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Copy file name to clipboardExpand all lines: docs/model-serving/storage/providers/azure.md
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@@ -97,6 +97,25 @@ Apply the `sklearn-azure.yaml`.
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kubectl apply -f sklearn-azure.yaml
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```
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### Configuring blob download
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There are a number of environment variables that allow to configure azure blob download parallelization:
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```yaml
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kind: ClusterStorageContainer
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spec:
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container:
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env:
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- name: AZURE_MAX_FILE_CONCURRENCY
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value: 4
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- name: AZURE_MAX_CHUNK_CONCURRENCY
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value: 4
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```
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(only relevant entries shown)
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The above example shows the default values. Together they control how many files are downloaded in parallel, and the download parallelism of each file.
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Note that each download has a buffer of 8MiB.
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## Run a prediction
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Now, the ingress can be accessed at `${INGRESS_HOST}:${INGRESS_PORT}` or follow [this instruction](../../../getting-started/predictive-first-isvc.md#4-determine-the-ingress-ip-and-ports)
Copy file name to clipboardExpand all lines: docs/model-serving/storage/providers/s3/s3.md
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kubectl apply -f mnist-s3.yaml
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```
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### Configuring object download
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There are a number of environment variables that allow to configure S3 object download parallelization:
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```yaml
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kind: ClusterStorageContainer
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spec:
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container:
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env:
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- name: S3_MAX_FILE_CONCURRENCY
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value: 4
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```
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(only relevant entries shown)
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The above example shows the default value. It controls how many files are downloaded in parallel from S3.
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Note that each parallel download forks python and creates a new boto3 client.
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## Run a prediction
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Now, the ingress can be accessed at `${INGRESS_HOST}:${INGRESS_PORT}` or follow [this instruction](../../../../getting-started/predictive-first-isvc.md#4-determine-the-ingress-ip-and-ports)
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