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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==2.12.0
KERAS_PACKAGE: keras==2.12.0
PYTORCH_PACKAGE: torch==2.0.0+cpu
PYTORCH_LIGHTNING_PACKAGE: pytorch-lightning==1.5.9
TORCHVISION_PACKAGE: torchvision==0.15.1+cpu
MXNET_PACKAGE: mxnet==1.9.1
PYSPARK_PACKAGE: pyspark==3.4.0
SPARK_PACKAGE: spark-3.4.0/spark-3.4.0-bin-hadoop3.tgz
HOROVOD_BUILD_FLAGS: HOROVOD_WITH_GLOO=1
privileged: true
shm_size: 8gb
# our baseline first
test-cpu-gloo-py3_8-tf2_12_0-keras2_12_0-torch2_0_0-mxnet1_9_1-pyspark3_4_0:
extends: test-cpu-base
# permute MPI kinds
test-cpu-mpich-py3_8-tf2_12_0-keras2_12_0-torch2_0_0-mxnet1_9_1-pyspark3_4_0:
extends: test-cpu-base
build:
args:
MPI_KIND: MPICH
HOROVOD_BUILD_FLAGS: HOROVOD_WITHOUT_GLOO=1
test-cpu-oneccl-py3_8-tf2_12_0-keras2_12_0-torch2_0_0-mxnet1_9_1-pyspark3_4_0:
extends: test-cpu-base
build:
args:
MPI_KIND: ONECCL
HOROVOD_BUILD_FLAGS: HOROVOD_WITHOUT_GLOO=1
test-cpu-openmpi-py3_8-tf2_12_0-keras2_12_0-torch2_0_0-mxnet1_9_1-pyspark3_4_0:
extends: test-cpu-base
build:
args:
MPI_KIND: OpenMPI
HOROVOD_BUILD_FLAGS: HOROVOD_WITHOUT_GLOO=1
test-cpu-openmpi-gloo-py3_8-tf2_12_0-keras2_12_0-torch2_0_0-mxnet1_9_1-pyspark3_4_0:
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
# torch==1.8.1 is the latest we can test in this setup
# 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-cpu-gloo-py3_7-tf1_15_5-keras2_2_4-torch1_8_1-mxnet1_5_1_p0-pyspark3_4_0:
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_10_1-keras2_10_0-torch1_12_1-mxnet1_7_0_p2-pyspark3_4_0:
extends: test-cpu-base
build:
args:
TENSORFLOW_PACKAGE: tensorflow-cpu==2.10.1
KERAS_PACKAGE: keras==2.10.0
PYTORCH_PACKAGE: torch==1.12.1+cpu
TORCHVISION_PACKAGE: torchvision==0.13.1+cpu
MXNET_PACKAGE: mxnet==1.7.0.post2
test-cpu-gloo-py3_8-tf2_11_1-keras2_11_0-torch1_13_1-mxnet1_8_0_p0-pyspark3_4_0:
extends: test-cpu-base
build:
args:
TENSORFLOW_PACKAGE: tensorflow-cpu==2.11.1
KERAS_PACKAGE: keras==2.11.0
PYTORCH_PACKAGE: torch==1.13.1+cpu
TORCHVISION_PACKAGE: torchvision==0.14.1+cpu
MXNET_PACKAGE: mxnet==1.8.0.post0
# then our baseline again, omitted ...
test-cpu-openmpi-gloo-py3_8-tfhead-keras_none-torchhead-mxnethead-pyspark3_4_0:
extends: test-cpu-base
build:
args:
MPI_KIND: OpenMPI
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-openmpi-gloo-py3_7-tfmin-kerasmin-torchmin-mxnetmin-pysparkmin:
extends: test-cpu-base
build:
args:
UBUNTU_VERSION: 18.04
PYTHON_VERSION: 3.7
MPI_KIND: OpenMPI
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
# we deviate from baseline here because PySpark 2.4 requires Python 3.7 and
# Tensorflow 2.11.0 is the last version that supports that Python
# Torch 1.13.1 is the last version that supports that Python
test-cpu-gloo-py3_7-tf2_11_0-keras2_11_0-torch1_13_1-mxnet1_9_1-pyspark2_4_8:
extends: test-cpu-base
build:
args:
# PySpark 2.4.8 is only available for Python 3.7
# Python 3.7 is only available on Ubuntu 18.04
# Tensorflow 2.11.0 is the last version supporting that Python
# Torch 1.13.1 is the last version supporting that Python
UBUNTU_VERSION: 18.04
PYTHON_VERSION: 3.7
TENSORFLOW_PACKAGE: tensorflow-cpu==2.11.0
KERAS_PACKAGE: keras==2.11.0
PYTORCH_PACKAGE: torch==1.13.1+cpu
TORCHVISION_PACKAGE: torchvision==0.14.1+cpu
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_12_0-keras2_12_0-torch2_0_0-mxnet1_9_1-pyspark3_3_2:
extends: test-cpu-base
build:
args:
PYTHON_VERSION: 3.8
PYSPARK_PACKAGE: pyspark==3.3.2
SPARK_PACKAGE: spark-3.3.2/spark-3.3.2-bin-hadoop2.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==3.4.0
SPARK_PACKAGE: spark-3.4.0/spark-3.4.0-bin-hadoop3.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
# available versions for CUDNN_VERSION and NCCL_VERSION_OVERRIDE can be found at
# https://developer.download.nvidia.com/compute/cuda/repos/{OS}/x86_64/
# Mainline tensorflow-gpu==1.15.5 is compiled against and linked to CUDA 10.0, but appropriate containers aren't
# available anymore. Hence, we use the updated Python 3.8 wheel provided by Nvidia, see
# https://github.com/NVIDIA/tensorflow. For this reason versions of torch and mxnet also deviate from the CPU path.
test-gpu-gloo-py3_8-tf1_15_5-keras2_2_4-torch1_12_1-mxnet1_8_0_p0-pyspark3_4_0:
extends: test-gpu-base
build:
args:
CUDA_DOCKER_VERSION: 11.6.2-devel-ubuntu20.04
CUDNN_VERSION: 8.4.1.50-1+cuda11.6
NCCL_VERSION_OVERRIDE: 2.11.4-1+cuda11.6
PYTHON_VERSION: 3.8
TENSORFLOW_PACKAGE: nvidia-tensorflow==1.15.5+nv22.4
KERAS_PACKAGE: keras==2.2.4
PYTORCH_PACKAGE: torch==1.12.1+cu116
PYTORCH_LIGHTNING_PACKAGE: pytorch-lightning==1.5.9
TORCHVISION_PACKAGE: torchvision==0.13.1+cu116
MXNET_PACKAGE: mxnet-cu112==1.8.0.post0
# The container isn't provided for CUDA 10 anymore. The lowest version of mxnet available for cu112 is 1.8.0.post0.
test-gpu-gloo-py3_8-tf2_10_1-keras2_10_0-torch1_12_1-mxnet1_8_0_p0-pyspark3_4_0:
extends: test-gpu-base
build:
args:
CUDA_DOCKER_VERSION: 11.6.2-devel-ubuntu20.04
CUDNN_VERSION: 8.4.1.50-1+cuda11.6
NCCL_VERSION_OVERRIDE: 2.11.4-1+cuda11.6
TENSORFLOW_PACKAGE: tensorflow-gpu==2.10.1
KERAS_PACKAGE: keras==2.10.0
PYTORCH_PACKAGE: torch==1.12.1+cu116
PYTORCH_LIGHTNING_PACKAGE: pytorch-lightning==1.5.9
TORCHVISION_PACKAGE: torchvision==0.13.1+cu116
MXNET_PACKAGE: mxnet-cu112==1.8.0.post0
test-gpu-gloo-py3_8-tf2_11_1-keras2_11_0-torch1_13_1-mxnet1_8_0_p0-pyspark3_4_0:
extends: test-gpu-base
build:
args:
CUDA_DOCKER_VERSION: 11.6.2-devel-ubuntu20.04
CUDNN_VERSION: 8.4.1.50-1+cuda11.6
NCCL_VERSION_OVERRIDE: 2.11.4-1+cuda11.6
# tensorflow package supports GPU from 2.11.1 and 2.12.0 on
TENSORFLOW_PACKAGE: tensorflow==2.11.1
KERAS_PACKAGE: keras==2.11.0
PYTORCH_PACKAGE: torch==1.13.1+cu116
PYTORCH_LIGHTNING_PACKAGE: pytorch-lightning==1.5.9
TORCHVISION_PACKAGE: torchvision==0.14.1+cu116
MXNET_PACKAGE: mxnet-cu112==1.8.0.post0
test-gpu-openmpi-gloo-py3_8-tf2_12_0-keras2_12_0-torch2_0_0-mxnet1_9_1-pyspark3_4_0:
extends: test-gpu-base
build:
args:
CUDA_DOCKER_VERSION: 11.8.0-devel-ubuntu20.04
CUDNN_VERSION: 8.6.0.163-1+cuda11.8
NCCL_VERSION_OVERRIDE: 2.16.5-1+cuda11.8
MPI_KIND: OpenMPI
# tensorflow package supports GPU from 2.11.1 and 2.12.0 on
TENSORFLOW_PACKAGE: tensorflow==2.12.0
KERAS_PACKAGE: keras==2.12.0
PYTORCH_PACKAGE: torch==2.0.0+cu118
PYTORCH_LIGHTNING_PACKAGE: pytorch-lightning==1.5.9
TORCHVISION_PACKAGE: torchvision==0.15.1+cu118
MXNET_PACKAGE: mxnet-cu112==1.9.1
test-gpu-openmpi-gloo-py3_8-tfhead-keras_none-torchhead-mxnethead-pyspark3_4_0:
extends: test-gpu-base
build:
args:
CUDA_DOCKER_VERSION: 11.8.0-devel-ubuntu20.04
CUDNN_VERSION: 8.6.0.163-1+cuda11.8
NCCL_VERSION_OVERRIDE: 2.16.5-1+cuda11.8
MPI_KIND: OpenMPI
TENSORFLOW_PACKAGE: tf-nightly
KERAS_PACKAGE: None
PYTORCH_PACKAGE: torch-nightly-cu118
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 on the CUDA 11.x container, but they are not tested.
# Versions of python, mxnet, and pyspark differ from the CPU build with minimum versions.
test-gpu-openmpi-gloo-py3_8-tfmin-kerasmin-torchmin-mxnetmin-pysparkmin:
extends: test-gpu-base
build:
args:
CUDA_DOCKER_VERSION: 11.6.2-devel-ubuntu20.04
CUDNN_VERSION: 8.4.1.50-1+cuda11.6
NCCL_VERSION_OVERRIDE: 2.11.4-1+cuda11.6
MPI_KIND: OpenMPI
PYTHON_VERSION: 3.8
TENSORFLOW_PACKAGE: nvidia-tensorflow==1.15.5+nv22.4
KERAS_PACKAGE: keras==2.2.4
# torch ships its own CUDA libraries
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-cu112==1.8.0.post0
# On Python 3.8 Spark 3.0.0 is the lowest supported version
PYSPARK_PACKAGE: pyspark==3.0.0
SPARK_PACKAGE: spark-3.0.0/spark-3.0.0-bin-hadoop2.7.tgz
test-mixed-openmpi-gloo-py3_8-tf2_12_0-keras2_12_0-torch2_0_0-mxnet1_9_1-pyspark3_4_0:
extends: test-gpu-base
build:
args:
CUDA_DOCKER_VERSION: 11.8.0-devel-ubuntu20.04
CUDNN_VERSION: 8.6.0.163-1+cuda11.8
NCCL_VERSION_OVERRIDE: 2.16.5-1+cuda11.8
MPI_KIND: OpenMPI
# tensorflow package supports GPU from 2.11.1 and 2.12.0 on
TENSORFLOW_PACKAGE: tensorflow==2.12.0
KERAS_PACKAGE: keras==2.12.0
PYTORCH_PACKAGE: torch==2.0.0+cu118
PYTORCH_LIGHTNING_PACKAGE: pytorch-lightning==1.5.9
TORCHVISION_PACKAGE: torchvision==0.15.1+cu118
MXNET_PACKAGE: mxnet-cu112==1.9.1
HOROVOD_BUILD_FLAGS: ""
HOROVOD_MIXED_INSTALL: 1