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docker-compose.test.yml
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docker-compose.test.yml
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version: '2.3'
services:
test-cpu-base:
build:
context: .
dockerfile: Dockerfile.test.cpu
args:
UBUNTU_VERSION: 18.04
GPP_VERSION: 7
MPI_KIND: None
PYTHON_VERSION: 3.8
TENSORFLOW_PACKAGE: tensorflow-cpu==2.5.0
KERAS_PACKAGE: None
PYTORCH_PACKAGE: torch==1.9.0+cpu
PYTORCH_LIGHTNING_PACKAGE: pytorch-lightning==1.3.7.post0
TORCHVISION_PACKAGE: torchvision==0.10.0+cpu
MXNET_PACKAGE: mxnet==1.8.0.post0
PYSPARK_PACKAGE: pyspark==3.1.2
SPARK_PACKAGE: spark-3.1.2/spark-3.1.2-bin-hadoop2.7.tgz
HOROVOD_BUILD_FLAGS: HOROVOD_WITH_GLOO=1
privileged: true
shm_size: 8gb
# our baseline first
test-cpu-gloo-py3_8-tf2_5_0-keras_none-torch1_9_0-mxnet1_8_0_p0-pyspark3_1_2:
extends: test-cpu-base
test-cpu-mpich-py3_8-tf2_5_0-keras_none-torch1_9_0-mxnet1_8_0_p0-pyspark3_1_2:
extends: test-cpu-base
build:
args:
MPI_KIND: MPICH
HOROVOD_BUILD_FLAGS: HOROVOD_WITHOUT_GLOO=1
test-cpu-oneccl-py3_8-tf2_5_0-keras_none-torch1_9_0-mxnet1_8_0_p0-pyspark3_1_2:
extends: test-cpu-base
build:
args:
MPI_KIND: ONECCL
HOROVOD_BUILD_FLAGS: HOROVOD_WITHOUT_GLOO=1
test-cpu-openmpi-py3_8-tf2_5_0-keras_none-torch1_9_0-mxnet1_8_0_p0-pyspark3_1_2:
extends: test-cpu-base
build:
args:
MPI_KIND: OpenMPI
HOROVOD_BUILD_FLAGS: HOROVOD_WITHOUT_GLOO=1
test-cpu-openmpi-gloo-py3_8-tf2_5_0-keras_none-torch1_9_0-mxnet1_8_0_p0-pyspark3_1_2:
extends: test-cpu-base
build:
args:
MPI_KIND: OpenMPI
test-cpu-gloo-py3_7-tf1_15_5-keras2_2_4-torch1_6_0-mxnet1_5_1_p0-pyspark3_1_2:
extends: test-cpu-base
build:
args:
PYTHON_VERSION: 3.7
# there is no tensorflow-cpu>1.15.0, so we use tensorflow==1.15.5
TENSORFLOW_PACKAGE: tensorflow==1.15.5
KERAS_PACKAGE: keras==2.2.4
PYTORCH_PACKAGE: torch==1.6.0+cpu
PYTORCH_LIGHTNING_PACKAGE: pytorch_lightning==1.1.0
TORCHVISION_PACKAGE: torchvision==0.7.0+cpu
MXNET_PACKAGE: mxnet==1.5.1.post0
# there is no mxnet-1.6.0.post0 and mxnet-1.6.0 does not work with horovod
# https://github.com/apache/incubator-mxnet/issues/16193
# however, there is an mxnet-cu101-1.6.0.post0, so we test this with gpu instead of cpu
# this cpu test variation is defined as gpu in gpu frameworks variations below
# test-cpu-gloo-py3_8-tf2_3_2-keras2_3_1-torch1_7_1-mxnet1_6_0_p0-pyspark3_1_2:
test-cpu-gloo-py3_8-tf2_4_1-keras2_4_3-torch1_8_1-mxnet1_7_0_p2-pyspark3_1_2:
extends: test-cpu-base
build:
args:
TENSORFLOW_PACKAGE: tensorflow==2.4.1
KERAS_PACKAGE: keras==2.3.4
PYTORCH_PACKAGE: torch==1.8.1+cpu
TORCHVISION_PACKAGE: torchvision==0.9.1
MXNET_PACKAGE: mxnet==1.7.0.post2
# then our baseline again, omitted ...
test-cpu-gloo-py3_8-tfhead-keras_none-torchhead-mxnethead-pyspark3_1_2:
extends: test-cpu-base
build:
args:
TENSORFLOW_PACKAGE: tf-nightly
KERAS_PACKAGE: None
PYTORCH_PACKAGE: torch-nightly
TORCHVISION_PACKAGE: torchvision
MXNET_PACKAGE: mxnet-nightly
test-cpu-gloo-py3_7-tf2_5_0-keras_none-torch1_9_0-mxnet1_8_0_p0-pyspark2_4_8:
extends: test-cpu-base
build:
args:
PYTHON_VERSION: 3.7
PYSPARK_PACKAGE: pyspark==2.4.8
SPARK_PACKAGE: spark-2.4.8/spark-2.4.8-bin-hadoop2.7.tgz
test-cpu-gloo-py3_8-tf2_5_0-keras_none-torch1_9_0-mxnet1_8_0_p0-pyspark3_0_3:
extends: test-cpu-base
build:
args:
PYTHON_VERSION: 3.8
PYSPARK_PACKAGE: pyspark==3.0.3
SPARK_PACKAGE: spark-3.0.3/spark-3.0.3-bin-hadoop2.7.tgz
# then our baseline again, omitted ...
test-gpu-base:
build:
context: .
dockerfile: Dockerfile.test.gpu
args:
GPP_VERSION: 7
MPI_KIND: None
PYTHON_VERSION: 3.8
PYSPARK_PACKAGE: pyspark==3.1.2
SPARK_PACKAGE: spark-3.1.2/spark-3.1.2-bin-hadoop2.7.tgz
HOROVOD_BUILD_FLAGS: HOROVOD_GPU_OPERATIONS=NCCL
HOROVOD_MIXED_INSTALL: 0
runtime: nvidia
# We plumb CUDA_VISIBLE_DEVICES instead of NVIDIA_VISIBLE_DEVICES because
# the latter does not work in privileged mode that we use in the containers.
environment:
- CUDA_VISIBLE_DEVICES
privileged: true
shm_size: 8gb
# torch==1.3.1+cu100 requires torchvision==0.4.2+cu100
test-gpu-gloo-py3_7-tf1_15_5-keras2_2_4-torch1_3_1-mxnet1_5_1_p0-pyspark3_1_2:
extends: test-gpu-base
build:
args:
CUDA_DOCKER_VERSION: 10.0-devel-ubuntu18.04
CUDNN_VERSION: 7.6.5.32-1+cuda10.1
NCCL_VERSION_OVERRIDE: 2.7.8-1+cuda10.1
PYTHON_VERSION: 3.7
TENSORFLOW_PACKAGE: tensorflow-gpu==1.15.5
KERAS_PACKAGE: keras==2.2.4
PYTORCH_PACKAGE: torch==1.3.1+cu100
PYTORCH_LIGHTNING_PACKAGE: pytorch_lightning==1.1.0
TORCHVISION_PACKAGE: torchvision==0.4.2+cu100
MXNET_PACKAGE: mxnet-cu100==1.5.1.post0
# this is required as we cannot test mxnet-1.6.0.post0 with cpu
test-gpu-gloo-py3_8-tf2_3_2-keras2_3_1-torch1_7_1-mxnet1_6_0_p0-pyspark3_1_2:
extends: test-gpu-base
build:
args:
CUDA_DOCKER_VERSION: 10.1-devel-ubuntu18.04
CUDNN_VERSION: 7.6.5.32-1+cuda10.1
NCCL_VERSION_OVERRIDE: 2.7.8-1+cuda10.1
TENSORFLOW_PACKAGE: tensorflow-gpu==2.3.2
KERAS_PACKAGE: keras==2.3.1
PYTORCH_PACKAGE: torch==1.7.1+cu101
PYTORCH_LIGHTNING_PACKAGE: pytorch_lightning==1.2.9
TORCHVISION_PACKAGE: torchvision==0.8.2+cu101
MXNET_PACKAGE: mxnet-cu101==1.6.0.post0
# we additionally test the previous framework combination (CUDA 10.x) with mxnet 1.7.x
# as mxnet 1.7.x only supports CUDA 10.x, but next framework combination targets CUAA 11.x
test-gpu-gloo-py3_8-tf2_3_2-keras2_3_1-torch1_7_1-mxnet1_7_0_p1-pyspark3_1_2:
extends: test-gpu-base
build:
args:
CUDA_DOCKER_VERSION: 10.1-devel-ubuntu18.04
CUDNN_VERSION: 7.6.5.32-1+cuda10.1
NCCL_VERSION_OVERRIDE: 2.7.8-1+cuda10.1
TENSORFLOW_PACKAGE: tensorflow-gpu==2.3.2
KERAS_PACKAGE: keras==2.3.1
PYTORCH_PACKAGE: torch==1.7.1+cu101
PYTORCH_LIGHTNING_PACKAGE: pytorch_lightning==1.2.9
TORCHVISION_PACKAGE: torchvision==0.8.2+cu101
MXNET_PACKAGE: mxnet-cu101==1.7.0.post1
# we deviate from mxnet1_7_0_p2 here as other frameworks target CUDA 11.x and
# mxnet 1.7.x only supports CUDA 10.x, with mxnet 1.8.x we have CUDA 11.x packages
test-gpu-gloo-py3_8-tf2_4_1-keras2_4_3-torch1_8_1-mxnet1_8_0_p0-pyspark3_1_2:
extends: test-gpu-base
build:
args:
CUDA_DOCKER_VERSION: 11.2.2-devel-ubuntu18.04
CUDNN_VERSION: 8.1.1.33-1+cuda11.2
NCCL_VERSION_OVERRIDE: 2.8.4-1+cuda11.2
TENSORFLOW_PACKAGE: tensorflow-gpu==2.4.1
KERAS_PACKAGE: keras==2.4.3
PYTORCH_PACKAGE: torch==1.8.1+cu111
PYTORCH_LIGHTNING_PACKAGE: pytorch_lightning==1.2.9
TORCHVISION_PACKAGE: torchvision==0.9.1+cu111
MXNET_PACKAGE: mxnet-cu112==1.8.0.post0
test-gpu-openmpi-gloo-py3_8-tf2_5_0-keras_none-torch1_9_0-mxnet1_8_0_p0-pyspark3_1_2:
extends: test-gpu-base
build:
args:
CUDA_DOCKER_VERSION: 11.2.2-devel-ubuntu18.04
CUDNN_VERSION: 8.1.1.33-1+cuda11.2
NCCL_VERSION_OVERRIDE: 2.8.4-1+cuda11.2
MPI_KIND: OpenMPI
TENSORFLOW_PACKAGE: tensorflow-gpu==2.5.0
KERAS_PACKAGE: None
PYTORCH_PACKAGE: torch==1.9.0+cu111
PYTORCH_LIGHTNING_PACKAGE: pytorch-lightning==1.3.7.post0
TORCHVISION_PACKAGE: torchvision==0.10.0+cu111
MXNET_PACKAGE: mxnet-cu112==1.8.0.post0
test-gpu-gloo-py3_8-tfhead-keras_none-torchhead-mxnethead-pyspark3_1_2:
extends: test-gpu-base
build:
args:
CUDA_DOCKER_VERSION: 11.2.2-devel-ubuntu18.04
CUDNN_VERSION: 8.1.1.33-1+cuda11.2
NCCL_VERSION_OVERRIDE: 2.8.4-1+cuda11.2
TENSORFLOW_PACKAGE: tf-nightly-gpu
KERAS_PACKAGE: None
PYTORCH_PACKAGE: torch-nightly-cu111
PYTORCH_LIGHTNING_PACKAGE: pytorch_lightning==1.2.9
TORCHVISION_PACKAGE: torchvision
MXNET_PACKAGE: mxnet-nightly-cu112
test-mixed-openmpi-gloo-py3_8-tf2_5_0-keras_none-torch1_9_0-mxnet1_8_0_p0-pyspark3_1_2:
extends: test-gpu-base
build:
args:
CUDA_DOCKER_VERSION: 11.2.2-devel-ubuntu18.04
CUDNN_VERSION: 8.1.1.33-1+cuda11.2
NCCL_VERSION_OVERRIDE: 2.8.4-1+cuda11.2
MPI_KIND: OpenMPI
TENSORFLOW_PACKAGE: tensorflow-gpu==2.5.0
KERAS_PACKAGE: None
PYTORCH_PACKAGE: torch==1.9.0+cu111
PYTORCH_LIGHTNING_PACKAGE: pytorch_lightning==1.3.7.post0
TORCHVISION_PACKAGE: torchvision==0.10.0+cu111
MXNET_PACKAGE: mxnet-cu112==1.8.0.post0
HOROVOD_BUILD_FLAGS: ""
HOROVOD_MIXED_INSTALL: 1