{
"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.air.config import RunConfig, ScalingConfig\n",
"from ray.air.result import Result\n",
"from ray.train.xgboost import XGBoostTrainer\n",
"from ray.air.callbacks.comet import CometLoggerCallback"
]
},
{
"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",
" dataset = ray.data.read_csv(\"s3://anonymous@air-example-data/breast_cancer.csv\")\n",
" return dataset"
]
},
{
"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=ScalingConfig(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 ==<br>Current time: 2022-05-19 15:19:35 (running for 00:00:14.95)<br>Memory usage on this node: 10.2/16.0 GiB<br>Using FIFO scheduling algorithm.<br>Resources requested: 0/16 CPUs, 0/0 GPUs, 0.0/5.12 GiB heap, 0.0/2.0 GiB objects<br>Result logdir: /Users/kai/ray_results/XGBoostTrainer_2022-05-19_15-19-19<br>Number of trials: 1/1 (1 TERMINATED)<br><table>\n",
"<thead>\n",
"<tr><th>Trial name </th><th>status </th><th>loc </th><th style=\"text-align: right;\"> iter</th><th style=\"text-align: right;\"> total time (s)</th><th style=\"text-align: right;\"> train-rmse</th></tr>\n",
"</thead>\n",
"<tbody>\n",
"<tr><td>XGBoostTrainer_ac544_00000</td><td>TERMINATED</td><td>127.0.0.1:19852</td><td style=\"text-align: right;\"> 10</td><td style=\"text-align: right;\"> 9.7203</td><td style=\"text-align: right;\"> 0.030717</td></tr>\n",
"</tbody>\n",
"</table><br><br>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"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"
]
},
{
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"COMET WARNING: The given value of the metric timesteps_total was None; ignoring\n",
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"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",
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"COMET INFO: Artifact 'checkpoint_XGBoostTrainer_ac544_00000' version 7.0.0 created (previous was: 6.0.0)\n",
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"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",
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"COMET INFO: Artifact 'checkpoint_XGBoostTrainer_ac544_00000' version 8.0.0 created (previous was: 7.0.0)\n",
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"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",
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"COMET INFO: Artifact 'checkpoint_XGBoostTrainer_ac544_00000' version 9.0.0 created (previous was: 8.0.0)\n",
"COMET INFO: Scheduling the upload of 3 assets for a size of 14.36 KB, this can take some time\n",
"COMET INFO: Artifact '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"
]
},
{
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]
},
{
"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!"
]
}
],
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"cell_metadata_filter": "-all",
"main_language": "python",
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