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Oleksandr Shchur 2025-12-30 15:10:39 +00:00
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notebooks/deploy-chronos-to-amazon-sagemaker.ipynb
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@ -21,22 +21,22 @@
"### Deployment Options\n",
"This notebook covers three deployment modes on SageMaker:\n",
"\n",
"**[Real-time Inference](https://docs.aws.amazon.com/sagemaker/latest/dg/realtime-endpoints.html)** (Section 1)\n",
"- ✅ Highest throughput, consistently low latency, supports both GPU and CPU instances\n",
"- ✅ Simple setup via JumpStart\n",
"- ❌ By default, you pay for the time the endpoint is running (can be configured to [scale to zero](https://docs.aws.amazon.com/sagemaker/latest/dg/endpoint-auto-scaling-zero-instances.html))\n",
"1. **[Real-time Inference](https://docs.aws.amazon.com/sagemaker/latest/dg/realtime-endpoints.html)**\n",
" - ✅ Highest throughput, consistently low latency, supports both GPU and CPU instances\n",
" - ✅ Simple setup via JumpStart\n",
" - ❌ By default, you pay for the time the endpoint is running (can be configured to [scale to zero](https://docs.aws.amazon.com/sagemaker/latest/dg/endpoint-auto-scaling-zero-instances.html))\n",
"\n",
"**[Serverless Inference](https://docs.aws.amazon.com/sagemaker/latest/dg/serverless-endpoints.html)** (Section 2)\n",
"- ✅ Pay only for active inference time, no infrastructure management\n",
"- ✅ Cost-efficient for intermittent or unpredictable traffic\n",
"- ❌ Cold start latency on first request after idle, CPU only, lowest throughput of all options\n",
"- ❌ More complex setup (requires repackaging model artifacts)\n",
"2. **[Serverless Inference (CPU only)](https://docs.aws.amazon.com/sagemaker/latest/dg/serverless-endpoints.html)**\n",
" - ✅ Pay only for active inference time, no infrastructure management\n",
" - ✅ Cost-efficient for intermittent or unpredictable traffic\n",
" - ❌ Cold start latency on first request after idle, lowest throughput of all options\n",
" - ❌ More complex setup (requires repackaging model artifacts)\n",
"\n",
"**[Batch Transform](https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform.html)** (Section 3)\n",
"- ✅ Pay only for active compute time, no persistent infrastructure\n",
"- ✅ Cost-efficient for large-scale batch prediction jobs\n",
"- ❌ Initialization takes severa minutes for each job (not for real-time use), CPU only, requires data in S3\n",
"- ❌ More complex setup (requires repackaging model artifacts)\n",
"3. **[Batch Transform](https://docs.aws.amazon.com/sagemaker/latest/dg/batch-transform.html)**\n",
" - ✅ Pay only for active compute time, no persistent infrastructure\n",
" - ✅ Cost-efficient for large-scale batch prediction jobs\n",
" - ❌ Initialization takes severa minutes for each job (not for real-time use), requires data in S3\n",
" - ❌ More complex setup (requires repackaging model artifacts)\n",
"\n",
"**Reference benchmark** on a dataset with 1M rows (2000 time series with 500 observations each) and prediction length of 28:\n",
"| Mode | Instance | Inference time (s) |\n",
@ -1410,8 +1410,14 @@
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"notebook_metadata_filter": "-all"
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