whereas in MinIO erasure code you can lose as many as half of drives and still the data remains safe. Further, MinIO's erasure code is at the object level and can heal one object at a time. For RAID, healing can be done only at the volume level which translates into high downtime. As MinIO encodes each object individually, it can heal objects incrementally. Storage servers once deployed should not require drive replacement or healing for the lifetime of the server. MinIO's erasure coded backend is...

source ci/official/envs/linux_x86
TFCI_BAZEL_COMMON_ARGS="--repo_env=TF_PYTHON_VERSION=$TFCI_PYTHON_VERSION --config release_cpu_linux --config=tpu"
TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_tpu
TFCI_BUILD_PIP_PACKAGE_ARGS="--repo_env=WHEEL_NAME=tensorflow_tpu"
TFCI_LIB_SUFFIX="-tpu-linux-x86_64"
TFCI_WHL_BAZEL_TEST_ENABLE=0
TFCI_WHL_IMPORT_TEST_ENABLE=0
TFCI_WHL_SIZE_LIMIT=580M

set -euxo pipefail

# Run this from inside the tensorflow github directory.
# Usage: setup_venv_test.sh venv_and_symlink_name "glob pattern for one wheel file"
# Example: setup_venv_test.sh bazel_pip "/tf/pkg/*.whl"
# 
# This will create a venv with that wheel file installed in it, and a symlink
# in ./venv_and_symlink_name/tensorflow to ./tensorflow. We use this for the
# "pip" tests.

python -m venv /$1
mkdir -p $1

		}
		globalCompressConfigMu.Lock()
		globalCompressConfig = cmpCfg
		globalCompressConfigMu.Unlock()
	case config.HealSubSys:
		healCfg, err := heal.LookupConfig(s[config.HealSubSys][config.Default])
		if err != nil {
			errs = append(errs, fmt.Errorf("Unable to apply heal config: %w", err))
		} else {
			globalHealConfig.Update(healCfg)
		}
	case config.BatchSubSys:

TFCI_BAZEL_COMMON_ARGS="--repo_env=TF_PYTHON_VERSION=$TFCI_PYTHON_VERSION --config release_macos_arm64"
TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=macos_arm64
TFCI_BUILD_PIP_PACKAGE_ARGS="--repo_env=WHEEL_NAME=tensorflow"
TFCI_INDEX_HTML_ENABLE=1
TFCI_LIB_SUFFIX="-cpu-darwin-arm64"
TFCI_MACOS_BAZEL_TEST_DIR_ENABLE=1
TFCI_MACOS_BAZEL_TEST_DIR_PATH="/Volumes/BuildData/bazel_output"
TFCI_OUTPUT_DIR=build_output

# JAX requirements, passed into container by defining the ARG 
# REQUIREMENTS_FILE=jax.requirements.txt


setuptools
wheel
cloudpickle
colorama>=0.4.4
matplotlib
pillow>=9.1.0
rich
absl-py
portpicker
six
opt-einsum
auditwheel
typing_extensions
importlib_metadata>=4.6
numpy==1.26.0;python_version=="3.12"
numpy==1.23.4;python_version=="3.11"
numpy==1.22.4;python_version<"3.11"
scipy==1.11.2;python_version=="3.12"

# TODO(b/304751256): Adjust the numpy pin to a single version, when ready
numpy ~= 1.23.5 ; python_version <= "3.11"
numpy ~= 1.26.0 ; python_version >= "3.12"
wheel ~= 0.41.2
h5py >= 3.10.0
lit ~= 17.0.2
opt_einsum == 3.3.0
astunparse == 1.6.3
dill == 0.3.7
astor == 0.7.1
typing_extensions == 4.8.0
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0

# Install tensorboard, and keras

source ci/official/envs/linux_x86
TFCI_BAZEL_COMMON_ARGS="--repo_env=TF_PYTHON_VERSION=$TFCI_PYTHON_VERSION --config release_gpu_linux"
TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_cuda
TFCI_BUILD_PIP_PACKAGE_ARGS="--repo_env=WHEEL_NAME=tensorflow"
TFCI_DOCKER_ARGS="--gpus all"
TFCI_LIB_SUFFIX="-gpu-linux-x86_64"

func _() {
	// An "invalid array index" compiler error signifies that the constant values have changed.
	// Re-run the stringer command to generate them again.
	var x [1]struct{}
	_ = x[lcEventSrc_None-0]
	_ = x[lcEventSrc_Heal-1]
	_ = x[lcEventSrc_Scanner-2]
	_ = x[lcEventSrc_Decom-3]
	_ = x[lcEventSrc_Rebal-4]
	_ = x[lcEventSrc_s3HeadObject-5]
	_ = x[lcEventSrc_s3GetObject-6]
	_ = x[lcEventSrc_s3ListObjects-7]

#### Policy management permissions

- admin:CreatePolicy
- admin:DeletePolicy
- admin:GetPolicy
- admin:AttachUserOrGroupPolicy
- admin:ListUserPolicies

#### Heal management permissions

- admin:Heal

#### Service account management permissions

- admin:CreateServiceAccount
- admin:UpdateServiceAccount
- admin:RemoveServiceAccount
- admin:ListServiceAccounts