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Torchserve_sanity script fails for MNIST models

See original GitHub issue

Context

python torchserve_sanity.py fails for MNIST models in the gradle build inside a conda environment of a fresh ubuntu instance. torchserve==0.2.0 torch-model-archiver==0.2.0

Python version: 3.8 (64-bit runtime) Python executable: /home/ubuntu/anaconda3/envs/fbserve/bin/python3

Versions of relevant python libraries: numpy==1.19.2 torch==1.4.0

Java Version: openjdk 11.0.5 2019-10-15 OpenJDK Runtime Environment (build 11.0.5+10-post-Ubuntu-2ubuntu116.04) OpenJDK 64-Bit Server VM (build 11.0.5+10-post-Ubuntu-2ubuntu116.04, mixed mode, sharing)

OS: Ubuntu 16.04.7 LTS GCC version: (Ubuntu 5.4.0-6ubuntu1~16.04.12) 5.4.0 20160609

Your Environment

Installed using source? [yes/no]: yes
Are you planning to deploy it using docker container? [yes/no]: no
Is it a CPU or GPU environment?: CPU
Using a default/custom handler? [If possible upload/share custom handler/model]: NA
What kind of model is it e.g. vision, text, audio?: vision
Are you planning to use local models from model-store or public url being used e.g. from S3 bucket etc.? [If public url then provide link.]: NA
Provide config.properties, logs [ts.log] and parameters used for model registration/update APIs: NA
Link to your project [if any]: NA

Expected Behavior

The torchserve_sanity.py should pass for the master branch.

Current Behavior

It fails with the cases:

1) TorchServeSuite > TorchServe > org.pytorch.serve.ModelServerTest > testLoadMNISTEagerModel FAILED
    java.lang.AssertionError at ModelServerTest.java:1698
2) TorchServeSuite > TorchServe > org.pytorch.serve.ModelServerTest > testPredictionMNISTEagerModel FAILED
    java.lang.AssertionError at ModelServerTest.java:1727
3) TorchServeSuite > TorchServe > org.pytorch.serve.ModelServerTest > testUnregistedMNISTEagerModel FAILED
    java.lang.AssertionError at ModelServerTest.java:1650
4) TorchServeSuite > TorchServe > org.pytorch.serve.ModelServerTest > testLoadMNISTScriptedModel FAILED
    java.lang.AssertionError at ModelServerTest.java:1698

TorchServeSuite > TorchServe > org.pytorch.serve.ModelServerTest > testPredictionMNISTScriptedModel FAILED
    java.lang.AssertionError at ModelServerTest.java:1727
5)TorchServeSuite > TorchServe > org.pytorch.serve.ModelServerTest > testUnregistedMNISTScriptedModel FAILED
    java.lang.AssertionError at ModelServerTest.java:1650
6) TorchServeSuite > TorchServe > org.pytorch.serve.ModelServerTest > testLoadMNISTTracedModel FAILED
    java.lang.AssertionError at ModelServerTest.java:1698
7) TorchServeSuite > TorchServe > org.pytorch.serve.ModelServerTest > testPredictionMNISTTracedModel FAILED
    java.lang.AssertionError at ModelServerTest.java:1727

TorchServeSuite > TorchServe > org.pytorch.serve.ModelServerTest > testUnregistedMNISTTracedModel STANDARD_OUT
    2020-11-25 15:03:48,323 [WARN ] epollEventLoopGroup-3-5 org.pytorch.serve.wlm.ModelManager - Model not found: mnist_traced
    2020-11-25 15:03:48,323 [INFO ] epollEventLoopGroup-3-5 ACCESS_LOG - /127.0.0.1:42678 "DELETE /models/mnist_traced HTTP/1.1" 404 0
    2020-11-25 15:03:48,323 [INFO ] epollEventLoopGroup-3-5 TS_METRICS - Requests4XX.Count:1|#Level:Host|#hostname:ip-172-31-61-118,timestamp:null

TorchServeSuite > TorchServe > org.pytorch.serve.ModelServerTest > testUnregistedMNISTTracedModel FAILED
    java.lang.AssertionError at ModelServerTest.java:1650

Steps to Reproduce

Install java jdk
Install the requirements as mentioned in the comment
Run python torchserve_sanit.py

Failure Logs [if any]

ec2_explain.log master_ec2_c5_sanity_fail.log

Issue Analytics

State:
Created 3 years ago
Comments:12 (7 by maintainers)

Top GitHub Comments

1reaction

jeremiahschungcommented, Dec 4, 2020

The recommendation from the pytorch release eng team is to use one of the two options:

Use the folder-specific FilePath: https://download.pytorch.org/whl/cu102/torch_stable.html
Download Torch 1.6.0 from PyPI instead of pytorch.org (e.g. pip install torch==1.6.0)

0reactions

harshbafnacommented, Dec 9, 2020

Issues related to install_dependency on CPU and different CUDA environments has been fixed as part of #836.

Closing the issue.

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