![\"\"](\"https://i.ibb.co/VDpCKT4/gpu-multiprocessing.png\")
\n\nTL;DR: The code is here
\n
As we know, GPU makes everything faster. Moreover, oftentimes the GPU device we use is so powerful that our code doesn't utilize the GPU code to 100% potential. In such cases, running multiple GPU instances in parallel can come in handy and save us some extra time.
\nI've made a notebook that introduces a method for running parallel jobs on GPU devices. It is a bit tricky since most packages that support GPU usage aren't built from the ground up for such use cases.
\nThe \"trick\" is simple: We simply do not call the GPU in the main process, the first time we call for any GPU utilization should be from the child processes.
To demonstrate the power of this concept, I made a notebook that runs Parallel Hyperparameters Optimization on the GPU for LightGBM. As it is assumed, the GPU enables us to train our models faster (much faster) and by
\nleveraging that in combination with the parallel execution we get to search a large space of parameter configurations for fully maximizing our model's performance (Since we can now try many combinations).
\nThe Optimization framework is optuna, the popular framework for optimizing the hyperparameters. Again, this is just as an example of the approach. You can use the same approach for many other GPU-enabled use cases.
Personally, This concept helped me over and over as it is easily transferable to many different GPU-based models including Neural Networks (Tensorflow, Pytorch..), Other GBM models (Catboost, XGBoost..), Feature Engineering (Pandas, CuPy), and more.
", "messages": [ { "id": 1630302, "postDate": "2021-12-27T06:52:57.167Z", "content": "\n\nTL;DR: The code is here
\n
As we know, GPU makes everything faster. Moreover, oftentimes the GPU device we use is so powerful that our code doesn't utilize the GPU code to 100% potential. In such cases, running multiple GPU instances in parallel can come in handy and save us some extra time.
\nI've made a notebook that introduces a method for running parallel jobs on GPU devices. It is a bit tricky since most packages that support GPU usage aren't built from the ground up for such use cases.
\nThe \"trick\" is simple: We simply do not call the GPU in the main process, the first time we call for any GPU utilization should be from the child processes.
To demonstrate the power of this concept, I made a notebook that runs Parallel Hyperparameters Optimization on the GPU for LightGBM. As it is assumed, the GPU enables us to train our models faster (much faster) and by
\nleveraging that in combination with the parallel execution we get to search a large space of parameter configurations for fully maximizing our model's performance (Since we can now try many combinations).
\nThe Optimization framework is optuna, the popular framework for optimizing the hyperparameters. Again, this is just as an example of the approach. You can use the same approach for many other GPU-enabled use cases.
Personally, This concept helped me over and over as it is easily transferable to many different GPU-based models including Neural Networks (Tensorflow, Pytorch..), Other GBM models (Catboost, XGBoost..), Feature Engineering (Pandas, CuPy), and more.
", "rawMarkdown": "### GPU Multiprocessing for Parameter Optimization\n\n>TL;DR: The code is [here](https://www.kaggle.com/yamqwe/gpu-multiprocessing-for-parameter-optimization)\n\nAs we know, GPU makes everything faster. Moreover, oftentimes the GPU device we use is so powerful that our code doesn't utilize the GPU code to 100% potential. In such cases, running multiple GPU instances in parallel can come in handy and save us some extra time.\nI've made a notebook that introduces a method for running parallel jobs on GPU devices. It is a bit tricky since most packages that support GPU usage aren't built from the ground up for such use cases.\n**The \"trick\" is simple:** We simply do not call the GPU in the main process, the first time we call for any GPU utilization should be from the child processes. \n\n\n\n\n\nTo demonstrate the power of this concept, I made a notebook that runs Parallel Hyperparameters Optimization on the GPU for LightGBM. As it is assumed, the GPU enables us to train our models faster (much faster) and by \nleveraging that in combination with the parallel execution we get to search a large space of parameter configurations for fully maximizing our model's performance (Since we can now try many combinations).\nThe Optimization framework is optuna, the popular framework for optimizing the hyperparameters. Again, this is just as an example of the approach. You can use the same approach for many other GPU-enabled use cases. \n\n\nPersonally, This concept helped me over and over as it is easily transferable to many different GPU-based models including Neural Networks (Tensorflow, Pytorch..), Other GBM models (Catboost, XGBoost..), Feature Engineering (Pandas, CuPy), and more.\n\n\n\n", "votes": 3 } ], "comments": [], "raw_markdown_by_id": { "1630302": "### GPU Multiprocessing for Parameter Optimization\n\n>TL;DR: The code is [here](https://www.kaggle.com/yamqwe/gpu-multiprocessing-for-parameter-optimization)\n\nAs we know, GPU makes everything faster. Moreover, oftentimes the GPU device we use is so powerful that our code doesn't utilize the GPU code to 100% potential. In such cases, running multiple GPU instances in parallel can come in handy and save us some extra time.\nI've made a notebook that introduces a method for running parallel jobs on GPU devices. It is a bit tricky since most packages that support GPU usage aren't built from the ground up for such use cases.\n**The \"trick\" is simple:** We simply do not call the GPU in the main process, the first time we call for any GPU utilization should be from the child processes. \n\n\n\n\n\nTo demonstrate the power of this concept, I made a notebook that runs Parallel Hyperparameters Optimization on the GPU for LightGBM. As it is assumed, the GPU enables us to train our models faster (much faster) and by \nleveraging that in combination with the parallel execution we get to search a large space of parameter configurations for fully maximizing our model's performance (Since we can now try many combinations).\nThe Optimization framework is optuna, the popular framework for optimizing the hyperparameters. Again, this is just as an example of the approach. You can use the same approach for many other GPU-enabled use cases. \n\n\nPersonally, This concept helped me over and over as it is easily transferable to many different GPU-based models including Neural Networks (Tensorflow, Pytorch..), Other GBM models (Catboost, XGBoost..), Feature Engineering (Pandas, CuPy), and more.\n\n\n\n" } }