From 2657305b81df22d697e201efe76147c9f3d175f3 Mon Sep 17 00:00:00 2001 From: revital Date: Tue, 22 Aug 2023 14:51:12 +0300 Subject: [PATCH 01/10] Add ClearML experiment management tutorial Signed-off-by: revital --- README.md | 3 + .../unet_segmentation_3d_ignite_clearml.ipynb | 630 ++++++++++++++++++ 2 files changed, 633 insertions(+) create mode 100644 experiment_management/unet_segmentation_3d_ignite_clearml.ipynb diff --git a/README.md b/README.md index d59d2f9beb..401e541fad 100644 --- a/README.md +++ b/README.md @@ -146,6 +146,9 @@ An example of experiment management with [Aim](https://aimstack.io/aim-monai-tut An example of experiment management with [MLFlow](https://www.mlflow.org/docs/latest/tracking.html), using 3D spleen segmentation as an example. ##### [MONAI bundle integrates MLFlow](./experiment_management/bundle_integrate_mlflow.ipynb) An example shows how to easily enable and customize the MLFlow for experiment management in MONAI bundle. +##### [ClearML](./experiment_management/unet_segmentation_3d_ignite_clearml) +An example of experiment management with [ClearML](https://clear.ml/docs/latest/docs/), using 3D Segmentation with UNet as an example. + #### **Federated Learning** ##### [NVFlare](./federated_learning/nvflare) diff --git a/experiment_management/unet_segmentation_3d_ignite_clearml.ipynb b/experiment_management/unet_segmentation_3d_ignite_clearml.ipynb new file mode 100644 index 0000000000..1a7ffc2a2f --- /dev/null +++ b/experiment_management/unet_segmentation_3d_ignite_clearml.ipynb @@ -0,0 +1,630 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "3mqoATXZGTAX" + }, + "source": [ + "Copyright (c) MONAI Consortium \n", + "Licensed under the Apache License, Version 2.0 (the \"License\"); \n", + "you may not use this file except in compliance with the License. \n", + "You may obtain a copy of the License at \n", + "    http://www.apache.org/licenses/LICENSE-2.0 \n", + "Unless required by applicable law or agreed to in writing, software \n", + "distributed under the License is distributed on an \"AS IS\" BASIS, \n", + "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. \n", + "See the License for the specific language governing permissions and \n", + "limitations under the License.\n", + "\n", + "# Experiment Management with ClearML\n", + "\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/3d_segmentation/unet_segmentation_3d_ignite.ipynb)\n", + "\n", + "This tutorial shows how to use ClearML to manage MONAI experiments. You can integrate ClearML into your code using Monai's built-in handlers: `ClearMLImageHandler`, `ClearMLStatsHandler`, and `ModelCheckpoint`.\n", + "\n", + "The MONAI example used here is [3D segmentation with UNet](https://github.com/Project-MONAI/tutorials/blob/main/3d_segmentation/unet_segmentation_3d_ignite.ipynb)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VUefxTkGGTAc" + }, + "source": [ + "## Setup environment\n", + "\n", + "`clearml` comes as part of the `monai[all]` installation. For more information about integrating ClearML into your Monai code, see [here](https://clear.ml/docs/latest/docs/integrations/monai). For more information about using ClearML (experiment management, data management, pipelines, model serving, and more), see [ClearML's official documentation](https://clear.ml/docs/latest/docs/)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "OUMIxMjvGTAd" + }, + "outputs": [], + "source": [ + "!python -c \"import monai\" || pip install -q \"monai-weekly[ignite, nibabel, tensorboard, clearml]\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "s-ORDi_qZTYM" + }, + "source": [ + "### Set up ClearML ⚓\n", + "\n", + "1. To keep track of your experiments and/or data, ClearML needs to communicate to a server. You have 2 server options:\n", + "\n", + " * Sign up for free to the [ClearML Hosted Service](https://app.clear.ml/)\n", + " * Set up your own server, see [here](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server).\n", + "\n", + "\n", + "\n", + "2. Add you ClearML credentials below. ClearML will use these credentials to connect to your server (see instructions for generating credentials [here](https://clear.ml/docs/latest/docs/getting_started/ds/ds_first_steps/#jupyter-notebook))." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ofvwifHWG0SK" + }, + "outputs": [], + "source": [ + "# clearml credentials\n", + "%env CLEARML_WEB_HOST=''\n", + "%env CLEARML_API_HOST=''\n", + "%env CLEARML_FILES_HOST=''\n", + "%env CLEARML_API_ACCESS_KEY=''\n", + "%env CLEARML_API_SECRET_KEY=''\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "G-L36u4sGTAg" + }, + "source": [ + "## Setup imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "VAWF4EftGTAh" + }, + "outputs": [], + "source": [ + "import glob\n", + "import logging\n", + "import os\n", + "from pathlib import Path\n", + "import shutil\n", + "import sys\n", + "import tempfile\n", + "\n", + "import nibabel as nib\n", + "import numpy as np\n", + "from monai.config import print_config\n", + "from monai.data import ArrayDataset, create_test_image_3d, decollate_batch, DataLoader\n", + "from monai.handlers import (\n", + " MeanDice,\n", + " StatsHandler,\n", + ")\n", + "# import the clearml handlers\n", + "from monai.handlers.clearml_handlers import ClearMLImageHandler, ClearMLStatsHandler\n", + "from monai.losses import DiceLoss\n", + "from monai.networks.nets import UNet\n", + "from monai.transforms import (\n", + " Activations,\n", + " EnsureChannelFirst,\n", + " AsDiscrete,\n", + " Compose,\n", + " LoadImage,\n", + " RandSpatialCrop,\n", + " Resize,\n", + " ScaleIntensity,\n", + ")\n", + "from monai.utils import first\n", + "\n", + "import ignite\n", + "import torch\n", + "print_config()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cBN8rQJcGTAk" + }, + "source": [ + "## Setup data directory\n", + "\n", + "You can specify a directory with the `MONAI_DATA_DIRECTORY` environment variable. \n", + "This allows you to save results and reuse downloads. \n", + "If not specified a temporary directory will be used." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "BSjqI-ZBGTAk" + }, + "outputs": [], + "source": [ + "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n", + "root_dir = tempfile.mkdtemp() if directory is None else directory\n", + "print(root_dir)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LzxP-l1nGTAl" + }, + "source": [ + "## Setup logging" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "GGHooZZwGTAl" + }, + "outputs": [], + "source": [ + "logging.basicConfig(stream=sys.stdout, level=logging.INFO)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eIDTWNVxGTAm" + }, + "source": [ + "## Setup demo data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "l0jR_jz8GTAn" + }, + "outputs": [], + "source": [ + "for i in range(40):\n", + " im, seg = create_test_image_3d(128, 128, 128, num_seg_classes=1)\n", + "\n", + " n = nib.Nifti1Image(im, np.eye(4))\n", + " nib.save(n, os.path.join(root_dir, f\"im{i}.nii.gz\"))\n", + "\n", + " n = nib.Nifti1Image(seg, np.eye(4))\n", + " nib.save(n, os.path.join(root_dir, f\"seg{i}.nii.gz\"))\n", + "\n", + "images = sorted(glob.glob(os.path.join(root_dir, \"im*.nii.gz\")))\n", + "segs = sorted(glob.glob(os.path.join(root_dir, \"seg*.nii.gz\")))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8FUIYNO0GTAn" + }, + "source": [ + "## Setup transforms, dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "g5gz3rECGTAo" + }, + "outputs": [], + "source": [ + "# Define transforms for image and segmentation\n", + "imtrans = Compose(\n", + " [\n", + " LoadImage(image_only=True),\n", + " ScaleIntensity(),\n", + " EnsureChannelFirst(),\n", + " RandSpatialCrop((96, 96, 96), random_size=False),\n", + " ]\n", + ")\n", + "segtrans = Compose(\n", + " [\n", + " LoadImage(image_only=True),\n", + " EnsureChannelFirst(),\n", + " RandSpatialCrop((96, 96, 96), random_size=False),\n", + " ]\n", + ")\n", + "\n", + "# Define nifti dataset, dataloader\n", + "ds = ArrayDataset(images, imtrans, segs, segtrans)\n", + "# loader = DataLoader(ds, batch_size=10, num_workers=2, pin_memory=torch.cuda.is_available())\n", + "loader = DataLoader(ds, batch_size=10, num_workers=2, pin_memory=False)\n", + "im, seg = first(loader)\n", + "print(im.shape, seg.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6zl4_zeSGTAp" + }, + "source": [ + "## Create Model, Loss, Optimizer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "XT1D-a1-GTAp" + }, + "outputs": [], + "source": [ + "# Create UNet, DiceLoss and Adam optimizer\n", + "\n", + "device = None # torch.device(\"cuda:0\")\n", + "net = UNet(\n", + " spatial_dims=3,\n", + " in_channels=1,\n", + " out_channels=1,\n", + " channels=(16, 32, 64, 128, 256),\n", + " strides=(2, 2, 2, 2),\n", + " num_res_units=2,\n", + ").to(device)\n", + "\n", + "loss = DiceLoss(sigmoid=True)\n", + "lr = 1e-3\n", + "opt = torch.optim.Adam(net.parameters(), lr)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Xvduwsk8GTBm" + }, + "source": [ + "## Create supervised_trainer using ignite" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "9Ocpt_MOGTBn" + }, + "outputs": [], + "source": [ + "# Create trainer\n", + "trainer = ignite.engine.create_supervised_trainer(net, opt, loss, device, False)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5vo9Eq33GTBn" + }, + "source": [ + "## Set up event handlers for checkpointing and logging\n", + "\n", + "Using a ClearML handler creates a ClearML Task, which captures your experiment's models, scalars, images, and more.\n", + "\n", + "The console output displays the task ID and a link to the task's page in the ClearML web UI." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "cZTtFnySGTBo" + }, + "outputs": [], + "source": [ + "# optional section for checkpoint and clearml logging\n", + "# adding checkpoint handler to save models (network\n", + "# params and optimizer stats) during training\n", + "log_dir = os.path.join(root_dir, \"logs\")\n", + "checkpoint_handler = ignite.handlers.ModelCheckpoint(log_dir, \"net\", n_saved=10, require_empty=False)\n", + "trainer.add_event_handler(\n", + " event_name=ignite.engine.Events.EPOCH_COMPLETED,\n", + " handler=checkpoint_handler,\n", + " to_save={\"net\": net, \"opt\": opt},\n", + ")\n", + "\n", + "# StatsHandler prints loss at every iteration\n", + "# user can also customize print functions and can use output_transform to convert\n", + "# engine.state.output if it's not a loss value\n", + "train_stats_handler = StatsHandler(name=\"trainer\", output_transform=lambda x: x)\n", + "train_stats_handler.attach(trainer)\n", + "\n", + "\n", + "# ClearMLStatsHandler plots loss at every iteration\n", + "# Creates a ClearML Task which logs the scalar plots\n", + "task_name = \"UNet segmentation 3d\"\n", + "project_name = \"Monai example\"\n", + "\n", + "train_clearml_stats_handler = ClearMLStatsHandler(task_name=task_name,\n", + " project_name=project_name, log_dir=log_dir, output_transform=lambda x: x)\n", + "train_clearml_stats_handler.attach(trainer)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hBv81ENGGTBp" + }, + "source": [ + "## Add Validation every N epochs" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "VYte_Cw6GTBp" + }, + "outputs": [], + "source": [ + "# optional section for model validation during training\n", + "validation_every_n_epochs = 1\n", + "# Set parameters for validation\n", + "metric_name = \"Mean_Dice\"\n", + "# add evaluation metric to the evaluator engine\n", + "val_metrics = {metric_name: MeanDice()}\n", + "post_pred = Compose([Activations(sigmoid=True), AsDiscrete(threshold=0.5)])\n", + "post_label = Compose([AsDiscrete(threshold=0.5)])\n", + "# Ignite evaluator expects batch=(img, seg) and\n", + "# returns output=(y_pred, y) at every iteration,\n", + "# user can add output_transform to return other values\n", + "evaluator = ignite.engine.create_supervised_evaluator(\n", + " net,\n", + " val_metrics,\n", + " device,\n", + " True,\n", + " output_transform=lambda x, y, y_pred: (\n", + " [post_pred(i) for i in decollate_batch(y_pred)],\n", + " [post_label(i) for i in decollate_batch(y)],\n", + " ),\n", + ")\n", + "\n", + "# create a validation data loader\n", + "val_imtrans = Compose(\n", + " [\n", + " LoadImage(image_only=True),\n", + " ScaleIntensity(),\n", + " EnsureChannelFirst(),\n", + " Resize((96, 96, 96)),\n", + " ]\n", + ")\n", + "val_segtrans = Compose(\n", + " [\n", + " LoadImage(image_only=True),\n", + " EnsureChannelFirst(),\n", + " Resize((96, 96, 96)),\n", + " ]\n", + ")\n", + "val_ds = ArrayDataset(images[21:], val_imtrans, segs[21:], val_segtrans)\n", + "# val_loader = DataLoader(val_ds, batch_size=5, num_workers=8, pin_memory=torch.cuda.is_available())\n", + "val_loader = DataLoader(val_ds, batch_size=5, num_workers=8, pin_memory=False)\n", + "\n", + "\n", + "@trainer.on(ignite.engine.Events.EPOCH_COMPLETED(every=validation_every_n_epochs))\n", + "def run_validation(engine):\n", + " evaluator.run(val_loader)\n", + "\n", + "\n", + "# Add stats event handler to print validation stats via evaluator\n", + "val_stats_handler = StatsHandler(\n", + " name=\"evaluator\",\n", + " # no need to print loss value, so disable per iteration output\n", + " output_transform=lambda x: None,\n", + " # fetch global epoch number from trainer\n", + " global_epoch_transform=lambda x: trainer.state.epoch,\n", + ")\n", + "val_stats_handler.attach(evaluator)\n", + "\n", + "# add handler to record metrics to clearml at every validation epoch\n", + "val_clearml_stats_handler = ClearMLStatsHandler(\n", + " log_dir=log_dir,\n", + " # no need to plot loss value, so disable per iteration output\n", + " output_transform=lambda x: None,\n", + " # fetch global epoch number from trainer\n", + " global_epoch_transform=lambda x: trainer.state.epoch,\n", + ")\n", + "val_clearml_stats_handler.attach(evaluator)\n", + "\n", + "# add handler to draw the first image and the corresponding\n", + "# label and model output in the last batch\n", + "# here we draw the 3D output as GIF format along Depth\n", + "# axis, at every validation epoch\n", + "val_clearml_image_handler = ClearMLImageHandler(task_name=task_name,\n", + " project_name=project_name,\n", + " log_dir=log_dir,\n", + " batch_transform=lambda batch: (batch[0], batch[1]),\n", + " output_transform=lambda output: output[0],\n", + " global_iter_transform=lambda x: trainer.state.epoch,\n", + ")\n", + "evaluator.add_event_handler(\n", + " event_name=ignite.engine.Events.EPOCH_COMPLETED,\n", + " handler=val_clearml_image_handler,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VxXixOfHGTBq" + }, + "source": [ + "## Run training loop" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "kW0K4mxIGTBt" + }, + "outputs": [], + "source": [ + "# create a training data loader\n", + "train_ds = ArrayDataset(images[:20], imtrans, segs[:20], segtrans)\n", + "train_loader = DataLoader(\n", + " train_ds,\n", + " batch_size=5,\n", + " shuffle=True,\n", + " num_workers=8,\n", + " # pin_memory=torch.cuda.is_available(),\n", + " pin_memory=False\n", + ")\n", + "\n", + "max_epochs = 10\n", + "trainer.run(train_loader, max_epochs)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Visualize results\n", + "\n", + "ClearML captures the models, scalar plots, and images logged with `ModelCheckpoint`, `ClearMLImageHandler`, and `ClearMLStatsHandler` respectively. View them in ClearML's web UI. When a task is created, the console output displays the task ID and a link to the task's page in the ClearML web UI.\n", + "\n", + "### Models\n", + "All model checkpoints logged with ModelCheckpoint can be viewed in the task's **Artifacts** tab.\n", + "\n", + "\n", + "\n" + ], + "metadata": { + "id": "7bPDjtbE7sCP" + } + }, + { + "cell_type": "markdown", + "source": [ + "![monai_clearml_models.png](data:image/png;base64,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+ ], + "metadata": { + "id": "Uya6hN7At-C6" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Scalars\n", + "\n", + "View the logged metric plots in the task's **Scalars** tab.\n", + "\n" + ], + "metadata": { + "id": "kLs1tEXRt-Pp" + } + }, + { + "cell_type": "markdown", + "source": [ + 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)" + ], + "metadata": { + "id": "DCLhp4e2uKsd" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Debug Samples\n", + "\n", + "View all images logged through ClearMLImageHandler in the task's **Debug Samples** tab. You can view the samples by metric at any iteration." + ], + "metadata": { + "id": "yjfeorgJuLUN" + } + }, + { + "cell_type": "markdown", + "source": [ + 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+ ], + "metadata": { + "id": "AFLJauKxt-Yy" + } + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UFlaQUTXGTBv" + }, + "source": [ + "## Cleanup data directory" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "w6JE9lVQGTBw" + }, + "outputs": [], + "source": [ + "if directory is None:\n", + " shutil.rmtree(root_dir)" + ] + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "## Close the ClearML Task\n", + "This changes the task status to `Completed`." + ], + "metadata": { + "id": "sT-5Zxg_7Ajm" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "BAD6ACGGOLU3" + }, + "outputs": [], + "source": [ + "val_clearml_image_handler.clearml_task.close()" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "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": "3.8.13" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From cad859d632a32c6c78db95bab2672f4e0515b1c8 Mon Sep 17 00:00:00 2001 From: revital Date: Tue, 22 Aug 2023 15:00:24 +0300 Subject: [PATCH 02/10] Add images Signed-off-by: revital --- .../unet_segmentation_3d_ignite_clearml.ipynb | 6 +++--- figures/monai_clearml_debug_samples.png | Bin 0 -> 41855 bytes figures/monai_clearml_models.png | Bin 0 -> 57683 bytes figures/monai_clearml_scalars.png | Bin 0 -> 77667 bytes 4 files changed, 3 insertions(+), 3 deletions(-) create mode 100644 figures/monai_clearml_debug_samples.png create mode 100644 figures/monai_clearml_models.png create mode 100644 figures/monai_clearml_scalars.png diff --git a/experiment_management/unet_segmentation_3d_ignite_clearml.ipynb b/experiment_management/unet_segmentation_3d_ignite_clearml.ipynb index 1a7ffc2a2f..b070c661c6 100644 --- a/experiment_management/unet_segmentation_3d_ignite_clearml.ipynb +++ b/experiment_management/unet_segmentation_3d_ignite_clearml.ipynb @@ -512,7 +512,7 @@ { "cell_type": "markdown", "source": [ - 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+ "![Monai ClearML Models](./../figures/monai_clearml_models.png)" ], "metadata": { "id": "Uya6hN7At-C6" @@ -533,7 +533,7 @@ { "cell_type": "markdown", "source": [ - 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AAAAAIA24S2rUPGKrTZAWLhglSqzc22HJIlXTVa3b8+0HZSEDktXzLlnKWnmFMWOG+I+E8CJEChspni36jE9HwMAAAAAcGZVF5aoeOlG5fzjfRUtWquqnAJFDuqlpGsuUNK1FypqcG+7XPTZ/RQ6cajiLxpj5wNoGgKFzRQVEqWIkEhV1lSoyFvopgIAAAAAgNZSlVuookXrbAnCos822oBh1NC+Sr5+qi0xGNm/h7skgNNBoPAU9AxPt4/byzfZRwAAAAAA0PKqsnNV+NEqW4KweMUWW+U4esQApdx0iRIvn6iI9FR3SQAtgUDhKRgUOczZcaHKqtynQqogAwAAAADQokynJKZzkqOz56tkzQ4pJEQxYwary6zpSpg+TuE9urhLAmhJBApPQURIhPpFDLTjW8s32kcAAAAAAHB6KvYcVP67nynnpQUq3bhbnuhIxY4bagOE8Refo7Buvg5GAbQOAoWnaFDkUIWGhCm7Kkv51bluKgAAAAAAaK7ynfuV9+anyn39E5VtzVRofIxizxuulFnTFXfBKIUmx7tLAmhNBApPUVhIuAZEDLbjW8s32EcAAAAAANB0ZVv2Kve1j5X31mKV7zpgA4ImMGgDhJOG24AhgDOHQOFpGBBxlg0YHqnKVk71ETcVAAAAAAA0qqZGpet3KefFD5X/3lJV7D1kqxSbqsWmirGpamyqHAM48wgUngZT9XhgxBA7vrlsnX0EAAAAAADHq6moUsnq7baDkoIPVqgy66jtlMR0TmIChKazkpDwMHdpAG2BQOFp6h85yHZuYtopPFx1yE0FAAAAAACGt6RMxZ9v1tHZ76tw4WpVHc5TRHqqEi+fqJSbLlH0iAG2V2MAbY9A4WnyKFSDIofZ8S3l6+0jAAAAAACdXXV+sYqWbLAlCIsWr1d1XpEi+/dQ0swpSr5+qqKG9nWXBBAsCBS2gL4RAxQZEqnC6nwdrDrgpgIAAAAA0PlUHc1X4cdrbAnC4mWb5C0qVdTg3kq69kIlXXOBIgf1cpcEEGwIFLaAEGc3nhU53I5vK6MHZAAAAABA51N5MEcFH65Uzj/mq2TVNtWUVyrq7H5K/vLFSrxqsiL7pblLAghWBApbSO+IforxxKrIW6gDlZluKgAAAAAAHVvFvmzl/3uZcv71gUrX7VSNkxY9aqBSvnKpEmdMUETvbr4FAQQ9AoUtJMT5Gxx5th3fVr7RuTCaSyMAAAAAAB1T+e4s5b29RLmvLFTZ5j0KiQhXzLln2R6MEy4Zq/C0FHdJAO0FgcIW1DM8XXGeeJV4i7W/co+bCgAAAABAx1G2fZ/y3likvDmLVO6Me2KiFDthmA0Qxl80RmFdE90lAbQ3BApbkClVWNtWYfkm1chrxwEAAAAAaO9KN2Uo95WPlP/2EpVnZCk0IVZxk0fYAGHclJEKTYpzlwTQXhEobGFp4b0UH5qoMm+p9lbsdlMBAAAAAAh+lfuPqGxrpioys+10TbXXtjuY888PVDDvc1XsO6ywlATFXzhaKV+brtiJZ8sTF22XBdD+EShsBUMiR9jHHeWb5VW1HQcAAAAAIJjlvfmpcl5eoPx3P1Puqwt15Nl3lDP7fduTceWhHIWnJit+2rk2QBgzdog8kRHuMwF0FAQKW0FqWJoSQ5NVXlOujIqdbioAAAAAAMGpfMd+le864E75VBcUq+pogcJ7dVXCZeOVMmu6YkYPUkhoqLsEgI6m3QYKV6xYoenTp9vh1ltvVW5urjvneLt27dL1119fu3xTnnO6hkaOtI+7yrequqbKjgMAAAAAEIyq84vdsWNF9u+hlBumKXp4fzcFQEfWLgOFJvD31FNP6emnn9b8+fM1c+ZMPfbYYyorK3OXOF5CQkLt8mZ47rnnlJyc7M5teV3CuikltKsqaiq0u2KHmwoAAID6zM1bcxPXf0PX3BA+kfo3gd944w13jvT73/++Nj1wONk6AaAzK9+dpdKNu9ypY4X36OqOAegM2mWgcO3atRo9erQGDBhgp814VlaWDhw4tph0WxsWNco+7qrYpqqaSjsOAACAOuZGr7nha278mpu55sauuSFsgoENMUHFhx9+WPfdd59d/uWXX9bcuXNrA4F33nln7Y1h//qGDx+ugQMH2vkAgDpVR/NV8P5y5c1ZZKsYKyTEnVMnclBPdwxAZ9AuA4V79+51x3z8JQNzcnLsY0MKCgp022232TvKrV3t2M+0U9gtLM0GCXdWbHNTAQAA4Gdu9BYXF2vq1Kl2umfPnurRo4e9MdwQfx4uJSXFPkZHR9vlMzMz7XR9Joh40UUXtWpNEgBob2oqqlS8dKNyXlyg0o27FRIWansvTrlxmu2kJOqsdMWcM1gpX7lUYV0S3WcB6AzabRuFffr0ccdOzpQ8fPXVV2vvLJsSiCerqtxShro9IGdU7LDVkAEAAFDH3Og1N3T9oqKilJqaetyNYT+TrzN5uXvvvdeWOqwfaAxk5u/evbvBeQDQWZnAYM5LH6ros42qqahU1LC+SrnpEsVNHqHwHl0Uf+FoJV55nuKnnqPwNN9NGQCdR0iNwx1vN0zbM4apWmKYO8t33323br/9do0bN86mnYjJNP7hD3/QQw89ZO8ut3bpwm2e9Toaclg9atLVzzvYTQUAAG2pI5Yw27knVwP7tq/tMlWGTRuDP/nJT2yQ0Kif16vPPMdUTzYBxvz8fJsHvPbaa925dcx6zM3lhuah+UyemZKZwYVjEnyC+ZhUZGarZOVW2x6hEdG7m2LOHaLIgR23ajGfkeDDMWk5zc33meUbUn8d7TJQaDKT5i6zP/NoAn+mrZoHH3ywtt3CE6kfKGxtRd4CfVI0345fEn+VIkMi7ThaFxcg+HEuwI9zAR1dew0UmqDf448/Xvv5PFGgsKEbvuaGsWnjMDAg2NB6DbM8AHQmNYUlqt64R94t++x0SFy0PMP7KnRYup0GOoOO+BuAQGGA+oFBEzg0mUH/nWgzbdqj8WcM33//fQ0aNKg2iHiyu9StYWXJEh2qytLo6PHqFd70atM4dQQE4Me5AD/OBXR07TFQWD/wZ5qG+eUvf2lriTRUErApJRBPtg6cGq6hwYdjEnyC6ph4vSpeuc2WIvSWlju//mVLEMaOHSJPrO/62dHxGQk+HJOW01qBwnbZRqEJ+JkqJv7OSUxQ8J577qnNLNZnGrv2L2uG7OxsO30mJYf5upTPq268wxUAAIDOxnReEhsbq4ULF9pp0+ZgVlaWbYfQMIHE66+/3gYIDZOv27p1qzZs2GCnzQ8O0/FJYPvVZp5ZB20TAuisyrbutR2VFH26zgYJTeckKTdeYtsf7CxBQgCnpl2WKGyPjlYd1rKST5QUmqLJsRe7qWhN3KmAH+cC/DgX0NG1xxKFhvlsmurD/p6LH3300dp2p02g0HRcct9999WmmaDh/fffb8eNwDYK/etqatvVaDquocGHYxJ82vqYVB44ouJV21S+3VfN2HRG4u/FuDPiMxJ8OCYtp7VKFBIoPEOqnb95BXMUIo++kHC1fUTr4gIEP84F+HEuoKNrr4FCtA9cQ4MPxyT4tNUxqS4qtVWMS1Zts9Om1KCvmvFZzq/+EJvWGfEZCT4ck5bTWoFColVnSKjzF+eJV428KqgucFMBAAAAADh1Jau3KffFD2uDhDFjBtlqxrHjhnTqICGAU0Og8AxKDE2xj/m0UwgAAAAAOA3lO/Yr5+UFKly4RtWFJYoc0FPJX75Y8Refq9DEWHcpAGgeAoVnUFKorzhnfnXDxT0BAAAAADiRyuxc5b+3VHlzF6ty/xGFdUtS4owJSrr6fEX07uYuBQCnhkDhGZToBgrzCBQCAAAAAJrB9F5ctHi9rWZctmWvQiLDFTd5hK1mHHV2P3cpADg9BArPoMTQJNuJSZG3wHZuAgAAAADAyZSu26mcFxeo+PPNqqn2KnrEAKXcdIliJ56tkPBQdykAOH0ECs8gEyRMCE1QjfNXQKlCAAAAAMAJlGdkKfe1j1Xw4UpV5xUqom93JV97oRKmj1NYSoK7FAC0HAKFZ5i/QxOqHwMAAAAAGlJ1tEAF85cr741Fqth7SGHJ8Uq4dJySv3SRIvqluUsBQMsjUHiG0aEJAAAAAKAhNZVVKl66UTkvfqjSDbsVEhaq2AnDlHzTJYoeOcBdCgBaD4HCM6yuQ5Mc+wgAAAAAQOmmDBsgLPpso2oqKhU1rK/tqCRuykh5oiLcpQCgdREoPMPiPAkKdf5KvMWqqql0UwEAAAAAnVFFZrby5ixSwbzPVXUkXxG9uinpi1OU+IWJCktNcpcCgDODQOEZFuL8JVCqEAAAAAA6ter8IhUuWKXcVxeqfHeWQhNiFD/1HCXfcLEiB/ZylwKAM4tAYRvwt1NIhyYAAAAA0Ml4a1S8YotyXlygkrU7bFLM2CG2mnHMOYPtNAC0FQKFbcDfTiEdmgAAAABA51G2da+vHcJF6+QtKVPU4N5KuekSxV84Wp64aHcpAGg7BArbQFJoin3MrT5qHwEAAAAAHVdl1lHlv71E+e8uVeWhHIWnpSjxivOUeNVkhffo4i4FAG2PQGEbiPHEKiwkXBU15SqrKXVTAQAAAAAdibeoVIWfrLGlCMu275MnJkpxF4yy1YyjhqS7SwFA8CBQ2Eb8pQqpfgwAAAAA7VvV0QKVrNmu6g0ZtmMSo2T1dhsgLFm5zU7HjB5kqxnHjhvq/BIPsWkAEGwIFLYROjQBAAAAgPavfOcBHf2/f6vwo9WqXrFdeXMW6fBf5qpwoTNdWKLIAT2VfP1UxU87V6GJse6zACA4EShsI4m1JQpz7CMAAAAAoP0pXb/THavjLS5VWEqCEmZMUNLV5ysiPdWdAwDBjUBhG0l2A4WUKAQAAACA9suUGmxIwmXjFX12P3cKANoHAoVtJCIkUlEh0aqqqVSxt8hNBQAAAAC0F5UHjshbXOZOHSs0PsYdA4D2g0BhG0p02ymkQxMAAAAAaEdqpOKlG5Xzykfylpa7iXWihvWTJy7anQKA9oNAYRvqbD0fV9dUaXfFdh2pynZTAAAAAKB9qdhzyAYIiz7bKHlrFDNmkJKuuUBxk0codFR/JV4+SYlfmOAuDQDtC4HCNtTZej7eXL5em8vW6fOSRbbKNQAAAAC0FzWVVSpatE65r3+syv2HFd49WUlfnKL4i89VZP8eip14tkLPHaSooX3cZwBA+0OgsA35qx4XVOeqxpRd78Byqo9ob8Uud0rKrT7qjgEAAABAcCvfuV+5L3+k4hVb7HTs+KFK/vLFihzYy04DQEdBoLANhYWEK9YTp2rnr6i6wE3teMz2rStdYcejPb4GfU3gEAAAAACCmbekTIUfrVLeW4tVmZ2riN7dlHzdRYo7f5RCwsPcpQCg4yBQ2Mb8pQrzvDn2sSPaVrZRJd5i2ybj8KhzbFpOFYFCAAAAAMGrbMte5b6yUCVrdigk1GPbIDSlCCP6dHeXAICOh0BhG+voHZrkVefYDkxCnL/R0ePVJbSrHTfpXlW7SwEAAABAcKjOL1bB+8uV/95SVeUU2PYHTYDQtEEIAB0dgcI2VluisAMGCk27i2tLl9vxQZHDbDXr0JAwxYcm2HkmWAgAAAAAwaJ0/S7lvvKRSjfulicqQvEXjbE9Gof36OIuAQAdG4HCNpYQmmRL2BVW56tGXje1Y9hevlnF3iLFeRI0KHKomyolh3a1j1Q/BgAAABAMqo7mK/+dz1TwwQpVF5Yo6qx0W4ow5tyz3CUAoHMI2kBhbm6ubr31Vk2fPt0OK1b4OsM4mbKyMj3wwAN2MOPGrl27dP3119euywxm3eY12lqo8+cvYZdfneemtn9F3gLtLN9ig6BjosfbR78Uf6CQDk0AAAAAtLGS1dttj8Zl2zIVGh+jhEvHKfHK8xTWNdFdAgA6j6AMFJoA32OPPaaZM2dq/vz5evrpp/XUU0/ZgN+JmOf98pe/1Oeff+6m1ElISLDrMeszw3PPPafkZF+137aW6PG1U9hRquKaoOea0uX2sV/EIFtqMlDXsFT7mFN11C4DAAAAAGda5cEc5b35qQoXrpa3rELRw/vbUoTRIwe4SwBA5xOUgcIDBw6ouLhYU6dOtdM9e/ZUjx49tHbtWjvdGBMIHDdunG6//XY3pX3wt1PYUTo02VW+TQXVeYr2xOisqOFuap3wkAjFeOJsZyZmOQAAAAA4k4o/32zbIizfdUBhKQlKvHySEi4br9DEWHcJAOicgjJQmJOTo4KCAndKioqKUmpqqvbu3eumHO/3v/+9fbz22mvtY31mfbfddltQVTv2S+pAHZqYNgm3l2+y46aXY1O1uiFUPwYAAH7NbXKmfrMyb7zxhjvHx98UTWPzAXReFZnZyn11oYoWr1dNVbVixgy2pQijhvZxlwCAzi2kxuGOBw2TOTQZup/85Cc2SGj4A4F33nmnfQxkljVBRP88M23WEfj8QGZd2dnZtfPbPmhYo6WhC2013AnVFypUYW56+7Pes0JFIQXqXtNTA7x1HZjUdzgkSzs8m5VS01VDvKPcVAAAzpxgaYKkJe3ck6uBfdvXdvmbjjG1QswNXxMEfPjhh/Xggw9qwIDjq/+ZfNvdd99ta5CY59Sfrr8+tByzrzvi56Y945g0XU21V8VLN9qShEZ49xTFTjxbkQN72umWwjEJLhyP4MMxaTnNzfeZ5RtSfx1BGyg0bRI+/vjjtSfQiQKFZt7bb7/tTtWZMGFCg8FCkwH9wx/+oIceeihoTtDPihcqt/qoJsZcqC5h3dzU9iWjYqc2la1RZEikpsZ9QaEhjQc8S7zFWlj0b1sNeXr8TDe1ZXEBgh/nAvw4F9DRtcdAYf182ckCffUDifWXr38DGS2Ha2jw4Zg0TfnuLBUv26TKrKN2Onb8MMVOOlshYQ3XfjodHJPgwvEIPhyTltNagcKgrHqckpJiOx/xMxlAUwKwT5+Gi4ObjKC/kxIzmDvKjQUJg5W/ncL22qFJqbdEW8vW2/GR0WNPGCQ0YjyxigyJUmVNhe0hGQAAdE7NbXLGBAdHjx6te++91wYN67dtbZ5nbiD7qx2bKspmOQCdj+mgpPDjNcqbs8gGCSN6pyr5uqmKO39kqwQJAaAjCMoShSergmKmTebwvvvus8vUV7/q8fvvv69BgwbVVl85UenEtrK/cq/Wli5XWngvnRs9yU1tP5YWf2zbG+wZnq4x0RPc1BNbXbpMWZX7NCLqHPWJaPmexbhTAT/OBfhxLqCja48lCpvb5Izhr31iAoz5+fn2JrHJM/rzkGbcn0c06547d25tTRVzHQDQ8XkzDql67S7V5BZJoR6Fjh6g0FH93bkAOpuO+BugtUoUBmWg0DCZONPeTGZmpp1+9NFHazN8zQ0UmvH777/fndt4leS2VOwt1MdF7yvKE61pcVe4qe3DvsoMrStdqYiQCF0YN8M+NsWeip3aWLamWcHF5iAgAD/OBfhxLqCja6+BwuY0OWPygYFVlf15xpkzZ+ryyy8/LlDon+9vwxCnjmto8OGYHK+6qNS2RVi63leSOLJ/D1vNODyti51ubRyT4MLxCD4ck5bTWoHCoKx6bJgT57nnnqutThyYsTMlA1999dVGM3smc/jII4/UBgLNcoFVkwPnBYtYT7zCQsJV5i1VRU25mxr8yp33uqlsnR0/O2p0k4OERkqYr+fjo1WH7SMAAOh8mtvkjKmqHBsbq+joaDtt8oymKrKpcmzyd6basv9Gs59Zv3kdAB1b6aYM5b78kQ0SeqIiFD91jJKuueCMBQkBoCMI2kBhZ5TUDtspXF+6QlU1lUoNS1PP8IYz9I2J9yTazkzKa8psG4donCl9+W7Ba1pW8ombAgBAx9CzZ08b+Fu4cKGdNm0OZmVl2eCfYUoQmnYGTclDwwT8tm7dqg0bNthpUzJh7dq1tYHFKVOmaPbs2fZ5hlmvWb95HQAdU3VuofL/vUwF8z5XdX6Ros5KV/INFyvmnLPcJQAATUWgMIj4OzTJr24fbeccqMxUdtVB23HJiOhz3dTmSQ713d0z7RuicaZHbKO9dnYDAEBjTCnAe+65x7YjaDofue2222w1YX/b0vWZdNP8jGlWxix/ww032GrHpkaJYWqSzJo1y67HzDfrNesPttokAFpGydodynnlI5Vt3qPQ+BglTB+nxCvPU1iXRHcJAEBzBG0bhZ3RwaoDWlXymbqFpWl8zBQ3NTiZUoQLi/6tipqK0+qMZFfFNm0pW+88v7+znlMLNjamI7V98FHRe7WlLqfGfcH2Go2mox0M+HEuoKNrj20Uov3gGhp8OvMxqcrOVdGyTSrfsd9OR4/or9iJZys0oW3zyXxOggvHI/hwTFpOp2ujsDPyVz1uDyUKD1UdsEHCpNCU0+qxOCXU105hThUlChtTv2p2eylxCgAAALSG4hVbbSlCEyQMS0lQ4hWTlDB9fJsHCQGgIyBQGESiQqIVERJpOzMp8Ra7qcHpUFWWfezVzHYJ6zPVrT0KVZG3UJU1FW4qAtUPohIoBAAAQGdUeeCIcl//REWL1qqmokox5wy2bRFGDTm93yQAgDoECoNMcqivR76C6jz7GKwOVx2yj93DT69h8BDnLznMt81Hq+n9uCH+9gnjQ33trBR4g/vcAAAAAFpUTY2Kl25UzssfqWLPQYWnpSjp6vMVP/UceaIj3YUAAC2BQGGQ8XdoEsydVpggYXVNlRJCE20pyNNF9eMT8wcK+0cMto95lCgEAABAJ2ECg6aacdFnG23AMHbCMCV/+WJFDqAncwBoDQQKg0yiW6IwmINBpn1CIzWsZb6cU0K72cdcej4+jlfVtVWNe4b3VlhIuO1IJrDNQgAAAKCjMVWLixats1WNK/cfUUR6qpKvn6q4KSMVEhbqLgUAaGkECoOMv+pxfnWOapy/YJRd6QsUdg/rYR9Pl6l6bKogF1Tn25KKqJNb5StN6G/LsT11eAMAAACcCtNJiSlFWLxiiw0KmuCgCRKaYCEAoHURKAwypsRYjCdW1c5fsbfQTQ0epu3Espoy2+mKv5r06TIBMLMuExjNcavZwse/P5JDu9hH/z7P9xIoBAAAQPtWU15RW2ow763FKlm5VYULVilv7mJVZefa6sWmmrGpbgwAODMIFAahunYKgy8YlO32dpwW3ss+tpTadgqpfnyMvPqBQo+/RGFwdWiyo3yLtpdvsgFuAAAAoClMcNCUGjTtEJbv3K/CT9aqZO0O20GJ6ajEdFhiOi4BAJw5BAqDUFJt9ePgCxQecgOFqS1U7dgvJYwOTeozJSz9HZkku/snITTJPgZTZzemyvi28o3aXr5Zhc44AAAAcDLV+UWq2HfYnapjgoSmFGHMOb6O/AAAZxaBwiBUV6IwuHo+NlWOTfAyNCRUXcNatn2QLm6HJqYEXbC2zXimFVYXqKqmStGeGEWFRNk0Uy092Do0OVqd7Y6Z90ygEAAAACdnOitpiCc2SmFdEtwpAMCZRqAwCJlAob9zjxp53dS25+/EpGtod+fEadlTJzQkTPGexGNK0XV2/l6g/dWO/RLdUoX53uCofnyk6pA7JhV4CRQCAADg5EKT4tyxY4V1SXTHAABtgUBhEAp1/mI98TZIaIKFwcLfPmH38JatduxH9eNj1VY7rh8odNspLAiCqukmsHu0qq7KCCUKAQAA0BQNVTsOTYhV7Pih7hQAoC0QKAxSwdZOYXVNtbKrDtrxlm6f0M/foYm/JF1n5w8U+veLX23Px0HQoYl5j17nL8oTbacLvQX2sbWY9ZvAZEVNuZsCAACA9qbqaIEK5n1ux+MmDVfydRcp5cZp6vrNKxXWzVd7BgDQNggUBqmkIOv5+IjbDp0p3RYREmnHW5q/RCFVj33tQZo2CMNMlezQY6tf1FY9DoJzw1/tuGdYug0WVtZUtGrbiWtLP9eykk+UVbnPTQEAAEB7UlNVrYL3P5e3tFwxYwYr9rzhiujTXeE9j705DgBoGwQKg1SwdWhyyG2fsLVKExqRIVG2sw7TgUcw9vh8JuW61a+T6lU7NmI8cbZDGVOqrsxb6qa2jSNVvgByl7BU28ak0VrtFJqq+KaDFyOYquQDAACg6UyQsPJgjiL791D8xee4qQCAYEGgMEj5A4VF3gJV1xzbI1i182eCRCXeIlv9NKf6sA5VZWl/5V7tqdipneVbW7xU1+FWbp/Qr676cecuVVhX7fj4QKFRWzW9DTs0MT0v+wPZ5rgluCUfW6udQl/nPr4esQvpNAUAAKDdKVq8XmVbM22vxgkzJripAIBgQqAwiPmDQYuKP9RHRe9pfuFcvVvwmuYVzNEHhW9rYdE8LXbmLS3+RCtLlmht6XJtLFujreUbtLNiq31uSzDBoPKaclvaL86T4Ka2jtoOTTp5O4X+QGFyvfYJ/RI8vurHbdmhyZFqXwPUXcK62RKO/vfUWkG8wFKmBAoBAADal9L1u1T8+WaFhDn5xssmyBPdOs0ZAQBOD4HCIOYvVWhKDpoSgqb9NyM8JFzRnhgbtDOBpJTQbscMxt6KXSqraZlqqbW9HYf1tI+tyV+isDP3fGw6jvEHxfxtVdYXDB2aHHWrHXcNTbWPCW7biQWt9J4CS0+afVTiLXanAAAAEMwq9h5SwQcr7LgpSRie5isQAQAIPgQKg9jAiCGaEjtNF8XN0CXxV2pG/NW6IuE6TY//oi6Ou1wXxk3XebEXaVLshccMPcJ72+fvKt9mH0/XoUpfoLA12yf0M+3vmbYKTdXqIm+hm9q5+KvzmmBgaEiYHa+vLlDYdm1Y+jsyMe0TGrHOsfMoVMXeIhvIa2n+AGRYSLh9LGjDatcAAABomur8IhW8v9yOx50/SlFnpdtxAEBwIlAYxEwvsiYgZAIwJnjWWNCovsGRw+xjZsVuG3A7Hab3XVPN0wRn/NWCW1tt9eNOWqow1612nexWPW+IOSdMdV9TJbzcOUZnmulExQQEzTnpryJvxIf6qqa3RtVgfynLnuG+zKW/YxMAAAAEr4J5y1VdWKLoUQMVO36omwoACFYECjugOE+CrSZsOj053VKFByv32cfUsDSFOH9ngr9dPn/ArLM5WfuEfokef6nCM99O4ZFqX2lCf7Vjv9qej1u4QxN/aUITIPUHJgvbsNo1AAAATq5g3ueq2H9YEX3TlHDJWDcVABDMCBR2UP5ShXsqd6myptKOnwp/+4RnotqxX207hZ0wUGh69a0NFJ6kBKe/TcC2aKfwiL99QrfasV9tz8ctXKLQ3z6hKWFbG4ykQxMAAICgVbx0o0o3ZSg0KU6Jl413UwEAwY5AYQdlgkjdwrqruqZKGRXb3dTmqaqp1NGqw7YkoSlReKaY6qumSqvpwKUtqtW2paLqAme/V9nOaqJCotzUhvnbKWyLno8PVx20j40FClu6RKG/1KQ5r835Yc5J05mJOb8BAAAQXMo271HRZxudX5shSpwxQZ64aHcOACDYESjswAa5pQozKnacUkDlcNUhW8KtS1i32g4kzgQTBOri9t5sApWnwrTNWBCSa5/fnKEtqvEGqqt23MU+nkhthyZnuFOPwup8W0rVtKEZ64l3U33iPb5Sji1dotBf9dhUt/Y4f6YKslHopZ1CAACAYFJ54Ijy5y2z44mXTVB4zxPXkgEABBcChR2YCTaZIJ8J6mRU7HRTm64tqh371XZocgrVj3OqD+uTovna6FmtZSWfNGtYXLxAe05hX7WU5gQK4zzxzgc41HYscrqd1jTHkWq32nFod/sYKDwk3JaGNKVRTYnQluLvCdpf3dr/2BqdpgAAAODUeItKlT/vc9OejuImj1DUsL7uHABAe0GgsIMbFOErVbi7Yrvt3KQ5/IFC0zHKmVbbTmEzez7eUb5FS4s/sYGzmJo4Zz3dmjz4g3Mby9ZoX2WGHT/TmtqRiV9ibTuFZ64k5JEqtyOTetWO/eraEGyZko7+YGCMJ9YGIo14j6935Zau4gwAAIBTl//+clXnFSl6eH/FTjzbTQUAtCcECjs4U6LQBMBM4CyzYrebenKmVJ4piWgCMqaE2JlmqtWa0nJF3gLnfVS4qY0zJdg+L1mkbeUbbdXlIZEjNNo7QZNiL2zycF7sVJ0V6cvQrC9dpSy3x+czpaymzLa7Z7bbtMPXFHWBwjNT/dhURTdVtI1GA4Ut3E6hf9v8pQgN/2uYatAAAABoewUfrlTFnoOKSE9VAp2XAEC71W4DhStWrND06dPtcOuttyo3t2klqvzPM4+dxaDIofZxZ/lWeZ2/pjhU6VY7Dj/zpQkNE+xLDkux40dPUv3YtF+3qPgD2xNvZEiUJsVepIGRQ9y5zWPadRwQcZYNiK0p/VyHqg64c1pfrlt6MiWsi93+pkjwt1N4hkoUmhKe5hwypQYjQiLd1GMluCUKW6pasH/bTPuEfv5Si2e6fUYAAAAcr3jFFpWu26nQhFiChADQzrXLQOGuXbv01FNP6emnn9b8+fM1c+ZMPfbYYyorO3EPuSY4eP/997tTnUe3sDRbGsv0ILyvomlVav0Bsu5t0D6hn7/6rT+A1hDTnuCS4o9se3im5OQFcZfWViE+VUOjRio9or8NFq4qWWoDkGdCc6sdG/4ShS1VzfdkatsnbKQ0odHSPR/XdmQSUKLQlHI1PWObTnpMG40AAABoG2XbMlW0aJ0dN0FCEywEALRf7TJQuHbtWo0ePVoDBgyw02Y8KytLBw40XvrLBBf/8Y9/2OBienq6m9p5DHZ7QN5ZsdUGwE6kyFtoq8CaEmNJob5SfW2htp3CBkoUmgDRqtKltj1BU8LNlCA0JQkbK+XWXCOjzlWv8D52X60oWXJKnao0V3M6MvGL8yQoxPkYm0CpqX7d2o66QdMTBQpjPHHOO/I451CRc5ya1y5mfWb/+4Og9c/FBLeH5QI6NAEAAGgTlYdyVWA6L3GYIKGpdgwAaN/aZaBw79697phPcrKvSmJOjq9n1PpMkPDhhx/W97///dplOxvTIYkJKpmA0v7KY/dffXWdmLRdaULDXwXXlCjzBnTEYgKZnxZ/qIOV+xUWEq7xMVNsm4RNra7bVKOix9n9Zl57ecmntT3vtgbzGmY7zTaY7W4qs7y/pF1rvj/DtFlpXsO+xxOUejTza9spPM2SjsXOsTbBRlOC0BzrQP6Si7RTCAAAcOZ5yypUMG+ZaqqqbcclpgMTAED7F1LjcMfbjd///vfq06ePrr32Wjtt2ie8++67dfvtt2vcuHE2zc/M+/nPf26DhKYEYkPLNrV9w/buaMghbfNsVJRidE71RCel4cDaBs8qFYbkaah3lJJrml4NtjWs96xQUUiBhnnHKKkmxdmGbO3wbLKlCE2vxuY9Rjpb1FpMibatnnXKDTmqUOdvuPdcxdbEu3NbTn5IrjZ5Vtt1j/I2r12X3Z6tOhiyX328A9Wrpq+b2vJyQg47+2K9EmqS7H44kZ2eLcoOOaAB3iHqXtPLTW2+wyEH7fFOqemmId6RbqrPIWebdznb3rWmuwZ7h7upANB0HfHm4c49uRrYt3PeFEXrM3nmznrTPVi15THJe/NTle86oKhhfZX4BfPbAgafk+DC8Qg+HJOW09x8n1m+IfXX0W4Dhcadd95pH82J1lig0JQmvPfee5Wff3ypo0cfffS45Tu6T4retyXyzomeqB7hvd3UOqaH4fmFc2V63p2eMNMGx9rSlrL12lWxzVYtNiXa9lbssul9IgZoRNQ5drwxLXkBWlayyFa7DQ+JsFWcTW/QLWlH+RbbY3O/iIE6O2qMm9o0+yoztK50pT2e5ri2FlPN27QJeVbk8NoOchpjljPLN+U4ncjmsnXaXbFdQ5zXHFjvNU3pRtM+penYxLRNeSJ8GcGPc+HUrCtdoaPVhzUwYoj9XCN4tddAoT8vl5mZaadPlkern78zeUD/DeSG8n6m2ZnHH3+cz/9p4hoafNrqmBQuXKOS1dsU3rOrUr58seRp2Zo97Rmfk+DC8Qg+HJOW01qBwnZZ9diUJgxkTjQjJeX49vRMKcJXX33VdnpihpdfftlmFjtjkNDwB1t2VGyxj/VlVx20j6YNurYOEhopYb4SjabHZhMkNAHM0dHjTyv4dCrGRU+2bQeaQOqy4k9U7C1y57SMU+nIxM/fG3Br93x8pOqQfTxR+4R+/qrHRdUF9vFU5bsdmfh7dw7kD9a2VO/KABpm2j89ULnPNl3h/44AWpLpjM50Smc6pzN5NdOetOm0zgT8GmLyfaZJmfvuu682bzd37lzbaZ1fQkJCbad3Znjuuef4UQK0kJLV222Q0BMXrcQZEwgSAkAH0y4DhabzEtOhiT8DacZ79Oihnj172uk33nhDt956a20AEXVMBx0xnljbrpu/LcJAtb0dh7dt+4R+gR17mE4ypsRNs9twpoWGhGpCzPlKDE1WRU25lhZ/bDt8aSm5bmcpyc1on9DPBOVMANW8H1PqsjWU1ZTa4KhpJ7ApHdwkeFqmjcJ8r+8zHNjjsZ/p9dicy0ZL9bAM4HgHKjNr24lt7RsS6JxMZ3TFxcWaOnWqnTb5OZOvM/m7htS/QRwdHW2X95dGBNB6TFXjwoWr7Xii6eE4Kc6OAwA6jnYZKDSlBE0Vk9tuu03Tp0+3d5HvueceRUW1Xlt1HYmpOmZsL99kH/1Me3yH3dIiphOPYGCq+ppOWEzHKhfEXtLiVX6bwwSmTLAwzhOv8poyLS352D6eLhO0raqpUpQnWlEh0W5q88SH+vZLa/2IP+yWJuwS2s0+nowJKJoOSP4/e/cBGGV9/3H8e5e9SYCwCXspgoo4cCtua1FLa+20g9YOWle1aK1/bW2r1mJbWlq1ra0dVEXFhTioigNxIMiQvSFAQvbO/Z/v735PcrlcIOOSXO7eLzyf5/d7nlzu7rm7PPe539D71d5AVYNJnd1aH5OWZrPWbseqpIOBJICW7ajZatecD4jOe16Vr8qWgPDQyeiKixtboOv5XG5ubrPJ61x6HqhfGmv3Yv3SODhoVHp97nkiXx4D4VF7sEiKXnjHrGeec7wk5vU36wCA6NIjxyhEx71S8pxpJTY19bSGrqTatXR5+RumxdgpaWeZukig3U9DtSg7Ev1Q0BndjLRF4VtlS02QleZNl5PTzmwxyGoN7VK9uvIDGZgwRCanTLW1baM/r9czLnmijEgcY2vD58OK5aZV0VHJkyUvcaStPbz3yt+UfbV75PjUk9sVPO+p2SkfVLxjQuLjU0+xtU1p2L2haq25z3rfW9JZzwX0PDwX2qakvlheL10iCSb8TzOzs5+Qeqr0je9n90Ck6YljFGqXYe0NcttttzV86Rs8HnUw/RntnqyBoI5FGDhGYTC9rvz8/IbrJzQE2qG2TmqeXyG+g8USN3GYxB0/2m4AgJ4hGj8DdNYYhQSFMcqdbCInrq+clHa6qWvLZBU9QWcGApX1FfJm+atmqS0MNSzU1o/tsbLiXdlVs71NIVywHTVbZFXF+502oclLJc+YgPSM9PMkzbm/rfFJ1RrZWLVWRidNcC7jbW3ruRPZHO7n99bskvcr3pY+8f1Ma8+WEA7BxXOhbdZUrpSt1RvNREtqq/M3ItTkQogcPTUo1NAvcLKRwwWF2orwgQcekNtvv93sr69rnQhFxzgMFRYG74/24z008nTVMSl65i2p3LBDkscMkayLT7a1CIXXSWTheEQejkn4dFZQ2CO7HqPjNJBK8iRJQd3+hok03DELI2V8wkim3YRPSj3DeQyTzSzSGla1V+NEJm0fn9DlTmiirX3CTcf/05BQuwC3NiRU7jiFJe28TY3jE7b8xudOmqLdtxEZtCWotvTUlmjo+fRLDDUkYXjDpEJFdPVHmOlYgzr5iEsnN9EWgMGT17m0q3JaWpoZm1Dphw3titxSV2UAHVP6xkcmJEzo77xWdfISAEBUIyiMYSOS/GMVaqsvDVp0RksdV84d9w2HpxNpaBdtfcwO1u433X/bSsf70jH8dPzDzHZ0r3a5P6vdoXWG0nA6WJdvlq2Z7TiQG+IVtzMwcmc8Ply3c+36rbNz6+OoYSa6V6VzHLS7uHYHX1f5ka1FT6Whr870rmG9vp4zvf7XYmd8IYHYppOXaPC3dOlSU9YxB/fs2WPCP6UtAq+88sqGWY01WFy/fr2sXr3alLVlgk584gaLL774YsOEd0rHsh4+fDitF4B2qFi1ScreXSfelCTJPG+qeOLj7BYAQLQiKIxhQxNGSKIn0UxUsaF6ranT8eDQehoSTkk9xQR9OkagXtqisLbjrQldbsu7js40HEzHrlTavbctTIjn0dmY/ZOStIWGpxp4aqtXbbV5OOl2IpeSOlqwdTf90sGl7yuH6gpsCT2RO4nJkIRhZplpw3/39QmEi44bqJPSaaCnk4/oJCQ65qBOWhKK1t98881yyy23mP1nzpzZpNuxBonuRCZ60daJWgbQNtXb9knxS++ZdW1JGN+7+yYVBAB0HYLCGKYhzvBE/0DEOtabipTZjnsSbYE5OeUEs66tCrV1YWsV1h0wy3AEhW6rQrclXrgU2NvY1haFym2dWlzftq7BboulrLgcszwct5VTSRt/B8JLwyM3KHe/cNBxKtEzaQtz90sCnWjJ1VlfSADa2u/hhx+WJUuWmMuUKVPsFn8w+NhjjzWp03V3X70Ejk0YvO1nP/tZwyQpAFqn7lBpwwzHGWcdK0nDaUwAALGCoDDGDUscZVrDKQ0Oc+L7mnW0jQasOumG0vEKdZKT1igIw/iEriwbmBXVhW82Rw0963x1kuHNbNfMzu0dQ9Adn7A13bEzbItCukN2Lx2XUGmoNDFlivPHJc4ETbQq7Jl22taEgxKGSrwnwawrd+zRcH8hAQCIID6fFC1+R+rLKyX1uDGSOpkZjgEglhAUxjgNCUckjjbdO/MSRorH+Yf20Zl5+ycMMmN6LS9/44jdbTWAc0O97Fa0nDuShpY+YfwA3zg+Ydu6HbvcUKH9LQqPPJ6U2x2SyTO6j07o4056oYG5DmkwPGmUKa+v+tgs0bPsrNlmlkMSh5ulyw3vCeYBIHoVLV4uNbsPStKoQZJxxmRbCwCIFQSFMB/sz8m4WMYlT7Q1aK9JKSeY1nel9cVHnAnZbWmlH7zdVp0doYGZBr0a2mgIGQ46zpzq3Y5ux6q9LQoL7WPjtpI8HLd7s/4On/MPXe8TGwbqWHY6NqUanjjGtCo8WJtPq8IeRluCupNb5cT1sbV+nTXEAQAgMpS+9bFUrt0m8X17SdZ5zHAMALGIoBAII52B94TUU003XQ3ZDteaqjCM3Y6Vx3k5u91w3a67HVHjqzEtHjV87B0UFrRWlrft45lpQKETJWh3x2Rviq1tWYInUZI9yVLv/NNZn9G1NKDVMU71+ed2v1eBrQrXVfpnJkXP4E5iohNeBdMxQfU9oay+RHzOaw4AED0q1myVsrc/Fk9SgmSdP9UsAQCxh6AQCDMNt3QmZA1ONlWtkz01O+2WpsI5kYkr0wZz4Rin0O12rLevvS0eddxLbZWkLRx1sovWcEPFtnTHbm/LRXTcuqpVZpmXOLxZsKutCvW5U1C3v02T/KD7aEi/t2a3CQOHJPpnOw6kr2ltNaqtd2lVCADRo3rXfilevNysa0tCbVEIAIhNBIVAJ+gVlyMTU44z6ysr3g05nle4WxSqrDB2CzxQ6w8K29vt2NU4TmHrbpMbcma2YnxCV0P3Y2Y+7lJ6rLTlrHYxHhXQmtClrQqHJY406+uraFXYE+jYhNpSsG98/xYnMGKcQgCILnWlFVLsznB8xmQzNiEAIHYRFAKdZHBCnglJtEvsu+XLpNpXZbf4A61aX63pMqst7sKlcUKTjrcodIPCPh0MCjNsqNDa1n5uyOmGnq3htigspkVhl1pb+ZFZahdjDQVDGZk41rQq1HEK3ecUIte26k1mGTyJSSD3faaoDUMKAAAil4aEdcXlZnZjneUYABDbCAqBTjQ+eZJpkVflqzRhoTumV2GtbU0Y376x/1qi4Vo4JjSprK+Q8vpSE/Bo68iOaGhR2Oqg0B9yuuMbtob7O2hR2HV0wouCugPmOaJhYEt0rMnhif6xCt1JTxCZ9LWn43xqS8Lc+P62tjkdp1DRohAAer7iJe9K9Y58SRo+QDLOOtbWAgBiGUEh0Ik0tDsu5URJ9aaZD+EfVrxr6g/ZMCyc3Y6VjovYMCtpByY02V/nn+24b1yuuQ8d0dDarxWtjyp9lablpYZLbWlpmR6X6dxzr5kIpc5Xa2vRmdbasQlHJ403x+twNEjUfbRVoc6CjMik3Y7VwIQhh33du18eaPjPTOMA0HOVLV8rFau3SHzvTMk8nxmOAQB+BIVAJ9NZeaekTjMtr3Rik01V6wPGJwxvi0LV2P24/a193DCnT3w/s+wInfhAZ4PWEE9nUj4c9zb3asP4hEpDDXfG52JaFXa6fbW7TVfyJE9ywxiEhxPY6vCTqjVmichSL3Wyu2aHWR+a2Hy240CNE5rUS2l9sa3tXtoNvqWJowAAzVWu3yGly1aJJz5OMs+bKt6U0OPSAgBiD0Eh0AXSvRlybMqJZl0ndSirLzHhSVvG4Wstt1ug24W3PbRbqeroRCauhlmJjxDiubfZvQ9t0TChCeMUdrr1lf4uxKOSxjl/ROLM+pFooKihuYbk7vMLkUNnOq7xVZvWgvp+dSQNLZcjoPuxtlTdUr1BPqh4R14seVo+rvyw4csYAAhWd6hUKtZslYqPt0htfvvPlXqymr0FUvTC22ZdWxIm9O/YMDMAgOhCUAh0ER3za2zS0bakrQk756SsozMfa3fCal+1JHtSTKuhcGgICo8Q4rktCt1WkW3RGEZGRgunw9ExIDXI0BZQGnAcrN1vt0S+3TXbTSsy7Rqe14rWhC4Nxkck+gdIp1Vh5NlRs9Ushx5mEpNAbphfEgFBob6GXLW+GjMhy1tlS+XV0udlQ9VaKa8vs1sBxLrK9dvlwF+ek+LFy6X4xXfl4KNLpOzdtXZrbKivqJIineG43ifppx4jyWOG2C0AAPgRFAJdaGTSWDP+lwZhbQlZ2sIN2TTMqXP+tdXBOn+3475h6HbsapjQ5EgtCu24iu0JChsmNOnmFoXaxVpDQO3Gubn6E1lTuVLer3hb3ix7VV4peVaeK35cXil9zgQZ2gJKu0y+U/6aLC9/3bSMinRuyKdjE7aVtirU2ZH1fu6nVWHE0OesDjcQ74mXAfGDbe3hdfQLiXDR2+52OT47/SI5Je0s896qrVd12wbn+bq09AV5u+x/sqNmi5ltHkDsKn//E7vWqGz5OrsWG8wMx4UlknLMSEk7YZytBQCgEUEh0MUmp0yVaWlnS7/4gbYm/Nxuge0ZpzDc3Y5Va26PtmLUlnY66YVO/tJWjZOmdH1QqIGEdnd8ofhJ04pJQ8APK5bLuspVsrV6o+yt2WXCMZ2sRSV7ks1ENjlxfZ2Lf5zKA7X5JkxcUb4sIrpzhrKjeotpnaUtTQcnDLO1raetCoc3tCpkBuRI4bYmHJAwxByj1si0YX5RKyYp6kz6+lL6BUyyN8V0nT4qebJMz7hUpqSe4twnf/CpM3SvqnhfXix5yrw282v3mHoAsaW2sNSuNfJV15ix+moPRP/QJSWvvC9VW/dKYl5/yTzneFsLAEBTBIVAFHK7BbYlKNTxyXT8RLelV58wBoUZXv9EIyV1LXcLdscnbO+4jYmeJHPRroca3HUFfXy1VaCGg9rdUSeE0BZz2iKyX/wA07JJu5trOHxy2plyVvqFclHmFXJ2xsWmfFLa6c7lDNMSakiCv8tnfu1eWVb2srxf/lbEdaN2WxOOSTrKLNtjWOIoc5z0eOt9RffbaYPCIW0If/V5rsGczjLeXV17tXXg9potZt0NoAPlOq9BHRv2vIzL5JiUKdI7vq+p19a+K8rflJdKnjFlANFPu9tqV2NfVbWtaUpn/z3498Vy4MFnpPjl96Rq827x1bW9V0YkK3/vEylfuVHieqVL1nkn2FoAAJojKASiUGO3wCMP0q0t+bT7q3aH1RmZlc7GrGFOuGgrQR3TToO0svrm3+arYjco9La927Ers5WTpnSUtv7T7sJvlL1suj3q/dNu5edmXOJcLjUtRo9PPcW0bHK7m2sLQn0MQtHAZWLKcXJm+gUyKGGoqdtbu1teL11iui3r5DfdTVtuVfkqTctNt5VWe+iMuSOSurZVYZ2vTgrrDpj7sNG7xszaDD/9YkBb8qZ7M01rvLYIx8RJHaFdiTWo1Ja5h/uCQbtUD07IkxNTTzehvIb3en91tnQAUc7nk5LXVsr+Pz5lJi8RT/PXvbauSz12jCT0y5a6knKp+GiTHHrqDcl/4HE59OTrJlzTCVB6sqpNu5zH4UPnk59Hss6fKt70FLsFAIDm4n7qsOtAVKmsrJSUlNg9EdLuhD7nfDgvcYStaUpbEOr4XR9ULDchis/5p0HWhORJcpRzCbeC2gMm8NKuthlx/haGgbbWbJRSZ7u2wnO7EbeVTsSiXXy1BWNOvL9LrwrXc2FnzTZZWblCtlR/Ylotapg6OnmCaTGok9W0tttmS3Rctf4Jg0ywqMdHWxTqY6KtFbXVlnbh1n26moYxGljqmJfHpBwvaa2YFfdw9H643Zi19WVHry+QhoJF9QUmDNTHTZ/ja6pWmmOnoVi5p1Tya/aYMTg1oI11GtbqeKY6g3Vbg0J9PWuX3rS49LC2QG4tbc2rrQqPSpnc6ueQhvr63qDvM/o603K0KSyqlJxePLfROXrSuZVOUlL42P+kZvcBU045arhpSZc4tJ/EpSVLQr8cSZnknHOcMVmShvWXlIkjJSnP2ZadIZ74OKkvrzTdkau37JHyDzdI1YadUl9crt94SVxm24dI6SxHOiZ6HzTwlPp6yTr/REka0XlD38Av1j+DRBqOR+ThmIRPW8/7dP9Qgq+DoBBRK5bfgBK8ibKpap1U+6pkRNJY8QY0HtY67UKqAaF+yNeAUMcEnJA82XTPc8cTDDcNvPT3pcdlhBz/cF3VKtNteGzy0aZbY3toizcNiBK9SU1avXXkuaDB07aaTSaU2FWz3Tx+GjCZLsWpJ5jWTF5PeBtnawCpgWF/5z7o79PHTltJaos4bf3lDwy7LuDQYFTHdNNQb3zyMba2/czz0eNvmand0VsKs4/kSKGgPmb6+GkrRm3RqY+pp9Yr5Z4y8zP6HOnKxzHSaGvijypWmHUNu/Vxaota59+emh2mxZ7bEraraPdhPc4aEGrL3faIxpBQERSiM/WEc6uKVZvl0JOvSdWm3aZFYdKoQWY8Pm016E1LlvjemZI0bIAkDR8gCblNezHEZaRK4qA+kjxuqCSPHSrxznZvSpLzhlcntQXFJnSsXLNVyt9f7x/T0Kn3JCeKN6n73k8Od0x8NbUmJKwvrZD0U46W1Mmj7RZ0JkKQyMLxiDwck/AhKATaKJbfgDSI2VOzy4QkOlaeBltVzroGKdoyTFvdaUCok1Loh+yJKcc3dNvtLNpCbk+tv5uutuQJ5B8f8WPTIq8jQZTPuWh3RF0LnFW6Pc8FDVE2Vq8zAaGGZNpySR+v8ckTZVLKCab1lScggO0MSR5/4KkT31T6Kky37eL6QyYw1OMXzglnWlLjq3GeM+9IvfNP73d7JpoJRbuKbq/eIhW+MtOCVLuCtoa25NQx9bQl3KrK95uFgkqDVA0FhyaOkLHJR5kQfHDiMNOKML0iS6oSy83+GlRqwOVtY0AWLbbXbDaTF+nrsT1BX5zzT5+L+hzRLvZdSVv26hcDY5OOMgE2GhEUojNF8rmVtvgrWrRMKtZsFV9NnSQOzjWtBdNPPqpdLQC9SYmS0LeXJI0cKKmTR0niwN7mejxej9QVlUntgUNStXGXmUm5etteqS+rFE+c14SNXelwx6To2bekZtd+05pSHwt0DUKQyMLxiDwck/AhKATaKNbfgDQM1DAkyZtiWk99YANCleHNMgHh0SnHtbubb1tpGKOhggZOwxObfqOtt2tXzTbJjssxgU57JXuTZUPVWhPyjU6aYGvb/lzQ8OT1spdMl2y9vRpqHZVyrBydfGyntbg8nCTnfg1MGCp94vuZYE3DMm2d2Te+f6d3n91Uvc4EatplfExy+ycxCWZCVtuqsLROu5y33KqwsO6gCbXWVq40gbIeH3fCmnRvhvO49HeeN3nOMR9vukZrQKhdwfW4JXmSzX4ufS4Myxgp++v84WJRfaEMSsizW2OLzgKs4aoGqe0JgLU15rbqzSboH+I85tqysCvoc1/HU9WWt5NSp3R6YN/TEBSiM0XiuVX1znwpeu5tE9jVV1ZLfN9ekn7qRMk481iJz2ndl1CtoZOAJA7JlZQJw5xLnumi7E1MMLMm1x4sluod+VKxeotz2Sx1hSXiq60zrRE9CZ373tjSMdExCSs/3mpuc69Lp9ladIVY/wwSaTgekYdjEj6dFRRydg1EKbeVzcaqtSagUxpyHZd6spyWfm6HJqRoDw0itGujBjzaxTiQhjUqHC2DNDhSRW2Y8TmYTu6idJbUE1JPlWlp50j/+O4f00e7z+qEDG7rrTWVK82ys2iItKVqg1nXLuHhNixxpGk1qYHd3ppdttbfpVjD7Y8q3pOXShbJW2VLTTDkzgKt4+FpuKUTU5yefp5MTjnBhM/6+LSGtlw9IXWaGZNTg0r9PbFGw3kdm1Bfl+5swO3htkR2JyPqClvtc1JbDXslNluDArBj7y18XQr/u1Rq9hw0rf201Vzvq6ebFnSdSX9X6qRRknXRSdLn65dIr0+fJqnHjZGE/jmmm692f9bWfPvnPy2FT7xmQsyafV33PqkTsOgsx3o7M5nhGADQRgSFQJQKbPmmAZzOwntqNwde7iypxfVNZyUutqHe4WYubS33fpcG/Y7W0q6sGkhpiKShnHZXjTSjEseZ1lQa9gQGbOG2pvIjM4GJtlxsbQjXFhryjEwaZ9a1G7GOM/hu+TJZXPKkvFf+lulirK1Dtbu6do09LuUkOT/jMpmaepoJGTvSmlJbGmoIrNetv8ed8TtW6H1WQxLa34JXNc583P5gvi3K60vNjOCqvWNbAujZNIgreuEdOfj3xVK1dY94khIk7aQJkvP5c01YF2pm486m4x1qSJlz1bnS52sXS/ppk0yXZW9youmWXPK/D6Xgn0vk4N9eMOs6C7GvqumXpuFStdX5fa+8b9Y1JGxPt2sAQGwjKASiVJbzAV7DnSmp02Ra2tlmrMLu5nZzLqlrGuIV2dZImWFoUagzHiudAbmt6qVOPqn82KzrZCWRSlvEjUnydwPWSWA6w8HafNlds92s6zhwnWVo4nDTqlC7r35c+aHsr91r6rWlm7YSPCntDDkv41NmfEQdd7CjM0sH0tanU1JPNuvrq1bLnpqdZj3aafirk4GoQR3o6q/cVsAlXRQUbrGto7V7uYblAGKHr65OSpZ+IPv/vEgq124zdTo5R+/PT5f0k4/2TzoSATSYS5syVnp96lTp++1Pm9aGKRNHmElUdEIUbV146Ollzv142rQ6LP9wo39ilDDQLs/Fz79t1jUk1G7HAAC0FUEhEKU0UDk57UwzVlukCBXi6SQhOkmHdkt2uw13REMY2Y4WhVurN5lJQ/Q6gidciTQasOmMr+X1ZQ1dy8NFA1Nt4af093TmuIyBrQo12NaA9vT06XJm+gVmYhsdG7Ez6azVk1OmmnWduEbHQ4x22p1cX3e5OtFR0BiObeV2PS6q7/ygUMPkHdX+lpDDE0eZJYDYUPb2GtONt/wD/9ADyePzTAvCjLOONWMHRjJtbZh57hTp/aULGlobJg7t5/yx9UnlJzuk5NX3TevIgn+9JKVvfGRaBOr4hm3mXF/R8++YcRrTTpzQ6d2vAQDRK2KDwsLCQrnmmmtk+vTp5rJixQq7JbS5c+c27Bu8/+bNm+XKK69ssl2vW38HgK7jBk46c6+r2I5P6HZh7CidqEW1tSukhhAbq9aZ9fFJ7Z95uSvpDMxKJ3AJHvexI/Rx0ABSu+eOTfL/js40JHG4nJtxiQm2dfzF9FbOgBwuGgq7LUhXlL9p7nu00RaiqyrekxdLnjYzRqshHWxNqDSsdsce1ddQZ9JZsjXE1oBTfy+A6Kdj7R146FkpfWu16aqbNGKgZF9xhmRdcKIk9Muxe/UcbmtDvQ+537/S39pwwjBTX7O3QMreXSeHFr4mBx5+1nSv1rEOtZVgaxS98LbU7CswIWr6KZHbKwIAEPkiMijUWXDuvfdeufTSS2XJkiUyf/58mTdvngn8QnEDv0WLFrW4f2ZmpqnX7Xp5+OGHJTu7490cAbSeGwZq12Of80+5gV44xidUOragtqbU0KLKF3pWp1A0HNOwTSd20MkyegINTLQVXmDI2VFl9SUN4/VNSJ5kZrbtbHHOv+7uRqoB5eCEPPNYvlv+RqeHXl1Bu/TrxDwvlzwr75S/LjtqtupE06aVqIay/cI0XmlXjFOo7xdbqv0tiYJnTQcQfSrXb5eDj75oxtqrKy6ThEF9Jevik6XXZaf6W+NFCdPa8PyppqWh29owYUBvqS+rNN2ri19aIQf++rwU/vdVKX3rYzOzsvj85086u3LBv1+WmsfekAMPPuM8ZjskYWAfyTrP30oeAID2isigcPfu3VJWViZnnnmmKQ8cOFAGDBggK1eGnuFTA7/Zs2dLcrK/C5UbABYUFJglgMigLY907Ll6559OSqDCOT6hK9O2KgweC7El2t1Yux0rDcd6Ep39V+nYbZVtCEZbojMAayjTJ75fl8+M3d0mphxvQmLtCq8tC/V52tPo62pD1Rr5X+liWVb2ignXdPZqHYLg2JQT5ZyMS+To5OPCOjlNqJbC4barZru5HzosQEdmaQYQ2aq37ZPCx5ZK0XNvS23+IYnvk2W67ebMPEuSx0T2kCAd5bY2zPncOdLvhzMbWhvq2IvVO/dL2dsfm8dGg0NtdVi8ZIWZ7dlXWiF1JeXmOtJPHO98uuv6yVwAANElIoNCDfiKi4ttSUwAmJubK9u3+wfWP5JNmzaZn8/JaeySoOVZs2bR7RjoZm7XYHecwnDOeOxyxykMnl25JesrV4tP6k0XVPf29RT6uOnt1tu/rvIjW9s+O6q3mDH6tIXfMcnH29rY4XH+HZ9ysnkO6OPwYcVyuyWyactZDQQ1GFxauth0RdewUwM8Db7PybjYTGqkwa+3E/7sNwSFndii0G3lOjJxrFkCiC41+YVy6Jk3pfCJ/5lWc3EZqZJ+2jGS8/npZiKQWOS2Nuz7rcsaWxv2z5G6Q6VmHMNQaouib+gMAEDXi9gxCrUFYUpKii21jjsW4S233CI333yzjBjhP7HQ5WOPPdbQ7XjSpEmma7N2cQbQtdxQQScbqfPVSml9ifNGFBfWgK4tLQo1sNTWSiqSZzo+HPd260y2bgvNttLWWu4MymOTj5Zkb9vef6OFdlufmnaq6cK+t2aXfFK1xm6JTOsqV5muxdrFWI+9duEekTjGTAhzato5MixxVKd369YZ1lVnBYU6vqJ2idfnZKRPMgSgbbRbsbaMK3h0iVRt2CmexHhJmzreTFSSNmWceOIi9qNKl2pobXjVuaa1YdLoFlr81/e8lvAAgMjj8TnsesTQiUh0jMH77ruvoRuxTlaitIvxkWhrweuvv16uvfZamTJliq1tpIHiAw88ILfffru5floXAl2nwLNf1ntXSbavjwzy5clq73uS4cuUo+ubv1bbq8RTZK43zZcux9Qffqyetd6VcshzUAb4hsiw+p479tk270bZ7dkumb5eclT9cba29TZ418gBz15J92XIxPoTbG3sqpAyWRX3ntRJrYyunyB9fJEze7iryrmV78e9Zdb7+PpJX+c29vKFr0txW7wV94pZnlh3hgn+w2md8xotdF6jefWjZKBvqK0Nj2gcq3jTtkIZmccYzOgces4cjteNr7pWyt75WMo/3Ngww2/qpFGSMnm0xOcwWdGRVK7bLkXPv21LjbKvPFMSh/SMcZajWbheJwgPjkfk4ZiET1vP+3T/UIKvIyKDwuAgT1v+3XnnnSb0mzFjht3r8DRYHDp0aMj9g68f0Yk3oMikM8ouLX3BtNjSSQnWVK6UvMSRcpQday8ctKXi4pKnTDfL8zM/LYcKD4V8Lhys3S/vlL9mWpGdnX6hJHgS7ZaeRydiedV5XHUSjuNTT27TRBXu46Bdb09LP7fLZx3uSm15XyioOyDvlL1u1k9MO1Vy4iJrbLyVFStkV802GZIwXCamtD0cDqc3yl42LQp1kpRwjn+oLQn/V/qixJvX6MVmicMjKERnCse5VdnytVL+4QYzYYdKHjdUUiePNpN4oPV0ohOdFdmlLQ61ezK6H59BIgvHI/JwTMKns4LCiGzPr5OXpKWlydKlS01ZJzfZs2eP6TKs3C7G2vLQLT/44INmXWn59ddflyFD/F2UXnzxRVPn0tmRhw8fzpMT6AY6mYkGcxX15VJQu9/UhXN8QqXX706aokFDS9yutqMSx/bokFDFexJkdNJ4s65jLrZWvdTJqsr3zLoGt9EcErZVTlwfmZQyxYz/+EH5cueR8rd6iQQ6BqGGhGpU0jiz7E6d1f14s53peGjCCEJCoIer+GiTHHzkBSldtsqEhDoGX68Zp0vWhScREraDTvKSe+0MSbhimuR+93JCQgBA2ERkUKiTl9xwww0m0NPJR3QSEu1G7I45GEyDxS1btph93f11jEK327FOauJOZKKX/Px8UwbQPdwxBPfW7jbLTG/4Q3s38HInTQnmjueX5EmS4Uk9t8txoLzEEaalpo77uM3O4nwkOvGFtvLU8d9GJ0+wtXDpmHhZcdlmwpCtVRttbff7pOpjsxxqj3l3c8ceLQpjUKitY3dVbzMtXYcljbK1iFbaukAnm3PP1dwvg1vifmns7r9w4UK7pSm9ntZcHzpP5fodUvCfV6T45fek9mCxJAzsI1kXnWRm9U0aFnnDOvQknqQE8WSkiieBL1IAAOETkV2PgXCgSXPkWl35gWyv9rfy9YhXLsj8tAkDwkmDlI1V62Rk0ljJLR/U5LmgLcS0O6MGZEcnHydDE4fbLT3fnpqd8kHFO6aF5NnpF0mcp+Xx4rS15WulS5zHwydTU0+VPvH97Jbo1Z73hcK6A/JW2f9Mq03toq7L7qRB8GvO81e71p+VcaEkeZLtlu6js0S/VbZUMuOy5NS0c21tx2iIvaFqjQlrJ6ccfqxRNOqJXY+Dh5jREPCuu+6SW2+9NeSXxPo6DhyLOrjs0nBQJ7hTd999d5NtaJ+2vIdWb9trxiCs2uz/UjC+d6bpYpxyzEhTRnhwvht5OCaRheMReTgm4RNTXY8BRDe3RaHSYCHcIaFyZ1EO1aJwW/VmExKmedNlSOIwWxsdBiQMNl25tTXWxup1tja0jyreMyGhBjGxEBK2V3ZcH+nrPD46DuSm6vW2tvt8YruWa2vCSAgJVabtelxSV2yC+I7SYQO2VvtbcI5MHGuWiF46xExZWZmceeaZpqw9RQYMGCArV6405WD6AUNpjxGVkpJi9t+xY4cpKw0b//GPf8j8+fMbhqJB16jZWyBFL7wjhU+8ZkJCb3qKpJ860czYS0gIAEDkIygE0OUy4hqDQu3W2Rnc31ESFBTqRCfaUkmNTZ7YKSFld5tgJ4bZUrVBKn3+weKDba/eYlqBaes4d3+0bJzzXFFbqjZKla/KrHcHDb61y77OLKytZSOFtlxN82aY4Lm4rtjWtp+Ov6hhd+/4vk3eLxCdCgoKpLi48XmjQ9Dk5ubK9u3bbU1T2spQx62+6aabTCAYHDS6LRK///3v02KhC9UdKpWSpR9Iwb9eksq128ST4LwvnDBOel91rrMcT/dYAAB6CLoeI2rRpDly1fnqZHHJk2a9s7r+amCxuPhJ0zLphLrTpW+2f8Zat0uyBpTT0s42ddHo/fK3TKA0OCFPjklp2t2u2lct/zMzJNc424539omuVpWH05H3Be3SrV27wz1Ld1usKF8m+bV7zcQz45OPsbWR4cOK5WbsT52BWWdi7ggdGkC7xk9JPUVy4wfYWrRGT+x6rF2EdYzB2267zYSEau7cuWY5e/ZsswymPzNv3jwTMBYVFZlux9ptWV/jd9xxhwkJNVDUcnC3ZLdFIsKkqkbq1m53LjvMuvKOHSxx44aIJzvdlAEA6G7RmA10VtdjgkJELYLCyKahS1V9lRydMrnTZtpdVvaKmbDkqPrjJK/XcDMhxcslz5ptJ6edKdlx0TvLos6K+7/SxWb9tPTpkhHwGLuBjs7qe1LaGbY2NnTkfSHwMT0r/cIun0REn8v6nI5z/unYhImeJLslMmyu/kTWVa7qcJC6v3afvFv+hhka4Iz0820tWqunBoUa+t13330Nr8/DBYXaYvCBBx6Q22+/3ezvhoGXXnppQ0tDDQ+DMU5hx/iqa6Vwx27J6pMjcVn+ALD8g0+cy0apKyo15eQxQ8w4hAmD+pgyOh/nu5GHYxJZOB6Rh2MSPp0VFNL1GEC3ODblRDkp7fROCwmVG46Vif8DjE6MoPrFD4jqkFBpyKKBjVpT+aFZqoO1+01IqBNhBLc0xOGZMS1t68tP7HOpK62v8o9NqDMAR1pIqNxxCjXQ7Igt1Z+Y5YjEMWaJ6KdjDWZmNv4t0MlN8vPzZejQobamKe2qnJaWZsYmVPphQwNC7aqsrQgfe+wxWbJkibksWLDAjFFISNgxZe+ulfzfPyE1T78tBx5+Tg7+7QUpeHSJlCz90ISEiXn9pddlp0rWxScTEgIA0MMRFAKIWplx/uCiwlNqujHquHxKxyaMBaOTJki8J96Egwdq90m91MmqyvfMtlFJ4yXVm2bW0Xqjk48ySx1DT2cf7io6nuSB2nyJc45npAZoOomOKqlv3pKrtfbX7jX3U4PQIVE0GzkOTycv0eBv6dKlpqxjDu7Zs8eEf0pbEF555ZWm5aHSYHH9+vWyerU/PNeWCTrxSUvBIjqmrqRcSt9YZUt+tQXFUpNfKAn9e0vWBSdK9uWnS9KIgXYrAADoyQgKAUStwBaF66s+NuvaIizdm2HWo12iJ1FGJo4z62sqPzKTuOhsz3r/RyX569E2yZ5kMz6gcmcf7grapVfp705wjmsk0tul3bF1DNL2hKg6ecmqivfN+uik8WaJ2KDjEt5www2yaNEimT59usyaNcuMKaitA0PR+ptvvlluueUWs//MmTNNt2MdoxDhVXugSMpWhJ5BP753puRcdY4kj8+zNQAAIBowRiGiFmMfQIOHJSWLbEm/GfGP7ZYUgd02O4u2Ilxa8kKT2Y9PSTtLesXl2FJsCcf7gj6vXil93sygrRPidNbM3a6DtfnyTvnrZobqs9MvNMtI9V75W7KvdrdMTjlBBia0rXXXivI3Jb92j+TE9TXDEqB9euIYhYgcvqpqqd6531xq9JLf8lACSaMHS69LTrEldBfOdyMPxySycDwiD8ckfBijEADaSFs4JXn8M2iq4UmjYiokVBqOBna11nELYzUkDBd9Xrndf9dVNe2O1xnc8RBHJI6O6JBQud39i+va1v14Z81WExLqY3ts6lRbC6Ar1OwrlPL31suhp96Q/D88JYeeXibl739iQkJvsvN3dNQg8SQ1b8mcNKy/XQMAANGEoBBAVMuI83c/TvAkyMjEsWY91gxKGGoCHA1NxyYdbWvREdoFWLt26/iPeuksOgOwjk+oz99htstzJHPHKSyqP2SWraHd4T+uXGnWj0k5vkm4DyD86ssqpXL9Dil+5T058PCzUvDPJVLy2kqp2rxbxOeThIF9JO2EcdLr06dJn69fIr0unSbZV54hyWOHiic73UxWknHmsZJydOiu4QAAoGcjKAQQ1bQFXR9fPxmffEzEt8bqTOOTjpGjkiebyU3Qcfo4jrTjPHZmq8JP7NiaIxLH9ohjl2m7YRfXtT4o/LBiuenGPThhmPSLZzIEoDPU7D4gZcvXSuFjS2X/n56WoufekoqVm6SuqEziMlIl5ajhknn+VOlzzcWS89mzJf3UYyRp+ADxJPjfdxJysyXropMk4bKTJWfm2ZJ6bOR/cQEAANqH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+ "![monai_clearml_scalars.png](./../figures/monai_clearml_scalars.png)" ], "metadata": { "id": "DCLhp4e2uKsd" @@ -553,7 +553,7 @@ { "cell_type": "markdown", "source": [ - 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deletion(-) diff --git a/README.md b/README.md index 401e541fad..9e072c9ae1 100644 --- a/README.md +++ b/README.md @@ -146,7 +146,7 @@ An example of experiment management with [Aim](https://aimstack.io/aim-monai-tut An example of experiment management with [MLFlow](https://www.mlflow.org/docs/latest/tracking.html), using 3D spleen segmentation as an example. ##### [MONAI bundle integrates MLFlow](./experiment_management/bundle_integrate_mlflow.ipynb) An example shows how to easily enable and customize the MLFlow for experiment management in MONAI bundle. -##### [ClearML](./experiment_management/unet_segmentation_3d_ignite_clearml) +##### [ClearML](./experiment_management/unet_segmentation_3d_ignite_clearml.ipynb) An example of experiment management with [ClearML](https://clear.ml/docs/latest/docs/), using 3D Segmentation with UNet as an example. From ebb9ea6f1be03c729ae3d1b8fcc293e03c108fdc Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 22 Aug 2023 12:13:54 +0000 Subject: [PATCH 04/10] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- .../unet_segmentation_3d_ignite_clearml.ipynb | 18 +++++++++++------- 1 file changed, 11 insertions(+), 7 deletions(-) diff --git a/experiment_management/unet_segmentation_3d_ignite_clearml.ipynb b/experiment_management/unet_segmentation_3d_ignite_clearml.ipynb index b070c661c6..9bb14cfd82 100644 --- a/experiment_management/unet_segmentation_3d_ignite_clearml.ipynb +++ b/experiment_management/unet_segmentation_3d_ignite_clearml.ipynb @@ -79,7 +79,7 @@ "%env CLEARML_API_HOST=''\n", "%env CLEARML_FILES_HOST=''\n", "%env CLEARML_API_ACCESS_KEY=''\n", - "%env CLEARML_API_SECRET_KEY=''\n" + "%env CLEARML_API_SECRET_KEY=''" ] }, { @@ -115,6 +115,7 @@ " MeanDice,\n", " StatsHandler,\n", ")\n", + "\n", "# import the clearml handlers\n", "from monai.handlers.clearml_handlers import ClearMLImageHandler, ClearMLStatsHandler\n", "from monai.losses import DiceLoss\n", @@ -133,6 +134,7 @@ "\n", "import ignite\n", "import torch\n", + "\n", "print_config()" ] }, @@ -273,7 +275,7 @@ "source": [ "# Create UNet, DiceLoss and Adam optimizer\n", "\n", - "device = None # torch.device(\"cuda:0\")\n", + "device = None # torch.device(\"cuda:0\")\n", "net = UNet(\n", " spatial_dims=3,\n", " in_channels=1,\n", @@ -353,9 +355,10 @@ "task_name = \"UNet segmentation 3d\"\n", "project_name = \"Monai example\"\n", "\n", - "train_clearml_stats_handler = ClearMLStatsHandler(task_name=task_name,\n", - " project_name=project_name, log_dir=log_dir, output_transform=lambda x: x)\n", - "train_clearml_stats_handler.attach(trainer)\n" + "train_clearml_stats_handler = ClearMLStatsHandler(\n", + " task_name=task_name, project_name=project_name, log_dir=log_dir, output_transform=lambda x: x\n", + ")\n", + "train_clearml_stats_handler.attach(trainer)" ] }, { @@ -447,7 +450,8 @@ "# label and model output in the last batch\n", "# here we draw the 3D output as GIF format along Depth\n", "# axis, at every validation epoch\n", - "val_clearml_image_handler = ClearMLImageHandler(task_name=task_name,\n", + "val_clearml_image_handler = ClearMLImageHandler(\n", + " task_name=task_name,\n", " project_name=project_name,\n", " log_dir=log_dir,\n", " batch_transform=lambda batch: (batch[0], batch[1]),\n", @@ -485,7 +489,7 @@ " shuffle=True,\n", " num_workers=8,\n", " # pin_memory=torch.cuda.is_available(),\n", - " pin_memory=False\n", + " pin_memory=False,\n", ")\n", "\n", "max_epochs = 10\n", From 557e7da90baba0447c2d2a06f7c7408b5f80fc73 Mon Sep 17 00:00:00 2001 From: revital Date: Tue, 29 Aug 2023 09:40:21 +0300 Subject: [PATCH 05/10] Edits --- .../unet_segmentation_3d_ignite_clearml.ipynb | 81 +++++++++---------- 1 file changed, 39 insertions(+), 42 deletions(-) diff --git a/experiment_management/unet_segmentation_3d_ignite_clearml.ipynb b/experiment_management/unet_segmentation_3d_ignite_clearml.ipynb index b070c661c6..2addd6948a 100644 --- a/experiment_management/unet_segmentation_3d_ignite_clearml.ipynb +++ b/experiment_management/unet_segmentation_3d_ignite_clearml.ipynb @@ -28,59 +28,25 @@ }, { "cell_type": "markdown", - "metadata": { - "id": "VUefxTkGGTAc" - }, "source": [ "## Setup environment\n", "\n", "`clearml` comes as part of the `monai[all]` installation. For more information about integrating ClearML into your Monai code, see [here](https://clear.ml/docs/latest/docs/integrations/monai). For more information about using ClearML (experiment management, data management, pipelines, model serving, and more), see [ClearML's official documentation](https://clear.ml/docs/latest/docs/)." - ] + ], + "metadata": { + "collapsed": false + } }, { "cell_type": "code", "execution_count": null, - "metadata": { - "id": "OUMIxMjvGTAd" - }, "outputs": [], "source": [ "!python -c \"import monai\" || pip install -q \"monai-weekly[ignite, nibabel, tensorboard, clearml]\"" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "s-ORDi_qZTYM" - }, - "source": [ - "### Set up ClearML ⚓\n", - "\n", - "1. To keep track of your experiments and/or data, ClearML needs to communicate to a server. You have 2 server options:\n", - "\n", - " * Sign up for free to the [ClearML Hosted Service](https://app.clear.ml/)\n", - " * Set up your own server, see [here](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server).\n", - "\n", - "\n", - "\n", - "2. Add you ClearML credentials below. ClearML will use these credentials to connect to your server (see instructions for generating credentials [here](https://clear.ml/docs/latest/docs/getting_started/ds/ds_first_steps/#jupyter-notebook))." - ] - }, - { - "cell_type": "code", - "execution_count": null, + ], "metadata": { - "id": "ofvwifHWG0SK" - }, - "outputs": [], - "source": [ - "# clearml credentials\n", - "%env CLEARML_WEB_HOST=''\n", - "%env CLEARML_API_HOST=''\n", - "%env CLEARML_FILES_HOST=''\n", - "%env CLEARML_API_ACCESS_KEY=''\n", - "%env CLEARML_API_SECRET_KEY=''\n" - ] + "collapsed": false + } }, { "cell_type": "markdown", @@ -102,7 +68,6 @@ "import glob\n", "import logging\n", "import os\n", - "from pathlib import Path\n", "import shutil\n", "import sys\n", "import tempfile\n", @@ -136,6 +101,38 @@ "print_config()" ] }, + { + "cell_type": "markdown", + "source": [ + "## Set up ClearML ⚓\n", + "\n", + "1. To keep track of your experiments and/or data, ClearML needs to communicate to a server. You have 2 server options:\n", + "\n", + " * Sign up for free to the [ClearML Hosted Service](https://app.clear.ml/)\n", + " * Set up your own server, see [here](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server).\n", + "\n", + "2. Add you ClearML credentials below. ClearML will use these credentials to connect to your server (see instructions for generating credentials [here](https://clear.ml/docs/latest/docs/getting_started/ds/ds_first_steps/#jupyter-notebook))." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "# clearml credentials\n", + "%env CLEARML_WEB_HOST=''\n", + "%env CLEARML_API_HOST=''\n", + "%env CLEARML_FILES_HOST=''\n", + "%env CLEARML_API_ACCESS_KEY=''\n", + "%env CLEARML_API_SECRET_KEY=''\n" + ], + "metadata": { + "collapsed": false + } + }, { "cell_type": "markdown", "metadata": { From 468d0a729870712a39c443ac728388d1ddb2b057 Mon Sep 17 00:00:00 2001 From: revital Date: Tue, 29 Aug 2023 09:46:54 +0300 Subject: [PATCH 06/10] fix link --- .../unet_segmentation_3d_ignite_clearml.ipynb | 29 +++++++------------ 1 file changed, 11 insertions(+), 18 deletions(-) diff --git a/experiment_management/unet_segmentation_3d_ignite_clearml.ipynb b/experiment_management/unet_segmentation_3d_ignite_clearml.ipynb index 7a19a495af..847f13c918 100644 --- a/experiment_management/unet_segmentation_3d_ignite_clearml.ipynb +++ b/experiment_management/unet_segmentation_3d_ignite_clearml.ipynb @@ -19,7 +19,7 @@ "\n", "# Experiment Management with ClearML\n", "\n", - "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/3d_segmentation/unet_segmentation_3d_ignite.ipynb)\n", + "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/experiment_management/unet_segmentation_3d_ignite_clearml.ipynb)\n", "\n", "This tutorial shows how to use ClearML to manage MONAI experiments. You can integrate ClearML into your code using Monai's built-in handlers: `ClearMLImageHandler`, `ClearMLStatsHandler`, and `ModelCheckpoint`.\n", "\n", @@ -28,21 +28,18 @@ }, { "cell_type": "markdown", - "metadata": { - "id": "VUefxTkGGTAc" - }, "source": [ "## Setup environment\n", "\n", "`clearml` comes as part of the `monai[all]` installation. For more information about integrating ClearML into your Monai code, see [here](https://clear.ml/docs/latest/docs/integrations/monai). For more information about using ClearML (experiment management, data management, pipelines, model serving, and more), see [ClearML's official documentation](https://clear.ml/docs/latest/docs/)." - ] + ], + "metadata": { + "collapsed": false + } }, { "cell_type": "code", "execution_count": null, - "metadata": { - "id": "OUMIxMjvGTAd" - }, "outputs": [], "source": [ "!python -c \"import monai\" || pip install -q \"monai-weekly[ignite, nibabel, tensorboard, clearml]\"" @@ -83,7 +80,6 @@ " MeanDice,\n", " StatsHandler,\n", ")\n", - "\n", "# import the clearml handlers\n", "from monai.handlers.clearml_handlers import ClearMLImageHandler, ClearMLStatsHandler\n", "from monai.losses import DiceLoss\n", @@ -102,7 +98,6 @@ "\n", "import ignite\n", "import torch\n", - "\n", "print_config()" ] }, @@ -275,7 +270,7 @@ "source": [ "# Create UNet, DiceLoss and Adam optimizer\n", "\n", - "device = None # torch.device(\"cuda:0\")\n", + "device = None # torch.device(\"cuda:0\")\n", "net = UNet(\n", " spatial_dims=3,\n", " in_channels=1,\n", @@ -355,10 +350,9 @@ "task_name = \"UNet segmentation 3d\"\n", "project_name = \"Monai example\"\n", "\n", - "train_clearml_stats_handler = ClearMLStatsHandler(\n", - " task_name=task_name, project_name=project_name, log_dir=log_dir, output_transform=lambda x: x\n", - ")\n", - "train_clearml_stats_handler.attach(trainer)" + "train_clearml_stats_handler = ClearMLStatsHandler(task_name=task_name,\n", + " project_name=project_name, log_dir=log_dir, output_transform=lambda x: x)\n", + "train_clearml_stats_handler.attach(trainer)\n" ] }, { @@ -450,8 +444,7 @@ "# label and model output in the last batch\n", "# here we draw the 3D output as GIF format along Depth\n", "# axis, at every validation epoch\n", - "val_clearml_image_handler = ClearMLImageHandler(\n", - " task_name=task_name,\n", + "val_clearml_image_handler = ClearMLImageHandler(task_name=task_name,\n", " project_name=project_name,\n", " log_dir=log_dir,\n", " batch_transform=lambda batch: (batch[0], batch[1]),\n", @@ -489,7 +482,7 @@ " shuffle=True,\n", " num_workers=8,\n", " # pin_memory=torch.cuda.is_available(),\n", - " pin_memory=False,\n", + " pin_memory=False\n", ")\n", "\n", "max_epochs = 10\n", From 8d504276328451959f4c266535018130f4b48457 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Tue, 29 Aug 2023 06:47:55 +0000 Subject: [PATCH 07/10] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- .../unet_segmentation_3d_ignite_clearml.ipynb | 18 +++++++++++------- 1 file changed, 11 insertions(+), 7 deletions(-) diff --git a/experiment_management/unet_segmentation_3d_ignite_clearml.ipynb b/experiment_management/unet_segmentation_3d_ignite_clearml.ipynb index 847f13c918..f96f97c1a0 100644 --- a/experiment_management/unet_segmentation_3d_ignite_clearml.ipynb +++ b/experiment_management/unet_segmentation_3d_ignite_clearml.ipynb @@ -80,6 +80,7 @@ " MeanDice,\n", " StatsHandler,\n", ")\n", + "\n", "# import the clearml handlers\n", "from monai.handlers.clearml_handlers import ClearMLImageHandler, ClearMLStatsHandler\n", "from monai.losses import DiceLoss\n", @@ -98,6 +99,7 @@ "\n", "import ignite\n", "import torch\n", + "\n", "print_config()" ] }, @@ -127,7 +129,7 @@ "%env CLEARML_API_HOST=''\n", "%env CLEARML_FILES_HOST=''\n", "%env CLEARML_API_ACCESS_KEY=''\n", - "%env CLEARML_API_SECRET_KEY=''\n" + "%env CLEARML_API_SECRET_KEY=''" ], "metadata": { "collapsed": false @@ -270,7 +272,7 @@ "source": [ "# Create UNet, DiceLoss and Adam optimizer\n", "\n", - "device = None # torch.device(\"cuda:0\")\n", + "device = None # torch.device(\"cuda:0\")\n", "net = UNet(\n", " spatial_dims=3,\n", " in_channels=1,\n", @@ -350,9 +352,10 @@ "task_name = \"UNet segmentation 3d\"\n", "project_name = \"Monai example\"\n", "\n", - "train_clearml_stats_handler = ClearMLStatsHandler(task_name=task_name,\n", - " project_name=project_name, log_dir=log_dir, output_transform=lambda x: x)\n", - "train_clearml_stats_handler.attach(trainer)\n" + "train_clearml_stats_handler = ClearMLStatsHandler(\n", + " task_name=task_name, project_name=project_name, log_dir=log_dir, output_transform=lambda x: x\n", + ")\n", + "train_clearml_stats_handler.attach(trainer)" ] }, { @@ -444,7 +447,8 @@ "# label and model output in the last batch\n", "# here we draw the 3D output as GIF format along Depth\n", "# axis, at every validation epoch\n", - "val_clearml_image_handler = ClearMLImageHandler(task_name=task_name,\n", + "val_clearml_image_handler = ClearMLImageHandler(\n", + " task_name=task_name,\n", " project_name=project_name,\n", " log_dir=log_dir,\n", " batch_transform=lambda batch: (batch[0], batch[1]),\n", @@ -482,7 +486,7 @@ " shuffle=True,\n", " num_workers=8,\n", " # pin_memory=torch.cuda.is_available(),\n", - " pin_memory=False\n", + " pin_memory=False,\n", ")\n", "\n", "max_epochs = 10\n", From 47cdbad9fba0859f0b572e41798e572efa5bd98c Mon Sep 17 00:00:00 2001 From: revital Date: Tue, 29 Aug 2023 09:48:26 +0300 Subject: [PATCH 08/10] DCO Remediation Commit for revital I, revital , hereby add my Signed-off-by to this commit: 557e7da90baba0447c2d2a06f7c7408b5f80fc73 I, revital , hereby add my Signed-off-by to this commit: 468d0a729870712a39c443ac728388d1ddb2b057 Signed-off-by: revital --- experiment_management/unet_segmentation_3d_ignite_clearml.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/experiment_management/unet_segmentation_3d_ignite_clearml.ipynb b/experiment_management/unet_segmentation_3d_ignite_clearml.ipynb index 847f13c918..a848af2eee 100644 --- a/experiment_management/unet_segmentation_3d_ignite_clearml.ipynb +++ b/experiment_management/unet_segmentation_3d_ignite_clearml.ipynb @@ -104,7 +104,7 @@ { "cell_type": "markdown", "source": [ - "## Set up ClearML ⚓\n", + "## Setup ClearML ⚓\n", "\n", "1. To keep track of your experiments and/or data, ClearML needs to communicate to a server. You have 2 server options:\n", "\n", From db6e8b07793533640ac118f4bff58b2760e6a07b Mon Sep 17 00:00:00 2001 From: revital Date: Thu, 23 Nov 2023 15:37:18 +0200 Subject: [PATCH 09/10] Edit ClearML example --- .../unet_segmentation_3d_ignite_clearml.ipynb | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/experiment_management/unet_segmentation_3d_ignite_clearml.ipynb b/experiment_management/unet_segmentation_3d_ignite_clearml.ipynb index 5121b45dfd..ba30c27bb0 100644 --- a/experiment_management/unet_segmentation_3d_ignite_clearml.ipynb +++ b/experiment_management/unet_segmentation_3d_ignite_clearml.ipynb @@ -21,7 +21,7 @@ "\n", "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/tutorials/blob/main/experiment_management/unet_segmentation_3d_ignite_clearml.ipynb)\n", "\n", - "This tutorial shows how to use ClearML to manage MONAI experiments. You can integrate ClearML into your code using Monai's built-in handlers: `ClearMLImageHandler`, `ClearMLStatsHandler`, and `ModelCheckpoint`.\n", + "This tutorial shows how to use ClearML to manage MONAI experiments. You can integrate ClearML into your code using MONAI's built-in handlers: `ClearMLImageHandler`, `ClearMLStatsHandler`, and `ModelCheckpoint`.\n", "\n", "The MONAI example used here is [3D segmentation with UNet](https://github.com/Project-MONAI/tutorials/blob/main/3d_segmentation/unet_segmentation_3d_ignite.ipynb)." ] @@ -31,7 +31,7 @@ "source": [ "## Setup environment\n", "\n", - "`clearml` comes as part of the `monai[all]` installation. For more information about integrating ClearML into your Monai code, see [here](https://clear.ml/docs/latest/docs/integrations/monai). For more information about using ClearML (experiment management, data management, pipelines, model serving, and more), see [ClearML's official documentation](https://clear.ml/docs/latest/docs/)." + "`clearml` comes as part of the `monai[all]` installation. For more information about integrating ClearML into your MONAI code, see [here](https://clear.ml/docs/latest/docs/integrations/monai). For more information about using ClearML (experiment management, data management, pipelines, model serving, and more), see [ClearML's official documentation](https://clear.ml/docs/latest/docs/)." ], "metadata": { "collapsed": false @@ -350,7 +350,7 @@ "# ClearMLStatsHandler plots loss at every iteration\n", "# Creates a ClearML Task which logs the scalar plots\n", "task_name = \"UNet segmentation 3d\"\n", - "project_name = \"Monai example\"\n", + "project_name = \"MONAI example\"\n", "\n", "train_clearml_stats_handler = ClearMLStatsHandler(\n", " task_name=task_name, project_name=project_name, log_dir=log_dir, output_transform=lambda x: x\n", @@ -513,7 +513,7 @@ { "cell_type": "markdown", "source": [ - "![Monai ClearML Models](./../figures/monai_clearml_models.png)" + "![MONAI ClearML Models](./../figures/monai_clearml_models.png)" ], "metadata": { "id": "Uya6hN7At-C6" @@ -534,7 +534,7 @@ { "cell_type": "markdown", "source": [ - "![monai_clearml_scalars.png](./../figures/monai_clearml_scalars.png)" + "![MONAI ClearML scalars.png](./../figures/monai_clearml_scalars.png)" ], "metadata": { "id": "DCLhp4e2uKsd" @@ -554,7 +554,7 @@ { "cell_type": "markdown", "source": [ - "![monai_clearml_debug_samples.png](./../figures/monai_clearml_debug_samples.png)" + "![MONAI ClearML Debug Samples.png](./../figures/monai_clearml_debug_samples.png)" ], "metadata": { "id": "AFLJauKxt-Yy" From 60020e1dafe4c1e29969d9d070d51ca26db19d1f Mon Sep 17 00:00:00 2001 From: revital Date: Thu, 23 Nov 2023 15:46:16 +0200 Subject: [PATCH 10/10] DCO Remediation Commit for revital I, revital , hereby add my Signed-off-by to this commit: db6e8b07793533640ac118f4bff58b2760e6a07b Signed-off-by: revital --- experiment_management/unet_segmentation_3d_ignite_clearml.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/experiment_management/unet_segmentation_3d_ignite_clearml.ipynb b/experiment_management/unet_segmentation_3d_ignite_clearml.ipynb index ba30c27bb0..e0c7cb8073 100644 --- a/experiment_management/unet_segmentation_3d_ignite_clearml.ipynb +++ b/experiment_management/unet_segmentation_3d_ignite_clearml.ipynb @@ -113,7 +113,7 @@ " * Sign up for free to the [ClearML Hosted Service](https://app.clear.ml/)\n", " * Set up your own server, see [here](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server).\n", "\n", - "2. Add you ClearML credentials below. ClearML will use these credentials to connect to your server (see instructions for generating credentials [here](https://clear.ml/docs/latest/docs/getting_started/ds/ds_first_steps/#jupyter-notebook))." + "2. Add your ClearML credentials below. ClearML will use these credentials to connect to your server (see instructions for generating credentials [here](https://clear.ml/docs/latest/docs/getting_started/ds/ds_first_steps/#jupyter-notebook))." ], "metadata": { "collapsed": false