Criteo HugeCTR Inference Configuration Fix (#1522)

* API Overhaul

First draft of the API overhauls changes. Adds most core functionality, including
defining workflow graphs with a ColumnGroup class, the workflow and dataset changes
, most operators converted to use the new api, etc.

* remove debug print statement

* Fix test_io unittest

Also partially fix some tests inside test_workflow

* Handle multi-column joint/combo categorify

* Update JoinGroupby

* Fix differencelag

* add dependencies method (#498)

* Convert TargetEncoding op

* Update nvtabular/workflow.py

Co-authored-by: Richard (Rick) Zamora <rzamora217@gmail.com>

* Update nvtabular/workflow.py

Co-authored-by: Richard (Rick) Zamora <rzamora217@gmail.com>

* Remove workflow code from dataloaders

We should be doing online transforms like
```KerasSequenceLoader(workflow.transform(dataset), ...```  instead of
```KerasSequenceLoader(dataset, workflows=[workflow], ...``` now

* Unittest ops + bugfix in Bucketize (#496)

* test_minmix

* updates test

* unittest ops

* First draft get_embedding_sizes support

Re-add get_embedding_sizes . Note that this changes how we support multi-hot columns here
(sizes are returned same as single hot, and we don't use this method to distinguish between
multi and singlehot columns)

* isort

* Remove groupbystatistics

* implement serialization of statistics

add save_stats/load_stats/clear_stats methods to the workflow, with each statoperator getting
called as appropiate

* Fix TF dataloader unittests

* test_torch_dataloader fixes

* doc strings

* add comma to ps.json

Co-authored-by: Ben Frederickson <github@benfrederickson.com>
Co-authored-by: rnyak <ronayak@hotmail.com>
Co-authored-by: Richard (Rick) Zamora <rzamora217@gmail.com>
Co-authored-by: root <root@dgx06.aselab.nvidia.com>

examples/scaling-criteo/04-Triton-Inference-with-HugeCTR.ipynb

@@ -289,18 +289,18 @@
     "            \"model\": \"criteo\",\n",
     "            \"sparse_files\": [\"/model/criteo/1/0_sparse_9600.model\"],\n",
     "            \"dense_file\": \"/model/criteo/1/_dense_9600.model\",\n",
-    "            \"network_file\": \"/model/criteo/1/criteo.json\"\n",
+    "            \"network_file\": \"/model/criteo/1/criteo.json\",\n",
     "            \"max_batch_size\": \"64\",\n",
-    "            \"gpucache\":\"true\",\n",
-    "            \"hit_rate_threshold\":\"0.9\",\n",
-    "            \"gpucacheper\":\"0.5\",\n",
+    "            \"gpucache\": \"true\",\n",
+    "            \"hit_rate_threshold\": \"0.9\",\n",
+    "            \"gpucacheper\": \"0.5\",\n",
     "            \"num_of_worker_buffer_in_pool\": \"4\",\n",
-    "            \"num_of_refresher_buffer_in_pool\":\"1\",\n",
-    "            \"cache_refresh_percentage_per_iteration\":0.2,\n",
-    "            \"deployed_device_list\":[\"0\"],\n",
-    "            \"default_value_for_each_table\":[\"0.0\",\"0.0\"]\n",
+    "            \"num_of_refresher_buffer_in_pool\": \"1\",\n",
+    "            \"cache_refresh_percentage_per_iteration\": 0.2,\n",
+    "            \"deployed_device_list\": [\"0\"],\n",
+    "            \"default_value_for_each_table\": [\"0.0\", \"0.0\"],\n",
     "        }\n",
-    "    ]\n",
+    "    ],\n",
     "}"
    ]
   },