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Update Tutorial 6
This commit is contained in:
Malte Pietsch
parent
8a9f97fad3
commit
c36f8c991e
@ -65,37 +65,9 @@
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Fri Jul 3 09:43:18 2020 \r\n",
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"+-----------------------------------------------------------------------------+\r\n",
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"| NVIDIA-SMI 435.21 Driver Version: 435.21 CUDA Version: 10.1 |\r\n",
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"|-------------------------------+----------------------+----------------------+\r\n",
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"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\r\n",
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"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\r\n",
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"|===============================+======================+======================|\r\n",
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"| 0 GeForce 940MX Off | 00000000:02:00.0 Off | N/A |\r\n",
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"| N/A 41C P0 N/A / N/A | 567MiB / 2004MiB | 4% Default |\r\n",
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"+-------------------------------+----------------------+----------------------+\r\n",
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" \r\n",
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"+-----------------------------------------------------------------------------+\r\n",
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"| Processes: GPU Memory |\r\n",
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"| GPU PID Type Process name Usage |\r\n",
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"|=============================================================================|\r\n",
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"| 0 1507 G /usr/lib/xorg/Xorg 212MiB |\r\n",
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"| 0 1735 G /usr/bin/gnome-shell 85MiB |\r\n",
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"| 0 3310 G ...uest-channel-token=10103706267471532991 48MiB |\r\n",
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"| 0 3507 G ...AAAAAAAAAAAACAAAAAAAAAA= --shared-files 195MiB |\r\n",
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"| 0 22962 G ...p/pycharm-professional/201/jbr/bin/java 22MiB |\r\n",
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"+-----------------------------------------------------------------------------+\r\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"# Make sure you have a GPU running\n",
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"!nvidia-smi"
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@ -107,7 +79,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"! pip install git+git://github.com/deepset-ai/haystack.git@07ecfb60b944d9682f6d50317a15ffe5501ae456"
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"! pip install git+git://github.com/deepset-ai/haystack.git@8a9f97fad37241b0101c4561d10a49f2fbc6ee52"
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]
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},
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{
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@ -153,9 +125,9 @@
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],
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"source": [
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"# Recommended: Start Elasticsearch using Docker\n",
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"! docker run -d -p 9200:9200 -e \"discovery.type=single-node\" elasticsearch:7.6.2\n",
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"#! docker run -d -p 9200:9200 -e \"discovery.type=single-node\" elasticsearch:7.6.2\n",
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"# wait until ES has started\n",
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"! sleep 30"
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"#! sleep 30"
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]
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},
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{
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@ -165,18 +137,18 @@
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"outputs": [],
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"source": [
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"# In Colab / No Docker environments: Start Elasticsearch from source\n",
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"#! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.6.2-linux-x86_64.tar.gz -q\n",
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"#! tar -xzf elasticsearch-7.6.2-linux-x86_64.tar.gz\n",
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"#! chown -R daemon:daemon elasticsearch-7.6.2\n",
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"! wget https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-7.6.2-linux-x86_64.tar.gz -q\n",
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"! tar -xzf elasticsearch-7.6.2-linux-x86_64.tar.gz\n",
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"! chown -R daemon:daemon elasticsearch-7.6.2\n",
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"\n",
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"#import os\n",
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"#from subprocess import Popen, PIPE, STDOUT\n",
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"#es_server = Popen(['elasticsearch-7.6.2/bin/elasticsearch'],\n",
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"# stdout=PIPE, stderr=STDOUT,\n",
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"# preexec_fn=lambda: os.setuid(1) # as daemon\n",
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"# )\n",
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"import os\n",
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"from subprocess import Popen, PIPE, STDOUT\n",
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"es_server = Popen(['elasticsearch-7.6.2/bin/elasticsearch'],\n",
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" stdout=PIPE, stderr=STDOUT,\n",
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" preexec_fn=lambda: os.setuid(1) # as daemon\n",
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" )\n",
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"# wait until ES has started\n",
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"#! sleep 30"
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"! sleep 30"
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]
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},
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{
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@ -340,7 +312,7 @@
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" do_lower_case=True, gpu=True)\n",
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"\n",
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"# Important: \n",
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"# Now that we have the DPR initialized, we need to call update_embeddings() to iterate over all \n",
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"# Now that after we have the DPR initialized, we need to call update_embeddings() to iterate over all\n",
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"# previously indexed documents and update their embedding representation. \n",
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"# While this can be a time consuming operation (depending on corpus size), it only needs to be done once. \n",
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"# At query time, we only need to embed the query and compare it the existing doc embeddings which is very fast.\n",
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@ -396,7 +368,7 @@
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"# Load a local model or any of the QA models on\n",
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"# Hugging Face's model hub (https://huggingface.co/models)\n",
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"\n",
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"reader = FARMReader(model_name_or_path=\"deepset/roberta-base-squad2\", use_gpu=False)"
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"reader = FARMReader(model_name_or_path=\"deepset/roberta-base-squad2\", use_gpu=True)"
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]
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},
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{
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@ -459,15 +431,8 @@
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"source": [
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"# You can configure how many candidates the reader and retriever shall return\n",
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"# The higher top_k_retriever, the better (but also the slower) your answers. \n",
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"prediction = finder.get_answers(question=\"Who created the Dothraki vocabulary?\", top_k_retriever=10, top_k_reader=5)"
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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": 26,
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"metadata": {},
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"outputs": [],
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"source": [
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"prediction = finder.get_answers(question=\"Who created the Dothraki vocabulary?\", top_k_retriever=10, top_k_reader=5)\n",
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"\n",
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"#prediction = finder.get_answers(question=\"Who is the father of Arya Stark?\", top_k_retriever=10, top_k_reader=5)\n",
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"#prediction = finder.get_answers(question=\"Who is the sister of Sansa?\", top_k_retriever=10, top_k_reader=5)"
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]
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@ -516,13 +481,6 @@
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"source": [
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"print_answers(prediction, details=\"minimal\")"
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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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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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@ -48,6 +48,11 @@ document_store.write_documents(dicts[:16])
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### Retriever
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retriever = DensePassageRetriever(document_store=document_store, embedding_model="dpr-bert-base-nq",
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do_lower_case=True, gpu=True)
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# Important:
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# Now that after we have the DPR initialized, we need to call update_embeddings() to iterate over all
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# previously indexed documents and update their embedding representation.
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# While this can be a time consuming operation (depending on corpus size), it only needs to be done once.
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# At query time, we only need to embed the query and compare it the existing doc embeddings which is very fast.
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document_store.update_embeddings(retriever)
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### Reader
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