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@@ -101,6 +102,7 @@ if [ "$is_release_branch" = "true" ]; then
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! -path "./bin/*" \
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! -path "./.git/*" \
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! -name ".*" \
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! -name "*.bin" \
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-exec grep -R -A 5 -e "CORTEX_VERSION" {} \;)
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output=$(echo "$output"| grep -e "master"|| true)
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if [[ $output ]];then
@@ -119,6 +121,7 @@ if [ "$is_release_branch" = "true" ]; then
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! -path "./bin/*" \
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! -path "./.git/*" \
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! -name ".*" \
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! -name "*.bin" \
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-exec grep -l "WARNING: you are on the master branch" {} \;)
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if [[ $output ]];then
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echo"file(s) have the master version warning:"
@@ -132,6 +135,7 @@ if [ "$is_release_branch" = "true" ]; then
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! -name "*.json" \
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! -name "*.txt" \
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! -name ".*" \
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! -name "*.bin" \
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-exec grep -L -e "this is an example for cortex release ${git_branch} and may not deploy correctly on other releases of cortex" {} \;)
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if [[ $output ]];then
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echo"examples file(s) are missing appropriate version comment:"
@@ -148,6 +152,7 @@ else
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! -name "*.json" \
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! -name "*.txt" \
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! -name ".*" \
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! -name "*.bin" \
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-exec grep -L "WARNING: you are on the master branch, please refer to the docs on the branch that matches your \`cortex version\`" {} \;)
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if [[ $output ]];then
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echo"docs file(s) are missing appropriate version comment:"
@@ -162,6 +167,7 @@ else
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! -name "*.json" \
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! -name "*.txt" \
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! -name ".*" \
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! -name "*.bin" \
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-exec grep -L "WARNING: you are on the master branch; please refer to examples on the branch corresponding to your \`cortex version\` (e\.g\. for version [0-9]*\.[0-9]*\.\*, run \`git checkout -b [0-9]*\.[0-9]*\` or switch to the \`[0-9]*\.[0-9]*\` branch on GitHub)" {} \;)
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if [[ $output ]];then
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echo"example file(s) are missing version appropriate comment:"
Copy file name to clipboardExpand all lines: examples/tensorflow/image-classifier-resnet50/README.md
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@@ -55,6 +55,16 @@ Then, deploy each API one at a time and check the results:
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1. Running `python ../../utils/throughput_test.py -i 30 -p 4 -t 24` with the [cortex_gpu.yaml](cortex_gpu.yaml) API running on an `g4dn.xlarge` instance will get **~125 inferences/sec** with an average latency of **85 ms**. Optimizing the model with TensorRT to use FP16 on TF-serving only seems to achieve a 10% performance improvement - one thing to consider is that the TensorRT engines hadn't been built beforehand, so this might have affected the results negatively.
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1. Running `python ../../utils/throughput_test.py -i 30 -p 4 -t 60` with the [cortex_gpu_server_side_batching.yaml](cortex_gpu_batch_sized.yaml) API running on an `g4dn.xlarge` instance will get **~186 inferences/sec** with an average latency of **500 ms**. This achieves a 49% higher throughput than the [cortex_gpu.yaml](cortex_gpu.yaml) API, at the expense of increased latency.
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Alternatively to [throughput_test.py](../../utils/throughput_test.py), the `ab` GNU utility can also be used to benchmark the API. This has the advantage that it's not as taxing on your local machine, but the disadvantage that it doesn't implement a cooldown period. You can run `ab` like this:
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```bash
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# for making octet-stream requests, which is the default for throughput_test script
*Note: `inf1.xlarge` isn't used because the major bottleneck with `inf` instances for this example is with the CPU, and `inf1.2xlarge` has twice the amount of CPU cores for same number of Inferentia ASICs (which is 1), which translates to almost double the throughput.*
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