Added a page documenting scikit-learn for Aurora #573
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just an observation: we don't have a scikit-learn page in any other Machine section. are there architecture/platform-specific performance notes we want to convey for scikit-learn users on Polaris, Sophia, etc? Or is it all straightforward with cuML integration on NVIDIA platforms. What about scaling?
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| ## Environment Setup | ||
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| Intel Extension for Scikit-learn is already pre-installed on Aurora, available in the `frameworks` |
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| Intel Extension for Scikit-learn is already pre-installed on Aurora, available in the `frameworks` | |
| Intel Extension for scikit-learn is already pre-installed on Aurora, available in the `frameworks` |
I would be consistent with not capitalizing scikit-learn
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It's aligned with official scikit-learn-intelex documentation: https://www.intel.com/content/www/us/en/developer/tools/oneapi/scikit-learn.html
| from sklearnex import patch_sklearn | ||
| patch_sklearn() | ||
| ``` | ||
| It is important to note that this needs to happen before importing scikit-learn. To explicitly only patch certain estimators, you can import particular functions from sklearnex instead of sklearn, like this: |
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| It is important to note that this needs to happen before importing scikit-learn. To explicitly only patch certain estimators, you can import particular functions from sklearnex instead of sklearn, like this: | |
| It is important to note that this needs to happen before importing scikit-learn. To explicitly only patch certain estimators, you can import particular functions from `sklearnex` instead of `sklearn`, like this: |
use inline code formatting when discussing the actual module imports, etc.
| ``` | ||
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| ### GPU Acceleration | ||
| Intel Extension for Scikit-learn can execute algorithms on the GPU via the [dpctl](https://intelpython.github.io/dpctl/latest/index.html) package, which should be included in the frameworks module. (If not, see the [Python page](../python.md)) dpctl implements oneAPI concepts like queues and devices. |
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| Intel Extension for Scikit-learn can execute algorithms on the GPU via the [dpctl](https://intelpython.github.io/dpctl/latest/index.html) package, which should be included in the frameworks module. (If not, see the [Python page](../python.md)) dpctl implements oneAPI concepts like queues and devices. | |
| Intel Extension for scikit-learn can execute algorithms on the GPU via the [dpctl](https://intelpython.github.io/dpctl/latest/index.html) package, which should be included in the frameworks module. (If not, see the [Python page](../python.md)) dpctl implements oneAPI concepts like queues and devices. |
| As described in more detail in Intel's documentation [here](https://uxlfoundation.github.io/scikit-learn-intelex/latest/oneapi-gpu.html), there are two ways to run on the GPU. | ||
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| 1. Pass the input data to the algorithm as `dpctl.tensor.usm_ndarray`. Then the algorithm will run on the same device as the data and return the result as a usm_array on the same device. | ||
| 2. Configure Intel Extension for Scikit-learn, for example, by setting a context: `sklearnex.config_context`. |
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| 2. Configure Intel Extension for Scikit-learn, for example, by setting a context: `sklearnex.config_context`. | |
| 2. Configure Intel Extension for scikit-learn, for example, by setting a context: `sklearnex.config_context`. |
| 2. Configure Intel Extension for Scikit-learn, for example, by setting a context: `sklearnex.config_context`. | ||
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| ### Distributed Mode | ||
| To distribute an sklearnex algorithm across multiple GPUs, we need several ingredients, which we will demonstrate in an example below. We recommend using the MPI backend rather than the CCL backend since it is more tested on Aurora. |
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| To distribute an sklearnex algorithm across multiple GPUs, we need several ingredients, which we will demonstrate in an example below. We recommend using the MPI backend rather than the CCL backend since it is more tested on Aurora. | |
| To distribute an `sklearnex` algorithm across multiple GPUs, we need several ingredients demonstrated in an example below. We recommend using the MPI backend rather than the CCL backend since it is more tested on Aurora. |
"more tested" phrasing sounds awkward to me. Maybe "since it is tested more thoroughly on Aurora."
| if dpctl.has_gpu_devices(): | ||
| q = dpctl.SyclQueue("gpu") | ||
| else: | ||
| raise RuntimeError( | ||
| "GPU devices unavailable." | ||
| ) |
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Also for the sake of a concise demo:
| if dpctl.has_gpu_devices(): | |
| q = dpctl.SyclQueue("gpu") | |
| else: | |
| raise RuntimeError( | |
| "GPU devices unavailable." | |
| ) | |
| # Create a GPU SYCL queue to store data on device | |
| q = dpctl.SyclQueue("gpu") |
| module use /soft/modulefiles | ||
| module load frameworks | ||
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| export NUMEXPR_NUM_THREADS=64 |
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Is this an Aurora-specific issue? Because afaik its not required for sklearnex spmd usage
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| # Launch the script | ||
| mpiexec -np ${NRANKS} -ppn ${NRANKS_PER_NODE} \ | ||
| --cpu-bind ${CPU_BIND} gpu_tile_compact.sh \ |
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I also run with hostfile flag and PBS_NODEFILE arg. Not sure if its necessary or covered by gpu_tile_compact.sh
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| ## Environment Setup | ||
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| Intel Extension for Scikit-learn is already pre-installed on Aurora, available in the `frameworks` |
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It's aligned with official scikit-learn-intelex documentation: https://www.intel.com/content/www/us/en/developer/tools/oneapi/scikit-learn.html
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| scikit-learn (abbreviated "sklearn") is built for CPUs. However, Intel(R) Extension for Scikit-learn (abbreviated "sklearnex") is a free Python package that speeds up scikit-learn on Intel CPUs & GPUs. For more information, see the [scikit-learn-intelex Github page](https://github.com/uxlfoundation/scikit-learn-intelex), [the documentation](https://uxlfoundation.github.io/scikit-learn-intelex/latest/index.html), or [Intel's website](https://www.intel.com/content/www/us/en/developer/tools/oneapi/scikit-learn.html#gs.b2f4sw). | ||
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| The acceleration is by patching: replacing stock scikit-learn algorithms with the versions from Intel(R) oneAPI Data Analytics Library (oneDAL). |
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| The acceleration is by patching: replacing stock scikit-learn algorithms with the versions from Intel(R) oneAPI Data Analytics Library (oneDAL). | |
| The accelerated interfaces are available via patching: replacing stock scikit-learn algorithms with versions that utilize Intel(R) oneAPI Data Analytics Library (oneDAL). |
| from sklearnex import patch_sklearn | ||
| patch_sklearn() | ||
| ``` | ||
| It is important to note that this needs to happen before importing scikit-learn. To explicitly only patch certain estimators, you can import particular functions from sklearnex instead of sklearn, like this: |
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One other comment on patching - it's not necessary to run distributed GPU algos. Patching allows a user to import and use the CPU estimator the exact same way they would from scikit-learn, or make use of single-GPU interfaces easier, but our spmd algos are available in a different path and thus are unrelated to the patching. If the main goal of this documentation is to support users who will be running these algos on multiple GPUs it may be worth reducing emphasis on patching details
| 1. Create an MPI communicator using mpi4py if you need to use the rank. (mpi4py is also included in the frameworks module.) | ||
| 2. Check for GPU devices. | ||
| 3. Use dpctl to create a SYCL queue (connection to the GPU devices you choose). | ||
| 4. Using dpctl and your queue, move your data to the GPU devices. | ||
| 5. Run the algorithm on that data. The compute will happen where the data is. The algorithm should be from `sklearnex.spmd`. |
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Steps 1 and 2 are not actually required and could be removed for simplicity. mpi4py is a required dependency but no lines of code with mpi4py are actually necessary to run our spmd estimators. however in most cases a user would use this for arranging their data across various devices.
If these are removed, a comment could be added to the code snippet where the communicator is created to indicated that it is being used for the data setup
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