Huggingface models
Text Generation Model
cat ./models/hf-text-gen.yaml
apiVersion: mlops.seldon.io/v1alpha1
kind: Model
metadata:
name: text-gen
spec:
storageUri: "gs://seldon-models/mlserver/huggingface/text-generation"
requirements:
- huggingface
Load the model
kubectl apply -f ./models/hf-text-gen.yaml
model.mlops.seldon.io/text-gen created
Wait for the model to be ready
kubectl get model text-gen -n ${NAMESPACE} -o json | jq -r '.status.conditions[] | select(.message == "ModelAvailable") | .status'
True
Do a REST inference call
curl --location 'http://${MESH_IP}:9000/v2/models/text-gen/infer' \
--header 'Content-Type: application/json' \
--data '{"inputs": [{"name": "args","shape": [1],"datatype": "BYTES","data": ["Once upon a time in a galaxy far away"]}]}'
{
"model_name": "text-gen_1",
"model_version": "1",
"id": "121ff5f4-1d4a-46d0-9a5e-4cd3b11040df",
"parameters": {},
"outputs": [
{
"name": "output",
"shape": [
1,
1
],
"datatype": "BYTES",
"parameters": {
"content_type": "hg_jsonlist"
},
"data": [
"{\"generated_text\": \"Once upon a time in a galaxy far away, the planet is full of strange little creatures. A very strange combination of creatures in that universe, that is. A strange combination of creatures in that universe, that is. A kind of creature that is\"}"
]
}
]
}
res = !seldon model infer text-gen --inference-mode grpc \
'{"inputs":[{"name":"args","contents":{"bytes_contents":["T25jZSB1cG9uIGEgdGltZSBpbiBhIGdhbGF4eSBmYXIgYXdheQo="]},"datatype":"BYTES","shape":[1]}]}'
import json
import base64
r = json.loads(res[0])
base64.b64decode(r["outputs"][0]["contents"]["bytesContents"][0])
b'{"generated_text": "Once upon a time in a galaxy far away\\n\\nThe Universe is a big and massive place. How can you feel any of this? Your body doesn\'t make sense if the Universe is in full swing \\u2014 you don\'t have to remember whether the"}'
Unload the model
kubectl delete model text-gen
Custom Text Generation Model
cat ./models/hf-text-gen-custom-tiny-stories.yaml
apiVersion: mlops.seldon.io/v1alpha1
kind: Model
metadata:
name: custom-tiny-stories-text-gen
spec:
storageUri: "gs://seldon-models/scv2/samples/mlserver_1.3.5/huggingface-text-gen-custom-tiny-stories"
requirements:
- huggingface
Load the model
kubectl apply -f ./models/hf-text-gen-custom-tiny-stories.yaml
model.mlops.seldon.io/custom-tiny-stories-text-gen created
kubectl get model custom-tiny-stories-text-gen -n ${NAMESPACE} -o json | jq -r '.status.conditions[] | select(.message == "ModelAvailable") | .status'
True
curl --location 'http://${MESH_IP}:9000/v2/models/custom-tiny-stories-text-gen/infer' \
--header 'Content-Type: application/json' \
--data '{"inputs": [{"name": "args","shape": [1],"datatype": "BYTES","data": ["Once upon a time in a galaxy far away"]}]}'
{
"model_name": "custom-tiny-stories-text-gen_1",
"model_version": "1",
"id": "d0fce59c-76e2-4f81-9711-1c93d08bcbf9",
"parameters": {},
"outputs": [
{
"name": "output",
"shape": [
1,
1
],
"datatype": "BYTES",
"parameters": {
"content_type": "hg_jsonlist"
},
"data": [
"{\"generated_text\": \"Once upon a time in a galaxy far away. It was a very special place to live.\\n\"}"
]
}
]
}
res = !seldon model infer custom-tiny-stories-text-gen --inference-mode grpc \
'{"inputs":[{"name":"args","contents":{"bytes_contents":["T25jZSB1cG9uIGEgdGltZSBpbiBhIGdhbGF4eSBmYXIgYXdheQo="]},"datatype":"BYTES","shape":[1]}]}'
import json
import base64
r = json.loads(res[0])
base64.b64decode(r["outputs"][0]["contents"]["bytesContents"][0])
b'{"generated_text": "Once upon a time in a galaxy far away\\nOne night, a little girl named Lily went to"}'
Unload the model
kubectl delete custom-tiny-stories-text-gen
As a next step, why not try running a larger-scale model? You can find a definition for one in ./models/hf-text-gen-custom-gpt2.yaml. However, you may need to request and allocate more memory!
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