BERT for TensorFlow Jupyter Notebook

This notebook provides a worked example for utilising the BERT for TensorFlow model scripts.

For convenience, it is launched inside of NVIDIA’s Tensorflow Docker container hosted on NGC to have a reproducible environment that is easy to set up.

View in NGC

To take a look at this, or any other, notebook; follow these steps:

Navigate to the File Browser tab of the asset in NGC
Select the version you'd like to see
Under the actions menu (three dots) for the .ipynb file select \"View Jupyter\"
There you have it! You can read a notebook for documentation and copy code samples without ever leaving NGC.

Quick Start

note - This method will not allow you to run the notebook's contents.

Assuming you have Jupyter installed, to simply read through the notebook and code samples follow these steps:

Download the latest version of the notebook from this page
Navigate to the downloaded directory
Deploy the notebook with this command:

jupyter notebook BERT_Jupyter.ipynb\n

You'll now be able to view the notebook from your web browser.

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\n\n# BERT Question Answering in TensorFlow with Mixed Precision\n\nCopyright 2021 NVIDIA Corporation. All Rights Reserved.\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\n\n```python\n!nvidia-smi\n```\n\n Tue Mar 16 00:26:48 2021 \n +-----------------------------------------------------------------------------+\n | NVIDIA-SMI 460.32.03 Driver Version: 460.32.03 CUDA Version: 11.2 |\n |-------------------------------+----------------------+----------------------+\n | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n | | | MIG M. |\n |===============================+======================+======================|\n | 0 Tesla V100-DGXS... On | 00000000:08:00.0 Off | 0 |\n | N/A 37C P0 39W / 300W | 5MiB / 32508MiB | 0% Default |\n | | | N/A |\n +-------------------------------+----------------------+----------------------+\n \n +-----------------------------------------------------------------------------+\n | Processes: |\n | GPU GI CI PID Type Process name GPU Memory |\n | ID ID Usage |\n |=============================================================================|\n +-----------------------------------------------------------------------------+\n\n\n## 1. Overview\n\nBidirectional Embedding Representations from Transformers (BERT), is a method of pre-training language representations which obtains state-of-the-art results on a wide array of Natural Language Processing (NLP) tasks. \n\nThe original paper can be found here: https://arxiv.org/abs/1810.04805.\n\nNVIDIA's BERT is an optimized version of Google's official implementation, leveraging mixed precision arithmetic and tensor cores on V100 GPUS for faster training times while maintaining target accuracy.\n\n### Learning objectives\n\nThis notebook demonstrates:\n- Inference on Question Answering (QA) task with BERT Large model\n- The use/download of fine-tuned NVIDIA BERT models from [NGC](https://ngc.nvidia.com)\n- Use of Mixed Precision models for Inference\n\n## 2. Setup\n\n### Pre-Trained NVIDIA BERT TensorFlow Models on NGC\n\n

\n\nWe will be using the following configuration of BERT in this example:\n\n| **Model** | **Hidden layers** | **Hidden unit size** | **Attention heads** | **Feedforward filter size** | **Max sequence length** | **Parameters** |\n|:---------:|:----------:|:----:|:---:|:--------:|:---:|:----:|\n|BERTLARGE|24 encoder|1024| 16|4 x 1024|512|330M|\n\n**To do so, we will take advantage of the pre-trained models available on the [NGC Model Registry](https://ngc.nvidia.com/catalog/models).**\n\nAmong the many configurations available we will download one of these two:\n\n - **bert_tf_ckpt_large_qa_squad2_amp_384**\n\nwhich are trained on the [SQuaD 2.0 Dataset](https://rajpurkar.github.io/SQuAD-explorer/).\n\nWe can choose the mixed precision model (which takes much less time to train than the fp32 version) without losing accuracy, with the following flag: \n\n\n```python\nuse_mixed_precision_model = True\n```\n\n\n```python\n# bert_tf_ckpt_large_qa_squad2_amp_384\nDATA_DIR_FT = '/workspace/bert/data/finetuned_large_model_SQUAD2.0'\n!mkdir -p $DATA_DIR_FT\n \n!wget --content-disposition -O $DATA_DIR_FT/bert_tf_ckpt_large_qa_squad2_amp_384_19.03.1.zip \\\nhttps://api.ngc.nvidia.com/v2/models/nvidia/bert_tf_ckpt_large_qa_squad2_amp_384/versions/19.03.1/zip \\\n&& unzip -n -d $DATA_DIR_FT/ $DATA_DIR_FT/bert_tf_ckpt_large_qa_squad2_amp_384_19.03.1.zip \\\n&& rm -rf $DATA_DIR_FT/bert_tf_ckpt_large_qa_squad2_amp_384_19.03.1.zip\n```\n\n --2021-03-16 00:19:46-- https://api.ngc.nvidia.com/v2/models/nvidia/bert_tf_ckpt_large_qa_squad2_amp_384/versions/19.03.1/zip\n Resolving api.ngc.nvidia.com (api.ngc.nvidia.com)... 52.9.83.149, 52.9.91.161\n Connecting to api.ngc.nvidia.com (api.ngc.nvidia.com)|52.9.83.149|:443... connected.\n HTTP request sent, awaiting response... 302 \n Location: https://s3.us-west-2.amazonaws.com/prod-model-registry-ngc-bucket/org/nvidia/models/bert_tf_ckpt_large_qa_squad2_amp_384/versions/19.03.1/files.zip?response-content-disposition=attachment%3B%20filename%3D%22files.zip%22&response-content-type=application%2Fzip&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEM%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLXdlc3QtMSJIMEYCIQCrGyjQ8LQkZNyTMr6nchH4edRFx%2F25OXI%2FNE2G%2BBF8SQIhAKduF2AEyjRXyw4e5%2BsfnhA81SY%2BN4DOACIaawtQOboyKr0DCPj%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEQAxoMNzg5MzYzMTM1MDI3IgxYmy9jrsgCYn3zEQkqkQOLHUCtPKMUjTXYRa2arifO0nTVXmCxHLV%2FCbZmmZOF5J1HGHQgnyZu1y3EPoh8cf5Kn5VfzORhYQlq3KvbKN5%2FXJq0170myZrAOjqrSRUE1DstQl8P0ym3V1S9EWnjYurXuNMa%2FQaqzzZLsVE%2FhuB7N2x0dlI4EZmZ%2B6fnqUlltvMqmc6p1Uh0FQnz6dQ9L1LTTTg2nPx6OAbcbLHjalsPB%2FFBNOSoVRtphUkh4d6iM9uxByZYiYAVr23aJExAU0GAJtjexOZ8bPLzgWcnDRTBzMk0LwKlOtWePK3GFFJQUMD4GOLO3wVXsXv6LHBRjOS7icqrDm7zdHS0OeUD5usHQPxwwbg0zId%2BgJTKmz6W2S9lSfHGwwv7ukxKraY%2BI3iV00qmq757DeYu0M%2FAEqOa9CyTTCq1TL%2FOqhND1Nm%2BrmqKCp%2FuKqTzz%2BzMEZNUvgA5CTAT7D4M8cg2AL0zlREf8XhnX%2FOrfEwG1mrFN9WyKJavYb13m2pX5%2FellyjGv7PeZ5Q1A2czRjqlt8FdXybAkzCywb%2BCBjrqAfbuvTtMpWQy%2B5VFa2TcyJLuc4bJ2QaGinWaSAAf%2BkOkuVUjcslbodHTDSsHEjI0ezBSvNzcLJe9HUum%2Bqx9YtKrGRqnVFzwhD15D4pD0UN737M8UJk0Zoto%2BQz0ytkhi6ST1SKhu6cJUj0iXyAZd9lpNv7MDhXgUL3dL74ngRqxbyfquO%2BmBGHbfV1%2BP%2B9V3pJqATmXNrNQ6pezbu%2FjdsMXzIO1J%2F8ThEC2JBnq%2FCPCXDNRGJ0l4zqgA35YriFsh%2BmUDvOyB6LPSfJ8DuVpiLU%2BbnyUc1s0WduA6VhlhPTaBlqUznrbMp0fRw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20210316T001946Z&X-Amz-SignedHeaders=host&X-Amz-Expires=3600&X-Amz-Credential=ASIA3PSNVSIZYFQQ2SH4%2F20210316%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Signature=f987fe14d3094638bb6e6abdbe47c066e0d75ba623cf1b77cf02275f52bd8e12 [following]\n --2021-03-16 00:19:46-- https://s3.us-west-2.amazonaws.com/prod-model-registry-ngc-bucket/org/nvidia/models/bert_tf_ckpt_large_qa_squad2_amp_384/versions/19.03.1/files.zip?response-content-disposition=attachment%3B%20filename%3D%22files.zip%22&response-content-type=application%2Fzip&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEM%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLXdlc3QtMSJIMEYCIQCrGyjQ8LQkZNyTMr6nchH4edRFx%2F25OXI%2FNE2G%2BBF8SQIhAKduF2AEyjRXyw4e5%2BsfnhA81SY%2BN4DOACIaawtQOboyKr0DCPj%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEQAxoMNzg5MzYzMTM1MDI3IgxYmy9jrsgCYn3zEQkqkQOLHUCtPKMUjTXYRa2arifO0nTVXmCxHLV%2FCbZmmZOF5J1HGHQgnyZu1y3EPoh8cf5Kn5VfzORhYQlq3KvbKN5%2FXJq0170myZrAOjqrSRUE1DstQl8P0ym3V1S9EWnjYurXuNMa%2FQaqzzZLsVE%2FhuB7N2x0dlI4EZmZ%2B6fnqUlltvMqmc6p1Uh0FQnz6dQ9L1LTTTg2nPx6OAbcbLHjalsPB%2FFBNOSoVRtphUkh4d6iM9uxByZYiYAVr23aJExAU0GAJtjexOZ8bPLzgWcnDRTBzMk0LwKlOtWePK3GFFJQUMD4GOLO3wVXsXv6LHBRjOS7icqrDm7zdHS0OeUD5usHQPxwwbg0zId%2BgJTKmz6W2S9lSfHGwwv7ukxKraY%2BI3iV00qmq757DeYu0M%2FAEqOa9CyTTCq1TL%2FOqhND1Nm%2BrmqKCp%2FuKqTzz%2BzMEZNUvgA5CTAT7D4M8cg2AL0zlREf8XhnX%2FOrfEwG1mrFN9WyKJavYb13m2pX5%2FellyjGv7PeZ5Q1A2czRjqlt8FdXybAkzCywb%2BCBjrqAfbuvTtMpWQy%2B5VFa2TcyJLuc4bJ2QaGinWaSAAf%2BkOkuVUjcslbodHTDSsHEjI0ezBSvNzcLJe9HUum%2Bqx9YtKrGRqnVFzwhD15D4pD0UN737M8UJk0Zoto%2BQz0ytkhi6ST1SKhu6cJUj0iXyAZd9lpNv7MDhXgUL3dL74ngRqxbyfquO%2BmBGHbfV1%2BP%2B9V3pJqATmXNrNQ6pezbu%2FjdsMXzIO1J%2F8ThEC2JBnq%2FCPCXDNRGJ0l4zqgA35YriFsh%2BmUDvOyB6LPSfJ8DuVpiLU%2BbnyUc1s0WduA6VhlhPTaBlqUznrbMp0fRw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20210316T001946Z&X-Amz-SignedHeaders=host&X-Amz-Expires=3600&X-Amz-Credential=ASIA3PSNVSIZYFQQ2SH4%2F20210316%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Signature=f987fe14d3094638bb6e6abdbe47c066e0d75ba623cf1b77cf02275f52bd8e12\n Resolving s3.us-west-2.amazonaws.com (s3.us-west-2.amazonaws.com)... 52.218.250.176\n Connecting to s3.us-west-2.amazonaws.com (s3.us-west-2.amazonaws.com)|52.218.250.176|:443... connected.\n HTTP request sent, awaiting response... 200 OK\n Length: 3672271344 (3.4G) [application/zip]\n Saving to: ‘/workspace/bert/data/finetuned_large_model_SQUAD2.0/bert_tf_ckpt_large_qa_squad2_amp_384_19.03.1.zip’\n \n /workspace/bert/dat 100%[===================>] 3.42G 36.8MB/s in 1m 47s \n \n 2021-03-16 00:21:34 (32.6 MB/s) - ‘/workspace/bert/data/finetuned_large_model_SQUAD2.0/bert_tf_ckpt_large_qa_squad2_amp_384_19.03.1.zip’ saved [3672271344/3672271344]\n \n Archive: /workspace/bert/data/finetuned_large_model_SQUAD2.0/bert_tf_ckpt_large_qa_squad2_amp_384_19.03.1.zip\n inflating: /workspace/bert/data/finetuned_large_model_SQUAD2.0/bert_config.json \n inflating: /workspace/bert/data/finetuned_large_model_SQUAD2.0/model.ckpt.data-00000-of-00001 \n inflating: /workspace/bert/data/finetuned_large_model_SQUAD2.0/model.ckpt.index \n inflating: /workspace/bert/data/finetuned_large_model_SQUAD2.0/model.ckpt.meta \n inflating: /workspace/bert/data/finetuned_large_model_SQUAD2.0/tf_bert_squad_1n_fp16_gbs32.190523090758.log \n inflating: /workspace/bert/data/finetuned_large_model_SQUAD2.0/vocab.txt \n\n\n### NGC Model Scripts\n\nWhile we're at it, we'll also pull down some BERT helper scripts from the [NGC Model Scripts Registry](https://ngc.nvidia.com/catalog/model-scripts/nvidia:bert_for_tensorflow)\n\n\n```python\n# Download BERT helper scripts\n!wget -nc --show-progress -O bert_scripts.zip \\\n https://api.ngc.nvidia.com/v2/recipes/nvidia/bert_for_tensorflow/versions/1/zip\n!mkdir -p /workspace/bert\n!unzip -n -d /workspace/bert bert_scripts.zip\n```\n\n --2021-03-16 00:22:16-- https://api.ngc.nvidia.com/v2/recipes/nvidia/bert_for_tensorflow/versions/1/zip\n Resolving api.ngc.nvidia.com (api.ngc.nvidia.com)... 52.9.83.149, 52.9.91.161\n Connecting to api.ngc.nvidia.com (api.ngc.nvidia.com)|52.9.83.149|:443... connected.\n HTTP request sent, awaiting response... 302 \n Location: https://s3.us-west-2.amazonaws.com/prod-model-registry-ngc-bucket/org/nvidia/recipes/bert_for_tensorflow/versions/1/files.zip?response-content-disposition=attachment%3B%20filename%3D%22files.zip%22&response-content-type=application%2Fzip&X-Amz-Security-Token=IQoJb3JpZ2luX2VjEM%2F%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEaCXVzLXdlc3QtMSJIMEYCIQCrGyjQ8LQkZNyTMr6nchH4edRFx%2F25OXI%2FNE2G%2BBF8SQIhAKduF2AEyjRXyw4e5%2BsfnhA81SY%2BN4DOACIaawtQOboyKr0DCPj%2F%2F%2F%2F%2F%2F%2F%2F%2F%2FwEQAxoMNzg5MzYzMTM1MDI3IgxYmy9jrsgCYn3zEQkqkQOLHUCtPKMUjTXYRa2arifO0nTVXmCxHLV%2FCbZmmZOF5J1HGHQgnyZu1y3EPoh8cf5Kn5VfzORhYQlq3KvbKN5%2FXJq0170myZrAOjqrSRUE1DstQl8P0ym3V1S9EWnjYurXuNMa%2FQaqzzZLsVE%2FhuB7N2x0dlI4EZmZ%2B6fnqUlltvMqmc6p1Uh0FQnz6dQ9L1LTTTg2nPx6OAbcbLHjalsPB%2FFBNOSoVRtphUkh4d6iM9uxByZYiYAVr23aJExAU0GAJtjexOZ8bPLzgWcnDRTBzMk0LwKlOtWePK3GFFJQUMD4GOLO3wVXsXv6LHBRjOS7icqrDm7zdHS0OeUD5usHQPxwwbg0zId%2BgJTKmz6W2S9lSfHGwwv7ukxKraY%2BI3iV00qmq757DeYu0M%2FAEqOa9CyTTCq1TL%2FOqhND1Nm%2BrmqKCp%2FuKqTzz%2BzMEZNUvgA5CTAT7D4M8cg2AL0zlREf8XhnX%2FOrfEwG1mrFN9WyKJavYb13m2pX5%2FellyjGv7PeZ5Q1A2czRjqlt8FdXybAkzCywb%2BCBjrqAfbuvTtMpWQy%2B5VFa2TcyJLuc4bJ2QaGinWaSAAf%2BkOkuVUjcslbodHTDSsHEjI0ezBSvNzcLJe9HUum%2Bqx9YtKrGRqnVFzwhD15D4pD0UN737M8UJk0Zoto%2BQz0ytkhi6ST1SKhu6cJUj0iXyAZd9lpNv7MDhXgUL3dL74ngRqxbyfquO%2BmBGHbfV1%2BP%2B9V3pJqATmXNrNQ6pezbu%2FjdsMXzIO1J%2F8ThEC2JBnq%2FCPCXDNRGJ0l4zqgA35YriFsh%2BmUDvOyB6LPSfJ8DuVpiLU%2BbnyUc1s0WduA6VhlhPTaBlqUznrbMp0fRw%3D%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20210316T002217Z&X-Amz-SignedHeaders=host&X-Amz-Expires=3599&X-Amz-Credential=ASIA3PSNVSIZYFQQ2SH4%2F20210316%2Fus-west-2%2Fs3%2Faws4_request&X-Amz-Signature=ed2fba6c87fcfe93220e5ce6bc1207ffb9568596c5a73aefd9d83034434c4cf2 [following]\n --2021-03-16 00:22:17-- 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100%[===================>] 226.08K 364KB/s in 0.6s \n \n 2021-03-16 00:22:21 (364 KB/s) - ‘/workspace/bert/config.qa/vocab.txt’ saved [231508/231508]\n \n\n\n\n```python\n%%writefile /workspace/bert/config.qa/bert_config.json\n{\n \"attention_probs_dropout_prob\": 0.1,\n \"hidden_act\": \"gelu\",\n \"hidden_dropout_prob\": 0.1,\n \"hidden_size\": 1024,\n \"initializer_range\": 0.02,\n \"intermediate_size\": 4096,\n \"max_position_embeddings\": 512,\n \"num_attention_heads\": 16,\n \"num_hidden_layers\": 24,\n \"type_vocab_size\": 2,\n \"vocab_size\": 30522\n}\n```\n\n Writing /workspace/bert/config.qa/bert_config.json\n\n\n### Helper Functions\n\n\n```python\n# Create dynamic JSON files based on user inputs\ndef write_input_file(context, qinputs, predict_file):\n # Remove quotes and new lines from text for valid JSON\n context = context.replace('\"', '').replace('\\n', '')\n # Create JSON dict to write\n json_dict = {\n \"data\": [\n {\n \"title\": \"BERT QA\",\n \"paragraphs\": [\n {\n \"context\": context,\n \"qas\": qinputs\n }\n ]\n }\n ]\n }\n # Write JSON to input file\n with open(predict_file, 'w') as json_file:\n import json\n json.dump(json_dict, json_file, indent=2)\n \n# Display Inference Results as HTML Table\ndef display_results(predict_file, output_prediction_file):\n import json\n from IPython.display import display, HTML\n\n # Here we show only the prediction results, nbest prediction is also available in the output directory\n results = \"\"\n with open(predict_file, 'r') as query_file:\n queries = json.load(query_file)\n input_data = queries[\"data\"]\n with open(output_prediction_file, 'r') as result_file:\n data = json.load(result_file)\n for entry in input_data:\n for paragraph in entry[\"paragraphs\"]:\n for qa in paragraph[\"qas\"]:\n results += \"{}{}{}\".format(qa[\"id\"], qa[\"question\"], data[qa[\"id\"]])\n\n display(HTML(\"{}

Id	Question	Answer

\".format(results)))\n```\n\n## 3. BERT Inference: Question Answering\n\nWe can run inference on a fine-tuned BERT model for tasks like Question Answering.\n\nHere we use a BERT model fine-tuned on a [SQuaD 2.0 Dataset](https://rajpurkar.github.io/SQuAD-explorer/) which contains 100,000+ question-answer pairs on 500+ articles combined with over 50,000 new, unanswerable questions.\n\n### Paragraph and Queries\n\nIn this example we will ask our BERT model questions related to the following paragraph:\n\n**The Apollo Program**\n_\"The Apollo program, also known as Project Apollo, was the third United States human spaceflight program carried out by the National Aeronautics and Space Administration (NASA), which accomplished landing the first humans on the Moon from 1969 to 1972. First conceived during Dwight D. Eisenhower's administration as a three-man spacecraft to follow the one-man Project Mercury which put the first Americans in space, Apollo was later dedicated to President John F. Kennedy's national goal of landing a man on the Moon and returning him safely to the Earth by the end of the 1960s, which he proposed in a May 25, 1961, address to Congress. Project Mercury was followed by the two-man Project Gemini. The first manned flight of Apollo was in 1968. Apollo ran from 1961 to 1972, and was supported by the two-man Gemini program which ran concurrently with it from 1962 to 1966. Gemini missions developed some of the space travel techniques that were necessary for the success of the Apollo missions. Apollo used Saturn family rockets as launch vehicles. Apollo/Saturn vehicles were also used for an Apollo Applications Program, which consisted of Skylab, a space station that supported three manned missions in 1973-74, and the Apollo-Soyuz Test Project, a joint Earth orbit mission with the Soviet Union in 1975.\"_\n\n \n---\n\nThe paragraph and the questions can be easily customized by changing the code below:\n\n---\n\n\n```python\n# Create BERT input file with (1) context and (2) questions to be answered based on that context\npredict_file = '/workspace/bert/config.qa/input.json'\n```\n\n\n```python\n%%writefile $predict_file\n{\"data\": \n [\n {\"title\": \"Project Apollo\",\n \"paragraphs\": [\n {\"context\":\"The Apollo program, also known as Project Apollo, was the third United States human spaceflight program carried out by the National Aeronautics and Space Administration (NASA), which accomplished landing the first humans on the Moon from 1969 to 1972. First conceived during Dwight D. Eisenhower's administration as a three-man spacecraft to follow the one-man Project Mercury which put the first Americans in space, Apollo was later dedicated to President John F. Kennedy's national goal of landing a man on the Moon and returning him safely to the Earth by the end of the 1960s, which he proposed in a May 25, 1961, address to Congress. Project Mercury was followed by the two-man Project Gemini. The first manned flight of Apollo was in 1968. Apollo ran from 1961 to 1972, and was supported by the two man Gemini program which ran concurrently with it from 1962 to 1966. Gemini missions developed some of the space travel techniques that were necessary for the success of the Apollo missions. Apollo used Saturn family rockets as launch vehicles. Apollo/Saturn vehicles were also used for an Apollo Applications Program, which consisted of Skylab, a space station that supported three manned missions in 1973-74, and the Apollo-Soyuz Test Project, a joint Earth orbit mission with the Soviet Union in 1975.\", \n \"qas\": [\n { \"question\": \"What project put the first Americans into space?\", \n \"id\": \"Q1\"\n },\n { \"question\": \"What program was created to carry out these projects and missions?\",\n \"id\": \"Q2\"\n },\n { \"question\": \"What year did the first manned Apollo flight occur?\",\n \"id\": \"Q3\"\n }, \n { \"question\": \"What President is credited with the notion of putting Americans on the moon?\",\n \"id\": \"Q4\"\n },\n { \"question\": \"Who did the U.S. collaborate with on an Earth orbit mission in 1975?\",\n \"id\": \"Q5\"\n },\n { \"question\": \"How long did Project Apollo run?\",\n \"id\": \"Q6\"\n }, \n { \"question\": \"What program helped develop space travel techniques that Project Apollo used?\",\n \"id\": \"Q7\"\n }, \n {\"question\": \"What space station supported three manned missions in 1973-1974?\",\n \"id\": \"Q8\"\n }\n]}]}]}\n```\n\n Writing /workspace/bert/config.qa/input.json\n\n\n## 4. Running Question/Answer Inference\n\nTo run QA inference we will launch the script run_squad.py with the following parameters:\n\n\n```python\nimport os\n\n# This specifies the model architecture.\nbert_config_file = '/workspace/bert/config.qa/bert_config.json'\n\n# The vocabulary file that the BERT model was trained on.\nvocab_file = '/workspace/bert/config.qa/vocab.txt'\n\n# Initiate checkpoint to the fine-tuned BERT Large model\ninit_checkpoint = os.path.join('/workspace/bert/data/finetuned_large_model_SQUAD2.0/model.ckpt')\n\n# Create the output directory where all the results are saved.\noutput_dir = '/workspace/bert/results'\noutput_prediction_file = os.path.join(output_dir,'predictions.json')\n \n# Whether to lower case the input - True for uncased models / False for cased models.\ndo_lower_case = True\n \n# Total batch size for predictions\npredict_batch_size = 8\n\n# Whether to run eval on the dev set.\ndo_predict = True\n\n# When splitting up a long document into chunks, how much stride to take between chunks.\ndoc_stride = 128\n\n# The maximum total input sequence length after WordPiece tokenization.\n# Sequences longer than this will be truncated, and sequences shorter than this will be padded.\nmax_seq_length = 384\n```\n\n### 4a. Run Inference\n\n\n```python\n# Ask BERT questions\n!python /workspace/bert/run_squad.py \\\n --bert_config_file=$bert_config_file \\\n --vocab_file=$vocab_file \\\n --init_checkpoint=$init_checkpoint \\\n --output_dir=$output_dir \\\n --do_predict=$do_predict \\\n --predict_file=$predict_file \\\n --predict_batch_size=$predict_batch_size \\\n --doc_stride=$doc_stride \\\n --max_seq_length=$max_seq_length\n```\n\n 2021-03-16 00:22:33.385548: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n WARNING:tensorflow:Deprecation warnings have been disabled. Set TF_ENABLE_DEPRECATION_WARNINGS=1 to re-enable them.\n WARNING:tensorflow:From /workspace/bert/optimization.py:110: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n \n WARNING:tensorflow:From /workspace/bert/run_squad.py:162: The name tf.train.SessionRunHook is deprecated. Please use tf.estimator.SessionRunHook instead.\n \n WARNING:tensorflow:From /workspace/bert/run_squad.py:1409: The name tf.app.run is deprecated. Please use tf.compat.v1.app.run instead.\n \n WARNING:tensorflow:From /workspace/bert/run_squad.py:1174: The name tf.logging.set_verbosity is deprecated. Please use tf.compat.v1.logging.set_verbosity instead.\n \n W0316 00:22:34.840889 140590680020800 module_wrapper.py:137] From /workspace/bert/run_squad.py:1174: The name tf.logging.set_verbosity is deprecated. Please use tf.compat.v1.logging.set_verbosity instead.\n \n WARNING:tensorflow:From /workspace/bert/run_squad.py:1174: The name tf.logging.INFO is deprecated. Please use tf.compat.v1.logging.INFO instead.\n \n W0316 00:22:34.841060 140590680020800 module_wrapper.py:137] From /workspace/bert/run_squad.py:1174: The name tf.logging.INFO is deprecated. Please use tf.compat.v1.logging.INFO instead.\n \n WARNING:tensorflow:From /workspace/bert/modeling.py:94: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.\n \n W0316 00:22:34.841254 140590680020800 module_wrapper.py:137] From /workspace/bert/modeling.py:94: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.\n \n WARNING:tensorflow:From /workspace/bert/run_squad.py:1183: The name tf.gfile.MakeDirs is deprecated. Please use tf.io.gfile.makedirs instead.\n \n W0316 00:22:34.842063 140590680020800 module_wrapper.py:137] From /workspace/bert/run_squad.py:1183: The name tf.gfile.MakeDirs is deprecated. Please use tf.io.gfile.makedirs instead.\n \n WARNING:tensorflow:From /workspace/bert/run_squad.py:1199: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n \n W0316 00:22:34.972105 140590680020800 module_wrapper.py:137] From /workspace/bert/run_squad.py:1199: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n \n WARNING:tensorflow:\n The TensorFlow contrib module will not be included in TensorFlow 2.0.\n For more information, please see:\n * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n * https://github.com/tensorflow/addons\n * https://github.com/tensorflow/io (for I/O related ops)\n If you depend on functionality not listed there, please file an issue.\n \n W0316 00:22:34.972314 140590680020800 lazy_loader.py:50] \n The TensorFlow contrib module will not be included in TensorFlow 2.0.\n For more information, please see:\n * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n * https://github.com/tensorflow/addons\n * https://github.com/tensorflow/io (for I/O related ops)\n If you depend on functionality not listed there, please file an issue.\n \n WARNING:tensorflow:Estimator's model_fn (.model_fn at 0x7fdd090fc3a0>) includes params argument, but params are not passed to Estimator.\n W0316 00:22:35.720636 140590680020800 estimator.py:1992] Estimator's model_fn (.model_fn at 0x7fdd090fc3a0>) includes params argument, but params are not passed to Estimator.\n INFO:tensorflow:Using config: {'_model_dir': '/workspace/bert/results', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 1000, '_save_checkpoints_secs': None, '_session_config': , '_keep_checkpoint_max': 1, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': None, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': , '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1, '_tpu_config': TPUConfig(iterations_per_loop=1000, num_shards=8, num_cores_per_replica=None, per_host_input_for_training=3, tpu_job_name=None, initial_infeed_sleep_secs=None, input_partition_dims=None, eval_training_input_configuration=2, experimental_host_call_every_n_steps=1), '_cluster': None}\n I0316 00:22:35.721187 140590680020800 estimator.py:212] Using config: {'_model_dir': '/workspace/bert/results', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 1000, '_save_checkpoints_secs': None, '_session_config': , '_keep_checkpoint_max': 1, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': None, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': , '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1, '_tpu_config': TPUConfig(iterations_per_loop=1000, num_shards=8, num_cores_per_replica=None, per_host_input_for_training=3, tpu_job_name=None, initial_infeed_sleep_secs=None, input_partition_dims=None, eval_training_input_configuration=2, experimental_host_call_every_n_steps=1), '_cluster': None}\n INFO:tensorflow:_TPUContext: eval_on_tpu True\n I0316 00:22:35.721480 140590680020800 tpu_context.py:220] _TPUContext: eval_on_tpu True\n WARNING:tensorflow:eval_on_tpu ignored because use_tpu is False.\n W0316 00:22:35.721641 140590680020800 tpu_context.py:222] eval_on_tpu ignored because use_tpu is False.\n WARNING:tensorflow:From /workspace/bert/run_squad.py:266: The name tf.gfile.Open is deprecated. Please use tf.io.gfile.GFile instead.\n \n W0316 00:22:35.721812 140590680020800 module_wrapper.py:137] From /workspace/bert/run_squad.py:266: The name tf.gfile.Open is deprecated. Please use tf.io.gfile.GFile instead.\n \n WARNING:tensorflow:From /workspace/bert/run_squad.py:1112: The name tf.python_io.TFRecordWriter is deprecated. Please use tf.io.TFRecordWriter instead.\n \n W0316 00:22:35.723536 140590680020800 module_wrapper.py:137] From /workspace/bert/run_squad.py:1112: The name tf.python_io.TFRecordWriter is deprecated. Please use tf.io.TFRecordWriter instead.\n \n WARNING:tensorflow:From /workspace/bert/run_squad.py:1354: The name tf.logging.info is deprecated. Please use tf.compat.v1.logging.info instead.\n \n W0316 00:22:35.794478 140590680020800 module_wrapper.py:137] From /workspace/bert/run_squad.py:1354: The name tf.logging.info is deprecated. Please use tf.compat.v1.logging.info instead.\n \n INFO:tensorflow:***** Running predictions *****\n I0316 00:22:35.794595 140590680020800 run_squad.py:1354] ***** Running predictions *****\n INFO:tensorflow: Num orig examples = 8\n I0316 00:22:35.794691 140590680020800 run_squad.py:1355] Num orig examples = 8\n INFO:tensorflow: Num split examples = 8\n I0316 00:22:35.794868 140590680020800 run_squad.py:1356] Num split examples = 8\n INFO:tensorflow: Batch size = 8\n I0316 00:22:35.794964 140590680020800 run_squad.py:1357] Batch size = 8\n WARNING:tensorflow:From /workspace/bert/run_squad.py:731: The name tf.FixedLenFeature is deprecated. Please use tf.io.FixedLenFeature instead.\n \n W0316 00:22:35.795106 140590680020800 module_wrapper.py:137] From /workspace/bert/run_squad.py:731: The name tf.FixedLenFeature is deprecated. Please use tf.io.FixedLenFeature instead.\n \n INFO:tensorflow:Could not find trained model in model_dir: /workspace/bert/results, running initialization to predict.\n I0316 00:22:35.795398 140590680020800 estimator.py:614] Could not find trained model in model_dir: /workspace/bert/results, running initialization to predict.\n WARNING:tensorflow:Entity .input_fn.. at 0x7fdcff6d63a0> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: 'arguments' object has no attribute 'defaults'\n W0316 00:22:35.851542 140590680020800 ag_logging.py:146] Entity .input_fn.. at 0x7fdcff6d63a0> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: 'arguments' object has no attribute 'defaults'\n WARNING:tensorflow:From /workspace/bert/run_squad.py:743: The name tf.parse_single_example is deprecated. Please use tf.io.parse_single_example instead.\n \n W0316 00:22:35.851733 140590680020800 module_wrapper.py:137] From /workspace/bert/run_squad.py:743: The name tf.parse_single_example is deprecated. Please use tf.io.parse_single_example instead.\n \n INFO:tensorflow:Calling model_fn.\n I0316 00:22:35.867721 140590680020800 estimator.py:1148] Calling model_fn.\n INFO:tensorflow:Running infer on CPU\n I0316 00:22:35.867883 140590680020800 tpu_estimator.py:3124] Running infer on CPU\n WARNING:tensorflow:From /workspace/bert/modeling.py:175: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n \n W0316 00:22:35.870865 140590680020800 module_wrapper.py:137] From /workspace/bert/modeling.py:175: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n \n WARNING:tensorflow:From /workspace/bert/modeling.py:413: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.\n \n W0316 00:22:35.872210 140590680020800 module_wrapper.py:137] From /workspace/bert/modeling.py:413: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.\n \n WARNING:tensorflow:From /workspace/bert/modeling.py:494: The name tf.assert_less_equal is deprecated. Please use tf.compat.v1.assert_less_equal instead.\n \n W0316 00:22:35.897977 140590680020800 module_wrapper.py:137] From /workspace/bert/modeling.py:494: The name tf.assert_less_equal is deprecated. Please use tf.compat.v1.assert_less_equal instead.\n \n WARNING:tensorflow:From /workspace/bert/run_squad.py:655: The name tf.trainable_variables is deprecated. Please use tf.compat.v1.trainable_variables instead.\n \n W0316 00:22:39.388511 140590680020800 module_wrapper.py:137] From /workspace/bert/run_squad.py:655: The name tf.trainable_variables is deprecated. Please use tf.compat.v1.trainable_variables instead.\n \n WARNING:tensorflow:From /workspace/bert/run_squad.py:670: The name tf.train.init_from_checkpoint is deprecated. Please use tf.compat.v1.train.init_from_checkpoint instead.\n \n W0316 00:22:39.394683 140590680020800 module_wrapper.py:137] From /workspace/bert/run_squad.py:670: The name tf.train.init_from_checkpoint is deprecated. Please use tf.compat.v1.train.init_from_checkpoint instead.\n \n INFO:tensorflow:Done calling model_fn.\n I0316 00:22:40.470538 140590680020800 estimator.py:1150] Done calling model_fn.\n INFO:tensorflow:Graph was finalized.\n I0316 00:22:41.255115 140590680020800 monitored_session.py:240] Graph was finalized.\n 2021-03-16 00:22:41.283573: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2198615000 Hz\n 2021-03-16 00:22:41.286205: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x54540d0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n 2021-03-16 00:22:41.286256: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version\n 2021-03-16 00:22:41.290408: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1\n 2021-03-16 00:22:41.533042: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x54339f0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n 2021-03-16 00:22:41.533093: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Tesla V100-DGXS-32GB, Compute Capability 7.0\n 2021-03-16 00:22:41.537140: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1665] Found device 0 with properties: \n name: Tesla V100-DGXS-32GB major: 7 minor: 0 memoryClockRate(GHz): 1.53\n pciBusID: 0000:08:00.0\n 2021-03-16 00:22:41.537196: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n 2021-03-16 00:22:41.554704: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n 2021-03-16 00:22:41.562981: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n 2021-03-16 00:22:41.565560: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n 2021-03-16 00:22:41.581478: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.11\n 2021-03-16 00:22:41.585296: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11\n 2021-03-16 00:22:41.585491: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n 2021-03-16 00:22:41.590070: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1793] Adding visible gpu devices: 0\n 2021-03-16 00:22:41.590119: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n 2021-03-16 00:22:42.007522: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1206] Device interconnect StreamExecutor with strength 1 edge matrix:\n 2021-03-16 00:22:42.007563: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1212] 0 \n 2021-03-16 00:22:42.007576: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1225] 0: N \n 2021-03-16 00:22:42.011538: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1351] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 31026 MB memory) -> physical GPU (device: 0, name: Tesla V100-DGXS-32GB, pci bus id: 0000:08:00.0, compute capability: 7.0)\n INFO:tensorflow:Running local_init_op.\n I0316 00:22:44.737054 140590680020800 session_manager.py:500] Running local_init_op.\n INFO:tensorflow:Done running local_init_op.\n I0316 00:22:44.841894 140590680020800 session_manager.py:502] Done running local_init_op.\n 2021-03-16 00:22:46.448377: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n INFO:tensorflow:Processing example: 0\n I0316 00:22:47.535837 140590680020800 run_squad.py:1373] Processing example: 0\n INFO:tensorflow:prediction_loop marked as finished\n I0316 00:22:47.610052 140590680020800 error_handling.py:101] prediction_loop marked as finished\n INFO:tensorflow:prediction_loop marked as finished\n I0316 00:22:47.610238 140590680020800 error_handling.py:101] prediction_loop marked as finished\n INFO:tensorflow:-----------------------------\n I0316 00:22:47.610357 140590680020800 run_squad.py:1388] -----------------------------\n INFO:tensorflow:0 Total Inference Time = 11.82 Inference Time W/O start up overhead = 2.01 Sentences processed = 8\n I0316 00:22:47.610444 140590680020800 run_squad.py:1389] 0 Total Inference Time = 11.82 Inference Time W/O start up overhead = 2.01 Sentences processed = 8\n INFO:tensorflow:0 Inference Performance = 3.9767 sentences/sec\n I0316 00:22:47.610535 140590680020800 run_squad.py:1392] 0 Inference Performance = 3.9767 sentences/sec\n INFO:tensorflow:-----------------------------\n I0316 00:22:47.610617 140590680020800 run_squad.py:1393] -----------------------------\n INFO:tensorflow:Writing predictions to: /workspace/bert/results/predictions.json\n I0316 00:22:47.610741 140590680020800 run_squad.py:792] Writing predictions to: /workspace/bert/results/predictions.json\n INFO:tensorflow:Writing nbest to: /workspace/bert/results/nbest_predictions.json\n I0316 00:22:47.610820 140590680020800 run_squad.py:793] Writing nbest to: /workspace/bert/results/nbest_predictions.json\n\n\n### 4b. Display Results:\n\n\n```python\ndisplay_results(predict_file, output_prediction_file)\n```\n\n\n

Id	Question	Answer
Q1	What project put the first Americans into space?	Project Mercury
Q2	What program was created to carry out these projects and missions?	The Apollo program
Q3	What year did the first manned Apollo flight occur?	1968
Q4	What President is credited with the notion of putting Americans on the moon?	John F. Kennedy
Q5	Who did the U.S. collaborate with on an Earth orbit mission in 1975?	Soviet Union
Q6	How long did Project Apollo run?	1961 to 1972
Q7	What program helped develop space travel techniques that Project Apollo used?	Gemini missions
Q8	What space station supported three manned missions in 1973-1974?	Skylab

\n\n\n

Click to reveal expected answers to the questions above

\n \n| Id | Question | Answer |\n|----|----------|--------|\n| Q1 | What project put the first Americans into space? | Project Mercury |\n| Q2 | What program was created to carry out these projects and missions? | The Apollo program |\n| Q3 | What year did the first manned Apollo flight occur? | 1968 |\n| Q4 | What President is credited with the notion of putting Americans on the moon?\t | John F. Kennedy |\n| Q5 | Who did the U.S. collaborate with on an Earth orbit mission in 1975? | Soviet Union |\n| Q6 | How long did Project Apollo run? | 1961 to 1972 |\n| Q7 | What program helped develop space travel techniques that Project Apollo used? | Gemini missions |\n| Q8 | What space station supported three manned missions in 1973-1974? | Skylab |\n\n

\n\n## 5. Custom Inputs\n\nNow that you are familiar with running QA Inference on BERT, you may want to try\nyour own paragraphs and queries.\n\n\n1. Copy and paste your context from Wikipedia, news articles, etc. when prompted below\n2. Enter questions based on the context when prompted below.\n3. Run the inference script\n4. Display the inference results\n\n\n```python\npredict_file = '/workspace/bert/config.qa/custom_input.json'\nnum_questions = 3 # You can configure this number\n```\n\n\n```python\n# Create your own context to ask questions about.\ncontext = input(\"Paste your context here: \")\n```\n\n Paste your context here: Alan Mathison Turing OBE FRS (/ˈtjʊərɪŋ/; 23 June 1912 – 7 June 1954) was an English[6] mathematician, computer scientist, logician, cryptanalyst, philosopher and theoretical biologist.[7] Turing was highly influential in the development of theoretical computer science, providing a formalisation of the concepts of algorithm and computation with the Turing machine, which can be considered a model of a general-purpose computer.[8][9][10] Turing is widely considered to be the father of theoretical computer science and artificial intelligence.[11] Despite these accomplishments, he was not fully recognised in his home country during his lifetime, due to his homosexuality, and because much of his work was covered by the Official Secrets Act. During the Second World War, Turing worked for the Government Code and Cypher School (GC&CS) at Bletchley Park, Britain's codebreaking centre that produced Ultra intelligence. For a time he led Hut 8, the section that was responsible for German naval cryptanalysis. Here, he devised a number of techniques for speeding the breaking of German ciphers, including improvements to the pre-war Polish bombe method, an electromechanical machine that could find settings for the Enigma machine. Turing played a pivotal role in cracking intercepted coded messages that enabled the Allies to defeat the Nazis in many crucial engagements, including the Battle of the Atlantic, and in so doing helped win the war.[12][13] Due to the problems of counterfactual history, it's hard to estimate what effect Ultra intelligence had on the war,[14] but at the upper end it has been estimated that this work shortened the war in Europe by more than two years and saved over 14 million lives.[12] After the war, Turing worked at the National Physical Laboratory, where he designed the Automatic Computing Engine, which was one of the first designs for a stored-program computer. In 1948, Turing joined Max Newman's Computing Machine Laboratory at the Victoria University of Manchester, where he helped develop the Manchester computers[15] and became interested in mathematical biology. He wrote a paper on the chemical basis of morphogenesis[1] and predicted oscillating chemical reactions such as the Belousov–Zhabotinsky reaction, first observed in the 1960s. Turing was prosecuted in 1952 for homosexual acts; the Labouchere Amendment of 1885 had mandated that \"gross indecency\" was a criminal offence in the UK. He accepted chemical castration treatment, with DES, as an alternative to prison. Turing died in 1954, 16 days before his 42nd birthday, from cyanide poisoning. An inquest determined his death as a suicide, but it has been noted that the known evidence is also consistent with accidental poisoning. In 2009, following an Internet campaign, British Prime Minister Gordon Brown made an official public apology on behalf of the British government for \"the appalling way he was treated\". Queen Elizabeth II granted Turing a posthumous pardon in 2013. The Alan Turing law is now an informal term for a 2017 law in the United Kingdom that retroactively pardoned men cautioned or convicted under historical legislation that outlawed homosexual acts.[16] On 15 July 2019 the Bank of England announced that Turing would be depicted on the United Kingdom's new £50 note.\n\n\n\n```python\n# Get questions from user input\nquestions = [input(\"Question {}/{}: \".format(i+1, num_questions)) for i in range(num_questions)]\n# Format questions and write to JSON input file\nqinputs = [{ \"question\":q, \"id\":\"Q{}\".format(i+1)} for i,q in enumerate(questions)]\nwrite_input_file(context, qinputs, predict_file)\n```\n\n Question 1/3: Who is Alan Turing?\n Question 2/3: When was Alan Turing born?\n Question 3/3: Which laboratory Turing joined in 1948?\n\n\n\n```python\n# Ask BERT questions\n!python /workspace/bert/run_squad.py \\\n --bert_config_file=$bert_config_file \\\n --vocab_file=$vocab_file \\\n --init_checkpoint=$init_checkpoint \\\n --output_dir=$output_dir \\\n --do_predict=$do_predict \\\n --predict_file=$predict_file \\\n --predict_batch_size=$predict_batch_size \\\n --doc_stride=$doc_stride \\\n --max_seq_length=$max_seq_length\n```\n\n 2021-03-16 00:25:20.406731: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n WARNING:tensorflow:Deprecation warnings have been disabled. Set TF_ENABLE_DEPRECATION_WARNINGS=1 to re-enable them.\n WARNING:tensorflow:From /workspace/bert/optimization.py:110: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n \n WARNING:tensorflow:From /workspace/bert/run_squad.py:162: The name tf.train.SessionRunHook is deprecated. Please use tf.estimator.SessionRunHook instead.\n \n WARNING:tensorflow:From /workspace/bert/run_squad.py:1409: The name tf.app.run is deprecated. Please use tf.compat.v1.app.run instead.\n \n WARNING:tensorflow:From /workspace/bert/run_squad.py:1174: The name tf.logging.set_verbosity is deprecated. Please use tf.compat.v1.logging.set_verbosity instead.\n \n W0316 00:25:21.868111 140608677435200 module_wrapper.py:137] From /workspace/bert/run_squad.py:1174: The name tf.logging.set_verbosity is deprecated. Please use tf.compat.v1.logging.set_verbosity instead.\n \n WARNING:tensorflow:From /workspace/bert/run_squad.py:1174: The name tf.logging.INFO is deprecated. Please use tf.compat.v1.logging.INFO instead.\n \n W0316 00:25:21.868280 140608677435200 module_wrapper.py:137] From /workspace/bert/run_squad.py:1174: The name tf.logging.INFO is deprecated. Please use tf.compat.v1.logging.INFO instead.\n \n WARNING:tensorflow:From /workspace/bert/modeling.py:94: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.\n \n W0316 00:25:21.868470 140608677435200 module_wrapper.py:137] From /workspace/bert/modeling.py:94: The name tf.gfile.GFile is deprecated. Please use tf.io.gfile.GFile instead.\n \n WARNING:tensorflow:From /workspace/bert/run_squad.py:1183: The name tf.gfile.MakeDirs is deprecated. Please use tf.io.gfile.makedirs instead.\n \n W0316 00:25:21.869253 140608677435200 module_wrapper.py:137] From /workspace/bert/run_squad.py:1183: The name tf.gfile.MakeDirs is deprecated. Please use tf.io.gfile.makedirs instead.\n \n WARNING:tensorflow:From /workspace/bert/run_squad.py:1199: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n \n W0316 00:25:21.998484 140608677435200 module_wrapper.py:137] From /workspace/bert/run_squad.py:1199: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n \n WARNING:tensorflow:\n The TensorFlow contrib module will not be included in TensorFlow 2.0.\n For more information, please see:\n * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n * https://github.com/tensorflow/addons\n * https://github.com/tensorflow/io (for I/O related ops)\n If you depend on functionality not listed there, please file an issue.\n \n W0316 00:25:21.998684 140608677435200 lazy_loader.py:50] \n The TensorFlow contrib module will not be included in TensorFlow 2.0.\n For more information, please see:\n * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n * https://github.com/tensorflow/addons\n * https://github.com/tensorflow/io (for I/O related ops)\n If you depend on functionality not listed there, please file an issue.\n \n WARNING:tensorflow:Estimator's model_fn (.model_fn at 0x7fe139ce63a0>) includes params argument, but params are not passed to Estimator.\n W0316 00:25:22.744968 140608677435200 estimator.py:1992] Estimator's model_fn (.model_fn at 0x7fe139ce63a0>) includes params argument, but params are not passed to Estimator.\n INFO:tensorflow:Using config: {'_model_dir': '/workspace/bert/results', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 1000, '_save_checkpoints_secs': None, '_session_config': , '_keep_checkpoint_max': 1, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': None, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': , '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1, '_tpu_config': TPUConfig(iterations_per_loop=1000, num_shards=8, num_cores_per_replica=None, per_host_input_for_training=3, tpu_job_name=None, initial_infeed_sleep_secs=None, input_partition_dims=None, eval_training_input_configuration=2, experimental_host_call_every_n_steps=1), '_cluster': None}\n I0316 00:25:22.745511 140608677435200 estimator.py:212] Using config: {'_model_dir': '/workspace/bert/results', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': 1000, '_save_checkpoints_secs': None, '_session_config': , '_keep_checkpoint_max': 1, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': None, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': , '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1, '_tpu_config': TPUConfig(iterations_per_loop=1000, num_shards=8, num_cores_per_replica=None, per_host_input_for_training=3, tpu_job_name=None, initial_infeed_sleep_secs=None, input_partition_dims=None, eval_training_input_configuration=2, experimental_host_call_every_n_steps=1), '_cluster': None}\n INFO:tensorflow:_TPUContext: eval_on_tpu True\n I0316 00:25:22.745804 140608677435200 tpu_context.py:220] _TPUContext: eval_on_tpu True\n WARNING:tensorflow:eval_on_tpu ignored because use_tpu is False.\n W0316 00:25:22.745965 140608677435200 tpu_context.py:222] eval_on_tpu ignored because use_tpu is False.\n WARNING:tensorflow:From /workspace/bert/run_squad.py:266: The name tf.gfile.Open is deprecated. Please use tf.io.gfile.GFile instead.\n \n W0316 00:25:22.746137 140608677435200 module_wrapper.py:137] From /workspace/bert/run_squad.py:266: The name tf.gfile.Open is deprecated. Please use tf.io.gfile.GFile instead.\n \n WARNING:tensorflow:From /workspace/bert/run_squad.py:1112: The name tf.python_io.TFRecordWriter is deprecated. Please use tf.io.TFRecordWriter instead.\n \n W0316 00:25:22.749643 140608677435200 module_wrapper.py:137] From /workspace/bert/run_squad.py:1112: The name tf.python_io.TFRecordWriter is deprecated. Please use tf.io.TFRecordWriter instead.\n \n WARNING:tensorflow:From /workspace/bert/run_squad.py:1354: The name tf.logging.info is deprecated. Please use tf.compat.v1.logging.info instead.\n \n W0316 00:25:22.830683 140608677435200 module_wrapper.py:137] From /workspace/bert/run_squad.py:1354: The name tf.logging.info is deprecated. Please use tf.compat.v1.logging.info instead.\n \n INFO:tensorflow:***** Running predictions *****\n I0316 00:25:22.830802 140608677435200 run_squad.py:1354] ***** Running predictions *****\n INFO:tensorflow: Num orig examples = 3\n I0316 00:25:22.830897 140608677435200 run_squad.py:1355] Num orig examples = 3\n INFO:tensorflow: Num split examples = 12\n I0316 00:25:22.831071 140608677435200 run_squad.py:1356] Num split examples = 12\n INFO:tensorflow: Batch size = 8\n I0316 00:25:22.831164 140608677435200 run_squad.py:1357] Batch size = 8\n WARNING:tensorflow:From /workspace/bert/run_squad.py:731: The name tf.FixedLenFeature is deprecated. Please use tf.io.FixedLenFeature instead.\n \n W0316 00:25:22.831306 140608677435200 module_wrapper.py:137] From /workspace/bert/run_squad.py:731: The name tf.FixedLenFeature is deprecated. Please use tf.io.FixedLenFeature instead.\n \n INFO:tensorflow:Could not find trained model in model_dir: /workspace/bert/results, running initialization to predict.\n I0316 00:25:22.831603 140608677435200 estimator.py:614] Could not find trained model in model_dir: /workspace/bert/results, running initialization to predict.\n WARNING:tensorflow:Entity .input_fn.. at 0x7fe1302d1a60> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: 'arguments' object has no attribute 'defaults'\n W0316 00:25:22.888306 140608677435200 ag_logging.py:146] Entity .input_fn.. at 0x7fe1302d1a60> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: 'arguments' object has no attribute 'defaults'\n WARNING:tensorflow:From /workspace/bert/run_squad.py:743: The name tf.parse_single_example is deprecated. Please use tf.io.parse_single_example instead.\n \n W0316 00:25:22.888502 140608677435200 module_wrapper.py:137] From /workspace/bert/run_squad.py:743: The name tf.parse_single_example is deprecated. Please use tf.io.parse_single_example instead.\n \n INFO:tensorflow:Calling model_fn.\n I0316 00:25:22.904676 140608677435200 estimator.py:1148] Calling model_fn.\n INFO:tensorflow:Running infer on CPU\n I0316 00:25:22.904836 140608677435200 tpu_estimator.py:3124] Running infer on CPU\n WARNING:tensorflow:From /workspace/bert/modeling.py:175: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n \n W0316 00:25:22.907857 140608677435200 module_wrapper.py:137] From /workspace/bert/modeling.py:175: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n \n WARNING:tensorflow:From /workspace/bert/modeling.py:413: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.\n \n W0316 00:25:22.909193 140608677435200 module_wrapper.py:137] From /workspace/bert/modeling.py:413: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.\n \n WARNING:tensorflow:From /workspace/bert/modeling.py:494: The name tf.assert_less_equal is deprecated. Please use tf.compat.v1.assert_less_equal instead.\n \n W0316 00:25:22.935186 140608677435200 module_wrapper.py:137] From /workspace/bert/modeling.py:494: The name tf.assert_less_equal is deprecated. Please use tf.compat.v1.assert_less_equal instead.\n \n WARNING:tensorflow:From /workspace/bert/run_squad.py:655: The name tf.trainable_variables is deprecated. Please use tf.compat.v1.trainable_variables instead.\n \n W0316 00:25:26.434307 140608677435200 module_wrapper.py:137] From /workspace/bert/run_squad.py:655: The name tf.trainable_variables is deprecated. Please use tf.compat.v1.trainable_variables instead.\n \n WARNING:tensorflow:From /workspace/bert/run_squad.py:670: The name tf.train.init_from_checkpoint is deprecated. Please use tf.compat.v1.train.init_from_checkpoint instead.\n \n W0316 00:25:26.440835 140608677435200 module_wrapper.py:137] From /workspace/bert/run_squad.py:670: The name tf.train.init_from_checkpoint is deprecated. Please use tf.compat.v1.train.init_from_checkpoint instead.\n \n INFO:tensorflow:Done calling model_fn.\n I0316 00:25:27.519404 140608677435200 estimator.py:1150] Done calling model_fn.\n INFO:tensorflow:Graph was finalized.\n I0316 00:25:28.309463 140608677435200 monitored_session.py:240] Graph was finalized.\n 2021-03-16 00:25:28.339586: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2198615000 Hz\n 2021-03-16 00:25:28.341293: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x5978d70 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n 2021-03-16 00:25:28.341321: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version\n 2021-03-16 00:25:28.344109: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1\n 2021-03-16 00:25:28.580121: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x8b42f70 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n 2021-03-16 00:25:28.580169: I tensorflow/compiler/xla/service/service.cc:176] StreamExecutor device (0): Tesla V100-DGXS-32GB, Compute Capability 7.0\n 2021-03-16 00:25:28.583618: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1665] Found device 0 with properties: \n name: Tesla V100-DGXS-32GB major: 7 minor: 0 memoryClockRate(GHz): 1.53\n pciBusID: 0000:08:00.0\n 2021-03-16 00:25:28.583675: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n 2021-03-16 00:25:28.589475: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n 2021-03-16 00:25:28.591388: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n 2021-03-16 00:25:28.591844: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n 2021-03-16 00:25:28.596971: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.11\n 2021-03-16 00:25:28.597815: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11\n 2021-03-16 00:25:28.597983: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n 2021-03-16 00:25:28.601731: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1793] Adding visible gpu devices: 0\n 2021-03-16 00:25:28.601770: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n 2021-03-16 00:25:28.994633: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1206] Device interconnect StreamExecutor with strength 1 edge matrix:\n 2021-03-16 00:25:28.994679: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1212] 0 \n 2021-03-16 00:25:28.994692: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1225] 0: N \n 2021-03-16 00:25:28.998624: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1351] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 31026 MB memory) -> physical GPU (device: 0, name: Tesla V100-DGXS-32GB, pci bus id: 0000:08:00.0, compute capability: 7.0)\n INFO:tensorflow:Running local_init_op.\n I0316 00:25:31.745624 140608677435200 session_manager.py:500] Running local_init_op.\n INFO:tensorflow:Done running local_init_op.\n I0316 00:25:31.858720 140608677435200 session_manager.py:502] Done running local_init_op.\n 2021-03-16 00:25:33.689415: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n INFO:tensorflow:Processing example: 0\n I0316 00:25:34.384404 140608677435200 run_squad.py:1373] Processing example: 0\n INFO:tensorflow:prediction_loop marked as finished\n I0316 00:25:34.586190 140608677435200 error_handling.py:101] prediction_loop marked as finished\n INFO:tensorflow:prediction_loop marked as finished\n I0316 00:25:34.586374 140608677435200 error_handling.py:101] prediction_loop marked as finished\n INFO:tensorflow:-----------------------------\n I0316 00:25:34.586491 140608677435200 run_squad.py:1388] -----------------------------\n INFO:tensorflow:0 Total Inference Time = 11.76 Inference Time W/O start up overhead = 1.89 Sentences processed = 16\n I0316 00:25:34.586577 140608677435200 run_squad.py:1389] 0 Total Inference Time = 11.76 Inference Time W/O start up overhead = 1.89 Sentences processed = 16\n INFO:tensorflow:0 Inference Performance = 8.4703 sentences/sec\n I0316 00:25:34.586667 140608677435200 run_squad.py:1392] 0 Inference Performance = 8.4703 sentences/sec\n INFO:tensorflow:-----------------------------\n I0316 00:25:34.586749 140608677435200 run_squad.py:1393] -----------------------------\n INFO:tensorflow:Writing predictions to: /workspace/bert/results/predictions.json\n I0316 00:25:34.586873 140608677435200 run_squad.py:792] Writing predictions to: /workspace/bert/results/predictions.json\n INFO:tensorflow:Writing nbest to: /workspace/bert/results/nbest_predictions.json\n I0316 00:25:34.586952 140608677435200 run_squad.py:793] Writing nbest to: /workspace/bert/results/nbest_predictions.json\n\n\n\n```python\ndisplay_results(predict_file, output_prediction_file)\n```\n\n\n

Id	Question	Answer
Q1	Who is Alan Turing?	father of theoretical computer science and artificial intelligence
Q2	When was Alan Turing born?	23 June 1912
Q3	Which laboratory Turing joined in 1948?	Max Newman's Computing Machine Laboratory at the Victoria University of Manchester

\n\n\n\n```python\n\n```","hasQuickStartGuide","advanced","","hasAdvanced","performance","hasPerformance","policies",[],{"_79":22,"_81":211,"_180":-5,"_187":32,"_189":32,"_203":210,"_191":32},{"_79":24,"_81":24,"_180":-5,"_187":32,"_189":32,"_203":210,"_191":32},{"_79":208,"_81":208,"_180":-5,"_187":32,"_189":32,"_203":209,"_191":32},"1",[],[],"Latest (2)","nvidia/bert_tf_jupyter_notebook","key","nvidia/resources/bert_tf_jupyter_notebook","resourceType","resources","resourceId","bert_tf_jupyter_notebook","shortDescription","Jupyter Notebook example for Question Answering with BERT for TensorFlow","# BERT for TensorFlow Jupyter Notebook\n \nThis notebook provides a worked example for utilising the [BERT for TensorFlow](https://ngc.nvidia.com/catalog/model-scripts/nvidia:bert_for_tensorflow/performance) model scripts. \n\nFor convenience, it is launched inside of [NVIDIA’s Tensorflow Docker container](https://ngc.nvidia.com/catalog/containers/nvidia:tensorflow) hosted on NGC to have a reproducible environment that is easy to set up.\n\n## View in NGC\n\nTo take a look at this, or any other, notebook; follow these steps:\n\n1. Navigate to the File Browser tab of the asset in NGC\n2. Select the version you'd like to see\n3. Under the actions menu (three dots) for the .ipynb file select \"View Jupyter\"\n4. There you have it! You can read a notebook for documentation and copy code samples without ever leaving NGC.\n\n## Quick Start\n\n*note* - This method will not allow you to run the notebook's contents. \n\nAssuming you have Jupyter installed, to simply read through the notebook and code samples follow these steps:\n\n1. Download the latest version of the notebook from this page\n2. Navigate to the downloaded directory\n3. Deploy the notebook with this command: \n\n```\njupyter notebook BERT_Jupyter.ipynb\n```\n\nYou'll now be able to view the notebook from your web browser.","displayName","BERT for TensorFlow Jupyter Notebook",["D",1709056673277],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/-/resources/bert_tf_jupyter_notebook","rawImageUrl","https://assets.nvidiagrid.net/ngc/logos/Tutorials.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[254,255,256,257],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_222":272,"_259":272,"_260":273,"_262":263},{"_222":269,"_259":270,"_260":271,"_262":263},{"_222":265,"_259":266,"_260":267,"_262":263},{"_222":258,"_259":258,"_260":261,"_262":263},"Tutorial","value","filter",{"_264":76,"_259":258},"category","label","field","Question Answering","uscs_question_answering",{"_264":268,"_259":266},"application","Natural Language Understanding","uscs_natural_language_understanding",{"_264":268,"_259":270},"BERT",{"_264":263,"_259":272},{"_119":1448,"_121":1449},"ngcApps",[1401],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1382":1383,"_1384":1385,"_1386":1387,"_1388":1389,"_1390":285,"_150":309,"_1391":1392,"_1393":57,"_1394":98,"_1395":98,"_1396":1397,"_1398":98,"_1399":1400},"amplitude",{"_1379":1380,"_981":-7,"_1381":32},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1373":1374,"_1375":1376,"_1377":1378},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1256":199,"_1257":1258,"_1259":1260,"_1261":1262},"announcements",[],"consentState",{"_347":1246,"_1247":32,"_1248":32,"_1249":98,"_1250":1251},"consentConfig",{"_329":330,"_331":332,"_333":334,"_335":336,"_337":338,"_339":340,"_341":342,"_343":344,"_345":346,"_347":348},"showContactAdmin",1783885477049,[],[],[323,324,325,326,327],{"_46":61,"_48":72,"_50":114,"_52":-5,"_54":-5,"_56":57},{"_46":61,"_48":70,"_50":114,"_52":-5,"_54":-5,"_56":57},{"_46":61,"_48":62,"_50":114,"_52":-5,"_54":-5,"_56":57},{"_46":58,"_48":59,"_50":51,"_52":53,"_54":328,"_56":57},{"_46":47,"_48":49,"_50":51,"_52":53,"_54":55,"_56":57},8,"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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For more information about our privacy practices, please see our Privacy Policy.","mainText","Cookie Settings","alertNoticeText",{"_1238":1239,"_1240":1241,"_1242":1243,"_1244":1245},"bannerAcceptLabel","Accept All","bannerRejectLabel","Reject Optional","bannerSettingsLabel","Manage Settings","preferenceSaveLabel","Save Choices","preferenceRejectLabel","Decline All","alwaysActiveText","Always Active","groups",[349,350,351,352,353],{"_354":890,"_81":891,"_178":892,"_358":98,"_359":18,"_361":893,"_363":894},{"_354":680,"_81":681,"_178":682,"_358":32,"_359":60,"_361":683,"_363":684},{"_354":580,"_81":581,"_178":582,"_358":32,"_359":55,"_361":583,"_363":584},{"_354":365,"_81":366,"_178":367,"_358":32,"_359":368,"_361":369,"_363":370},{"_354":355,"_81":356,"_178":357,"_358":32,"_359":360,"_361":362,"_363":364},"optanonGroupId","C0005","Social Media Cookies","These cookies are set by a range of social media services that we have added to the site to enable you to share our content with your friends and networks. They are capable of tracking your browser across other sites and building up a profile of your interests. This may impact the content and messages you see on other websites you visit. If you do not allow these cookies you may not be able to use or see these sharing tools.","alwaysActive","order",11,"cookies",[],"hosts",[],"C0004","Advertising Cookies","These cookies record your visit to our websites, the pages you have visited and the links you have followed to influence the advertisements that you see on other websites. These cookies and the information they collect may be managed by other companies, including our advertising partners, and may be used to build a profile of your interests and show you relevant advertising on other sites. We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[498,499,500,501,502,503,504,505,506,507,508,509,510,511,512,513,514,515,516,517,518,519,520,521,522,523,524,525,526,527,528,529,530,531,532],[371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387],{"_388":494,"_222":494,"_178":199,"_361":495},{"_388":489,"_222":489,"_178":199,"_361":490},{"_388":485,"_222":485,"_178":199,"_361":486},{"_388":480,"_222":480,"_178":199,"_361":481},{"_388":475,"_222":475,"_178":199,"_361":476},{"_388":463,"_222":463,"_178":199,"_361":464},{"_388":451,"_222":451,"_178":199,"_361":452},{"_388":446,"_222":446,"_178":199,"_361":447},{"_388":435,"_222":435,"_178":199,"_361":436},{"_388":430,"_222":430,"_178":199,"_361":431},{"_388":425,"_222":425,"_178":199,"_361":426},{"_388":420,"_222":420,"_178":199,"_361":421},{"_388":415,"_222":415,"_178":199,"_361":416},{"_388":410,"_222":410,"_178":199,"_361":411},{"_388":405,"_222":405,"_178":199,"_361":406},{"_388":398,"_222":398,"_178":199,"_361":399},{"_388":389,"_222":389,"_178":199,"_361":390},"hostName","amazon-adsystem.com",[391,392],{"_81":397,"_394":389,"_395":32,"_178":396},{"_81":393,"_394":389,"_395":32,"_178":396},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[400,401],{"_81":404,"_394":398,"_395":32,"_178":403},{"_81":402,"_394":398,"_395":32,"_178":403},"IDE","This domain is owned by Doubleclick (Google). 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It is used as a URL shortening service, which allows Twitter to collect data on how links are shared.","snowplow.com",[427],{"_81":428,"_394":425,"_395":32,"_178":429},"sp","Testing","google.com",[432],{"_81":433,"_394":430,"_395":32,"_178":434},"NID","This domain is owned by Google Inc. Although Google is primarily known as a search engine, the company provides a diverse range of products and services. Its main source of revenue however is advertising. Google tracks users extensively both through its own products and sites, and the numerous technologies embedded into many millions of websites around the world. It uses the data gathered from most of these services to profile the interests of web users and sell advertising space to organisations based on such interest profiles as well as aligning adverts to the content on the pages where its customer's adverts appear.","tags.srv.stackadapt.com",[437,438,439,440],{"_81":445,"_394":435,"_395":32,"_178":442},{"_81":444,"_394":435,"_395":32,"_178":442},{"_81":443,"_394":435,"_395":32,"_178":442},{"_81":441,"_394":435,"_395":32,"_178":442},"sa-user-id-v3","This domain is associated with StackAdapt, a platform for programmatic advertising, which suggests that it is used for targeting and advertising purposes.","sa-user-id","sa-user-id-v2","sa-user-id-v4","linkedin.com",[448],{"_81":449,"_394":446,"_395":32,"_178":450},"bcookie","This domain is owned by LinkedIn, the business networking platform. It typically acts as a third party host where website owners have placed one of its content sharing buttons in their pages, although its content and services can be embedded in other ways. Although such buttons add functionality to the website they are on, cookies are set regardless of whether or not the visitor has an active Linkedin profile, or agreed to their terms and conditions. For this reason it is classified as a primarily tracking/targeting domain.","twitter.com",[453,454,455,456],{"_81":462,"_394":451,"_395":32,"_178":458},{"_81":460,"_394":451,"_395":32,"_178":461},{"_81":459,"_394":451,"_395":32,"_178":458},{"_81":457,"_394":451,"_395":32,"_178":458},"personalization_id","This domain is owned by Twitter. The main business activity is: Social Networking Services. Where twitter acts as a third party host, it collects data through a range of plug-ins and integrations, that is primarily used for tracking and targeting.","guest_id_marketing","guest_id","Twitter does not currently provide information on the use of specific cookies.","guest_id_ads","youtube.com",[465,466,467,468],{"_81":474,"_394":463,"_395":32,"_178":472},{"_81":473,"_394":463,"_395":32,"_178":472},{"_81":471,"_394":463,"_395":32,"_178":472},{"_81":469,"_394":463,"_395":32,"_178":470},"VISITOR_INFO1_LIVE","This cookie is used as a unique identifier to track viewing of videos","__Secure-YNID","YouTube is a Google owned platform for hosting and sharing videos. YouTube collects user data through videos embedded in websites, which is aggregated with profile data from other Google services in order to display targeted advertising to web visitors across a broad range of their own and other websites.","__Secure-YEC","__Secure-ROLLOUT_TOKEN","demdex.net",[477],{"_81":478,"_394":475,"_395":32,"_178":479},"demdex","This cookie helps Adobe Audience Manger perform basic functions such as visitor identification, ID synchronization, segmentation, modeling, reporting, etc.","im-apps.net",[482],{"_81":483,"_394":480,"_395":32,"_178":484},"receive-cookie-deprecation","This domain is owned by im-apps.net which provides information and resources about various instant messaging (IM) applications to help users make informed decisions based on their communication needs.","influ2.com",[487],{"_81":488,"_394":485,"_395":32,"_178":429},"R","6sc.co",[491],{"_81":492,"_394":489,"_395":32,"_178":493},"6suuid","This domain is associated with the company 6Sense Insights, Inc., which provides predictive analytics and account engagement services. 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It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier.","kndctr_F207D74D549850760A4C98C6_AdobeOrg_identity","_rdt_pn","www.nvidia.com","This cookie name is associated with www.mind.org.uk which is used for marketing purposes.","ai-podcast.nvidia.com","This cookie is used to identify the user's session on the website and is often associated with analytics or personalization.","_gcl_au","Used by Google AdSense for experimenting with advertisement efficiency across websites using their services","_mkto_trk","This cookie is associated with an email marketing service provided by Marketo. This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","_gat_gtag_UA_140825383_2","Google Analytics","_zitok","This cookie name is associated with ZoomInfo which is used to maintain a unique visitor identifier and store Personally Identifiable Information.","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","AMP_91bf70adfe","Supports Amplitude product analytics.","This cookie name is associated with StackAdapt Inc. which is used to support the effective management and optimization of digital advertising campaigns by handling information such as visitor actions on websites, visited pages, time spent, page load speed, and more.","build.nvidia.com","kndctr_F207D74D549850760A4C98C6_AdobeOrg_cluster","_cs_nnnnnnnnnnnnn","This is a session cookie. 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It is commonly used for analytics and personalization of content.","marketplace.nvidia.com","AMP_MKTG_92dea1e784","This cookie is typically associated with SnackTools, which is a suite of web applications for creating and sharing multimedia content. It is used to identify a user across sessions and track user activities.","forums.developer.nvidia.com","This cookie is typically associated with the use of Taptap Digital, a company specializing in digital advertising. It is used for tracking and ad personalization purposes.","_tt_enable_cookie","This cookie name is associated with TikTok which is used to collect data about behavior and purchases on the website and to measure the effect of advertising. It is also used to track website activity to help optimize advertising efforts.","AMCV_xxxxxAdobeOrg","This cookie is used to identify a unique visitor for tracking purposes by Adobe Experience Cloud.This is a pattern type cookie name associated with Adobe Marketing Cloud. It stores a unique visitor identifier, and uses an organisation identifier to allow a company to track users across their domains and services.","ttcsid","This cookie name is associated with TikTok which is used to enable TikTok Ads session tracking by assigning a unique session ID to users who click on TikTok ads, allowing for the attribution of user behavior and conversions within the same session.This cookie is associated with TikTok. It helps track your activity on the platform, including how you interact with their embedded services on other websites.","AMP_MKTG_91bf70adfe","C0003","Personalization Cookies","These cookies collect data about how you have interacted with our website to help us improve your web experience, such as which pages you have visited. These cookies may store a unique ID so that our system will remember you when you return. They may be set by us or by third party providers whose services we have added to our pages. These cookies enable us to provide enhanced website functionality and personalization as well as make the marketing messages we send to you more relevant to your interests. If you do not allow these cookies, then some or all of these services may not function properly.",[620,621,622,623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640],[585,586,587,588,589],{"_388":480,"_222":480,"_178":199,"_361":611},{"_388":446,"_222":446,"_178":199,"_361":606},{"_388":599,"_222":599,"_178":199,"_361":600},{"_388":595,"_222":595,"_178":199,"_361":596},{"_388":590,"_222":590,"_178":199,"_361":591},"nvcrm.my.site.com",[592],{"_81":593,"_394":590,"_395":32,"_178":594},"BrowserId","If you know anything about this cookie and its uses that are not specific to a particular site please add your contributions.","nvidia.partners",[597],{"_81":598,"_394":595,"_395":32,"_178":429},"edge","github.com",[601,602],{"_81":605,"_394":599,"_395":32,"_178":604},{"_81":603,"_394":599,"_395":32,"_178":604},"logged_in","This domain is owned by Github. 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The data is aggregated and analyzed; therefore, it remains anonymous upon use.\n","nvHeadless","This cookie is likely used to track if a user is using the site in a 'headless' non-GUI mode, which could be for accessibility reasons or for automated processes.","_biz_nA","C0002","Performance Cookies","These cookies are used to provide quantitative measures of our website visitors, such as the number of times you visit, time on page, your mouse movements, scrolling, clicks and keystroke activity on the websites; other browsing, search, or product research behavior; and what brought you to our site. These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website 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BERT_Jupyter.ipynbView Notebook	80.89 KB	March 17, 2021 UTC	curl wget Download