Remove scrapingh & fix broken links
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9 changed files with 31 additions and 146 deletions
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@ -1,104 +0,0 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import httpx\n",
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"\n",
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"transport = httpx.AsyncHTTPTransport(retries=2)\n",
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"limits = httpx.Limits(max_connections=None)\n",
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"timeout = httpx.Timeout(connect=60.0, read=300, write=60, pool=None)\n",
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"async with httpx.AsyncClient(\n",
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" transport=transport, limits=limits, timeout=timeout\n",
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") as client:\n",
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" pass"
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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": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\u001b[38;5;226m2022-07-11 19:00:27 | WARNING | Environment variable ENVIRONMENT is not set, defaulting to development mode ‼️\u001b[0m\n",
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"\u001b[38;5;226m2022-07-11 19:00:27 | WARNING | Cannot find credentials files, defaulting to using ParallelTinyDbDriver\u001b[0m\n",
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"\u001b[38;5;226m2022-07-11 19:00:27 | WARNING | The selected tracing database (ParallelTinyDbDriver) is not recommended for production\u001b[0m\n",
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"\u001b[38;5;226m2022-07-11 19:00:27 | WARNING | Cannot find credentials files, defaulting to using LargeFileLocal\u001b[0m\n",
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"\u001b[38;5;39m2022-07-11 19:00:27 | INFO | GreatAI (v0.1.3): configured ✅\u001b[0m\n",
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"\u001b[38;5;39m2022-07-11 19:00:27 | INFO | 🔩 tracing_database: ParallelTinyDbDriver\u001b[0m\n",
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"\u001b[38;5;39m2022-07-11 19:00:27 | INFO | 🔩 large_file_implementation: LargeFileLocal\u001b[0m\n",
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"\u001b[38;5;39m2022-07-11 19:00:27 | INFO | 🔩 is_production: False\u001b[0m\n",
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"\u001b[38;5;39m2022-07-11 19:00:27 | INFO | 🔩 should_log_exception_stack: True\u001b[0m\n",
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"\u001b[38;5;39m2022-07-11 19:00:27 | INFO | 🔩 prediction_cache_size: 512\u001b[0m\n",
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"\u001b[38;5;39m2022-07-11 19:00:27 | INFO | 🔩 dashboard_table_size: 50\u001b[0m\n",
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"\u001b[38;5;226m2022-07-11 19:00:27 | WARNING | You still need to check whether you follow all best practices before trusting your deployment.\u001b[0m\n",
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"\u001b[38;5;226m2022-07-11 19:00:27 | WARNING | > Find out more at https://se-ml.github.io/practices\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"Trace[str]({'created': '2022-07-11T17:00:27.064568',\n",
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" 'exception': None,\n",
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" 'feedback': None,\n",
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" 'logged_values': {'arg:your_name:length': 3, 'arg:your_name:value': 'Bob'},\n",
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" 'models': [],\n",
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" 'original_execution_time_ms': 0.0896,\n",
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" 'output': 'Hi Bob',\n",
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" 'tags': ['greeter', 'online', 'development'],\n",
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" 'trace_id': '8ff5b268-2613-4e85-96ae-f248666a051f'})"
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]
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},
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"execution_count": 1,
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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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"from great_ai import configure, RouteConfig\n",
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"from great_ai import GreatAI\n",
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"\n",
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"\n",
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"@GreatAI.create\n",
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"def greeter(your_name: str) -> str:\n",
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" return f\"Hi {your_name}\"\n",
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"\n",
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"\n",
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"greeter(\"Bob\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.10.4 ('.env': venv)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.4"
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},
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"orig_nbformat": 4,
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"vscode": {
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"interpreter": {
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"hash": "02dd6d3afbfa9fbbe1037d64ad9014965528a1ccad21929d6e72f466389a68ad"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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@ -10,10 +10,10 @@ Well-tested tools that can be used in production with confidence. The toolbox of
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::: great_ai.utilities.clean
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::: great_ai.utilities.get_sentences
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::: great_ai.utilities.predict_language
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::: great_ai.utilities.is_english
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::: great_ai.utilities.english_name_of_language
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::: great_ai.utilities.evaluate_ranking
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::: great_ai.utilities.language.predict_language
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::: great_ai.utilities.language.english_name_of_language
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::: great_ai.utilities.language.is_english
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::: great_ai.utilities.evaluate_ranking.evaluate_ranking
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## Parallel processing
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@ -29,7 +29,7 @@ Multiprocessing and multithreading-based parallelism with support for `async` fu
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## Composable parallel processing
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Because both [threaded_parallel_map][great_ai.utilities.parallel_map.threaded_parallel_map.threaded_parallel_map] and [parallel_map][great_ai.utilities.parallel_map.parallel_map] have a streaming interface, it is easy to compose them and end up with, for example, a process for each CPU core with its own thread-pool or event-loop. Longer pipelines are also easy to imagine. The chunking methods help in these compositions.
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Because both [threaded_parallel_map][great_ai.utilities.parallel_map.threaded_parallel_map.threaded_parallel_map] and [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] have a streaming interface, it is easy to compose them and end up with, for example, a process for each CPU core with its own thread-pool or event-loop. Longer pipelines are also easy to imagine. The chunking methods help in these compositions.
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For more info, check-out [the scraping guide](/how-to-guides/scraping).
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