Merge pull request #342 from mlrun/marketplace-doc-gen-f7e4fd1

Marketplace update from marketplace-doc-gen-f7e4fd1
mlrun · Feb 13, 2024 · caff285 · caff285
2 parents 9cf6f7e + dc10f55
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diff --git a/README.md b/README.md
@@ -1,3 +1,67 @@
+### Change log [2024-02-13 16:05:23]
+1. Item Updated: `load_dask` (from version: `1.1.0` to `1.1.0`)
+2. Item Updated: `sklearn_classifier_dask` (from version: `1.1.1` to `1.1.1`)
+3. Item Updated: `test_classifier` (from version: `1.1.0` to `1.1.0`)
+4. Item Updated: `xgb_custom` (from version: `1.1.0` to `1.1.0`)
+5. Item Updated: `concept_drift_streaming` (from version: `1.1.0` to `1.1.0`)
+6. Item Updated: `auto_trainer` (from version: `1.7.0` to `1.7.0`)
+7. Item Updated: `v2_model_server` (from version: `1.1.0` to `1.1.0`)
+8. Item Updated: `concept_drift` (from version: `1.1.0` to `1.1.0`)
+9. Item Updated: `pii_recognizer` (from version: `0.2.0` to `0.2.0`)
+10. Item Updated: `pyannote_audio` (from version: `1.0.0` to `1.0.0`)
+11. Item Updated: `structured_data_generator` (from version: `1.3.0` to `1.3.0`)
+12. Item Updated: `describe_dask` (from version: `1.1.0` to `1.1.0`)
+13. Item Updated: `pandas_profiling_report` (from version: `1.1.0` to `1.1.0`)
+14. Item Updated: `open_archive` (from version: `1.1.0` to `1.1.0`)
+15. Item Updated: `tf1_serving` (from version: `1.1.0` to `1.1.0`)
+16. Item Updated: `rnn_serving` (from version: `1.1.0` to `1.1.0`)
+17. Item Updated: `gen_class_data` (from version: `1.2.0` to `1.2.0`)
+18. Item Updated: `send_email` (from version: `1.2.0` to `1.2.0`)
+19. Item Updated: `tf2_serving_v2` (from version: `1.1.0` to `1.1.0`)
+20. Item Updated: `hugging_face_classifier_trainer` (from version: `0.1.0` to `0.1.0`)
+21. Item Updated: `model_monitoring_stream` (from version: `1.1.0` to `1.1.0`)
+22. Item Updated: `sklearn_classifier` (from version: `1.1.1` to `1.1.1`)
+23. Item Updated: `batch_inference` (from version: `1.7.0` to `1.7.0`)
+24. Item Updated: `churn_server` (from version: `1.1.0` to `1.1.0`)
+25. Item Updated: `get_offline_features` (from version: `1.2.0` to `1.2.0`)
+26. Item Updated: `github_utils` (from version: `1.1.0` to `1.1.0`)
+27. Item Updated: `translate` (from version: `0.0.2` to `0.0.2`)
+28. Item Updated: `model_server` (from version: `1.1.0` to `1.1.0`)
+29. Item Updated: `text_to_audio_generator` (from version: `1.1.0` to `1.1.0`)
+30. Item Updated: `sql_to_file` (from version: `1.1.0` to `1.1.0`)
+31. Item Updated: `snowflake_dask` (from version: `1.1.0` to `1.1.0`)
+32. Item Updated: `silero_vad` (from version: `1.1.0` to `1.1.0`)
+33. Item Updated: `virtual_drift` (from version: `1.1.0` to `1.1.0`)
+34. Item Updated: `batch_inference_v2` (from version: `2.5.0` to `2.5.0`)
+35. Item Updated: `feature_selection` (from version: `1.4.0` to `1.4.0`)
+36. Item Updated: `feature_perms` (from version: `1.1.0` to `1.1.0`)
+37. Item Updated: `azureml_serving` (from version: `1.1.0` to `1.1.0`)
+38. Item Updated: `slack_notify` (from version: `1.1.0` to `1.1.0`)
+39. Item Updated: `describe_spark` (from version: `1.1.0` to `1.1.0`)
+40. Item Updated: `tf2_serving` (from version: `1.1.0` to `1.1.0`)
+41. Item Updated: `coxph_trainer` (from version: `1.1.0` to `1.1.0`)
+42. Item Updated: `validate_great_expectations` (from version: `1.1.0` to `1.1.0`)
+43. Item Updated: `question_answering` (from version: `0.3.1` to `0.3.1`)
+44. Item Updated: `stream_to_parquet` (from version: `1.1.0` to `1.1.0`)
+45. Item Updated: `load_dataset` (from version: `1.1.0` to `1.1.0`)
+46. Item Updated: `xgb_test` (from version: `1.1.1` to `1.1.1`)
+47. Item Updated: `ingest` (from version: `1.1.0` to `1.1.0`)
+48. Item Updated: `aggregate` (from version: `1.3.0` to `1.3.0`)
+49. Item Updated: `transcribe` (from version: `1.0.0` to `1.0.0`)
+50. Item Updated: `arc_to_parquet` (from version: `1.4.1` to `1.4.1`)
+51. Item Updated: `bert_embeddings` (from version: `1.2.0` to `1.2.0`)
+52. Item Updated: `model_monitoring_batch` (from version: `1.1.0` to `1.1.0`)
+53. Item Updated: `describe` (from version: `1.2.0` to `1.2.0`)
+54. Item Updated: `huggingface_auto_trainer` (from version: `1.0.0` to `1.0.0`)
+55. Item Updated: `xgb_trainer` (from version: `1.1.1` to `1.1.1`)
+56. Item Updated: `coxph_test` (from version: `1.1.0` to `1.1.0`)
+57. Item Updated: `azureml_utils` (from version: `1.3.0` to `1.3.0`)
+58. Item Updated: `xgb_serving` (from version: `1.1.2` to `1.1.2`)
+59. Item Updated: `v2_model_tester` (from version: `1.1.0` to `1.1.0`)
+60. Item Updated: `hugging_face_serving` (from version: `1.0.0` to `1.0.0`)
+61. Item Updated: `model_server_tester` (from version: `1.1.0` to `1.1.0`)
+62. Item Updated: `onnx_utils` (from version: `1.2.0` to `1.2.0`)
+
 ### Change log [2024-02-11 12:52:07]
 1. Item Updated: `batch_inference` (from version: `1.7.0` to `1.7.0`)
 2. Item Updated: `structured_data_generator` (from version: `1.3.0` to `1.3.0`)

diff --git a/catalog.json b/catalog.json
diff --git a/functions/development/batch_inference_v2/2.5.0/src/batch_inference_v2.ipynb b/functions/development/batch_inference_v2/2.5.0/src/batch_inference_v2.ipynb
@@ -478,10 +478,7 @@
     "# Import the `batch_inference_v2` function from the functions hub:\n",
     "batch_inference_function = mlrun.import_function('hub://batch_inference_v2')\n",
     "# you can import the function from the current directory as well: \n",
-    "# batch_inference_function = mlrun.import_function(\"function.yaml\")\n",
-    "\n",
-    "# Set the desired artifact path:\n",
-    "artifact_path = \"./\""
+    "# batch_inference_function = mlrun.import_function(\"function.yaml\")\n"
    ]
   },
   {
@@ -1448,23 +1445,18 @@
     "# 1. Generate data:\n",
     "generate_data_run = demo_function.run(\n",
     "    handler=\"generate_data\",\n",
-    "    artifact_path=artifact_path,\n",
     "    returns=[\"training_set : dataset\", \"prediction_set : dataset\"],\n",
-    "    local=True,\n",
     ")\n",
     "\n",
     "# 2. Train a model:\n",
     "train_run = demo_function.run(\n",
     "    handler=\"train\",\n",
-    "    artifact_path=artifact_path,\n",
     "    inputs={\"training_set\": generate_data_run.outputs[\"training_set\"]},\n",
-    "    local=True,\n",
     ")\n",
     "\n",
     "# 3. Perform batch prediction:\n",
     "batch_inference_run = batch_inference_function.run(\n",
     "    handler=\"infer\",\n",
-    "    artifact_path=artifact_path,\n",
     "    inputs={\"dataset\": generate_data_run.outputs[\"prediction_set\"]},\n",
     "    params={\n",
     "        \"model_path\": train_run.outputs[\"model\"],\n",
@@ -1474,7 +1466,6 @@
     "        \"model_endpoint_drift_threshold\": 0.2,\n",
     "        \"model_endpoint_possible_drift_threshold\": 0.1,\n",
     "    },\n",
-    "    local=True,\n",
     ")"
    ]
   },

diff --git a/functions/development/batch_inference_v2/2.5.0/static/example.html b/functions/development/batch_inference_v2/2.5.0/static/example.html
@@ -815,9 +815,6 @@ <h3>4.2. Run the Example with MLRun<a class="headerlink" href="#run-the-example-
 <span class="n">batch_inference_function</span> <span class="o">=</span> <span class="n">mlrun</span><span class="o">.</span><span class="n">import_function</span><span class="p">(</span><span class="s1">'hub://batch_inference_v2'</span><span class="p">)</span>
 <span class="c1"># you can import the function from the current directory as well: </span>
 <span class="c1"># batch_inference_function = mlrun.import_function("function.yaml")</span>
-
-<span class="c1"># Set the desired artifact path:</span>
-<span class="n">artifact_path</span> <span class="o">=</span> <span class="s2">"./"</span>
 </pre></div>
 </div>
 </div>
@@ -833,23 +830,18 @@ <h3>4.2. Run the Example with MLRun<a class="headerlink" href="#run-the-example-
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># 1. Generate data:</span>
 <span class="n">generate_data_run</span> <span class="o">=</span> <span class="n">demo_function</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
     <span class="n">handler</span><span class="o">=</span><span class="s2">"generate_data"</span><span class="p">,</span>
-    <span class="n">artifact_path</span><span class="o">=</span><span class="n">artifact_path</span><span class="p">,</span>
     <span class="n">returns</span><span class="o">=</span><span class="p">[</span><span class="s2">"training_set : dataset"</span><span class="p">,</span> <span class="s2">"prediction_set : dataset"</span><span class="p">],</span>
-    <span class="n">local</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
 <span class="p">)</span>
 
 <span class="c1"># 2. Train a model:</span>
 <span class="n">train_run</span> <span class="o">=</span> <span class="n">demo_function</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
     <span class="n">handler</span><span class="o">=</span><span class="s2">"train"</span><span class="p">,</span>
-    <span class="n">artifact_path</span><span class="o">=</span><span class="n">artifact_path</span><span class="p">,</span>
     <span class="n">inputs</span><span class="o">=</span><span class="p">{</span><span class="s2">"training_set"</span><span class="p">:</span> <span class="n">generate_data_run</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">"training_set"</span><span class="p">]},</span>
-    <span class="n">local</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
 <span class="p">)</span>
 
 <span class="c1"># 3. Perform batch prediction:</span>
 <span class="n">batch_inference_run</span> <span class="o">=</span> <span class="n">batch_inference_function</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
     <span class="n">handler</span><span class="o">=</span><span class="s2">"infer"</span><span class="p">,</span>
-    <span class="n">artifact_path</span><span class="o">=</span><span class="n">artifact_path</span><span class="p">,</span>
     <span class="n">inputs</span><span class="o">=</span><span class="p">{</span><span class="s2">"dataset"</span><span class="p">:</span> <span class="n">generate_data_run</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">"prediction_set"</span><span class="p">]},</span>
     <span class="n">params</span><span class="o">=</span><span class="p">{</span>
         <span class="s2">"model_path"</span><span class="p">:</span> <span class="n">train_run</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">"model"</span><span class="p">],</span>
@@ -859,7 +851,6 @@ <h3>4.2. Run the Example with MLRun<a class="headerlink" href="#run-the-example-
         <span class="s2">"model_endpoint_drift_threshold"</span><span class="p">:</span> <span class="mf">0.2</span><span class="p">,</span>
         <span class="s2">"model_endpoint_possible_drift_threshold"</span><span class="p">:</span> <span class="mf">0.1</span><span class="p">,</span>
     <span class="p">},</span>
-    <span class="n">local</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
 <span class="p">)</span>
 </pre></div>
 </div>

diff --git a/functions/development/batch_inference_v2/latest/src/batch_inference_v2.ipynb b/functions/development/batch_inference_v2/latest/src/batch_inference_v2.ipynb
@@ -478,10 +478,7 @@
     "# Import the `batch_inference_v2` function from the functions hub:\n",
     "batch_inference_function = mlrun.import_function('hub://batch_inference_v2')\n",
     "# you can import the function from the current directory as well: \n",
-    "# batch_inference_function = mlrun.import_function(\"function.yaml\")\n",
-    "\n",
-    "# Set the desired artifact path:\n",
-    "artifact_path = \"./\""
+    "# batch_inference_function = mlrun.import_function(\"function.yaml\")\n"
    ]
   },
   {
@@ -1448,23 +1445,18 @@
     "# 1. Generate data:\n",
     "generate_data_run = demo_function.run(\n",
     "    handler=\"generate_data\",\n",
-    "    artifact_path=artifact_path,\n",
     "    returns=[\"training_set : dataset\", \"prediction_set : dataset\"],\n",
-    "    local=True,\n",
     ")\n",
     "\n",
     "# 2. Train a model:\n",
     "train_run = demo_function.run(\n",
     "    handler=\"train\",\n",
-    "    artifact_path=artifact_path,\n",
     "    inputs={\"training_set\": generate_data_run.outputs[\"training_set\"]},\n",
-    "    local=True,\n",
     ")\n",
     "\n",
     "# 3. Perform batch prediction:\n",
     "batch_inference_run = batch_inference_function.run(\n",
     "    handler=\"infer\",\n",
-    "    artifact_path=artifact_path,\n",
     "    inputs={\"dataset\": generate_data_run.outputs[\"prediction_set\"]},\n",
     "    params={\n",
     "        \"model_path\": train_run.outputs[\"model\"],\n",
@@ -1474,7 +1466,6 @@
     "        \"model_endpoint_drift_threshold\": 0.2,\n",
     "        \"model_endpoint_possible_drift_threshold\": 0.1,\n",
     "    },\n",
-    "    local=True,\n",
     ")"
    ]
   },

diff --git a/functions/development/batch_inference_v2/latest/static/example.html b/functions/development/batch_inference_v2/latest/static/example.html
@@ -815,9 +815,6 @@ <h3>4.2. Run the Example with MLRun<a class="headerlink" href="#run-the-example-
 <span class="n">batch_inference_function</span> <span class="o">=</span> <span class="n">mlrun</span><span class="o">.</span><span class="n">import_function</span><span class="p">(</span><span class="s1">'hub://batch_inference_v2'</span><span class="p">)</span>
 <span class="c1"># you can import the function from the current directory as well: </span>
 <span class="c1"># batch_inference_function = mlrun.import_function("function.yaml")</span>
-
-<span class="c1"># Set the desired artifact path:</span>
-<span class="n">artifact_path</span> <span class="o">=</span> <span class="s2">"./"</span>
 </pre></div>
 </div>
 </div>
@@ -833,23 +830,18 @@ <h3>4.2. Run the Example with MLRun<a class="headerlink" href="#run-the-example-
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># 1. Generate data:</span>
 <span class="n">generate_data_run</span> <span class="o">=</span> <span class="n">demo_function</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
     <span class="n">handler</span><span class="o">=</span><span class="s2">"generate_data"</span><span class="p">,</span>
-    <span class="n">artifact_path</span><span class="o">=</span><span class="n">artifact_path</span><span class="p">,</span>
     <span class="n">returns</span><span class="o">=</span><span class="p">[</span><span class="s2">"training_set : dataset"</span><span class="p">,</span> <span class="s2">"prediction_set : dataset"</span><span class="p">],</span>
-    <span class="n">local</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
 <span class="p">)</span>
 
 <span class="c1"># 2. Train a model:</span>
 <span class="n">train_run</span> <span class="o">=</span> <span class="n">demo_function</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
     <span class="n">handler</span><span class="o">=</span><span class="s2">"train"</span><span class="p">,</span>
-    <span class="n">artifact_path</span><span class="o">=</span><span class="n">artifact_path</span><span class="p">,</span>
     <span class="n">inputs</span><span class="o">=</span><span class="p">{</span><span class="s2">"training_set"</span><span class="p">:</span> <span class="n">generate_data_run</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">"training_set"</span><span class="p">]},</span>
-    <span class="n">local</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
 <span class="p">)</span>
 
 <span class="c1"># 3. Perform batch prediction:</span>
 <span class="n">batch_inference_run</span> <span class="o">=</span> <span class="n">batch_inference_function</span><span class="o">.</span><span class="n">run</span><span class="p">(</span>
     <span class="n">handler</span><span class="o">=</span><span class="s2">"infer"</span><span class="p">,</span>
-    <span class="n">artifact_path</span><span class="o">=</span><span class="n">artifact_path</span><span class="p">,</span>
     <span class="n">inputs</span><span class="o">=</span><span class="p">{</span><span class="s2">"dataset"</span><span class="p">:</span> <span class="n">generate_data_run</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">"prediction_set"</span><span class="p">]},</span>
     <span class="n">params</span><span class="o">=</span><span class="p">{</span>
         <span class="s2">"model_path"</span><span class="p">:</span> <span class="n">train_run</span><span class="o">.</span><span class="n">outputs</span><span class="p">[</span><span class="s2">"model"</span><span class="p">],</span>
@@ -859,7 +851,6 @@ <h3>4.2. Run the Example with MLRun<a class="headerlink" href="#run-the-example-
         <span class="s2">"model_endpoint_drift_threshold"</span><span class="p">:</span> <span class="mf">0.2</span><span class="p">,</span>
         <span class="s2">"model_endpoint_possible_drift_threshold"</span><span class="p">:</span> <span class="mf">0.1</span><span class="p">,</span>
     <span class="p">},</span>
-    <span class="n">local</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
 <span class="p">)</span>
 </pre></div>
 </div>

diff --git a/functions/development/catalog.json b/functions/development/catalog.json
diff --git a/functions/development/tags.json b/functions/development/tags.json
@@ -1 +1 @@
-{"categories": ["deep-learning", "model-serving", "utils", "Data Generation", "data-analysis", "NLP", "data-preparation", "machine-learning", "Audio", "Deep Learning", "PyTorch", "data-validation", "feature-store", "GenAI", "Data Preparation", "model-testing", "etl", "Huggingface", "monitoring", "model-training"], "kind": ["serving", "dask", "job", "nuclio", "nuclio:serving"]}
+{"kind": ["nuclio", "nuclio:serving", "serving", "job", "dask"], "categories": ["GenAI", "deep-learning", "model-serving", "data-validation", "machine-learning", "Data Preparation", "PyTorch", "utils", "feature-store", "model-training", "etl", "data-analysis", "model-testing", "monitoring", "Audio", "Deep Learning", "NLP", "Data Generation", "Huggingface", "data-preparation"]}
Original file line number	Diff line number	Diff line change
		@@ -1 +1 @@
		{"categories": ["deep-learning", "model-serving", "utils", "Data Generation", "data-analysis", "NLP", "data-preparation", "machine-learning", "Audio", "Deep Learning", "PyTorch", "data-validation", "feature-store", "GenAI", "Data Preparation", "model-testing", "etl", "Huggingface", "monitoring", "model-training"], "kind": ["serving", "dask", "job", "nuclio", "nuclio:serving"]}
		{"kind": ["nuclio", "nuclio:serving", "serving", "job", "dask"], "categories": ["GenAI", "deep-learning", "model-serving", "data-validation", "machine-learning", "Data Preparation", "PyTorch", "utils", "feature-store", "model-training", "etl", "data-analysis", "model-testing", "monitoring", "Audio", "Deep Learning", "NLP", "Data Generation", "Huggingface", "data-preparation"]}