https://github.com/douban/tfmesos

Tensorflow in Docker on Mesos #tfmesos #tensorflow #mesos
https://github.com/douban/tfmesos

deep-learning deep-neural-networks distributed docker machine-learning mesos ml neural-network nvidia-docker tensorflow

Last synced: about 7 hours ago
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Tensorflow in Docker on Mesos #tfmesos #tensorflow #mesos

• Host: GitHub
• URL: https://github.com/douban/tfmesos
• Owner: douban
• License: bsd-3-clause
• Created: 2016-04-18T06:44:24.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2023-03-24T21:54:14.000Z (over 1 year ago)
• Last Synced: 2024-10-12T23:07:14.212Z (24 days ago)
• Topics: deep-learning, deep-neural-networks, distributed, docker, machine-learning, mesos, ml, neural-network, nvidia-docker, tensorflow
• Language: Python
• Homepage:
• Size: 117 KB
• Stars: 191
• Watchers: 28
• Forks: 48
• Open Issues: 5
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE.txt

Awesome Lists containing this project

• awesome-mesos - TensorFlow

README

        
TFMesos 

========

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``TFMesos`` is a lightweight framework to help running distributed `Tensorflow `_ Machine Learning tasks on `Apache Mesos `_ within `Docker `_ and `Nvidia-Docker `_ .

``TFMesos`` dynamically allocates resources from a ``Mesos`` cluster, builds a distributed training cluster for ``Tensorflow``, and makes different training tasks mangeed and isolated in the shared ``Mesos`` cluster with the help of ``Docker``.

Prerequisites

--------------

* For ``Mesos >= 1.0.0``:

1. ``Mesos`` Cluster (cf: `Mesos Getting Started `_). All nodes in the cluster should be reachable using their hostnames, and all nodes have identical ``/etc/passwd`` and ``/etc/group``.

  

2. Setup ``Mesos Agent`` to enable `Mesos Containerizer `_ and `Mesos Nvidia GPU Support `_ (optional). eg: ``mesos-agent --containerizers=mesos --image_providers=docker --isolation=filesystem/linux,docker/runtime,cgroups/devices,gpu/nvidia``

    

3. (optional) A Distributed Filesystem (eg: `MooseFS `_)

  

4. Ensure latest ``TFMesos`` docker image (`tfmesos/tfmesos `_) is pulled across the whole cluster

* For ``Mesos < 1.0.0``:

1. ``Mesos`` Cluster (cf: `Mesos Getting Started `_). All nodes in the cluster should be reachable using their hostnames, and all nodes have identical ``/etc/passwd`` and ``/etc/group``.

2. ``Docker`` (cf: `Docker Get Start Tutorial `_)

3. ``Mesos Docker Containerizer Support`` (cf: `Mesos Docker Containerizer `_)

4. (optional) ``Nvidia-docker`` installation (cf: `Nvidia-docker installation `_) and make sure nvidia-plugin is accessible from remote host (with ``-l 0.0.0.0:3476``)

5. (optional) A Distributed Filesystem (eg: `MooseFS `_)

6. Ensure latest ``TFMesos`` docker image (`tfmesos/tfmesos `_) is pulled across the whole cluster

If you are using ``AWS G2`` instance, here is a `sample `_ script to setup most of there prerequisites.

Running simple Test

------------------------

After setting up the mesos and pulling the docker image on a single node (or a cluser), you should be able to use the following command to run a simple test.

.. code:: bash

    $ docker run -e MESOS_MASTER=mesos-master:5050 \

        -e DOCKER_IMAGE=tfmesos/tfmesos \

        --net=host \

        -v /path-to-your-tfmesos-code/tfmesos/examples/plus.py:/tmp/plus.py \

        --rm \

        -it \

        tfmesos/tfmesos \

        python /tmp/plus.py mesos-master:5050

Successfully running the test should result in an output of 42 on the console.

Running in replica mode

------------------------

This mode is called `Between-graph replication` in official `Distributed Tensorflow Howto `_

Most distributed training models that Google has open sourced (such as `mnist_replica `_ and `inception `_) are using this mode. In this mode, two kind of Jobs are defined with the names `'ps'` and `'worker'`. `'ps'` tasks act as `'Parameter Server'` and `'worker'` tasks run the actual training process.

Here we use our modified `'mnist_replica' `_ as example:

1. Checkout the `mnist` example codes into a directory in shared filesystem, eg: `/nfs/mnist`

2. Assume Mesos master is `mesos-master:5050`

3. Now we can launch this script using following commands:

CPU:

.. code:: bash

    $ docker run --rm -it -e MESOS_MASTER=mesos-master:5050 \

                 --net=host \

                 -v /nfs/mnist:/nfs/mnist \

                 -v /etc/passwd:/etc/passwd:ro \

                 -v /etc/group:/etc/group:ro \

                 -u `id -u` \

                 -w /nfs/mnist \

                 tfmesos/tfmesos \

                 tfrun -w 1 -s 1  \

                 -V /nfs/mnist:/nfs/mnist \

                 -- python mnist_replica.py \

                 --ps_hosts {ps_hosts} --worker_hosts {worker_hosts} \

                 --job_name {job_name} --worker_index {task_index}

GPU (1 GPU per worker):

.. code:: bash

    $ nvidia-docker run --rm -it -e MESOS_MASTER=mesos-master:5050 \

                 --net=host \

                 -v /nfs/mnist:/nfs/mnist \

                 -v /etc/passwd:/etc/passwd:ro \

                 -v /etc/group:/etc/group:ro \

                 -u `id -u` \

                 -w /nfs/mnist \

                 tfmesos/tfmesos \

                 tfrun -w 1 -s 1 -Gw 1 -- python mnist_replica.py \

                 --ps_hosts {ps_hosts} --worker_hosts {worker_hosts} \

                 --job_name {job_name} --worker_index {task_index}

Note:

In this mode, `tfrun` is used to prepare the cluster and launch the training script on each node, and worker #0 (the chief worker) will be launched in the local container.

`tfrun` will substitute `{ps_hosts}`, `{worker_hosts}`, `{job_name}`, `{task_index}` with corresponding values of each task.

Running in fine-grained mode

-----------------------------

This mode is called `In-graph replication` in official `Distributed Tensorflow Howto `_

In this mode, we have more control over the cluster spec. All nodes in the cluster is remote and just running a `Grpc` server. Each worker is driven by a local thread to run the training task.

Here we use our modified `mnist `_ as example:

1. Checkout the `mnist` example codes into a directory, eg: `/tmp/mnist`

2. Assume Mesos master is `mesos-master:5050`

3. Now we can launch this script using following commands:

CPU:

.. code:: bash

    $ docker run --rm -it -e MESOS_MASTER=mesos-master:5050 \

                 --net=host \

                 -v /tmp/mnist:/tmp/mnist \

                 -v /etc/passwd:/etc/passwd:ro \

                 -v /etc/group:/etc/group:ro \

                 -u `id -u` \

                 -w /tmp/mnist \

                 tfmesos/tfmesos \

                 python mnist.py 

GPU (1 GPU per worker):

.. code:: bash

    $ nvidia-docker run --rm -it -e MESOS_MASTER=mesos-master:5050 \

                 --net=host \

                 -v /tmp/mnist:/tmp/mnist \

                 -v /etc/passwd:/etc/passwd:ro \

                 -v /etc/group:/etc/group:ro \

                 -u `id -u` \

                 -w /tmp/mnist \

                 tfmesos/tfmesos \

                 python mnist.py --worker-gpus 1