cuda. Otherwise, the indexing starts from 0, so you can count and verify. ``` torch.device('cuda') ``` ## Check GPU Availability This code uses the nvidia\_smi library to check if gpu device is present on your device or not. If it doesn’t exist, you’ll get an error. ``` import nvidia_smi devices = nvidia_smi.nvml.DeviceGetAll() if not devices: raise RuntimeError("No GPUs detected") ``` ## Set Device Ordinal/ GPU id You have the feasibility to set the device ordinal all by yourself. For this purpose, the torch.cuda.set\_device() function is present in the torch library of Python. Check the GPU that you want to use and replace it in the given code. ``` import torch gpu_id = 0 # Replace with the desired GPU ordinal torch.cuda.set_device(gpu_id) ``` Or you can explicitly mention it in this way as well: ``` var = EmotionRecognition(device='gpu', gpu_id=1) ``` Popular now [\[Fixed\] typeerror can’t compare datetime.datetime to datetime.date](https://www.pythonpool.com/fixed-typeerror-cant-compare-datetime-datetime-to-datetime-date/) ## Check CUDA availability The presence of the CUDA environment is extremely important. The torch.cuda.is\_available() under the torch library enables you to check whether CUDA is available or not. ``` import torch if not torch.cuda.is_available(): print("CUDA is not available") exit() ``` ## GPU Usage check It is suggested to check if you have enough GPU hardware devices or not. The torch.cuda.device\_count() of the torch library is an excellent way to check this. It draws a comparison with the desired GPUs. ``` import torch num_gpus = torch.cuda.device_count() if num_gpus < desired_gpus: print("Not enough GPUs available") exit() ``` ## runtimeerror: cuda error: invalid device ordinal PyTorch The GPU id is counted from 0. Therefore, you should mention the GPU id correctly. If the device has only one GPU, then it is better to state 0. This refers to the first GPU. If you want to avoid the specifications, use torch.device(‘cuda’) directly. Apart from this, check the `CUDA_VISIBLE_DEVICES` environment variable to know the details. ## runtimeerror: cuda error: invalid device ordinal torchrun While using torchrun if you are specifying the local rank using os.environ\[“LOCAL\_RANK”\] and have set it equal to a GPU ID, check if you have done the right assignment [![\[Fixed\] nameerror: name Unicode is not defined](data:image/svg+xml,%3Csvg%20xmlns='http://www.w3.org/2000/svg'%20viewBox='0%200%200%200'%3E%3C/svg%3E)![\[Fixed\] nameerror: name Unicode is not defined](https://www.pythonpool.com/wp-content/uploads/2024/01/Fixed-nameerror-name-Unicode-is-not-defined-300x157.webp)](https://www.pythonpool.com/fixed-nameerror-name-unicode-is-not-defined/) Trending [\[Fixed\] nameerror: name Unicode is not defined](https://www.pythonpool.com/fixed-nameerror-name-unicode-is-not-defined/) ## Other tips while working with CUDA and GPU - Set the device ordinal yourself if more than one GPU exists. - Work with try-except blocks for exception handling. - Nvidia-semi is an exception tool to monitor your memory usage. - If you plan to work on deep learning and scientific computing projects, CUDA libraries like cuDNN (CUDA Deep Neural Networks) and cuSPARSE (CUDA Sparse Matrix Library) are quite efficient. ## FAQs **How do I update my CUDA drivers?** NVIDIA’s website has a list of CUDA drivers. You can install the one that suits your device. ## Conclusion This article covers the runtimeerror: cuda error: invalid device ordinal error in Python. It explains different reasons for the error and how you can resolve it. ## Trending Now - [![\[Fixed\] typeerror can’t compare datetime.datetime to datetime.date](data:image/svg+xml,%3Csvg%20xmlns='http://www.w3.org/2000/svg'%20viewBox='0%200%200%200'%3E%3C/svg%3E) ![\[Fixed\] typeerror can’t compare datetime.datetime to datetime.date](https://www.pythonpool.com/wp-content/uploads/2024/01/typeerror-cant-compare-datetime.datetime-to-datetime.date_-300x157.webp)\[Fixed\] typeerror can’t compare datetime.datetime to datetime.date by Namrata Gulati●January 11, 2024](https://www.pythonpool.com/fixed-typeerror-cant-compare-datetime-datetime-to-datetime-date/) - [![\[Fixed\] nameerror: name Unicode is not defined](data:image/svg+xml,%3Csvg%20xmlns='http://www.w3.org/2000/svg'%20viewBox='0%200%200%200'%3E%3C/svg%3E) ![\[Fixed\] nameerror: name Unicode is not defined](https://www.pythonpool.com/wp-content/uploads/2024/01/Fixed-nameerror-name-Unicode-is-not-defined-300x157.webp)\[Fixed\] nameerror: name Unicode is not defined by Namrata Gulati●January 2, 2024](https://www.pythonpool.com/fixed-nameerror-name-unicode-is-not-defined/) - [![\[Fixed\] typeerror: type numpy.ndarray doesn’t define \_\_round\_\_ method](data:image/svg+xml,%3Csvg%20xmlns='http://www.w3.org/2000/svg'%20viewBox='0%200%200%200'%3E%3C/svg%3E) ![\[Fixed\] typeerror: type numpy.ndarray doesn’t define \_\_round\_\_ method](https://www.pythonpool.com/wp-content/uploads/2024/01/Fixed-typeerror-type-numpy.ndarray-doesnt-define-__round__-method-300x157.webp)\[Fixed\] typeerror: type numpy.ndarray doesn’t define \_\_round\_\_ method by Namrata Gulati●January 2, 2024](https://www.pythonpool.com/fixed-typeerror-type-numpy-ndarray-doesnt-define-__round__-method/) - [![Cannot mask with non-boolean array containing NA/NaN values](data:image/svg+xml,%3Csvg%20xmlns='http://www.w3.org/2000/svg'%20viewBox='0%200%200%200'%3E%3C/svg%3E) ![Cannot mask with non-boolean array containing NA/NaN values](https://www.pythonpool.com/wp-content/uploads/2024/01/Cannot-mask-with-non-boolean-array-containing-NANaN-values-300x157.webp)Cannot mask with non-boolean array containing NA/NaN values by Namrata Gulati●January 2, 2024](https://www.pythonpool.com/cannot-mask-with-non-boolean-array-containing-na-nan-values/) [\[Fixed\] typeerror: type numpy.ndarray doesn’t define \_\_round\_\_ method](https://www.pythonpool.com/fixed-typeerror-type-numpy-ndarray-doesnt-define-__round__-method/) [\[Fixed\] nameerror: name Unicode is not defined](https://www.pythonpool.com/fixed-nameerror-name-unicode-is-not-defined/) Subscribe [Login](https://www.pythonpool.com/link2access/?redirect_to=https%3A%2F%2Fwww.pythonpool.com%2Fsolved-runtimeerror-cuda-error-invalid-device-ordinal%2F) 0 Comments Oldest Newest Most Voted Inline Feedbacks View all comments ## About us Python Pool is a platform where you can learn and become an expert in every aspect of Python programming language as well as in AI, ML, and Data Science. ## Quick Links - 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Readable Markdown	The ‘runtimeerror: cuda error: invalid device ordinal’ error happens when the CUDA runtime finds an unrecognizable, non-existent, or unavailable GPU device. Scroll down to know why this error occurs and how you can prevent this on your system. Contents - [Reasons of error](https://www.pythonpool.com/solved-runtimeerror-cuda-error-invalid-device-ordinal/#Reasons_of_error) - [Methods of resolving the error](https://www.pythonpool.com/solved-runtimeerror-cuda-error-invalid-device-ordinal/#Methods_of_resolving_the_error) - [Check GPU Availability](https://www.pythonpool.com/solved-runtimeerror-cuda-error-invalid-device-ordinal/#Check_GPU_Availability) - [Set Device Ordinal/ GPU id](https://www.pythonpool.com/solved-runtimeerror-cuda-error-invalid-device-ordinal/#Set_Device_Ordinal_GPU_id) - [Check CUDA availability](https://www.pythonpool.com/solved-runtimeerror-cuda-error-invalid-device-ordinal/#Check_CUDA_availability) - [GPU Usage check](https://www.pythonpool.com/solved-runtimeerror-cuda-error-invalid-device-ordinal/#GPU_Usage_check) - [runtimeerror: cuda error: invalid device ordinal PyTorch](https://www.pythonpool.com/solved-runtimeerror-cuda-error-invalid-device-ordinal/#runtimeerror_cuda_error_invalid_device_ordinal_PyTorch) - [runtimeerror: cuda error: invalid device ordinal torchrun](https://www.pythonpool.com/solved-runtimeerror-cuda-error-invalid-device-ordinal/#runtimeerror_cuda_error_invalid_device_ordinal_torchrun) - [Other tips while working with CUDA and GPU](https://www.pythonpool.com/solved-runtimeerror-cuda-error-invalid-device-ordinal/#Other_tips_while_working_with_CUDA_and_GPU) - [FAQs](https://www.pythonpool.com/solved-runtimeerror-cuda-error-invalid-device-ordinal/#FAQs) - [Conclusion](https://www.pythonpool.com/solved-runtimeerror-cuda-error-invalid-device-ordinal/#Conclusion) - [Trending Now](https://www.pythonpool.com/solved-runtimeerror-cuda-error-invalid-device-ordinal/#Trending_Now) ## Reasons of error You get the “runtimeerror: cuda error: invalid device ordinal” error because of a couple of reasons. [Check this list](https://www.pythonpool.com/python-check-if-list-is-empty/) out to know more: - Your system might not have a GPU, or if it exists, it may not be configured correctly. CUDA-compatible GPUs are necessary for a system, and thus, you need to check first. - The GPU ID that you have used for accessing is not correct. In this case, the GPU ID should be valid. - Your CUDA driver might not be updated. Outdated drivers are not able to recognize the GPU on your device. - Your CUDA environment may not be properly set. See if the CUDA\_HOME directory has the CUDA installation details only. - If two conflicting libraries try to access the CUDA device simultaneously, it throws an error. - If your GPU usage exceeds what is available to you, you may get this error. ## Methods of resolving the error You can go through the following pointers in order to resolve the “runtimeerror: cuda error: invalid device ordinal” error. - Check if GPU actually exists on your system. For this, use the Nvidia-smi command-line tool. It lists the GPU on your system. It provides information about device IDs, status, and temperature. In case no such GPUs exist, you got the reason of your error. You need to have physical GPU hardware to run this command. - Your CUDA driver may need an update. Go to the NVIDIA website: <https://www.nvidia.com/download/index.aspx> in order to download the latest driver. You can choose your operating system and GPU model from the site itself. - The environment variable should have the correct CUDA installation directory. Use the following command to check this: ``` echo $CUDA_HOME ``` - If you change CUDA configurations, driver installations, or environment variables in any way, consider restarting the system once. - If you don’t know which GPU you should point out in your command, you can simply write cuda. Otherwise, the indexing starts from 0, so you can count and verify. ``` torch.device('cuda') ``` ## Check GPU Availability This code uses the nvidia\_smi library to check if gpu device is present on your device or not. If it doesn’t exist, you’ll get an error. ``` import nvidia_smi devices = nvidia_smi.nvml.DeviceGetAll() if not devices: raise RuntimeError("No GPUs detected") ``` ## Set Device Ordinal/ GPU id You have the feasibility to set the device ordinal all by yourself. For this purpose, the torch.cuda.set\_device() function is present in the torch library of Python. Check the GPU that you want to use and replace it in the given code. ``` import torch gpu_id = 0 # Replace with the desired GPU ordinal torch.cuda.set_device(gpu_id) ``` Or you can explicitly mention it in this way as well: ``` var = EmotionRecognition(device='gpu', gpu_id=1) ``` Popular now ## Check CUDA availability The presence of the CUDA environment is extremely important. The torch.cuda.is\_available() under the torch library enables you to check whether CUDA is available or not. ``` import torch if not torch.cuda.is_available(): print("CUDA is not available") exit() ``` ## GPU Usage check It is suggested to check if you have enough GPU hardware devices or not. The torch.cuda.device\_count() of the torch library is an excellent way to check this. It draws a comparison with the desired GPUs. ``` import torch num_gpus = torch.cuda.device_count() if num_gpus < desired_gpus: print("Not enough GPUs available") exit() ``` The GPU id is counted from 0. Therefore, you should mention the GPU id correctly. If the device has only one GPU, then it is better to state 0. This refers to the first GPU. If you want to avoid the specifications, use torch.device(‘cuda’) directly. Apart from this, check the `CUDA_VISIBLE_DEVICES` environment variable to know the details. ## runtimeerror: cuda error: invalid device ordinal torchrun While using torchrun if you are specifying the local rank using os.environ\[“LOCAL\_RANK”\] and have set it equal to a GPU ID, check if you have done the right assignment [![\[Fixed\] nameerror: name Unicode is not defined](https://www.pythonpool.com/wp-content/uploads/2024/01/Fixed-nameerror-name-Unicode-is-not-defined-300x157.webp)](https://www.pythonpool.com/fixed-nameerror-name-unicode-is-not-defined/) Trending ## Other tips while working with CUDA and GPU - Set the device ordinal yourself if more than one GPU exists. - Work with try-except blocks for exception handling. - Nvidia-semi is an exception tool to monitor your memory usage. - If you plan to work on deep learning and scientific computing projects, CUDA libraries like cuDNN (CUDA Deep Neural Networks) and cuSPARSE (CUDA Sparse Matrix Library) are quite efficient. ## FAQs **How do I update my CUDA drivers?** NVIDIA’s website has a list of CUDA drivers. You can install the one that suits your device. ## Conclusion This article covers the runtimeerror: cuda error: invalid device ordinal error in Python. It explains different reasons for the error and how you can resolve it. ## Trending Now - [![\[Fixed\] typeerror can’t compare datetime.datetime to datetime.date](https://www.pythonpool.com/wp-content/uploads/2024/01/typeerror-cant-compare-datetime.datetime-to-datetime.date_-300x157.webp)\[Fixed\] typeerror can’t compare datetime.datetime to datetime.date by Namrata Gulati●January 11, 2024](https://www.pythonpool.com/fixed-typeerror-cant-compare-datetime-datetime-to-datetime-date/) - [![\[Fixed\] nameerror: name Unicode is not defined](https://www.pythonpool.com/wp-content/uploads/2024/01/Fixed-nameerror-name-Unicode-is-not-defined-300x157.webp)\[Fixed\] nameerror: name Unicode is not defined by Namrata Gulati●January 2, 2024](https://www.pythonpool.com/fixed-nameerror-name-unicode-is-not-defined/) - [![\[Fixed\] typeerror: type numpy.ndarray doesn’t define \_\_round\_\_ method](https://www.pythonpool.com/wp-content/uploads/2024/01/Fixed-typeerror-type-numpy.ndarray-doesnt-define-__round__-method-300x157.webp)\[Fixed\] typeerror: type numpy.ndarray doesn’t define \_\_round\_\_ method by Namrata Gulati●January 2, 2024](https://www.pythonpool.com/fixed-typeerror-type-numpy-ndarray-doesnt-define-__round__-method/) - [![Cannot mask with non-boolean array containing NA/NaN values](https://www.pythonpool.com/wp-content/uploads/2024/01/Cannot-mask-with-non-boolean-array-containing-NANaN-values-300x157.webp)Cannot mask with non-boolean array containing NA/NaN values by Namrata Gulati●January 2, 2024](https://www.pythonpool.com/cannot-mask-with-non-boolean-array-containing-na-nan-values/)
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