{ "cells": [ { "cell_type": "markdown", "id": "98d7c620", "metadata": {}, "source": [ "# Logging results and uploading models to Comet ML\n", "In this example, we train a simple XGBoost model and log the training\n", "results to Comet ML. We also save the resulting model checkpoints\n", "as artifacts." ] }, { "cell_type": "markdown", "id": "c6e66577", "metadata": {}, "source": [ "Let's start with installing our dependencies:" ] }, { "cell_type": "code", "execution_count": 1, "id": "6d6297ef", "metadata": {}, "outputs": [], "source": [ "!pip install -qU \"ray[tune]\" sklearn xgboost_ray comet_ml" ] }, { "cell_type": "markdown", "id": "c2e21446", "metadata": {}, "source": [ "Then we need some imports:" ] }, { "cell_type": "code", "execution_count": 2, "id": "dffff484", "metadata": {}, "outputs": [], "source": [ "import ray\n", "\n", "from ray.ml import RunConfig\n", "from ray.ml.result import Result\n", "from ray.ml.train.integrations.xgboost import XGBoostTrainer\n", "from ray.tune.integration.comet import CometLoggerCallback\n", "from sklearn.datasets import load_breast_cancer" ] }, { "cell_type": "markdown", "id": "29fcd93b", "metadata": {}, "source": [ "We define a simple function that returns our training dataset as a Ray Dataset:" ] }, { "cell_type": "code", "execution_count": 3, "id": "cf830706", "metadata": {}, "outputs": [], "source": [ "def get_train_dataset() -> ray.data.Dataset:\n", " \"\"\"Return the \"Breast cancer\" dataset as a Ray dataset.\"\"\"\n", " data_raw = load_breast_cancer(as_frame=True)\n", " df = data_raw[\"data\"]\n", " df[\"target\"] = data_raw[\"target\"]\n", " return ray.data.from_pandas(df)" ] }, { "cell_type": "markdown", "id": "0f48f948", "metadata": {}, "source": [ "Now we define a simple training function. All the magic happens within the `CometLoggerCallback`:\n", "\n", "```python\n", "CometLoggerCallback(\n", " project_name=comet_project,\n", " save_checkpoints=True,\n", ")\n", "```\n", "\n", "It will automatically log all results to Comet ML and upload the checkpoints as artifacts. It assumes you're logged in into Comet via an API key or your `~./.comet.config`." ] }, { "cell_type": "code", "execution_count": 4, "id": "230f23a3", "metadata": {}, "outputs": [], "source": [ "def train_model(train_dataset: ray.data.Dataset, comet_project: str) -> Result:\n", " \"\"\"Train a simple XGBoost model and return the result.\"\"\"\n", " trainer = XGBoostTrainer(\n", " scaling_config={\"num_workers\": 2},\n", " params={\"tree_method\": \"auto\"},\n", " label_column=\"target\",\n", " datasets={\"train\": train_dataset},\n", " num_boost_round=10,\n", " run_config=RunConfig(\n", " callbacks=[\n", " # This is the part needed to enable logging to Comet ML.\n", " # It assumes Comet ML can find a valid API (e.g. by setting\n", " # the ``COMET_API_KEY`` environment variable).\n", " CometLoggerCallback(\n", " project_name=comet_project,\n", " save_checkpoints=True,\n", " )\n", " ]\n", " ),\n", " )\n", " result = trainer.fit()\n", " return result" ] }, { "cell_type": "markdown", "id": "711b1d7d", "metadata": {}, "source": [ "Let's kick off a run:" ] }, { "cell_type": "code", "execution_count": 5, "id": "9bfd9a8d", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2022-05-19 15:19:17,237\tINFO services.py:1483 -- View the Ray dashboard at \u001b[1m\u001b[32mhttp://127.0.0.1:8265\u001b[39m\u001b[22m\n" ] }, { "data": { "text/html": [ "== Status ==
Current time: 2022-05-19 15:19:35 (running for 00:00:14.95)
Memory usage on this node: 10.2/16.0 GiB
Using FIFO scheduling algorithm.
Resources requested: 0/16 CPUs, 0/0 GPUs, 0.0/5.12 GiB heap, 0.0/2.0 GiB objects
Result logdir: /Users/kai/ray_results/XGBoostTrainer_2022-05-19_15-19-19
Number of trials: 1/1 (1 TERMINATED)
\n", "\n", "\n", "\n", "\n", "\n", "\n", "
Trial name status loc iter total time (s) train-rmse
XGBoostTrainer_ac544_00000TERMINATED127.0.0.1:19852 10 9.7203 0.030717


" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "COMET WARNING: As you are running in a Jupyter environment, you will need to call `experiment.end()` when finished to ensure all metrics and code are logged before exiting.\n", "\u001b[2m\u001b[33m(raylet)\u001b[0m 2022-05-19 15:19:21,584\tINFO context.py:70 -- Exec'ing worker with command: exec /Users/kai/.pyenv/versions/3.7.7/bin/python3.7 /Users/kai/coding/ray/python/ray/workers/default_worker.py --node-ip-address=127.0.0.1 --node-manager-port=61222 --object-store-name=/tmp/ray/session_2022-05-19_15-19-14_632568_19778/sockets/plasma_store --raylet-name=/tmp/ray/session_2022-05-19_15-19-14_632568_19778/sockets/raylet --redis-address=None --storage=None --temp-dir=/tmp/ray --metrics-agent-port=62873 --logging-rotate-bytes=536870912 --logging-rotate-backup-count=5 --gcs-address=127.0.0.1:61938 --redis-password=5241590000000000 --startup-token=16 --runtime-env-hash=-2010331134\n", "COMET INFO: Experiment is live on comet.ml https://www.comet.ml/krfricke/ray-air-example/ecd3726ca127497ba7386003a249fad6\n", "\n", "COMET WARNING: Failed to add tag(s) None to the experiment\n", "\n", "COMET WARNING: Empty mapping given to log_params({}); ignoring\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=19852)\u001b[0m UserWarning: Dataset 'train' has 1 blocks, which is less than the `num_workers` 2. This dataset will be automatically repartitioned to 2 blocks.\n", "\u001b[2m\u001b[33m(raylet)\u001b[0m 2022-05-19 15:19:24,628\tINFO context.py:70 -- Exec'ing worker with command: exec /Users/kai/.pyenv/versions/3.7.7/bin/python3.7 /Users/kai/coding/ray/python/ray/workers/default_worker.py --node-ip-address=127.0.0.1 --node-manager-port=61222 --object-store-name=/tmp/ray/session_2022-05-19_15-19-14_632568_19778/sockets/plasma_store --raylet-name=/tmp/ray/session_2022-05-19_15-19-14_632568_19778/sockets/raylet --redis-address=None --storage=None --temp-dir=/tmp/ray --metrics-agent-port=62873 --logging-rotate-bytes=536870912 --logging-rotate-backup-count=5 --gcs-address=127.0.0.1:61938 --redis-password=5241590000000000 --startup-token=17 --runtime-env-hash=-2010331069\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=19852)\u001b[0m 2022-05-19 15:19:25,961\tINFO main.py:980 -- [RayXGBoost] Created 2 new actors (2 total actors). Waiting until actors are ready for training.\n", "\u001b[2m\u001b[33m(raylet)\u001b[0m 2022-05-19 15:19:26,830\tINFO context.py:70 -- Exec'ing worker with command: exec /Users/kai/.pyenv/versions/3.7.7/bin/python3.7 /Users/kai/coding/ray/python/ray/workers/default_worker.py --node-ip-address=127.0.0.1 --node-manager-port=61222 --object-store-name=/tmp/ray/session_2022-05-19_15-19-14_632568_19778/sockets/plasma_store --raylet-name=/tmp/ray/session_2022-05-19_15-19-14_632568_19778/sockets/raylet --redis-address=None --storage=None --temp-dir=/tmp/ray --metrics-agent-port=62873 --logging-rotate-bytes=536870912 --logging-rotate-backup-count=5 --gcs-address=127.0.0.1:61938 --redis-password=5241590000000000 --startup-token=18 --runtime-env-hash=-2010331069\n", "\u001b[2m\u001b[33m(raylet)\u001b[0m 2022-05-19 15:19:26,918\tINFO context.py:70 -- Exec'ing worker with command: exec /Users/kai/.pyenv/versions/3.7.7/bin/python3.7 /Users/kai/coding/ray/python/ray/workers/default_worker.py --node-ip-address=127.0.0.1 --node-manager-port=61222 --object-store-name=/tmp/ray/session_2022-05-19_15-19-14_632568_19778/sockets/plasma_store --raylet-name=/tmp/ray/session_2022-05-19_15-19-14_632568_19778/sockets/raylet --redis-address=None --storage=None --temp-dir=/tmp/ray --metrics-agent-port=62873 --logging-rotate-bytes=536870912 --logging-rotate-backup-count=5 --gcs-address=127.0.0.1:61938 --redis-password=5241590000000000 --startup-token=20 --runtime-env-hash=-2010331134\n", "\u001b[2m\u001b[33m(raylet)\u001b[0m 2022-05-19 15:19:26,922\tINFO context.py:70 -- Exec'ing worker with command: exec /Users/kai/.pyenv/versions/3.7.7/bin/python3.7 /Users/kai/coding/ray/python/ray/workers/default_worker.py --node-ip-address=127.0.0.1 --node-manager-port=61222 --object-store-name=/tmp/ray/session_2022-05-19_15-19-14_632568_19778/sockets/plasma_store --raylet-name=/tmp/ray/session_2022-05-19_15-19-14_632568_19778/sockets/raylet --redis-address=None --storage=None --temp-dir=/tmp/ray --metrics-agent-port=62873 --logging-rotate-bytes=536870912 --logging-rotate-backup-count=5 --gcs-address=127.0.0.1:61938 --redis-password=5241590000000000 --startup-token=21 --runtime-env-hash=-2010331134\n", "\u001b[2m\u001b[33m(raylet)\u001b[0m 2022-05-19 15:19:26,922\tINFO context.py:70 -- Exec'ing worker with command: exec /Users/kai/.pyenv/versions/3.7.7/bin/python3.7 /Users/kai/coding/ray/python/ray/workers/default_worker.py --node-ip-address=127.0.0.1 --node-manager-port=61222 --object-store-name=/tmp/ray/session_2022-05-19_15-19-14_632568_19778/sockets/plasma_store --raylet-name=/tmp/ray/session_2022-05-19_15-19-14_632568_19778/sockets/raylet --redis-address=None --storage=None --temp-dir=/tmp/ray --metrics-agent-port=62873 --logging-rotate-bytes=536870912 --logging-rotate-backup-count=5 --gcs-address=127.0.0.1:61938 --redis-password=5241590000000000 --startup-token=22 --runtime-env-hash=-2010331134\n", "\u001b[2m\u001b[33m(raylet)\u001b[0m 2022-05-19 15:19:26,923\tINFO context.py:70 -- Exec'ing worker with command: exec /Users/kai/.pyenv/versions/3.7.7/bin/python3.7 /Users/kai/coding/ray/python/ray/workers/default_worker.py --node-ip-address=127.0.0.1 --node-manager-port=61222 --object-store-name=/tmp/ray/session_2022-05-19_15-19-14_632568_19778/sockets/plasma_store --raylet-name=/tmp/ray/session_2022-05-19_15-19-14_632568_19778/sockets/raylet --redis-address=None --storage=None --temp-dir=/tmp/ray --metrics-agent-port=62873 --logging-rotate-bytes=536870912 --logging-rotate-backup-count=5 --gcs-address=127.0.0.1:61938 --redis-password=5241590000000000 --startup-token=19 --runtime-env-hash=-2010331134\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=19852)\u001b[0m 2022-05-19 15:19:29,272\tINFO main.py:1025 -- [RayXGBoost] Starting XGBoost training.\n", "\u001b[2m\u001b[36m(_RemoteRayXGBoostActor pid=19876)\u001b[0m [15:19:29] task [xgboost.ray]:4505889744 got new rank 1\n", "\u001b[2m\u001b[36m(_RemoteRayXGBoostActor pid=19875)\u001b[0m [15:19:29] task [xgboost.ray]:6941849424 got new rank 0\n", "COMET WARNING: The given value of the metric episodes_total was None; ignoring\n", "COMET WARNING: The given value of the metric timesteps_total was None; ignoring\n", "COMET INFO: Artifact 'checkpoint_XGBoostTrainer_ac544_00000' version 1.0.0 created\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Result for XGBoostTrainer_ac544_00000:\n", " date: 2022-05-19_15-19-30\n", " done: false\n", " experiment_id: d3007bd6a2734b328fd90385485c5a8d\n", " hostname: Kais-MacBook-Pro.local\n", " iterations_since_restore: 1\n", " node_ip: 127.0.0.1\n", " pid: 19852\n", " should_checkpoint: true\n", " time_since_restore: 6.529659032821655\n", " time_this_iter_s: 6.529659032821655\n", " time_total_s: 6.529659032821655\n", " timestamp: 1652969970\n", " timesteps_since_restore: 0\n", " train-rmse: 0.357284\n", " training_iteration: 1\n", " trial_id: ac544_00000\n", " warmup_time: 0.003961086273193359\n", " \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "COMET INFO: Scheduling the upload of 3 assets for a size of 2.48 KB, this can take some time\n", "COMET INFO: Artifact 'krfricke/checkpoint_XGBoostTrainer_ac544_00000:1.0.0' has started uploading asynchronously\n", "COMET WARNING: The given value of the metric episodes_total was None; ignoring\n", "COMET WARNING: The given value of the metric timesteps_total was None; ignoring\n", "COMET INFO: Artifact 'checkpoint_XGBoostTrainer_ac544_00000' version 2.0.0 created (previous was: 1.0.0)\n", "COMET INFO: Scheduling the upload of 3 assets for a size of 3.86 KB, this can take some time\n", "COMET INFO: Artifact 'krfricke/checkpoint_XGBoostTrainer_ac544_00000:2.0.0' has started uploading asynchronously\n", "COMET WARNING: The given value of the metric episodes_total was None; ignoring\n", "COMET WARNING: The given value of the metric timesteps_total was None; ignoring\n", "COMET INFO: Artifact 'checkpoint_XGBoostTrainer_ac544_00000' version 3.0.0 created (previous was: 2.0.0)\n", "COMET INFO: Artifact 'krfricke/checkpoint_XGBoostTrainer_ac544_00000:1.0.0' has been fully uploaded successfully\n", "COMET INFO: Scheduling the upload of 3 assets for a size of 5.31 KB, this can take some time\n", "COMET INFO: Artifact 'krfricke/checkpoint_XGBoostTrainer_ac544_00000:3.0.0' has started uploading asynchronously\n", "COMET WARNING: The given value of the metric episodes_total was None; ignoring\n", "COMET WARNING: The given value of the metric timesteps_total was None; ignoring\n", "COMET INFO: Artifact 'checkpoint_XGBoostTrainer_ac544_00000' version 4.0.0 created (previous was: 3.0.0)\n", "COMET INFO: Artifact 'krfricke/checkpoint_XGBoostTrainer_ac544_00000:2.0.0' has been fully uploaded successfully\n", "COMET INFO: Scheduling the upload of 3 assets for a size of 6.76 KB, this can take some time\n", "COMET INFO: Artifact 'krfricke/checkpoint_XGBoostTrainer_ac544_00000:4.0.0' has started uploading asynchronously\n", "COMET WARNING: The given 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Scheduling the upload of 3 assets for a size of 9.87 KB, this can take some time\n", "COMET INFO: Artifact 'krfricke/checkpoint_XGBoostTrainer_ac544_00000:6.0.0' has started uploading asynchronously\n", "COMET WARNING: The given value of the metric episodes_total was None; ignoring\n", "COMET WARNING: The given value of the metric timesteps_total was None; ignoring\n", "COMET INFO: Artifact 'krfricke/checkpoint_XGBoostTrainer_ac544_00000:5.0.0' has been fully uploaded successfully\n", "COMET INFO: Artifact 'checkpoint_XGBoostTrainer_ac544_00000' version 7.0.0 created (previous was: 6.0.0)\n", "COMET INFO: Scheduling the upload of 3 assets for a size of 11.46 KB, this can take some time\n", "COMET INFO: Artifact 'krfricke/checkpoint_XGBoostTrainer_ac544_00000:7.0.0' has started uploading asynchronously\n", "COMET WARNING: The given value of the metric episodes_total was None; ignoring\n", "COMET WARNING: The given value of the metric timesteps_total was None; ignoring\n", "COMET INFO: 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'krfricke/checkpoint_XGBoostTrainer_ac544_00000:9.0.0' has started uploading asynchronously\n", "COMET WARNING: The given value of the metric episodes_total was None; ignoring\n", "COMET WARNING: The given value of the metric timesteps_total was None; ignoring\n", "COMET INFO: Artifact 'krfricke/checkpoint_XGBoostTrainer_ac544_00000:8.0.0' has been fully uploaded successfully\n", "COMET INFO: Artifact 'checkpoint_XGBoostTrainer_ac544_00000' version 10.0.0 created (previous was: 9.0.0)\n", "COMET INFO: Scheduling the upload of 3 assets for a size of 16.37 KB, this can take some time\n", "COMET INFO: Artifact 'krfricke/checkpoint_XGBoostTrainer_ac544_00000:10.0.0' has started uploading asynchronously\n", "\u001b[2m\u001b[36m(GBDTTrainable pid=19852)\u001b[0m 2022-05-19 15:19:33,890\tINFO main.py:1519 -- [RayXGBoost] Finished XGBoost training on training data with total N=569 in 7.96 seconds (4.61 pure XGBoost training time).\n", "COMET INFO: Artifact 'krfricke/checkpoint_XGBoostTrainer_ac544_00000:9.0.0' has been fully uploaded successfully\n", "COMET INFO: Artifact 'checkpoint_XGBoostTrainer_ac544_00000' version 11.0.0 created (previous was: 10.0.0)\n", "COMET INFO: Scheduling the upload of 3 assets for a size of 16.39 KB, this can take some time\n", "COMET INFO: Artifact 'krfricke/checkpoint_XGBoostTrainer_ac544_00000:11.0.0' has started uploading asynchronously\n", "COMET INFO: ---------------------------\n", "COMET INFO: Comet.ml Experiment Summary\n", "COMET INFO: ---------------------------\n", "COMET INFO: Data:\n", "COMET INFO: display_summary_level : 1\n", "COMET INFO: url : https://www.comet.ml/krfricke/ray-air-example/ecd3726ca127497ba7386003a249fad6\n", "COMET INFO: Metrics [count] (min, max):\n", "COMET INFO: iterations_since_restore [10] : (1, 10)\n", "COMET INFO: time_since_restore [10] : (6.529659032821655, 9.720295906066895)\n", "COMET INFO: time_this_iter_s [10] : (0.3124058246612549, 6.529659032821655)\n", "COMET INFO: time_total_s [10] : (6.529659032821655, 9.720295906066895)\n", "COMET INFO: timestamp [10] : (1652969970, 1652969973)\n", "COMET INFO: timesteps_since_restore : 0\n", "COMET INFO: train-rmse [10] : (0.030717, 0.357284)\n", "COMET INFO: training_iteration [10] : (1, 10)\n", "COMET INFO: warmup_time : 0.003961086273193359\n", "COMET INFO: Others:\n", "COMET INFO: Created from : Ray\n", "COMET INFO: Name : XGBoostTrainer_ac544_00000\n", "COMET INFO: experiment_id : d3007bd6a2734b328fd90385485c5a8d\n", "COMET INFO: trial_id : ac544_00000\n", "COMET INFO: System Information:\n", "COMET INFO: date : 2022-05-19_15-19-33\n", "COMET INFO: hostname : Kais-MacBook-Pro.local\n", "COMET INFO: node_ip : 127.0.0.1\n", "COMET INFO: pid : 19852\n", "COMET INFO: Uploads:\n", "COMET INFO: artifact assets : 33 (107.92 KB)\n", "COMET INFO: artifacts : 11\n", "COMET INFO: environment details : 1\n", "COMET INFO: filename : 1\n", "COMET INFO: installed packages : 1\n", "COMET INFO: notebook : 1\n", "COMET INFO: source_code : 1\n", "COMET INFO: ---------------------------\n", "COMET INFO: Uploading metrics, params, and assets to Comet before program termination (may take several seconds)\n", "COMET INFO: The Python SDK has 3600 seconds to finish before aborting...\n", "COMET INFO: Waiting for completion of the file uploads (may take several seconds)\n", "COMET INFO: The Python SDK has 10800 seconds to finish before aborting...\n", "COMET INFO: Still uploading 6 file(s), remaining 21.05 KB/116.69 KB\n", "COMET INFO: Artifact 'krfricke/checkpoint_XGBoostTrainer_ac544_00000:10.0.0' has been fully uploaded successfully\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "COMET INFO: Artifact 'krfricke/checkpoint_XGBoostTrainer_ac544_00000:11.0.0' has been fully uploaded successfully\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Result for XGBoostTrainer_ac544_00000:\n", " date: 2022-05-19_15-19-33\n", " done: true\n", " experiment_id: d3007bd6a2734b328fd90385485c5a8d\n", " experiment_tag: '0'\n", " hostname: Kais-MacBook-Pro.local\n", " iterations_since_restore: 10\n", " node_ip: 127.0.0.1\n", " pid: 19852\n", " should_checkpoint: true\n", " time_since_restore: 9.720295906066895\n", " time_this_iter_s: 0.39761900901794434\n", " time_total_s: 9.720295906066895\n", " timestamp: 1652969973\n", " timesteps_since_restore: 0\n", " train-rmse: 0.030717\n", " training_iteration: 10\n", " trial_id: ac544_00000\n", " warmup_time: 0.003961086273193359\n", " \n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "2022-05-19 15:19:35,621\tINFO tune.py:753 -- Total run time: 15.75 seconds (14.94 seconds for the tuning loop).\n" ] } ], "source": [ "comet_project = \"ray_air_example\"\n", "\n", "train_dataset = get_train_dataset()\n", "result = train_model(train_dataset=train_dataset, comet_project=comet_project)" ] }, { "cell_type": "markdown", "id": "be28bdd3", "metadata": {}, "source": [ "Check out your [Comet ML](https://www.comet.ml/) project to see the results!" ] } ], "metadata": { "jupytext": { "cell_metadata_filter": "-all", "main_language": "python", "notebook_metadata_filter": "-all" }, "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": "3.7.7" } }, "nbformat": 4, "nbformat_minor": 5 }