emacski/tensorflow-serving

By emacski

•Updated about 5 years ago

Project images from https://github.com/emacski/tensorflow-serving-arm

Image

50K+

Overview Tags

emacski/tensorflow-serving repository overview

⁠TensorFlow Serving on ARM

⁠Quick Start

On many consumer / developer 64-bit and 32-bit arm platforms you can simply:

docker pull emacski/tensorflow-serving:latest
# or
docker pull emacski/tensorflow-serving:2.5.1

Refer to TensorFlow Serving with Docker⁠ for configuration and setting up a model for serving.

⁠Images

⁠`emacski/tensorflow-serving:[Tag]`

Tag	ARM Core Compatability
`[Version]-linux_amd64_avx_sse4.2`	N/A
`[Version]-linux_arm64_armv8-a`	Cortex-A35 / A53 / A57 / A72 / A73
`[Version]-linux_arm64_armv8.2-a`	Cortex-A55 / A75 / A76
`[Version]-linux_arm_armv7-a_neon_vfpv3`	Cortex-A8
`[Version]-linux_arm_armv7-a_neon_vfpv4`	Cortex-A7 / A12 / A15 / A17

Example

# on beaglebone black
docker pull emacski/tensorflow-serving:2.5.1-linux_arm_armv7-a_neon_vfpv3

⁠Aliases

⁠`emacski/tensorflow-serving:[Alias]`

Alias	Tag	Notes
`[Version]-linux_amd64`	`[Version]-linux_amd64_avx_sse4.2`	default `linux_amd64` image
`[Version]-linux_arm64`	`[Version]-linux_arm64_armv8-a`	Should work on most 64-bit raspberry pi and compatible platforms
`[Version]-linux_arm`	`[Version]-linux_arm_armv7-a_neon_vfpv4`	Should work on most 32-bit raspberry pi and compatible platforms
`latest-linux_amd64`	`[Latest-Version]-linux_amd64`
`latest-linux_arm64`	`[Latest-Version]-linux_arm64`
`latest-linux_arm`	`[Latest-Version]-linux_arm`

Examples

# on Raspberry PI 3 B+
docker pull emacski/tensorflow-serving:2.5.1-linux_arm64
# or
docker pull emacski/tensorflow-serving:latest-linux_arm64

⁠Manifest Lists

⁠`emacski/tensorflow-serving:latest`

Image	OS	Arch
`emacski/tensorflow-serving:latest-linux_arm`	`linux`	`arm`
`emacski/tensorflow-serving:latest-linux_arm64`	`linux`	`arm64`
`emacski/tensorflow-serving:latest-linux_amd64`	`linux`	`amd64`

Examples

# on Raspberry PI 3 B+
docker pull emacski/tensorflow-serving
# or
docker pull emacski/tensorflow-serving:latest
# the actual image used is emacski/tensorflow-serving:latest-linux_arm64
# itself actually being emacski/tensorflow-serving:[Latest-Version]-linux_arm64_armv8-a

⁠`emacski/tensorflow-serving:[Version]`

Image	OS	Arch
`emacski/tensorflow-serving:[Version]-linux_arm`	`linux`	`arm`
`emacski/tensorflow-serving:[Version]-linux_arm64`	`linux`	`arm64`
`emacski/tensorflow-serving:[Version]-linux_amd`	`linux`	`amd64`

Example

# on Raspberry PI 3 B+
docker pull emacski/tensorflow-serving:2.5.1
# the actual image used is emacski/tensorflow-serving:2.5.1-linux_arm64
# itself actually being emacski/tensorflow-serving:2.5.1-linux_arm64_armv8-a

⁠Debug Images

As of version 2.0.0, debug images are also built and published to docker hub. These images are identical to the non-debug images with the addition of busybox utils. The utils are located at /busybox/bin which is also included in the image's system PATH.

For any image above, add debug after the [Version] and before the platform suffix (if one is required) in the image tag.

# multi-arch
docker pull emacski/tensorflow-serving:2.5.1-debug
# specific image
docker pull emacski/tensorflow-serving:2.5.1-debug-linux_arm64_armv8-a
# specific alias
docker pull emacski/tensorflow-serving:latest-debug-linux_arm64

Example Usage

# start a new container with an interactive ash (busybox) shell
docker run -ti --entrypoint /busybox/bin/sh emacski/tensorflow-serving:latest-debug-linux_arm64
# with an interactive dash (system) shell
docker run -ti --entrypoint sh emacski/tensorflow-serving:latest-debug-linux_arm64
# start an interactive ash shell in a running debug container
docker exec -ti my_running_container /busybox/bin/sh

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Last updated

about 5 years ago

docker pull emacski/tensorflow-serving