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hhsecond
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pytorch example wip
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‎DecisionTreeWithApacheSpark.ipynb

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‎ImageClassificationWithPytorch.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "f0c2a6e5",
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"metadata": {},
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"source": [
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"# Image Classification with PyTorch\n",
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"Pytorch has been both researcher's and engineer's preferred choice of framework for DL development but when it comes to productionizing pytorch models, there still hasn't been a consensus on what to use. This guide run you through building a simple image classification model using Pytorch and then deploying that to RedisAI"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "1e657632",
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"metadata": {},
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"outputs": [],
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"source": [
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"import torchvision.models as models\n",
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"import torch"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "56edea97",
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"metadata": {},
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"outputs": [],
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"source": [
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"model = models.resnet50(pretrained=True)\n",
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"model.eval()\n",
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"\n",
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"scripted_model = torch.jit.script(model)\n",
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"torch.jit.save(scripted_model, 'resnet50.pt')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "4ffd3d48",
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"import time\n",
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"from redisai import Client\n",
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"import ml2rt\n",
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"from skimage import io"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "59b6599a",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"from redisai import Client\n",
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"\n",
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"REDIS_HOST = os.getenv(\"REDIS_HOST\", \"localhost\")\n",
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"REDIS_PORT = int(os.getenv(\"REDIS_PORT\", 6379))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"id": "97bbea43",
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"metadata": {},
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"outputs": [],
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"source": [
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"con = Client(host=REDIS_HOST, port=REDIS_PORT)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"id": "95325dca",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"True"
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]
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},
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"execution_count": 17,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"con.ping()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"id": "f7ddde68",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'OK'"
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]
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},
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"execution_count": 18,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"model = ml2rt.load_model(\"resnet50.pt\")\n",
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"con.modelstore(\"pytorch_model\", backend=\"TORCH\", device=\"CPU\", data=model)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"id": "50bb90b1",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'OK'"
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]
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},
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"execution_count": 28,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"script = \"\"\"\n",
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"def pre_process(tensors: List[Tensor], keys: List[str], args: List[str]):\n",
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" image = tensors[0]\n",
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" mean = torch.zeros(3).float().to(image.device)\n",
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" std = torch.zeros(3).float().to(image.device)\n",
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" mean[0], mean[1], mean[2] = 0.485, 0.456, 0.406\n",
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" std[0], std[1], std[2] = 0.229, 0.224, 0.225\n",
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" mean = mean.unsqueeze(1).unsqueeze(1)\n",
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" std = std.unsqueeze(1).unsqueeze(1)\n",
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" temp = image.float().div(255).permute(2, 0, 1)\n",
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" return temp.sub(mean).div(std).unsqueeze(0)\n",
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"\n",
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"\n",
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"def post_process(tensors: List[Tensor], keys: List[str], args: List[str]):\n",
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" output = tensors[0]\n",
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" return output.max(1)[1]\n",
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"\"\"\"\n",
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"con.scriptstore(\"processing_script\", device=\"CPU\", script=script, entry_points=(\"pre_process\", \"post_process\"))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"id": "f24ce05d",
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"metadata": {},
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"outputs": [],
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"source": [
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"image = io.imread(\"../data/cat.jpg\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"id": "40e02215",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'OK'"
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]
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},
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"execution_count": 30,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"con.tensorset('image', image)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"id": "cacc9eb6",
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"metadata": {},
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"outputs": [
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{
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"ename": "ResponseError",
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"evalue": "The following operation failed in the TorchScript interpreter. Traceback of TorchScript (most recent call last): File \"<string>\", line 10, in pre_process mean = mean.unsqueeze(1).unsqueeze(1) std = std.unsqueeze(1).unsqueeze(1) temp = image.float().div(255).permute(2, 0, 1) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE return temp.sub(mean).div(std).unsqueeze(0) RuntimeError: number of dims don't match in permute ",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mResponseError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m/var/folders/66/g3bgwk8s0mq9fmm1d32nmb8c0000gq/T/ipykernel_4521/4111896467.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcon\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscriptexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'processing_script'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'pre_process'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'image'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'processed'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
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"\u001b[0;32m~/asgard/redisai-examples/venv/lib/python3.8/site-packages/redisai/client.py\u001b[0m in \u001b[0;36mscriptexecute\u001b[0;34m(self, key, function, keys, inputs, args, outputs, timeout)\u001b[0m\n\u001b[1;32m 786\u001b[0m \"\"\"\n\u001b[1;32m 787\u001b[0m \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuilder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscriptexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 788\u001b[0;31m \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecute_command\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 789\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0menable_postprocess\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mprocessor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscriptexecute\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mres\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 790\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/asgard/redisai-examples/venv/lib/python3.8/site-packages/redis/client.py\u001b[0m in \u001b[0;36mexecute_command\u001b[0;34m(self, *args, **options)\u001b[0m\n\u001b[1;32m 899\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 900\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend_command\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 901\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparse_response\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcommand_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0moptions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 902\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mConnectionError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 903\u001b[0m \u001b[0mconn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/asgard/redisai-examples/venv/lib/python3.8/site-packages/redis/client.py\u001b[0m in \u001b[0;36mparse_response\u001b[0;34m(self, connection, command_name, **options)\u001b[0m\n\u001b[1;32m 913\u001b[0m \u001b[0;34m\"Parses a response from the Redis server\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 914\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 915\u001b[0;31m \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconnection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_response\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 916\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mResponseError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 917\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mEMPTY_RESPONSE\u001b[0m \u001b[0;32min\u001b[0m \u001b[0moptions\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/asgard/redisai-examples/venv/lib/python3.8/site-packages/redis/connection.py\u001b[0m in \u001b[0;36mread_response\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 754\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 755\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mResponseError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 756\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 757\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 758\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mResponseError\u001b[0m: The following operation failed in the TorchScript interpreter. Traceback of TorchScript (most recent call last): File \"<string>\", line 10, in pre_process mean = mean.unsqueeze(1).unsqueeze(1) std = std.unsqueeze(1).unsqueeze(1) temp = image.float().div(255).permute(2, 0, 1) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE return temp.sub(mean).div(std).unsqueeze(0) RuntimeError: number of dims don't match in permute "
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]
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}
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],
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"source": [
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"con.scriptexecute('processing_script', 'pre_process', 'image', 'processed')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c63c7ff8",
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"id": "848627bd",
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"metadata": {},
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"outputs": [],
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"source": []

‎requirements.txt

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widgetsnbextension==3.5.2
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wrapt==1.13.3
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zipp==3.6.0
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torch==1.10.0
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torchvision==0.11.1
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scikit-image==0.18.3

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