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[pytorch] Change Torchvision version from 0.6.0 to 0.6.1 for PyTorch 1.5.1 #650
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@SergTogul @saimidu Tagging because I can't seem to add reviewers. Please take a look at this PR so we can resolve the mentioned issue! The EC2 test is failing because of the AMP test (introduced in a later version of PyTorch 1.6). I added an if-statement in the test as a workaround, per @saimidu 's suggestion but a more universal solution for disabling tests depending on framework version would be great. |
@@ -123,6 +123,9 @@ def test_nvapex(pytorch_training, ec2_connection, gpu_only): | |||
@pytest.mark.parametrize("ec2_instance_type", PT_EC2_GPU_INSTANCE_TYPE, indirect=True) | |||
@pytest.mark.skipif(PT_EC2_GPU_INSTANCE_TYPE == ["g3.4xlarge"], reason="Skipping AMP DDP test on single gpu instance") | |||
def test_pytorch_amp(pytorch_training, ec2_connection, gpu_only): | |||
from packaging import Version |
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When the CI tests on this PR are completed successfully, please move this import line to the top of the pytest script along with all the other standard module imports (i.e., below import os
for standard module os
, and above import pytest
for 3P module pytest
).
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Also, this should be from packaging.version import Version
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As discussed offline with @saimidu , I have reverted the buildspec file back to the current (1.6) file. For test results of code changes relevant to this PR, please refer to commit d9f09ce. I have also tested the container produced by the PR for the above commit by installing detectron2-0.1.3 and do not run into the import error that is mentioned in the issue. |
Issue #, if available:
PR Checklist
Pytest Marker Checklist
@pytest.mark.model("<model-type>")
to the new tests which I have added, to specify the Deep Learning model that is used in the test (use"N/A"
if the test doesn't use a model)@pytest.mark.integration("<feature-being-tested>")
to the new tests which I have added, to specify the feature that will be tested@pytest.mark.multinode(<integer-num-nodes>)
to the new tests which I have added, to specify the number of nodes used on a multi-node test@pytest.mark.processor(<"cpu"/"gpu"/"eia"/"neuron">)
to the new tests which I have added, if a test is specifically applicable to only one processor typeEIA/NEURON Checklist
src/config/build_config.py
in my PR branch by settingENABLE_EI_MODE = True
orENABLE_NEURON_MODE = True
Benchmark Checklist
src/config/test_config.py
in my PR branch by settingENABLE_BENCHMARK_DEV_MODE = True
Reviewer Checklist
Description: Fix #629 by updating the PyTorch 1.5.1 Dockerfiles with the S3 path to torchvision 0.6.1 binaries.
Tests run:
DLC image/dockerfile: PyTorch 1.5.1 images (CPU/GPU, training/inference).
Additional context:
By submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license. I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.