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fix od batch scoring notebook #2220

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merged 3 commits into from
Apr 21, 2023
Merged

fix od batch scoring notebook #2220

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d902c5a
fix od batch scoring notebook
sharma-riti Apr 20, 2023
6813089
format file
sharma-riti Apr 20, 2023
46d3e20
update broken path
sharma-riti Apr 21, 2023
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43 changes: 36 additions & 7 deletions ...sk-fridge-items-batch-scoring/image-object-detection-batch-scoring-non-mlflow-model.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -799,7 +799,7 @@
"## 6.2 Configure environment\n",
"It is recommended to use the same envionment for model deployment as the model training, therefore we are creating the Environment object using the conda environment file downloaded as artifacts. We also need to specify the base image, which in case of vision tasks, is `mcr.microsoft.com/azureml/openmpi4.1.0-cuda11.1-cudnn8-ubuntu18.04`\n",
"\n",
"To read more about environments, please follow this [notebook](./../../assets/environment/environment.ipynb)"
"To read more about environments, please follow this [notebook](./../../../assets/environment/environment.ipynb)"
]
},
{
Expand All @@ -813,7 +813,7 @@
"env = Environment(\n",
" name=\"automl-images-env\",\n",
" description=\"environment for automl images inference\",\n",
" image=\"mcr.microsoft.com/azureml/openmpi4.1.0-cuda11.1-cudnn8-ubuntu18.04\",\n",
" image=\"mcr.microsoft.com/azureml/openmpi4.1.0-cuda11.1-cudnn8-ubuntu20.04\",\n",
" conda_file=\"artifact_downloads/outputs/conda_env_v_1_0_0.yml\",\n",
")"
]
Expand Down Expand Up @@ -1079,7 +1079,7 @@
"# get the details of the job\n",
"job_name = job.name\n",
"batch_job = ml_client.jobs.get(name=job_name)\n",
"print(batch_job.status)\n",
"print(batch_job)\n",
"# stream the job logs\n",
"ml_client.jobs.stream(name=job_name)"
]
Expand All @@ -1097,13 +1097,42 @@
"metadata": {},
"outputs": [],
"source": [
"output_dir = \"./scoring_results\"\n",
"ml_client.jobs.download(\n",
" name=job.name, output_name=deployment.output_file_name, download_path=output_dir\n",
"from azureml.core import Experiment, Workspace\n",
"from azureml.pipeline.core import PipelineRun, StepRun, PortDataReference\n",
"\n",
"workspace_obj = Workspace(\n",
" subscription_id=ml_client.subscription_id,\n",
" resource_group=ml_client.resource_group_name,\n",
" workspace_name=ml_client.workspace_name,\n",
")\n",
"\n",
"# Get the Pipeline Run\n",
"experiment = Experiment(workspace=workspace_obj, name=batch_job.experiment_name)\n",
"pipeline_run = PipelineRun(experiment, batch_job.name)\n",
"\n",
"# Get the Step Run\n",
"step_run_id = pipeline_run.find_step_run(\"BatchScoring\")[0].id\n",
"node_id = pipeline_run.get_graph().node_name_dict[\"BatchScoring\"][0].node_id\n",
"print(\n",
" \"Pipeline Run ID: {} Step Run ID: {}, Step Run Node ID: {}\".format(\n",
" pipeline_run.id, step_run_id, node_id\n",
" )\n",
")\n",
"step_run = StepRun(experiment, step_run_id, pipeline_run.id, node_id)\n",
"\n",
"# Download Data\n",
"port_data_reference = step_run.get_output_data(\"score\")\n",
"port_data_reference.download(local_path=\".\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Print the first five lines of the output\n",
"result_file = os.path.join(output_dir, deployment.output_file_name)\n",
"result_file = os.path.join(\"azureml\", step_run_id, \"score\", deployment.output_file_name)\n",
"with open(result_file) as f:\n",
" for x in range(5):\n",
" print(next(f))"
Expand Down