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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: 20.04
GPP_VERSION: 7
MPI_KIND: None
PYTHON_VERSION: 3.8
TENSORFLOW_PACKAGE: tensorflow-cpu==2.9.1
KERAS_PACKAGE: keras==2.9.0
PYTORCH_PACKAGE: torch==1.11.0+cpu
PYTORCH_LIGHTNING_PACKAGE: pytorch-lightning==1.5.9
TORCHVISION_PACKAGE: torchvision==0.12.0+cpu
MXNET_PACKAGE: mxnet==1.9.1
PYSPARK_PACKAGE: pyspark==3.2.1
SPARK_PACKAGE: spark-3.2.1/spark-3.2.1-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_9_1-keras2_9_0-torch1_11_0-mxnet1_9_1-pyspark3_2_1:
extends: test-cpu-base
# permute MPI kinds
test-cpu-mpich-py3_8-tf2_9_1-keras2_9_0-torch1_11_0-mxnet1_9_1-pyspark3_2_1:
extends: test-cpu-base
build:
args:
MPI_KIND: MPICH
HOROVOD_BUILD_FLAGS: HOROVOD_WITHOUT_GLOO=1
test-cpu-oneccl-py3_8-tf2_9_1-keras2_9_0-torch1_11_0-mxnet1_9_1-pyspark3_2_1:
extends: test-cpu-base
build:
args:
MPI_KIND: ONECCL
HOROVOD_BUILD_FLAGS: HOROVOD_WITHOUT_GLOO=1
test-cpu-openmpi-py3_8-tf2_9_1-keras2_9_0-torch1_11_0-mxnet1_9_1-pyspark3_2_1:
extends: test-cpu-base
build:
args:
MPI_KIND: OpenMPI
HOROVOD_BUILD_FLAGS: HOROVOD_WITHOUT_GLOO=1
test-cpu-openmpi-gloo-py3_8-tf2_9_1-keras2_9_0-torch1_11_0-mxnet1_9_1-pyspark3_2_1:
extends: test-cpu-base
build:
args:
MPI_KIND: OpenMPI
# run_gloo_integration expects tf1 to have Gloo mpi kind to run 'Elastic Spark * Tests'
# Tensorflow 1.15.5 is only available for Python 3.7
# Python 3.7 is only available on Ubuntu 18.04
# see test-gpu-gloo-py3_7-tf1_15_5-... below why we have to test with mxnet 1.5.1 here
test-cpu-gloo-py3_7-tf1_15_5-keras2_2_4-torch1_8_1-mxnet1_5_1_p0-pyspark3_2_1:
extends: test-cpu-base
build:
args:
# On Ubuntu 18.04 our setup.py will pull in a recent CMake and use that only to build Horovod
UBUNTU_VERSION: 18.04
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==1.8.1+cpu
TORCHVISION_PACKAGE: torchvision==0.9.1+cpu
MXNET_PACKAGE: mxnet==1.5.1.post0
test-cpu-gloo-py3_8-tf2_7_3-keras2_7_0-torch1_9_1-mxnet1_7_0_p2-pyspark3_2_1:
extends: test-cpu-base
build:
args:
TENSORFLOW_PACKAGE: tensorflow-cpu==2.7.3
KERAS_PACKAGE: keras==2.7.0
PYTORCH_PACKAGE: torch==1.9.1+cpu
TORCHVISION_PACKAGE: torchvision==0.10.1+cpu
MXNET_PACKAGE: mxnet==1.7.0.post2
test-cpu-gloo-py3_8-tf2_8_2-keras2_8_0-torch1_10_2-mxnet1_8_0_p0-pyspark3_2_1:
extends: test-cpu-base
build:
args:
TENSORFLOW_PACKAGE: tensorflow-cpu==2.8.2
KERAS_PACKAGE: keras==2.8.0
PYTORCH_PACKAGE: torch==1.10.2+cpu
TORCHVISION_PACKAGE: torchvision==0.11.3+cpu
MXNET_PACKAGE: mxnet==1.8.0.post0
# then our baseline again, omitted ...
test-cpu-gloo-py3_8-tfhead-keras_none-torchhead-mxnethead-pyspark3_2_1:
extends: test-cpu-base
build:
args:
TENSORFLOW_PACKAGE: tf-nightly
KERAS_PACKAGE: None
PYTORCH_PACKAGE: torch-nightly
TORCHVISION_PACKAGE: torchvision
PYTORCH_LIGHTNING_PACKAGE: pytorch-lightning==1.5.9
MXNET_PACKAGE: mxnet-nightly
# these are the lowest framework versions that Horovod compiles with, but they are not tested
test-cpu-gloo-py3_7-tfmin-kerasmin-torchmin-mxnetmin-pysparkmin:
extends: test-cpu-base
build:
args:
UBUNTU_VERSION: 18.04
PYTHON_VERSION: 3.7
TENSORFLOW_PACKAGE: tensorflow-cpu==1.15.0
KERAS_PACKAGE: keras==2.2.4
PYTORCH_PACKAGE: torch==1.5.0+cpu
PYTORCH_LIGHTNING_PACKAGE: pytorch-lightning==0.7.3
TORCHVISION_PACKAGE: torchvision==0.6.0+cpu
MXNET_PACKAGE: mxnet==1.4.1
PYSPARK_PACKAGE: pyspark==2.4.0
SPARK_PACKAGE: spark-2.4.0/spark-2.4.0-bin-hadoop2.7.tgz
test-cpu-gloo-py3_7-tf2_9_1-keras2_9_0-torch1_11_0-mxnet1_9_1-pyspark2_4_8:
extends: test-cpu-base
build:
args:
# Tensorflow 1.15.5 is only available for Python 3.7
# Python 3.7 is only available on Ubuntu 18.04
UBUNTU_VERSION: 18.04
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_9_1-keras2_9_0-torch1_11_0-mxnet1_9_1-pyspark3_1_3:
extends: test-cpu-base
build:
args:
PYTHON_VERSION: 3.8
PYSPARK_PACKAGE: pyspark==3.1.3
SPARK_PACKAGE: spark-3.1.3/spark-3.1.3-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==3.2.1
SPARK_PACKAGE: spark-3.2.1/spark-3.2.1-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
# we need CUDA 10.0 as tensorflow-gpu==1.15.5 is compiled against and linked to CUDA 10.0
# mxnet-cu100==1.7.0 does not contain mkldnn headers, and there is no 1.7.0.postx that would have them
# 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
# so we test with mxnet 1.5.1
test-gpu-gloo-py3_7-tf1_15_5-keras2_2_4-torch1_8_1-mxnet1_5_1_p0-pyspark3_2_1:
extends: test-gpu-base
build:
args:
# Tensorflow 1.15.5 is only available for Python 3.7
# Python 3.7 is only available on Ubuntu 18.04
CUDA_DOCKER_VERSION: 10.0-devel-ubuntu18.04
CUDNN_VERSION: 8.0.5.39-1+cuda10.1
NCCL_VERSION_OVERRIDE: 2.8.4-1+cuda10.2
PYTHON_VERSION: 3.7
TENSORFLOW_PACKAGE: tensorflow-gpu==1.15.5
KERAS_PACKAGE: keras==2.2.4
PYTORCH_PACKAGE: torch==1.8.1+cu101
PYTORCH_LIGHTNING_PACKAGE: pytorch-lightning==1.5.9
TORCHVISION_PACKAGE: torchvision==0.9.1+cu101
MXNET_PACKAGE: mxnet-cu100==1.5.1.post0
# here we deviate from mxnet==1.7.0.post2 as there is none for cu101, only post1
test-gpu-gloo-py3_8-tf2_7_3-keras2_7_0-torch1_9_1-mxnet1_7_0_p1-pyspark3_2_1:
extends: test-gpu-base
build:
args:
# CUDA 10.x devel image is only available with Ubuntu 18.04
CUDA_DOCKER_VERSION: 10.1-devel-ubuntu18.04
CUDNN_VERSION: 8.0.5.39-1+cuda10.1
NCCL_VERSION_OVERRIDE: 2.8.4-1+cuda10.2
TENSORFLOW_PACKAGE: tensorflow-gpu==2.7.3
KERAS_PACKAGE: keras==2.7.0
PYTORCH_PACKAGE: torch==1.9.1+cu102
PYTORCH_LIGHTNING_PACKAGE: pytorch-lightning==1.5.9
TORCHVISION_PACKAGE: torchvision==0.10.1+cu102
MXNET_PACKAGE: mxnet-cu101==1.7.0.post1
test-gpu-gloo-py3_8-tf2_8_2-keras2_8_0-torch1_10_2-mxnet1_8_0_p0-pyspark3_2_1:
extends: test-gpu-base
build:
args:
CUDA_DOCKER_VERSION: 11.2.2-devel-ubuntu20.04
CUDNN_VERSION: 8.1.1.33-1+cuda11.2
NCCL_VERSION_OVERRIDE: 2.8.4-1+cuda11.2
TENSORFLOW_PACKAGE: tensorflow-gpu==2.8.2
KERAS_PACKAGE: keras==2.8.0
PYTORCH_PACKAGE: torch==1.10.2+cu111
PYTORCH_LIGHTNING_PACKAGE: pytorch-lightning==1.5.9
TORCHVISION_PACKAGE: torchvision==0.11.3+cu111
MXNET_PACKAGE: mxnet-cu112==1.8.0.post0
test-gpu-openmpi-gloo-py3_8-tf2_9_1-keras2_9_0-torch1_11_0-mxnet1_9_1-pyspark3_2_1:
extends: test-gpu-base
build:
args:
CUDA_DOCKER_VERSION: 11.2.2-devel-ubuntu20.04
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==2.9.1
KERAS_PACKAGE: keras==2.9.0
PYTORCH_PACKAGE: torch==1.11.0+cu113
PYTORCH_LIGHTNING_PACKAGE: pytorch-lightning==1.5.9
TORCHVISION_PACKAGE: torchvision==0.12.0+cu113
MXNET_PACKAGE: mxnet-cu112==1.9.0
test-gpu-gloo-py3_8-tfhead-keras_none-torchhead-mxnethead-pyspark3_2_1:
extends: test-gpu-base
build:
args:
CUDA_DOCKER_VERSION: 11.2.2-devel-ubuntu20.04
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==1.5.9
TORCHVISION_PACKAGE: torchvision
MXNET_PACKAGE: mxnet-nightly-cu112
# these are the lowest framework versions that Horovod compiles with, but they are not tested
test-gpu-gloo-py3_7-tfmin-kerasmin-torchmin-mxnetmin-pysparkmin:
extends: test-gpu-base
build:
args:
CUDA_DOCKER_VERSION: 10.0-devel-ubuntu18.04
CUDNN_VERSION: 8.0.5.39-1+cuda10.1
NCCL_VERSION_OVERRIDE: 2.8.4-1+cuda10.2
PYTHON_VERSION: 3.7
TENSORFLOW_PACKAGE: tensorflow-gpu==1.15.0
KERAS_PACKAGE: keras==2.2.4
PYTORCH_PACKAGE: torch==1.5.0+cu101
PYTORCH_LIGHTNING_PACKAGE: pytorch-lightning==0.7.3
TORCHVISION_PACKAGE: torchvision==0.6.0+cu101
MXNET_PACKAGE: mxnet-cu100==1.4.1
PYSPARK_PACKAGE: pyspark==2.4.0
SPARK_PACKAGE: spark-2.4.0/spark-2.4.0-bin-hadoop2.7.tgz
test-mixed-openmpi-gloo-py3_8-tf2_9_1-keras2_9_0-torch1_11_0-mxnet1_9_1-pyspark3_2_1:
extends: test-gpu-base
build:
args:
CUDA_DOCKER_VERSION: 11.2.2-devel-ubuntu20.04
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==2.9.1
KERAS_PACKAGE: keras==2.9.0
PYTORCH_PACKAGE: torch==1.11.0+cu113
PYTORCH_LIGHTNING_PACKAGE: pytorch-lightning==1.5.9
TORCHVISION_PACKAGE: torchvision==0.12.0+cu113
MXNET_PACKAGE: mxnet-cu112==1.9.0
HOROVOD_BUILD_FLAGS: ""
HOROVOD_MIXED_INSTALL: 1