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update samples from Release-136 as a part of SDK release

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amlrelsa-ms
2022-04-25 17:08:42 +00:00
parent a4dfcc4693
commit 1903f78285
22 changed files with 120 additions and 108 deletions
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40
how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb
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@@ -100,7 +100,7 @@
"\n",
"# Check core SDK version number\n",
"\n",
"print(\"This notebook was created using SDK version 1.40.0, you are currently running version\", azureml.core.VERSION)"
"print(\"This notebook was created using SDK version 1.41.0, you are currently running version\", azureml.core.VERSION)"
]
},
{
@@ -363,6 +363,43 @@
"run.log_image(name='Hyperbolic Tangent', plot=plt)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Logging for when more Metric Names are required\n",
"\n",
"Limits on logging are internally enforced to ensure a smooth experience, however these can sometimes be limiting, particularly in terms of the limit on metric names.\n",
"\n",
"The \"Logging Vectors\" or \"Logging Tables\" examples previously can be expanded upon to use up to 15 columns to increase this limit, with the information still being presented in Run Details as a chart, and being directly comparable in experiment reports.\n",
"\n",
"**Note:** see [Azure Machine Learning Limits Documentation](https://aka.ms/azure-machine-learning-limits) for more information on service limits.\n",
"**Note:** tables logged into the run are expected to be relatively small. Logging very large tables into Azure ML can result in reduced performance. If you need to store large amounts of data associated with the run, you can write the data to file that will be uploaded."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import random\n",
"metricNames = [ \"Accuracy\", \"Precision\", \"Recall\" ]\n",
"columnNames = [ \"expected\", \"actual\", \"calculated\", \"inferred\", \"determined\", \"predicted\", \"forecast\", \"speculated\", \"assumed\", \"required\", \"intended\", \"deduced\", \"theorized\", \"hoped\", \"hypothesized\" ]\n",
"\n",
"for step in range(1000):\n",
" for metricName in metricNames:\n",
"\n",
" metricKeyValueDictionary={}\n",
" for column in columnNames:\n",
" metricKeyValueDictionary[column] = random.randrange(0, step + 1)\n",
"\n",
" run.log_row(\n",
" metricName,\n",
" \"Example row for metric \" + metricName,\n",
" **metricKeyValueDictionary)"
]
},
{
"cell_type": "markdown",
"metadata": {},
@@ -498,7 +535,6 @@
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"os.makedirs('files', exist_ok=True)\n",
"\n",
"for f in run.get_file_names():\n",