diff --git a/README.md b/README.md index b236f804..f33f4842 100644 --- a/README.md +++ b/README.md @@ -101,6 +101,17 @@ We'll start by setting up our cluster with the environment and compute configura +### Credentials +```bash +touch .env +``` +```bash +# Inside .env +GITHUB_USERNAME="CHANGE_THIS_TO_YOUR_USERNAME" # ← CHANGE THIS +```bash +source .env +``` + ### Git setup Create a repository by following these instructions: [Create a new repository](https://github.com/new) → name it `Made-With-ML` → Toggle `Add a README file` (**very important** as this creates a `main` branch) → Click `Create repository` (scroll down) @@ -109,7 +120,7 @@ Now we're ready to clone the repository that has all of our code: ```bash git clone https://github.com/GokuMohandas/Made-With-ML.git . -git remote set-url origin https://github.com/GITHUB_USERNAME/Made-With-ML.git # <-- CHANGE THIS to your username +git remote set-url origin https://github.com/$GITHUB_USERNAME/Made-With-ML.git # <-- CHANGE THIS to your username git checkout -b dev ``` @@ -317,15 +328,7 @@ python madewithml/predict.py predict \ python madewithml/serve.py --run_id $RUN_ID ``` - While the application is running, we can use it via cURL, Python, etc.: - - ```bash - # via cURL - curl -X POST -H "Content-Type: application/json" -d '{ - "title": "Transfer learning with transformers", - "description": "Using transformers for transfer learning on text classification tasks." - }' http://127.0.0.1:8000/predict - ``` + Once the application is running, we can use it via cURL, Python, etc.: ```python # via Python @@ -341,13 +344,6 @@ python madewithml/predict.py predict \ ray stop # shutdown ``` -```bash -export HOLDOUT_LOC="https://raw.githubusercontent.com/GokuMohandas/Made-With-ML/main/datasets/holdout.csv" -curl -X POST -H "Content-Type: application/json" -d '{ - "dataset_loc": "https://raw.githubusercontent.com/GokuMohandas/Made-With-ML/main/datasets/holdout.csv" - }' http://127.0.0.1:8000/evaluate -``` -
@@ -362,15 +358,7 @@ curl -X POST -H "Content-Type: application/json" -d '{ python madewithml/serve.py --run_id $RUN_ID ``` - While the application is running, we can use it via cURL, Python, etc.: - - ```bash - # via cURL - curl -X POST -H "Content-Type: application/json" -d '{ - "title": "Transfer learning with transformers", - "description": "Using transformers for transfer learning on text classification tasks." - }' http://127.0.0.1:8000/predict - ``` + Once the application is running, we can use it via cURL, Python, etc.: ```python # via Python @@ -399,7 +387,7 @@ export RUN_ID=$(python madewithml/predict.py get-best-run-id --experiment-name $ pytest --run-id=$RUN_ID tests/model --verbose --disable-warnings # Coverage -python3 -m pytest --cov madewithml --cov-report html +python3 -m pytest tests/code --cov madewithml --cov-report html --disable-warnings ``` ## Production diff --git a/madewithml/__init__.py b/madewithml/__init__.py new file mode 100644 index 00000000..bf6bd6c5 --- /dev/null +++ b/madewithml/__init__.py @@ -0,0 +1,3 @@ +from dotenv import load_dotenv + +load_dotenv() diff --git a/madewithml/config.py b/madewithml/config.py index 2319419f..93b1de9d 100644 --- a/madewithml/config.py +++ b/madewithml/config.py @@ -1,5 +1,6 @@ # config.py import logging +import os import sys from pathlib import Path @@ -10,9 +11,10 @@ ROOT_DIR = Path(__file__).parent.parent.absolute() LOGS_DIR = Path(ROOT_DIR, "logs") LOGS_DIR.mkdir(parents=True, exist_ok=True) +EFS_DIR = Path(f"/efs/shared_storage/madewithml/{os.environ.get('GITHUB_USERNAME', '')}") # Config MLflow -MODEL_REGISTRY = Path("/tmp/mlflow") +MODEL_REGISTRY = Path(f"{EFS_DIR}/mlflow") Path(MODEL_REGISTRY).mkdir(parents=True, exist_ok=True) MLFLOW_TRACKING_URI = "file://" + str(MODEL_REGISTRY.absolute()) mlflow.set_tracking_uri(MLFLOW_TRACKING_URI) diff --git a/madewithml/predict.py b/madewithml/predict.py index b97e9f89..45f92a2b 100644 --- a/madewithml/predict.py +++ b/madewithml/predict.py @@ -125,7 +125,7 @@ def predict( # Load components best_checkpoint = get_best_checkpoint(run_id=run_id) predictor = TorchPredictor.from_checkpoint(best_checkpoint) - preprocessor = predictor.get_preprocessor() + # preprocessor = predictor.get_preprocessor() # Predict sample_df = pd.DataFrame([{"title": title, "description": description, "tag": "other"}]) diff --git a/madewithml/serve.py b/madewithml/serve.py index 2e30ab28..331a74b6 100644 --- a/madewithml/serve.py +++ b/madewithml/serve.py @@ -1,4 +1,5 @@ import argparse +import os from http import HTTPStatus from typing import Dict @@ -75,5 +76,5 @@ async def _predict(self, request: Request) -> Dict: parser.add_argument("--run_id", help="run ID to use for serving.") parser.add_argument("--threshold", type=float, default=0.9, help="threshold for `other` class.") args = parser.parse_args() - ray.init() + ray.init(runtime_env={"env_vars": {"GITHUB_USERNAME": os.environ["GITHUB_USERNAME"]}}) serve.run(ModelDeployment.bind(run_id=args.run_id, threshold=args.threshold)) diff --git a/madewithml/train.py b/madewithml/train.py index 00e1a44f..75a63f7c 100644 --- a/madewithml/train.py +++ b/madewithml/train.py @@ -1,5 +1,6 @@ import datetime import json +import os from typing import Tuple import numpy as np @@ -23,7 +24,7 @@ from typing_extensions import Annotated from madewithml import data, models, utils -from madewithml.config import MLFLOW_TRACKING_URI, logger +from madewithml.config import EFS_DIR, MLFLOW_TRACKING_URI, logger # Initialize Typer CLI app app = typer.Typer() @@ -200,10 +201,7 @@ def train_model( ) # Run config - run_config = RunConfig( - callbacks=[mlflow_callback], - checkpoint_config=checkpoint_config, - ) + run_config = RunConfig(callbacks=[mlflow_callback], checkpoint_config=checkpoint_config, storage_path=EFS_DIR) # Dataset ds = data.load_data(dataset_loc=dataset_loc, num_samples=train_loop_config["num_samples"]) @@ -252,5 +250,5 @@ def train_model( if __name__ == "__main__": # pragma: no cover, application if ray.is_initialized(): ray.shutdown() - ray.init() + ray.init(runtime_env={"env_vars": {"GITHUB_USERNAME": os.environ["GITHUB_USERNAME"]}}) app() diff --git a/madewithml/tune.py b/madewithml/tune.py index 77e21f4f..13d0d437 100644 --- a/madewithml/tune.py +++ b/madewithml/tune.py @@ -1,5 +1,6 @@ import datetime import json +import os import ray import typer @@ -19,7 +20,7 @@ from typing_extensions import Annotated from madewithml import data, train, utils -from madewithml.config import MLFLOW_TRACKING_URI, logger +from madewithml.config import EFS_DIR, MLFLOW_TRACKING_URI, logger # Initialize Typer CLI app app = typer.Typer() @@ -117,10 +118,7 @@ def tune_models( experiment_name=experiment_name, save_artifact=True, ) - run_config = RunConfig( - callbacks=[mlflow_callback], - checkpoint_config=checkpoint_config, - ) + run_config = RunConfig(callbacks=[mlflow_callback], checkpoint_config=checkpoint_config, storage_path=EFS_DIR) # Hyperparameters to start with initial_params = json.loads(initial_params) @@ -178,5 +176,5 @@ def tune_models( if __name__ == "__main__": # pragma: no cover, application if ray.is_initialized(): ray.shutdown() - ray.init() + ray.init(runtime_env={"env_vars": {"GITHUB_USERNAME": os.environ["GITHUB_USERNAME"]}}) app() diff --git a/notebooks/madewithml.ipynb b/notebooks/madewithml.ipynb index b0f40895..04ab696d 100644 --- a/notebooks/madewithml.ipynb +++ b/notebooks/madewithml.ipynb @@ -1,7 +1,6 @@ { "cells": [ { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "acbetMKBt825" @@ -29,7 +28,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "oh-HuNfDrPg0" @@ -39,7 +37,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "XTNsIiUrqoJW" @@ -53,7 +50,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "tags": [] @@ -63,7 +59,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -72,13 +67,15 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 163, "metadata": { "tags": [] }, "outputs": [], "source": [ - "import ray" + "import ray\n", + "from dotenv import load_dotenv; load_dotenv()\n", + "import warnings; warnings.filterwarnings(\"ignore\")" ] }, { @@ -92,17 +89,16 @@ "name": "stderr", "output_type": "stream", "text": [ - "Snapshotting files: 100%|██████████| 4/4 [00:00<00:00, 578.90file/s]\n", - "2023-07-24 18:09:33,959\tINFO worker.py:1431 -- Connecting to existing Ray cluster at address: 10.0.56.150:6379...\n", - "2023-07-24 18:09:34,005\tINFO worker.py:1612 -- Connected to Ray cluster. View the dashboard at \u001b[1m\u001b[32mhttps://session-cqhhlg21by7asj3ryvctbha9ek.i.anyscaleuserdata.com \u001b[39m\u001b[22m\n", - "2023-07-24 18:09:34,010\tINFO packaging.py:346 -- Pushing file package 'gcs://_ray_pkg_6914fbe58df99e42f77368975fb6b629.zip' (1.24MiB) to Ray cluster...\n", - "2023-07-24 18:09:34,014\tINFO packaging.py:359 -- Successfully pushed file package 'gcs://_ray_pkg_6914fbe58df99e42f77368975fb6b629.zip'.\n" + "2023-09-14 15:00:03,106\tINFO worker.py:1431 -- Connecting to existing Ray cluster at address: 10.0.41.50:6379...\n", + "2023-09-14 15:00:03,149\tINFO worker.py:1612 -- Connected to Ray cluster. View the dashboard at \u001b[1m\u001b[32mhttps://session-59p8a57mrjelmhz1ec7m8c7yke.i.anyscaleuserdata.com \u001b[39m\u001b[22m\n", + "2023-09-14 15:00:03,155\tINFO packaging.py:346 -- Pushing file package 'gcs://_ray_pkg_b89128278b4b23728836e7ef293f2994.zip' (1.46MiB) to Ray cluster...\n", + "2023-09-14 15:00:03,159\tINFO packaging.py:359 -- Successfully pushed file package 'gcs://_ray_pkg_b89128278b4b23728836e7ef293f2994.zip'.\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "58d1ddc62cd74bf19166797b77a5da12", + "model_id": "0600f97bbc1944ac9a46fc1edce91293", "version_major": 2, "version_minor": 0 }, @@ -134,7 +130,7 @@ " \n", " \n", " Dashboard:\n", - " http://session-cqhhlg21by7asj3ryvctbha9ek.i.anyscaleuserdata.com\n", + " http://session-59p8a57mrjelmhz1ec7m8c7yke.i.anyscaleuserdata.com\n", "\n", "\n", "\n", @@ -143,7 +139,7 @@ "\n" ], "text/plain": [ - "RayContext(dashboard_url='session-cqhhlg21by7asj3ryvctbha9ek.i.anyscaleuserdata.com', python_version='3.10.8', ray_version='2.6.0', ray_commit='0db82e31e249eac614f7c8e7da1c4f8f05c9064a', protocol_version=None)" + "RayContext(dashboard_url='session-59p8a57mrjelmhz1ec7m8c7yke.i.anyscaleuserdata.com', python_version='3.10.8', ray_version='2.6.0', ray_commit='0db82e31e249eac614f7c8e7da1c4f8f05c9064a', protocol_version=None)" ] }, "execution_count": 2, @@ -168,11 +164,13 @@ { "data": { "text/plain": [ - "{'CPU': 8.0,\n", - " 'object_store_memory': 9492578304.0,\n", - " 'memory': 34359738368.0,\n", + "{'node:10.0.41.50': 1.0,\n", + " 'CPU': 24.0,\n", " 'node:__internal_head__': 1.0,\n", - " 'node:10.0.56.150': 1.0}" + " 'object_store_memory': 28764595813.0,\n", + " 'memory': 103079215104.0,\n", + " 'GPU': 1.0,\n", + " 'node:10.0.50.151': 1.0}" ] }, "execution_count": 3, @@ -185,7 +183,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -206,7 +203,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -219,7 +215,27 @@ ] }, { - "attachments": {}, + "cell_type": "code", + "execution_count": 165, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/efs/shared_storage/madewithml/GokuMohandas\n" + ] + } + ], + "source": [ + "# Storage\n", + "EFS_DIR = f\"/efs/shared_storage/madewithml/{os.environ['GITHUB_USERNAME']}\"\n", + "print (EFS_DIR)" + ] + }, + { "cell_type": "markdown", "metadata": {}, "source": [ @@ -227,7 +243,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "tags": [] @@ -238,7 +253,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": { "tags": [] }, @@ -249,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { "tags": [] }, @@ -343,7 +358,7 @@ "4 A PyTorch Implementation of \"Watch Your Step: ... other " ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -356,7 +371,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "tags": [] @@ -367,7 +381,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": { "tags": [] }, @@ -378,7 +392,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": { "tags": [] }, @@ -394,7 +408,7 @@ "Name: count, dtype: int64" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -406,7 +420,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": { "tags": [] }, @@ -419,7 +433,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": { "tags": [] }, @@ -435,7 +449,7 @@ "Name: count, dtype: int64" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -447,7 +461,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": { "tags": [] }, @@ -463,7 +477,7 @@ "Name: count, dtype: int64" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -474,7 +488,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "WuCrsbxbNkSV" @@ -484,7 +497,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "eOJ3nlEgnSTJ" @@ -495,7 +507,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": { "id": "tHdQmqTBNkSV", "tags": [] @@ -511,7 +523,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": { "tags": [] }, @@ -525,7 +537,7 @@ " ('mlops', 63)]" ] }, - "execution_count": 13, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -538,7 +550,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -571,7 +583,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "pfjVstecaFC5" @@ -582,7 +593,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -605,16 +616,16 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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", + "image/png": "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", "text/plain": [ "
" ] @@ -637,7 +648,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "b8ua3MFhrOaX" @@ -647,7 +657,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "HFifXKl_eKsN" @@ -657,7 +666,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "RxAZ1AmteRaD" @@ -668,7 +676,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": { "tags": [] }, @@ -682,7 +690,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "6VgTwEQboTGc" @@ -692,7 +699,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "U_001GPyMZsC" @@ -703,7 +709,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": { "id": "3x1ldAFQNkSU", "tags": [] @@ -715,7 +721,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -724,7 +729,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -750,7 +755,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": { "id": "VfdWkkV8LlNR", "tags": [] @@ -778,7 +783,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -805,7 +810,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -814,7 +818,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": { "tags": [] }, @@ -883,7 +887,7 @@ "4 attentionwalk pytorch implementation watch ste... other" ] }, - "execution_count": 21, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -897,7 +901,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -905,7 +908,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -914,7 +916,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": { "tags": [] }, @@ -928,7 +930,7 @@ " 'other': 3}" ] }, - "execution_count": 22, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -943,7 +945,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": { "tags": [] }, @@ -1012,7 +1014,7 @@ "4 attentionwalk pytorch implementation watch ste... 3" ] }, - "execution_count": 23, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -1025,7 +1027,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": { "tags": [] }, @@ -1037,7 +1039,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "metadata": { "tags": [] }, @@ -1048,7 +1050,7 @@ "['computer-vision', 'computer-vision', 'other', 'other', 'other']" ] }, - "execution_count": 25, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -1059,7 +1061,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1067,7 +1068,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1076,7 +1076,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 27, "metadata": { "tags": [] }, @@ -1088,7 +1088,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 28, "metadata": { "tags": [] }, @@ -1115,7 +1115,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 29, "metadata": { "tags": [] }, @@ -1129,7 +1129,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 30, "metadata": { "tags": [] }, @@ -1144,7 +1144,7 @@ " 'targets': array([2])}" ] }, - "execution_count": 29, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -1155,7 +1155,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1163,7 +1162,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1172,7 +1170,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 31, "metadata": { "tags": [] }, @@ -1191,7 +1189,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 32, "metadata": { "tags": [] }, @@ -1243,7 +1241,7 @@ " 2, 3, 2, 2, 0, 1, 2, 2, 2, 0, 1, 2, 1, 3, 0, 2, 3])}" ] }, - "execution_count": 31, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -1254,7 +1252,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1262,7 +1259,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -1271,7 +1267,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 33, "metadata": { "tags": [] }, @@ -1285,7 +1281,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 34, "metadata": { "tags": [] }, @@ -1294,11 +1290,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-07-24 18:09:40,715\tINFO read_api.py:374 -- To satisfy the requested parallelism of 16, each read task output will be split into 16 smaller blocks.\n", - "2023-07-24 18:09:40,719\tINFO dataset.py:2180 -- Tip: Use `take_batch()` instead of `take() / show()` to return records in pandas or numpy batch format.\n", - "2023-07-24 18:09:40,720\tINFO streaming_executor.py:92 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadCSV->SplitBlocks(16)] -> AllToAllOperator[RandomShuffle]\n", - "2023-07-24 18:09:40,721\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", - "2023-07-24 18:09:40,722\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + "2023-09-14 15:00:09,809\tINFO read_api.py:374 -- To satisfy the requested parallelism of 48, each read task output will be split into 48 smaller blocks.\n", + "2023-09-14 15:00:09,814\tINFO dataset.py:2180 -- Tip: Use `take_batch()` instead of `take() / show()` to return records in pandas or numpy batch format.\n", + "2023-09-14 15:00:09,816\tINFO streaming_executor.py:92 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadCSV->SplitBlocks(48)] -> AllToAllOperator[RandomShuffle]\n", + "2023-09-14 15:00:09,817\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-09-14 15:00:09,817\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" ] }, { @@ -1309,7 +1305,7 @@ "version_minor": 0 }, "text/plain": [ - "- RandomShuffle 1: 0%| | 0/16 [00:00 TaskPoolMapOperator[ReadCSV->SplitBlocks(16)] -> AllToAllOperator[RandomShuffle] -> LimitOperator[limit=1]\n", - "2023-07-24 18:09:41,438\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", - "2023-07-24 18:09:41,439\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + "2023-09-14 15:00:13,415\tINFO streaming_executor.py:92 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadCSV->SplitBlocks(48)] -> AllToAllOperator[RandomShuffle] -> LimitOperator[limit=1]\n", + "2023-09-14 15:00:13,416\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-09-14 15:00:13,418\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" ] }, { @@ -1403,7 +1399,7 @@ "version_minor": 0 }, "text/plain": [ - "- RandomShuffle 1: 0%| | 0/16 [00:00 TaskPoolMapOperator[ReadCSV->SplitBlocks(16)] -> AllToAllOperator[RandomShuffle] -> AllToAllOperator[Sort] -> AllToAllOperator[MapBatches(group_fn)->MapBatches(_filter_split)->RandomShuffle] -> LimitOperator[limit=1]\n", - "2023-07-24 18:09:45,960\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", - "2023-07-24 18:09:45,961\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + "2023-09-14 15:00:15,649\tINFO streaming_executor.py:92 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadCSV->SplitBlocks(48)] -> AllToAllOperator[RandomShuffle] -> AllToAllOperator[Sort] -> AllToAllOperator[MapBatches(group_fn)->MapBatches(_filter_split)->RandomShuffle] -> LimitOperator[limit=1]\n", + "2023-09-14 15:00:15,650\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-09-14 15:00:15,650\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" ] }, { @@ -1482,7 +1478,7 @@ "version_minor": 0 }, "text/plain": [ - "- RandomShuffle 1: 0%| | 0/16 [00:00MapBatches(_filter_split)->RandomShuffle 8: 0%| | 0/16 [00:00MapBatches(_filter_split)->RandomShuffle 8: 0%| | 0/48 [00:00 TaskPoolMapOperator[ReadCSV->SplitBlocks(16)] -> AllToAllOperator[RandomShuffle] -> AllToAllOperator[Sort] -> AllToAllOperator[MapBatches(group_fn)->MapBatches(_filter_split)->RandomShuffle] -> AllToAllOperator[Aggregate] -> TaskPoolMapOperator[MapBatches()]\n", - "2023-07-24 18:09:47,955\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", - "2023-07-24 18:09:47,957\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + "2023-09-14 15:00:17,755\tINFO streaming_executor.py:92 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadCSV->SplitBlocks(48)] -> AllToAllOperator[RandomShuffle] -> AllToAllOperator[Sort] -> AllToAllOperator[MapBatches(group_fn)->MapBatches(_filter_split)->RandomShuffle] -> AllToAllOperator[Aggregate] -> TaskPoolMapOperator[MapBatches()]\n", + "2023-09-14 15:00:17,756\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-09-14 15:00:17,757\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" ] }, { @@ -1659,7 +1655,7 @@ "version_minor": 0 }, "text/plain": [ - "- RandomShuffle 1: 0%| | 0/16 [00:00MapBatches(_filter_split)->RandomShuffle 8: 0%| | 0/16 [00:00MapBatches(_filter_split)->RandomShuffle 8: 0%| | 0/48 [00:00 TaskPoolMapOperator[ReadCSV->SplitBlocks(16)] -> AllToAllOperator[RandomShuffle] -> AllToAllOperator[Sort] -> AllToAllOperator[MapBatches(group_fn)->MapBatches(_filter_split)->RandomShuffle] -> TaskPoolMapOperator[MapBatches(preprocess)]\n", - "2023-07-24 18:09:48,802\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", - "2023-07-24 18:09:48,802\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + "2023-09-14 15:00:19,882\tINFO streaming_executor.py:92 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadCSV->SplitBlocks(48)] -> AllToAllOperator[RandomShuffle] -> AllToAllOperator[Sort] -> AllToAllOperator[MapBatches(group_fn)->MapBatches(_filter_split)->RandomShuffle] -> TaskPoolMapOperator[MapBatches(preprocess)]\n", + "2023-09-14 15:00:19,882\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-09-14 15:00:19,883\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" ] }, { @@ -1904,7 +1902,7 @@ "version_minor": 0 }, "text/plain": [ - "- RandomShuffle 1: 0%| | 0/16 [00:00MapBatches(_filter_split)->RandomShuffle 8: 0%| | 0/16 [00:00MapBatches(_filter_split)->RandomShuffle 8: 0%| | 0/48 [00:00 TaskPoolMapOperator[ReadCSV->SplitBlocks(16)] -> AllToAllOperator[RandomShuffle] -> AllToAllOperator[Sort] -> AllToAllOperator[MapBatches(group_fn)->MapBatches(_filter_split)->RandomShuffle] -> TaskPoolMapOperator[MapBatches(preprocess)]\n", - "2023-07-24 18:09:52,026\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", - "2023-07-24 18:09:52,026\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + "2023-09-14 15:00:23,986\tINFO streaming_executor.py:92 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadCSV->SplitBlocks(48)] -> AllToAllOperator[RandomShuffle] -> AllToAllOperator[Sort] -> AllToAllOperator[MapBatches(group_fn)->MapBatches(_filter_split)->RandomShuffle] -> TaskPoolMapOperator[MapBatches(preprocess)]\n", + "2023-09-14 15:00:23,987\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-09-14 15:00:23,988\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" ] }, { @@ -2460,7 +2451,7 @@ "version_minor": 0 }, "text/plain": [ - "- RandomShuffle 1: 0%| | 0/16 [00:00MapBatches(_filter_split)->RandomShuffle 8: 0%| | 0/16 [00:00MapBatches(_filter_split)->RandomShuffle 8: 0%| | 0/48 [00:00 TaskPoolMapOperator[ReadCSV->SplitBlocks(16)] -> AllToAllOperator[RandomShuffle] -> LimitOperator[limit=1]\n", - "2023-07-24 18:09:53,142\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", - "2023-07-24 18:09:53,143\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + "2023-09-14 15:00:26,135\tINFO read_api.py:374 -- To satisfy the requested parallelism of 48, each read task output will be split into 48 smaller blocks.\n", + "2023-09-14 15:00:26,138\tINFO streaming_executor.py:92 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadCSV->SplitBlocks(48)] -> AllToAllOperator[RandomShuffle] -> LimitOperator[limit=1]\n", + "2023-09-14 15:00:26,139\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-09-14 15:00:26,139\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" ] }, { @@ -2889,7 +2876,7 @@ "version_minor": 0 }, "text/plain": [ - "- RandomShuffle 1: 0%| | 0/16 [00:00 TaskPoolMapOperator[ReadCSV->SplitBlocks(16)] -> AllToAllOperator[RandomShuffle] -> AllToAllOperator[Sort] -> AllToAllOperator[MapBatches(group_fn)->MapBatches(_filter_split)->RandomShuffle] -> LimitOperator[limit=1]\n", - "2023-07-24 18:09:53,361\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", - "2023-07-24 18:09:53,362\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + "2023-09-14 15:00:26,512\tINFO streaming_executor.py:92 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadCSV->SplitBlocks(48)] -> AllToAllOperator[RandomShuffle] -> AllToAllOperator[Sort] -> AllToAllOperator[MapBatches(group_fn)->MapBatches(_filter_split)->RandomShuffle] -> LimitOperator[limit=1]\n", + "2023-09-14 15:00:26,513\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-09-14 15:00:26,513\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" ] }, { @@ -2969,7 +2955,7 @@ "version_minor": 0 }, "text/plain": [ - "- RandomShuffle 1: 0%| | 0/16 [00:00MapBatches(_filter_split)->RandomShuffle 8: 0%| | 0/16 [00:00MapBatches(_filter_split)->RandomShuffle 8: 0%| | 0/48 [00:00 TaskPoolMapOperator[ReadCSV->SplitBlocks(16)] -> AllToAllOperator[RandomShuffle] -> AllToAllOperator[Sort] -> AllToAllOperator[MapBatches(group_fn)->MapBatches(_filter_split)->RandomShuffle] -> AllToAllOperator[Aggregate] -> TaskPoolMapOperator[MapBatches()]\n", - "2023-07-24 18:09:53,953\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", - "2023-07-24 18:09:53,953\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + "2023-09-14 15:00:27,771\tINFO streaming_executor.py:92 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadCSV->SplitBlocks(48)] -> AllToAllOperator[RandomShuffle] -> AllToAllOperator[Sort] -> AllToAllOperator[MapBatches(group_fn)->MapBatches(_filter_split)->RandomShuffle] -> AllToAllOperator[Aggregate] -> TaskPoolMapOperator[MapBatches()]\n", + "2023-09-14 15:00:27,772\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-09-14 15:00:27,773\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" ] }, { @@ -3146,7 +3132,7 @@ "version_minor": 0 }, "text/plain": [ - "- RandomShuffle 1: 0%| | 0/16 [00:00MapBatches(_filter_split)->RandomShuffle 8: 0%| | 0/16 [00:00MapBatches(_filter_split)->RandomShuffle 8: 0%| | 0/48 [00:00 TaskPoolMapOperator[ReadCSV->SplitBlocks(16)] -> AllToAllOperator[RandomShuffle] -> AllToAllOperator[Sort] -> AllToAllOperator[MapBatches(group_fn)->MapBatches(_filter_split)->RandomShuffle] -> TaskPoolMapOperator[MapBatches(CustomPreprocessor._transform_pandas)]\n", - "2023-07-24 18:09:54,693\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", - "2023-07-24 18:09:54,693\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + "2023-09-14 15:00:29,414\tINFO streaming_executor.py:92 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadCSV->SplitBlocks(48)] -> AllToAllOperator[RandomShuffle] -> AllToAllOperator[Sort] -> AllToAllOperator[MapBatches(group_fn)->MapBatches(_filter_split)->RandomShuffle] -> TaskPoolMapOperator[MapBatches(CustomPreprocessor._transform_pandas)]\n", + "2023-09-14 15:00:29,415\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-09-14 15:00:29,416\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" ] }, { @@ -3379,7 +3365,7 @@ "version_minor": 0 }, "text/plain": [ - "- RandomShuffle 1: 0%| | 0/16 [00:00MapBatches(_filter_split)->RandomShuffle 8: 0%| | 0/16 [00:00MapBatches(_filter_split)->RandomShuffle 8: 0%| | 0/48 [00:00 TaskPoolMapOperator[ReadCSV->SplitBlocks(16)] -> AllToAllOperator[RandomShuffle] -> AllToAllOperator[Sort] -> AllToAllOperator[MapBatches(group_fn)->MapBatches(_filter_split)->RandomShuffle] -> TaskPoolMapOperator[MapBatches(CustomPreprocessor._transform_pandas)]\n", - "2023-07-24 18:09:55,680\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", - "2023-07-24 18:09:55,681\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + "2023-09-14 15:00:31,130\tINFO streaming_executor.py:92 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadCSV->SplitBlocks(48)] -> AllToAllOperator[RandomShuffle] -> AllToAllOperator[Sort] -> AllToAllOperator[MapBatches(group_fn)->MapBatches(_filter_split)->RandomShuffle] -> TaskPoolMapOperator[MapBatches(CustomPreprocessor._transform_pandas)]\n", + "2023-09-14 15:00:31,130\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-09-14 15:00:31,131\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" ] }, { @@ -3556,7 +3542,7 @@ "version_minor": 0 }, "text/plain": [ - "- RandomShuffle 1: 0%| | 0/16 [00:00MapBatches(_filter_split)->RandomShuffle 8: 0%| | 0/16 [00:00MapBatches(_filter_split)->RandomShuffle 8: 0%| | 0/48 [00:00Tune Status\n", " \n", "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "
Current time:2023-07-24 18:11:25
Running for: 00:01:29.04
Memory: 7.6/30.9 GiB
Current time:2023-09-14 15:02:25
Running for: 00:01:52.74
Memory: 6.4/30.9 GiB
\n", " \n", "
\n", "
\n", "

System Info

\n", - " Using FIFO scheduling algorithm.
Logical resource usage: 4.0/12 CPUs, 1.0/1 GPUs\n", + " Using FIFO scheduling algorithm.
Logical resource usage: 4.0/24 CPUs, 1.0/1 GPUs\n", "
\n", " \n", " \n", @@ -3804,10 +3790,10 @@ "

Trial Status

\n", " \n", "\n", - "\n", + "\n", "\n", "\n", - "\n", + "\n", "\n", "
Trial name status loc iter total time (s) epoch lr train_loss
Trial name status loc iter total time (s) epoch lr train_loss
TorchTrainer_f80f6_00000TERMINATED10.0.56.150:281250 10 77.6459 90.0001 0.0364764
TorchTrainer_2044b_00000TERMINATED10.0.50.151:49611 10 106.8 98e-05 0.0400874
\n", " \n", @@ -3850,48 +3836,40 @@ "metadata": {}, "output_type": "display_data" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(autoscaler +30s) Tip: use `ray status` to view detailed cluster status. To disable these messages, set RAY_SCHEDULER_EVENTS=0.\n", - "(autoscaler +30s) Resized to 12 CPUs, 1 GPUs.\n" - ] - }, { "name": "stderr", "output_type": "stream", "text": [ - "(TorchTrainer pid=281250) The dict form of `dataset_config` is deprecated. Use the DataConfig class instead. Support for this will be dropped in a future release.\n", - "(TorchTrainer pid=281250) The `preprocessor` arg to Trainer is deprecated. Apply preprocessor transformations ahead of time by calling `preprocessor.transform(ds)`. Support for the preprocessor arg will be dropped in a future release.\n", - "(TorchTrainer pid=281250) Starting distributed worker processes: ['2349 (10.0.6.57)']\n", - "(RayTrainWorker pid=2349, ip=10.0.6.57) Setting up process group for: env:// [rank=0, world_size=1]\n", - "Downloading (…)lve/main/config.json: 100%|██████████| 385/385 [00:00<00:00, 2.83MB/s]\n", - "Downloading pytorch_model.bin: 0%| | 0.00/442M [00:00\n", " 0\n", " 0\n", - " 0.0001\n", - " 0.578165\n", - " 0.492538\n", - " 1690247421\n", - " 19.374968\n", + " 0.00010\n", + " 0.579703\n", + " 0.498519\n", + " 1694728846\n", + " 16.904209\n", " True\n", " False\n", " 1\n", - " f80f6_00000\n", - " 2023-07-24_18-10-25\n", - " 19.374968\n", - " 281250\n", - " ip-10-0-56-150\n", - " 10.0.56.150\n", - " 19.374968\n", + " 2044b_00000\n", + " 2023-09-14_15-00-53\n", + " 16.904209\n", + " 49611\n", + " ip-10-0-50-151\n", + " 10.0.50.151\n", + " 16.904209\n", " 1\n", " \n", " \n", " 1\n", " 1\n", - " 0.0001\n", - " 0.486276\n", - " 0.419530\n", - " 1690247428\n", - " 6.751568\n", + " 0.00010\n", + " 0.480723\n", + " 0.407913\n", + " 1694728856\n", + " 10.286396\n", " True\n", " False\n", " 2\n", - " f80f6_00000\n", - " 2023-07-24_18-10-31\n", - " 26.126536\n", - " 281250\n", - " ip-10-0-56-150\n", - " 10.0.56.150\n", - " 26.126536\n", + " 2044b_00000\n", + " 2023-09-14_15-01-03\n", + " 27.190605\n", + " 49611\n", + " ip-10-0-50-151\n", + " 10.0.50.151\n", + " 27.190605\n", " 2\n", " \n", " \n", " 2\n", " 2\n", - " 0.0001\n", - " 0.398447\n", - " 0.317161\n", - " 1690247435\n", - " 6.416867\n", + " 0.00010\n", + " 0.402693\n", + " 0.324105\n", + " 1694728867\n", + " 9.887647\n", " True\n", " False\n", " 3\n", - " f80f6_00000\n", - " 2023-07-24_18-10-38\n", - " 32.543403\n", - " 281250\n", - " ip-10-0-56-150\n", - " 10.0.56.150\n", - " 32.543403\n", + " 2044b_00000\n", + " 2023-09-14_15-01-13\n", + " 37.078252\n", + " 49611\n", + " ip-10-0-50-151\n", + " 10.0.50.151\n", + " 37.078252\n", " 3\n", " \n", " \n", " 3\n", " 3\n", - " 0.0001\n", - " 0.286960\n", - " 0.234889\n", - " 1690247441\n", - " 6.434473\n", + " 0.00010\n", + " 0.278217\n", + " 0.256301\n", + " 1694728877\n", + " 9.893445\n", " True\n", " False\n", " 4\n", - " f80f6_00000\n", - " 2023-07-24_18-10-44\n", - " 38.977876\n", - " 281250\n", - " ip-10-0-56-150\n", - " 10.0.56.150\n", - " 38.977876\n", + " 2044b_00000\n", + " 2023-09-14_15-01-23\n", + " 46.971696\n", + " 49611\n", + " ip-10-0-50-151\n", + " 10.0.50.151\n", + " 46.971696\n", " 4\n", " \n", " \n", " 4\n", " 4\n", - " 0.0001\n", - " 0.208955\n", - " 0.199119\n", - " 1690247448\n", - " 6.407677\n", + " 0.00010\n", + " 0.203579\n", + " 0.249053\n", + " 1694728887\n", + " 9.843567\n", " True\n", " False\n", " 5\n", - " f80f6_00000\n", - " 2023-07-24_18-10-51\n", - " 45.385553\n", - " 281250\n", - " ip-10-0-56-150\n", - " 10.0.56.150\n", - " 45.385553\n", + " 2044b_00000\n", + " 2023-09-14_15-01-33\n", + " 56.815263\n", + " 49611\n", + " ip-10-0-50-151\n", + " 10.0.50.151\n", + " 56.815263\n", " 5\n", " \n", " \n", " 5\n", " 5\n", - " 0.0001\n", - " 0.141784\n", - " 0.161738\n", - " 1690247454\n", - " 6.420556\n", + " 0.00010\n", + " 0.152601\n", + " 0.180287\n", + " 1694728896\n", + " 10.026406\n", " True\n", " False\n", " 6\n", - " f80f6_00000\n", - " 2023-07-24_18-10-57\n", - " 51.806109\n", - " 281250\n", - " ip-10-0-56-150\n", - " 10.0.56.150\n", - " 51.806109\n", + " 2044b_00000\n", + " 2023-09-14_15-01-43\n", + " 66.841670\n", + " 49611\n", + " ip-10-0-50-151\n", + " 10.0.50.151\n", + " 66.841670\n", " 6\n", " \n", " \n", " 6\n", " 6\n", - " 0.0001\n", - " 0.098122\n", - " 0.152620\n", - " 1690247460\n", - " 6.416981\n", + " 0.00010\n", + " 0.105872\n", + " 0.184052\n", + " 1694728906\n", + " 9.971822\n", " True\n", " False\n", " 7\n", - " f80f6_00000\n", - " 2023-07-24_18-11-03\n", - " 58.223091\n", - " 281250\n", - " ip-10-0-56-150\n", - " 10.0.56.150\n", - " 58.223091\n", + " 2044b_00000\n", + " 2023-09-14_15-01-53\n", + " 76.813491\n", + " 49611\n", + " ip-10-0-50-151\n", + " 10.0.50.151\n", + " 76.813491\n", " 7\n", " \n", " \n", " 7\n", " 7\n", - " 0.0001\n", - " 0.069849\n", - " 0.133828\n", - " 1690247467\n", - " 6.472243\n", + " 0.00010\n", + " 0.074756\n", + " 0.184786\n", + " 1694728916\n", + " 9.988855\n", " True\n", " False\n", " 8\n", - " f80f6_00000\n", - " 2023-07-24_18-11-10\n", - " 64.695333\n", - " 281250\n", - " ip-10-0-56-150\n", - " 10.0.56.150\n", - " 64.695333\n", + " 2044b_00000\n", + " 2023-09-14_15-02-03\n", + " 86.802346\n", + " 49611\n", + " ip-10-0-50-151\n", + " 10.0.50.151\n", + " 86.802346\n", " 8\n", " \n", " \n", " 8\n", " 8\n", - " 0.0001\n", - " 0.046368\n", - " 0.135197\n", - " 1690247473\n", - " 6.461530\n", + " 0.00010\n", + " 0.055098\n", + " 0.201344\n", + " 1694728926\n", + " 9.961005\n", " True\n", " False\n", " 9\n", - " f80f6_00000\n", - " 2023-07-24_18-11-16\n", - " 71.156864\n", - " 281250\n", - " ip-10-0-56-150\n", - " 10.0.56.150\n", - " 71.156864\n", + " 2044b_00000\n", + " 2023-09-14_15-02-13\n", + " 96.763352\n", + " 49611\n", + " ip-10-0-50-151\n", + " 10.0.50.151\n", + " 96.763352\n", " 9\n", " \n", " \n", " 9\n", " 9\n", - " 0.0001\n", - " 0.036476\n", - " 0.123047\n", - " 1690247480\n", - " 6.489086\n", + " 0.00008\n", + " 0.040087\n", + " 0.210514\n", + " 1694728936\n", + " 10.036337\n", " True\n", " False\n", " 10\n", - " f80f6_00000\n", - " 2023-07-24_18-11-23\n", - " 77.645949\n", - " 281250\n", - " ip-10-0-56-150\n", - " 10.0.56.150\n", - " 77.645949\n", + " 2044b_00000\n", + " 2023-09-14_15-02-23\n", + " 106.799689\n", + " 49611\n", + " ip-10-0-50-151\n", + " 10.0.50.151\n", + " 106.799689\n", " 10\n", " \n", " \n", @@ -4154,56 +4132,56 @@ "" ], "text/plain": [ - " epoch lr train_loss val_loss timestamp time_this_iter_s \n", - "0 0 0.0001 0.578165 0.492538 1690247421 19.374968 \\\n", - "1 1 0.0001 0.486276 0.419530 1690247428 6.751568 \n", - "2 2 0.0001 0.398447 0.317161 1690247435 6.416867 \n", - "3 3 0.0001 0.286960 0.234889 1690247441 6.434473 \n", - "4 4 0.0001 0.208955 0.199119 1690247448 6.407677 \n", - "5 5 0.0001 0.141784 0.161738 1690247454 6.420556 \n", - "6 6 0.0001 0.098122 0.152620 1690247460 6.416981 \n", - "7 7 0.0001 0.069849 0.133828 1690247467 6.472243 \n", - "8 8 0.0001 0.046368 0.135197 1690247473 6.461530 \n", - "9 9 0.0001 0.036476 0.123047 1690247480 6.489086 \n", + " epoch lr train_loss val_loss timestamp time_this_iter_s \n", + "0 0 0.00010 0.579703 0.498519 1694728846 16.904209 \\\n", + "1 1 0.00010 0.480723 0.407913 1694728856 10.286396 \n", + "2 2 0.00010 0.402693 0.324105 1694728867 9.887647 \n", + "3 3 0.00010 0.278217 0.256301 1694728877 9.893445 \n", + "4 4 0.00010 0.203579 0.249053 1694728887 9.843567 \n", + "5 5 0.00010 0.152601 0.180287 1694728896 10.026406 \n", + "6 6 0.00010 0.105872 0.184052 1694728906 9.971822 \n", + "7 7 0.00010 0.074756 0.184786 1694728916 9.988855 \n", + "8 8 0.00010 0.055098 0.201344 1694728926 9.961005 \n", + "9 9 0.00008 0.040087 0.210514 1694728936 10.036337 \n", "\n", " should_checkpoint done training_iteration trial_id \n", - "0 True False 1 f80f6_00000 \\\n", - "1 True False 2 f80f6_00000 \n", - "2 True False 3 f80f6_00000 \n", - "3 True False 4 f80f6_00000 \n", - "4 True False 5 f80f6_00000 \n", - "5 True False 6 f80f6_00000 \n", - "6 True False 7 f80f6_00000 \n", - "7 True False 8 f80f6_00000 \n", - "8 True False 9 f80f6_00000 \n", - "9 True False 10 f80f6_00000 \n", - "\n", - " date time_total_s pid hostname node_ip \n", - "0 2023-07-24_18-10-25 19.374968 281250 ip-10-0-56-150 10.0.56.150 \\\n", - "1 2023-07-24_18-10-31 26.126536 281250 ip-10-0-56-150 10.0.56.150 \n", - "2 2023-07-24_18-10-38 32.543403 281250 ip-10-0-56-150 10.0.56.150 \n", - "3 2023-07-24_18-10-44 38.977876 281250 ip-10-0-56-150 10.0.56.150 \n", - "4 2023-07-24_18-10-51 45.385553 281250 ip-10-0-56-150 10.0.56.150 \n", - "5 2023-07-24_18-10-57 51.806109 281250 ip-10-0-56-150 10.0.56.150 \n", - "6 2023-07-24_18-11-03 58.223091 281250 ip-10-0-56-150 10.0.56.150 \n", - "7 2023-07-24_18-11-10 64.695333 281250 ip-10-0-56-150 10.0.56.150 \n", - "8 2023-07-24_18-11-16 71.156864 281250 ip-10-0-56-150 10.0.56.150 \n", - "9 2023-07-24_18-11-23 77.645949 281250 ip-10-0-56-150 10.0.56.150 \n", + "0 True False 1 2044b_00000 \\\n", + "1 True False 2 2044b_00000 \n", + "2 True False 3 2044b_00000 \n", + "3 True False 4 2044b_00000 \n", + "4 True False 5 2044b_00000 \n", + "5 True False 6 2044b_00000 \n", + "6 True False 7 2044b_00000 \n", + "7 True False 8 2044b_00000 \n", + "8 True False 9 2044b_00000 \n", + "9 True False 10 2044b_00000 \n", + "\n", + " date time_total_s pid hostname node_ip \n", + "0 2023-09-14_15-00-53 16.904209 49611 ip-10-0-50-151 10.0.50.151 \\\n", + "1 2023-09-14_15-01-03 27.190605 49611 ip-10-0-50-151 10.0.50.151 \n", + "2 2023-09-14_15-01-13 37.078252 49611 ip-10-0-50-151 10.0.50.151 \n", + "3 2023-09-14_15-01-23 46.971696 49611 ip-10-0-50-151 10.0.50.151 \n", + "4 2023-09-14_15-01-33 56.815263 49611 ip-10-0-50-151 10.0.50.151 \n", + "5 2023-09-14_15-01-43 66.841670 49611 ip-10-0-50-151 10.0.50.151 \n", + "6 2023-09-14_15-01-53 76.813491 49611 ip-10-0-50-151 10.0.50.151 \n", + "7 2023-09-14_15-02-03 86.802346 49611 ip-10-0-50-151 10.0.50.151 \n", + "8 2023-09-14_15-02-13 96.763352 49611 ip-10-0-50-151 10.0.50.151 \n", + "9 2023-09-14_15-02-23 106.799689 49611 ip-10-0-50-151 10.0.50.151 \n", "\n", " time_since_restore iterations_since_restore \n", - "0 19.374968 1 \n", - "1 26.126536 2 \n", - "2 32.543403 3 \n", - "3 38.977876 4 \n", - "4 45.385553 5 \n", - "5 51.806109 6 \n", - "6 58.223091 7 \n", - "7 64.695333 8 \n", - "8 71.156864 9 \n", - "9 77.645949 10 " + "0 16.904209 1 \n", + "1 27.190605 2 \n", + "2 37.078252 3 \n", + "3 46.971696 4 \n", + "4 56.815263 5 \n", + "5 66.841670 6 \n", + "6 76.813491 7 \n", + "7 86.802346 8 \n", + "8 96.763352 9 \n", + "9 106.799689 10 " ] }, - "execution_count": 62, + "execution_count": 63, "metadata": {}, "output_type": "execute_result" } @@ -4215,7 +4193,7 @@ }, { "cell_type": "code", - "execution_count": 63, + "execution_count": 64, "metadata": { "tags": [] }, @@ -4223,22 +4201,22 @@ { "data": { "text/plain": [ - "[(TorchCheckpoint(local_path=/home/ray/ray_results/llm/TorchTrainer_f80f6_00000_0_2023-07-24_18-09-56/checkpoint_000009),\n", - " {'epoch': 9,\n", + "[(TorchCheckpoint(local_path=/efs/shared_storage/madewithml/GokuMohandas/llm/TorchTrainer_2044b_00000_0_2023-09-14_15-00-33/checkpoint_000005),\n", + " {'epoch': 5,\n", " 'lr': 0.0001,\n", - " 'train_loss': 0.03647640720009804,\n", - " 'val_loss': 0.12304694950580597,\n", - " 'timestamp': 1690247480,\n", - " 'time_this_iter_s': 6.489085674285889,\n", + " 'train_loss': 0.152601088086764,\n", + " 'val_loss': 0.1802874505519867,\n", + " 'timestamp': 1694728896,\n", + " 'time_this_iter_s': 10.026406288146973,\n", " 'should_checkpoint': True,\n", - " 'done': True,\n", - " 'training_iteration': 10,\n", - " 'trial_id': 'f80f6_00000',\n", - " 'date': '2023-07-24_18-11-23',\n", - " 'time_total_s': 77.6459493637085,\n", - " 'pid': 281250,\n", - " 'hostname': 'ip-10-0-56-150',\n", - " 'node_ip': '10.0.56.150',\n", + " 'done': False,\n", + " 'training_iteration': 6,\n", + " 'trial_id': '2044b_00000',\n", + " 'date': '2023-09-14_15-01-43',\n", + " 'time_total_s': 66.84166955947876,\n", + " 'pid': 49611,\n", + " 'hostname': 'ip-10-0-50-151',\n", + " 'node_ip': '10.0.50.151',\n", " 'config': {'train_loop_config': {'dropout_p': 0.5,\n", " 'lr': 0.0001,\n", " 'lr_factor': 0.8,\n", @@ -4246,12 +4224,12 @@ " 'num_epochs': 10,\n", " 'batch_size': 256,\n", " 'num_classes': 4}},\n", - " 'time_since_restore': 77.6459493637085,\n", - " 'iterations_since_restore': 10,\n", + " 'time_since_restore': 66.84166955947876,\n", + " 'iterations_since_restore': 6,\n", " 'experiment_tag': '0'})]" ] }, - "execution_count": 63, + "execution_count": 64, "metadata": {}, "output_type": "execute_result" } @@ -4262,7 +4240,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -4271,7 +4248,7 @@ }, { "cell_type": "code", - "execution_count": 64, + "execution_count": 65, "metadata": { "tags": [] }, @@ -4283,7 +4260,7 @@ }, { "cell_type": "code", - "execution_count": 65, + "execution_count": 66, "metadata": { "tags": [] }, @@ -4297,7 +4274,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 67, "metadata": { "tags": [] }, @@ -4306,10 +4283,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-07-24 18:11:26,923\tINFO read_api.py:374 -- To satisfy the requested parallelism of 24, each read task output will be split into 24 smaller blocks.\n", - "2023-07-24 18:11:26,927\tINFO streaming_executor.py:92 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadCSV->SplitBlocks(24)] -> TaskPoolMapOperator[MapBatches(CustomPreprocessor._transform_pandas)]\n", - "2023-07-24 18:11:26,927\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", - "2023-07-24 18:11:26,928\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + "2023-09-14 15:02:31,960\tINFO read_api.py:374 -- To satisfy the requested parallelism of 48, each read task output will be split into 48 smaller blocks.\n", + "2023-09-14 15:02:31,963\tINFO streaming_executor.py:92 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadCSV->SplitBlocks(48)] -> TaskPoolMapOperator[MapBatches(CustomPreprocessor._transform_pandas)]\n", + "2023-09-14 15:02:31,964\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-09-14 15:02:31,965\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" ] }, { @@ -4320,7 +4297,7 @@ "version_minor": 0 }, "text/plain": [ - "Running 0: 0%| | 0/24 [00:00 TaskPoolMapOperator[ReadCSV->SplitBlocks(24)] -> TaskPoolMapOperator[MapBatches(CustomPreprocessor._transform_pandas)->MapBatches()]\n", - "2023-07-24 18:11:27,397\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", - "2023-07-24 18:11:27,397\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + "2023-09-14 15:02:32,386\tINFO streaming_executor.py:92 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadCSV->SplitBlocks(48)] -> TaskPoolMapOperator[MapBatches(CustomPreprocessor._transform_pandas)->MapBatches()]\n", + "2023-09-14 15:02:32,387\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-09-14 15:02:32,388\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" ] }, { @@ -4373,19 +4349,12 @@ "version_minor": 0 }, "text/plain": [ - "Running 0: 0%| | 0/24 [00:00 TaskPoolMapOperator[ReadCSV->SplitBlocks(24)] -> TaskPoolMapOperator[MapBatches(CustomPreprocessor._transform_pandas)->MapBatches()]\n", - "2023-07-24 18:11:36,377\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", - "2023-07-24 18:11:36,379\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + "2023-09-14 15:02:36,046\tINFO streaming_executor.py:92 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadCSV->SplitBlocks(48)] -> TaskPoolMapOperator[MapBatches(CustomPreprocessor._transform_pandas)->MapBatches()]\n", + "2023-09-14 15:02:36,047\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-09-14 15:02:36,047\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" ] }, { @@ -4536,7 +4505,7 @@ "version_minor": 0 }, "text/plain": [ - "Running 0: 0%| | 0/24 [00:00 TaskPoolMapOperator[ReadCSV->SplitBlocks(24)] -> AllToAllOperator[RandomShuffle] -> LimitOperator[limit=1]\n", - "2023-07-24 18:11:40,817\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", - "2023-07-24 18:11:40,818\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + "2023-09-14 15:02:39,948\tINFO read_api.py:374 -- To satisfy the requested parallelism of 48, each read task output will be split into 48 smaller blocks.\n", + "2023-09-14 15:02:39,951\tINFO streaming_executor.py:92 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadCSV->SplitBlocks(48)] -> AllToAllOperator[RandomShuffle] -> LimitOperator[limit=1]\n", + "2023-09-14 15:02:39,952\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-09-14 15:02:39,952\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" ] }, { @@ -4773,7 +4740,7 @@ "version_minor": 0 }, "text/plain": [ - "- RandomShuffle 1: 0%| | 0/24 [00:00 TaskPoolMapOperator[ReadCSV->SplitBlocks(24)] -> AllToAllOperator[RandomShuffle] -> AllToAllOperator[Sort] -> AllToAllOperator[MapBatches(group_fn)->MapBatches(_filter_split)->RandomShuffle] -> LimitOperator[limit=1]\n", - "2023-07-24 18:11:41,067\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", - "2023-07-24 18:11:41,068\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + "2023-09-14 15:02:40,402\tINFO streaming_executor.py:92 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadCSV->SplitBlocks(48)] -> AllToAllOperator[RandomShuffle] -> AllToAllOperator[Sort] -> AllToAllOperator[MapBatches(group_fn)->MapBatches(_filter_split)->RandomShuffle] -> LimitOperator[limit=1]\n", + "2023-09-14 15:02:40,403\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-09-14 15:02:40,405\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" ] }, { @@ -4852,7 +4819,7 @@ "version_minor": 0 }, "text/plain": [ - "- RandomShuffle 1: 0%| | 0/24 [00:00MapBatches(_filter_split)->RandomShuffle 8: 0%| | 0/24 [00:00MapBatches(_filter_split)->RandomShuffle 8: 0%| | 0/48 [00:00 TaskPoolMapOperator[ReadCSV->SplitBlocks(24)] -> AllToAllOperator[RandomShuffle] -> AllToAllOperator[Sort] -> AllToAllOperator[MapBatches(group_fn)->MapBatches(_filter_split)->RandomShuffle] -> AllToAllOperator[Aggregate] -> TaskPoolMapOperator[MapBatches()]\n", - "2023-07-24 18:11:41,891\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", - "2023-07-24 18:11:41,892\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + "2023-09-14 15:02:41,749\tINFO streaming_executor.py:92 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadCSV->SplitBlocks(48)] -> AllToAllOperator[RandomShuffle] -> AllToAllOperator[Sort] -> AllToAllOperator[MapBatches(group_fn)->MapBatches(_filter_split)->RandomShuffle] -> AllToAllOperator[Aggregate] -> TaskPoolMapOperator[MapBatches()]\n", + "2023-09-14 15:02:41,750\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-09-14 15:02:41,751\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" ] }, { @@ -5029,7 +4996,7 @@ "version_minor": 0 }, "text/plain": [ - "- RandomShuffle 1: 0%| | 0/24 [00:00MapBatches(_filter_split)->RandomShuffle 8: 0%| | 0/24 [00:00MapBatches(_filter_split)->RandomShuffle 8: 0%| | 0/48 [00:00 TaskPoolMapOperator[ReadCSV->SplitBlocks(24)] -> AllToAllOperator[RandomShuffle] -> AllToAllOperator[Sort] -> AllToAllOperator[MapBatches(group_fn)->MapBatches(_filter_split)->RandomShuffle] -> TaskPoolMapOperator[MapBatches(CustomPreprocessor._transform_pandas)]\n", - "2023-07-24 18:11:42,979\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", - "2023-07-24 18:11:42,982\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + "2023-09-14 15:02:43,296\tINFO streaming_executor.py:92 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadCSV->SplitBlocks(48)] -> AllToAllOperator[RandomShuffle] -> AllToAllOperator[Sort] -> AllToAllOperator[MapBatches(group_fn)->MapBatches(_filter_split)->RandomShuffle] -> TaskPoolMapOperator[MapBatches(CustomPreprocessor._transform_pandas)]\n", + "2023-09-14 15:02:43,297\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-09-14 15:02:43,297\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" ] }, { @@ -5262,7 +5229,7 @@ "version_minor": 0 }, "text/plain": [ - "- RandomShuffle 1: 0%| | 0/24 [00:00MapBatches(_filter_split)->RandomShuffle 8: 0%| | 0/24 [00:00MapBatches(_filter_split)->RandomShuffle 8: 0%| | 0/48 [00:00 TaskPoolMapOperator[ReadCSV->SplitBlocks(24)] -> AllToAllOperator[RandomShuffle] -> AllToAllOperator[Sort] -> AllToAllOperator[MapBatches(group_fn)->MapBatches(_filter_split)->RandomShuffle] -> TaskPoolMapOperator[MapBatches(CustomPreprocessor._transform_pandas)]\n", - "2023-07-24 18:11:44,083\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", - "2023-07-24 18:11:44,083\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + "2023-09-14 15:02:45,162\tINFO streaming_executor.py:92 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadCSV->SplitBlocks(48)] -> AllToAllOperator[RandomShuffle] -> AllToAllOperator[Sort] -> AllToAllOperator[MapBatches(group_fn)->MapBatches(_filter_split)->RandomShuffle] -> TaskPoolMapOperator[MapBatches(CustomPreprocessor._transform_pandas)]\n", + "2023-09-14 15:02:45,163\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-09-14 15:02:45,164\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" ] }, { @@ -5439,7 +5406,7 @@ "version_minor": 0 }, "text/plain": [ - "- RandomShuffle 1: 0%| | 0/24 [00:00MapBatches(_filter_split)->RandomShuffle 8: 0%| | 0/24 [00:00MapBatches(_filter_split)->RandomShuffle 8: 0%| | 0/48 [00:00Tune Status\n", " \n", "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "
Current time:2023-07-24 18:13:07
Running for: 00:01:22.27
Memory: 8.3/30.9 GiB
Current time:2023-09-14 15:04:48
Running for: 00:02:01.46
Memory: 7.4/30.9 GiB
\n", " \n", "
\n", "
\n", "

System Info

\n", - " Using FIFO scheduling algorithm.
Logical resource usage: 4.0/12 CPUs, 1.0/1 GPUs\n", + " Using FIFO scheduling algorithm.
Logical resource usage: 4.0/24 CPUs, 1.0/1 GPUs\n", "
\n", " \n", " \n", @@ -5672,10 +5639,10 @@ "

Trial Status

\n", " \n", "\n", - "\n", + "\n", "\n", "\n", - "\n", + "\n", "\n", "
Trial name status loc iter total time (s) epoch lr train_loss
Trial name status loc iter total time (s) epoch lr train_loss
TorchTrainer_38d2b_00000TERMINATED10.0.56.150:282335 10 75.3763 90.0001 0.0344348
TorchTrainer_701e4_00000TERMINATED10.0.50.151:50696 10 106.195 90.0001 0.0361183
\n", " \n", @@ -5722,51 +5689,47 @@ "name": "stderr", "output_type": "stream", "text": [ - "(TorchTrainer pid=282335) The dict form of `dataset_config` is deprecated. Use the DataConfig class instead. Support for this will be dropped in a future release.\n", - "(TorchTrainer pid=282335) The `preprocessor` arg to Trainer is deprecated. Apply preprocessor transformations ahead of time by calling `preprocessor.transform(ds)`. Support for the preprocessor arg will be dropped in a future release.\n", - "(TorchTrainer pid=282335) Starting distributed worker processes: ['2994 (10.0.6.57)']\n", - "(RayTrainWorker pid=2994, ip=10.0.6.57) Setting up process group for: env:// [rank=0, world_size=1]\n", - "(RayTrainWorker pid=2994, ip=10.0.6.57) Some weights of the model checkpoint at allenai/scibert_scivocab_uncased were not used when initializing BertModel: ['cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.bias']\n", - "(RayTrainWorker pid=2994, ip=10.0.6.57) - This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", - "(RayTrainWorker pid=2994, ip=10.0.6.57) - This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n", - "(RayTrainWorker pid=2994, ip=10.0.6.57) Moving model to device: cuda:0\n", - "(RayTrainWorker pid=2994, ip=10.0.6.57) /tmp/ipykernel_280376/1209796013.py:7: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)\n" + "(TorchTrainer pid=50696, ip=10.0.50.151) The dict form of `dataset_config` is deprecated. Use the DataConfig class instead. Support for this will be dropped in a future release.\n", + "(TorchTrainer pid=50696, ip=10.0.50.151) The `preprocessor` arg to Trainer is deprecated. Apply preprocessor transformations ahead of time by calling `preprocessor.transform(ds)`. Support for the preprocessor arg will be dropped in a future release.\n", + "(TorchTrainer pid=50696, ip=10.0.50.151) Starting distributed worker processes: ['50741 (10.0.50.151)']\n", + "(RayTrainWorker pid=50741, ip=10.0.50.151) Setting up process group for: env:// [rank=0, world_size=1]\n", + "(RayTrainWorker pid=50741, ip=10.0.50.151) Some weights of the model checkpoint at allenai/scibert_scivocab_uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.predictions.decoder.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.decoder.weight']\n", + "(RayTrainWorker pid=50741, ip=10.0.50.151) - This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", + "(RayTrainWorker pid=50741, ip=10.0.50.151) - This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n", + "(RayTrainWorker pid=50741, ip=10.0.50.151) Moving model to device: cuda:0\n", + "(RayTrainWorker pid=50741, ip=10.0.50.151) /tmp/ipykernel_117917/1209796013.py:7: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "copying /home/ray/ray_results/TorchTrainer_2023-07-24_18-11-45/TorchTrainer_38d2b_00000_0_2023-07-24_18-11-45/progress.csv -> /tmp/mlflow/598544898920467811/8991516da6674b5395affb9fb6217964/artifacts\n", - "copying /home/ray/ray_results/TorchTrainer_2023-07-24_18-11-45/TorchTrainer_38d2b_00000_0_2023-07-24_18-11-45/events.out.tfevents.1690247508.ip-10-0-56-150 -> /tmp/mlflow/598544898920467811/8991516da6674b5395affb9fb6217964/artifacts\n", - "copying /home/ray/ray_results/TorchTrainer_2023-07-24_18-11-45/TorchTrainer_38d2b_00000_0_2023-07-24_18-11-45/result.json -> /tmp/mlflow/598544898920467811/8991516da6674b5395affb9fb6217964/artifacts\n", - "creating /tmp/mlflow/598544898920467811/8991516da6674b5395affb9fb6217964/artifacts/checkpoint_000008\n", - "copying /home/ray/ray_results/TorchTrainer_2023-07-24_18-11-45/TorchTrainer_38d2b_00000_0_2023-07-24_18-11-45/checkpoint_000008/.is_checkpoint -> /tmp/mlflow/598544898920467811/8991516da6674b5395affb9fb6217964/artifacts/checkpoint_000008\n", - "copying /home/ray/ray_results/TorchTrainer_2023-07-24_18-11-45/TorchTrainer_38d2b_00000_0_2023-07-24_18-11-45/checkpoint_000008/.tune_metadata -> /tmp/mlflow/598544898920467811/8991516da6674b5395affb9fb6217964/artifacts/checkpoint_000008\n", - "copying /home/ray/ray_results/TorchTrainer_2023-07-24_18-11-45/TorchTrainer_38d2b_00000_0_2023-07-24_18-11-45/checkpoint_000008/dict_checkpoint.pkl -> /tmp/mlflow/598544898920467811/8991516da6674b5395affb9fb6217964/artifacts/checkpoint_000008\n", - "copying /home/ray/ray_results/TorchTrainer_2023-07-24_18-11-45/TorchTrainer_38d2b_00000_0_2023-07-24_18-11-45/checkpoint_000008/.metadata.pkl -> /tmp/mlflow/598544898920467811/8991516da6674b5395affb9fb6217964/artifacts/checkpoint_000008\n", - "copying /home/ray/ray_results/TorchTrainer_2023-07-24_18-11-45/TorchTrainer_38d2b_00000_0_2023-07-24_18-11-45/params.json -> /tmp/mlflow/598544898920467811/8991516da6674b5395affb9fb6217964/artifacts\n", - "copying /home/ray/ray_results/TorchTrainer_2023-07-24_18-11-45/TorchTrainer_38d2b_00000_0_2023-07-24_18-11-45/params.pkl -> /tmp/mlflow/598544898920467811/8991516da6674b5395affb9fb6217964/artifacts\n", - "creating /tmp/mlflow/598544898920467811/8991516da6674b5395affb9fb6217964/artifacts/checkpoint_000009\n", - "copying /home/ray/ray_results/TorchTrainer_2023-07-24_18-11-45/TorchTrainer_38d2b_00000_0_2023-07-24_18-11-45/checkpoint_000009/.is_checkpoint -> /tmp/mlflow/598544898920467811/8991516da6674b5395affb9fb6217964/artifacts/checkpoint_000009\n", - "copying /home/ray/ray_results/TorchTrainer_2023-07-24_18-11-45/TorchTrainer_38d2b_00000_0_2023-07-24_18-11-45/checkpoint_000009/.tune_metadata -> /tmp/mlflow/598544898920467811/8991516da6674b5395affb9fb6217964/artifacts/checkpoint_000009\n", - "copying /home/ray/ray_results/TorchTrainer_2023-07-24_18-11-45/TorchTrainer_38d2b_00000_0_2023-07-24_18-11-45/checkpoint_000009/dict_checkpoint.pkl -> /tmp/mlflow/598544898920467811/8991516da6674b5395affb9fb6217964/artifacts/checkpoint_000009\n", - "copying /home/ray/ray_results/TorchTrainer_2023-07-24_18-11-45/TorchTrainer_38d2b_00000_0_2023-07-24_18-11-45/checkpoint_000009/.metadata.pkl -> /tmp/mlflow/598544898920467811/8991516da6674b5395affb9fb6217964/artifacts/checkpoint_000009\n" + "creating /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/cb6a74df7c4e43988598be5aee45e660/artifacts/checkpoint_000009\n", + "copying /efs/shared_storage/madewithml/GokuMohandas/TorchTrainer_2023-09-14_15-02-46/TorchTrainer_701e4_00000_0_2023-09-14_15-02-46/checkpoint_000009/dict_checkpoint.pkl -> /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/cb6a74df7c4e43988598be5aee45e660/artifacts/checkpoint_000009\n", + "copying /efs/shared_storage/madewithml/GokuMohandas/TorchTrainer_2023-09-14_15-02-46/TorchTrainer_701e4_00000_0_2023-09-14_15-02-46/checkpoint_000009/.metadata.pkl -> /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/cb6a74df7c4e43988598be5aee45e660/artifacts/checkpoint_000009\n", + "copying /efs/shared_storage/madewithml/GokuMohandas/TorchTrainer_2023-09-14_15-02-46/TorchTrainer_701e4_00000_0_2023-09-14_15-02-46/checkpoint_000009/.tune_metadata -> /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/cb6a74df7c4e43988598be5aee45e660/artifacts/checkpoint_000009\n", + "copying /efs/shared_storage/madewithml/GokuMohandas/TorchTrainer_2023-09-14_15-02-46/TorchTrainer_701e4_00000_0_2023-09-14_15-02-46/checkpoint_000009/.is_checkpoint -> /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/cb6a74df7c4e43988598be5aee45e660/artifacts/checkpoint_000009\n", + "copying /efs/shared_storage/madewithml/GokuMohandas/TorchTrainer_2023-09-14_15-02-46/TorchTrainer_701e4_00000_0_2023-09-14_15-02-46/params.json -> /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/cb6a74df7c4e43988598be5aee45e660/artifacts\n", + "copying /efs/shared_storage/madewithml/GokuMohandas/TorchTrainer_2023-09-14_15-02-46/TorchTrainer_701e4_00000_0_2023-09-14_15-02-46/events.out.tfevents.1694728971.ip-10-0-41-50 -> /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/cb6a74df7c4e43988598be5aee45e660/artifacts\n", + "copying /efs/shared_storage/madewithml/GokuMohandas/TorchTrainer_2023-09-14_15-02-46/TorchTrainer_701e4_00000_0_2023-09-14_15-02-46/params.pkl -> /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/cb6a74df7c4e43988598be5aee45e660/artifacts\n", + "creating /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/cb6a74df7c4e43988598be5aee45e660/artifacts/rank_0\n", + "copying /efs/shared_storage/madewithml/GokuMohandas/TorchTrainer_2023-09-14_15-02-46/TorchTrainer_701e4_00000_0_2023-09-14_15-02-46/result.json -> /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/cb6a74df7c4e43988598be5aee45e660/artifacts\n", + "copying /efs/shared_storage/madewithml/GokuMohandas/TorchTrainer_2023-09-14_15-02-46/TorchTrainer_701e4_00000_0_2023-09-14_15-02-46/progress.csv -> /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/cb6a74df7c4e43988598be5aee45e660/artifacts\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-07-24 18:13:07,524\tINFO tune.py:1148 -- Total run time: 82.33 seconds (82.27 seconds for the tuning loop).\n" + "2023-09-14 15:04:48,385\tINFO tune.py:1148 -- Total run time: 121.54 seconds (121.43 seconds for the tuning loop).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 1.82 s, sys: 981 ms, total: 2.8 s\n", - "Wall time: 1min 22s\n" + "CPU times: user 1.72 s, sys: 758 ms, total: 2.47 s\n", + "Wall time: 2min 1s\n" ] } ], @@ -5778,7 +5741,7 @@ }, { "cell_type": "code", - "execution_count": 84, + "execution_count": 85, "metadata": { "tags": [] }, @@ -5828,200 +5791,200 @@ " 0\n", " 0\n", " 0.0001\n", - " 0.581092\n", - " 0.493169\n", - " 1690247520\n", - " 14.871574\n", + " 0.575292\n", + " 0.488448\n", + " 1694728980\n", + " 16.911473\n", " True\n", " False\n", " 1\n", - " 38d2b_00000\n", - " 2023-07-24_18-12-03\n", - " 14.871574\n", - " 282335\n", - " ip-10-0-56-150\n", - " 10.0.56.150\n", - " 14.871574\n", + " 701e4_00000\n", + " 2023-09-14_15-03-07\n", + " 16.911473\n", + " 50696\n", + " ip-10-0-50-151\n", + " 10.0.50.151\n", + " 16.911473\n", " 1\n", " \n", " \n", " 1\n", " 1\n", " 0.0001\n", - " 0.478663\n", - " 0.423611\n", - " 1690247527\n", - " 6.952936\n", + " 0.477536\n", + " 0.394798\n", + " 1694728991\n", + " 10.108304\n", " True\n", " False\n", " 2\n", - " 38d2b_00000\n", - " 2023-07-24_18-12-10\n", - " 21.824510\n", - " 282335\n", - " ip-10-0-56-150\n", - " 10.0.56.150\n", - " 21.824510\n", + " 701e4_00000\n", + " 2023-09-14_15-03-18\n", + " 27.019777\n", + " 50696\n", + " ip-10-0-50-151\n", + " 10.0.50.151\n", + " 27.019777\n", " 2\n", " \n", " \n", " 2\n", " 2\n", " 0.0001\n", - " 0.386111\n", - " 0.367975\n", - " 1690247534\n", - " 6.707607\n", + " 0.352722\n", + " 0.354552\n", + " 1694729001\n", + " 9.832275\n", " True\n", " False\n", " 3\n", - " 38d2b_00000\n", - " 2023-07-24_18-12-17\n", - " 28.532117\n", - " 282335\n", - " ip-10-0-56-150\n", - " 10.0.56.150\n", - " 28.532117\n", + " 701e4_00000\n", + " 2023-09-14_15-03-27\n", + " 36.852052\n", + " 50696\n", + " ip-10-0-50-151\n", + " 10.0.50.151\n", + " 36.852052\n", " 3\n", " \n", " \n", " 3\n", " 3\n", " 0.0001\n", - " 0.287210\n", - " 0.304643\n", - " 1690247541\n", - " 6.656057\n", + " 0.270199\n", + " 0.237122\n", + " 1694729011\n", + " 9.836403\n", " True\n", " False\n", " 4\n", - " 38d2b_00000\n", - " 2023-07-24_18-12-24\n", - " 35.188173\n", - " 282335\n", - " ip-10-0-56-150\n", - " 10.0.56.150\n", - " 35.188173\n", + " 701e4_00000\n", + " 2023-09-14_15-03-37\n", + " 46.688455\n", + " 50696\n", + " ip-10-0-50-151\n", + " 10.0.50.151\n", + " 46.688455\n", " 4\n", " \n", " \n", " 4\n", " 4\n", " 0.0001\n", - " 0.209951\n", - " 0.278133\n", - " 1690247547\n", - " 6.671985\n", + " 0.192776\n", + " 0.225016\n", + " 1694729021\n", + " 9.903057\n", " True\n", " False\n", " 5\n", - " 38d2b_00000\n", - " 2023-07-24_18-12-30\n", - " 41.860158\n", - " 282335\n", - " ip-10-0-56-150\n", - " 10.0.56.150\n", - " 41.860158\n", + " 701e4_00000\n", + " 2023-09-14_15-03-47\n", + " 56.591512\n", + " 50696\n", + " ip-10-0-50-151\n", + " 10.0.50.151\n", + " 56.591512\n", " 5\n", " \n", " \n", " 5\n", " 5\n", " 0.0001\n", - " 0.152847\n", - " 0.258787\n", - " 1690247554\n", - " 6.679954\n", + " 0.141014\n", + " 0.196045\n", + " 1694729031\n", + " 9.963775\n", " True\n", " False\n", " 6\n", - " 38d2b_00000\n", - " 2023-07-24_18-12-37\n", - " 48.540112\n", - " 282335\n", - " ip-10-0-56-150\n", - " 10.0.56.150\n", - " 48.540112\n", + " 701e4_00000\n", + " 2023-09-14_15-03-57\n", + " 66.555288\n", + " 50696\n", + " ip-10-0-50-151\n", + " 10.0.50.151\n", + " 66.555288\n", " 6\n", " \n", " \n", " 6\n", " 6\n", " 0.0001\n", - " 0.102787\n", - " 0.248396\n", - " 1690247561\n", - " 6.690619\n", + " 0.093777\n", + " 0.201736\n", + " 1694729041\n", + " 9.954871\n", " True\n", " False\n", " 7\n", - " 38d2b_00000\n", - " 2023-07-24_18-12-44\n", - " 55.230731\n", - " 282335\n", - " ip-10-0-56-150\n", - " 10.0.56.150\n", - " 55.230731\n", + " 701e4_00000\n", + " 2023-09-14_15-04-07\n", + " 76.510159\n", + " 50696\n", + " ip-10-0-50-151\n", + " 10.0.50.151\n", + " 76.510159\n", " 7\n", " \n", " \n", " 7\n", " 7\n", " 0.0001\n", - " 0.066364\n", - " 0.232449\n", - " 1690247567\n", - " 6.750745\n", + " 0.070405\n", + " 0.174259\n", + " 1694729051\n", + " 9.840459\n", " True\n", " False\n", " 8\n", - " 38d2b_00000\n", - " 2023-07-24_18-12-50\n", - " 61.981476\n", - " 282335\n", - " ip-10-0-56-150\n", - " 10.0.56.150\n", - " 61.981476\n", + " 701e4_00000\n", + " 2023-09-14_15-04-17\n", + " 86.350618\n", + " 50696\n", + " ip-10-0-50-151\n", + " 10.0.50.151\n", + " 86.350618\n", " 8\n", " \n", " \n", " 8\n", " 8\n", " 0.0001\n", - " 0.049473\n", - " 0.219698\n", - " 1690247574\n", - " 6.681975\n", + " 0.048866\n", + " 0.159393\n", + " 1694729060\n", + " 9.945646\n", " True\n", " False\n", " 9\n", - " 38d2b_00000\n", - " 2023-07-24_18-12-57\n", - " 68.663451\n", - " 282335\n", - " ip-10-0-56-150\n", - " 10.0.56.150\n", - " 68.663451\n", + " 701e4_00000\n", + " 2023-09-14_15-04-27\n", + " 96.296264\n", + " 50696\n", + " ip-10-0-50-151\n", + " 10.0.50.151\n", + " 96.296264\n", " 9\n", " \n", " \n", " 9\n", " 9\n", " 0.0001\n", - " 0.034435\n", - " 0.221598\n", - " 1690247581\n", - " 6.712862\n", + " 0.036118\n", + " 0.137778\n", + " 1694729070\n", + " 9.898459\n", " True\n", " False\n", " 10\n", - " 38d2b_00000\n", - " 2023-07-24_18-13-04\n", - " 75.376313\n", - " 282335\n", - " ip-10-0-56-150\n", - " 10.0.56.150\n", - " 75.376313\n", + " 701e4_00000\n", + " 2023-09-14_15-04-37\n", + " 106.194723\n", + " 50696\n", + " ip-10-0-50-151\n", + " 10.0.50.151\n", + " 106.194723\n", " 10\n", " \n", " \n", @@ -6030,55 +5993,55 @@ ], "text/plain": [ " epoch lr train_loss val_loss timestamp time_this_iter_s \n", - "0 0 0.0001 0.581092 0.493169 1690247520 14.871574 \\\n", - "1 1 0.0001 0.478663 0.423611 1690247527 6.952936 \n", - "2 2 0.0001 0.386111 0.367975 1690247534 6.707607 \n", - "3 3 0.0001 0.287210 0.304643 1690247541 6.656057 \n", - "4 4 0.0001 0.209951 0.278133 1690247547 6.671985 \n", - "5 5 0.0001 0.152847 0.258787 1690247554 6.679954 \n", - "6 6 0.0001 0.102787 0.248396 1690247561 6.690619 \n", - "7 7 0.0001 0.066364 0.232449 1690247567 6.750745 \n", - "8 8 0.0001 0.049473 0.219698 1690247574 6.681975 \n", - "9 9 0.0001 0.034435 0.221598 1690247581 6.712862 \n", + "0 0 0.0001 0.575292 0.488448 1694728980 16.911473 \\\n", + "1 1 0.0001 0.477536 0.394798 1694728991 10.108304 \n", + "2 2 0.0001 0.352722 0.354552 1694729001 9.832275 \n", + "3 3 0.0001 0.270199 0.237122 1694729011 9.836403 \n", + "4 4 0.0001 0.192776 0.225016 1694729021 9.903057 \n", + "5 5 0.0001 0.141014 0.196045 1694729031 9.963775 \n", + "6 6 0.0001 0.093777 0.201736 1694729041 9.954871 \n", + "7 7 0.0001 0.070405 0.174259 1694729051 9.840459 \n", + "8 8 0.0001 0.048866 0.159393 1694729060 9.945646 \n", + "9 9 0.0001 0.036118 0.137778 1694729070 9.898459 \n", "\n", " should_checkpoint done training_iteration trial_id \n", - "0 True False 1 38d2b_00000 \\\n", - "1 True False 2 38d2b_00000 \n", - "2 True False 3 38d2b_00000 \n", - "3 True False 4 38d2b_00000 \n", - "4 True False 5 38d2b_00000 \n", - "5 True False 6 38d2b_00000 \n", - "6 True False 7 38d2b_00000 \n", - "7 True False 8 38d2b_00000 \n", - "8 True False 9 38d2b_00000 \n", - "9 True False 10 38d2b_00000 \n", - "\n", - " date time_total_s pid hostname node_ip \n", - "0 2023-07-24_18-12-03 14.871574 282335 ip-10-0-56-150 10.0.56.150 \\\n", - "1 2023-07-24_18-12-10 21.824510 282335 ip-10-0-56-150 10.0.56.150 \n", - "2 2023-07-24_18-12-17 28.532117 282335 ip-10-0-56-150 10.0.56.150 \n", - "3 2023-07-24_18-12-24 35.188173 282335 ip-10-0-56-150 10.0.56.150 \n", - "4 2023-07-24_18-12-30 41.860158 282335 ip-10-0-56-150 10.0.56.150 \n", - "5 2023-07-24_18-12-37 48.540112 282335 ip-10-0-56-150 10.0.56.150 \n", - "6 2023-07-24_18-12-44 55.230731 282335 ip-10-0-56-150 10.0.56.150 \n", - "7 2023-07-24_18-12-50 61.981476 282335 ip-10-0-56-150 10.0.56.150 \n", - "8 2023-07-24_18-12-57 68.663451 282335 ip-10-0-56-150 10.0.56.150 \n", - "9 2023-07-24_18-13-04 75.376313 282335 ip-10-0-56-150 10.0.56.150 \n", + "0 True False 1 701e4_00000 \\\n", + "1 True False 2 701e4_00000 \n", + "2 True False 3 701e4_00000 \n", + "3 True False 4 701e4_00000 \n", + "4 True False 5 701e4_00000 \n", + "5 True False 6 701e4_00000 \n", + "6 True False 7 701e4_00000 \n", + "7 True False 8 701e4_00000 \n", + "8 True False 9 701e4_00000 \n", + "9 True False 10 701e4_00000 \n", + "\n", + " date time_total_s pid hostname node_ip \n", + "0 2023-09-14_15-03-07 16.911473 50696 ip-10-0-50-151 10.0.50.151 \\\n", + "1 2023-09-14_15-03-18 27.019777 50696 ip-10-0-50-151 10.0.50.151 \n", + "2 2023-09-14_15-03-27 36.852052 50696 ip-10-0-50-151 10.0.50.151 \n", + "3 2023-09-14_15-03-37 46.688455 50696 ip-10-0-50-151 10.0.50.151 \n", + "4 2023-09-14_15-03-47 56.591512 50696 ip-10-0-50-151 10.0.50.151 \n", + "5 2023-09-14_15-03-57 66.555288 50696 ip-10-0-50-151 10.0.50.151 \n", + "6 2023-09-14_15-04-07 76.510159 50696 ip-10-0-50-151 10.0.50.151 \n", + "7 2023-09-14_15-04-17 86.350618 50696 ip-10-0-50-151 10.0.50.151 \n", + "8 2023-09-14_15-04-27 96.296264 50696 ip-10-0-50-151 10.0.50.151 \n", + "9 2023-09-14_15-04-37 106.194723 50696 ip-10-0-50-151 10.0.50.151 \n", "\n", " time_since_restore iterations_since_restore \n", - "0 14.871574 1 \n", - "1 21.824510 2 \n", - "2 28.532117 3 \n", - "3 35.188173 4 \n", - "4 41.860158 5 \n", - "5 48.540112 6 \n", - "6 55.230731 7 \n", - "7 61.981476 8 \n", - "8 68.663451 9 \n", - "9 75.376313 10 " + "0 16.911473 1 \n", + "1 27.019777 2 \n", + "2 36.852052 3 \n", + "3 46.688455 4 \n", + "4 56.591512 5 \n", + "5 66.555288 6 \n", + "6 76.510159 7 \n", + "7 86.350618 8 \n", + "8 96.296264 9 \n", + "9 106.194723 10 " ] }, - "execution_count": 84, + "execution_count": 85, "metadata": {}, "output_type": "execute_result" } @@ -6089,7 +6052,7 @@ }, { "cell_type": "code", - "execution_count": 85, + "execution_count": 86, "metadata": { "tags": [] }, @@ -6121,19 +6084,19 @@ " artifact_uri\n", " start_time\n", " end_time\n", - " metrics.time_since_restore\n", - " metrics.time_this_iter_s\n", - " metrics.config/train_loop_config/lr\n", - " metrics.done\n", + " metrics.pid\n", + " metrics.epoch\n", + " metrics.config/train_loop_config/batch_size\n", + " metrics.iterations_since_restore\n", " ...\n", - " metrics.val_loss\n", - " params.train_loop_config/num_classes\n", + " metrics.lr\n", " params.train_loop_config/dropout_p\n", - " params.train_loop_config/num_epochs\n", + " params.train_loop_config/batch_size\n", + " params.train_loop_config/lr_factor\n", + " params.train_loop_config/num_classes\n", " params.train_loop_config/lr_patience\n", + " params.train_loop_config/num_epochs\n", " params.train_loop_config/lr\n", - " params.train_loop_config/lr_factor\n", - " params.train_loop_config/batch_size\n", " tags.trial_name\n", " tags.mlflow.runName\n", " \n", @@ -6141,68 +6104,68 @@ " \n", " \n", " 0\n", - " 8991516da6674b5395affb9fb6217964\n", - " 598544898920467811\n", + " cb6a74df7c4e43988598be5aee45e660\n", + " 917462352875586010\n", " FINISHED\n", - " file:///tmp/mlflow/598544898920467811/8991516d...\n", - " 2023-07-25 01:11:48.948000+00:00\n", - " 2023-07-25 01:13:07.513000+00:00\n", - " 75.376313\n", - " 6.712862\n", - " 0.0001\n", - " 0.0\n", + " file:///efs/shared_storage/madewithml/GokuMoha...\n", + " 2023-09-14 22:02:50.670000+00:00\n", + " 2023-09-14 22:04:48.292000+00:00\n", + " 50696.0\n", + " 9.0\n", + " 256.0\n", + " 10.0\n", " ...\n", - " 0.221598\n", - " 4\n", + " 0.0001\n", " 0.5\n", - " 10\n", + " 256\n", + " 0.8\n", + " 4\n", " 3\n", + " 10\n", " 0.0001\n", - " 0.8\n", - " 256\n", - " TorchTrainer_38d2b_00000\n", - " TorchTrainer_38d2b_00000\n", + " TorchTrainer_701e4_00000\n", + " TorchTrainer_701e4_00000\n", " \n", " \n", "\n", - "

1 rows × 35 columns

\n", + "

1 rows × 36 columns

\n", "" ], "text/plain": [ " run_id experiment_id status \n", - "0 8991516da6674b5395affb9fb6217964 598544898920467811 FINISHED \\\n", + "0 cb6a74df7c4e43988598be5aee45e660 917462352875586010 FINISHED \\\n", "\n", " artifact_uri \n", - "0 file:///tmp/mlflow/598544898920467811/8991516d... \\\n", + "0 file:///efs/shared_storage/madewithml/GokuMoha... \\\n", "\n", " start_time end_time \n", - "0 2023-07-25 01:11:48.948000+00:00 2023-07-25 01:13:07.513000+00:00 \\\n", + "0 2023-09-14 22:02:50.670000+00:00 2023-09-14 22:04:48.292000+00:00 \\\n", "\n", - " metrics.time_since_restore metrics.time_this_iter_s \n", - "0 75.376313 6.712862 \\\n", + " metrics.pid metrics.epoch metrics.config/train_loop_config/batch_size \n", + "0 50696.0 9.0 256.0 \\\n", "\n", - " metrics.config/train_loop_config/lr metrics.done ... metrics.val_loss \n", - "0 0.0001 0.0 ... 0.221598 \\\n", + " metrics.iterations_since_restore ... metrics.lr \n", + "0 10.0 ... 0.0001 \\\n", "\n", - " params.train_loop_config/num_classes params.train_loop_config/dropout_p \n", - "0 4 0.5 \\\n", + " params.train_loop_config/dropout_p params.train_loop_config/batch_size \n", + "0 0.5 256 \\\n", "\n", - " params.train_loop_config/num_epochs params.train_loop_config/lr_patience \n", - "0 10 3 \\\n", + " params.train_loop_config/lr_factor params.train_loop_config/num_classes \n", + "0 0.8 4 \\\n", "\n", - " params.train_loop_config/lr params.train_loop_config/lr_factor \n", - "0 0.0001 0.8 \\\n", + " params.train_loop_config/lr_patience params.train_loop_config/num_epochs \n", + "0 3 10 \\\n", "\n", - " params.train_loop_config/batch_size tags.trial_name \n", - "0 256 TorchTrainer_38d2b_00000 \\\n", + " params.train_loop_config/lr tags.trial_name \n", + "0 0.0001 TorchTrainer_701e4_00000 \\\n", "\n", " tags.mlflow.runName \n", - "0 TorchTrainer_38d2b_00000 \n", + "0 TorchTrainer_701e4_00000 \n", "\n", - "[1 rows x 35 columns]" + "[1 rows x 36 columns]" ] }, - "execution_count": 85, + "execution_count": 86, "metadata": {}, "output_type": "execute_result" } @@ -6215,7 +6178,7 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": 87, "metadata": { "tags": [] }, @@ -6223,45 +6186,46 @@ { "data": { "text/plain": [ - "run_id 8991516da6674b5395affb9fb6217964\n", - "experiment_id 598544898920467811\n", + "run_id cb6a74df7c4e43988598be5aee45e660\n", + "experiment_id 917462352875586010\n", "status FINISHED\n", - "artifact_uri file:///tmp/mlflow/598544898920467811/8991516d...\n", - "start_time 2023-07-25 01:11:48.948000+00:00\n", - "end_time 2023-07-25 01:13:07.513000+00:00\n", - "metrics.time_since_restore 75.376313\n", - "metrics.time_this_iter_s 6.712862\n", - "metrics.config/train_loop_config/lr 0.0001\n", - "metrics.done 0.0\n", - "metrics.pid 282335.0\n", + "artifact_uri file:///efs/shared_storage/madewithml/GokuMoha...\n", + "start_time 2023-09-14 22:02:50.670000+00:00\n", + "end_time 2023-09-14 22:04:48.292000+00:00\n", + "metrics.pid 50696.0\n", + "metrics.epoch 9.0\n", + "metrics.config/train_loop_config/batch_size 256.0\n", "metrics.iterations_since_restore 10.0\n", - "metrics.training_iteration 10.0\n", - "metrics.config/train_loop_config/num_classes 4.0\n", - "metrics.lr 0.0001\n", - "metrics.config/train_loop_config/num_epochs 10.0\n", - "metrics.timestamp 1690247581.0\n", - "metrics.time_total_s 75.376313\n", "metrics.config/train_loop_config/lr_patience 3.0\n", - "metrics.config/train_loop_config/batch_size 256.0\n", - "metrics.epoch 9.0\n", - "metrics.should_checkpoint 1.0\n", - "metrics.train_loss 0.034435\n", + "metrics.done 0.0\n", "metrics.config/train_loop_config/dropout_p 0.5\n", + "metrics.config/train_loop_config/num_classes 4.0\n", + "metrics.time_total_s 106.194723\n", + "metrics.config/train_loop_config/lr 0.0001\n", "metrics.config/train_loop_config/lr_factor 0.8\n", - "metrics.val_loss 0.221598\n", - "params.train_loop_config/num_classes 4\n", + "metrics.should_checkpoint 1.0\n", + "metrics.val_loss 0.137778\n", + "metrics.train_loss 0.036118\n", + "metrics.timestamp 1694729070.0\n", + "metrics.time_since_restore 106.194723\n", + "metrics.time_this_iter_s 9.898459\n", + "metrics.config/train_loop_config/num_epochs 10.0\n", + "metrics.training_iteration 10.0\n", + "metrics.trial_id inf\n", + "metrics.lr 0.0001\n", "params.train_loop_config/dropout_p 0.5\n", - "params.train_loop_config/num_epochs 10\n", + "params.train_loop_config/batch_size 256\n", + "params.train_loop_config/lr_factor 0.8\n", + "params.train_loop_config/num_classes 4\n", "params.train_loop_config/lr_patience 3\n", + "params.train_loop_config/num_epochs 10\n", "params.train_loop_config/lr 0.0001\n", - "params.train_loop_config/lr_factor 0.8\n", - "params.train_loop_config/batch_size 256\n", - "tags.trial_name TorchTrainer_38d2b_00000\n", - "tags.mlflow.runName TorchTrainer_38d2b_00000\n", + "tags.trial_name TorchTrainer_701e4_00000\n", + "tags.mlflow.runName TorchTrainer_701e4_00000\n", "Name: 0, dtype: object" ] }, - "execution_count": 86, + "execution_count": 87, "metadata": {}, "output_type": "execute_result" } @@ -6273,7 +6237,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -6281,19 +6244,17 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Let's view what we've tracked from our experiment. MLFlow serves a dashboard for us to view and explore our experiments on a localhost port:\n", "\n", "```bash\n", - "mlflow server -h 0.0.0.0 -p 8080 --backend-store-uri /tmp/mlflow/\n", + "mlflow server -h 0.0.0.0 -p 8080 --backend-store-uri $EFS_DIR/mlflow\n", "```" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -6311,7 +6272,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -6320,7 +6280,7 @@ }, { "cell_type": "code", - "execution_count": 87, + "execution_count": 88, "metadata": { "tags": [] }, @@ -6332,7 +6292,7 @@ }, { "cell_type": "code", - "execution_count": 88, + "execution_count": 89, "metadata": { "tags": [] }, @@ -6346,7 +6306,7 @@ }, { "cell_type": "code", - "execution_count": 89, + "execution_count": 90, "metadata": { "tags": [] }, @@ -6355,9 +6315,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-07-24 18:13:08,541\tINFO streaming_executor.py:92 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadCSV->SplitBlocks(24)] -> TaskPoolMapOperator[MapBatches(CustomPreprocessor._transform_pandas)->MapBatches()]\n", - "2023-07-24 18:13:08,542\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", - "2023-07-24 18:13:08,543\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + "2023-09-14 15:04:49,356\tINFO streaming_executor.py:92 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadCSV->SplitBlocks(48)] -> TaskPoolMapOperator[MapBatches(CustomPreprocessor._transform_pandas)->MapBatches()]\n", + "2023-09-14 15:04:49,357\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-09-14 15:04:49,357\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" ] }, { @@ -6368,7 +6328,7 @@ "version_minor": 0 }, "text/plain": [ - "Running 0: 0%| | 0/24 [00:00 TaskPoolMapOperator[ReadCSV->SplitBlocks(24)] -> AllToAllOperator[RandomShuffle] -> LimitOperator[limit=1]\n", - "2023-07-24 18:13:12,895\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", - "2023-07-24 18:13:12,895\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + "2023-09-14 15:04:52,757\tINFO read_api.py:374 -- To satisfy the requested parallelism of 48, each read task output will be split into 48 smaller blocks.\n", + "2023-09-14 15:04:52,760\tINFO streaming_executor.py:92 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadCSV->SplitBlocks(48)] -> AllToAllOperator[RandomShuffle] -> LimitOperator[limit=1]\n", + "2023-09-14 15:04:52,761\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-09-14 15:04:52,761\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" ] }, { @@ -6520,7 +6481,7 @@ "version_minor": 0 }, "text/plain": [ - "- RandomShuffle 1: 0%| | 0/24 [00:00 TaskPoolMapOperator[ReadCSV->SplitBlocks(24)] -> AllToAllOperator[RandomShuffle] -> AllToAllOperator[Sort] -> AllToAllOperator[MapBatches(group_fn)->MapBatches(_filter_split)->RandomShuffle] -> LimitOperator[limit=1]\n", - "2023-07-24 18:13:14,122\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", - "2023-07-24 18:13:14,123\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + "2023-09-14 15:04:53,537\tINFO streaming_executor.py:92 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadCSV->SplitBlocks(48)] -> AllToAllOperator[RandomShuffle] -> AllToAllOperator[Sort] -> AllToAllOperator[MapBatches(group_fn)->MapBatches(_filter_split)->RandomShuffle] -> LimitOperator[limit=1]\n", + "2023-09-14 15:04:53,538\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-09-14 15:04:53,539\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" ] }, { @@ -6599,7 +6560,7 @@ "version_minor": 0 }, "text/plain": [ - "- RandomShuffle 1: 0%| | 0/24 [00:00MapBatches(_filter_split)->RandomShuffle 8: 0%| | 0/24 [00:00MapBatches(_filter_split)->RandomShuffle 8: 0%| | 0/48 [00:00 TaskPoolMapOperator[ReadCSV->SplitBlocks(24)] -> AllToAllOperator[RandomShuffle] -> AllToAllOperator[Sort] -> AllToAllOperator[MapBatches(group_fn)->MapBatches(_filter_split)->RandomShuffle] -> AllToAllOperator[Aggregate] -> TaskPoolMapOperator[MapBatches()]\n", - "2023-07-24 18:13:15,060\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", - "2023-07-24 18:13:15,061\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + "2023-09-14 15:04:54,839\tINFO streaming_executor.py:92 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadCSV->SplitBlocks(48)] -> AllToAllOperator[RandomShuffle] -> AllToAllOperator[Sort] -> AllToAllOperator[MapBatches(group_fn)->MapBatches(_filter_split)->RandomShuffle] -> AllToAllOperator[Aggregate] -> TaskPoolMapOperator[MapBatches()]\n", + "2023-09-14 15:04:54,841\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-09-14 15:04:54,842\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" ] }, { @@ -6776,7 +6737,7 @@ "version_minor": 0 }, "text/plain": [ - "- RandomShuffle 1: 0%| | 0/24 [00:00MapBatches(_filter_split)->RandomShuffle 8: 0%| | 0/24 [00:00MapBatches(_filter_split)->RandomShuffle 8: 0%| | 0/48 [00:00 TaskPoolMapOperator[ReadCSV->SplitBlocks(24)] -> AllToAllOperator[RandomShuffle] -> AllToAllOperator[Sort] -> AllToAllOperator[MapBatches(group_fn)->MapBatches(_filter_split)->RandomShuffle] -> TaskPoolMapOperator[MapBatches(CustomPreprocessor._transform_pandas)]\n", - "2023-07-24 18:13:16,029\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", - "2023-07-24 18:13:16,032\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + "2023-09-14 15:04:56,618\tINFO streaming_executor.py:92 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadCSV->SplitBlocks(48)] -> AllToAllOperator[RandomShuffle] -> AllToAllOperator[Sort] -> AllToAllOperator[MapBatches(group_fn)->MapBatches(_filter_split)->RandomShuffle] -> TaskPoolMapOperator[MapBatches(CustomPreprocessor._transform_pandas)]\n", + "2023-09-14 15:04:56,619\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-09-14 15:04:56,620\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" ] }, { @@ -7009,7 +6970,7 @@ "version_minor": 0 }, "text/plain": [ - "- RandomShuffle 1: 0%| | 0/24 [00:00MapBatches(_filter_split)->RandomShuffle 8: 0%| | 0/24 [00:00MapBatches(_filter_split)->RandomShuffle 8: 0%| | 0/48 [00:00 TaskPoolMapOperator[ReadCSV->SplitBlocks(24)] -> AllToAllOperator[RandomShuffle] -> AllToAllOperator[Sort] -> AllToAllOperator[MapBatches(group_fn)->MapBatches(_filter_split)->RandomShuffle] -> TaskPoolMapOperator[MapBatches(CustomPreprocessor._transform_pandas)]\n", - "2023-07-24 18:13:17,237\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", - "2023-07-24 18:13:17,237\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + "2023-09-14 15:04:58,287\tINFO streaming_executor.py:92 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadCSV->SplitBlocks(48)] -> AllToAllOperator[RandomShuffle] -> AllToAllOperator[Sort] -> AllToAllOperator[MapBatches(group_fn)->MapBatches(_filter_split)->RandomShuffle] -> TaskPoolMapOperator[MapBatches(CustomPreprocessor._transform_pandas)]\n", + "2023-09-14 15:04:58,288\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-09-14 15:04:58,289\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" ] }, { @@ -7186,7 +7147,7 @@ "version_minor": 0 }, "text/plain": [ - "- RandomShuffle 1: 0%| | 0/24 [00:00MapBatches(_filter_split)->RandomShuffle 8: 0%| | 0/24 [00:00MapBatches(_filter_split)->RandomShuffle 8: 0%| | 0/48 [00:00Tune Status\n", " \n", "\n", - "\n", - "\n", - "\n", + "\n", + "\n", + "\n", "\n", "
Current time:2023-07-24 18:15:25
Running for: 00:02:06.87
Memory: 9.0/30.9 GiB
Current time:2023-09-14 15:08:20
Running for: 00:03:20.50
Memory: 7.7/30.9 GiB
\n", " \n", "
\n", "
\n", "

System Info

\n", - " Using AsyncHyperBand: num_stopped=2
Bracket: Iter 5.000: -0.3085140064358711
Logical resource usage: 4.0/12 CPUs, 1.0/1 GPUs\n", + " Using AsyncHyperBand: num_stopped=2
Bracket: Iter 5.000: -0.2885470949113369
Logical resource usage: 4.0/24 CPUs, 1.0/1 GPUs\n", "
\n", " \n", " \n", @@ -7532,14 +7493,14 @@ "

Trial Status

\n", " \n", "\n", - "\n", + "_patience\n", "\n", "\n", - "\n", - "\n", + "\n", + "\n", "\n", "
Trial name status loc train_loop_config/dr\n", + "
Trial name status loc train_loop_config/dr\n", "opout_p train_loop_config/lr train_loop_config/lr\n", "_factor train_loop_config/lr\n", - "_patience iter total time (s) epoch lr train_loss
iter total time (s) epoch lr train_loss
TorchTrainer_578373f0TERMINATED10.0.56.150:2833230.5 0.0001 0.8 3 10 76.0183 90.0001 0.0386915
TorchTrainer_50d8c90fTERMINATED10.0.56.150:2841490.356927 2.63429e-050.1487252.50789 5 42.2436 42.63429e-05 0.407936
TorchTrainer_55490282TERMINATED10.0.50.151:514600.5 0.0001 0.8 3 10 107.565 90.0001 0.0393358
TorchTrainer_45b199dfTERMINATED10.0.50.151:520630.797623 2.6943e-050.2671435.21456 5 57.5858 42.6943e-05 0.482763
\n", " \n", @@ -7586,79 +7547,86 @@ "name": "stderr", "output_type": "stream", "text": [ - "(TorchTrainer pid=283323) The dict form of `dataset_config` is deprecated. Use the DataConfig class instead. Support for this will be dropped in a future release.\n", - "(TorchTrainer pid=283323) The `preprocessor` arg to Trainer is deprecated. Apply preprocessor transformations ahead of time by calling `preprocessor.transform(ds)`. Support for the preprocessor arg will be dropped in a future release.\n", - "(TorchTrainer pid=283323) Starting distributed worker processes: ['3541 (10.0.6.57)']\n", - "(RayTrainWorker pid=3541, ip=10.0.6.57) Setting up process group for: env:// [rank=0, world_size=1]\n", - "(RayTrainWorker pid=3541, ip=10.0.6.57) Some weights of the model checkpoint at allenai/scibert_scivocab_uncased were not used when initializing BertModel: ['cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.weight', 'cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.decoder.bias', 'cls.predictions.transform.LayerNorm.bias']\n", - "(RayTrainWorker pid=3541, ip=10.0.6.57) - This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", - "(RayTrainWorker pid=3541, ip=10.0.6.57) - This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n", - "(RayTrainWorker pid=3541, ip=10.0.6.57) Moving model to device: cuda:0\n", - "(RayTrainWorker pid=3541, ip=10.0.6.57) /tmp/ipykernel_280376/1209796013.py:7: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)\n" + "(TorchTrainer pid=51460, ip=10.0.50.151) The dict form of `dataset_config` is deprecated. Use the DataConfig class instead. Support for this will be dropped in a future release.\n", + "(TorchTrainer pid=51460, ip=10.0.50.151) The `preprocessor` arg to Trainer is deprecated. Apply preprocessor transformations ahead of time by calling `preprocessor.transform(ds)`. Support for the preprocessor arg will be dropped in a future release.\n", + "(TorchTrainer pid=51460, ip=10.0.50.151) Starting distributed worker processes: ['51502 (10.0.50.151)']\n", + "(RayTrainWorker pid=51502, ip=10.0.50.151) Setting up process group for: env:// [rank=0, world_size=1]\n", + "(RayTrainWorker pid=51502, ip=10.0.50.151) Some weights of the model checkpoint at allenai/scibert_scivocab_uncased were not used when initializing BertModel: ['cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.predictions.decoder.bias']\n", + "(RayTrainWorker pid=51502, ip=10.0.50.151) - This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", + "(RayTrainWorker pid=51502, ip=10.0.50.151) - This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n", + "(RayTrainWorker pid=51502, ip=10.0.50.151) Moving model to device: cuda:0\n", + "(RayTrainWorker pid=51502, ip=10.0.50.151) /tmp/ipykernel_117917/1209796013.py:7: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "copying /home/ray/ray_results/TorchTrainer_2023-07-24_18-13-18/TorchTrainer_578373f0_1_dropout_p=0.5000,lr=0.0001,lr_factor=0.8000,lr_patience=3.0000_2023-07-24_18-13-18/events.out.tfevents.1690247602.ip-10-0-56-150 -> /tmp/mlflow/598544898920467811/6b854ffda94844e28013fc6deb157af4/artifacts\n", - "copying /home/ray/ray_results/TorchTrainer_2023-07-24_18-13-18/TorchTrainer_578373f0_1_dropout_p=0.5000,lr=0.0001,lr_factor=0.8000,lr_patience=3.0000_2023-07-24_18-13-18/progress.csv -> /tmp/mlflow/598544898920467811/6b854ffda94844e28013fc6deb157af4/artifacts\n", - "copying /home/ray/ray_results/TorchTrainer_2023-07-24_18-13-18/TorchTrainer_578373f0_1_dropout_p=0.5000,lr=0.0001,lr_factor=0.8000,lr_patience=3.0000_2023-07-24_18-13-18/result.json -> /tmp/mlflow/598544898920467811/6b854ffda94844e28013fc6deb157af4/artifacts\n", - "copying /home/ray/ray_results/TorchTrainer_2023-07-24_18-13-18/TorchTrainer_578373f0_1_dropout_p=0.5000,lr=0.0001,lr_factor=0.8000,lr_patience=3.0000_2023-07-24_18-13-18/params.json -> /tmp/mlflow/598544898920467811/6b854ffda94844e28013fc6deb157af4/artifacts\n", - "copying /home/ray/ray_results/TorchTrainer_2023-07-24_18-13-18/TorchTrainer_578373f0_1_dropout_p=0.5000,lr=0.0001,lr_factor=0.8000,lr_patience=3.0000_2023-07-24_18-13-18/.lazy_checkpoint_marker -> /tmp/mlflow/598544898920467811/6b854ffda94844e28013fc6deb157af4/artifacts\n", - "copying /home/ray/ray_results/TorchTrainer_2023-07-24_18-13-18/TorchTrainer_578373f0_1_dropout_p=0.5000,lr=0.0001,lr_factor=0.8000,lr_patience=3.0000_2023-07-24_18-13-18/params.pkl -> /tmp/mlflow/598544898920467811/6b854ffda94844e28013fc6deb157af4/artifacts\n", - "creating /tmp/mlflow/598544898920467811/6b854ffda94844e28013fc6deb157af4/artifacts/checkpoint_000009\n", - "copying /home/ray/ray_results/TorchTrainer_2023-07-24_18-13-18/TorchTrainer_578373f0_1_dropout_p=0.5000,lr=0.0001,lr_factor=0.8000,lr_patience=3.0000_2023-07-24_18-13-18/checkpoint_000009/.is_checkpoint -> /tmp/mlflow/598544898920467811/6b854ffda94844e28013fc6deb157af4/artifacts/checkpoint_000009\n", - "copying /home/ray/ray_results/TorchTrainer_2023-07-24_18-13-18/TorchTrainer_578373f0_1_dropout_p=0.5000,lr=0.0001,lr_factor=0.8000,lr_patience=3.0000_2023-07-24_18-13-18/checkpoint_000009/.tune_metadata -> /tmp/mlflow/598544898920467811/6b854ffda94844e28013fc6deb157af4/artifacts/checkpoint_000009\n", - "copying /home/ray/ray_results/TorchTrainer_2023-07-24_18-13-18/TorchTrainer_578373f0_1_dropout_p=0.5000,lr=0.0001,lr_factor=0.8000,lr_patience=3.0000_2023-07-24_18-13-18/checkpoint_000009/dict_checkpoint.pkl -> /tmp/mlflow/598544898920467811/6b854ffda94844e28013fc6deb157af4/artifacts/checkpoint_000009\n", - "copying /home/ray/ray_results/TorchTrainer_2023-07-24_18-13-18/TorchTrainer_578373f0_1_dropout_p=0.5000,lr=0.0001,lr_factor=0.8000,lr_patience=3.0000_2023-07-24_18-13-18/checkpoint_000009/.metadata.pkl -> /tmp/mlflow/598544898920467811/6b854ffda94844e28013fc6deb157af4/artifacts/checkpoint_000009\n" + "creating /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/97a3692584eb4a51999f7e97176e3297/artifacts/checkpoint_000009\n", + "copying /efs/shared_storage/madewithml/GokuMohandas/TorchTrainer_2023-09-14_15-05-00/TorchTrainer_55490282_1_dropout_p=0.5000,lr=0.0001,lr_factor=0.8000,lr_patience=3.0000_2023-09-14_15-05-00/checkpoint_000009/dict_checkpoint.pkl -> /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/97a3692584eb4a51999f7e97176e3297/artifacts/checkpoint_000009\n", + "copying /efs/shared_storage/madewithml/GokuMohandas/TorchTrainer_2023-09-14_15-05-00/TorchTrainer_55490282_1_dropout_p=0.5000,lr=0.0001,lr_factor=0.8000,lr_patience=3.0000_2023-09-14_15-05-00/checkpoint_000009/.metadata.pkl -> /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/97a3692584eb4a51999f7e97176e3297/artifacts/checkpoint_000009\n", + "copying /efs/shared_storage/madewithml/GokuMohandas/TorchTrainer_2023-09-14_15-05-00/TorchTrainer_55490282_1_dropout_p=0.5000,lr=0.0001,lr_factor=0.8000,lr_patience=3.0000_2023-09-14_15-05-00/checkpoint_000009/.tune_metadata -> /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/97a3692584eb4a51999f7e97176e3297/artifacts/checkpoint_000009\n", + "copying /efs/shared_storage/madewithml/GokuMohandas/TorchTrainer_2023-09-14_15-05-00/TorchTrainer_55490282_1_dropout_p=0.5000,lr=0.0001,lr_factor=0.8000,lr_patience=3.0000_2023-09-14_15-05-00/checkpoint_000009/.is_checkpoint -> /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/97a3692584eb4a51999f7e97176e3297/artifacts/checkpoint_000009\n", + "copying /efs/shared_storage/madewithml/GokuMohandas/TorchTrainer_2023-09-14_15-05-00/TorchTrainer_55490282_1_dropout_p=0.5000,lr=0.0001,lr_factor=0.8000,lr_patience=3.0000_2023-09-14_15-05-00/params.json -> /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/97a3692584eb4a51999f7e97176e3297/artifacts\n", + "copying /efs/shared_storage/madewithml/GokuMohandas/TorchTrainer_2023-09-14_15-05-00/TorchTrainer_55490282_1_dropout_p=0.5000,lr=0.0001,lr_factor=0.8000,lr_patience=3.0000_2023-09-14_15-05-00/.lazy_checkpoint_marker -> /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/97a3692584eb4a51999f7e97176e3297/artifacts\n", + "copying /efs/shared_storage/madewithml/GokuMohandas/TorchTrainer_2023-09-14_15-05-00/TorchTrainer_55490282_1_dropout_p=0.5000,lr=0.0001,lr_factor=0.8000,lr_patience=3.0000_2023-09-14_15-05-00/params.pkl -> /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/97a3692584eb4a51999f7e97176e3297/artifacts\n", + "creating /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/97a3692584eb4a51999f7e97176e3297/artifacts/rank_0\n", + "copying /efs/shared_storage/madewithml/GokuMohandas/TorchTrainer_2023-09-14_15-05-00/TorchTrainer_55490282_1_dropout_p=0.5000,lr=0.0001,lr_factor=0.8000,lr_patience=3.0000_2023-09-14_15-05-00/events.out.tfevents.1694729104.ip-10-0-41-50 -> /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/97a3692584eb4a51999f7e97176e3297/artifacts\n", + "copying /efs/shared_storage/madewithml/GokuMohandas/TorchTrainer_2023-09-14_15-05-00/TorchTrainer_55490282_1_dropout_p=0.5000,lr=0.0001,lr_factor=0.8000,lr_patience=3.0000_2023-09-14_15-05-00/result.json -> /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/97a3692584eb4a51999f7e97176e3297/artifacts\n", + "creating /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/97a3692584eb4a51999f7e97176e3297/artifacts/checkpoint_000008\n", + "copying /efs/shared_storage/madewithml/GokuMohandas/TorchTrainer_2023-09-14_15-05-00/TorchTrainer_55490282_1_dropout_p=0.5000,lr=0.0001,lr_factor=0.8000,lr_patience=3.0000_2023-09-14_15-05-00/checkpoint_000008/dict_checkpoint.pkl -> /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/97a3692584eb4a51999f7e97176e3297/artifacts/checkpoint_000008\n", + "copying /efs/shared_storage/madewithml/GokuMohandas/TorchTrainer_2023-09-14_15-05-00/TorchTrainer_55490282_1_dropout_p=0.5000,lr=0.0001,lr_factor=0.8000,lr_patience=3.0000_2023-09-14_15-05-00/checkpoint_000008/.metadata.pkl -> /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/97a3692584eb4a51999f7e97176e3297/artifacts/checkpoint_000008\n", + "copying /efs/shared_storage/madewithml/GokuMohandas/TorchTrainer_2023-09-14_15-05-00/TorchTrainer_55490282_1_dropout_p=0.5000,lr=0.0001,lr_factor=0.8000,lr_patience=3.0000_2023-09-14_15-05-00/checkpoint_000008/.tune_metadata -> /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/97a3692584eb4a51999f7e97176e3297/artifacts/checkpoint_000008\n", + "copying /efs/shared_storage/madewithml/GokuMohandas/TorchTrainer_2023-09-14_15-05-00/TorchTrainer_55490282_1_dropout_p=0.5000,lr=0.0001,lr_factor=0.8000,lr_patience=3.0000_2023-09-14_15-05-00/checkpoint_000008/.is_checkpoint -> /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/97a3692584eb4a51999f7e97176e3297/artifacts/checkpoint_000008\n", + "copying /efs/shared_storage/madewithml/GokuMohandas/TorchTrainer_2023-09-14_15-05-00/TorchTrainer_55490282_1_dropout_p=0.5000,lr=0.0001,lr_factor=0.8000,lr_patience=3.0000_2023-09-14_15-05-00/progress.csv -> /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/97a3692584eb4a51999f7e97176e3297/artifacts\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "(TorchTrainer pid=284149) The dict form of `dataset_config` is deprecated. Use the DataConfig class instead. Support for this will be dropped in a future release.\n", - "(TorchTrainer pid=284149) The `preprocessor` arg to Trainer is deprecated. Apply preprocessor transformations ahead of time by calling `preprocessor.transform(ds)`. Support for the preprocessor arg will be dropped in a future release.\n", - "(TorchTrainer pid=284149) Starting distributed worker processes: ['3934 (10.0.6.57)']\n", - "(RayTrainWorker pid=3934, ip=10.0.6.57) Setting up process group for: env:// [rank=0, world_size=1]\n", - "(RayTrainWorker pid=3934, ip=10.0.6.57) Some weights of the model checkpoint at allenai/scibert_scivocab_uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.decoder.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.weight']\n", - "(RayTrainWorker pid=3934, ip=10.0.6.57) - This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", - "(RayTrainWorker pid=3934, ip=10.0.6.57) - This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n", - "(RayTrainWorker pid=3934, ip=10.0.6.57) Moving model to device: cuda:0\n", - "(RayTrainWorker pid=3934, ip=10.0.6.57) /tmp/ipykernel_280376/1209796013.py:7: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)\n" + "(TorchTrainer pid=52063, ip=10.0.50.151) The dict form of `dataset_config` is deprecated. Use the DataConfig class instead. Support for this will be dropped in a future release.\n", + "(TorchTrainer pid=52063, ip=10.0.50.151) The `preprocessor` arg to Trainer is deprecated. Apply preprocessor transformations ahead of time by calling `preprocessor.transform(ds)`. Support for the preprocessor arg will be dropped in a future release.\n", + "(TorchTrainer pid=52063, ip=10.0.50.151) Starting distributed worker processes: ['52105 (10.0.50.151)']\n", + "(RayTrainWorker pid=52105, ip=10.0.50.151) Setting up process group for: env:// [rank=0, world_size=1]\n", + "(RayTrainWorker pid=52105, ip=10.0.50.151) Some weights of the model checkpoint at allenai/scibert_scivocab_uncased were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.predictions.decoder.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.weight']\n", + "(RayTrainWorker pid=52105, ip=10.0.50.151) - This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n", + "(RayTrainWorker pid=52105, ip=10.0.50.151) - This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n", + "(RayTrainWorker pid=52105, ip=10.0.50.151) Moving model to device: cuda:0\n", + "(RayTrainWorker pid=52105, ip=10.0.50.151) /tmp/ipykernel_117917/1209796013.py:7: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:206.)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "copying /home/ray/ray_results/TorchTrainer_2023-07-24_18-13-18/TorchTrainer_50d8c90f_2_dropout_p=0.3569,lr=0.0000,lr_factor=0.1487,lr_patience=2.5079_2023-07-24_18-13-22/progress.csv -> /tmp/mlflow/598544898920467811/85444db0a2794c43a14accae3054d0e2/artifacts\n", - "copying /home/ray/ray_results/TorchTrainer_2023-07-24_18-13-18/TorchTrainer_50d8c90f_2_dropout_p=0.3569,lr=0.0000,lr_factor=0.1487,lr_patience=2.5079_2023-07-24_18-13-22/events.out.tfevents.1690247682.ip-10-0-56-150 -> /tmp/mlflow/598544898920467811/85444db0a2794c43a14accae3054d0e2/artifacts\n", - "creating /tmp/mlflow/598544898920467811/85444db0a2794c43a14accae3054d0e2/artifacts/checkpoint_000004\n", - "copying /home/ray/ray_results/TorchTrainer_2023-07-24_18-13-18/TorchTrainer_50d8c90f_2_dropout_p=0.3569,lr=0.0000,lr_factor=0.1487,lr_patience=2.5079_2023-07-24_18-13-22/checkpoint_000004/.is_checkpoint -> /tmp/mlflow/598544898920467811/85444db0a2794c43a14accae3054d0e2/artifacts/checkpoint_000004\n", - "copying /home/ray/ray_results/TorchTrainer_2023-07-24_18-13-18/TorchTrainer_50d8c90f_2_dropout_p=0.3569,lr=0.0000,lr_factor=0.1487,lr_patience=2.5079_2023-07-24_18-13-22/checkpoint_000004/.tune_metadata -> /tmp/mlflow/598544898920467811/85444db0a2794c43a14accae3054d0e2/artifacts/checkpoint_000004\n", - "copying /home/ray/ray_results/TorchTrainer_2023-07-24_18-13-18/TorchTrainer_50d8c90f_2_dropout_p=0.3569,lr=0.0000,lr_factor=0.1487,lr_patience=2.5079_2023-07-24_18-13-22/checkpoint_000004/dict_checkpoint.pkl -> /tmp/mlflow/598544898920467811/85444db0a2794c43a14accae3054d0e2/artifacts/checkpoint_000004\n", - "copying /home/ray/ray_results/TorchTrainer_2023-07-24_18-13-18/TorchTrainer_50d8c90f_2_dropout_p=0.3569,lr=0.0000,lr_factor=0.1487,lr_patience=2.5079_2023-07-24_18-13-22/checkpoint_000004/.metadata.pkl -> /tmp/mlflow/598544898920467811/85444db0a2794c43a14accae3054d0e2/artifacts/checkpoint_000004\n", - "copying /home/ray/ray_results/TorchTrainer_2023-07-24_18-13-18/TorchTrainer_50d8c90f_2_dropout_p=0.3569,lr=0.0000,lr_factor=0.1487,lr_patience=2.5079_2023-07-24_18-13-22/result.json -> /tmp/mlflow/598544898920467811/85444db0a2794c43a14accae3054d0e2/artifacts\n", - "copying /home/ray/ray_results/TorchTrainer_2023-07-24_18-13-18/TorchTrainer_50d8c90f_2_dropout_p=0.3569,lr=0.0000,lr_factor=0.1487,lr_patience=2.5079_2023-07-24_18-13-22/params.json -> /tmp/mlflow/598544898920467811/85444db0a2794c43a14accae3054d0e2/artifacts\n", - "copying /home/ray/ray_results/TorchTrainer_2023-07-24_18-13-18/TorchTrainer_50d8c90f_2_dropout_p=0.3569,lr=0.0000,lr_factor=0.1487,lr_patience=2.5079_2023-07-24_18-13-22/.lazy_checkpoint_marker -> /tmp/mlflow/598544898920467811/85444db0a2794c43a14accae3054d0e2/artifacts\n", - "copying /home/ray/ray_results/TorchTrainer_2023-07-24_18-13-18/TorchTrainer_50d8c90f_2_dropout_p=0.3569,lr=0.0000,lr_factor=0.1487,lr_patience=2.5079_2023-07-24_18-13-22/params.pkl -> /tmp/mlflow/598544898920467811/85444db0a2794c43a14accae3054d0e2/artifacts\n" + "copying /efs/shared_storage/madewithml/GokuMohandas/TorchTrainer_2023-09-14_15-05-00/TorchTrainer_45b199df_2_dropout_p=0.7976,lr=0.0000,lr_factor=0.2671,lr_patience=5.2146_2023-09-14_15-05-04/params.json -> /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/57c609dd76c9477896863f04f2a46bb5/artifacts\n", + "copying /efs/shared_storage/madewithml/GokuMohandas/TorchTrainer_2023-09-14_15-05-00/TorchTrainer_45b199df_2_dropout_p=0.7976,lr=0.0000,lr_factor=0.2671,lr_patience=5.2146_2023-09-14_15-05-04/events.out.tfevents.1694729233.ip-10-0-41-50 -> /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/57c609dd76c9477896863f04f2a46bb5/artifacts\n", + "copying /efs/shared_storage/madewithml/GokuMohandas/TorchTrainer_2023-09-14_15-05-00/TorchTrainer_45b199df_2_dropout_p=0.7976,lr=0.0000,lr_factor=0.2671,lr_patience=5.2146_2023-09-14_15-05-04/.lazy_checkpoint_marker -> /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/57c609dd76c9477896863f04f2a46bb5/artifacts\n", + "copying /efs/shared_storage/madewithml/GokuMohandas/TorchTrainer_2023-09-14_15-05-00/TorchTrainer_45b199df_2_dropout_p=0.7976,lr=0.0000,lr_factor=0.2671,lr_patience=5.2146_2023-09-14_15-05-04/params.pkl -> /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/57c609dd76c9477896863f04f2a46bb5/artifacts\n", + "creating /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/57c609dd76c9477896863f04f2a46bb5/artifacts/checkpoint_000004\n", + "copying /efs/shared_storage/madewithml/GokuMohandas/TorchTrainer_2023-09-14_15-05-00/TorchTrainer_45b199df_2_dropout_p=0.7976,lr=0.0000,lr_factor=0.2671,lr_patience=5.2146_2023-09-14_15-05-04/checkpoint_000004/dict_checkpoint.pkl -> /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/57c609dd76c9477896863f04f2a46bb5/artifacts/checkpoint_000004\n", + "copying /efs/shared_storage/madewithml/GokuMohandas/TorchTrainer_2023-09-14_15-05-00/TorchTrainer_45b199df_2_dropout_p=0.7976,lr=0.0000,lr_factor=0.2671,lr_patience=5.2146_2023-09-14_15-05-04/checkpoint_000004/.metadata.pkl -> /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/57c609dd76c9477896863f04f2a46bb5/artifacts/checkpoint_000004\n", + "copying /efs/shared_storage/madewithml/GokuMohandas/TorchTrainer_2023-09-14_15-05-00/TorchTrainer_45b199df_2_dropout_p=0.7976,lr=0.0000,lr_factor=0.2671,lr_patience=5.2146_2023-09-14_15-05-04/checkpoint_000004/.tune_metadata -> /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/57c609dd76c9477896863f04f2a46bb5/artifacts/checkpoint_000004\n", + "copying /efs/shared_storage/madewithml/GokuMohandas/TorchTrainer_2023-09-14_15-05-00/TorchTrainer_45b199df_2_dropout_p=0.7976,lr=0.0000,lr_factor=0.2671,lr_patience=5.2146_2023-09-14_15-05-04/checkpoint_000004/.is_checkpoint -> /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/57c609dd76c9477896863f04f2a46bb5/artifacts/checkpoint_000004\n", + "creating /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/57c609dd76c9477896863f04f2a46bb5/artifacts/rank_0\n", + "copying /efs/shared_storage/madewithml/GokuMohandas/TorchTrainer_2023-09-14_15-05-00/TorchTrainer_45b199df_2_dropout_p=0.7976,lr=0.0000,lr_factor=0.2671,lr_patience=5.2146_2023-09-14_15-05-04/result.json -> /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/57c609dd76c9477896863f04f2a46bb5/artifacts\n", + "copying /efs/shared_storage/madewithml/GokuMohandas/TorchTrainer_2023-09-14_15-05-00/TorchTrainer_45b199df_2_dropout_p=0.7976,lr=0.0000,lr_factor=0.2671,lr_patience=5.2146_2023-09-14_15-05-04/progress.csv -> /efs/shared_storage/madewithml/GokuMohandas/mlflow/917462352875586010/57c609dd76c9477896863f04f2a46bb5/artifacts\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "2023-07-24 18:15:25,696\tINFO tune.py:1148 -- Total run time: 126.91 seconds (126.86 seconds for the tuning loop).\n" + "2023-09-14 15:08:20,659\tINFO tune.py:1148 -- Total run time: 200.56 seconds (200.45 seconds for the tuning loop).\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 3.49 s, sys: 1.41 s, total: 4.9 s\n", - "Wall time: 2min 6s\n" + "CPU times: user 3.63 s, sys: 2.11 s, total: 5.73 s\n", + "Wall time: 3min 20s\n" ] } ], @@ -7670,7 +7638,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 107, "metadata": { "tags": [] }, @@ -7724,49 +7692,49 @@ " 0\n", " 9\n", " 0.000100\n", - " 0.038692\n", - " 0.226971\n", - " 1690247675\n", - " 6.857913\n", + " 0.039336\n", + " 0.160408\n", + " 1694729205\n", + " 10.023823\n", " True\n", " True\n", " 10\n", - " 578373f0\n", + " 55490282\n", " ...\n", - " 283323\n", - " ip-10-0-56-150\n", - " 10.0.56.150\n", - " 76.018291\n", + " 51460\n", + " ip-10-0-50-151\n", + " 10.0.50.151\n", + " 107.564980\n", " 10\n", " 0.500000\n", " 0.000100\n", " 0.800000\n", " 3.000000\n", - " /home/ray/ray_results/TorchTrainer_2023-07-24_...\n", + " /efs/shared_storage/madewithml/GokuMohandas/To...\n", " \n", " \n", " 1\n", " 4\n", - " 0.000026\n", - " 0.407936\n", - " 0.389877\n", - " 1690247722\n", - " 6.744698\n", + " 0.000027\n", + " 0.482763\n", + " 0.436918\n", + " 1694729285\n", + " 10.070868\n", " True\n", " True\n", " 5\n", - " 50d8c90f\n", + " 45b199df\n", " ...\n", - " 284149\n", - " ip-10-0-56-150\n", - " 10.0.56.150\n", - " 42.243630\n", + " 52063\n", + " ip-10-0-50-151\n", + " 10.0.50.151\n", + " 57.585811\n", " 5\n", - " 0.356927\n", - " 0.000026\n", - " 0.148725\n", - " 2.507894\n", - " /home/ray/ray_results/TorchTrainer_2023-07-24_...\n", + " 0.797623\n", + " 0.000027\n", + " 0.267143\n", + " 5.214562\n", + " /efs/shared_storage/madewithml/GokuMohandas/To...\n", " \n", " \n", "\n", @@ -7775,33 +7743,33 @@ ], "text/plain": [ " epoch lr train_loss val_loss timestamp time_this_iter_s \n", - "0 9 0.000100 0.038692 0.226971 1690247675 6.857913 \\\n", - "1 4 0.000026 0.407936 0.389877 1690247722 6.744698 \n", + "0 9 0.000100 0.039336 0.160408 1694729205 10.023823 \\\n", + "1 4 0.000027 0.482763 0.436918 1694729285 10.070868 \n", "\n", - " should_checkpoint done training_iteration trial_id ... pid \n", - "0 True True 10 578373f0 ... 283323 \\\n", - "1 True True 5 50d8c90f ... 284149 \n", + " should_checkpoint done training_iteration trial_id ... pid \n", + "0 True True 10 55490282 ... 51460 \\\n", + "1 True True 5 45b199df ... 52063 \n", "\n", " hostname node_ip time_since_restore iterations_since_restore \n", - "0 ip-10-0-56-150 10.0.56.150 76.018291 10 \\\n", - "1 ip-10-0-56-150 10.0.56.150 42.243630 5 \n", + "0 ip-10-0-50-151 10.0.50.151 107.564980 10 \\\n", + "1 ip-10-0-50-151 10.0.50.151 57.585811 5 \n", "\n", " config/train_loop_config/dropout_p config/train_loop_config/lr \n", "0 0.500000 0.000100 \\\n", - "1 0.356927 0.000026 \n", + "1 0.797623 0.000027 \n", "\n", " config/train_loop_config/lr_factor config/train_loop_config/lr_patience \n", "0 0.800000 3.000000 \\\n", - "1 0.148725 2.507894 \n", + "1 0.267143 5.214562 \n", "\n", " logdir \n", - "0 /home/ray/ray_results/TorchTrainer_2023-07-24_... \n", - "1 /home/ray/ray_results/TorchTrainer_2023-07-24_... \n", + "0 /efs/shared_storage/madewithml/GokuMohandas/To... \n", + "1 /efs/shared_storage/madewithml/GokuMohandas/To... \n", "\n", "[2 rows x 22 columns]" ] }, - "execution_count": 106, + "execution_count": 107, "metadata": {}, "output_type": "execute_result" } @@ -7813,7 +7781,7 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 108, "metadata": { "tags": [] }, @@ -7863,200 +7831,200 @@ " 0\n", " 0\n", " 0.0001\n", - " 0.581037\n", - " 0.501312\n", - " 1690247613\n", - " 14.747473\n", + " 0.577241\n", + " 0.493672\n", + " 1694729113\n", + " 16.759313\n", " True\n", " False\n", " 1\n", - " 578373f0\n", - " 2023-07-24_18-13-36\n", - " 14.747473\n", - " 283323\n", - " ip-10-0-56-150\n", - " 10.0.56.150\n", - " 14.747473\n", + " 55490282\n", + " 2023-09-14_15-05-20\n", + " 16.759313\n", + " 51460\n", + " ip-10-0-50-151\n", + " 10.0.50.151\n", + " 16.759313\n", " 1\n", " \n", " \n", " 1\n", " 1\n", " 0.0001\n", - " 0.486921\n", - " 0.432752\n", - " 1690247620\n", - " 7.031596\n", + " 0.476221\n", + " 0.417544\n", + " 1694729124\n", + " 10.431773\n", " True\n", " False\n", " 2\n", - " 578373f0\n", - " 2023-07-24_18-13-44\n", - " 21.779069\n", - " 283323\n", - " ip-10-0-56-150\n", - " 10.0.56.150\n", - " 21.779069\n", + " 55490282\n", + " 2023-09-14_15-05-31\n", + " 27.191086\n", + " 51460\n", + " ip-10-0-50-151\n", + " 10.0.50.151\n", + " 27.191086\n", " 2\n", " \n", " \n", " 2\n", " 2\n", " 0.0001\n", - " 0.383275\n", - " 0.357377\n", - " 1690247627\n", - " 6.705714\n", + " 0.368710\n", + " 0.373515\n", + " 1694729135\n", + " 10.071463\n", " True\n", " False\n", " 3\n", - " 578373f0\n", - " 2023-07-24_18-13-50\n", - " 28.484783\n", - " 283323\n", - " ip-10-0-56-150\n", - " 10.0.56.150\n", - " 28.484783\n", + " 55490282\n", + " 2023-09-14_15-05-41\n", + " 37.262549\n", + " 51460\n", + " ip-10-0-50-151\n", + " 10.0.50.151\n", + " 37.262549\n", " 3\n", " \n", " \n", " 3\n", " 3\n", " 0.0001\n", - " 0.276686\n", - " 0.300713\n", - " 1690247634\n", - " 6.760320\n", + " 0.292983\n", + " 0.275497\n", + " 1694729145\n", + " 10.122023\n", " True\n", " False\n", " 4\n", - " 578373f0\n", - " 2023-07-24_18-13-57\n", - " 35.245103\n", - " 283323\n", - " ip-10-0-56-150\n", - " 10.0.56.150\n", - " 35.245103\n", + " 55490282\n", + " 2023-09-14_15-05-51\n", + " 47.384572\n", + " 51460\n", + " ip-10-0-50-151\n", + " 10.0.50.151\n", + " 47.384572\n", " 4\n", " \n", " \n", " 4\n", " 4\n", " 0.0001\n", - " 0.208469\n", - " 0.281393\n", - " 1690247641\n", - " 6.757150\n", + " 0.210210\n", + " 0.239090\n", + " 1694729155\n", + " 9.991925\n", " True\n", " False\n", " 5\n", - " 578373f0\n", - " 2023-07-24_18-14-04\n", - " 42.002253\n", - " 283323\n", - " ip-10-0-56-150\n", - " 10.0.56.150\n", - " 42.002253\n", + " 55490282\n", + " 2023-09-14_15-06-01\n", + " 57.376497\n", + " 51460\n", + " ip-10-0-50-151\n", + " 10.0.50.151\n", + " 57.376497\n", " 5\n", " \n", " \n", " 5\n", " 5\n", " 0.0001\n", - " 0.146135\n", - " 0.260962\n", - " 1690247647\n", - " 6.796252\n", + " 0.161824\n", + " 0.197741\n", + " 1694729165\n", + " 10.084616\n", " True\n", " False\n", " 6\n", - " 578373f0\n", - " 2023-07-24_18-14-11\n", - " 48.798505\n", - " 283323\n", - " ip-10-0-56-150\n", - " 10.0.56.150\n", - " 48.798505\n", + " 55490282\n", + " 2023-09-14_15-06-11\n", + " 67.461113\n", + " 51460\n", + " ip-10-0-50-151\n", + " 10.0.50.151\n", + " 67.461113\n", " 6\n", " \n", " \n", " 6\n", " 6\n", " 0.0001\n", - " 0.099608\n", - " 0.260589\n", - " 1690247654\n", - " 6.804206\n", + " 0.113558\n", + " 0.159887\n", + " 1694729175\n", + " 9.953290\n", " True\n", " False\n", " 7\n", - " 578373f0\n", - " 2023-07-24_18-14-17\n", - " 55.602711\n", - " 283323\n", - " ip-10-0-56-150\n", - " 10.0.56.150\n", - " 55.602711\n", + " 55490282\n", + " 2023-09-14_15-06-21\n", + " 77.414402\n", + " 51460\n", + " ip-10-0-50-151\n", + " 10.0.50.151\n", + " 77.414402\n", " 7\n", " \n", " \n", " 7\n", " 7\n", " 0.0001\n", - " 0.072397\n", - " 0.253754\n", - " 1690247661\n", - " 6.785470\n", + " 0.076707\n", + " 0.174182\n", + " 1694729185\n", + " 10.031824\n", " True\n", " False\n", " 8\n", - " 578373f0\n", - " 2023-07-24_18-14-24\n", - " 62.388181\n", - " 283323\n", - " ip-10-0-56-150\n", - " 10.0.56.150\n", - " 62.388181\n", + " 55490282\n", + " 2023-09-14_15-06-31\n", + " 87.446226\n", + " 51460\n", + " ip-10-0-50-151\n", + " 10.0.50.151\n", + " 87.446226\n", " 8\n", " \n", " \n", " 8\n", " 8\n", " 0.0001\n", - " 0.049509\n", - " 0.235428\n", - " 1690247668\n", - " 6.772196\n", + " 0.056161\n", + " 0.152457\n", + " 1694729195\n", + " 10.094931\n", " True\n", " False\n", " 9\n", - " 578373f0\n", - " 2023-07-24_18-14-31\n", - " 69.160377\n", - " 283323\n", - " ip-10-0-56-150\n", - " 10.0.56.150\n", - " 69.160377\n", + " 55490282\n", + " 2023-09-14_15-06-41\n", + " 97.541157\n", + " 51460\n", + " ip-10-0-50-151\n", + " 10.0.50.151\n", + " 97.541157\n", " 9\n", " \n", " \n", " 9\n", " 9\n", " 0.0001\n", - " 0.038692\n", - " 0.226971\n", - " 1690247675\n", - " 6.857913\n", + " 0.039336\n", + " 0.160408\n", + " 1694729205\n", + " 10.023823\n", " True\n", " True\n", " 10\n", - " 578373f0\n", - " 2023-07-24_18-14-38\n", - " 76.018291\n", - " 283323\n", - " ip-10-0-56-150\n", - " 10.0.56.150\n", - " 76.018291\n", + " 55490282\n", + " 2023-09-14_15-06-51\n", + " 107.564980\n", + " 51460\n", + " ip-10-0-50-151\n", + " 10.0.50.151\n", + " 107.564980\n", " 10\n", " \n", " \n", @@ -8065,55 +8033,55 @@ ], "text/plain": [ " epoch lr train_loss val_loss timestamp time_this_iter_s \n", - "0 0 0.0001 0.581037 0.501312 1690247613 14.747473 \\\n", - "1 1 0.0001 0.486921 0.432752 1690247620 7.031596 \n", - "2 2 0.0001 0.383275 0.357377 1690247627 6.705714 \n", - "3 3 0.0001 0.276686 0.300713 1690247634 6.760320 \n", - "4 4 0.0001 0.208469 0.281393 1690247641 6.757150 \n", - "5 5 0.0001 0.146135 0.260962 1690247647 6.796252 \n", - "6 6 0.0001 0.099608 0.260589 1690247654 6.804206 \n", - "7 7 0.0001 0.072397 0.253754 1690247661 6.785470 \n", - "8 8 0.0001 0.049509 0.235428 1690247668 6.772196 \n", - "9 9 0.0001 0.038692 0.226971 1690247675 6.857913 \n", + "0 0 0.0001 0.577241 0.493672 1694729113 16.759313 \\\n", + "1 1 0.0001 0.476221 0.417544 1694729124 10.431773 \n", + "2 2 0.0001 0.368710 0.373515 1694729135 10.071463 \n", + "3 3 0.0001 0.292983 0.275497 1694729145 10.122023 \n", + "4 4 0.0001 0.210210 0.239090 1694729155 9.991925 \n", + "5 5 0.0001 0.161824 0.197741 1694729165 10.084616 \n", + "6 6 0.0001 0.113558 0.159887 1694729175 9.953290 \n", + "7 7 0.0001 0.076707 0.174182 1694729185 10.031824 \n", + "8 8 0.0001 0.056161 0.152457 1694729195 10.094931 \n", + "9 9 0.0001 0.039336 0.160408 1694729205 10.023823 \n", "\n", " should_checkpoint done training_iteration trial_id \n", - "0 True False 1 578373f0 \\\n", - "1 True False 2 578373f0 \n", - "2 True False 3 578373f0 \n", - "3 True False 4 578373f0 \n", - "4 True False 5 578373f0 \n", - "5 True False 6 578373f0 \n", - "6 True False 7 578373f0 \n", - "7 True False 8 578373f0 \n", - "8 True False 9 578373f0 \n", - "9 True True 10 578373f0 \n", - "\n", - " date time_total_s pid hostname node_ip \n", - "0 2023-07-24_18-13-36 14.747473 283323 ip-10-0-56-150 10.0.56.150 \\\n", - "1 2023-07-24_18-13-44 21.779069 283323 ip-10-0-56-150 10.0.56.150 \n", - "2 2023-07-24_18-13-50 28.484783 283323 ip-10-0-56-150 10.0.56.150 \n", - "3 2023-07-24_18-13-57 35.245103 283323 ip-10-0-56-150 10.0.56.150 \n", - "4 2023-07-24_18-14-04 42.002253 283323 ip-10-0-56-150 10.0.56.150 \n", - "5 2023-07-24_18-14-11 48.798505 283323 ip-10-0-56-150 10.0.56.150 \n", - "6 2023-07-24_18-14-17 55.602711 283323 ip-10-0-56-150 10.0.56.150 \n", - "7 2023-07-24_18-14-24 62.388181 283323 ip-10-0-56-150 10.0.56.150 \n", - "8 2023-07-24_18-14-31 69.160377 283323 ip-10-0-56-150 10.0.56.150 \n", - "9 2023-07-24_18-14-38 76.018291 283323 ip-10-0-56-150 10.0.56.150 \n", + "0 True False 1 55490282 \\\n", + "1 True False 2 55490282 \n", + "2 True False 3 55490282 \n", + "3 True False 4 55490282 \n", + "4 True False 5 55490282 \n", + "5 True False 6 55490282 \n", + "6 True False 7 55490282 \n", + "7 True False 8 55490282 \n", + "8 True False 9 55490282 \n", + "9 True True 10 55490282 \n", + "\n", + " date time_total_s pid hostname node_ip \n", + "0 2023-09-14_15-05-20 16.759313 51460 ip-10-0-50-151 10.0.50.151 \\\n", + "1 2023-09-14_15-05-31 27.191086 51460 ip-10-0-50-151 10.0.50.151 \n", + "2 2023-09-14_15-05-41 37.262549 51460 ip-10-0-50-151 10.0.50.151 \n", + "3 2023-09-14_15-05-51 47.384572 51460 ip-10-0-50-151 10.0.50.151 \n", + "4 2023-09-14_15-06-01 57.376497 51460 ip-10-0-50-151 10.0.50.151 \n", + "5 2023-09-14_15-06-11 67.461113 51460 ip-10-0-50-151 10.0.50.151 \n", + "6 2023-09-14_15-06-21 77.414402 51460 ip-10-0-50-151 10.0.50.151 \n", + "7 2023-09-14_15-06-31 87.446226 51460 ip-10-0-50-151 10.0.50.151 \n", + "8 2023-09-14_15-06-41 97.541157 51460 ip-10-0-50-151 10.0.50.151 \n", + "9 2023-09-14_15-06-51 107.564980 51460 ip-10-0-50-151 10.0.50.151 \n", "\n", " time_since_restore iterations_since_restore \n", - "0 14.747473 1 \n", - "1 21.779069 2 \n", - "2 28.484783 3 \n", - "3 35.245103 4 \n", - "4 42.002253 5 \n", - "5 48.798505 6 \n", - "6 55.602711 7 \n", - "7 62.388181 8 \n", - "8 69.160377 9 \n", - "9 76.018291 10 " + "0 16.759313 1 \n", + "1 27.191086 2 \n", + "2 37.262549 3 \n", + "3 47.384572 4 \n", + "4 57.376497 5 \n", + "5 67.461113 6 \n", + "6 77.414402 7 \n", + "7 87.446226 8 \n", + "8 97.541157 9 \n", + "9 107.564980 10 " ] }, - "execution_count": 107, + "execution_count": 108, "metadata": {}, "output_type": "execute_result" } @@ -8126,7 +8094,7 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 109, "metadata": { "tags": [] }, @@ -8137,7 +8105,7 @@ "{'dropout_p': 0.5, 'lr': 0.0001, 'lr_factor': 0.8, 'lr_patience': 3.0}" ] }, - "execution_count": 108, + "execution_count": 109, "metadata": {}, "output_type": "execute_result" } @@ -8149,7 +8117,7 @@ }, { "cell_type": "code", - "execution_count": 109, + "execution_count": 110, "metadata": { "tags": [] }, @@ -8181,19 +8149,19 @@ " artifact_uri\n", " start_time\n", " end_time\n", - " metrics.time_since_restore\n", - " metrics.time_this_iter_s\n", - " metrics.config/train_loop_config/lr\n", - " metrics.done\n", + " metrics.pid\n", + " metrics.epoch\n", + " metrics.config/train_loop_config/batch_size\n", + " metrics.iterations_since_restore\n", " ...\n", - " metrics.val_loss\n", - " params.train_loop_config/num_classes\n", + " metrics.lr\n", " params.train_loop_config/dropout_p\n", - " params.train_loop_config/num_epochs\n", + " params.train_loop_config/batch_size\n", + " params.train_loop_config/lr_factor\n", + " params.train_loop_config/num_classes\n", " params.train_loop_config/lr_patience\n", + " params.train_loop_config/num_epochs\n", " params.train_loop_config/lr\n", - " params.train_loop_config/lr_factor\n", - " params.train_loop_config/batch_size\n", " tags.trial_name\n", " tags.mlflow.runName\n", " \n", @@ -8201,136 +8169,136 @@ " \n", " \n", " 0\n", - " 8991516da6674b5395affb9fb6217964\n", - " 598544898920467811\n", + " cb6a74df7c4e43988598be5aee45e660\n", + " 917462352875586010\n", " FINISHED\n", - " file:///tmp/mlflow/598544898920467811/8991516d...\n", - " 2023-07-25 01:11:48.948000+00:00\n", - " 2023-07-25 01:13:07.513000+00:00\n", - " 75.376313\n", - " 6.712862\n", - " 0.000100\n", - " 0.0\n", + " file:///efs/shared_storage/madewithml/GokuMoha...\n", + " 2023-09-14 22:02:50.670000+00:00\n", + " 2023-09-14 22:04:48.292000+00:00\n", + " 50696.0\n", + " 9.0\n", + " 256.0\n", + " 10.0\n", " ...\n", - " 0.221598\n", - " 4\n", + " 0.000100\n", " 0.5\n", - " 10\n", + " 256\n", + " 0.8\n", + " 4\n", " 3\n", + " 10\n", " 0.0001\n", - " 0.8\n", - " 256\n", - " TorchTrainer_38d2b_00000\n", - " TorchTrainer_38d2b_00000\n", + " TorchTrainer_701e4_00000\n", + " TorchTrainer_701e4_00000\n", " \n", " \n", " 1\n", - " 6b854ffda94844e28013fc6deb157af4\n", - " 598544898920467811\n", + " 97a3692584eb4a51999f7e97176e3297\n", + " 917462352875586010\n", " FINISHED\n", - " file:///tmp/mlflow/598544898920467811/6b854ffd...\n", - " 2023-07-25 01:13:22.223000+00:00\n", - " 2023-07-25 01:14:38.804000+00:00\n", - " 76.018291\n", - " 6.857913\n", - " 0.000100\n", - " 1.0\n", + " file:///efs/shared_storage/madewithml/GokuMoha...\n", + " 2023-09-14 22:05:03.880000+00:00\n", + " 2023-09-14 22:07:09.776000+00:00\n", + " 51460.0\n", + " 9.0\n", + " NaN\n", + " 10.0\n", " ...\n", - " 0.226971\n", - " None\n", + " 0.000100\n", " 0.5\n", " None\n", - " 3.0\n", - " 0.0001\n", " 0.8\n", " None\n", - " TorchTrainer_578373f0\n", - " TorchTrainer_578373f0\n", + " 3.0\n", + " None\n", + " 0.0001\n", + " TorchTrainer_55490282\n", + " TorchTrainer_55490282\n", " \n", " \n", " 2\n", - " 85444db0a2794c43a14accae3054d0e2\n", - " 598544898920467811\n", + " 57c609dd76c9477896863f04f2a46bb5\n", + " 917462352875586010\n", " FINISHED\n", - " file:///tmp/mlflow/598544898920467811/85444db0...\n", - " 2023-07-25 01:14:42.883000+00:00\n", - " 2023-07-25 01:15:25.678000+00:00\n", - " 42.243630\n", - " 6.744698\n", - " 0.000026\n", - " 1.0\n", + " file:///efs/shared_storage/madewithml/GokuMoha...\n", + " 2023-09-14 22:07:13.514000+00:00\n", + " 2023-09-14 22:08:20.525000+00:00\n", + " 52063.0\n", + " 4.0\n", + " NaN\n", + " 5.0\n", " ...\n", - " 0.389877\n", + " 0.000027\n", + " 0.7976231355896662\n", " None\n", - " 0.3569270357483055\n", + " 0.267142900040756\n", " None\n", - " 2.507894040733857\n", - " 2.6342924831000413e-05\n", - " 0.14872459915992878\n", + " 5.214561723373508\n", " None\n", - " TorchTrainer_50d8c90f\n", - " TorchTrainer_50d8c90f\n", + " 2.6942959077294993e-05\n", + " TorchTrainer_45b199df\n", + " TorchTrainer_45b199df\n", " \n", " \n", "\n", - "

3 rows × 35 columns

\n", + "

3 rows × 36 columns

\n", "" ], "text/plain": [ " run_id experiment_id status \n", - "0 8991516da6674b5395affb9fb6217964 598544898920467811 FINISHED \\\n", - "1 6b854ffda94844e28013fc6deb157af4 598544898920467811 FINISHED \n", - "2 85444db0a2794c43a14accae3054d0e2 598544898920467811 FINISHED \n", + "0 cb6a74df7c4e43988598be5aee45e660 917462352875586010 FINISHED \\\n", + "1 97a3692584eb4a51999f7e97176e3297 917462352875586010 FINISHED \n", + "2 57c609dd76c9477896863f04f2a46bb5 917462352875586010 FINISHED \n", "\n", " artifact_uri \n", - "0 file:///tmp/mlflow/598544898920467811/8991516d... \\\n", - "1 file:///tmp/mlflow/598544898920467811/6b854ffd... \n", - "2 file:///tmp/mlflow/598544898920467811/85444db0... \n", + "0 file:///efs/shared_storage/madewithml/GokuMoha... \\\n", + "1 file:///efs/shared_storage/madewithml/GokuMoha... \n", + "2 file:///efs/shared_storage/madewithml/GokuMoha... \n", "\n", " start_time end_time \n", - "0 2023-07-25 01:11:48.948000+00:00 2023-07-25 01:13:07.513000+00:00 \\\n", - "1 2023-07-25 01:13:22.223000+00:00 2023-07-25 01:14:38.804000+00:00 \n", - "2 2023-07-25 01:14:42.883000+00:00 2023-07-25 01:15:25.678000+00:00 \n", - "\n", - " metrics.time_since_restore metrics.time_this_iter_s \n", - "0 75.376313 6.712862 \\\n", - "1 76.018291 6.857913 \n", - "2 42.243630 6.744698 \n", - "\n", - " metrics.config/train_loop_config/lr metrics.done ... metrics.val_loss \n", - "0 0.000100 0.0 ... 0.221598 \\\n", - "1 0.000100 1.0 ... 0.226971 \n", - "2 0.000026 1.0 ... 0.389877 \n", - "\n", - " params.train_loop_config/num_classes params.train_loop_config/dropout_p \n", - "0 4 0.5 \\\n", - "1 None 0.5 \n", - "2 None 0.3569270357483055 \n", - "\n", - " params.train_loop_config/num_epochs params.train_loop_config/lr_patience \n", - "0 10 3 \\\n", - "1 None 3.0 \n", - "2 None 2.507894040733857 \n", - "\n", - " params.train_loop_config/lr params.train_loop_config/lr_factor \n", - "0 0.0001 0.8 \\\n", - "1 0.0001 0.8 \n", - "2 2.6342924831000413e-05 0.14872459915992878 \n", - "\n", - " params.train_loop_config/batch_size tags.trial_name \n", - "0 256 TorchTrainer_38d2b_00000 \\\n", - "1 None TorchTrainer_578373f0 \n", - "2 None TorchTrainer_50d8c90f \n", + "0 2023-09-14 22:02:50.670000+00:00 2023-09-14 22:04:48.292000+00:00 \\\n", + "1 2023-09-14 22:05:03.880000+00:00 2023-09-14 22:07:09.776000+00:00 \n", + "2 2023-09-14 22:07:13.514000+00:00 2023-09-14 22:08:20.525000+00:00 \n", + "\n", + " metrics.pid metrics.epoch metrics.config/train_loop_config/batch_size \n", + "0 50696.0 9.0 256.0 \\\n", + "1 51460.0 9.0 NaN \n", + "2 52063.0 4.0 NaN \n", + "\n", + " metrics.iterations_since_restore ... metrics.lr \n", + "0 10.0 ... 0.000100 \\\n", + "1 10.0 ... 0.000100 \n", + "2 5.0 ... 0.000027 \n", + "\n", + " params.train_loop_config/dropout_p params.train_loop_config/batch_size \n", + "0 0.5 256 \\\n", + "1 0.5 None \n", + "2 0.7976231355896662 None \n", + "\n", + " params.train_loop_config/lr_factor params.train_loop_config/num_classes \n", + "0 0.8 4 \\\n", + "1 0.8 None \n", + "2 0.267142900040756 None \n", + "\n", + " params.train_loop_config/lr_patience params.train_loop_config/num_epochs \n", + "0 3 10 \\\n", + "1 3.0 None \n", + "2 5.214561723373508 None \n", + "\n", + " params.train_loop_config/lr tags.trial_name \n", + "0 0.0001 TorchTrainer_701e4_00000 \\\n", + "1 0.0001 TorchTrainer_55490282 \n", + "2 2.6942959077294993e-05 TorchTrainer_45b199df \n", "\n", " tags.mlflow.runName \n", - "0 TorchTrainer_38d2b_00000 \n", - "1 TorchTrainer_578373f0 \n", - "2 TorchTrainer_50d8c90f \n", + "0 TorchTrainer_701e4_00000 \n", + "1 TorchTrainer_55490282 \n", + "2 TorchTrainer_45b199df \n", "\n", - "[3 rows x 35 columns]" + "[3 rows x 36 columns]" ] }, - "execution_count": 109, + "execution_count": 110, "metadata": {}, "output_type": "execute_result" } @@ -8343,7 +8311,7 @@ }, { "cell_type": "code", - "execution_count": 110, + "execution_count": 111, "metadata": { "tags": [] }, @@ -8352,9 +8320,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "2023-07-24 18:15:26,376\tINFO streaming_executor.py:92 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadCSV->SplitBlocks(24)] -> TaskPoolMapOperator[MapBatches(CustomPreprocessor._transform_pandas)->MapBatches()]\n", - "2023-07-24 18:15:26,377\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", - "2023-07-24 18:15:26,377\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + "2023-09-14 15:08:21,709\tINFO streaming_executor.py:92 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadCSV->SplitBlocks(48)] -> TaskPoolMapOperator[MapBatches(CustomPreprocessor._transform_pandas)->MapBatches()]\n", + "2023-09-14 15:08:21,710\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-09-14 15:08:21,711\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" ] }, { @@ -8365,7 +8333,7 @@ "version_minor": 0 }, "text/plain": [ - "Running 0: 0%| | 0/24 [00:00 TaskPoolMapOperator[ReadCSV->SplitBlocks(24)] -> TaskPoolMapOperator[MapBatches(CustomPreprocessor._transform_pandas)->MapBatches()]\n", - "2023-07-24 18:15:30,005\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", - "2023-07-24 18:15:30,006\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" + "2023-09-14 15:08:25,603\tINFO streaming_executor.py:92 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadCSV->SplitBlocks(48)] -> TaskPoolMapOperator[MapBatches(CustomPreprocessor._transform_pandas)->MapBatches()]\n", + "2023-09-14 15:08:25,603\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-09-14 15:08:25,604\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n" ] }, { @@ -8512,7 +8478,7 @@ "version_minor": 0 }, "text/plain": [ - "Running 0: 0%| | 0/24 [00:0084\n", " 2020-03-20 15:18:43\n", " Mention Classifier\n", - " Category prediction model\\r\\nThis repo contain...\n", + " Category prediction model\\nThis repo contains ...\n", " natural-language-processing\n", - " Mention Classifier Category prediction model\\r...\n", + " Mention Classifier Category prediction model\\n...\n", " natural-language-processing\n", " \n", " \n", @@ -8698,7 +8664,7 @@ "1 A PyTorch implementation of \"Graph Wavelet Neu... \n", "2 A PyTorch implementation of \"Capsule Graph Neu... \n", "3 Representing scenes as neural radiance fields ... \n", - "4 Category prediction model\\r\\nThis repo contain... \n", + "4 Category prediction model\\nThis repo contains ... \n", "\n", " tag \n", "0 other \\\n", @@ -8712,7 +8678,7 @@ "1 Graph Wavelet Neural Network A PyTorch impleme... \n", "2 Capsule Graph Neural Network A PyTorch impleme... \n", "3 NeRF: Neural Radiance Fields Representing scen... \n", - "4 Mention Classifier Category prediction model\\r... \n", + "4 Mention Classifier Category prediction model\\n... \n", "\n", " prediction \n", "0 computer-vision \n", @@ -8722,7 +8688,7 @@ "4 natural-language-processing " ] }, - "execution_count": 117, + "execution_count": 118, "metadata": {}, "output_type": "execute_result" } @@ -8736,7 +8702,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "TiXcls5JoNA8" @@ -8747,7 +8712,7 @@ }, { "cell_type": "code", - "execution_count": 118, + "execution_count": 119, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -8762,9 +8727,9 @@ "output_type": "stream", "text": [ "{\n", - " \"precision\": 0.9281010510531216,\n", - " \"recall\": 0.9267015706806283,\n", - " \"f1\": 0.9269438615952555,\n", + " \"precision\": 0.9206853613661888,\n", + " \"recall\": 0.9162303664921466,\n", + " \"f1\": 0.9167279878033617,\n", " \"num_samples\": 191.0\n", "}\n" ] @@ -8781,7 +8746,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "zl3xSuXRutKG" @@ -8792,7 +8756,7 @@ }, { "cell_type": "code", - "execution_count": 119, + "execution_count": 120, "metadata": { "id": "jqetm3ybN9C1", "tags": [] @@ -8804,7 +8768,7 @@ }, { "cell_type": "code", - "execution_count": 120, + "execution_count": 121, "metadata": { "id": "1zIAI4mwusoX", "tags": [] @@ -8824,7 +8788,7 @@ }, { "cell_type": "code", - "execution_count": 121, + "execution_count": 122, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -8839,9 +8803,9 @@ "output_type": "stream", "text": [ "{\n", - " \"precision\": 0.95,\n", + " \"precision\": 0.9156626506024096,\n", " \"recall\": 0.9743589743589743,\n", - " \"f1\": 0.9620253164556962,\n", + " \"f1\": 0.9440993788819876,\n", " \"num_samples\": 78.0\n", "}\n" ] @@ -8855,7 +8819,7 @@ }, { "cell_type": "code", - "execution_count": 122, + "execution_count": 123, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -8872,36 +8836,36 @@ "[\n", " \"natural-language-processing\",\n", " {\n", - " \"precision\": 0.95,\n", + " \"precision\": 0.9156626506024096,\n", " \"recall\": 0.9743589743589743,\n", - " \"f1\": 0.9620253164556962,\n", + " \"f1\": 0.9440993788819876,\n", " \"num_samples\": 78.0\n", " }\n", "]\n", "[\n", - " \"computer-vision\",\n", + " \"other\",\n", " {\n", - " \"precision\": 0.9552238805970149,\n", - " \"recall\": 0.9014084507042254,\n", - " \"f1\": 0.927536231884058,\n", - " \"num_samples\": 71.0\n", + " \"precision\": 0.9583333333333334,\n", + " \"recall\": 0.8846153846153846,\n", + " \"f1\": 0.9199999999999999,\n", + " \"num_samples\": 26.0\n", " }\n", "]\n", "[\n", - " \"other\",\n", + " \"computer-vision\",\n", " {\n", - " \"precision\": 0.8888888888888888,\n", - " \"recall\": 0.9230769230769231,\n", - " \"f1\": 0.9056603773584906,\n", - " \"num_samples\": 26.0\n", + " \"precision\": 0.9538461538461539,\n", + " \"recall\": 0.8732394366197183,\n", + " \"f1\": 0.9117647058823529,\n", + " \"num_samples\": 71.0\n", " }\n", "]\n", "[\n", " \"mlops\",\n", " {\n", - " \"precision\": 0.7647058823529411,\n", - " \"recall\": 0.8125,\n", - " \"f1\": 0.787878787878788,\n", + " \"precision\": 0.7368421052631579,\n", + " \"recall\": 0.875,\n", + " \"f1\": 0.7999999999999999,\n", " \"num_samples\": 16.0\n", " }\n", "]\n" @@ -8917,7 +8881,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "f-juex26zvBF" @@ -8927,7 +8890,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "xPUao0S4k99c" @@ -8942,7 +8904,7 @@ }, { "cell_type": "code", - "execution_count": 123, + "execution_count": 124, "metadata": { "id": "ZG2SgsPAzukL", "tags": [] @@ -8965,7 +8927,7 @@ }, { "cell_type": "code", - "execution_count": 124, + "execution_count": 125, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -8979,9 +8941,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "[4, 9, 12, 16, 17, 19, 23, 26, 29, 30, 31, 32, 33, 34, 42, 47, 49, 50, 54, 56, 65, 66, 68, 71, 75, 76, 77, 78, 79, 82, 92, 94, 95, 97, 99, 101, 109, 113, 114, 115, 118, 120, 122, 126, 128, 129, 130, 131, 133, 134, 135, 138, 139, 140, 141, 142, 144, 148, 149, 152, 159, 160, 161, 163, 166, 170, 172, 173, 174, 177, 179, 183, 184, 187, 189, 190]\n", - "[24, 104, 165, 188]\n", - "[25, 112]\n" + "[4, 9, 12, 16, 17, 19, 23, 25, 26, 29, 30, 31, 32, 33, 34, 42, 47, 49, 50, 54, 56, 65, 66, 68, 71, 75, 77, 78, 79, 82, 92, 94, 95, 97, 99, 101, 109, 113, 114, 115, 118, 120, 122, 126, 128, 129, 130, 131, 133, 134, 135, 138, 139, 140, 141, 142, 144, 148, 149, 152, 159, 160, 161, 163, 166, 170, 172, 173, 174, 177, 179, 183, 184, 187, 189, 190]\n", + "[41, 55, 61, 104, 107, 154, 165]\n", + "[76, 112]\n" ] } ], @@ -8993,7 +8955,7 @@ }, { "cell_type": "code", - "execution_count": 125, + "execution_count": 126, "metadata": {}, "outputs": [ { @@ -9017,27 +8979,27 @@ "\n", "\n", "=== False positives ===\n", - "Keras OCR A packaged and flexible version of the CRAFT text detector and Keras CRNN recognition model. \n", - " true: computer-vision\n", + "How Docker Can Help You Become A More Effective Data Scientist A look at Docker from the perspective of a data scientist.\n", + " true: mlops\n", " pred: natural-language-processing\n", "\n", - "Open Compound Domain Adaptation Pytorch implementation for \"Open Compound Domain Adaptation\"\n", - " true: computer-vision\n", + "The AI Economist Improving Equality and Productivity with AI-Driven Tax Policies\n", + " true: other\n", " pred: natural-language-processing\n", "\n", - "Unpopular Opinion - Data Scientists Should Be More End-to-End I believe data scientists can be more effective by being end-to-end.\n", - " true: mlops\n", + "Differential Subspace Search in High-Dimensional Latent Space Differential subspace search to allow efficient iterative user exploration in such a space, without relying on domain- or data-specific assumptions.\n", + " true: computer-vision\n", " pred: natural-language-processing\n", "\n", "\n", "=== False negatives ===\n", - "Visualizing Memorization in RNNs Inspecting gradient magnitudes in context can be a powerful tool to see when recurrent units use short-term or long-term contextual understanding.\n", + "Get Subreddit Suggestions for a Post Trained on 4M Reddit posts from 4k Subreddits. End-to-end ML pipeline built with fasttext and FastAPI, deployed to Valohai.\n", " true: natural-language-processing\n", " pred: computer-vision\n", "\n", "Machine Learning Projects This Repo contains projects done by me while learning the basics. All the familiar types of regression, classification, and clustering methods have been used.\n", " true: natural-language-processing\n", - " pred: other\n", + " pred: mlops\n", "\n" ] } @@ -9056,7 +9018,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "6S5LZdP2Myjh" @@ -9066,7 +9027,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "ZW5nY_h-M08p" @@ -9089,7 +9049,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "OuN8xKFZlo2t" @@ -9100,7 +9059,7 @@ }, { "cell_type": "code", - "execution_count": 126, + "execution_count": 127, "metadata": { "id": "3FCrRUb2GANr", "tags": [] @@ -9115,7 +9074,7 @@ }, { "cell_type": "code", - "execution_count": 127, + "execution_count": 128, "metadata": { "id": "sKQxFU0iU-w-", "tags": [] @@ -9137,7 +9096,7 @@ }, { "cell_type": "code", - "execution_count": 128, + "execution_count": 129, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -9150,17 +9109,17 @@ { "data": { "text/plain": [ - "[{'text': 'Visualizing Memorization in RNNs Inspecting gradient magnitudes in context can be a powerful tool to see when recurrent units use short-term or long-term contextual understanding.',\n", + "[{'text': 'Get Subreddit Suggestions for a Post Trained on 4M Reddit posts from 4k Subreddits. End-to-end ML pipeline built with fasttext and FastAPI, deployed to Valohai.',\n", " 'true': 'natural-language-processing',\n", " 'pred': 'computer-vision',\n", - " 'prob': 0.0070185387},\n", + " 'prob': 0.00060260075},\n", " {'text': 'Machine Learning Projects This Repo contains projects done by me while learning the basics. All the familiar types of regression, classification, and clustering methods have been used.',\n", " 'true': 'natural-language-processing',\n", - " 'pred': 'other',\n", - " 'prob': 0.006060462}]" + " 'pred': 'mlops',\n", + " 'prob': 0.0076674907}]" ] }, - "execution_count": 128, + "execution_count": 129, "metadata": {}, "output_type": "execute_result" } @@ -9170,7 +9129,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "JwL1ltdiUjH2" @@ -9194,7 +9152,7 @@ }, { "cell_type": "code", - "execution_count": 129, + "execution_count": 130, "metadata": { "id": "XX3cORGPPXXM", "tags": [] @@ -9207,7 +9165,7 @@ }, { "cell_type": "code", - "execution_count": 130, + "execution_count": 131, "metadata": { "tags": [] }, @@ -9216,38 +9174,38 @@ "name": "stderr", "output_type": "stream", "text": [ - "*** SIGTERM received at time=1690247734 on cpu 6 ***\n", - "*** SIGTERM received at time=1690247734 on cpu 1 ***\n", - "*** SIGTERM received at time=1690247734 on cpu 0 ***\n", - "*** SIGTERM received at time=1690247734 on cpu 4 ***\n", - "PC: @ 0x7fb69c9c011b (unknown) _Exit\n", - " @ 0x7fb69cc3d420 28547728 (unknown)\n", - "PC: @ 0x4edd8a (unknown) _PyEval_MakeFrameVector\n", - " @ 0x7fb69cc3d420 (unknown) (unknown)\n", - "PC: @ 0x4ef4bc (unknown) _PyEval_EvalFrameDefault\n", - "PC: @ 0x4eddcd (unknown) _PyEval_MakeFrameVector\n", - " @ 0x7260e0 (unknown) (unknown)\n", - " @ 0x7fb69cc3d420 (unknown) (unknown)\n", - " @ 0x7fb69cc3d420 (unknown) (unknown)\n", + "*** SIGTERM received at time=1694729309 on cpu 5 ***\n", + "*** SIGTERM received at time=1694729309 on cpu 4 ***\n", + "*** SIGTERM received at time=1694729309 on cpu 0 ***\n", + "*** SIGTERM received at time=1694729309 on cpu 2 ***\n", + "PC: @ 0x4fefa3 (unknown) _PyObject_GetMethod\n", + "PC: @ 0x4fce88 (unknown) _PyObject_GenericGetAttrWithDict\n", + " @ 0x7fe11d01f420 27754944 (unknown)\n", + " @ 0x7fe11d01f420 26229312 (unknown)\n", + "PC: @ 0x4f369b (unknown) _PyEval_EvalFrameDefault\n", + "PC: @ 0x4ff156 (unknown) _PyObject_GetMethod\n", + " @ 0x7fe11d01f420 (unknown) (unknown)\n", + " @ 0x7fe11d01f420 27580544 (unknown)\n", " @ ... and at least 1 more frames\n", - "[2023-07-24 18:15:34,914 E 284736 280376] logging.cc:361: *** SIGTERM received at time=1690247734 on cpu 6 ***\n", - "[2023-07-24 18:15:34,915 E 284736 280376] logging.cc:361: PC: @ 0x7fb69c9c011b (unknown) _Exit\n", - "[2023-07-24 18:15:34,915 E 284738 280376] logging.cc:361: *** SIGTERM received at time=1690247734 on cpu 4 ***\n", - "[2023-07-24 18:15:34,915 E 284738 280376] logging.cc:361: PC: @ 0x4ef4bc (unknown) _PyEval_EvalFrameDefault\n", - "[2023-07-24 18:15:34,915 E 284738 280376] logging.cc:361: @ 0x7fb69cc3d420 (unknown) (unknown)\n", - "[2023-07-24 18:15:34,915 E 284738 280376] logging.cc:361: @ ... and at least 1 more frames\n", - " @ 0x72c720 (unknown) (unknown)\n", - "[2023-07-24 18:15:34,917 E 284737 280376] logging.cc:361: *** SIGTERM received at time=1690247734 on cpu 1 ***\n", - "[2023-07-24 18:15:34,917 E 284737 280376] logging.cc:361: PC: @ 0x4edd8a (unknown) _PyEval_MakeFrameVector\n", - "[2023-07-24 18:15:34,918 E 284736 280376] logging.cc:361: @ 0x7fb69cc3d420 28547728 (unknown)\n", - " @ 0x72c720 (unknown) (unknown)\n", - "[2023-07-24 18:15:34,920 E 284737 280376] logging.cc:361: @ 0x7fb69cc3d420 (unknown) (unknown)\n", - "[2023-07-24 18:15:34,920 E 284735 280376] logging.cc:361: *** SIGTERM received at time=1690247734 on cpu 0 ***\n", - "[2023-07-24 18:15:34,921 E 284735 280376] logging.cc:361: PC: @ 0x4eddcd (unknown) _PyEval_MakeFrameVector\n", - "[2023-07-24 18:15:34,922 E 284736 280376] logging.cc:361: @ 0x7260e0 (unknown) (unknown)\n", - "[2023-07-24 18:15:34,924 E 284737 280376] logging.cc:361: @ 0x72c720 (unknown) (unknown)\n", - "[2023-07-24 18:15:34,926 E 284735 280376] logging.cc:361: @ 0x7fb69cc3d420 (unknown) (unknown)\n", - "[2023-07-24 18:15:34,932 E 284735 280376] logging.cc:361: @ 0x72c720 (unknown) (unknown)\n" + "[2023-09-14 15:08:29,998 E 123487 117917] logging.cc:361: *** SIGTERM received at time=1694729309 on cpu 4 ***\n", + "[2023-09-14 15:08:29,998 E 123487 117917] logging.cc:361: PC: @ 0x4f369b (unknown) _PyEval_EvalFrameDefault\n", + "[2023-09-14 15:08:29,998 E 123487 117917] logging.cc:361: @ 0x7fe11d01f420 (unknown) (unknown)\n", + "[2023-09-14 15:08:29,998 E 123487 117917] logging.cc:361: @ ... and at least 1 more frames\n", + " @ 0x742da0 (unknown) (unknown)\n", + " @ 0x742da0 (unknown) (unknown)\n", + "[2023-09-14 15:08:30,000 E 123488 117917] logging.cc:361: *** SIGTERM received at time=1694729309 on cpu 5 ***\n", + "[2023-09-14 15:08:30,000 E 123488 117917] logging.cc:361: PC: @ 0x4fefa3 (unknown) _PyObject_GetMethod\n", + "[2023-09-14 15:08:30,000 E 123489 117917] logging.cc:361: *** SIGTERM received at time=1694729309 on cpu 2 ***\n", + "[2023-09-14 15:08:30,000 E 123489 117917] logging.cc:361: PC: @ 0x4fce88 (unknown) _PyObject_GenericGetAttrWithDict\n", + "[2023-09-14 15:08:30,003 E 123488 117917] logging.cc:361: @ 0x7fe11d01f420 27754944 (unknown)\n", + "[2023-09-14 15:08:30,003 E 123489 117917] logging.cc:361: @ 0x7fe11d01f420 26229312 (unknown)\n", + " @ 0x742da0 (unknown) (unknown)\n", + "[2023-09-14 15:08:30,003 E 123486 117917] logging.cc:361: *** SIGTERM received at time=1694729309 on cpu 0 ***\n", + "[2023-09-14 15:08:30,003 E 123486 117917] logging.cc:361: PC: @ 0x4ff156 (unknown) _PyObject_GetMethod\n", + "[2023-09-14 15:08:30,007 E 123488 117917] logging.cc:361: @ 0x742da0 (unknown) (unknown)\n", + "[2023-09-14 15:08:30,007 E 123489 117917] logging.cc:361: @ 0x742da0 (unknown) (unknown)\n", + "[2023-09-14 15:08:30,009 E 123486 117917] logging.cc:361: @ 0x7fe11d01f420 27580544 (unknown)\n", + "[2023-09-14 15:08:30,015 E 123486 117917] logging.cc:361: @ 0x742da0 (unknown) (unknown)\n" ] }, { @@ -9281,15 +9239,6 @@ " \n", " \n", " \n", - " 165\n", - " 2137\n", - " 2020-08-13 02:10:03\n", - " Unpopular Opinion - Data Scientists Should Be ...\n", - " I believe data scientists can be more effectiv...\n", - " mlops\n", - " natural-language-processing\n", - " \n", - " \n", " 103\n", " 1459\n", " 2020-06-16 03:06:10\n", @@ -9299,31 +9248,40 @@ " computer-vision\n", " \n", " \n", - " 188\n", - " 2413\n", - " 2020-10-01 23:50:04\n", - " Keeping Data Pipelines healthy w/ Great Expect...\n", - " We show you how you can use GitHub Actions tog...\n", + " 165\n", + " 2137\n", + " 2020-08-13 02:10:03\n", + " Unpopular Opinion - Data Scientists Should Be ...\n", + " I believe data scientists can be more effectiv...\n", " mlops\n", " natural-language-processing\n", " \n", " \n", - " 112\n", - " 1524\n", - " 2020-06-20 10:42:25\n", - " Machine Learning Projects\n", - " This Repo contains projects done by me while l...\n", + " 76\n", + " 916\n", + " 2020-05-19 08:11:05\n", + " Get Subreddit Suggestions for a Post\n", + " Trained on 4M Reddit posts from 4k Subreddits....\n", " natural-language-processing\n", - " other\n", + " computer-vision\n", " \n", " \n", - " 25\n", - " 384\n", - " 2020-04-08 21:22:25\n", - " Visualizing Memorization in RNNs\n", - " Inspecting gradient magnitudes in context can ...\n", + " 41\n", + " 561\n", + " 2020-04-16 16:27:31\n", + " How Docker Can Help You Become A More Effectiv...\n", + " A look at Docker from the perspective of a dat...\n", + " mlops\n", " natural-language-processing\n", + " \n", + " \n", + " 104\n", + " 1462\n", + " 2020-06-16 03:28:40\n", + " Open Compound Domain Adaptation\n", + " Pytorch implementation for \"Open Compound Doma...\n", " computer-vision\n", + " natural-language-processing\n", " \n", " \n", "\n", @@ -9331,35 +9289,35 @@ ], "text/plain": [ " id created_on \n", - "165 2137 2020-08-13 02:10:03 \\\n", - "103 1459 2020-06-16 03:06:10 \n", - "188 2413 2020-10-01 23:50:04 \n", - "112 1524 2020-06-20 10:42:25 \n", - "25 384 2020-04-08 21:22:25 \n", + "103 1459 2020-06-16 03:06:10 \\\n", + "165 2137 2020-08-13 02:10:03 \n", + "76 916 2020-05-19 08:11:05 \n", + "41 561 2020-04-16 16:27:31 \n", + "104 1462 2020-06-16 03:28:40 \n", "\n", " title \n", - "165 Unpopular Opinion - Data Scientists Should Be ... \\\n", - "103 SuperGlue: Learning Feature Matching with Grap... \n", - "188 Keeping Data Pipelines healthy w/ Great Expect... \n", - "112 Machine Learning Projects \n", - "25 Visualizing Memorization in RNNs \n", + "103 SuperGlue: Learning Feature Matching with Grap... \\\n", + "165 Unpopular Opinion - Data Scientists Should Be ... \n", + "76 Get Subreddit Suggestions for a Post \n", + "41 How Docker Can Help You Become A More Effectiv... \n", + "104 Open Compound Domain Adaptation \n", "\n", " description \n", - "165 I believe data scientists can be more effectiv... \\\n", - "103 SuperGlue, a neural network that matches two s... \n", - "188 We show you how you can use GitHub Actions tog... \n", - "112 This Repo contains projects done by me while l... \n", - "25 Inspecting gradient magnitudes in context can ... \n", + "103 SuperGlue, a neural network that matches two s... \\\n", + "165 I believe data scientists can be more effectiv... \n", + "76 Trained on 4M Reddit posts from 4k Subreddits.... \n", + "41 A look at Docker from the perspective of a dat... \n", + "104 Pytorch implementation for \"Open Compound Doma... \n", "\n", " tag prediction \n", - "165 mlops natural-language-processing \n", "103 other computer-vision \n", - "188 mlops natural-language-processing \n", - "112 natural-language-processing other \n", - "25 natural-language-processing computer-vision " + "165 mlops natural-language-processing \n", + "76 natural-language-processing computer-vision \n", + "41 mlops natural-language-processing \n", + "104 computer-vision natural-language-processing " ] }, - "execution_count": 130, + "execution_count": 131, "metadata": {}, "output_type": "execute_result" } @@ -9371,7 +9329,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "UtXjpKf9FU4C" @@ -9381,7 +9338,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "dvS3UpusXP_R" @@ -9391,7 +9347,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "eeWWMG38Ny4U" @@ -9409,7 +9364,7 @@ }, { "cell_type": "code", - "execution_count": 131, + "execution_count": 132, "metadata": { "id": "ZyueOtQsXdGm", "tags": [] @@ -9423,7 +9378,7 @@ }, { "cell_type": "code", - "execution_count": 132, + "execution_count": 133, "metadata": { "id": "coutP2KtXdLG", "tags": [] @@ -9441,7 +9396,7 @@ }, { "cell_type": "code", - "execution_count": 133, + "execution_count": 134, "metadata": { "id": "PbxmLvi-D7lq", "tags": [] @@ -9455,7 +9410,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "2Vxg5X9OD-Ax" @@ -9466,7 +9420,7 @@ }, { "cell_type": "code", - "execution_count": 134, + "execution_count": 135, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -9481,7 +9435,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 191/191 [00:00<00:00, 28411.25it/s]\n" + "100%|██████████| 191/191 [00:00<00:00, 27560.88it/s]\n" ] }, { @@ -9555,7 +9509,7 @@ "31 natural-language-processing " ] }, - "execution_count": 134, + "execution_count": 135, "metadata": {}, "output_type": "execute_result" } @@ -9567,7 +9521,7 @@ }, { "cell_type": "code", - "execution_count": 135, + "execution_count": 136, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -9582,7 +9536,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 191/191 [00:00<00:00, 52118.41it/s]\n" + "100%|██████████| 191/191 [00:00<00:00, 62729.00it/s]\n" ] }, { @@ -9656,7 +9610,7 @@ "140 natural-language-processing " ] }, - "execution_count": 135, + "execution_count": 136, "metadata": {}, "output_type": "execute_result" } @@ -9667,7 +9621,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "kZuDZwTNO93Q" @@ -9678,7 +9631,7 @@ }, { "cell_type": "code", - "execution_count": 136, + "execution_count": 137, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -9692,7 +9645,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████| 191/191 [00:00<00:00, 25025.37it/s]\n" + "100%|██████████| 191/191 [00:00<00:00, 26776.93it/s]\n" ] }, { @@ -9725,7 +9678,7 @@ " dtype=[('nlp_llm', '\n", " " @@ -47027,7 +46975,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "5Pkm_pH847x1" @@ -47038,7 +46985,7 @@ }, { "cell_type": "code", - "execution_count": 142, + "execution_count": 143, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -47054,7 +47001,7 @@ "['natural-language-processing', 'natural-language-processing']" ] }, - "execution_count": 142, + "execution_count": 143, "metadata": {}, "output_type": "execute_result" } @@ -47068,7 +47015,7 @@ }, { "cell_type": "code", - "execution_count": 143, + "execution_count": 144, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -47084,7 +47031,7 @@ "['natural-language-processing', 'computer-vision']" ] }, - "execution_count": 143, + "execution_count": 144, "metadata": {}, "output_type": "execute_result" } @@ -47098,7 +47045,7 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": 145, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -47114,7 +47061,7 @@ "['natural-language-processing', 'mlops']" ] }, - "execution_count": 144, + "execution_count": 145, "metadata": {}, "output_type": "execute_result" } @@ -47127,7 +47074,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "id": "OkBxFVAA47x2" @@ -47137,7 +47083,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -47145,7 +47090,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": { "tags": [] @@ -47156,7 +47100,7 @@ }, { "cell_type": "code", - "execution_count": 145, + "execution_count": 146, "metadata": { "tags": [] }, @@ -47169,7 +47113,7 @@ }, { "cell_type": "code", - "execution_count": 146, + "execution_count": 147, "metadata": { "tags": [] }, @@ -47182,7 +47126,7 @@ }, { "cell_type": "code", - "execution_count": 147, + "execution_count": 148, "metadata": { "tags": [] }, @@ -47200,7 +47144,7 @@ }, { "cell_type": "code", - "execution_count": 148, + "execution_count": 149, "metadata": { "tags": [] }, @@ -47217,17 +47161,17 @@ }, { "cell_type": "code", - "execution_count": 149, + "execution_count": 150, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "2023-07-24 18:16:03,510\tINFO streaming_executor.py:92 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadCSV->SplitBlocks(24)] -> ActorPoolMapOperator[MapBatches(Predictor)]\n", - "2023-07-24 18:16:03,511\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", - "2023-07-24 18:16:03,512\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n", - "2023-07-24 18:16:05,779\tINFO actor_pool_map_operator.py:117 -- MapBatches(Predictor): Waiting for 1 pool actors to start...\n" + "2023-09-14 15:08:58,658\tINFO streaming_executor.py:92 -- Executing DAG InputDataBuffer[Input] -> TaskPoolMapOperator[ReadCSV->SplitBlocks(48)] -> ActorPoolMapOperator[MapBatches(Predictor)]\n", + "2023-09-14 15:08:58,659\tINFO streaming_executor.py:93 -- Execution config: ExecutionOptions(resource_limits=ExecutionResources(cpu=None, gpu=None, object_store_memory=None), locality_with_output=False, preserve_order=True, actor_locality_enabled=True, verbose_progress=False)\n", + "2023-09-14 15:08:58,660\tINFO streaming_executor.py:95 -- Tip: For detailed progress reporting, run `ray.data.DataContext.get_current().execution_options.verbose_progress = True`\n", + "2023-09-14 15:09:01,060\tINFO actor_pool_map_operator.py:117 -- MapBatches(Predictor): Waiting for 1 pool actors to start...\n" ] }, { @@ -47238,7 +47182,7 @@ "version_minor": 0 }, "text/plain": [ - "Running 0: 0%| | 0/24 [00:00