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a/01_standalone_examples/image-segmentation-local/Dockerfile b/01_standalone_examples/image-segmentation-local/Dockerfile new file mode 100644 index 000000000..0e40249da --- /dev/null +++ b/01_standalone_examples/image-segmentation-local/Dockerfile @@ -0,0 +1,29 @@ +FROM jupyter/all-spark-notebook:notebook-6.5.3 + +ENV HADOOP_AWS_VERSION=3.3.1 +ENV AWS_SDK_VERSION=1.11.901 +ENV LAKEFS_VERSION=1.1.0 + +USER root + +RUN wget https://repo1.maven.org/maven2/com/amazonaws/aws-java-sdk-bundle/${AWS_SDK_VERSION}/aws-java-sdk-bundle-${AWS_SDK_VERSION}.jar -O ${SPARK_HOME}/jars/aws-java-sdk-bundle-${AWS_SDK_VERSION}.jar +RUN wget https://repo1.maven.org/maven2/org/apache/hadoop/hadoop-aws/${HADOOP_AWS_VERSION}/hadoop-aws-${HADOOP_AWS_VERSION}.jar -O ${SPARK_HOME}/jars/hadoop-aws-${HADOOP_AWS_VERSION}.jar +RUN wget https://github.com/treeverse/lakeFS/releases/download/v${LAKEFS_VERSION}/lakeFS_${LAKEFS_VERSION}_Linux_x86_64.tar.gz -O /usr/local/bin/lakeFS_${LAKEFS_VERSION}_Linux_x86_64.tar.gz +RUN tar -xvzf /usr/local/bin/lakeFS_${LAKEFS_VERSION}_Linux_x86_64.tar.gz -C /usr/local/bin/ && rm /usr/local/bin/lakeFS_${LAKEFS_VERSION}_Linux_x86_64.tar.gz + +RUN pip install lakefs-client==${LAKEFS_VERSION} +RUN pip install boto3==1.21.32 +RUN pip install tabulate +RUN pip install petastorm==0.12.1 +RUN pip install mlflow==2.6.0 +RUN pip install hyperopt==0.2.7 +RUN pip install pydantic==1.10.11 +RUN pip install torch==1.13.1 +RUN pip install torchvision==0.14.1 +RUN pip install segmentation-models-pytorch==0.3.3 +RUN pip install pytorch-lightning==1.5.4 + +USER $NB_UID + +# Disable the "Would you like to receive official Jupyter news?" popup +RUN jupyter labextension disable "@jupyterlab/apputils-extension:announcements" diff --git a/01_standalone_examples/image-segmentation-local/Image Segmentation.ipynb b/01_standalone_examples/image-segmentation-local/Image Segmentation.ipynb new file mode 100644 index 000000000..e7e64f510 --- /dev/null +++ b/01_standalone_examples/image-segmentation-local/Image Segmentation.ipynb @@ -0,0 +1,832 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "43e3aa9c-9a20-4468-9846-4c6f5902b7c4", + "showTitle": false, + "title": "" + } + }, + "source": [ + "\"lakeFS\n", + "\n", + "# ML Data Version Control and Reproducibility at Scale\n", + "\n", + "### In the ever-evolving landscape of machine learning (ML), data stands as the cornerstone upon which triumphant models are built. However, as ML projects expand and encompass larger and more complex datasets, the challenge of efficiently managing and controlling data at scale becomes more pronounced.\n", + "\n", + "### Breaking Down Conventional Approaches:\n", + "##### The Copy/Paste Predicament: In the world of data science, it's commonplace for data scientists to extract subsets of data to their local environments for model training. This method allows for iterative experimentation, but it introduces challenges that hinder the seamless evolution of ML projects.\n", + "\n", + "##### Reproducibility Constraints: Traditional practices of copying and modifying data locally lack the version control and audit-ability crucial for reproducibility. Iterating on models with various data subsets becomes a daunting task.\n", + "\n", + "##### Inefficient Data Transfer: Regularly shuttling data between the central repository and local environments strains resources and time, especially when choosing different subsets of data for each training run.\n", + "\n", + "### In this demo, we will demonstrate:\n", + "##### How to use lakeFS to version control your data when working with your data locally.\n", + "##### We will be leveraging the technology stack of: MinIO, Delta Lake, PyTorch and MLflow" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "b1a8d403-ec02-4dcc-8fc3-38da45734113", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "source": [ + "## Target Architecture\n", + "\n", + "\"target\n", + "\n", + "#### Source: Databricks Blogs:\n", + "##### [Accelerating Your Deep Learning with PyTorch Lightning on Databricks](https://www.databricks.com/blog/2022/09/07/accelerating-your-deep-learning-pytorch-lightning-databricks.html)\n", + "##### [Image Segmentation with Databricks](https://florent-brosse.medium.com/image-segmentation-with-databricks-6db19d23725d)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "### You can run this same notebook in local container. This picture explains the full procees:\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "0f3e1e04-ef13-45c9-8d5a-d637080fc5af", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "source": [ + "## Config" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "c377a867-3690-4c7e-b899-7f5d376c0bd8", + "showTitle": false, + "title": "" + } + }, + "source": [ + "### You can change repo name" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "d1144e38-f35e-43eb-9435-a656f5560af5", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "repo_name = \"image-segmentation-local-repo\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "d9b7d4ff-2a76-40a4-8629-8818f667bfb8", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "source": [ + "## Setup\n", + "\n", + "**(you shouldn't need to change anything in this section, just run it)**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "ddc2a99a-da97-441f-a734-663a6617f88f", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "%run ./ImageSegmentationSetup.ipynb" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create an empty Git repository and configure Git. Git will version control your code while lakeFS will version control your data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "!git init {repo_name}\n", + "!git config --global user.email \"you@example.com\"\n", + "!git config --global user.name \"Your Name\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "2ed0c446-1d2f-4b0a-a59b-4b4e7e33ae1a", + "showTitle": false, + "title": "" + } + }, + "source": [ + "# Main demo starts here 🚦 👇🏻" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "b873d753-63a9-4146-bf81-22d4ffd3c23c", + "showTitle": false, + "title": "" + } + }, + "source": [ + "## Import training data to experiment branch" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "3a1d6958-521d-4fc8-b422-ac64e5b7b34a", + "showTitle": false, + "title": "" + } + }, + "source": [ + "### Create branch for each experiment. Create a Git branch also" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "aaf98d56-be45-4093-bff1-e6dea24c76a8", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "experimentBranchN = experimentBranch+\"-1\"\n", + "\n", + "try:\n", + " lakefs.branches.get_branch(repo_name, experimentBranchN)\n", + " print(f\"{experimentBranchN} already exists\")\n", + "except NotFoundException as f:\n", + " !cd {repo_name} && git checkout -b {experimentBranchN}\n", + " lakefs.branches.create_branch(\n", + " repository=repo_name,\n", + " branch_creation=BranchCreation(\n", + " name=experimentBranchN,\n", + " source=emptyBranch))\n", + " print(f\"{experimentBranchN} branch created\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "66e5aeb9-d444-46ed-b45d-755e54642cd8", + "showTitle": false, + "title": "" + } + }, + "source": [ + "### Import training data to lakeFS repo\n", + "#### This is zero-copy operation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "dfec7383-400e-4a21-aae6-aa0683c3350d", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import_images()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "fd319b42-6da9-42d0-b681-8e970527b9f1", + "showTitle": false, + "title": "" + } + }, + "source": [ + "### Clone experiment branch locally\n", + "#### This will download images locally. You will notice \"image-segmentation-local-repo/lakefs_local\" folder in Jupyter File Browser on the left side panel. You can browse the files inside this folder." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "67daa1c8-db4f-4816-9969-3673bab22d28", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "lakeFSLocalCommand = f\"lakectl local clone lakefs://{repo.id}/{experimentBranchN}/ {repo_path}\"\n", + "response = ! $lakeFSLocalCommand\n", + "print_lakectl_response(response, 8)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Let's review \".gitignore\" file and \".lakefs_ref.yaml\" file created by previous \"lakectl local clone\" command.\n", + "#### You will notice in .gitignore file that Git will not commit any data files in \"lakefs_local\" folder but will commit \".lakefs_ref.yaml\" file which includes lakeFS commit information. This way code as well as commit information about data will be kept together in Git repo." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "!cat {repo_name}/.gitignore" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "!cat {repo_path}/.lakefs_ref.yaml" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Delete images smaller than 100KB in size locally. Add few new images." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "!find {training_data_path} -type f -name \"*.jpg\" -size -100k -delete\n", + "!cp /data/airbus-ship-detection/new-images/*.jpg {training_data_path}\n", + "diff_branch(repo.id, repo_path, experimentBranchN)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Add changes to Git repo and perform initial commit" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "!cd {repo_name} && git add -A && git status\n", + "!cd {repo_name} && git commit -m \"Initial commit\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Commit local changes to lakeFS repo" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "commitMessage = 'Deleted images smaller than 100KB in size and added few images'\n", + "commit(repo.id, repo_path, experimentBranchN, commitMessage)\n", + "lakefs_set_tag(repo.id, f\"{tagPrefix}-{experimentBranchN}-raw-images\", experimentBranchN)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "145e8baf-6bb3-4c9b-807d-27120c6ee422", + "showTitle": false, + "title": "" + } + }, + "source": [ + "### Verify that you can read the local dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "895b0ea9-855f-4368-a6ac-b71a7e1ffbb3", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "df = spark.read.format(\"image\").load(training_data_path)\n", + "df.select(\"image.origin\", \"image.width\", \"image.height\").show(truncate=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "62d1893d-9d3a-4901-859d-137f55465ffa", + "showTitle": false, + "title": "" + } + }, + "source": [ + "## Run the data pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "goldDatasetTagID = data_pipeline()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "b8276798-7707-44a4-afe1-d455811fe079", + "showTitle": false, + "title": "" + } + }, + "source": [ + "## Run the Image Segmentation model" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "ce0cb5aa-91c9-4241-8220-2f176bcdf158", + "showTitle": false, + "title": "" + } + }, + "source": [ + "### Split data as train/test datasets" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "f469b84f-bcdd-4a6f-ab15-a730db89bba2", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "gold_images_df = spark.read.format(\"delta\").load(f\"{gold_data_path}/{training_data_folder}\")\n", + "(images_train, images_test) = gold_images_df.randomSplit(weights = [0.8, 0.2], seed = 42)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "5e602614-98df-4ec3-a72f-3efcff223a6f", + "showTitle": false, + "title": "" + } + }, + "source": [ + "### Prepare the dataset in PyTorch format by using Petastorm" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "e826d87f-5a38-46df-ae0a-f04f7a351b5f", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Set the converter cache folder to petastorm_path\n", + "petastorm_path = 'file:///home/jovyan/petastorm/cache'\n", + "\n", + "spark.conf.set(SparkDatasetConverter.PARENT_CACHE_DIR_URL_CONF, petastorm_path)\n", + "# convert the image for pytorch\n", + "converter_train = make_spark_converter(images_train.coalesce(4)) # You can increase number of partitions from 4 if parquet file sizes generated by Petastorm are more than 50 MB\n", + "converter_test = make_spark_converter(images_test.coalesce(4))\n", + "print(f\"Images in training dataset: {len(converter_train)}, Images in test dataset: {len(converter_test)}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "61ccfd3a-743a-40cd-825e-d45a333d2760", + "showTitle": false, + "title": "" + } + }, + "source": [ + "## Train the base Model\n", + "\n", + "### Train the model with \"FPN\" architecture, \"resnet34\" encoder and learning rate of \"0.0001\"\n", + "\n", + "#### Model will return Intersection over Union (IoU) metric which is a widely-used evaluation metric in object detection and image segmentation tasks\n", + "#### IoU measures the overlap between predicted bounding boxes and ground truth boxes, with scores ranging from 0 to 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "05cfc88d-6ecd-49fa-a1f5-be1821d76e42", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "valid_per_image_iou = train_model(\"FPN\", \"resnet34\", 0.0001)\n", + "print(f\"Intersection over Union (IoU) metric value: {valid_per_image_iou}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train the base Model again with different parameters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "05cfc88d-6ecd-49fa-a1f5-be1821d76e42", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "valid_per_image_iou = train_model(\"FPN\", \"resnet50\", 0.0002)\n", + "print(f\"Intersection over Union (IoU) metric value: {valid_per_image_iou}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "1e273b5a-f534-4a08-9b51-7270e3f7d73d", + "showTitle": false, + "title": "" + } + }, + "source": [ + "### Save the best model to the MLflow registry (as a new version)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "fbbdb268-4c0c-4e47-82ed-557303c616bf", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# get the best model from the registry\n", + "best_model = \\\n", + "mlflow.search_runs(filter_string='attributes.status = \"FINISHED\" and tags.lakefs_demos = \"image_segmentation\"',\n", + " order_by=[\"metrics.valid_per_image_iou DESC\"], max_results=1).iloc[0]\n", + "model_registered = mlflow.register_model(\"runs:/\" + best_model.run_id + \"/model\", \"lakefs_demos_image_segmentation\")\n", + "print(model_registered)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "928d2cd5-82e2-4880-9e6a-d68dff4910c5", + "showTitle": false, + "title": "" + } + }, + "source": [ + "### Save the best model information in the lakeFS repository\n", + "\n", + "#### Commit log in the lakeFS repository also includes URL to go to best registered model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "c24b4e76-0868-47fd-bfc0-633d7f509962", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "pd.set_option('display.max_colwidth', -1)\n", + "f = open(f\"{repo_path}/best_model.txt\", \"w\")\n", + "f.write(best_model.to_string())\n", + "f.close()\n", + "\n", + "commitMetadata = commit_metadata_for_best_model(best_model, model_registered)\n", + "diff_branch(repo.id, repo_path, experimentBranchN)\n", + "\n", + "commitMessage = 'Information on best model'\n", + "commit_id = commit(repo.id, repo_path, experimentBranchN, commitMessage, commitMetadata)\n", + "lakefs_set_tag(repo.id, f\"{tagPrefix}-{experimentBranchN}-best-model\", experimentBranchN)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Copy notebooks (code) to Git repo. The \"git add\" command adds changes in the working directory to the staging area.\n", + "#### Git doesn't add data files to staging area while adds \".lakefs_ref.yaml\" file which includes lakeFS commit information" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "!cp -t {repo_name} 'Image Segmentation.ipynb' 'ImageSegmentationSetup.ipynb'\n", + "!cd {repo_name} && git add -A && git status" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## If you want to access MLflow UI then open [start-mlflow-ui](./start-mlflow-ui.ipynb) notebook, start MLflow server and go to [MLflow UI](http://127.0.0.1:5001/)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Run following cell to generate the hyperlink to go to the commit page in lakeFS" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "md(f\"
👉🏻 **Go to [the commit page in lakeFS]({lakefsUIEndPoint}/repositories/{repo_name}/commits/{commit_id}) \\\n", + "to see the commit made to the repository along with information for the best model.
Click on 'Open Registered Model UI' button on the commit page to \\\n", + "open the best model in MLflow UI.
Click on 'Source Run' link in MLflow UI to get run details including model pickle file(python_model.pkl).**\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "03ff8dba-f5d6-4aa9-8c93-7a74833abf3d", + "showTitle": false, + "title": "" + } + }, + "source": [ + "## More Questions?\n", + "\n", + "[\"lakeFS](https://lakefs.io/slack)" + ] + } + ], + "metadata": { + "application/vnd.databricks.v1+notebook": { + "dashboards": [], + "language": "python", + "notebookMetadata": { + "pythonIndentUnit": 4 + }, + "notebookName": "Image Segmentation", + "widgets": {} + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/01_standalone_examples/image-segmentation-local/ImageSegmentationSetup.ipynb b/01_standalone_examples/image-segmentation-local/ImageSegmentationSetup.ipynb new file mode 100644 index 000000000..0f0cfc46a --- /dev/null +++ b/01_standalone_examples/image-segmentation-local/ImageSegmentationSetup.ipynb @@ -0,0 +1,1781 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "43e3aa9c-9a20-4468-9846-4c6f5902b7c4", + "showTitle": false, + "title": "" + } + }, + "source": [ + "\"lakeFS\n", + "\n", + "# ML Data Version Control and Reproducibility at Scale" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "d9b7d4ff-2a76-40a4-8629-8818f667bfb8", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "source": [ + "## Setup\n", + "\n", + "**(you shouldn't need to change anything in this section, just run it)**" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### lakeFS endpoint and credentials" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "lakefsEndPoint = 'http://lakefs:8000' # e.g. 'https://username.aws_region_name.lakefscloud.io' \n", + "lakefsAccessKey = 'AKIAIOSFOLKFSSAMPLES'\n", + "lakefsSecretKey = 'wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Storage Information\n", + "\n", + "Change the Storage Namespace to a location in the bucket you’ve configured. The storage namespace is a location in the underlying storage where data for lakeFS repository will be stored." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "storageNamespace = 's3://example/' + repo_name # e.g. \"s3://bucket\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Are you running this demo in LOCAL container or in Databricks DISTRIBUTED cluster?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "localOrDistributedComputing = \"LOCAL\" # LOCAL or DISTRIBUTED" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Downloaded demo dataset from [Kaggle](https://www.kaggle.com/c/airbus-ship-detection) and uploaded to \"airbus-ship-detection\" folder in MinIO" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "bucketName = 'sample-data'\n", + "awsRegion = 'us-east-1'\n", + "prefix = \"airbus-ship-detection/\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "aws_access_key_id = 'aaaaaaaaaaaaa'\n", + "aws_secret_access_key = 'bbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbbb'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "c785bf65-a97a-4c81-8120-555028db4a0c", + "showTitle": false, + "title": "" + } + }, + "source": [ + "### Import libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "77b90199-50aa-4bc9-8183-6cc6759f8cc4", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "%xmode Minimal\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "import logging\n", + "logging.basicConfig()\n", + "logging.getLogger().setLevel(logging.ERROR)\n", + "\n", + "import lakefs_client\n", + "from lakefs_client.models import *\n", + "from lakefs_client.client import LakeFSClient\n", + "from lakefs_client.exceptions import NotFoundException\n", + "import os\n", + "\n", + "import boto3\n", + "import random\n", + "import time\n", + "import datetime\n", + "from pyspark.sql.functions import substring_index, col, pandas_udf, collect_list, size, desc, base64\n", + "from PIL import Image\n", + "import io\n", + "import pandas as pd\n", + "import numpy as np\n", + "from petastorm.spark import SparkDatasetConverter, make_spark_converter\n", + "from petastorm import TransformSpec\n", + "from functools import partial\n", + "import torch\n", + "import torchvision\n", + "from torchvision import transforms\n", + "import pytorch_lightning as pl\n", + "import segmentation_models_pytorch as smp\n", + "import base64\n", + "import mlflow\n", + "from hyperopt import fmin, rand, hp, SparkTrials, STATUS_OK, space_eval, tpe\n", + "import gc\n", + "import ipywidgets as widgets\n", + "from IPython.display import Markdown as md" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "cb2c1123-b5dd-4c95-965a-65393a8d9e8c", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "source": [ + "### Configuring lakeFSClient" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "172f81be-53c1-4c42-bbfc-ab3db4a39b2a", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# lakeFS credentials and endpoint\n", + "configuration = lakefs_client.Configuration()\n", + "configuration.username = lakefsAccessKey\n", + "configuration.password = lakefsSecretKey\n", + "configuration.host = lakefsEndPoint\n", + "\n", + "lakefs = LakeFSClient(configuration)\n", + "\n", + "if lakefsEndPoint.startswith('http://host.docker.internal'):\n", + " lakefsUIEndPoint = 'http://127.0.0.1:8000'\n", + "elif lakefsEndPoint.startswith('http://lakefs:8000'):\n", + " lakefsUIEndPoint = 'http://127.0.0.1:8003'\n", + "else:\n", + " lakefsUIEndPoint = lakefsEndPoint" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "43223700-ef0e-43f0-af99-2d7e6d0cea89", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "print(f\"lakeFS client version: {lakefs_client.__version__}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "ecc978df-d338-409a-8509-8c2142ebd298", + "showTitle": false, + "title": "" + } + }, + "source": [ + "### Define lakeFS Repository" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "3dc99a61-962d-45e9-8c0c-a6ab96ba4745", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "try:\n", + " repo=lakefs.repositories.get_repository(repo_name)\n", + " print(f\"Found existing repo {repo.id} using storage namespace {repo.storage_namespace}\")\n", + "except NotFoundException as f:\n", + " print(f\"Repository {repo_name} does not exist, so going to try and create it now.\")\n", + " try:\n", + " repo=lakefs.repositories.create_repository(repository_creation=RepositoryCreation(name=repo_name,\n", + " storage_namespace=f\"{storageNamespace}/{repo_name}\"))\n", + " print(f\"Created new repo {repo.id} using storage namespace {repo.storage_namespace}\")\n", + " except lakefs_client.ApiException as e:\n", + " print(f\"Error creating repo {repo_name}. Error is {e}\")\n", + " os._exit(00)\n", + "except lakefs_client.ApiException as e:\n", + " print(f\"Error getting repo {repo_name}: {e}\")\n", + " os._exit(00)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "3a9f2712-23f7-4ba5-b47b-6f7ae4202da5", + "showTitle": false, + "title": "" + } + }, + "source": [ + "### Configure lakectl for LOCAL" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "75c61607-f816-4aa3-9ec1-8107104f8d54", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "if localOrDistributedComputing == \"LOCAL\":\n", + " f = open(\".lakectl.yaml\", \"w\")\n", + " f.write(f\"credentials: \\n\\\n", + " access_key_id: {lakefsAccessKey}\\n\\\n", + " secret_access_key: {lakefsSecretKey}\\n\\\n", + "server: \\n\\\n", + " endpoint_url: {lakefsEndPoint}\")\n", + " f.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "01aca111-3337-4545-a209-29d6a0a13593", + "showTitle": false, + "title": "" + } + }, + "source": [ + "### Set up Spark for LOCAL" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "01cc56b3-f532-4c5e-9a0c-6e10f7137d72", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "if localOrDistributedComputing == \"LOCAL\":\n", + " from pyspark.sql import SparkSession\n", + " spark = SparkSession.builder.appName(\"lakeFS / Jupyter\") \\\n", + " .config(\"spark.hadoop.fs.s3.impl\", \"org.apache.hadoop.fs.s3a.S3AFileSystem\") \\\n", + " .config(\"spark.hadoop.fs.s3a.endpoint\", lakefsEndPoint) \\\n", + " .config(\"spark.hadoop.fs.s3a.path.style.access\", \"true\") \\\n", + " .config(\"spark.hadoop.fs.s3a.access.key\", lakefsAccessKey) \\\n", + " .config(\"spark.hadoop.fs.s3a.secret.key\", lakefsSecretKey) \\\n", + " .config(\"spark.jars.packages\", \"io.delta:delta-core_2.12:2.3.0\") \\\n", + " .config(\"spark.sql.extensions\", \"io.delta.sql.DeltaSparkSessionExtension\") \\\n", + " .config(\"spark.sql.catalog.spark_catalog\", \"org.apache.spark.sql.delta.catalog.DeltaCatalog\") \\\n", + " .getOrCreate()\n", + " \n", + "# .config(\"spark.jars.packages\", \"io.delta:delta-core_2.12:2.3.0,org.mlflow.mlflow-spark\") \\\n", + " spark.sparkContext.setLogLevel(\"INFO\")\n", + "\n", + " spark" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "a72ed0dc-a84f-4663-9fb3-2fd88c201e75", + "showTitle": false, + "title": "" + } + }, + "source": [ + "### Versioning Information" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "b92fe92a-b990-495e-941f-ee57619f988a", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "mainBranch = \"main\"\n", + "emptyBranch = \"empty\"\n", + "experimentBranch = localOrDistributedComputing + \"-experiment\"\n", + "commitMetadata=\"\"\n", + "tagPrefix = datetime.datetime.now().strftime(\"%Y_%m_%d_%H_%M_%S\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "fcef1ef4-d2e5-4f87-b4ec-370906cc895e", + "showTitle": false, + "title": "" + } + }, + "source": [ + "### Create empty branch" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "bb74dfb2-3abf-4de1-a112-c2a4e92bc309", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "try:\n", + " lakefs.branches.get_branch(repo_name, emptyBranch)\n", + "except NotFoundException as f:\n", + " lakefs.branches.create_branch(\n", + " repository=repo_name,\n", + " branch_creation=BranchCreation(\n", + " name=emptyBranch,\n", + " source=mainBranch))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "cf987466-9413-453d-9675-0e55ab633948", + "showTitle": false, + "title": "" + } + }, + "source": [ + "### Folder structure to implement Medallion Architecture" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "fbda8000-e809-4e2d-9fa1-c58710cedab4", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "raw_data_folder = \"raw/\"\n", + "bronze_data_folder = \"bronze/\"\n", + "silver_data_folder = \"silver/\"\n", + "gold_data_folder = \"gold/\"\n", + "training_data_folder = \"train_v2/\"\n", + "mask_data_folder = \"mask/\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "85126538-183e-4918-b647-07211cf6a27b", + "showTitle": false, + "title": "" + } + }, + "source": [ + "### Create S3 client" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "5b6649ff-7742-4f4a-903d-efcc0628c51d", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "s3 = boto3.client('s3',\n", + " endpoint_url='https://s3.' + awsRegion + '.amazonaws.com',\n", + " aws_access_key_id=aws_access_key_id,\n", + " aws_secret_access_key=aws_secret_access_key)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "e5e8bd4b-5160-44e3-a4d9-aa332cef1edb", + "showTitle": false, + "title": "" + } + }, + "source": [ + "### Get the list of images in training dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "1a646985-b578-4a6c-ad47-faf4034842af", + "showTitle": false, + "title": "" + } + }, + "outputs": [], + "source": [ + "def list_images():\n", + " s3_result = s3.list_objects_v2(Bucket=bucketName, Prefix=prefix+training_data_folder)\n", + "\n", + " file_list = []\n", + " for key in s3_result['Contents']:\n", + " file_list.append(key['Key'])\n", + "\n", + " while s3_result['IsTruncated']:\n", + " continuation_key = s3_result['NextContinuationToken']\n", + " s3_result = s3.list_objects_v2(Bucket=bucketName, Prefix=prefix, ContinuationToken=continuation_key)\n", + " for key in s3_result['Contents']:\n", + " file_list.append(key['Key'])\n", + " print(f\"Image count = {len(file_list)}\")\n", + " return file_list" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "52b5fcc4-0aa1-48d2-a80b-821df7eb8fcd", + "showTitle": false, + "title": "" + } + }, + "source": [ + "### Import subset of training data to lakeFS repo" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "0c718869-4e3b-4f1a-840c-748b65c7bbf5", + "showTitle": false, + "title": "" + } + }, + "outputs": [], + "source": [ + "def import_images():\n", + " paths = []\n", + " for file in file_list_random:\n", + " paths.append(ImportLocation(type=\"common_prefix\", path=\"s3://\"+bucketName+'/'+file, destination=raw_data_folder+training_data_folder))\n", + "\n", + " paths.append(ImportLocation(type=\"common_prefix\", path=\"s3://\"+bucketName+'/'+prefix+'train_ship_segmentations_v2.csv', destination=raw_data_folder))\n", + "\n", + " # Start Import\n", + " import_creation = ImportCreation(paths=paths, commit=CommitCreation(message=\"import images\"))\n", + " create_resp = lakefs.import_api.import_start(repo.id, experimentBranchN, import_creation)\n", + "\n", + " # Wait for import to finish\n", + " while True:\n", + " status_resp = lakefs.import_api.import_status(repo.id, experimentBranchN, create_resp.id)\n", + " print(status_resp)\n", + " if hasattr(status_resp, \"Error in import\"):\n", + " raise Exception(status_resp.err)\n", + " if status_resp.completed:\n", + " print(\"Import completed Successfully. Data imported into branch:\", experimentBranchN)\n", + " break\n", + " time.sleep(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "62d1893d-9d3a-4901-859d-137f55465ffa", + "showTitle": false, + "title": "" + } + }, + "source": [ + "## Build the data pipeline" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "95dea8a4-ea85-4e56-9e80-c943822b8f8d", + "showTitle": false, + "title": "" + } + }, + "source": [ + "### Ingest raw images as bronze data set and save as Delta table" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "e5867767-45b9-4454-ac76-aa14fc388f26", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def bronze_images():\n", + " return spark.read.format(\"binaryfile\").option(\"pathGlobFilter\", \"*.jpg\").load(training_data_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "219f4da1-6b3d-4a39-a478-949ed904857a", + "showTitle": false, + "title": "" + } + }, + "source": [ + "### Print Diff results" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "9fa59cb8-9ef1-4573-91c6-bea3fc3d2cb2", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def print_diff_refs(diff_refs):\n", + " results = map(\n", + " lambda n:[n.path,n.path_type,n.size_bytes,n.type],\n", + " diff_refs.results)\n", + " return results" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "0f62b5f2-0323-478f-8346-8a69a8be878e", + "showTitle": false, + "title": "" + } + }, + "source": [ + "### Diff the branch to find uncommitted changes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "c75ad558-3866-4b86-bb9a-a15a4fba5b53", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def diff_branch(repo, repo_path, branch):\n", + " if localOrDistributedComputing == \"LOCAL\":\n", + " lakeFSLocalCommand = f\"lakectl local status {repo_path}\"\n", + " ! $lakeFSLocalCommand\n", + " elif localOrDistributedComputing == \"DISTRIBUTED\":\n", + " for a in print_diff_refs(\n", + " lakefs.branches.diff_branch(\n", + " repository=repo,\n", + " branch=branch)):\n", + " print(a)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "96dd6218-ea87-49cf-a2ed-492b648fd79a", + "showTitle": false, + "title": "" + } + }, + "source": [ + "### Commit changes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "b112b57c-f56f-4278-a065-226479355595", + "showTitle": false, + "title": "" + } + }, + "outputs": [], + "source": [ + "def print_lakectl_response(response, lines):\n", + " if lines > len(response):\n", + " lines = len(response)\n", + " for x in range(lines):\n", + " print(response[-(lines-x)])\n", + "\n", + "def get_commit_id(commit_response):\n", + " responseLastNLinesSplit = [i.split(':') for i in commit_response]\n", + " for key_value in responseLastNLinesSplit:\n", + " for key in key_value:\n", + " if key == 'ID':\n", + " return key_value[1].strip()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "7f784f4a-3ed5-49de-98da-2afd4001e34c", + "showTitle": false, + "title": "" + } + }, + "outputs": [], + "source": [ + "def commit(repo, repo_path, branch, commitMessage, metadata=\"\"):\n", + " if localOrDistributedComputing == \"LOCAL\":\n", + " lakeFSLocalCommand = f\"lakectl local commit -m '{commitMessage}' --meta '{metadata}' {repo_path}\"\n", + " commit_response = ! $lakeFSLocalCommand\n", + " # Print last 15 lines\n", + " print_lakectl_response(commit_response, 15)\n", + " return get_commit_id(commit_response)\n", + " elif localOrDistributedComputing == \"DISTRIBUTED\":\n", + " if metadata == \"\":\n", + " metadata = {}\n", + " commit_response = lakefs.commits.commit(\n", + " repository=repo,\n", + " branch=branch,\n", + " commit_creation=CommitCreation(\n", + " message=commitMessage,\n", + " metadata=metadata))\n", + " return commit_response.id" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "5991ef81-03af-4e4a-b117-4d0f6a7c70d9", + "showTitle": false, + "title": "" + } + }, + "source": [ + "### Create lakeFS tag" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "8e47ac94-9317-42d5-8338-2c947cb21d89", + "showTitle": false, + "title": "" + } + }, + "outputs": [], + "source": [ + "def lakefs_set_tag(repo, tagID, branch):\n", + " return lakefs.tags.create_tag(\n", + " repository=repo,\n", + " tag_creation=TagCreation(\n", + " id=tagID, \n", + " ref=branch))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "9a7d4dd7-5581-4a12-a14e-6a959c2910cc", + "showTitle": false, + "title": "" + } + }, + "source": [ + "### Enrich dataset and save as silver dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "b0aded32-476b-41a0-abba-fe27c695402f", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "IMAGE_RESIZE = 320 # divisible by 32\n", + "\n", + "# Resize UDF function\n", + "@pandas_udf(\"binary\")\n", + "def resize_image_udf(content_series):\n", + " def resize_image(content):\n", + " \"\"\"resize image and serialize as jpeg\"\"\"\n", + " try:\n", + " image = Image.open(io.BytesIO(content)).resize((IMAGE_RESIZE, IMAGE_RESIZE), Image.NEAREST)\n", + " output = io.BytesIO()\n", + " image.save(output, format='JPEG')\n", + " return output.getvalue()\n", + " except Exception:\n", + " # some images are invalid\n", + " return None\n", + "\n", + " return content_series.apply(resize_image)\n", + "\n", + "# add the metadata to enable the image preview\n", + "image_meta = {\"spark.contentAnnotation\": '{\"mimeType\": \"image/jpeg\"}'}\n", + "\n", + "def silver_images(df_bronze_images):\n", + " return df_bronze_images.withColumn(\"image_id\", substring_index(col('path'), '/', -1)) \\\n", + " .withColumn(\"content\", resize_image_udf(col(\"content\")).alias(\"content\", metadata=image_meta)) \\\n", + " .filter(\"content is not null\") \\\n", + " .select(\"image_id\", \"content\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "851ccf08-2446-44dd-b448-bd7a992d5982", + "showTitle": false, + "title": "" + } + }, + "source": [ + "### Load the raw image mask" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "b4109119-51be-41c2-9b4b-9ca1b6869f70", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def bronze_mask():\n", + " annotationsDF = (spark.read.option(\"header\", \"true\")\n", + " .option(\"inferSchema\", \"true\")\n", + " .csv(f\"{raw_data_path}/train_ship_segmentations_v2.csv\"))\n", + " return (annotationsDF.withColumnRenamed(\"ImageId\", \"image_id\")\n", + " .withColumnRenamed(\"EncodedPixels\", \"encoded_pixels\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "1e6c26b1-8895-4782-81f1-5864c5098d9a", + "showTitle": false, + "title": "" + } + }, + "source": [ + "### Transforming masks into images" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "5b5894b8-1a57-414b-a5ad-bcb13ae116dc", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "IMAGE_SIZE = 768\n", + "\n", + "def rle_decode(mask_rle, shape=(IMAGE_SIZE, IMAGE_SIZE)):\n", + " s = mask_rle.split()\n", + " starts = np.asarray(s[0::2], dtype=int)\n", + " lengths = np.asarray(s[1::2], dtype=int)\n", + "\n", + " starts -= 1\n", + " ends = starts + lengths\n", + " img = np.zeros(shape[0] * shape[1], dtype=np.uint8)\n", + " for lo, hi in zip(starts, ends):\n", + " img[lo:hi] = 255\n", + " return img.reshape(shape).T\n", + "\n", + "\n", + "def mask(rle_masks):\n", + " if isinstance(rle_masks, np.ndarray):\n", + " all_masks = np.zeros((IMAGE_SIZE, IMAGE_SIZE), dtype=np.int8)\n", + " for mask in rle_masks.tolist():\n", + " all_masks += rle_decode(mask)\n", + " image = Image.fromarray(all_masks, mode=\"L\").resize((IMAGE_RESIZE, IMAGE_RESIZE), Image.NEAREST)\n", + " output = io.BytesIO()\n", + " image.save(output, format='JPEG')\n", + " return output.getvalue()\n", + " raise Exception(type(rle_masks))\n", + "\n", + "\n", + "@pandas_udf(\"binary\")\n", + "def computeMaskUDF(s: pd.Series) -> pd.Series:\n", + " return s.apply(mask)\n", + "\n", + "\n", + "def silver_mask(df_bronze_mask):\n", + " return (df_bronze_mask\n", + " .filter(\"encoded_pixels is not null\")\n", + " .groupBy(\"image_id\").agg(collect_list('encoded_pixels').alias('encoded_pixels'))\n", + " .withColumn(\"boat_number\", size(col(\"encoded_pixels\")))\n", + " .withColumn(\"mask\", computeMaskUDF(col(\"encoded_pixels\")).alias(\"mask\", metadata=image_meta)))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "6b86d14d-bf0d-44e5-9daa-9408cbac586c", + "showTitle": false, + "title": "" + } + }, + "source": [ + "### Joining image and mask both as the gold layer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "ba4f442a-bace-4722-a537-897e82cfaba8", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def gold_images(df_silver_images, df_silver_mask):\n", + " imagesWithMask = df_silver_mask.join(df_silver_images, \"image_id\")\n", + " return imagesWithMask.select(\"image_id\", \"boat_number\", \"mask\", \"content\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "5ff7e332-a275-47ea-a123-e3483f329087", + "showTitle": false, + "title": "" + } + }, + "source": [ + "### Decode the raw image bytes and apply standard ImageNet transforms" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "dc7b3f6d-7615-440a-b285-6e6cc75295a4", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def transform_row(is_train, pd_batch):\n", + " \"\"\"\n", + " The input and output of this function must be pandas dataframes.\n", + " Do data augmentation for the training dataset only.\n", + " \"\"\"\n", + " transformers = [transforms.Lambda(lambda x: Image.open(io.BytesIO(x)))]\n", + " if is_train:\n", + " transformers.extend([\n", + " transforms.RandomResizedCrop(224),\n", + " transforms.RandomHorizontalFlip(),\n", + " ])\n", + " else:\n", + " transformers.extend([\n", + " transforms.Resize(256),\n", + " transforms.CenterCrop(224),\n", + " ])\n", + " transformers.extend([\n", + " transforms.ToTensor(),\n", + " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n", + " ])\n", + " \n", + " transformersMask = [transforms.Lambda(lambda x: Image.open(io.BytesIO(x)))]\n", + " if is_train:\n", + " transformersMask.extend([\n", + " #transforms.RandomResizedCrop(224),\n", + " #transforms.RandomHorizontalFlip(),\n", + " transforms.Resize(256),\n", + " transforms.CenterCrop(224),\n", + " ])\n", + " else:\n", + " transformersMask.extend([\n", + " transforms.Resize(256),\n", + " transforms.CenterCrop(224),\n", + " ])\n", + " transformersMask.extend([\n", + " transforms.ToTensor(),\n", + " transforms.Normalize([0.5], [0.5]),\n", + " ])\n", + " \n", + " trans = transforms.Compose(transformers)\n", + " transMask = transforms.Compose(transformersMask)\n", + "\n", + " pd_batch['content'] = pd_batch['content'].map(lambda x: trans(x).numpy())\n", + " pd_batch['mask'] = pd_batch['mask'].map(lambda x: transMask(x).numpy())\n", + " #pd_batch = pd_batch.drop(labels=['content'], axis=1)\n", + " pd_batch = pd_batch.drop(labels=['image_id'], axis=1)\n", + " pd_batch = pd_batch.drop(labels=['boat_number'], axis=1)\n", + " #pd_batch = pd_batch.drop(labels=['mask'], axis=1)\n", + " return pd_batch\n", + "\n", + "def get_transform_spec(is_train=True):\n", + " # Note that the output shape of the `TransformSpec` is not automatically known by petastorm, \n", + " # so we need to specify the shape for new columns in `edit_fields` and specify the order of \n", + " # the output columns in `selected_fields`.\n", + " return TransformSpec(partial(transform_row, is_train), \n", + " edit_fields=[('content', np.float32, (3, 224, 224), False), ('mask', np.float32, (1, 224, 224), False)], # num_channels is 1 for greyscale images\n", + " #edit_fields=[('content', torch.bfloat16, (3, 224, 224), False), ('mask', torch.bfloat16, (1, 224, 224), False)], \n", + " selected_fields=['content', 'mask'])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "61ccfd3a-743a-40cd-825e-d45a333d2760", + "showTitle": false, + "title": "" + } + }, + "source": [ + "### Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "7e7ddf75-0583-4718-86e0-aa20db4086d9", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "class BoatModel(pl.LightningModule):\n", + "\n", + " def __init__(self, arch, encoder_name, in_channels, out_classes, lr, **kwargs):\n", + " super().__init__()\n", + " self.model = smp.create_model(\n", + " arch, encoder_name=encoder_name, in_channels=in_channels, classes=out_classes, **kwargs\n", + " )\n", + "\n", + " # preprocessing parameteres for image\n", + " params = smp.encoders.get_preprocessing_params(encoder_name)\n", + " self.register_buffer(\"std\", torch.tensor(params[\"std\"]).view(1, 3, 1, 1))\n", + " self.register_buffer(\"mean\", torch.tensor(params[\"mean\"]).view(1, 3, 1, 1))\n", + "\n", + " # for image segmentation dice loss could be the best first choice\n", + " self.loss_fn = smp.losses.DiceLoss(smp.losses.BINARY_MODE, from_logits=True)\n", + "\n", + " def forward(self, image):\n", + " # normalize image here\n", + " image = (image - self.mean) / self.std\n", + " mask = self.model(image)\n", + " return mask\n", + "\n", + " def shared_step(self, batch, stage):\n", + " \n", + " image = batch[\"content\"]\n", + "\n", + " # Shape of the image should be (batch_size, num_channels, height, width)\n", + " # if you work with grayscale images, expand channels dim to have [batch_size, 1, height, width]\n", + " assert image.ndim == 4\n", + "\n", + " # Check that image dimensions are divisible by 32, \n", + " # encoder and decoder connected by `skip connections` and usually encoder have 5 stages of \n", + " # downsampling by factor 2 (2 ^ 5 = 32); e.g. if we have image with shape 65x65 we will have \n", + " # following shapes of features in encoder and decoder: 84, 42, 21, 10, 5 -> 5, 10, 20, 40, 80\n", + " # and we will get an error trying to concat these features\n", + " h, w = image.shape[2:]\n", + " assert h % 32 == 0 and w % 32 == 0\n", + "\n", + " mask = batch[\"mask\"]\n", + "\n", + " # Shape of the mask should be [batch_size, num_classes, height, width]\n", + " # for binary segmentation num_classes = 1\n", + " assert mask.ndim == 4\n", + "\n", + " # Check that mask values in between 0 and 1, NOT 0 and 255 for binary segmentation\n", + " #assert mask.max() <= 1.0 and mask.min() >= 0 # Commented to avoid error\n", + "\n", + " logits_mask = self.forward(image)\n", + " \n", + " # Predicted mask contains logits, and loss_fn param `from_logits` is set to True\n", + " loss = self.loss_fn(logits_mask, mask)\n", + "\n", + " # Lets compute metrics for some threshold\n", + " # first convert mask values to probabilities, then \n", + " # apply thresholding\n", + " prob_mask = logits_mask.sigmoid()\n", + " pred_mask = (prob_mask > 0.5).float()\n", + "\n", + " # We will compute IoU metric by two ways\n", + " # 1. dataset-wise\n", + " # 2. image-wise\n", + " # but for now we just compute true positive, false positive, false negative and\n", + " # true negative 'pixels' for each image and class\n", + " # these values will be aggregated in the end of an epoch\n", + " tp, fp, fn, tn = smp.metrics.get_stats(pred_mask.long(), mask.long(), mode=\"binary\")\n", + "\n", + " return {\n", + " \"loss\": loss,\n", + " \"tp\": tp,\n", + " \"fp\": fp,\n", + " \"fn\": fn,\n", + " \"tn\": tn,\n", + " }\n", + "\n", + " def shared_epoch_end(self, outputs, stage):\n", + " # aggregate step metics\n", + " tp = torch.cat([x[\"tp\"] for x in outputs])\n", + " fp = torch.cat([x[\"fp\"] for x in outputs])\n", + " fn = torch.cat([x[\"fn\"] for x in outputs])\n", + " tn = torch.cat([x[\"tn\"] for x in outputs])\n", + " \n", + " # per image IoU means that we first calculate IoU score for each image \n", + " # and then compute mean over these scores\n", + " per_image_iou = smp.metrics.iou_score(tp, fp, fn, tn, reduction=\"micro-imagewise\")\n", + " \n", + " # dataset IoU means that we aggregate intersection and union over whole dataset\n", + " # and then compute IoU score. The difference between dataset_iou and per_image_iou scores\n", + " # in this particular case will not be much, however for dataset \n", + " # with \"empty\" images (images without target class) a large gap could be observed. \n", + " # Empty images influence a lot on per_image_iou and much less on dataset_iou.\n", + " dataset_iou = smp.metrics.iou_score(tp, fp, fn, tn, reduction=\"micro\")\n", + "\n", + " metrics = {\n", + " f\"{stage}_per_image_iou\": per_image_iou,\n", + " f\"{stage}_dataset_iou\": dataset_iou,\n", + " }\n", + " \n", + " self.log_dict(metrics, prog_bar=True)\n", + "\n", + " def training_step(self, batch, batch_idx):\n", + " return self.shared_step(batch, \"train\") \n", + "\n", + " def training_epoch_end(self, outputs):\n", + " return self.shared_epoch_end(outputs, \"train\")\n", + "\n", + " def validation_step(self, batch, batch_idx):\n", + " return self.shared_step(batch, \"valid\")\n", + "\n", + " def validation_epoch_end(self, outputs):\n", + " return self.shared_epoch_end(outputs, \"valid\")\n", + "\n", + " def test_step(self, batch, batch_idx):\n", + " return self.shared_step(batch, \"test\") \n", + "\n", + " def test_epoch_end(self, outputs):\n", + " return self.shared_epoch_end(outputs, \"test\")\n", + "\n", + " def predict_step(self, batch, batch_idx):\n", + " return self.shared_step(batch, \"test\") \n", + "\n", + " def predict_epoch_end(self, outputs):\n", + " return self.shared_epoch_end(outputs, \"test\")\n", + " \n", + " def configure_optimizers(self):\n", + " return torch.optim.Adam(self.parameters(), lr=0.0001)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "9883528c-0446-4be7-8e1a-94bfe35c1c06", + "showTitle": false, + "title": "" + } + }, + "source": [ + "### Model wrapper which tackles all the data transformations originally captured in transform spec." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "b7781d73-34fb-4772-b26c-ad4231659325", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "class CVModelWrapper(mlflow.pyfunc.PythonModel):\n", + " \n", + " def __init__(self, model): \n", + " # instantiate model in evaluation mode\n", + " self.model = model.eval()\n", + " \n", + " # define transformation pipeline\n", + " trans = torchvision.transforms.Compose([\n", + " torchvision.transforms.Lambda(lambda x: Image.open(io.BytesIO(x))),\n", + " torchvision.transforms.Resize(256),\n", + " torchvision.transforms.ToTensor(),\n", + " torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])\n", + " ])\n", + " self.transform = trans\n", + " \n", + " def predict(self, context, model_input):\n", + " for i in torch.utils.data.DataLoader(model_input):\n", + " output = model(i)\n", + " #probs = torch.nn.functional.softmax(output, dim=1)[:,1]\n", + " outputs += [output]\n", + " \n", + " ser = pd.Series(outputs)\n", + " return ser" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "37ca43f7-4343-4096-ac15-68923835fe73", + "showTitle": false, + "title": "" + } + }, + "source": [ + "### Train the base Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": { + "byteLimit": 2048000, + "rowLimit": 10000 + }, + "inputWidgets": {}, + "nuid": "2fdd782a-5370-4e76-9a8a-e5cdc0557fb9", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "BATCH_SIZE = 16\n", + "\n", + "#mlflow.autolog(log_models=False)\n", + "\n", + "def train_model(arch, encoder_name, lr, nested=False):\n", + " with mlflow.start_run(nested=nested) as run:\n", + " model = BoatModel(arch=arch, encoder_name=encoder_name, in_channels=3, out_classes=1, lr=lr)\n", + " trainer = pl.Trainer(\n", + " benchmark=True,\n", + " num_sanity_val_steps=0,\n", + " precision=32, # Changed precision from 16 to 32 to run model on CPUs\n", + " #accelerator=\"gpu\",\n", + " #gpus=\"1\",\n", + " log_every_n_steps=100,\n", + " default_root_dir=\"/tmp\",\n", + " max_epochs=1)\n", + "\n", + " mlflow.log_param(\"encoder\", encoder_name)\n", + " mlflow.log_param(\"arch\", arch)\n", + " mlflow.log_param(\"learning rate\", lr)\n", + " mlflow.set_tag(\"lakefs_demos\", \"image_segmentation\")\n", + " with converter_train.make_torch_dataloader(num_epochs=1, transform_spec=get_transform_spec(is_train=True),\n", + " batch_size=BATCH_SIZE) as train_dataloader, \\\n", + " converter_test.make_torch_dataloader(num_epochs=1, transform_spec=get_transform_spec(is_train=False),\n", + " batch_size=BATCH_SIZE) as valid_dataloader:\n", + " trainer.fit(model, train_dataloaders=train_dataloader, val_dataloaders=valid_dataloader)\n", + " delattr(model, \"trainer\")\n", + " # add model requirement\n", + " reqs = mlflow.pytorch.get_default_pip_requirements() + [\n", + " \"git+https://github.com/qubvel/segmentation_models.pytorch\", \"pytorch-lightning==\" + pl.__version__]\n", + " mlflow.pyfunc.log_model(artifact_path=\"model\", python_model=CVModelWrapper(model), pip_requirements=reqs)\n", + " # log and returns model accuracy\n", + " valid_metrics = trainer.validate(model, dataloaders=valid_dataloader, verbose=False)\n", + " valid_per_image_iou = valid_metrics[0]['valid_per_image_iou']\n", + " mlflow.log_metric(\"valid_per_image_iou\", valid_per_image_iou)\n", + " mlflow.log_metric(\"loss\", 1 - valid_per_image_iou)\n", + " \n", + " # Log information about dataset and lakeFS tags/commits\n", + " if localOrDistributedComputing == \"LOCAL\":\n", + " dataset = mlflow.data.load_delta(path=f\"{gold_data_path}/{training_data_folder}\")\n", + " mlflow.log_input(dataset, context=\"Gold Dataset\")\n", + " lakefs_dataset_tag = f\"{lakefsUIEndPoint}/repositories/{repo_name}/objects?ref={goldDatasetTagID}&path=gold/train_v2/\"\n", + " dictionary = {\"lakefs_dataset\": lakefs_dataset_tag}\n", + " mlflow.log_dict(dictionary, \"model/lakefs_dataset.json\")\n", + " mlflow.set_tag(\"lakefs_repo\", repo_name)\n", + " mlflow.set_tag(\"lakefs_branch\", experimentBranchN)\n", + " mlflow.set_tag(\"lakefs_dataset\", lakefs_dataset_tag)\n", + "\n", + " return valid_per_image_iou" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "a6065c3e-f3a4-4fb1-ab11-4caa5289fa10", + "showTitle": false, + "title": "" + } + }, + "source": [ + "### Other helper functions" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "4d585cb4-0881-4fbe-9310-f589b6a96f29", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def commit_metadata_for_best_model(best_model, model_registered):\n", + " if localOrDistributedComputing == \"LOCAL\":\n", + " commitMetadata='::lakefs::Registered Model::url[url:ui]=http://127.0.0.1:5001/#/models/lakefs_demos_image_segmentation/versions/' + str(model_registered.version) + \\\n", + " ',Model Name=' + model_registered.name + \\\n", + " ',Model Version=' + str(model_registered.version) + \\\n", + " ',Model Run Name=' + best_model[\"tags.mlflow.runName\"] + \\\n", + " ',Model Metric valid_per_image_iou=' + str(best_model[\"metrics.valid_per_image_iou\"])\n", + " elif localOrDistributedComputing == \"DISTRIBUTED\": \n", + " commitMetadata={'::lakefs::Registered Model::url[url:ui]' : 'https://'+spark.conf.get(\"spark.databricks.workspaceUrl\")+'/#mlflow/models/lakefs_demos_image_segmentation/versions/' + str(model_registered.version),\n", + " 'Model Name' : model_registered.name,\n", + " 'Model Version' : str(model_registered.version),\n", + " 'Model Run Name' : best_model[\"tags.mlflow.runName\"],\n", + " 'Model Metric valid_per_image_iou' : str(best_model[\"metrics.valid_per_image_iou\"]) \n", + " }\n", + " return commitMetadata " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "6d7fcd85-1afa-42f7-b3ff-127c564c952f", + "showTitle": false, + "title": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "def display_gold_images_header():\n", + " wi1 = widgets.Label(value='image_id', disabled=True, layout=widgets.Layout(width='120px'))\n", + " wi2 = widgets.Label(value='boats', disabled=True, layout=widgets.Layout(width='60px'))\n", + " wi3 = widgets.Label(value='mask', disabled=True, layout=widgets.Layout(width='150px'))\n", + " wi4 = widgets.Label(value='content', disabled=True, layout=widgets.Layout(width='150px'))\n", + " wid=widgets.HBox([wi1,wi2,wi3,wi4])\n", + " display(wid)\n", + " \n", + "def display_gold_images_row(row):\n", + " wi1 = widgets.Text(value=row['image_id'], disabled=True, layout=widgets.Layout(width='120px'))\n", + " wi2 = widgets.IntText(value=row['boat_number'], disabled=True, layout=widgets.Layout(width='60px'))\n", + " wi3 = widgets.Image(value=row['mask'], format='jpg', width=150, height=150)\n", + " wi4 = widgets.Image(value=row['content'], format='jpg', width=150, height=150)\n", + " wid=widgets.HBox([wi1,wi2,wi3,wi4])\n", + " display(wid)\n", + " \n", + "def display_gold_images_local(df_gold_images):\n", + " display_gold_images_header()\n", + " dataCollect = df_gold_images.collect()\n", + " for row in dataCollect:\n", + " display_gold_images_row(row)\n", + " \n", + "def display_gold_images(df_gold_images):\n", + " if localOrDistributedComputing == \"LOCAL\":\n", + " display_gold_images_local(df_gold_images)\n", + " elif localOrDistributedComputing == \"DISTRIBUTED\":\n", + " display(df_gold_images)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "application/vnd.databricks.v1+cell": { + "cellMetadata": {}, + "inputWidgets": {}, + "nuid": "bc46b71a-592c-4ef7-8b1f-225d43c23ced", + "showTitle": false, + "title": "" + } + }, + "outputs": [], + "source": [ + "# This works with Jupyter notebook but does not work with Databricks notebook\n", + "def display_capture(capture, lines):\n", + " capture_split = capture.stdout.split('\\n')\n", + " if lines > len(capture_split):\n", + " lines = len(capture_split)\n", + " for x in range(lines):\n", + " print(capture_split[-(lines-x)])\n", + " \n", + " capture_split = capture.stderr.split('\\n')\n", + " if lines > len(capture_split):\n", + " lines = len(capture_split)\n", + " for x in range(lines):\n", + " print(capture_split[-(lines-x)])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Added for Local demo" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "file_list_random = []\n", + "file_list_random.append(prefix+training_data_folder)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Work locally with smaller dataset or work with bigger dataset in Databricks cluster" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "if localOrDistributedComputing == \"LOCAL\":\n", + " repo_path = f\"{repo_name}/lakefs_local\"\n", + "elif localOrDistributedComputing == \"DISTRIBUTED\":\n", + " repo_path = f\"lakefs://{repo_name}/{experimentBranchN}\"\n", + "\n", + "raw_data_path = f\"{repo_path}/{raw_data_folder}\"\n", + "training_data_path = f\"{raw_data_path}{training_data_folder}\"\n", + "bronze_data_path = f\"{repo_path}/{bronze_data_folder}\"\n", + "silver_data_path = f\"{repo_path}/{silver_data_folder}\"\n", + "gold_data_path = f\"{repo_path}/{gold_data_folder}\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run the data pipeline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def data_pipeline():\n", + " print(\"############ Ingest raw images as bronze data set and save as Delta table ############\")\n", + " df_bronze_images = bronze_images()\n", + " df_bronze_images.write.format(\"delta\").mode(\"overwrite\").save(f\"{bronze_data_path}/{training_data_folder}\")\n", + " diff_branch(repo.id, repo_path, experimentBranchN)\n", + " \n", + " print(\"############ Commit bronze dataset to the lakeFS repository and tag it ############\")\n", + " commitMessage = 'Converted raw images to binary content and saved as Delta table'\n", + " commit(repo.id, repo_path, experimentBranchN, commitMessage)\n", + " lakefs_set_tag(repo.id, f\"{tagPrefix}-{experimentBranchN}-bronze-images\", experimentBranchN)\n", + " \n", + " print(\"############ Enrich dataset and save as silver dataset ############\")\n", + " df_silver_images = silver_images(df_bronze_images)\n", + " df_silver_images.write.format(\"delta\").mode(\"overwrite\").save(f\"{silver_data_path}/{training_data_folder}\")\n", + " diff_branch(repo.id, repo_path, experimentBranchN)\n", + " \n", + " print(\"############ Commit silver dataset to the lakeFS repository and tag it ############\")\n", + " commitMessage = 'Enriched dataset and saved as silver dataset'\n", + " commit(repo.id, repo_path, experimentBranchN, commitMessage)\n", + " lakefs_set_tag(repo.id, f\"{tagPrefix}-{experimentBranchN}-silver-images\", experimentBranchN)\n", + " \n", + " print(\"############ Load the raw image mask as bronze dataset ############\")\n", + " df_bronze_mask = bronze_mask()\n", + " df_bronze_mask.write.format(\"delta\").mode(\"overwrite\").save(f\"{bronze_data_path}/{mask_data_folder}\")\n", + " diff_branch(repo.id, repo_path, experimentBranchN)\n", + " \n", + " print(\"############ Commit bronze mask dataset to the lakeFS repository and tag it ############\")\n", + " commitMessage = 'Loaded the raw image mask and saved as Delta table'\n", + " commit(repo.id, repo_path, experimentBranchN, commitMessage)\n", + " lakefs_set_tag(repo.id, f\"{tagPrefix}-{experimentBranchN}-bronze-mask\", experimentBranchN)\n", + " \n", + " print(\"############ Transform masks into images ############\")\n", + " df_silver_mask = silver_mask(df_bronze_mask)\n", + " df_silver_mask.write.format(\"delta\").mode(\"overwrite\").save(f\"{silver_data_path}/{mask_data_folder}\")\n", + " diff_branch(repo.id, repo_path, experimentBranchN)\n", + " \n", + " print(\"############ Commit silver mask dataset to the lakeFS repository and tag it ############\")\n", + " commitMessage = 'Transformed masks into images'\n", + " commit(repo.id, repo_path, experimentBranchN, commitMessage)\n", + " lakefs_set_tag(repo.id, f\"{tagPrefix}-{experimentBranchN}-silver-mask\", experimentBranchN)\n", + " \n", + " print(\"############ To verify that pipeline ran successfully, join image and mask both as the gold layer and select top 10 images with maximum number of boats/ships ############\")\n", + " df_gold_images = gold_images(df_silver_images, df_silver_mask)\n", + " display_gold_images(df_gold_images.orderBy(desc(\"boat_number\")).limit(10))\n", + " \n", + " print(\"\\n\\n############ Save gold dataset ############\")\n", + " df_gold_images.write.format(\"delta\").mode(\"overwrite\").save(f\"{gold_data_path}/{training_data_folder}\")\n", + " diff_branch(repo.id, repo_path, experimentBranchN)\n", + " \n", + " print(\"############ Commit gold dataset to the lakeFS repository and tag it ############\")\n", + " commitMessage = 'Joined image and mask both as the gold layer'\n", + " commit(repo.id, repo_path, experimentBranchN, commitMessage)\n", + " goldDatasetTagID = f\"{tagPrefix}-{experimentBranchN}-gold-images\"\n", + " lakefs_set_tag(repo.id, goldDatasetTagID, experimentBranchN)\n", + " \n", + " return goldDatasetTagID" + ] + } + ], + "metadata": { + "application/vnd.databricks.v1+notebook": { + "dashboards": [], + "language": "python", + "notebookMetadata": { + "pythonIndentUnit": 4 + }, + "notebookName": "ImageSegmentationSetup", + "widgets": {} + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/01_standalone_examples/image-segmentation-local/README.md b/01_standalone_examples/image-segmentation-local/README.md new file mode 100644 index 000000000..cfb8b07fb --- /dev/null +++ b/01_standalone_examples/image-segmentation-local/README.md @@ -0,0 +1,42 @@ +# Image Segmentation Demo - Run Locally + +Start by ⭐️ starring [lakeFS open source](https://go.lakefs.io/oreilly-course) project. + +This repository includes a Jupyter Notebook which you can run on your local machine. The notebook demonstrates ML Data Version Control and Reproducibility at Scale. + +In the ever-evolving landscape of machine learning (ML), data stands as the cornerstone upon which triumphant models are built. However, as ML projects expand and encompass larger and more complex datasets, the challenge of efficiently managing and controlling data at scale becomes more pronounced. + +* Breaking Down Conventional Approaches: +1. The Copy/Paste Predicament: In the world of data science, it's commonplace for data scientists to extract subsets of data to their local environments for model training. This method allows for iterative experimentation, but it introduces challenges that hinder the seamless evolution of ML projects. +2. Reproducibility Constraints: Traditional practices of copying and modifying data locally lack the version control and audit-ability crucial for reproducibility. Iterating on models with various data subsets becomes a daunting task. +3. Inefficient Data Transfer: Regularly shuttling data between the central repository and local environments strains resources and time, especially when choosing different subsets of data for each training run. + +* In this demo, we will demonstrate: +1. How to use lakeFS to version control your data when working with your data locally. +3. We will be leveraging the technology stack of: MinIO, Delta Lake, PyTorch and MLflow + + +## Prerequisites +* Docker installed on your local machine + +## Setup + +1. Start by cloning this repository: + + ```bash + git clone https://github.com/treeverse/lakeFS-samples && cd lakeFS-samples/01_standalone_examples/image-segmentation + ``` + +2. Run the following to provision the full stack which includes lakeFS, MinIO, Python, Spark, Jupyter Notebook, JDK, Hadoop binaries and lakeFS Python client + + ```bash + docker compose --profile local-lakefs up + ``` + + If any of the port numbers (8893, 4043, 5001, 8003, 9002 and 9003) are already in use then change the port numbers in docker-compose.yml file to any available ports. + +3. Open JupyterLab UI [http://127.0.0.1:8893/](http://127.0.0.1:8893/) in your web browser. + +## Demo Instructions + +1. Once you have successfully completed setup then open "Image Segmentation" notebook from JupyterLab UI and follow the instructions. \ No newline at end of file diff --git a/01_standalone_examples/image-segmentation-local/docker-compose.yml b/01_standalone_examples/image-segmentation-local/docker-compose.yml new file mode 100644 index 000000000..81524c1c7 --- /dev/null +++ b/01_standalone_examples/image-segmentation-local/docker-compose.yml @@ -0,0 +1,99 @@ +version: "3.5" +name: lakefs-image-segmentation-local-demo +services: + jupyter-notebook: + build: . + environment: + # log-level is set to WARN because of noisy stdout problem + # -> See https://github.com/jupyter-server/jupyter_server/issues/1279 + - NOTEBOOK_ARGS=--log-level='WARN' --NotebookApp.token='' --NotebookApp.password='' --notebook-dir=/home/jovyan + ports: + - 8893:8888 # Jupyter + - 4043:4040 # Spark + - 5001:5000 # MLflow + volumes: + - $PWD:/home/jovyan + - ../../data:/data + + lakefs: + image: treeverse/lakefs:1.1.0 + depends_on: + - minio-setup + ports: + - "8003:8000" + environment: + - LAKEFS_DATABASE_TYPE=local + - LAKEFS_BLOCKSTORE_TYPE=s3 + - LAKEFS_BLOCKSTORE_S3_FORCE_PATH_STYLE=true + - LAKEFS_BLOCKSTORE_S3_ENDPOINT=http://minio:9000 + - LAKEFS_BLOCKSTORE_S3_CREDENTIALS_ACCESS_KEY_ID=minioadmin + - LAKEFS_BLOCKSTORE_S3_CREDENTIALS_SECRET_ACCESS_KEY=minioadmin + - LAKEFS_AUTH_ENCRYPT_SECRET_KEY=some random secret string + - LAKEFS_LOGGING_LEVEL=INFO + - LAKEFS_STATS_ENABLED=${LAKEFS_STATS_ENABLED:-1} + - LAKEFS_INSTALLATION_USER_NAME=everything-bagel + - LAKEFS_INSTALLATION_ACCESS_KEY_ID=AKIAIOSFOLKFSSAMPLES + - LAKEFS_INSTALLATION_SECRET_ACCESS_KEY=wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY + - LAKECTL_SERVER_ENDPOINT_URL=http://localhost:8000 + entrypoint: ["/bin/sh", "-c"] + command: + - | + lakefs run --local-settings & + echo "---- Creating repository ----" + wait-for -t 60 lakefs:8000 -- curl -u "$$LAKEFS_INSTALLATION_ACCESS_KEY_ID":"$$LAKEFS_INSTALLATION_SECRET_ACCESS_KEY" -X POST -H "Content-Type: application/json" -d '{ "name": "quickstart", "storage_namespace": "s3://quickstart", "default_branch": "main", "sample_data": true }' http://localhost:8000/api/v1/repositories || true + # wait-for -t 60 lakefs:8000 -- lakectl repo create lakefs://example s3://example || true + echo "" + wait-for -t 60 minio:9000 && echo '------------------------------------------------ + + MinIO admin: http://127.0.0.1:9003/ + + Username : minioadmin + Password : minioadmin + ' + echo "------------------------------------------------" + wait-for -t 60 jupyter-notebook:8888 && echo ' + + Jupyter: http://127.0.0.1:8893/ + ' + echo "------------------------------------------------" + echo "" + echo "lakeFS Web UI: http://127.0.0.1:8003/ >(._.)<" + echo " ( )_ " + echo "" + echo " Access Key ID : $$LAKEFS_INSTALLATION_ACCESS_KEY_ID" + echo " Secret Access Key: $$LAKEFS_INSTALLATION_SECRET_ACCESS_KEY" + echo "" + echo "-------- Let's go and have axolotl fun! --------" + echo "" + wait + profiles: + - local-lakefs + + minio-setup: + image: minio/mc:RELEASE.2023-05-18T16-59-00Z + environment: + - MC_HOST_lakefs=http://minioadmin:minioadmin@minio:9000 + depends_on: + - minio + volumes: + - ../../data:/data + entrypoint: ["/bin/sh", "-c"] + command: + - | + mc mb lakefs/quickstart lakefs/example lakefs/sample-data + mc cp --recursive /data/* lakefs/sample-data 1>/dev/null # don't be so noisy 🤫 + profiles: + - local-lakefs + + minio: + image: minio/minio:RELEASE.2023-05-18T00-05-36Z + ports: + - "9002:9000" + - "9003:9001" + entrypoint: ["minio", "server", "/data", "--console-address", ":9001"] + profiles: + - local-lakefs + +networks: + default: + name: lakefs-image-segmentation-local-network diff --git a/01_standalone_examples/image-segmentation-local/files/images/ImageSegmentation/ImageSegmentation.png b/01_standalone_examples/image-segmentation-local/files/images/ImageSegmentation/ImageSegmentation.png new file mode 100644 index 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a/01_standalone_examples/image-segmentation-local/start-mlflow-ui.ipynb b/01_standalone_examples/image-segmentation-local/start-mlflow-ui.ipynb new file mode 100644 index 000000000..bfceb83bd --- /dev/null +++ b/01_standalone_examples/image-segmentation-local/start-mlflow-ui.ipynb @@ -0,0 +1,53 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "aa891c5b-66c4-4df4-8d89-9202183c098c", + "metadata": {}, + "source": [ + "## Start MLflow server and go to [MLflow UI](http://127.0.0.1:5001/)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "be749b47-4833-4a69-aad9-5415f3d61284", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "!mlflow server -h 0.0.0.0" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cd4290a6-4256-4f6e-9a55-94f598f84a78", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, 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