aws / aws/sagemaker-python-sdk

Serverless Endpoint Can't Run Due to Insufficient Space

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#4,665 2 comments 0 reactions 0 assignees View on GitHub
component: Inference APIs and Interfaces type: bug
Dominant language
Python
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Description

**Describe the bug**
I am trying to run a serverless endpoint but the endpoint always fails to get created while trying to install dependencies. I understand that serverless endpoints do not have much space but I provisioned the full 6GB amount and it hasn't even gotten to downloading the model.

**To reproduce**
Create a sagemaker serverless endpoint withe the following configuration:

IMAGE:

763104351884.dkr.ecr.eu-west-2.amazonaws.com/pytorch-inference:2.2.0-cpu-py310-ubuntu20.04-sagemaker

REQUIRMENTS:

torchaudio==2.2.2
sox==1.5.0
huggingface_hub>=0.8.0
hyperpyyaml>=0.0.1
joblib>=0.14.1
numpy>=1.17.0
packaging
pandas>=1.0.1
pre-commit>=2.3.0
pygtrie>=2.1,<3.0
scipy>=1.4.1,<1.13.0
sentencepiece>=0.1.91
SoundFile; sys_platform == 'win32'
torch>=1.9.0,<=2.2.2
tqdm>=4.42.0
transformers>=4.30.0
speechbrain==1.0.0

Alternatively you could reduce this to the following but the others will be installed as dependencies anyway:

torchaudio==2.2.2
sox==1.5.0
speechbrain==1.0.0

MEMORY:

6GB

**Expected behavior**
Serverless endpoint is created

**Screenshots or logs**
![image](https://github.com/aws/sagemaker-python-sdk/assets/140638069/2a5da1d9-5ddd-4484-8ef0-e9f6572a2a97)

**System information**
A description of your system. Please provide:
- **SageMaker Python SDK version**: Used AWS Console
- **Framework name (eg. PyTorch) or algorithm (eg. KMeans)**: Pytorch, Speechbrain (speechbrain/spkrec-ecapa-voxceleb)
- **Framework version**: Speechbrain (1.0.0)
- **Python version**: 3.10
- **CPU or GPU**: CPU
- **Custom Docker image (Y/N)**: N

**Additional context**
On my local machine a virtual environment with the packages outlined in the requirements.txt file takes 842MB

Contributor guide

Open the contributing guide

Research direction

The report names no SageMaker Python SDK file, test, or SDK version; it was reproduced through the AWS Console with a PyTorch 2.2.0 image and the listed dependencies. Start by reproducing the serverless endpoint creation and inspecting the dependency-installation failure logs. Done means the endpoint is created successfully within the 6GB allocation, or the limitation is documented.

Written by the indexing model from the issue text.

Assessment

Tech stack
aws, python, pytorch
Domain
cloud, machine-learning
Issue type
Bug
Difficulty
4/5
Estimated time
3-5 days
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
20/100

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