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https://developer.download.nvidia.com/notebooks/dlsw-notebooks/merlin_merlin_scaling-criteo-04-triton-inference-with-hugectr/nvidia_logo.png

Scaling Criteo: Triton Inference with HugeCTR#

This notebook is created using the latest stable merlin-hugectr container.

Overview#

The last step is to deploy the ETL workflow and saved model to production. In the production setting, we want to transform the input data as during training (ETL). We need to apply the same mean/std for continuous features and use the same categorical mapping to convert the categories to continuous integer before we use the deep learning model for a prediction. Therefore, we deploy the NVTabular workflow with the HugeCTR model as an ensemble model to Triton Inference. The ensemble model guarantees that the same transformation are applied to the raw inputs.

../../_images/triton-hugectr.png

Learning objectives#

In this notebook, we learn how to deploy our models to production:

  • Use NVTabular to generate config and model files for Triton Inference Server

  • Deploy an ensemble of NVTabular workflow and HugeCTR model

  • Send example request to Triton Inference Server

Inference with Triton and HugeCTR#

First, we need to generate the Triton Inference Server configurations and save the models in the correct format. In the previous notebooks 02-ETL-with-NVTabular and 03-Training-with-HugeCTR we saved the NVTabular workflow and HugeCTR model to disk. We will load them.

Saving Ensemble Model for Triton Inference Server#

After training terminates, we can see that two .model files are generated. We need to move them inside a temporary folder, like criteo_hugectr/1. Let’s create these folders.

import os
import numpy as np

Now we move our saved .model files inside 1 folder. We use only the last snapshot after 9600 iterations.

os.system("mv *9600.model ./criteo_hugectr/1/")

Now we can save our models to be deployed at the inference stage. To do so we will use export_hugectr_ensemble method below. With this method, we can generate the config.pbtxt files automatically for each model. In doing so, we should also create a hugectr_params dictionary, and define the parameters like where the amazonreview.json file will be read, slots which corresponds to number of categorical features, embedding_vector_size, max_nnz, and n_outputs which is number of outputs.

The script below creates an ensemble triton server model where

  • workflow is the the nvtabular workflow used in preprocessing,

  • hugectr_model_path is the HugeCTR model that should be served.

  • This path includes the model files.

  • name is the base name of the various triton models.

  • output_path is the path where is model will be saved to.

  • cats are the categorical column names

  • conts are the continuous column names

We need to load the NVTabular workflow first

import nvtabular as nvt

BASE_DIR = os.environ.get("BASE_DIR", "/raid/data/criteo")
input_path = os.path.join(BASE_DIR, "test_dask/output")
workflow = nvt.Workflow.load(os.path.join(input_path, "workflow"))

Let’s clear the directory

os.system("rm -rf /model/*")
0
from nvtabular.inference.triton import export_hugectr_ensemble

hugectr_params = dict()
hugectr_params["config"] = "/model/criteo/1/criteo.json"
hugectr_params["slots"] = 26
hugectr_params["max_nnz"] = 1
hugectr_params["embedding_vector_size"] = 128
hugectr_params["n_outputs"] = 1
export_hugectr_ensemble(
    workflow=workflow,
    hugectr_model_path="./criteo_hugectr/1/",
    hugectr_params=hugectr_params,
    name="criteo",
    output_path="/model/",
    label_columns=["label"],
    cats=["C" + str(x) for x in range(1, 27)],
    conts=["I" + str(x) for x in range(1, 14)],
    max_batch_size=64,
)

We can take a look at the generated files.

!tree /model
/model
├── criteo
│   ├── 1
│   │   ├── 0_opt_sparse_9600.model
│   │   ├── 0_sparse_9600.model
│   │   │   ├── emb_vector
│   │   │   ├── key
│   │   │   └── slot_id
│   │   ├── _dense_9600.model
│   │   ├── _opt_dense_9600.model
│   │   └── criteo.json
│   └── config.pbtxt
├── criteo_ens
│   ├── 1
│   └── config.pbtxt
└── criteo_nvt
    ├── 1
    │   ├── model.py
    │   └── workflow
    │       ├── categories
    │       │   ├── unique.C1.parquet
    │       │   ├── unique.C10.parquet
    │       │   ├── unique.C11.parquet
    │       │   ├── unique.C12.parquet
    │       │   ├── unique.C13.parquet
    │       │   ├── unique.C14.parquet
    │       │   ├── unique.C15.parquet
    │       │   ├── unique.C16.parquet
    │       │   ├── unique.C17.parquet
    │       │   ├── unique.C18.parquet
    │       │   ├── unique.C19.parquet
    │       │   ├── unique.C2.parquet
    │       │   ├── unique.C20.parquet
    │       │   ├── unique.C21.parquet
    │       │   ├── unique.C22.parquet
    │       │   ├── unique.C23.parquet
    │       │   ├── unique.C24.parquet
    │       │   ├── unique.C25.parquet
    │       │   ├── unique.C26.parquet
    │       │   ├── unique.C3.parquet
    │       │   ├── unique.C4.parquet
    │       │   ├── unique.C5.parquet
    │       │   ├── unique.C6.parquet
    │       │   ├── unique.C7.parquet
    │       │   ├── unique.C8.parquet
    │       │   └── unique.C9.parquet
    │       ├── column_types.json
    │       ├── metadata.json
    │       └── workflow.pkl
    └── config.pbtxt

9 directories, 40 files

We need to write a configuration file with the stored model weights and model configuration.

%%writefile '/model/ps.json'
# fmt: off
{
    "supportlonglong": true,
    "models": [
        {
            "model": "criteo",
            "sparse_files": ["/model/criteo/1/0_sparse_9600.model"],
            "dense_file": "/model/criteo/1/_dense_9600.model",
            "network_file": "/model/criteo/1/criteo.json"
        }
    ]
}
Overwriting /model/ps.json

Loading Ensemble Model with Triton Inference Server#

We have only saved the models for Triton Inference Server. We started Triton Inference Server in explicit mode, meaning that we need to send a request that Triton will load the ensemble model.

We connect to the Triton Inference Server.

import tritonhttpclient

try:
    triton_client = tritonhttpclient.InferenceServerClient(url="localhost:8000", verbose=True)
    print("client created.")
except Exception as e:
    print("channel creation failed: " + str(e))
client created.
/usr/local/lib/python3.8/dist-packages/tritonhttpclient/__init__.py:31: DeprecationWarning: The package `tritonhttpclient` is deprecated and will be removed in a future version. Please use instead `tritonclient.http`
  warnings.warn(

We deactivate warnings.

import warnings

warnings.filterwarnings("ignore")

We check if the server is alive.

triton_client.is_server_live()
GET /v2/health/live, headers None
<HTTPSocketPoolResponse status=200 headers={'content-length': '0', 'content-type': 'text/plain'}>
True

We check the available models in the repositories:

  • criteo_ens: Ensemble

  • criteo_nvt: NVTabular

  • criteo: HugeCTR model

triton_client.get_model_repository_index()
POST /v2/repository/index, headers None

<HTTPSocketPoolResponse status=200 headers={'content-type': 'application/json', 'content-length': '93'}>
bytearray(b'[{"name":".ipynb_checkpoints"},{"name":"criteo"},{"name":"criteo_ens"},{"name":"criteo_nvt"}]')
[{'name': '.ipynb_checkpoints'},
 {'name': 'criteo'},
 {'name': 'criteo_ens'},
 {'name': 'criteo_nvt'}]

We load the models individually.

%%time

triton_client.load_model(model_name="criteo_nvt")
POST /v2/repository/models/criteo_nvt/load, headers None

<HTTPSocketPoolResponse status=200 headers={'content-type': 'application/json', 'content-length': '0'}>
Loaded model 'criteo_nvt'
CPU times: user 4.21 ms, sys: 258 µs, total: 4.47 ms
Wall time: 20.6 s
%%time

triton_client.load_model(model_name="criteo")
POST /v2/repository/models/criteo/load, headers None

<HTTPSocketPoolResponse status=200 headers={'content-type': 'application/json', 'content-length': '0'}>
Loaded model 'criteo'
CPU times: user 1.8 ms, sys: 3.01 ms, total: 4.81 ms
Wall time: 32.4 s
%%time

triton_client.load_model(model_name="criteo_ens")
POST /v2/repository/models/criteo_ens/load, headers None

<HTTPSocketPoolResponse status=200 headers={'content-type': 'application/json', 'content-length': '0'}>
Loaded model 'criteo_ens'
CPU times: user 4.7 ms, sys: 0 ns, total: 4.7 ms
Wall time: 20.2 s

Example Request to Triton Inference Server#

Now, the models are loaded and we can create a sample request. We read an example raw batch for inference.

# Get dataframe library - cudf or pandas
from merlin.core.dispatch import get_lib
df_lib = get_lib()

# read in the workflow (to get input/output schema to call triton with)
batch_path = os.path.join(BASE_DIR, "converted/criteo")
batch = df_lib.read_parquet(os.path.join(batch_path, "*.parquet"), num_rows=3)
batch = batch[[x for x in batch.columns if x != "label"]]
print(batch)
     I1   I2    I3    I4    I5  I6  I7  I8  I9  I10  ...        C17  \
0     5  110  <NA>    16  <NA>   1   0  14   7    1  ... -771205462   
1    32    3     5  <NA>     1   0   0  61   5    0  ... -771205462   
2  <NA>  233     1   146     1   0   0  99   7    0  ... -771205462   

          C18         C19         C20         C21        C22        C23  \
0 -1206449222 -1793932789 -1014091992   351689309  632402057 -675152885   
1 -1578429167 -1793932789   -20981661 -1556988767 -924717482  391309800   
2  1653545869 -1793932789 -1014091992   351689309  632402057 -675152885   

          C24         C25         C26  
0  2091868316   809724924  -317696227  
1  1966410890 -1726799382 -1218975401  
2   883538181   -10139646  -317696227  

[3 rows x 39 columns]

We prepare the batch for inference by using correct column names and data types. We use the same datatypes as defined in our dataframe.

batch.dtypes
I1     int32
I2     int32
I3     int32
I4     int32
I5     int32
I6     int32
I7     int32
I8     int32
I9     int32
I10    int32
I11    int32
I12    int32
I13    int32
C1     int32
C2     int32
C3     int32
C4     int32
C5     int32
C6     int32
C7     int32
C8     int32
C9     int32
C10    int32
C11    int32
C12    int32
C13    int32
C14    int32
C15    int32
C16    int32
C17    int32
C18    int32
C19    int32
C20    int32
C21    int32
C22    int32
C23    int32
C24    int32
C25    int32
C26    int32
dtype: object
import tritonclient.http as httpclient
from tritonclient.utils import np_to_triton_dtype

inputs = []

col_names = list(batch.columns)
col_dtypes = [np.int32] * len(col_names)

for i, col in enumerate(batch.columns):
    d = batch[col].values_host.astype(col_dtypes[i])
    d = d.reshape(len(d), 1)
    inputs.append(httpclient.InferInput(col_names[i], d.shape, np_to_triton_dtype(col_dtypes[i])))
    inputs[i].set_data_from_numpy(d)

We send the request to the triton server and collect the last output.

# placeholder variables for the output
outputs = [httpclient.InferRequestedOutput("OUTPUT0")]

# build a client to connect to our server.
# This InferenceServerClient object is what we'll be using to talk to Triton.
# make the request with tritonclient.http.InferInput object
response = triton_client.infer("criteo_ens", inputs, request_id="1", outputs=outputs)

print("predicted sigmoid result:\n", response.as_numpy("OUTPUT0"))
POST /v2/models/criteo_ens/infer, headers {'Inference-Header-Content-Length': 3383}
b'{"id":"1","inputs":[{"name":"I1","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"I2","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"I3","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"I4","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"I5","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"I6","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"I7","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"I8","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"I9","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"I10","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"I11","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"I12","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"I13","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"C1","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"C2","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"C3","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"C4","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"C5","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"C6","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"C7","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"C8","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"C9","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"C10","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"C11","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"C12","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"C13","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"C14","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"C15","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"C16","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"C17","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"C18","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"C19","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"C20","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"C21","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"C22","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"C23","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"C24","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"C25","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}},{"name":"C26","shape":[3,1],"datatype":"INT32","parameters":{"binary_data_size":12}}],"outputs":[{"name":"OUTPUT0","parameters":{"binary_data":true}}]}\x05\x00\x00\x00 \x00\x00\x00\x00\x00\x00\x00n\x00\x00\x00\x03\x00\x00\x00\xe9\x00\x00\x00\x00\x00\x00\x00\x05\x00\x00\x00\x01\x00\x00\x00\x10\x00\x00\x00\x00\x00\x00\x00\x92\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\x01\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x0e\x00\x00\x00=\x00\x00\x00c\x00\x00\x00\x07\x00\x00\x00\x05\x00\x00\x00\x07\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\x01\x00\x00\x002\x01\x00\x00U\x0c\x00\x00\x1d\x0c\x00\x00\x00\x00\x00\x00\x05\x00\x00\x00\x01\x00\x00\x00y\rwb\x8d\xfd\xf3\xe5y\rwbX]\x1f\xe2\xa6\xff\xaa\xa0\x03B\x98\xad/D\xea\xaf\xd5\x15\xaao\r\xc6\xbeb\xcf\x7f\\\x94!4\x8a\xda\xeeIl8H\'\xb08#\x9f\xd6<M\x06U\xe7\xcbm\xcdo\xcbm\xcdo\xcbm\xcdo!\xaa\x805\x81\xed\x16\xabb\xeb\xf5\xb5\x03\x89\x80()lBC\x8b\xcc\xf2\xd1\xa6\xdf\xdeFT\xe1\xf5\x1d\x1f\x82N.\xc1}\x02.\xa9\xc0\xe9}\xc1}\x02.1B|\x0cd\xdcRf1B|\x0c\x1f\x1d\x98\x95\'N\xeb\x99\x84aq\x12\xb7\xff\xc5\x00\xb7\xff\xc5\x00\xb7\xff\xc5\x007\xe5N\xbe7\xe5N\xbe7\xe5N\xbe\xcct\x0b\x8a\x99\xfe\xbb\xf3\x0b\r\x0f\xf7\xfa>\xdcL\xfa>\xdcL\xfa>\xdcL\xaaV\x08\xd2\xaaV\x08\xd2\xaaV\x08\xd2\xba\x0b\x17\xb8\x11\x15\xeb\xa1\x8d\x1b\x8fb\x0b\xc2\x12\x95\x0b\xc2\x12\x95\x0b\xc2\x12\x95(/\x8e\xc3c\xd8\xbf\xfe(/\x8e\xc3]Z\xf6\x14\xa1<2\xa3]Z\xf6\x14\x89\xb0\xb1%V\xee\xe1\xc8\x89\xb0\xb1%\x0b\xfc\xc1\xd7\xe8\xe9R\x17\x0b\xfc\xc1\xd7\x9c`\xaf|\x8a\x0c5u\x05\xb9\xa94\xfckC0\xea!\x13\x99\x02He\xff\x1dW\x10\xedW\xe9W\xb7\x1dW\x10\xed'
<HTTPSocketPoolResponse status=400 headers={'content-length': '122', 'content-type': 'text/plain'}>
---------------------------------------------------------------------------
InferenceServerException                  Traceback (most recent call last)
/tmp/ipykernel_2961/3517835948.py in <module>
      5 # This InferenceServerClient object is what we'll be using to talk to Triton.
      6 # make the request with tritonclient.http.InferInput object
----> 7 response = triton_client.infer("criteo_ens", inputs, request_id="1", outputs=outputs)
      8 
      9 print("predicted sigmoid result:\n", response.as_numpy("OUTPUT0"))

/usr/local/lib/python3.8/dist-packages/tritonclient/http/__init__.py in infer(self, model_name, inputs, model_version, outputs, request_id, sequence_id, sequence_start, sequence_end, priority, timeout, headers, query_params, request_compression_algorithm, response_compression_algorithm)
   1254                               headers=headers,
   1255                               query_params=query_params)
-> 1256         _raise_if_error(response)
   1257 
   1258         return InferResult(response, self._verbose)

/usr/local/lib/python3.8/dist-packages/tritonclient/http/__init__.py in _raise_if_error(response)
     62     error = _get_error(response)
     63     if error is not None:
---> 64         raise error
     65 
     66 

InferenceServerException: in ensemble 'criteo_ens', Failed to process the request(s), message: The stub process has exited unexpectedly.

Let’s unload the model. We need to unload each model.

triton_client.unload_model(model_name="criteo_ens")
triton_client.unload_model(model_name="criteo_nvt")
triton_client.unload_model(model_name="criteo")
POST /v2/repository/models/criteo_ens/unload, headers None
{"parameters":{"unload_dependents":false}}
<HTTPSocketPoolResponse status=200 headers={'content-type': 'application/json', 'content-length': '0'}>
Loaded model 'criteo_ens'
POST /v2/repository/models/criteo_nvt/unload, headers None
{"parameters":{"unload_dependents":false}}
<HTTPSocketPoolResponse status=200 headers={'content-type': 'application/json', 'content-length': '0'}>
Loaded model 'criteo_nvt'
POST /v2/repository/models/criteo/unload, headers None
{"parameters":{"unload_dependents":false}}
<HTTPSocketPoolResponse status=200 headers={'content-type': 'application/json', 'content-length': '0'}>
Loaded model 'criteo'