gpu

问题 Closed. This question does not meet Stack Overflow guidelines. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 2 years ago . I want to use nvidia-smi to monitor my GPU for my machine-learning/ AI projects. However, when I run nvidia-smi in my cmd, git bash or powershell, I get the following results: $ nvidia-smi Sun May 28 13:25:46 2017 +-----------------------------------------------------------------------

问题 Closed. This question does not meet Stack Overflow guidelines. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 2 years ago . I want to use nvidia-smi to monitor my GPU for my machine-learning/ AI projects. However, when I run nvidia-smi in my cmd, git bash or powershell, I get the following results: $ nvidia-smi Sun May 28 13:25:46 2017 +-----------------------------------------------------------------------

问题 After attempting to install nvidia toolkit on MAC by following guide : http://docs.nvidia.com/cuda/cuda-installation-guide-mac-os-x/index.html#axzz4FPTBCf7X I received error "Package manifest parsing error" which led me to this : NVidia CUDA toolkit 7.5.27 failing to install on OS X . I unmounted the dmg and upshot was that instead of receiving "Package manifest parsing error" the installer would not launch (it seemed to launch briefly , then quit). Installing via command brew install

问题 I am trying to debug a HLSL shader in VS2012 using NSight, but it can't start. When I click on "Start Graphics Debugging", it seems like it starts the app for a moment, and then closes it (output windows from NSight shows several "shader loaded"/"shader unloaded" lines). Windows Event log doesn't show anything (except "NVIDIA Network Service" failing to start, but if I understood well, this is something related to updates). On the other hand, if I start GPU Perfomannce analysis, then it runs

问题 I am trying to debug a HLSL shader in VS2012 using NSight, but it can't start. When I click on "Start Graphics Debugging", it seems like it starts the app for a moment, and then closes it (output windows from NSight shows several "shader loaded"/"shader unloaded" lines). Windows Event log doesn't show anything (except "NVIDIA Network Service" failing to start, but if I understood well, this is something related to updates). On the other hand, if I start GPU Perfomannce analysis, then it runs

问题 I want to test my Python library in GPU machine once a day. I decided to use AWS EC2 for testing. However, the fee of gpu machine is very high, so I want to stop the instance after the test ends. Thus, I want to do the followings once a day automatically Start EC2 instance (which is setup manually) Execute command (test -> push logs to S3) Stop EC2 (not remove) How to do this? 回答1: It is very simple... Run script on startup To run a script automatically when the instance starts ( every time

问题 I have an LSTM model that I want to train on multiple gpus. I transformed the code to do this and in nvidia-smi I could see that it is using all the memory of all the gpus and each of the gpus are utilizing around 40% BUT the estimated time for training of each batch was almost the same as 1 gpu. Can someone please guid me and tell me how I can train properly on multiple gpus? My code: import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import

问题 I have created a flask service for accepting requests with camera URLs as parameters for finding objects(table, chair etc...) in the camera frame. I have written code in flask for accepting POST requests. @app.route('/rest/detectObjects', methods=['GET','POST']) def detectObjects() ... json_result = function_call_for_detecting_objects() ... return In the function, its loads the tf model for object detection and returns the result. A large amount of request needs to be processed simultaneously

问题 I have created a flask service for accepting requests with camera URLs as parameters for finding objects(table, chair etc...) in the camera frame. I have written code in flask for accepting POST requests. @app.route('/rest/detectObjects', methods=['GET','POST']) def detectObjects() ... json_result = function_call_for_detecting_objects() ... return In the function, its loads the tf model for object detection and returns the result. A large amount of request needs to be processed simultaneously

问题 I've been using GPU for a while without questioning it but now I'm curious. Why can GPU do matrix multiplication much faster than CPU? Is it because of the parallel processing? But I didn't write any parallel processing code. Does it do it automatically by itself? Any intuition / high-level explanation will be appreciated! Thanks. 回答1: How do you parallelize the computations? GPU's are able to do a lot of parallel computations. A Lot more than a CPU could do. Look at this example of vector