Print pip list in CI

[jackalcooper]

打印用于跑测试的容器内的 pip 包，方便大家查询版本信息

[github-actions]

Speed stats:

GPU Name: GeForce GTX 1080 

✔️ OneFlow resnet50 time: 128.5ms (= 12849.2ms / 100, input_shape=[16, 3, 224, 224])
PyTorch resnet50 time: 137.3ms (= 13732.6ms / 100, input_shape=[16, 3, 224, 224])
✔️ Relative speed: 1.07 (= 137.3ms / 128.5ms)

✔️ OneFlow resnet50 time: 77.2ms (= 7721.4ms / 100, input_shape=[8, 3, 224, 224])
PyTorch resnet50 time: 86.2ms (= 8619.3ms / 100, input_shape=[8, 3, 224, 224])
✔️ Relative speed: 1.12 (= 86.2ms / 77.2ms)

OneFlow resnet50 time: 52.4ms (= 10471.7ms / 200, input_shape=[4, 3, 224, 224])
PyTorch resnet50 time: 58.1ms (= 11616.7ms / 200, input_shape=[4, 3, 224, 224])
✔️ Relative speed: 1.11 (= 58.1ms / 52.4ms)

OneFlow resnet50 time: 44.2ms (= 8843.8ms / 200, input_shape=[2, 3, 224, 224])
PyTorch resnet50 time: 46.4ms (= 9280.2ms / 200, input_shape=[2, 3, 224, 224])
✔️ Relative speed: 1.05 (= 46.4ms / 44.2ms)

OneFlow resnet50 time: 36.9ms (= 7370.1ms / 200, input_shape=[1, 3, 224, 224])
PyTorch resnet50 time: 42.1ms (= 8427.7ms / 200, input_shape=[1, 3, 224, 224])
✔️ Relative speed: 1.14 (= 42.1ms / 36.9ms)

OneFlow swin dataloader time: 0.255s (= 51.083s / 200, num_workers=1)
PyTorch swin dataloader time: 0.253s (= 50.536s / 200, num_workers=1)
✔️ Relative speed: 0.989 (= 0.253s / 0.255s)

OneFlow swin dataloader time: 0.067s (= 13.455s / 200, num_workers=4)
PyTorch swin dataloader time: 0.069s (= 13.706s / 200, num_workers=4)
✔️ Relative speed: 1.019 (= 0.069s / 0.067s)

OneFlow swin dataloader time: 0.036s (= 7.186s / 200, num_workers=8)
PyTorch swin dataloader time: 0.037s (= 7.496s / 200, num_workers=8)
✔️ Relative speed: 1.043 (= 0.037s / 0.036s)

✔️ OneFlow resnet50 time: 135.5ms (= 13553.9ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 156.7ms (= 15669.4ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.16 (= 156.7ms / 135.5ms)

OneFlow resnet50 time: 88.4ms (= 8843.5ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 107.3ms (= 10729.1ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.21 (= 107.3ms / 88.4ms)

OneFlow resnet50 time: 63.3ms (= 12655.6ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 75.0ms (= 15009.7ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
❌ Relative speed: 1.19 (= 75.0ms / 63.3ms)

OneFlow resnet50 time: 52.9ms (= 10582.6ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 66.4ms (= 13281.2ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.26 (= 66.4ms / 52.9ms)

OneFlow resnet50 time: 49.6ms (= 9919.1ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 63.8ms (= 12765.1ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.29 (= 63.8ms / 49.6ms)

[github-actions]

CI failed when running job: cuda-speed-test. PR label automerge has been removed

[github-actions]

Speed stats:

GPU Name: GeForce GTX 1080 

✔️ OneFlow resnet50 time: 128.5ms (= 12852.4ms / 100, input_shape=[16, 3, 224, 224])
PyTorch resnet50 time: 140.1ms (= 14014.6ms / 100, input_shape=[16, 3, 224, 224])
✔️ Relative speed: 1.09 (= 140.1ms / 128.5ms)

✔️ OneFlow resnet50 time: 77.2ms (= 7723.5ms / 100, input_shape=[8, 3, 224, 224])
PyTorch resnet50 time: 84.7ms (= 8474.7ms / 100, input_shape=[8, 3, 224, 224])
✔️ Relative speed: 1.10 (= 84.7ms / 77.2ms)

OneFlow resnet50 time: 54.2ms (= 10836.0ms / 200, input_shape=[4, 3, 224, 224])
PyTorch resnet50 time: 61.7ms (= 12337.5ms / 200, input_shape=[4, 3, 224, 224])
✔️ Relative speed: 1.14 (= 61.7ms / 54.2ms)

OneFlow resnet50 time: 44.8ms (= 8954.8ms / 200, input_shape=[2, 3, 224, 224])
PyTorch resnet50 time: 48.5ms (= 9700.0ms / 200, input_shape=[2, 3, 224, 224])
✔️ Relative speed: 1.08 (= 48.5ms / 44.8ms)

OneFlow resnet50 time: 38.8ms (= 7750.4ms / 200, input_shape=[1, 3, 224, 224])
PyTorch resnet50 time: 40.1ms (= 8014.1ms / 200, input_shape=[1, 3, 224, 224])
✔️ Relative speed: 1.03 (= 40.1ms / 38.8ms)

OneFlow swin dataloader time: 0.252s (= 50.345s / 200, num_workers=1)
PyTorch swin dataloader time: 0.255s (= 51.094s / 200, num_workers=1)
✔️ Relative speed: 1.015 (= 0.255s / 0.252s)

OneFlow swin dataloader time: 0.064s (= 12.844s / 200, num_workers=4)
PyTorch swin dataloader time: 0.069s (= 13.740s / 200, num_workers=4)
✔️ Relative speed: 1.070 (= 0.069s / 0.064s)

OneFlow swin dataloader time: 0.036s (= 7.264s / 200, num_workers=8)
PyTorch swin dataloader time: 0.038s (= 7.614s / 200, num_workers=8)
✔️ Relative speed: 1.048 (= 0.038s / 0.036s)

✔️ OneFlow resnet50 time: 135.6ms (= 13560.9ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 155.7ms (= 15566.8ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.15 (= 155.7ms / 135.6ms)

OneFlow resnet50 time: 88.8ms (= 8884.9ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 106.5ms (= 10646.6ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.20 (= 106.5ms / 88.8ms)

OneFlow resnet50 time: 61.9ms (= 12384.8ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 77.7ms (= 15539.5ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.25 (= 77.7ms / 61.9ms)

OneFlow resnet50 time: 52.3ms (= 10462.4ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 66.6ms (= 13320.5ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.27 (= 66.6ms / 52.3ms)

OneFlow resnet50 time: 48.6ms (= 9723.4ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 63.6ms (= 12728.7ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.31 (= 63.6ms / 48.6ms)

[github-actions]

View latest API docs preview at: https://staging.oneflow.info/docs/Oneflow-Inc/oneflow/pr/7952/