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2023-12-07 09:26:25 +00:00
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import dask\n",
"import random\n",
"import torch\n",
"from torch.utils.data import Dataset, DataLoader\n",
"\n",
"from dask.distributed import Client\n",
"import dask.dataframe as dd\n",
"import pandas as pd\n",
"\n",
"from dask_ml.preprocessing import MinMaxScaler\n",
"from dask_ml.model_selection import train_test_split\n",
"from dask_ml.linear_model import LinearRegression\n",
"\n",
"from daskdataset import DaskDataset, ShallowNet\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"client = Client()\n",
"\n",
"sample_dataset=\"/home/hpcrsaxe/Desktop/Code/Dataset/sample_train_data/dataset1.parquet\"\n",
"\n",
"df = dd.read_parquet(sample_dataset, engine=\"fastparquet\")#.repartition(npartitions=10) #using pyarrow throws error with numpy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Run this only on the cluster"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"client = Client(str(os.getenv('HOSTNAME')) + \"-ib:8786\")\n",
"\n",
"sample_datasets=[\"/lustre/nec/ws3/ws/hpcrsaxe-hpcrsaxe/datasets/dataset1.parquet\",\n",
" \"/lustre/nec/ws3/ws/hpcrsaxe-hpcrsaxe/datasets/dataset2.parquet\",\n",
" \"/lustre/nec/ws3/ws/hpcrsaxe-hpcrsaxe/datasets/dataset3.parquet\",\n",
" \"/lustre/nec/ws3/ws/hpcrsaxe-hpcrsaxe/datasets/dataset4.parquet\",\n",
" \"/lustre/nec/ws3/ws/hpcrsaxe-hpcrsaxe/datasets/dataset5.parquet\",\n",
" \"/lustre/nec/ws3/ws/hpcrsaxe-hpcrsaxe/datasets/dataset6.parquet\",\n",
" \"/lustre/nec/ws3/ws/hpcrsaxe-hpcrsaxe/datasets/dataset7.parquet\",\n",
" \"/lustre/nec/ws3/ws/hpcrsaxe-hpcrsaxe/datasets/dataset8.parquet\",]\n",
"\n",
"df = dd.read_parquet(sample_datasets, engine=\"fastparquet\") "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Convert old Parquet to new Parquet"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2023-11-28 16:43:12,666 - distributed.worker.memory - WARNING - Unmanaged memory use is high. This may indicate a memory leak or the memory may not be released to the OS; see https://distributed.dask.org/en/latest/worker-memory.html#memory-not-released-back-to-the-os for more information. -- Unmanaged memory: 2.72 GiB -- Worker memory limit: 3.86 GiB\n",
"2023-11-28 16:43:14,316 - distributed.worker.memory - WARNING - Worker is at 81% memory usage. Pausing worker. Process memory: 3.13 GiB -- Worker memory limit: 3.86 GiB\n",
"2023-11-28 16:43:15,668 - distributed.nanny.memory - WARNING - Worker tcp://127.0.0.1:33977 (pid=48276) exceeded 95% memory budget. Restarting...\n",
"2023-11-28 16:43:15,946 - distributed.nanny - WARNING - Restarting worker\n",
"2023-11-28 16:43:21,786 - distributed.worker.memory - WARNING - Unmanaged memory use is high. This may indicate a memory leak or the memory may not be released to the OS; see https://distributed.dask.org/en/latest/worker-memory.html#memory-not-released-back-to-the-os for more information. -- Unmanaged memory: 2.74 GiB -- Worker memory limit: 3.86 GiB\n",
"2023-11-28 16:43:23,443 - distributed.worker.memory - WARNING - Worker is at 80% memory usage. Pausing worker. Process memory: 3.09 GiB -- Worker memory limit: 3.86 GiB\n",
"2023-11-28 16:43:24,862 - distributed.nanny.memory - WARNING - Worker tcp://127.0.0.1:46711 (pid=48247) exceeded 95% memory budget. Restarting...\n",
"2023-11-28 16:43:25,144 - distributed.nanny - WARNING - Restarting worker\n",
"2023-11-28 16:43:31,078 - distributed.worker.memory - WARNING - Unmanaged memory use is high. This may indicate a memory leak or the memory may not be released to the OS; see https://distributed.dask.org/en/latest/worker-memory.html#memory-not-released-back-to-the-os for more information. -- Unmanaged memory: 2.76 GiB -- Worker memory limit: 3.86 GiB\n",
"2023-11-28 16:43:32,615 - distributed.worker.memory - WARNING - Worker is at 80% memory usage. Pausing worker. Process memory: 3.11 GiB -- Worker memory limit: 3.86 GiB\n",
"2023-11-28 16:43:34,068 - distributed.nanny.memory - WARNING - Worker tcp://127.0.0.1:39735 (pid=48375) exceeded 95% memory budget. Restarting...\n",
"2023-11-28 16:43:34,366 - distributed.nanny - WARNING - Restarting worker\n",
"2023-11-28 16:43:40,997 - distributed.worker.memory - WARNING - Unmanaged memory use is high. This may indicate a memory leak or the memory may not be released to the OS; see https://distributed.dask.org/en/latest/worker-memory.html#memory-not-released-back-to-the-os for more information. -- Unmanaged memory: 2.72 GiB -- Worker memory limit: 3.86 GiB\n",
"2023-11-28 16:43:42,760 - distributed.worker.memory - WARNING - Worker is at 81% memory usage. Pausing worker. Process memory: 3.13 GiB -- Worker memory limit: 3.86 GiB\n",
"2023-11-28 16:43:44,718 - distributed.nanny.memory - WARNING - Worker tcp://127.0.0.1:43435 (pid=48187) exceeded 95% memory budget. Restarting...\n",
"2023-11-28 16:43:45,089 - distributed.nanny - WARNING - Restarting worker\n"
]
},
{
"ename": "KilledWorker",
"evalue": "Attempted to run task ('read-parquet-dfab612cdfb5b1c27377f316ddefebac', 0) on 3 different workers, but all those workers died while running it. The last worker that attempt to run the task was tcp://127.0.0.1:43435. Inspecting worker logs is often a good next step to diagnose what went wrong. For more information see https://distributed.dask.org/en/stable/killed.html.",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKilledWorker\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[8], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Compute the Dask dataframe to get a Pandas dataframe\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m df_pandas \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;66;03m# Create a new Pandas dataframe with the expanded 'features' columns\u001b[39;00m\n\u001b[1;32m 5\u001b[0m features_df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(df_pandas[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfeatures\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mto_list(), columns\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfeature_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(df_pandas[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfeatures\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;241m0\u001b[39m]))])\n",
"File \u001b[0;32m~/miniconda3/envs/dask-env/lib/python3.8/site-packages/dask/base.py:314\u001b[0m, in \u001b[0;36mDaskMethodsMixin.compute\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m 290\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcompute\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 291\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Compute this dask collection\u001b[39;00m\n\u001b[1;32m 292\u001b[0m \n\u001b[1;32m 293\u001b[0m \u001b[38;5;124;03m This turns a lazy Dask collection into its in-memory equivalent.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 312\u001b[0m \u001b[38;5;124;03m dask.compute\u001b[39;00m\n\u001b[1;32m 313\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 314\u001b[0m (result,) \u001b[38;5;241m=\u001b[39m \u001b[43mcompute\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtraverse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 315\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
"File \u001b[0;32m~/miniconda3/envs/dask-env/lib/python3.8/site-packages/dask/base.py:599\u001b[0m, in \u001b[0;36mcompute\u001b[0;34m(traverse, optimize_graph, scheduler, get, *args, **kwargs)\u001b[0m\n\u001b[1;32m 596\u001b[0m keys\u001b[38;5;241m.\u001b[39mappend(x\u001b[38;5;241m.\u001b[39m__dask_keys__())\n\u001b[1;32m 597\u001b[0m postcomputes\u001b[38;5;241m.\u001b[39mappend(x\u001b[38;5;241m.\u001b[39m__dask_postcompute__())\n\u001b[0;32m--> 599\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mschedule\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdsk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeys\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 600\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m repack([f(r, \u001b[38;5;241m*\u001b[39ma) \u001b[38;5;28;01mfor\u001b[39;00m r, (f, a) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(results, postcomputes)])\n",
"File \u001b[0;32m~/miniconda3/envs/dask-env/lib/python3.8/site-packages/distributed/client.py:3224\u001b[0m, in \u001b[0;36mClient.get\u001b[0;34m(self, dsk, keys, workers, allow_other_workers, resources, sync, asynchronous, direct, retries, priority, fifo_timeout, actors, **kwargs)\u001b[0m\n\u001b[1;32m 3222\u001b[0m should_rejoin \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m 3223\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3224\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgather\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpacked\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43masynchronous\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43masynchronous\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdirect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdirect\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3225\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 3226\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m f \u001b[38;5;129;01min\u001b[39;00m futures\u001b[38;5;241m.\u001b[39mvalues():\n",
"File \u001b[0;32m~/miniconda3/envs/dask-env/lib/python3.8/site-packages/distributed/client.py:2359\u001b[0m, in \u001b[0;36mClient.gather\u001b[0;34m(self, futures, errors, direct, asynchronous)\u001b[0m\n\u001b[1;32m 2357\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 2358\u001b[0m local_worker \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 2359\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msync\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2360\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_gather\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2361\u001b[0m \u001b[43m \u001b[49m\u001b[43mfutures\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2362\u001b[0m \u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2363\u001b[0m \u001b[43m \u001b[49m\u001b[43mdirect\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdirect\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2364\u001b[0m \u001b[43m \u001b[49m\u001b[43mlocal_worker\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlocal_worker\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2365\u001b[0m \u001b[43m \u001b[49m\u001b[43masynchronous\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43masynchronous\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2366\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/miniconda3/envs/dask-env/lib/python3.8/site-packages/distributed/utils.py:351\u001b[0m, in \u001b[0;36mSyncMethodMixin.sync\u001b[0;34m(self, func, asynchronous, callback_timeout, *args, **kwargs)\u001b[0m\n\u001b[1;32m 349\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m future\n\u001b[1;32m 350\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 351\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43msync\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 352\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallback_timeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallback_timeout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m 353\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/miniconda3/envs/dask-env/lib/python3.8/site-packages/distributed/utils.py:418\u001b[0m, in \u001b[0;36msync\u001b[0;34m(loop, func, callback_timeout, *args, **kwargs)\u001b[0m\n\u001b[1;32m 416\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m error:\n\u001b[1;32m 417\u001b[0m typ, exc, tb \u001b[38;5;241m=\u001b[39m error\n\u001b[0;32m--> 418\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exc\u001b[38;5;241m.\u001b[39mwith_traceback(tb)\n\u001b[1;32m 419\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 420\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
"File \u001b[0;32m~/miniconda3/envs/dask-env/lib/python3.8/site-packages/distributed/utils.py:391\u001b[0m, in \u001b[0;36msync.<locals>.f\u001b[0;34m()\u001b[0m\n\u001b[1;32m 389\u001b[0m future \u001b[38;5;241m=\u001b[39m wait_for(future, callback_timeout)\n\u001b[1;32m 390\u001b[0m future \u001b[38;5;241m=\u001b[39m asyncio\u001b[38;5;241m.\u001b[39mensure_future(future)\n\u001b[0;32m--> 391\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01myield\u001b[39;00m future\n\u001b[1;32m 392\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m:\n\u001b[1;32m 393\u001b[0m error \u001b[38;5;241m=\u001b[39m sys\u001b[38;5;241m.\u001b[39mexc_info()\n",
"File \u001b[0;32m~/miniconda3/envs/dask-env/lib/python3.8/site-packages/tornado/gen.py:767\u001b[0m, in \u001b[0;36mRunner.run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 765\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 766\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 767\u001b[0m value \u001b[38;5;241m=\u001b[39m \u001b[43mfuture\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mresult\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 768\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 769\u001b[0m \u001b[38;5;66;03m# Save the exception for later. It's important that\u001b[39;00m\n\u001b[1;32m 770\u001b[0m \u001b[38;5;66;03m# gen.throw() not be called inside this try/except block\u001b[39;00m\n\u001b[1;32m 771\u001b[0m \u001b[38;5;66;03m# because that makes sys.exc_info behave unexpectedly.\u001b[39;00m\n\u001b[1;32m 772\u001b[0m exc: Optional[\u001b[38;5;167;01mException\u001b[39;00m] \u001b[38;5;241m=\u001b[39m e\n",
"File \u001b[0;32m~/miniconda3/envs/dask-env/lib/python3.8/site-packages/distributed/client.py:2222\u001b[0m, in \u001b[0;36mClient._gather\u001b[0;34m(self, futures, errors, direct, local_worker)\u001b[0m\n\u001b[1;32m 2220\u001b[0m exc \u001b[38;5;241m=\u001b[39m CancelledError(key)\n\u001b[1;32m 2221\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2222\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exception\u001b[38;5;241m.\u001b[39mwith_traceback(traceback)\n\u001b[1;32m 2223\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exc\n\u001b[1;32m 2224\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mskip\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
"\u001b[0;31mKilledWorker\u001b[0m: Attempted to run task ('read-parquet-dfab612cdfb5b1c27377f316ddefebac', 0) on 3 different workers, but all those workers died while running it. The last worker that attempt to run the task was tcp://127.0.0.1:43435. Inspecting worker logs is often a good next step to diagnose what went wrong. For more information see https://distributed.dask.org/en/stable/killed.html."
]
}
],
"source": [
"# Compute the Dask dataframe to get a Pandas dataframe\n",
"df_pandas = df.compute()\n",
"\n",
"# Create a new Pandas dataframe with the expanded 'features' columns\n",
"features_df = pd.DataFrame(df_pandas['features'].to_list(), columns=[f'feature_{i}' for i in range(len(df_pandas['features'][0]))])\n",
"labels_df = pd.DataFrame(df_pandas['labels'].to_list(), columns=[f'label_{i}' for i in range(len(df_pandas['labels'][0]))])\n",
"\n",
"# Concatenate the original dataframe with the expanded features and labels dataframes\n",
"df_pandas = pd.concat([features_df, labels_df], axis=1)\n",
"\n",
"#save df_pandas a parquet\n",
"df_pandas.to_parquet(\"/home/hpcrsaxe/Desktop/Code/Dataset/sample_train_data/dataset1.parquet\", engine=\"fastparquet\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"#separate the features and labels\n",
"df_labels = df.loc[:, df.columns.str.contains('label')]\n",
"\n",
"# Create a StandardScaler object\n",
"scaler_features = MinMaxScaler()\n",
"df_features_scaled = scaler_features.fit_transform(df.loc[:, df.columns.str.contains('feature')])\n",
"\n",
"#Split the data into training and test sets\n",
"X_train, X_test, y_train, y_test = train_test_split(df_features_scaled, df_labels, shuffle=True)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2023-11-28 16:58:28,387 - distributed.worker.memory - WARNING - Worker is at 81% memory usage. Pausing worker. Process memory: 3.16 GiB -- Worker memory limit: 3.86 GiB\n",
"2023-11-28 16:58:29,636 - distributed.worker.memory - WARNING - Worker is at 58% memory usage. Resuming worker. Process memory: 2.26 GiB -- Worker memory limit: 3.86 GiB\n"
]
}
],
"source": [
"X = torch.tensor(X_train.compute().values)\n",
"y = torch.tensor(y_train.compute().values)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2023-11-28 16:52:11,130 - distributed.worker.memory - WARNING - Worker is at 81% memory usage. Pausing worker. Process memory: 3.15 GiB -- Worker memory limit: 3.86 GiB\n",
"2023-11-28 16:52:12,561 - distributed.worker.memory - WARNING - Worker is at 58% memory usage. Resuming worker. Process memory: 2.25 GiB -- Worker memory limit: 3.86 GiB\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"4860\n"
]
}
],
"source": [
"from skorch import NeuralNetRegressor\n",
"import torch.optim as optim\n",
"\n",
"niceties = {\n",
" \"callbacks\": False,\n",
" \"warm_start\": False,\n",
" \"train_split\": None,\n",
" \"max_epochs\": 5,\n",
"}\n",
"\n",
"model = NeuralNetRegressor(\n",
" module=ShallowNet,\n",
" module__n_features=X.size(dim=1),\n",
" criterion=nn.MSELoss,\n",
" optimizer=optim.SGD,\n",
" optimizer__lr=0.1,\n",
" optimizer__momentum=0.9,\n",
" batch_size=64,\n",
" **niceties,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
"model = model.share_memory()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Re-initializing module because the following parameters were re-set: n_features.\n",
"Re-initializing criterion.\n",
"Re-initializing optimizer.\n",
" epoch train_loss dur\n",
"------- ------------ ------\n",
" 1 \u001b[36m0.3994\u001b[0m 4.5545\n"
]
},
{
"data": {
"text/plain": [
"<class 'skorch.regressor.NeuralNetRegressor'>[initialized](\n",
" module_=ShallowNet(\n",
" (layer1): Linear(in_features=4860, out_features=8, bias=True)\n",
" ),\n",
")"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.fit(X, y)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2023-11-28 17:00:11,618 - distributed.worker.memory - WARNING - Worker is at 81% memory usage. Pausing worker. Process memory: 3.13 GiB -- Worker memory limit: 3.86 GiB\n",
"2023-11-28 17:00:13,143 - distributed.worker.memory - WARNING - Worker is at 57% memory usage. Resuming worker. Process memory: 2.22 GiB -- Worker memory limit: 3.86 GiB\n"
]
}
],
"source": [
"test_X = torch.tensor(X_test.compute().values)\n",
"test_y = torch.tensor(y_test.compute().values)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-3.410176609207851\n"
]
}
],
"source": [
"print(model.score(test_X, test_y))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Define the loss function and optimizer\n",
"criterion = torch.nn.CrossEntropyLoss()\n",
"optimizer = torch.optim.SGD(model.parameters(), lr=0.01)\n",
"# Move the model to the GPU\n",
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
"model = model.to(device)\n",
"# Distribute the model across workers\n",
"model = model.share_memory()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Train the model\n",
"for epoch in range(10):\n",
" for batch in dataloader:\n",
" # Split the batch across workers\n",
" batch = [b.to(device) for b in batch]\n",
" futures = client.map(lambda data: model(data[0]), batch)\n",
" # Compute the loss\n",
" losses = client.map(criterion, futures, batch[1])\n",
" loss = client.submit(torch.mean, client.gather(losses))\n",
" # Compute the gradients and update the model parameters\n",
" optimizer.zero_grad()\n",
" gradients = client.map(lambda loss, future: torch.autograd.grad(loss, future)[0], loss, futures)\n",
" gradients =client.submit(torch.mean, client.gather(gradients))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"client.map(lambda parameter, gradient: parameter.grad.copy_(gradient), model.parameters(), gradients)\n",
"optimizer.step()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from dask_ml.linear_model import LinearRegression\n",
"from sklearn.linear_model import LinearRegression\n",
"\n",
"# Initialize the Linear Regression model\n",
"model = LinearRegression()\n",
"model.fit(X_train, y_train)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"new_dask_df = df['features'].apply(pd.Series, meta=meta)\n",
"new_dask_df.compute()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Use this code for PyArrow tables\n",
"\n",
"#import pyarrow as pa\n",
"\n",
"# Define schema for features and labels columns\n",
"#schema = pa.schema({\n",
" #'features': pa.list_(pa.float32()),\n",
" #'labels': pa.list_(pa.float32())\n",
"#})\n",
"\n",
"#import pyarrow.parquet as pq\n",
"#df = dd.from_pandas(pq.read_table(sample_dataset, schema=schema).to_pandas(), npartitions=10)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "dask-env",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.18"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Reference in a new issue Copy permalink