From c48ac6cefc5a0fd11d79c200aba35b9e683d2d4a Mon Sep 17 00:00:00 2001 From: Sujee Maniyam Date: Wed, 4 Sep 2024 13:58:00 -0700 Subject: [PATCH 1/3] updated code to work with local data. Added a colab notebook - simplified python and ray notebooks to work with 'Input-Test_Data' that is part of repo - removed 'Output-Test-Data' directory. This is generated - Added a notebook that is Google Colab friendly. This one requires no local setup. And easy to run - Updated project README.md accordingly --- README.md | 10 +- examples/notebooks/.gitignore | 3 +- .../attention_is_all_you_need.pdf | Bin 0 -> 2215244 bytes .../Output-Test-Data/GraniteCodePaper.parquet | Bin 65246 -> 0 bytes .../notebooks/Output-Test-Data/metadata.json | 62 - .../Run_your_first_transform_colab.ipynb | 3721 +++++++++++++++++ .../Run_your_first_transform_python.ipynb | 177 +- .../Run_your_first_transform_ray.ipynb | 209 +- 8 files changed, 3869 insertions(+), 313 deletions(-) create mode 100644 examples/notebooks/Input-Test-Data/attention_is_all_you_need.pdf delete mode 100644 examples/notebooks/Output-Test-Data/GraniteCodePaper.parquet delete mode 100644 examples/notebooks/Output-Test-Data/metadata.json create mode 100644 examples/notebooks/Run_your_first_transform_colab.ipynb diff --git a/README.md b/README.md index 753b70b42..a7aeb5af4 100644 --- a/README.md +++ b/README.md @@ -131,10 +131,14 @@ If there are no errors, you are good to go! Let's try a simple transform to extract content from PDF files. We have following notebooks that demonstrate how to run a data preparation transformation that extracts content from PDF files using the data-prep-kit. -- Option 1: Pure python notebook : [examples/notebooks/Run_your_first_transform_python.ipynb](examples/notebooks/Run_your_first_transform_python.ipynb) - easiest to get started -- Option 2: This one uses Ray framework for parallel execution while still allowing local processing : [examples/notebooks/Run_your_first_transform_ray.ipynb](examples/notebooks/Run_your_first_transform_ray.ipynb) +**Notebook versions** + +You can try one or all 😄 + +- Option 1: Google Colab friendly notebook (no setup necessary, easiest to get started): [examples/notebooks/Run_your_first_transform_colab.ipynb](examples/notebooks/Run_your_first_transform_colab.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/IBM/data-prep-kit/blob/dev/examples/notebooks/Run_your_first_transform_colab.ipynb) +- Option 2: Pure python notebook (runs locally) : [examples/notebooks/Run_your_first_transform_python.ipynb](examples/notebooks/Run_your_first_transform_python.ipynb) - easiest to get started +- Option 3: Ray version (runs locally): This one uses Ray framework for parallel execution while still allowing local processing - [examples/notebooks/Run_your_first_transform_ray.ipynb](examples/notebooks/Run_your_first_transform_ray.ipynb) -You can try either one, or both 😄 To run the notebooks, launch jupyter from the same virtual environment you created using the command below. diff --git a/examples/notebooks/.gitignore b/examples/notebooks/.gitignore index 65e0ef546..c89c13bc1 100644 --- a/examples/notebooks/.gitignore +++ b/examples/notebooks/.gitignore @@ -1,2 +1 @@ -Input-Test-Data -Output-Test-Data 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22:15:49", - "status": "success" - }, - "code": { - "github": "NA", - "commit_hash": "0", - "path": "path" - }, - "job_input_params": { - "artifacts_path": null, - "contents_type": "application/json", - "do_table_structure": true, - "do_ocr": false, - "checkpointing": false, - "max_files": -1, - "random_samples": -1, - "files_to_use": [ - ".pdf" - ], - "number of workers": 2, - "worker options": { - "num_cpus": 4.0, - "memory": 536870912.0, - "max_restarts": -1 - }, - "actor creation delay": 0 - }, - "execution_stats": { - "cpus": 16, - "gpus": 0, - "memory": 37.52686538733542, - "object_store": 2.0, - "execution time, min": 1.92 - }, - "job_output_stats": { - "source_files": 1, - "source_size": 1273644, - "result_files": 1, - "result_size": 65246, - "processing_time": 85.595, - "nrows": 1, - "nsuccess": 1, - "nfail": 0, - "nskip": 0 - }, - "source": { - "name": "/Users/himapatel/Work/Projects/MCD/OpenSource/reprg_folders/data-prep-kit/examples/notebooks/Input-Test-Data", - "type": "path" - }, - "target": { - "name": "/Users/himapatel/Work/Projects/MCD/OpenSource/reprg_folders/data-prep-kit/examples/notebooks/Output-Test-Data", - "type": "path" - } -} \ No newline at end of file diff --git a/examples/notebooks/Run_your_first_transform_colab.ipynb b/examples/notebooks/Run_your_first_transform_colab.ipynb new file mode 100644 index 000000000..ee179f9c5 --- /dev/null +++ b/examples/notebooks/Run_your_first_transform_colab.ipynb @@ -0,0 +1,3721 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "922vLtOCvgJD" + }, + "source": [ + "# Data Prep Kit - Hello World (Colab friendly)\n", + "\n", + "This notebook guides you through running your first data preparation transformation using the data-prep-kit. In this example, we will demonstrate a transformation that takes PDF files as input and extracts their content.\n", + "\n", + "**Notebook versions:**\n", + "\n", + "- Pure python (run locally): [Run_your_first_transform_python.ipynb](Run_your_first_transform_python.ipynb)\n", + "- Ray version (run locally): [Run_your_first_transform_ray.ipynb](Run_your_first_transform_ray.ipynb)\n", + "- Google Colab friendly notebook: this notebook | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/IBM/data-prep-kit/blob/dev/examples/notebooks/Run_your_first_transform_colab.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qnaUEB57vgJF" + }, + "source": [ + "## Step-1: Install data-prep-kit dependencies\n", + "\n" + ] + }, + { + "cell_type": "code", + "source": [ + "!pip install data-prep-toolkit-transforms-ray\n" + ], + "metadata": { + "id": "ENbZ2ZNsvqrh" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Restart Runtime\n", + "\n", + "After installing dependencies, be sure restart runtime, so libraries will be loaded\n", + "\n", + "You do this by going to **`Runtime --> Restart Session`**\n", + "\n", + "Then continue from **Step-2** below" + ], + "metadata": { + "id": "T8MnqTZ-wC2P" + } + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IZOmU1sxvgJG" + }, + "source": [ + "## Step-2: Get Data\n", + "\n", + "For this example, we will show PDF processing capabilities of DPK. And we will download and use this PDF documents\n", + "\n", + "- [IBM Granite model](https://arxiv.org/abs/2405.04324)\n", + "- [Attention is all you need](https://arxiv.org/pdf/1706.03762) - seminal paper on transformer/attention architecture\n", + "\n", + "The code below will download the PDF. Feel free to try your own PDFs to test it out" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "H5yF6_rYvgJG" + }, + "outputs": [], + "source": [ + "INPUT_DIR = 'Input-Test-Data'\n", + "OUTPUT_DIR = 'Output-Test-Data'" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "qfmO_eLnvgJH", + "outputId": "2735f68b-8313-488e-ecef-0775ede27543" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Input-Test-Data/Granite_code_models.pdf (1.27 MB) downloaded successfully.\n", + "\n", + "Input-Test-Data/attention_is_all_you_need.pdf (2.22 MB) downloaded successfully.\n" + ] + } + ], + "source": [ + "## This cell will download the input files\n", + "\n", + "import os\n", + "import shutil\n", + "import requests\n", + "from humanfriendly import format_size\n", + "\n", + "def download_file(url, local_file, chunk_size=1024*1024):\n", + " # Check if the local file already exists\n", + " if os.path.exists(local_file):\n", + " file_size = format_size(os.path.getsize(local_file))\n", + " print(f\"Local file '{local_file}' ({file_size}) already exists. Skipping download.\")\n", + " return\n", + "\n", + " # Create the directory if it doesn't exist\n", + " os.makedirs(os.path.dirname(local_file), exist_ok=True)\n", + "\n", + " # Stream the file download\n", + " with requests.get(url, stream=True) as r:\n", + " r.raise_for_status()\n", + " with open(local_file, 'wb') as f:\n", + " for chunk in r.iter_content(chunk_size=chunk_size):\n", + " if chunk: # filter out keep-alive new chunks\n", + " f.write(chunk)\n", + " print()\n", + " file_size = format_size(os.path.getsize(local_file))\n", + " print(f\"{local_file} ({file_size}) downloaded successfully.\")\n", + "## --- end: download_file ------\n", + "\n", + "## setup input/output directories\n", + "shutil.os.makedirs(INPUT_DIR, exist_ok=True)\n", + "shutil.rmtree(OUTPUT_DIR, ignore_errors=True)\n", + "shutil.os.makedirs(OUTPUT_DIR, exist_ok=True)\n", + "\n", + "## Download PDF files\n", + "download_file (url = 'https://arxiv.org/pdf/2405.04324', local_file = os.path.join(INPUT_DIR, 'Granite_code_models.pdf' ))\n", + "download_file (url = 'https://arxiv.org/pdf/1706.03762', local_file = os.path.join(INPUT_DIR, 'attention_is_all_you_need.pdf' ))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8J62IyS5vgJJ" + }, + "source": [ + "## Step-3: Extract Text from PDF\n", + "\n", + "This code is designed to set up a data transformation process that extracts text from PDF. We will save the output as parquet format." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "aRkeYDvuvgJJ" + }, + "outputs": [], + "source": [ + "import sys\n", + "import ast\n", + "\n", + "# Utilities from the data-prep-kit's data-processing-lib library provide functions and classes for parameter management,\n", + "from pdf2parquet_transform import (pdf2parquet_contents_type_cli_param, pdf2parquet_contents_types,)\n", + "from data_processing.utils import GB, ParamsUtils\n", + "\n", + "\n", + "ingest_config = {\n", + " pdf2parquet_contents_type_cli_param: pdf2parquet_contents_types.JSON,\n", + "}\n", + "\n", + "#local_conf: A dictionary specifying the local input and output folders where the PDF files will be read from and the transformed data will be saved.\n", + "local_conf = {\n", + " \"input_folder\": INPUT_DIR,\n", + " \"output_folder\": OUTPUT_DIR,\n", + "}\n", + "\n", + "#params: A dictionary containing various runtime parameters for the transformation.\n", + "#data_local_config: Configuration for local data access, such as input and output folders, converted into a format compatible with the transformation using ParamsUtils.convert_to_ast.\n", + "#data_files_to_use: Specifies that only PDF files (['.pdf']) will be used as input data.\n", + "\n", + "params = {\n", + " \"data_local_config\": ParamsUtils.convert_to_ast(local_conf),\n", + " \"data_files_to_use\": ast.literal_eval(\"['.pdf']\"),\n", + "}\n", + "sys.argv = ParamsUtils.dict_to_req(d=(params | ingest_config))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7EFL1Q7kvgJJ" + }, + "source": [ + "### 3 - Execute" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Q3xy8QnpvgJJ" + }, + "source": [ + "Now its time to run the transformation.\n", + "\n", + "You will notice, that the code will download models to execute the transformation. These models will be used to process PDFs." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 862, + "referenced_widgets": [ + "4da9634138834af6b53830e77822bbee", + "02ce2efff8214690a2d0cea6832492a4", + "86df69d190354ddaa33adce96936a6df", + "e93c215aa5304de39220f244ebc4b911", + "86c48f1756cd4b0fa73125e39dcb77ba", + "f3e425eac5734ff19306ae71f627e079", + "691f09e257d54b499faaf90f72e44a3b", + "30dc5f48be2d4b4da8716d2e0d23e372", + "08be842c2e5e46589fb13deeb9c036bc", + "ae837f1a69a74a3b863a9c19202da0e7", + "c92ce42d31784202b1b3b210ae77aae7", + "950921b442fc4f739fd186f051c76d81", + "93342e0027934b22b8e5967fb59af428", + "5dbede0d03044ce3b9a6f08f7a924c62", + "4fff11b9bfad46e98b7c933f2994a725", + "ae8e349ca5a741878cef03f248061f2e", + "58207c5fd9594c988308dcd6e32775b5", + "1f048b048640473bb0b215d7da9e1a92", + "15263bd25c9741c3b34b073010642944", + "91ba93a7c927481b9e5ee9793988e9de", + "8010d3af49824ca5a6f7f9dd302494ba", + "0e851e377db1415fbe9c62bc8bcc7d1a", + "88a9c683544b416e8b58c54627320901", + "71b4cb950be7449c9a63fc552fccf647", + "f0a4d1f74e0947c780e64be7e2d33f97", + "4b8d0c62fda9415ca77acd3b135d3fa0", + "b7eb047535a245a2b1e46c68aa9c86d8", + "1a02de7a971448fdb47d6b22c93476b3", + "4d5ceb4c8b51480cb82caa36d573ca0b", + "f5cde5c28a3e4788bbcf193dc64ebe57", + "a1939affb9954aca86cdcd9430ab569b", + "93232da807aa4bdfaa6df7af867ca183", + "4912fdf719054575a6f5695fb106e74d", + "1a1ce455b56c4c5abc61a67b04fa8983", + "295ffb18518d4f55bed187eae1bb4a91", + "7d1f4804da354359ba4ffa44db911ef2", + "463d76d334c9438882ac033baa6a2a71", + "98e88ee67f8940b0b7c1ab0f4e5ed7fe", + "cac6ab1306d4430f91be43b5f4fc4585", + "a4797fa4499941318dbbbdc06bd59912", + "9d8ea99d01c940e9a10889d1639d74e8", + "c3922d4ff33641f388ab101d7d4a981f", + "68f49fdff2154a72b9875756c8469dba", + "ac44b83e481e4a4482f323b6328e39ff", + "76c2058596f04b1c9adbd456a5799f36", + "8409eca6c9034f3f94fce36a2933e3f3", + "d69525c97a264fea8cf50f1243aab2db", + "fda7c65808ed440b917d7299b500b485", + "2eeb6f5268484ef196f0ad26c77307cb", + "dc5a5200d9f94dcf8c47c14590ff3952", + "7e426116c9ac4a228ba1f9d94c6519bf", + "136199f932974fc7a1b07d24dbf527bb", + "4b2609707adc42e980fe7bc8171c0d1d", + "1fc82f82701c4c918f39b85dab253723", + "c3c45d72c4e74a1faef0632f2c768608", + "e0ce578a163c40f684053b3d6f3db5ec", + "6e04886fc4e6485a9941d67b4577bfda", + "1196c066c5784a1bbcadc9a0202bfe02", + "677a6dc64b124372bb44e6df8066aa38", + "85ff85b8cc074a9caa85f9db2f669ac0", + "3e0010c2a38243d2ae4d4f27518b503b", + "091493736776456392e4493174f9fa0f", + "ebe75f475b9c43aca827b1f5cf68e511", + "30cacbb0c6674d4fa198381a502ab44e", + "a87905e174e64986b59575a4b0a1e799", + "c451b2d71a7d4291baabc7b59ed47b96", + "9959454762d64b199ab209eef345e8e3", + "657fccdce16d4e128d51b6cb06f91056", + "266a3a063fe34782a6546beac0eecb62", + "02192a51069f40a2a0f831ecc2809085", + "70834764cd4242708965f339e9f1f08e", + "e4efea17c0fe49409a9c970dbcba9a93", + "0873f21424a3404dbcb995ec862a9f44", + "2c22a39735d3452593db594323ed27dc", + "402b3286fec044b7a8bd12c9e46e3286", + "7c4a475bdb434c91a051d3408cda94d9", + "bfe2e357e7384315a3614797e9d530ae", + "8beac29537a4472ba369ccc91674dbf1", + "5797c0a5b8824a3f84b72e4ce2e6de16", + "b483da44bc634a0c9b7a6fbc9f8443db", + "5dc4d60351e54257907737d5deaea73a", + "904f7974a8d24a60a9b6fe65fd42f4a4", + "a425f8aa6b9c4b8bb95cdc59f20734cf", + "0756cd16dc96426ba54f20c75686e6e3", + "ef3251cf7d884620a30719e0ae5b49a0", + "0453e6e227e6466b8fb2f6f80be09b8a", + "8916ed32df05413ebc9fa43076f03f77", + "ee33612510194eee92a956bad872838c" + ] + }, + "id": "UIMqzaAtvgJK", + "outputId": "680e8934-483d-4878-92f5-dc3d708c2b67" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "20:39:40 INFO - pdf2parquet parameters are : {'artifacts_path': None, 'contents_type': , 'do_table_structure': True, 'do_ocr': False}\n", + "INFO:pdf2parquet_transform:pdf2parquet parameters are : {'artifacts_path': None, 'contents_type': , 'do_table_structure': True, 'do_ocr': False}\n", + "20:39:40 INFO - pipeline id pipeline_id\n", + "INFO:data_processing.runtime.execution_configuration:pipeline id pipeline_id\n", + "20:39:40 INFO - code location None\n", + "INFO:data_processing.runtime.execution_configuration:code location None\n", + "20:39:40 INFO - data factory data_ is using local data access: input_folder - Input-Test-Data output_folder - Output-Test-Data\n", + "INFO:data_processing.data_access.data_access_factory_basee9e219d6-eeb0-4e90-a0e6-12ea6b0bc0b7:data factory data_ is using local data access: input_folder - Input-Test-Data output_folder - Output-Test-Data\n", + "20:39:40 INFO - data factory data_ max_files -1, n_sample -1\n", + "INFO:data_processing.data_access.data_access_factory_basee9e219d6-eeb0-4e90-a0e6-12ea6b0bc0b7:data factory data_ max_files -1, n_sample -1\n", + "20:39:40 INFO - data factory data_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.pdf'], files to checkpoint ['.parquet']\n", + "INFO:data_processing.data_access.data_access_factory_basee9e219d6-eeb0-4e90-a0e6-12ea6b0bc0b7:data factory data_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.pdf'], files to checkpoint ['.parquet']\n", + "20:39:40 INFO - orchestrator pdf2parquet started at 2024-09-04 20:39:40\n", + "INFO:data_processing.runtime.pure_python.transform_orchestrator:orchestrator pdf2parquet started at 2024-09-04 20:39:40\n", + "20:39:40 INFO - Number of files is 2, source profile {'max_file_size': 2.112621307373047, 'min_file_size': 1.2146415710449219, 'total_file_size': 3.3272628784179688}\n", + "INFO:data_processing.runtime.pure_python.transform_orchestrator:Number of files is 2, source profile {'max_file_size': 2.112621307373047, 'min_file_size': 1.2146415710449219, 'total_file_size': 3.3272628784179688}\n", + "20:39:40 INFO - Initializing models\n", + "INFO:pdf2parquet_transform:Initializing models\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Fetching 7 files: 0%| | 0/7 [00:00\n", + "
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[cs.AI] 7 May 2024\\\",\\\"type\\\":\\\"page-header\\\",\\\"name\\\":\\\"Page-header\\\",\\\"prov\\\":[{\\\"bbox\\\":[17,238,36,576],\\\"page\\\":1,\\\"span\\\":[0,37]}]},{\\\"text\\\":\\\"IBM Granite Code Models\\\",\\\"type\\\":\\\"page-header\\\",\\\"name\\\":\\\"Page-header\\\",\\\"prov\\\":[{\\\"bbox\\\":[108,756,504,764],\\\"page\\\":1,\\\"span\\\":[0,23]}]},{\\\"text\\\":\\\"Granite Code Models: A Family of Open Foundation Models for Code Intelligence\\\",\\\"type\\\":\\\"subtitle-level-1\\\",\\\"name\\\":\\\"Section-header\\\",\\\"prov\\\":[{\\\"bbox\\\":[143,673,468,711],\\\"page\\\":1,\\\"span\\\":[0,77]}]},{\\\"text\\\":\\\"Mayank Mishra \\\\u22c6 Matt Stallone \\\\u22c6 Gaoyuan Zhang \\\\u22c6 Yikang Shen Aditya Prasad Adriana Meza Soria Michele Merler Parameswaran Selvam Saptha Surendran Shivdeep Singh Manish Sethi Xuan-Hong Dang Pengyuan Li Kun-Lung Wu Syed Zawad Andrew Coleman Matthew White Mark Lewis Raju Pavuluri Yan Koyfman Boris Lublinsky Maximilien de Bayser Ibrahim Abdelaziz Kinjal Basu Mayank Agarwal Yi Zhou Chris Johnson Aanchal Goyal Hima Patel Yousaf Shah Petros Zerfos Heiko Ludwig Asim Munawar Maxwell Crouse Pavan Kapanipathi Shweta Salaria Bob Calio Sophia Wen Seetharami Seelam Brian Belgodere Carlos Fonseca Amith Singhee Nirmit Desai David D. Cox Ruchir Puri \\\\u2020 Rameswar Panda \\\\u2020\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"meta-data\\\",\\\"prov\\\":[{\\\"bbox\\\":[108,538,504,647],\\\"page\\\":1,\\\"span\\\":[0,658]}]},{\\\"text\\\":\\\"IBM Research \\\\u22c6 Equal Contribution \\\\u2020 Corresponding Authors ruchir@us.ibm.com, rpanda@ibm.com\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"meta-data\\\",\\\"prov\\\":[{\\\"bbox\\\":[221,484,391,529],\\\"page\\\":1,\\\"span\\\":[0,91]}]},{\\\"text\\\":\\\"Abstract\\\",\\\"type\\\":\\\"subtitle-level-1\\\",\\\"name\\\":\\\"Section-header\\\",\\\"prov\\\":[{\\\"bbox\\\":[283,465,329,475],\\\"page\\\":1,\\\"span\\\":[0,8]}]},{\\\"text\\\":\\\"Large Language Models (LLMs) trained on code are revolutionizing the software development process. Increasingly, code LLMs are being integrated into software development environments to improve the productivity of human programmers, and LLM-based agents are beginning to show promise for handling complex tasks autonomously. Realizing the full potential of code LLMs requires a wide range of capabilities, including code generation, fixing bugs, explaining and documenting code, maintaining repositories, and more. In this work, we introduce the Granite series of decoder-only code models for code generative tasks, trained with code written in 116 programming languages. The Granite Code models family consists of models ranging in size from 3 to 34 billion parameters, suitable for applications ranging from complex application modernization tasks to on-device memory-constrained use cases. Evaluation on a comprehensive set of tasks demonstrates that Granite Code models consistently reaches state-of-the-art performance among available open-source code LLMs. The Granite Code model family was optimized for enterprise software development workflows and performs well across a range of coding tasks (e.g. code generation, fixing and explanation), making it a versatile \\\\\\\"all around\\\\\\\" code model. We release all our Granite Code models under an Apache 2.0 license for both research and commercial use.\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"text\\\",\\\"prov\\\":[{\\\"bbox\\\":[143,233,470,450],\\\"page\\\":1,\\\"span\\\":[0,1401]}]},{\\\"text\\\":\\\"\\\\u0089 https:\\\\/\\\\/github.com\\\\/ibm-granite\\\\/granite-code-models\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"reference\\\",\\\"prov\\\":[{\\\"bbox\\\":[177,211,434,222],\\\"page\\\":1,\\\"span\\\":[0,52]}]},{\\\"text\\\":\\\"1 Introduction\\\",\\\"type\\\":\\\"subtitle-level-1\\\",\\\"name\\\":\\\"Section-header\\\",\\\"prov\\\":[{\\\"bbox\\\":[108,179,195,188],\\\"page\\\":1,\\\"span\\\":[0,14]}]},{\\\"text\\\":\\\"Over the last several decades, software has been woven into the fabric of every aspect of our society. As demand for software development surges, it is more critical than ever to increase software development productivity, and LLMs provide promising path for augmenting human programmers. Prominent enterprise use cases for LLMs in software development productivity include code generation, code explanation, code fixing, unit test and documentation generation, application modernization, vulnerability detection, code translation, and more.\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"text\\\",\\\"prov\\\":[{\\\"bbox\\\":[107,88,504,164],\\\"page\\\":1,\\\"span\\\":[0,541]}]},{\\\"text\\\":\\\"Recent years have seen rapid progress in LLM's ability to generate and manipulate code, and a range of models with impressive coding abilities are available today. Models range in\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"text\\\",\\\"prov\\\":[{\\\"bbox\\\":[108,59,505,81],\\\"page\\\":1,\\\"span\\\":[0,179]}]},{\\\"text\\\":\\\"1\\\",\\\"type\\\":\\\"page-footer\\\",\\\"name\\\":\\\"Page-footer\\\",\\\"prov\\\":[{\\\"bbox\\\":[303,32,308,39],\\\"page\\\":1,\\\"span\\\":[0,1]}]},{\\\"text\\\":\\\"IBM Granite Code Models\\\",\\\"type\\\":\\\"page-header\\\",\\\"name\\\":\\\"Page-header\\\",\\\"prov\\\":[{\\\"bbox\\\":[108,756,504,764],\\\"page\\\":2,\\\"span\\\":[0,23]}]},{\\\"text\\\":\\\"Figure 1: Comparison of Granite-8B-Code (Base\\\\/Instruct) with other open source (code) LLMs of similar size on HumanEvalPack (Muennighoff et al. , 2023), spanning 3 coding tasks and 6 programming languages. See Tables 3 , 10 , 11 for more details. Best viewed in color.\\\",\\\"type\\\":\\\"caption\\\",\\\"name\\\":\\\"Caption\\\",\\\"prov\\\":[{\\\"bbox\\\":[107,358,505,391],\\\"page\\\":2,\\\"span\\\":[0,268]}]},{\\\"name\\\":\\\"Picture\\\",\\\"type\\\":\\\"figure\\\",\\\"$ref\\\":\\\"#\\\\/figures\\\\/0\\\"},{\\\"text\\\":\\\"size from single-digit billions of parameters (e.g. Llama-7B (Touvron et al. , 2023), Gemma7B (Gemma-Team et al. , 2024), etc.) to hundreds of billions: DBRX (Databricks), Arctic (Snowflake), Grok, Mixtral 8x22B (MistralAI), Command R+ (Cohere), and vary in the generality of intended use, with some models aiming to cover a range of uses outside of code, while others focus primarily on coding-related tasks (e.g. StarCoder (Li et al. , 2023a; Lozhkov et al. , 2024), CodeGen (Nijkamp et al. , 2023), CodeLlama (Roziere et al. ' ' , 2023), and CodeGemma (CodeGemma Team et al. , 2024)).\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"text\\\",\\\"prov\\\":[{\\\"bbox\\\":[107,253,505,329],\\\"page\\\":2,\\\"span\\\":[0,587]}]},{\\\"text\\\":\\\"However, there remain important gaps in the current field of LLMs for code, especially in the context of enterprise software development. First, while very large, generalist LLMs can achieve excellent coding performance, their size makes them expensive to deploy. Smaller code-focused models (Li et al. , 2023a; Lozhkov et al. , 2024; Nijkamp et al. , 2023; Roziere et al. ' ' , 2023; CodeGemma Team et al. , 2024) can achieve excellent code generation performance in a smaller and more flexible package, but performance in coding tasks beyond generation (e.g. fixing and explanation) can lag behind code generation performance.\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"text\\\",\\\"prov\\\":[{\\\"bbox\\\":[107,170,505,247],\\\"page\\\":2,\\\"span\\\":[0,628]}]},{\\\"text\\\":\\\"In many enterprise contexts, code LLM adoption can be further complicated by factors beyond the performance of the models. For instance, even open models are sometimes plagued by a lack of transparency about the data sources and data processing methods that went into model, which can erode trust in models in mission critical and regulated contexts. Furthermore, license terms in today's open LLMs can encumber and complicate an enterprise's ability to use a model.\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"text\\\",\\\"prov\\\":[{\\\"bbox\\\":[107,98,504,164],\\\"page\\\":2,\\\"span\\\":[0,466]}]},{\\\"text\\\":\\\"Here, we present Granite Code models, a series of highly capable code LLMs, designed to support enterprise software development across a wide range of coding tasks. Granite Code models has two main variants that we release in four different sizes (3B, 8B, 20B, and 34B):\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"text\\\",\\\"prov\\\":[{\\\"bbox\\\":[107,60,505,92],\\\"page\\\":2,\\\"span\\\":[0,270]}]},{\\\"text\\\":\\\"2\\\",\\\"type\\\":\\\"page-footer\\\",\\\"name\\\":\\\"Page-footer\\\",\\\"prov\\\":[{\\\"bbox\\\":[303,32,308,40],\\\"page\\\":2,\\\"span\\\":[0,1]}]},{\\\"text\\\":\\\"IBM Granite Code Models\\\",\\\"type\\\":\\\"page-header\\\",\\\"name\\\":\\\"Page-header\\\",\\\"prov\\\":[{\\\"bbox\\\":[108,756,504,764],\\\"page\\\":3,\\\"span\\\":[0,23]}]},{\\\"text\\\":\\\"\\\\u00b7 Granite Code Base: base foundation models for code-related tasks;\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"text\\\",\\\"prov\\\":[{\\\"bbox\\\":[133,698,439,708],\\\"page\\\":3,\\\"span\\\":[0,67]}]},{\\\"text\\\":\\\"\\\\u00b7 Granite Code Instruct: instruction following models finetuned using a combination of Git commits paired with human instructions and open-source synthetically generated code instruction datasets.\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"List-item\\\",\\\"prov\\\":[{\\\"bbox\\\":[133,657,504,690],\\\"page\\\":3,\\\"span\\\":[0,196]}]},{\\\"text\\\":\\\"The base models in the series have been trained from scratch with a two-phase training strategy. In phase 1, our model is trained on 3 to 4 trillion tokens sourced from 116 programming languages, ensuring a comprehensive understanding of programming languages and syntax. In phase 2, our model is further trained on 500 billion tokens with a carefully designed mixture of high-quality data from code and natural language domains to improve the model's ability to reason. We use the unsupervised language modeling objective to train the base models in both the phases of training. The instruct models are derived by further finetuning the above trained base models on a combination of a filtered variant of CommitPack (Muennighoff et al. , 2023), natural language instruction following datasets (OASST (Kopf et al. \\\\u00a8 \\\\u00a8 , 2023), HelpSteer (Wang et al. , 2023)) and open-source math datasets (MathInstruct (Yue et al. , 2023) and MetaMathQA (Yu et al. , 2023)), including synthetically generated code datasets for improving instruction following and reasoning capabilities.\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"text\\\",\\\"prov\\\":[{\\\"bbox\\\":[107,514,505,645],\\\"page\\\":3,\\\"span\\\":[0,1070]}]},{\\\"text\\\":\\\"We conduct extensive evaluations of our code LLMs on a comprehensive set of benchmarks, including HumanEvalPack (Muennighoff et al. , 2023), MBPP(+) (Austin et al. , 2021; Liu et al. , 2023a), RepoBench (Liu et al. , 2023b), ReCode (Wang et al. , 2022), and more. This set of benchmarks encompasses many different kinds of coding tasks beyond just code synthesis in Python, e.g., code fixing, code explanation, code editing, code translation, etc., across most major programming languages (Python, JavaScript, Java, Go, C++, Rust, etc.).\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"text\\\",\\\"prov\\\":[{\\\"bbox\\\":[107,442,505,508],\\\"page\\\":3,\\\"span\\\":[0,537]}]},{\\\"text\\\":\\\"Our findings reveal that among open-source models, the Granite Code models overall show very strong performance across all model sizes and benchmarks (often outperforming other open-source code models that are twice large compared to Granite). As an illustration, figure 1 (top) shows a comparison of Granite-8B-Code-Base with other open-source base code LLMs, including recent high-performing general purpose base LLMs like Mistral (Jiang et al. , 2023b) and LLama-3 (AI@Meta , 2024) on HumanEvalPack (Muennighoff et al. , 2023). While CodeGemma and StarCoder2 perform reasonably well in generating code, they perform significantly worse on the code fixing and explanation variants of HumanEvalPack. On average, Granite-8B-Code-Base outperforms the most competitive CodeGemma-8B model by almost 12 points on HumanEvalPack (33.2% vs 21.3%), despite being trained on significantly less number of tokens (4.5T vs 7.5T tokens). Besides base models, the instruction tuned variants of our Granite Code models also show strong performance on HumanEvalPack, outperforming other open-source (code) instruction models, demonstrating benefits to a wider set of coding tasks with natural language instructions (see figure 1 (bottom)).\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"text\\\",\\\"prov\\\":[{\\\"bbox\\\":[107,283,505,436],\\\"page\\\":3,\\\"span\\\":[0,1223]}]},{\\\"text\\\":\\\"Furthermore, since reasoning is critical for solving complicated questions and tasks, we also test our Granite-8B-Code-Base model on six mathematical benchmarks, including MATH (Cobbe et al. , 2021), GSM8K (Cobbe et al. , 2021) and problem solving with access to computational tools, where our Granite 8B model achieves better performance compared to most state-of-the-art 7B or 8B LLMs. For example, Granite-8B-Code-Base outperforms Llama-3-8B-Base by \\\\u223c12 points on GSM8K and \\\\u223c6 points on MATH (see table 15).\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"text\\\",\\\"prov\\\":[{\\\"bbox\\\":[107,211,504,276],\\\"page\\\":3,\\\"span\\\":[0,510]}]},{\\\"text\\\":\\\"The key advantages of Granite Code models include:\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"text\\\",\\\"prov\\\":[{\\\"bbox\\\":[107,194,341,205],\\\"page\\\":3,\\\"span\\\":[0,50]}]},{\\\"text\\\":\\\"\\\\u00b7 All-rounder Code LLM: Granite Code models achieve competitive or state-of-theart performance on different kinds of code-related tasks, including code generation, explanation, fixing, editing, translation, etc., demonstrating their ability to solve diverse coding tasks;\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"text\\\",\\\"prov\\\":[{\\\"bbox\\\":[133,138,506,183],\\\"page\\\":3,\\\"span\\\":[0,271]}]},{\\\"text\\\":\\\"\\\\u00b7 Trustworthy Enterprise-Grade LLM: All our models are trained on licensepermissible data collected following IBM's AI Ethics principles 1 and guided by IBM's Corporate Legal team for trustworthy enterprise usage. All the Granite Code models are released under the Apache 2.0 license.\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"text\\\",\\\"prov\\\":[{\\\"bbox\\\":[133,86,505,131],\\\"page\\\":3,\\\"span\\\":[0,284]}]},{\\\"text\\\":\\\"1 https:\\\\/\\\\/www.ibm.com\\\\/impact\\\\/ai-ethics\\\",\\\"type\\\":\\\"footnote\\\",\\\"name\\\":\\\"Footnote\\\",\\\"prov\\\":[{\\\"bbox\\\":[120,59,287,70],\\\"page\\\":3,\\\"span\\\":[0,38]}]},{\\\"text\\\":\\\"3\\\",\\\"type\\\":\\\"page-footer\\\",\\\"name\\\":\\\"Page-footer\\\",\\\"prov\\\":[{\\\"bbox\\\":[303,31,308,40],\\\"page\\\":3,\\\"span\\\":[0,1]}]},{\\\"text\\\":\\\"IBM Granite Code Models\\\",\\\"type\\\":\\\"page-header\\\",\\\"name\\\":\\\"Page-header\\\",\\\"prov\\\":[{\\\"bbox\\\":[108,756,504,764],\\\"page\\\":4,\\\"span\\\":[0,23]}]},{\\\"text\\\":\\\"We describe our entire data collection, filtering, and preprocessing pipeline in section 2. Section 3 describes the details of model architecture, followed by training details in Section 4. Section 5 provides the details about instruction tuning, and Section 6 describes the experiments and results comparing Granite Code models with other open-source LLMs.\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"text\\\",\\\"prov\\\":[{\\\"bbox\\\":[107,664,505,708],\\\"page\\\":4,\\\"span\\\":[0,357]}]},{\\\"text\\\":\\\"2 Data Collection\\\",\\\"type\\\":\\\"subtitle-level-1\\\",\\\"name\\\":\\\"Section-header\\\",\\\"prov\\\":[{\\\"bbox\\\":[108,638,211,648],\\\"page\\\":4,\\\"span\\\":[0,17]}]},{\\\"text\\\":\\\"In this section, we describe the process of crawling and filtering (Sec. 2.1), deduplication (Sec. 2.2), HAP\\\\/PII filtering (Sec. 2.3) used to prepare the code data for model training. We also provide an overview of high-quality natural language data used to enhance the model's language understanding and mathematical reasoning skills.\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"text\\\",\\\"prov\\\":[{\\\"bbox\\\":[107,582,504,626],\\\"page\\\":4,\\\"span\\\":[0,335]}]},{\\\"text\\\":\\\"2.1 Data Crawling and Filtering\\\",\\\"type\\\":\\\"subtitle-level-1\\\",\\\"name\\\":\\\"Section-header\\\",\\\"prov\\\":[{\\\"bbox\\\":[107,560,259,571],\\\"page\\\":4,\\\"span\\\":[0,31]}]},{\\\"text\\\":\\\"The pretraining code data was sourced from a combination of publicly available datasets like Github Code Clean 2 , StarCoderdata 3 , and additional public code repositories and issues from GitHub. We filter raw data to retain a list of 116 programming languages out of 300+ languages, as listed in Appendix A. The assignment of data to programming languages is performed based solely on file extension, similar to StarCoder (Li et al. , 2023a). After language filtering, we apply four key filtering rules to filter out lower-quality code (Li et al. , 2023a): (1) remove files with fewer than 25% alphabetic characters, (2) except for the XSLT language, filter out files where the string \\\\\\\", 'do_table_structure': True, 'do_ocr': False}\n", - "00:21:36 INFO - pipeline id pipeline_id\n", - "00:21:36 INFO - job details {'job category': 'preprocessing', 'job name': 'pdf2parquet', 'job type': 'pure python', 'job id': 'job_id'}\n", - "00:21:36 INFO - code location None\n", - "00:21:36 INFO - data factory data_ is using local data access: input_folder - Input-Test-Data output_folder - Output-Test-Data\n", - "00:21:36 INFO - data factory data_ max_files -1, n_sample -1\n", - "00:21:36 INFO - data factory data_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.pdf'], files to checkpoint ['.parquet']\n", - "00:21:36 INFO - orchestrator pdf2parquet started at 2024-09-04 00:21:36\n", - "00:21:36 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] } ], diff --git a/examples/notebooks/Run_your_first_transform_ray.ipynb b/examples/notebooks/Run_your_first_transform_ray.ipynb index 8157d85bc..3a8c2ec3f 100644 --- a/examples/notebooks/Run_your_first_transform_ray.ipynb +++ b/examples/notebooks/Run_your_first_transform_ray.ipynb @@ -8,9 +8,14 @@ "\n", "This notebook guides you through running your first data preparation transformation using the data-prep-kit. In this example, we will demonstrate a transformation that takes PDF files as input and extracts their content.\n", "\n", - "This notebook uses Ray framework, for pure python version see this notebook: [Run_your_first_transform_python.ipynb](Run_your_first_transform_python.ipynb)\n", + "[Ray](https://docs.ray.io/en/latest/index.html) is a powerful framework that enables parallelization while still allowing you to run it efficiently on a local machine, such as your laptop. \n", "\n", - "[Ray](https://docs.ray.io/en/latest/index.html) is a powerful framework that enables parallelization while still allowing you to run it efficiently on a local machine, such as your laptop. " + "\n", + "**Notebook versions:**\n", + "\n", + "- Pure python (run locally): [Run_your_first_transform_python.ipynb](Run_your_first_transform_python.ipynb)\n", + "- Ray version (run locally): this notebook\n", + "- Google Colab friendly notebook: [Run_your_first_transform_colab.ipynb](Run_your_first_transform_colab.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/IBM/data-prep-kit/blob/dev/examples/notebooks/Run_your_first_transform_colab.ipynb)" ] }, { @@ -26,14 +31,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Step-2: Get Data\n", + "## Step-2: Inspect Data\n", + "\n", + "For this example, we will show PDF processing capabilities of DPK. The PDF documents are in [Input-Test-Data](Input-Test-Data) folder.\n", "\n", - "For this example, we will show PDF processing capabilities of DPK. And we will download and use this PDF documents\n", + "- [IBM Granite model paper](Input-Test-Data/GraniteCodePaper.pdf) . [Arxiv link](https://arxiv.org/abs/2405.04324)\n", + "- [Attention is all you need paper](Input-Test-Data/attention_is_all_you_need.pdf) . [Arxiv link](https://arxiv.org/pdf/1706.03762) - seminal paper on transformer/attention architecture\n", "\n", - "- [IBM Granite model](https://arxiv.org/abs/2405.04324)\n", - "- [Attention is all you need](https://arxiv.org/pdf/1706.03762) - seminal paper on transformer/attention architecture\n", "\n", - "The code below will download the PDF. Feel free to try your own PDFs to test it out" + "**Try your own PDFs** : You can drop any PDF documents into [Input-Test-Data](Input-Test-Data) folder and run this code." ] }, { @@ -46,60 +52,6 @@ "OUTPUT_DIR = 'Output-Test-Data'" ] }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Local file 'Input-Test-Data/Granite_code_models.pdf' (1.27 MB) already exists. Skipping download.\n", - "Local file 'Input-Test-Data/attention_is_all_you_need.pdf' (2.22 MB) already exists. Skipping download.\n" - ] - } - ], - "source": [ - "## This cell will download the input files\n", - "\n", - "import os\n", - "import shutil\n", - "import requests\n", - "from humanfriendly import format_size\n", - "\n", - "def download_file(url, local_file, chunk_size=1024*1024):\n", - " # Check if the local file already exists\n", - " if os.path.exists(local_file):\n", - " file_size = format_size(os.path.getsize(local_file))\n", - " print(f\"Local file '{local_file}' ({file_size}) already exists. Skipping download.\")\n", - " return\n", - "\n", - " # Create the directory if it doesn't exist\n", - " os.makedirs(os.path.dirname(local_file), exist_ok=True)\n", - "\n", - " # Stream the file download\n", - " with requests.get(url, stream=True) as r:\n", - " r.raise_for_status()\n", - " with open(local_file, 'wb') as f:\n", - " for chunk in r.iter_content(chunk_size=chunk_size):\n", - " if chunk: # filter out keep-alive new chunks\n", - " f.write(chunk)\n", - " print()\n", - " file_size = format_size(os.path.getsize(local_file))\n", - " print(f\"{local_file} ({file_size}) downloaded successfully.\")\n", - "## --- end: download_file ------\n", - "\n", - "## setup input/output directories\n", - "shutil.os.makedirs(INPUT_DIR, exist_ok=True)\n", - "shutil.rmtree(OUTPUT_DIR, ignore_errors=True)\n", - "shutil.os.makedirs(OUTPUT_DIR, exist_ok=True)\n", - "\n", - "## Download PDF files\n", - "download_file (url = 'https://arxiv.org/pdf/2405.04324', local_file = os.path.join(INPUT_DIR, 'Granite_code_models.pdf' ))\n", - "download_file (url = 'https://arxiv.org/pdf/1706.03762', local_file = os.path.join(INPUT_DIR, 'attention_is_all_you_need.pdf' ))" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -111,11 +63,11 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "import sys\n", + "import os, sys\n", "import ast\n", "\n", "# Utilities from the data-prep-kit's data-processing-lib library provide functions and classes for parameter management, \n", @@ -182,40 +134,40 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "00:34:57 INFO - Running locally\n", - "00:34:57 INFO - pdf2parquet parameters are : {'artifacts_path': None, 'contents_type': , 'do_table_structure': True, 'do_ocr': False}\n", - "00:34:57 INFO - data factory data_ is using local data access: input_folder - Input-Test-Data output_folder - Output-Test-Data\n", - "00:34:57 INFO - data factory data_ max_files -1, n_sample -1\n", - "00:34:57 INFO - data factory data_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.pdf'], files to checkpoint ['.parquet']\n", - "00:34:57 INFO - pipeline id pipeline_id\n", - "00:34:57 INFO - code location None\n", - "00:34:57 INFO - number of workers 2 worker options {'num_cpus': 4.0, 'memory': 2147483648, 'max_restarts': -1}\n", - "00:34:57 INFO - actor creation delay 0\n", - "00:34:57 INFO - job details {'job category': 'preprocessing', 'job name': 'pdf2parquet', 'job type': 'ray', 'job id': 'job_id'}\n", - "2024-09-04 00:34:59,763\tINFO worker.py:1744 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32mhttp://127.0.0.1:8265 \u001b[39m\u001b[22m\n", - "\u001b[36m(orchestrate pid=313386)\u001b[0m 00:35:04 INFO - orchestrator started at 2024-09-04 00:35:04\n", - "\u001b[36m(orchestrate pid=313386)\u001b[0m 00:35:04 INFO - Number of files is 2, source profile {'max_file_size': 2.112621307373047, 'min_file_size': 1.2146415710449219, 'total_file_size': 3.3272628784179688}\n", - "\u001b[36m(orchestrate pid=313386)\u001b[0m 00:35:04 INFO - Cluster resources: {'cpus': 16, 'gpus': 1, 'memory': 5.692151642404497, 'object_store': 2.846075820736587}\n", - "\u001b[36m(orchestrate pid=313386)\u001b[0m 00:35:04 INFO - Number of workers - 2 with {'num_cpus': 4.0, 'memory': 2147483648, 'max_restarts': -1} each\n", - "\u001b[36m(orchestrate pid=313386)\u001b[0m 00:35:04 INFO - Completed 0 files (0.0%) in 7.74065653483073e-06 min. Waiting for completion\n", - "\u001b[36m(RayTransformFileProcessor pid=314343)\u001b[0m 00:35:08 INFO - Initializing models\n", - "Fetching 7 files: 100%|██████████| 7/7 [00:00<00:00, 102300.10it/s]\n", - "\u001b[36m(RayTransformFileProcessor pid=314343)\u001b[0m /home/sujee/apps/anaconda3/envs/data-prep-kit-3-py311/lib/python3.11/site-packages/torch/nn/modules/transformer.py:307: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance)\n", - "\u001b[36m(RayTransformFileProcessor pid=314343)\u001b[0m warnings.warn(f\"enable_nested_tensor is True, but self.use_nested_tensor is False because {why_not_sparsity_fast_path}\")\n", - "\u001b[36m(orchestrate pid=313386)\u001b[0m 00:36:16 INFO - Completed processing 2 files in 1.2009095589319865 min\n", - "\u001b[36m(orchestrate pid=313386)\u001b[0m 00:36:16 INFO - done flushing in 0.0009620189666748047 sec\n", - "\u001b[36m(RayTransformFileProcessor pid=314342)\u001b[0m 00:35:09 INFO - Initializing models\n", - "Fetching 7 files: 100%|██████████| 7/7 [00:00<00:00, 166818.91it/s]\n", - "\u001b[36m(RayTransformFileProcessor pid=314342)\u001b[0m /home/sujee/apps/anaconda3/envs/data-prep-kit-3-py311/lib/python3.11/site-packages/torch/nn/modules/transformer.py:307: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance)\n", - "\u001b[36m(RayTransformFileProcessor pid=314342)\u001b[0m warnings.warn(f\"enable_nested_tensor is True, but self.use_nested_tensor is False because {why_not_sparsity_fast_path}\")\n", - "00:36:26 INFO - Completed execution in 1.479572606086731 min, execution result 0\n" + "13:28:13 INFO - Running locally\n", + "13:28:13 INFO - pdf2parquet parameters are : {'artifacts_path': None, 'contents_type': , 'do_table_structure': True, 'do_ocr': False}\n", + "13:28:13 INFO - data factory data_ is using local data access: input_folder - Input-Test-Data output_folder - Output-Test-Data\n", + "13:28:13 INFO - data factory data_ max_files -1, n_sample -1\n", + "13:28:13 INFO - data factory data_ Not using data sets, checkpointing False, max files -1, random samples -1, files to use ['.pdf'], files to checkpoint ['.parquet']\n", + "13:28:13 INFO - pipeline id pipeline_id\n", + "13:28:13 INFO - code location None\n", + "13:28:13 INFO - number of workers 2 worker options {'num_cpus': 4.0, 'memory': 2147483648, 'max_restarts': -1}\n", + "13:28:13 INFO - actor creation delay 0\n", + "13:28:13 INFO - job details {'job category': 'preprocessing', 'job name': 'pdf2parquet', 'job type': 'ray', 'job id': 'job_id'}\n", + "2024-09-04 13:28:16,713\tINFO worker.py:1744 -- Started a local Ray instance. View the dashboard at \u001b[1m\u001b[32mhttp://127.0.0.1:8265 \u001b[39m\u001b[22m\n", + "\u001b[36m(orchestrate pid=1191546)\u001b[0m 13:28:21 INFO - orchestrator started at 2024-09-04 13:28:21\n", + "\u001b[36m(orchestrate pid=1191546)\u001b[0m 13:28:21 INFO - Number of files is 2, source profile {'max_file_size': 2.112621307373047, 'min_file_size': 1.2146415710449219, 'total_file_size': 3.3272628784179688}\n", + "\u001b[36m(orchestrate pid=1191546)\u001b[0m 13:28:21 INFO - Cluster resources: {'cpus': 16, 'gpus': 1, 'memory': 5.519162750802934, 'object_store': 2.759581374935806}\n", + "\u001b[36m(orchestrate pid=1191546)\u001b[0m 13:28:21 INFO - Number of workers - 2 with {'num_cpus': 4.0, 'memory': 2147483648, 'max_restarts': -1} each\n", + "\u001b[36m(orchestrate pid=1191546)\u001b[0m 13:28:21 INFO - Completed 0 files (0.0%) in 6.210803985595703e-06 min. Waiting for completion\n", + "\u001b[36m(RayTransformFileProcessor pid=1192507)\u001b[0m 13:28:25 INFO - Initializing models\n", + "Fetching 7 files: 100%|██████████| 7/7 [00:00<00:00, 58956.08it/s]\n", + "\u001b[36m(RayTransformFileProcessor pid=1192506)\u001b[0m /home/sujee/apps/anaconda3/envs/data-prep-kit-3-py311/lib/python3.11/site-packages/torch/nn/modules/transformer.py:307: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance)\n", + "\u001b[36m(RayTransformFileProcessor pid=1192506)\u001b[0m warnings.warn(f\"enable_nested_tensor is True, but self.use_nested_tensor is False because {why_not_sparsity_fast_path}\")\n", + "\u001b[36m(orchestrate pid=1191546)\u001b[0m 13:29:32 INFO - Completed processing 2 files in 1.1829445679982504 min\n", + "\u001b[36m(orchestrate pid=1191546)\u001b[0m 13:29:32 INFO - done flushing in 0.0008149147033691406 sec\n", + "\u001b[36m(RayTransformFileProcessor pid=1192506)\u001b[0m 13:28:25 INFO - Initializing models\n", + "Fetching 7 files: 100%|██████████| 7/7 [00:00<00:00, 164023.06it/s]\n", + "\u001b[36m(RayTransformFileProcessor pid=1192507)\u001b[0m /home/sujee/apps/anaconda3/envs/data-prep-kit-3-py311/lib/python3.11/site-packages/torch/nn/modules/transformer.py:307: UserWarning: enable_nested_tensor is True, but self.use_nested_tensor is False because encoder_layer.self_attn.batch_first was not True(use batch_first for better inference performance)\n", + "\u001b[36m(RayTransformFileProcessor pid=1192507)\u001b[0m warnings.warn(f\"enable_nested_tensor is True, but self.use_nested_tensor is False because {why_not_sparsity_fast_path}\")\n", + "13:29:42 INFO - Completed execution in 1.479382046063741 min, execution result 0\n" ] }, { @@ -223,7 +175,7 @@ "output_type": "stream", "text": [ "✅ Tranformation run completed successfully\n", - "CPU times: user 511 ms, sys: 264 ms, total: 775 ms\n", + "CPU times: user 485 ms, sys: 281 ms, total: 766 ms\n", "Wall time: 1min 30s\n" ] } @@ -256,7 +208,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -277,7 +229,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -318,61 +270,61 @@ " \n", " \n", " 0\n", + " GraniteCodePaper.pdf\n", + " {\"_name\":\"\",\"type\":\"pdf-document\",\"description...\n", + " 28\n", + " 17\n", + " 320\n", + " 6769e793-6e20-4dfc-8fe6-12ecc1944aac\n", + " pdf\n", + " d1f30543fce4e18d223cf4ba7728733e169201b4df5d5d...\n", + " 584819\n", + " 2024-09-04T13:29:32.380832\n", + " 53.872901\n", + " GraniteCodePaper.pdf\n", + " \n", + " \n", + " 1\n", " attention_is_all_you_need.pdf\n", " {\"_name\":\"\",\"type\":\"pdf-document\",\"description...\n", " 15\n", " 4\n", " 193\n", - " 39684ed7-3033-47fb-ae8f-26f470932cd3\n", + " 20925bb2-4e14-4e58-8ae1-0ce971dfc2bf\n", " pdf\n", " 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] } ], @@ -407,13 +359,6 @@ "column_json = json.dumps(column_list, indent=4)\n", "pprint.pprint(column_json[:2000]) # display first few lines" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From 944469f051d49ad199839cce10ceba4179b62c4f Mon Sep 17 00:00:00 2001 From: Sujee Maniyam Date: Wed, 4 Sep 2024 14:19:09 -0700 Subject: [PATCH 2/3] updated file links --- .../Run_your_first_transform_colab.ipynb | 1976 ++++++++--------- 1 file changed, 988 insertions(+), 988 deletions(-) diff --git a/examples/notebooks/Run_your_first_transform_colab.ipynb b/examples/notebooks/Run_your_first_transform_colab.ipynb index ee179f9c5..d15f37022 100644 --- a/examples/notebooks/Run_your_first_transform_colab.ipynb +++ b/examples/notebooks/Run_your_first_transform_colab.ipynb @@ -12,8 +12,8 @@ "\n", "**Notebook versions:**\n", "\n", - "- Pure python (run locally): [Run_your_first_transform_python.ipynb](Run_your_first_transform_python.ipynb)\n", - "- Ray version (run locally): [Run_your_first_transform_ray.ipynb](Run_your_first_transform_ray.ipynb)\n", + "- Pure python (run locally): [Run_your_first_transform_python.ipynb](https://github.com/IBM/data-prep-kit/blob/dev/examples/notebooks/Run_your_first_transform_python.ipynb)\n", + "- Ray version (run locally): [Run_your_first_transform_ray.ipynb](https://github.com/IBM/data-prep-kit/blob/dev/examples/notebooks/Run_your_first_transform_ray.ipynb)\n", "- Google Colab friendly notebook: this notebook | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/IBM/data-prep-kit/blob/dev/examples/notebooks/Run_your_first_transform_colab.ipynb)" ] }, @@ -29,17 +29,20 @@ }, { "cell_type": "code", - "source": [ - "!pip install data-prep-toolkit-transforms-ray\n" - ], + "execution_count": null, "metadata": { "id": "ENbZ2ZNsvqrh" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [ + "!pip install data-prep-toolkit-transforms-ray\n" + ] }, { "cell_type": "markdown", + "metadata": { + "id": "T8MnqTZ-wC2P" + }, "source": [ "## Restart Runtime\n", "\n", @@ -48,10 +51,7 @@ "You do this by going to **`Runtime --> Restart Session`**\n", "\n", "Then continue from **Step-2** below" - ], - "metadata": { - "id": "T8MnqTZ-wC2P" - } + ] }, { "cell_type": "markdown", @@ -93,8 +93,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "\n", "Input-Test-Data/Granite_code_models.pdf (1.27 MB) downloaded successfully.\n", @@ -314,8 +314,8 @@ }, "outputs": [ { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ "20:39:40 INFO - pdf2parquet parameters are : {'artifacts_path': None, 'contents_type': , 'do_table_structure': True, 'do_ocr': False}\n", "INFO:pdf2parquet_transform:pdf2parquet parameters are : {'artifacts_path': None, 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[cs.AI] 7 May 2024\\\",\\\"type\\\":\\\"page-header\\\",\\\"name\\\":\\\"Page-header\\\",\\\"prov\\\":[{\\\"bbox\\\":[17,238,36,576],\\\"page\\\":1,\\\"span\\\":[0,37]}]},{\\\"text\\\":\\\"IBM Granite Code Models\\\",\\\"type\\\":\\\"page-header\\\",\\\"name\\\":\\\"Page-header\\\",\\\"prov\\\":[{\\\"bbox\\\":[108,756,504,764],\\\"page\\\":1,\\\"span\\\":[0,23]}]},{\\\"text\\\":\\\"Granite Code Models: A Family of Open Foundation Models for Code Intelligence\\\",\\\"type\\\":\\\"subtitle-level-1\\\",\\\"name\\\":\\\"Section-header\\\",\\\"prov\\\":[{\\\"bbox\\\":[143,673,468,711],\\\"page\\\":1,\\\"span\\\":[0,77]}]},{\\\"text\\\":\\\"Mayank Mishra \\\\u22c6 Matt Stallone \\\\u22c6 Gaoyuan Zhang \\\\u22c6 Yikang Shen Aditya Prasad Adriana Meza Soria Michele Merler Parameswaran Selvam Saptha Surendran Shivdeep Singh Manish Sethi Xuan-Hong Dang Pengyuan Li Kun-Lung Wu Syed Zawad Andrew Coleman Matthew White Mark Lewis Raju Pavuluri Yan Koyfman Boris Lublinsky Maximilien de Bayser Ibrahim Abdelaziz Kinjal Basu Mayank Agarwal Yi Zhou Chris Johnson Aanchal Goyal Hima Patel Yousaf Shah Petros Zerfos Heiko Ludwig Asim Munawar Maxwell Crouse Pavan Kapanipathi Shweta Salaria Bob Calio Sophia Wen Seetharami Seelam Brian Belgodere Carlos Fonseca Amith Singhee Nirmit Desai David D. Cox Ruchir Puri \\\\u2020 Rameswar Panda \\\\u2020\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"meta-data\\\",\\\"prov\\\":[{\\\"bbox\\\":[108,538,504,647],\\\"page\\\":1,\\\"span\\\":[0,658]}]},{\\\"text\\\":\\\"IBM Research \\\\u22c6 Equal Contribution \\\\u2020 Corresponding Authors ruchir@us.ibm.com, rpanda@ibm.com\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"meta-data\\\",\\\"prov\\\":[{\\\"bbox\\\":[221,484,391,529],\\\"page\\\":1,\\\"span\\\":[0,91]}]},{\\\"text\\\":\\\"Abstract\\\",\\\"type\\\":\\\"subtitle-level-1\\\",\\\"name\\\":\\\"Section-header\\\",\\\"prov\\\":[{\\\"bbox\\\":[283,465,329,475],\\\"page\\\":1,\\\"span\\\":[0,8]}]},{\\\"text\\\":\\\"Large Language Models (LLMs) trained on code are revolutionizing the software development process. Increasingly, code LLMs are being integrated into software development environments to improve the productivity of human programmers, and LLM-based agents are beginning to show promise for handling complex tasks autonomously. Realizing the full potential of code LLMs requires a wide range of capabilities, including code generation, fixing bugs, explaining and documenting code, maintaining repositories, and more. In this work, we introduce the Granite series of decoder-only code models for code generative tasks, trained with code written in 116 programming languages. The Granite Code models family consists of models ranging in size from 3 to 34 billion parameters, suitable for applications ranging from complex application modernization tasks to on-device memory-constrained use cases. Evaluation on a comprehensive set of tasks demonstrates that Granite Code models consistently reaches state-of-the-art performance among available open-source code LLMs. The Granite Code model family was optimized for enterprise software development workflows and performs well across a range of coding tasks (e.g. code generation, fixing and explanation), making it a versatile \\\\\\\"all around\\\\\\\" code model. We release all our Granite Code models under an Apache 2.0 license for both research and commercial use.\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"text\\\",\\\"prov\\\":[{\\\"bbox\\\":[143,233,470,450],\\\"page\\\":1,\\\"span\\\":[0,1401]}]},{\\\"text\\\":\\\"\\\\u0089 https:\\\\/\\\\/github.com\\\\/ibm-granite\\\\/granite-code-models\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"reference\\\",\\\"prov\\\":[{\\\"bbox\\\":[177,211,434,222],\\\"page\\\":1,\\\"span\\\":[0,52]}]},{\\\"text\\\":\\\"1 Introduction\\\",\\\"type\\\":\\\"subtitle-level-1\\\",\\\"name\\\":\\\"Section-header\\\",\\\"prov\\\":[{\\\"bbox\\\":[108,179,195,188],\\\"page\\\":1,\\\"span\\\":[0,14]}]},{\\\"text\\\":\\\"Over the last several decades, software has been woven into the fabric of every aspect of our society. As demand for software development surges, it is more critical than ever to increase software development productivity, and LLMs provide promising path for augmenting human programmers. Prominent enterprise use cases for LLMs in software development productivity include code generation, code explanation, code fixing, unit test and documentation generation, application modernization, vulnerability detection, code translation, and more.\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"text\\\",\\\"prov\\\":[{\\\"bbox\\\":[107,88,504,164],\\\"page\\\":1,\\\"span\\\":[0,541]}]},{\\\"text\\\":\\\"Recent years have seen rapid progress in LLM's ability to generate and manipulate code, and a range of models with impressive coding abilities are available today. Models range in\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"text\\\",\\\"prov\\\":[{\\\"bbox\\\":[108,59,505,81],\\\"page\\\":1,\\\"span\\\":[0,179]}]},{\\\"text\\\":\\\"1\\\",\\\"type\\\":\\\"page-footer\\\",\\\"name\\\":\\\"Page-footer\\\",\\\"prov\\\":[{\\\"bbox\\\":[303,32,308,39],\\\"page\\\":1,\\\"span\\\":[0,1]}]},{\\\"text\\\":\\\"IBM Granite Code Models\\\",\\\"type\\\":\\\"page-header\\\",\\\"name\\\":\\\"Page-header\\\",\\\"prov\\\":[{\\\"bbox\\\":[108,756,504,764],\\\"page\\\":2,\\\"span\\\":[0,23]}]},{\\\"text\\\":\\\"Figure 1: Comparison of Granite-8B-Code (Base\\\\/Instruct) with other open source (code) LLMs of similar size on HumanEvalPack (Muennighoff et al. , 2023), spanning 3 coding tasks and 6 programming languages. See Tables 3 , 10 , 11 for more details. Best viewed in color.\\\",\\\"type\\\":\\\"caption\\\",\\\"name\\\":\\\"Caption\\\",\\\"prov\\\":[{\\\"bbox\\\":[107,358,505,391],\\\"page\\\":2,\\\"span\\\":[0,268]}]},{\\\"name\\\":\\\"Picture\\\",\\\"type\\\":\\\"figure\\\",\\\"$ref\\\":\\\"#\\\\/figures\\\\/0\\\"},{\\\"text\\\":\\\"size from single-digit billions of parameters (e.g. Llama-7B (Touvron et al. , 2023), Gemma7B (Gemma-Team et al. , 2024), etc.) to hundreds of billions: DBRX (Databricks), Arctic (Snowflake), Grok, Mixtral 8x22B (MistralAI), Command R+ (Cohere), and vary in the generality of intended use, with some models aiming to cover a range of uses outside of code, while others focus primarily on coding-related tasks (e.g. StarCoder (Li et al. , 2023a; Lozhkov et al. , 2024), CodeGen (Nijkamp et al. , 2023), CodeLlama (Roziere et al. ' ' , 2023), and CodeGemma (CodeGemma Team et al. , 2024)).\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"text\\\",\\\"prov\\\":[{\\\"bbox\\\":[107,253,505,329],\\\"page\\\":2,\\\"span\\\":[0,587]}]},{\\\"text\\\":\\\"However, there remain important gaps in the current field of LLMs for code, especially in the context of enterprise software development. First, while very large, generalist LLMs can achieve excellent coding performance, their size makes them expensive to deploy. Smaller code-focused models (Li et al. , 2023a; Lozhkov et al. , 2024; Nijkamp et al. , 2023; Roziere et al. ' ' , 2023; CodeGemma Team et al. , 2024) can achieve excellent code generation performance in a smaller and more flexible package, but performance in coding tasks beyond generation (e.g. fixing and explanation) can lag behind code generation performance.\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"text\\\",\\\"prov\\\":[{\\\"bbox\\\":[107,170,505,247],\\\"page\\\":2,\\\"span\\\":[0,628]}]},{\\\"text\\\":\\\"In many enterprise contexts, code LLM adoption can be further complicated by factors beyond the performance of the models. For instance, even open models are sometimes plagued by a lack of transparency about the data sources and data processing methods that went into model, which can erode trust in models in mission critical and regulated contexts. Furthermore, license terms in today's open LLMs can encumber and complicate an enterprise's ability to use a model.\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"text\\\",\\\"prov\\\":[{\\\"bbox\\\":[107,98,504,164],\\\"page\\\":2,\\\"span\\\":[0,466]}]},{\\\"text\\\":\\\"Here, we present Granite Code models, a series of highly capable code LLMs, designed to support enterprise software development across a wide range of coding tasks. Granite Code models has two main variants that we release in four different sizes (3B, 8B, 20B, and 34B):\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"text\\\",\\\"prov\\\":[{\\\"bbox\\\":[107,60,505,92],\\\"page\\\":2,\\\"span\\\":[0,270]}]},{\\\"text\\\":\\\"2\\\",\\\"type\\\":\\\"page-footer\\\",\\\"name\\\":\\\"Page-footer\\\",\\\"prov\\\":[{\\\"bbox\\\":[303,32,308,40],\\\"page\\\":2,\\\"span\\\":[0,1]}]},{\\\"text\\\":\\\"IBM Granite Code Models\\\",\\\"type\\\":\\\"page-header\\\",\\\"name\\\":\\\"Page-header\\\",\\\"prov\\\":[{\\\"bbox\\\":[108,756,504,764],\\\"page\\\":3,\\\"span\\\":[0,23]}]},{\\\"text\\\":\\\"\\\\u00b7 Granite Code Base: base foundation models for code-related tasks;\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"text\\\",\\\"prov\\\":[{\\\"bbox\\\":[133,698,439,708],\\\"page\\\":3,\\\"span\\\":[0,67]}]},{\\\"text\\\":\\\"\\\\u00b7 Granite Code Instruct: instruction following models finetuned using a combination of Git commits paired with human instructions and open-source synthetically generated code instruction datasets.\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"List-item\\\",\\\"prov\\\":[{\\\"bbox\\\":[133,657,504,690],\\\"page\\\":3,\\\"span\\\":[0,196]}]},{\\\"text\\\":\\\"The base models in the series have been trained from scratch with a two-phase training strategy. In phase 1, our model is trained on 3 to 4 trillion tokens sourced from 116 programming languages, ensuring a comprehensive understanding of programming languages and syntax. In phase 2, our model is further trained on 500 billion tokens with a carefully designed mixture of high-quality data from code and natural language domains to improve the model's ability to reason. We use the unsupervised language modeling objective to train the base models in both the phases of training. The instruct models are derived by further finetuning the above trained base models on a combination of a filtered variant of CommitPack (Muennighoff et al. , 2023), natural language instruction following datasets (OASST (Kopf et al. \\\\u00a8 \\\\u00a8 , 2023), HelpSteer (Wang et al. , 2023)) and open-source math datasets (MathInstruct (Yue et al. , 2023) and MetaMathQA (Yu et al. , 2023)), including synthetically generated code datasets for improving instruction following and reasoning capabilities.\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"text\\\",\\\"prov\\\":[{\\\"bbox\\\":[107,514,505,645],\\\"page\\\":3,\\\"span\\\":[0,1070]}]},{\\\"text\\\":\\\"We conduct extensive evaluations of our code LLMs on a comprehensive set of benchmarks, including HumanEvalPack (Muennighoff et al. , 2023), MBPP(+) (Austin et al. , 2021; Liu et al. , 2023a), RepoBench (Liu et al. , 2023b), ReCode (Wang et al. , 2022), and more. This set of benchmarks encompasses many different kinds of coding tasks beyond just code synthesis in Python, e.g., code fixing, code explanation, code editing, code translation, etc., across most major programming languages (Python, JavaScript, Java, Go, C++, Rust, etc.).\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"text\\\",\\\"prov\\\":[{\\\"bbox\\\":[107,442,505,508],\\\"page\\\":3,\\\"span\\\":[0,537]}]},{\\\"text\\\":\\\"Our findings reveal that among open-source models, the Granite Code models overall show very strong performance across all model sizes and benchmarks (often outperforming other open-source code models that are twice large compared to Granite). As an illustration, figure 1 (top) shows a comparison of Granite-8B-Code-Base with other open-source base code LLMs, including recent high-performing general purpose base LLMs like Mistral (Jiang et al. , 2023b) and LLama-3 (AI@Meta , 2024) on HumanEvalPack (Muennighoff et al. , 2023). While CodeGemma and StarCoder2 perform reasonably well in generating code, they perform significantly worse on the code fixing and explanation variants of HumanEvalPack. On average, Granite-8B-Code-Base outperforms the most competitive CodeGemma-8B model by almost 12 points on HumanEvalPack (33.2% vs 21.3%), despite being trained on significantly less number of tokens (4.5T vs 7.5T tokens). Besides base models, the instruction tuned variants of our Granite Code models also show strong performance on HumanEvalPack, outperforming other open-source (code) instruction models, demonstrating benefits to a wider set of coding tasks with natural language instructions (see figure 1 (bottom)).\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"text\\\",\\\"prov\\\":[{\\\"bbox\\\":[107,283,505,436],\\\"page\\\":3,\\\"span\\\":[0,1223]}]},{\\\"text\\\":\\\"Furthermore, since reasoning is critical for solving complicated questions and tasks, we also test our Granite-8B-Code-Base model on six mathematical benchmarks, including MATH (Cobbe et al. , 2021), GSM8K (Cobbe et al. , 2021) and problem solving with access to computational tools, where our Granite 8B model achieves better performance compared to most state-of-the-art 7B or 8B LLMs. For example, Granite-8B-Code-Base outperforms Llama-3-8B-Base by \\\\u223c12 points on GSM8K and \\\\u223c6 points on MATH (see table 15).\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"text\\\",\\\"prov\\\":[{\\\"bbox\\\":[107,211,504,276],\\\"page\\\":3,\\\"span\\\":[0,510]}]},{\\\"text\\\":\\\"The key advantages of Granite Code models include:\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"text\\\",\\\"prov\\\":[{\\\"bbox\\\":[107,194,341,205],\\\"page\\\":3,\\\"span\\\":[0,50]}]},{\\\"text\\\":\\\"\\\\u00b7 All-rounder Code LLM: Granite Code models achieve competitive or state-of-theart performance on different kinds of code-related tasks, including code generation, explanation, fixing, editing, translation, etc., demonstrating their ability to solve diverse coding tasks;\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"text\\\",\\\"prov\\\":[{\\\"bbox\\\":[133,138,506,183],\\\"page\\\":3,\\\"span\\\":[0,271]}]},{\\\"text\\\":\\\"\\\\u00b7 Trustworthy Enterprise-Grade LLM: All our models are trained on licensepermissible data collected following IBM's AI Ethics principles 1 and guided by IBM's Corporate Legal team for trustworthy enterprise usage. All the Granite Code models are released under the Apache 2.0 license.\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"text\\\",\\\"prov\\\":[{\\\"bbox\\\":[133,86,505,131],\\\"page\\\":3,\\\"span\\\":[0,284]}]},{\\\"text\\\":\\\"1 https:\\\\/\\\\/www.ibm.com\\\\/impact\\\\/ai-ethics\\\",\\\"type\\\":\\\"footnote\\\",\\\"name\\\":\\\"Footnote\\\",\\\"prov\\\":[{\\\"bbox\\\":[120,59,287,70],\\\"page\\\":3,\\\"span\\\":[0,38]}]},{\\\"text\\\":\\\"3\\\",\\\"type\\\":\\\"page-footer\\\",\\\"name\\\":\\\"Page-footer\\\",\\\"prov\\\":[{\\\"bbox\\\":[303,31,308,40],\\\"page\\\":3,\\\"span\\\":[0,1]}]},{\\\"text\\\":\\\"IBM Granite Code Models\\\",\\\"type\\\":\\\"page-header\\\",\\\"name\\\":\\\"Page-header\\\",\\\"prov\\\":[{\\\"bbox\\\":[108,756,504,764],\\\"page\\\":4,\\\"span\\\":[0,23]}]},{\\\"text\\\":\\\"We describe our entire data collection, filtering, and preprocessing pipeline in section 2. Section 3 describes the details of model architecture, followed by training details in Section 4. Section 5 provides the details about instruction tuning, and Section 6 describes the experiments and results comparing Granite Code models with other open-source LLMs.\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"text\\\",\\\"prov\\\":[{\\\"bbox\\\":[107,664,505,708],\\\"page\\\":4,\\\"span\\\":[0,357]}]},{\\\"text\\\":\\\"2 Data Collection\\\",\\\"type\\\":\\\"subtitle-level-1\\\",\\\"name\\\":\\\"Section-header\\\",\\\"prov\\\":[{\\\"bbox\\\":[108,638,211,648],\\\"page\\\":4,\\\"span\\\":[0,17]}]},{\\\"text\\\":\\\"In this section, we describe the process of crawling and filtering (Sec. 2.1), deduplication (Sec. 2.2), HAP\\\\/PII filtering (Sec. 2.3) used to prepare the code data for model training. We also provide an overview of high-quality natural language data used to enhance the model's language understanding and mathematical reasoning skills.\\\",\\\"type\\\":\\\"paragraph\\\",\\\"name\\\":\\\"text\\\",\\\"prov\\\":[{\\\"bbox\\\":[107,582,504,626],\\\"page\\\":4,\\\"span\\\":[0,335]}]},{\\\"text\\\":\\\"2.1 Data Crawling and Filtering\\\",\\\"type\\\":\\\"subtitle-level-1\\\",\\\"name\\\":\\\"Section-header\\\",\\\"prov\\\":[{\\\"bbox\\\":[107,560,259,571],\\\"page\\\":4,\\\"span\\\":[0,31]}]},{\\\"text\\\":\\\"The pretraining code data was sourced from a combination of publicly available datasets like Github Code Clean 2 , StarCoderdata 3 , and additional public code repositories and issues from GitHub. We filter raw data to retain a list of 116 programming languages out of 300+ languages, as listed in Appendix A. The assignment of data to programming languages is performed based solely on file extension, similar to StarCoder (Li et al. , 2023a). After language filtering, we apply four key filtering rules to filter out lower-quality code (Li et al. , 2023a): (1) remove files with fewer than 25% alphabetic characters, (2) except for the XSLT language, filter out files where the string \\\\\\\" Date: Wed, 4 Sep 2024 15:45:07 -0700 Subject: [PATCH 3/3] fixing pip install timeouts on google colab --- examples/notebooks/Run_your_first_transform_colab.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/notebooks/Run_your_first_transform_colab.ipynb b/examples/notebooks/Run_your_first_transform_colab.ipynb index d15f37022..8b99b8f36 100644 --- a/examples/notebooks/Run_your_first_transform_colab.ipynb +++ b/examples/notebooks/Run_your_first_transform_colab.ipynb @@ -35,7 +35,7 @@ }, "outputs": [], "source": [ - "!pip install data-prep-toolkit-transforms-ray\n" + "! pip install --default-timeout=100 data-prep-toolkit-transforms-ray\n" ] }, {