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sparsezoo.deployment_package
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bfineran
reviewed
Feb 13, 2023
Fix: Failing tests Refactor: Get `Dockerfile` path programmatically and also add it to `MANIFEST.in`
rahul-tuli
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dbogunowicz,
bfineran,
corey-nm and
KSGulin
February 15, 2023 04:10
rahul-tuli
changed the title
[WIP]
Feb 15, 2023
sparsezoo.deployment_package
sparsezoo.deployment_package
corey-nm
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Feb 16, 2023
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lgtm!
bfineran
approved these changes
Feb 16, 2023
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sparsezoo.deployment_package
This utility provides an easy to use
python-api
and acommand-line-interface
to find pre-trained, packaged models across both NLP and CV domains that are
ready for deployment out-of-the-box and are optimized for metrics that are most important to a user like accuracy, throughput, latency,
etc.
python-api
The python-api can be used to quickly fetch a model from SparseZoo, the
model.onnx
path printed the end of the following code snippet can be directlyused with the
Dockerfile
provided with this diffFor deployment instructions refer here
Output:
cli
The
sparsezoo.deployment_package
cli provides an interface to the python-apiand also prints out instructions to deploy the saved model directory with
Dockerfile present here
(sparsezoo3.9) ~ sparsezoo.deployment_package \ --dataset imagenette \ --optimizing_metric compression
Output:
Usage
Examples:
sparsezoo.deployment_package --dataset imagenette --optimizing_metric compression
sparsezoo.deployment_package --dataset imagenette --optimizing_metric accuracy
sparsezoo.deployment_package --task qa --dataset squad --optimizing_metric latency
sparsezoo.deployment_package --task qa --dataset squad \ --optimizing_metric "compression, accuracy"
Deployment
Assuming we know the path to the directory containing
model.onnx
eitherwith or without configuration files, the following instructions can be used to
deploy a deepsparse container serving the onnx model using the
Dockerfile
,exact instructions are also shown with the
cli