Add compute name and instance type param in sdk and cli

cli/foundation-models/system/evaluation/fill-mask/fill-mask-pipeline.yml

@@ -28,7 +28,8 @@ jobs:
         path: azureml://registries/azureml/models/bert-base-uncased/versions/4
       input_column_names: input_string
       label_column_name: title
-      device: gpu
+      device: auto
+      compute_name: gpu-cluster-big
       evaluation_config:
         path: "../../../../../sdk/python/foundation-models/system/evaluation/fill-mask/eval-config.json"
         type: uri_file

cli/foundation-models/system/evaluation/question-answering/extractive-qa-pipeline.yml

@@ -27,7 +27,8 @@ jobs:
         path: azureml://registries/azureml/models/distilbert-base-uncased-distilled-squad/versions/4
       input_column_names: context,question
       label_column_name: answer_text
-      device: gpu
+      device: auto
+      compute_name: gpu-cluster-big
       evaluation_config:
         path: "../../../../../sdk/python/foundation-models/system/evaluation/question-answering/eval-config.json"
         type: uri_file

cli/foundation-models/system/evaluation/summarization/abstractive-and-extractive-summarization-pipeline.yml

@@ -28,7 +28,8 @@ jobs:
         path: azureml://registries/azureml/models/sshleifer-distilbart-cnn-12-6/versions/4
       input_column_names: input_string
       label_column_name: summary
-      device: gpu
+      device: auto
+      compute_name: gpu-cluster-big
       evaluation_config:
         path: "../../../../../sdk/python/foundation-models/system/evaluation/summarization/eval-config.json"
         type: uri_file

cli/foundation-models/system/evaluation/text-classification/entailment-contradiction-pipeline.yml

@@ -28,7 +28,8 @@ jobs:
         path: azureml://registries/azureml/models/microsoft-deberta-base-mnli/versions/4
       input_column_names: input_string
       label_column_name: label_string
-      device: gpu
+      device: auto
+      compute_name: gpu-cluster-big
       evaluation_config:
         path: "../../../../../sdk/python/foundation-models/system/evaluation/text-classification/eval-config.json"
         type: uri_file

cli/foundation-models/system/evaluation/text-generation/text-generation-pipeline.yml

@@ -28,7 +28,8 @@ jobs:
         path: azureml://registries/azureml/models/gpt2/versions/4
       input_column_names: input_string
       label_column_name: ground_truth
-      device: gpu
+      device: auto
+      compute_name: gpu-cluster-big
       evaluation_config:
         path: "../../../../../sdk/python/foundation-models/system/evaluation/text-generation/eval-config.json"
         type: uri_file

cli/foundation-models/system/evaluation/token-classification/news-articles-entity-recognition-pipeline.yml

@@ -28,7 +28,8 @@ jobs:
         path: azureml://registries/azureml/models/jean-baptiste-camembert-ner/versions/4
       input_column_names: input_string
       label_column_name: ner_tags_str
-      device: gpu
+      device: auto
+      compute_name: gpu-cluster-big
       evaluation_config:
         path: "../../../../../sdk/python/foundation-models/system/evaluation/token-classification/eval-config.json"
         type: uri_file

cli/foundation-models/system/evaluation/translation/translation-pipeline.yml

@@ -28,7 +28,8 @@ jobs:
         path: azureml://registries/azureml/models/t5-base/versions/4
       input_column_names: input_string
       label_column_name: ro
-      device: gpu
+      device: auto
+      compute_name: gpu-cluster-big
       evaluation_config:
         path: "../../../../../sdk/python/foundation-models/system/evaluation/translation/eval-config.json"
         type: uri_file

sdk/python/foundation-models/system/evaluation/fill-mask/fill-mask.ipynb

@@ -117,6 +117,28 @@
     "registry_ml_client"
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating Compute\n",
+    "\n",
+    "There are two ways to submit a job - through a compute or a serverless job.\n",
+    "\n",
+    "##### Serverless Job:\n",
+    "\n",
+    "In a serverless job, there is no need to create a compute explicitly.\n",
+    "Simply pass the desired instance type value to the `instance_type` parameter while creating a pipeline job.\n",
+    "This allows for quick and convenient job submission without the need for managing a compute cluster.\n",
+    "\n",
+    "##### Compute Job:\n",
+    "\n",
+    "To submit a job through a compute, you need to create a compute cluster beforehand.\n",
+    "The following code below demonstrates how to create a gpu compute cluster.\n",
+    "After creating the compute cluster, pass the name of the compute cluster to the `compute_name` parameter while submitting the pipeline job. This ensures that the job runs on the specified compute cluster, allowing for more control and customization."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -326,10 +348,7 @@
     "    print(\"{} - {}\".format(model[\"name\"], test_data_fil_df.shape))\n",
     "    test_data_file_name = \"small-test-{}.jsonl\".format(model[\"name\"])\n",
     "    test_data_fil_df.to_json(test_data_file_name, lines=True, orient=\"records\")"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "attachments": {},
@@ -377,14 +396,18 @@
     "        # The following parameters map to the dataset fields\n",
     "        input_column_names=\"input_string\",\n",
     "        label_column_name=\"title\",\n",
+    "        # compute settings\n",
+    "        compute_name=compute_cluster,\n",
+    "        # specify the instance type for serverless job\n",
+    "        # instance_type= \"STANDARD_NC24\",\n",
     "        # Evaluation settings\n",
     "        task=\"fill-mask\",\n",
     "        # config file containing the details of evaluation metrics to calculate\n",
     "        evaluation_config=Input(type=AssetTypes.URI_FILE, path=\"./eval-config.json\"),\n",
     "        # evaluation_config_params=evaluation_config_params,\n",
     "        # config cluster/device job is running on\n",
     "        # set device to GPU/CPU on basis if GPU count was found\n",
-    "        device=\"gpu\" if gpu_count_found else \"cpu\",\n",
+    "        device=\"auto\",\n",
     "    )\n",
     "    return {\"evaluation_result\": evaluation_job.outputs.evaluation_result}"
    ]

sdk/python/foundation-models/system/evaluation/question-answering/question-answering.ipynb

@@ -117,6 +117,28 @@
     "registry_ml_client"
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating Compute\n",
+    "\n",
+    "There are two ways to submit a job - through a compute or a serverless job.\n",
+    "\n",
+    "##### Serverless Job:\n",
+    "\n",
+    "In a serverless job, there is no need to create a compute explicitly.\n",
+    "Simply pass the desired instance type value to the `instance_type` parameter while creating a pipeline job.\n",
+    "This allows for quick and convenient job submission without the need for managing a compute cluster.\n",
+    "\n",
+    "##### Compute Job:\n",
+    "\n",
+    "To submit a job through a compute, you need to create a compute cluster beforehand.\n",
+    "The following code below demonstrates how to create a gpu compute cluster.\n",
+    "After creating the compute cluster, pass the name of the compute cluster to the `compute_name` parameter while submitting the pipeline job. This ensures that the job runs on the specified compute cluster, allowing for more control and customization."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -363,13 +385,17 @@
     "        # The following parameters map to the dataset fields\n",
     "        input_column_names=\"context,question\",\n",
     "        label_column_name=\"answer_text\",\n",
+    "        # compute settings\n",
+    "        compute_name=compute_cluster,\n",
+    "        # specify the instance type for serverless job\n",
+    "        # instance_type= \"STANDARD_NC24\",\n",
     "        # Evaluation settings\n",
     "        task=\"question-answering\",\n",
     "        # config file containing the details of evaluation metrics to calculate\n",
     "        evaluation_config=Input(type=AssetTypes.URI_FILE, path=\"./eval-config.json\"),\n",
     "        # config cluster/device job is running on\n",
     "        # set device to GPU/CPU on basis if GPU count was found\n",
-    "        device=\"gpu\" if gpu_count_found else \"cpu\",\n",
+    "        device=\"auto\",\n",
     "    )\n",
     "    return {\"evaluation_result\": evaluation_job.outputs.evaluation_result}"
    ]

sdk/python/foundation-models/system/evaluation/summarization/abstractive-and-extractive-summarization.ipynb

@@ -115,6 +115,28 @@
     "registry_ml_client"
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating Compute\n",
+    "\n",
+    "There are two ways to submit a job - through a compute or a serverless job.\n",
+    "\n",
+    "##### Serverless Job:\n",
+    "\n",
+    "In a serverless job, there is no need to create a compute explicitly.\n",
+    "Simply pass the desired instance type value to the `instance_type` parameter while creating a pipeline job.\n",
+    "This allows for quick and convenient job submission without the need for managing a compute cluster.\n",
+    "\n",
+    "##### Compute Job:\n",
+    "\n",
+    "To submit a job through a compute, you need to create a compute cluster beforehand.\n",
+    "The following code below demonstrates how to create a gpu compute cluster.\n",
+    "After creating the compute cluster, pass the name of the compute cluster to the `compute_name` parameter while submitting the pipeline job. This ensures that the job runs on the specified compute cluster, allowing for more control and customization."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -271,6 +293,17 @@
     "test_data_df[\"summary\"] = test_data_df[\"highlights\"]"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# trucating the data to pass the tokenizer limit of the model\n",
+    "test_data_df[\"article\"] = test_data_df[\"article\"].str.slice(0, 200)\n",
+    "test_data_df[\"input_string\"] = test_data_df[\"input_string\"].str.slice(0, 200)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -359,14 +392,18 @@
     "        # The following parameters map to the dataset fields\n",
     "        input_column_names=\"input_string\",\n",
     "        label_column_name=\"summary\",\n",
+    "        # compute settings\n",
+    "        compute_name=compute_cluster,\n",
+    "        # specify the instance type for serverless job\n",
+    "        # instance_type= \"STANDARD_NC24\",\n",
     "        # Evaluation settings\n",
     "        task=\"text-summarization\",\n",
     "        # config file containing the details of evaluation metrics to calculate\n",
     "        evaluation_config=Input(type=AssetTypes.URI_FILE, path=\"./eval-config.json\"),\n",
     "        # evaluation_config_params=evaluation_config_params,\n",
     "        # config cluster/device job is running on\n",
     "        # set device to GPU/CPU on basis if GPU count was found\n",
-    "        device=\"gpu\" if gpu_count_found else \"cpu\",\n",
+    "        device=\"auto\",\n",
     "    )\n",
     "    return {\"evaluation_result\": evaluation_job.outputs.evaluation_result}"
    ]

sdk/python/foundation-models/system/evaluation/text-classification/entailment-contradiction.ipynb

@@ -122,6 +122,28 @@
     "workspace_ml_client"
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating Compute\n",
+    "\n",
+    "There are two ways to submit a job - through a compute or a serverless job.\n",
+    "\n",
+    "##### Serverless Job:\n",
+    "\n",
+    "In a serverless job, there is no need to create a compute explicitly.\n",
+    "Simply pass the desired instance type value to the `instance_type` parameter while creating a pipeline job.\n",
+    "This allows for quick and convenient job submission without the need for managing a compute cluster.\n",
+    "\n",
+    "##### Compute Job:\n",
+    "\n",
+    "To submit a job through a compute, you need to create a compute cluster beforehand.\n",
+    "The following code below demonstrates how to create a gpu compute cluster.\n",
+    "After creating the compute cluster, pass the name of the compute cluster to the `compute_name` parameter while submitting the pipeline job. This ensures that the job runs on the specified compute cluster, allowing for more control and customization."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -356,13 +378,17 @@
     "        # The following parameters map to the dataset fields\n",
     "        input_column_names=\"input_string\",\n",
     "        label_column_name=\"label_string\",\n",
+    "        # compute settings\n",
+    "        compute_name=compute_cluster,\n",
+    "        # specify the instance type for serverless job\n",
+    "        # instance_type= \"STANDARD_NC24\",\n",
     "        # Evaluation settings\n",
     "        task=\"text-classification\",\n",
     "        # config file containing the details of evaluation metrics to calculate\n",
     "        evaluation_config=Input(type=AssetTypes.URI_FILE, path=\"./eval-config.json\"),\n",
     "        # config cluster/device job is running on\n",
     "        # set device to GPU/CPU on basis if GPU count was found\n",
-    "        device=\"gpu\" if gpu_count_found else \"cpu\",\n",
+    "        device=\"auto\",\n",
     "    )\n",
     "    return {\"evaluation_result\": evaluation_job.outputs.evaluation_result}"
    ]

sdk/python/foundation-models/system/evaluation/text-generation/text-generation.ipynb

@@ -117,6 +117,28 @@
     "registry_ml_client"
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Creating Compute\n",
+    "\n",
+    "There are two ways to submit a job - through a compute or a serverless job.\n",
+    "\n",
+    "##### Serverless Job:\n",
+    "\n",
+    "In a serverless job, there is no need to create a compute explicitly.\n",
+    "Simply pass the desired instance type value to the `instance_type` parameter while creating a pipeline job.\n",
+    "This allows for quick and convenient job submission without the need for managing a compute cluster.\n",
+    "\n",
+    "##### Compute Job:\n",
+    "\n",
+    "To submit a job through a compute, you need to create a compute cluster beforehand.\n",
+    "The following code below demonstrates how to create a gpu compute cluster.\n",
+    "After creating the compute cluster, pass the name of the compute cluster to the `compute_name` parameter while submitting the pipeline job. This ensures that the job runs on the specified compute cluster, allowing for more control and customization."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -215,6 +237,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "models = []\n",
@@ -223,21 +248,18 @@
     "    reg_model = list(registry_ml_client.models.list(name=model[\"name\"]))[0]\n",
     "    print(reg_model.id)\n",
     "    models.append({**model, \"version\": reg_model.version})"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [],
    "source": [
     "models"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "attachments": {},
@@ -360,13 +382,17 @@
     "        # The following parameters map to the dataset fields\n",
     "        input_column_names=\"input_string\",\n",
     "        label_column_name=\"ground_truth\",\n",
+    "        # compute settings\n",
+    "        compute_name=compute_cluster,\n",
+    "        # specify the instance type for serverless job\n",
+    "        # instance_type= \"STANDARD_NC24\",\n",
     "        # Evaluation settings\n",
     "        task=\"text-generation\",\n",
     "        # config file containing the details of evaluation metrics to calculate\n",
     "        evaluation_config=Input(type=AssetTypes.URI_FILE, path=\"./eval-config.json\"),\n",
     "        # config cluster/device job is running on\n",
     "        # set device to GPU/CPU on basis if GPU count was found\n",
-    "        device=\"gpu\" if gpu_count_found else \"cpu\",\n",
+    "        device=\"auto\",\n",
     "    )\n",
     "    return {\"evaluation_result\": evaluation_job.outputs.evaluation_result}"
    ]