00-EnvironmentCheck.ipynb

{
 "cells": [
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   "cell_type": "markdown",
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   "source": [
    "# Environment check\n",
    "\n",
    "In this notebook we'll check if the system is initialized correctly. Before running anything please follow the instructions in the [Readme](./README.md) file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking if PyTorch is installed correctly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pytorch version: 2.2.1\n",
      "Apple Metal MPS acceleration ok.\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    import torch\n",
    "    print(f\"Pytorch version: {torch.__version__}\")\n",
    "    is_torch = True\n",
    "    if torch.backends.mps.is_available() is True:\n",
    "        print(\"Apple Metal MPS acceleration ok.\")\n",
    "    else:\n",
    "        print(\"Your version of Pytorch does not support MPS, Pytorch will be slow if used on MacOS.\")\n",
    "except:\n",
    "    print(\"Pytorch is not installed. Please install pytorch!\")\n",
    "    is_torch = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (Optional) checking if MLX is installed properly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLX version: 0.5.0\n",
      "Apple MLX framework is installed ok\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    import mlx.core as mx\n",
    "    print(f\"MLX version: {mx.__version__}\")\n",
    "    is_mlx = True\n",
    "    print(\"Apple MLX framework is installed ok\")\n",
    "except:\n",
    "    print(\"MLX is not installed, it's optional, so this is not a fatal error.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking if HuggingFace transformers is installed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformers version: 4.38.2\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    import transformers\n",
    "    from transformers import pipeline\n",
    "    print(f\"Transformers version: {transformers.__version__}\")\n",
    "    is_huggingface = True\n",
    "except:\n",
    "    print(\"HuggingFace transformers is not installed. This won't work!\")\n",
    "    is_huggingface = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sample sentiment analysis test\n",
    "\n",
    "> **Note:** when run first time required models will be downloaded (several hundred MB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No model was supplied, defaulted to distilbert/distilbert-base-uncased-finetuned-sst-2-english and revision af0f99b (https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english).\n",
      "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'label': 'POSITIVE', 'score': 0.9997795224189758}]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nlp = pipeline(\"sentiment-analysis\")\n",
    "nlp(\"We are very happy to show you the 🤗 Transformers library.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check if docker is setup correctly\n",
    "\n",
    "Verify if ElasticSearch and QDrant are up and running in docker."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elasticsearch is up and running.\n",
      "Qdrant is up and running.\n"
     ]
    }
   ],
   "source": [
    "from qdrant_client import QdrantClient\n",
    "from qdrant_client.models import Distance, VectorParams\n",
    "from elasticsearch import Elasticsearch\n",
    "\n",
    "es = Elasticsearch(\"http://localhost:9200\")\n",
    "qd = QdrantClient(host='localhost', port=6333)\n",
    "\n",
    "if (es.ping()):\n",
    "    print(\"Elasticsearch is up and running.\")\n",
    "else:\n",
    "    print(\"Elasticsearch is not running.\")\n",
    "\n",
    "if (qd.recreate_collection(\n",
    "    collection_name=\"test_collection\",     \n",
    "    vectors_config=VectorParams(size=384, distance=Distance.COSINE))):\n",
    "    print(\"Qdrant is up and running.\")\n",
    "else:\n",
    "    print(\"Qdrant is not running.\")"
   ]
  }
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