Timeline for GPU usage monitoring (CUDA)
Current License: CC BY-SA 4.0
16 events
| when toggle format | what | by | license | comment | |
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| Nov 29, 2022 at 7:53 | history | edited | Xuehai Pan | CC BY-SA 4.0 |
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| Apr 15, 2022 at 8:39 | history | edited | Xuehai Pan | CC BY-SA 4.0 |
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| Apr 15, 2022 at 5:11 | history | edited | Xuehai Pan | CC BY-SA 4.0 |
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| Apr 15, 2022 at 5:03 | history | edited | Xuehai Pan | CC BY-SA 4.0 |
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| Oct 6, 2021 at 7:38 | history | edited | Xuehai Pan | CC BY-SA 4.0 |
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| Oct 2, 2021 at 17:23 | comment | added | Hyperplane | I really like this because it shows Time-Series for both CPU & GPU. Many tools only show current usage, or Time-Series for either, but not for both. 👍 | |
| Jun 30, 2021 at 6:55 | history | edited | Xuehai Pan | CC BY-SA 4.0 |
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| Jun 29, 2021 at 18:11 | comment | added | Xuehai Pan |
For non-sudo users, pip install nvitop will install into ~/.local/bin by default. Users can add --user option to pip explicitly to make a user-wise install. Then you may need to add ~/.local/bin into your PATH environment variable. If there is no system Python installed, you can use Linuxbrew or conda to install Python in your home directory.
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| Jun 29, 2021 at 16:03 | comment | added | Sudharsan Madhavan | Works like charm with just conda virtual environment without sudo access if anyone is looking for a solution WITHOUT admin access. | |
| May 19, 2021 at 9:15 | history | edited | Xuehai Pan | CC BY-SA 4.0 |
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| May 19, 2021 at 8:58 | history | edited | Xuehai Pan | CC BY-SA 4.0 |
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| May 19, 2021 at 8:53 | history | edited | Xuehai Pan | CC BY-SA 4.0 |
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| May 19, 2021 at 8:47 | review | Late answers | |||
| May 19, 2021 at 10:33 | |||||
| May 19, 2021 at 8:39 | history | edited | Xuehai Pan | CC BY-SA 4.0 |
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| May 19, 2021 at 8:32 | review | First posts | |||
| May 21, 2021 at 11:26 | |||||
| May 19, 2021 at 8:29 | history | answered | Xuehai Pan | CC BY-SA 4.0 |