From e49f7b06833a1b3cc0924e9362f4d3427eb3fb4d Mon Sep 17 00:00:00 2001 From: Travis CI Date: Sat, 13 May 2017 03:06:52 +0000 Subject: [PATCH] Deploy to GitHub Pages: ae647bf30c94fba4edfa04fa363e2e6c2039f68d --- .../design/cluster_train/submit-job.md.txt | 127 ++++++ .../doc/design/cluster_train/submit-job.html | 373 +++++++++++++++++ develop/doc/objects.inv | Bin 2108 -> 2136 bytes develop/doc/searchindex.js | 2 +- .../design/cluster_train/submit-job.md.txt | 127 ++++++ .../design/cluster_train/submit-job.html | 381 ++++++++++++++++++ develop/doc_cn/objects.inv | Bin 2507 -> 2538 bytes develop/doc_cn/searchindex.js | 2 +- 8 files changed, 1010 insertions(+), 2 deletions(-) create mode 100644 develop/doc/_sources/design/cluster_train/submit-job.md.txt create mode 100644 develop/doc/design/cluster_train/submit-job.html create mode 100644 develop/doc_cn/_sources/design/cluster_train/submit-job.md.txt create mode 100644 develop/doc_cn/design/cluster_train/submit-job.html diff --git a/develop/doc/_sources/design/cluster_train/submit-job.md.txt b/develop/doc/_sources/design/cluster_train/submit-job.md.txt new file mode 100644 index 00000000000..8377d5489dc --- /dev/null +++ b/develop/doc/_sources/design/cluster_train/submit-job.md.txt @@ -0,0 +1,127 @@ +# Submit a Distributed Training Job + +The user can submit a distributed training job with Python code, rather than with a command-line interface. + +## Runtime Environment On Kubernetes + +For a distributed training job, there is two Docker image called *runtime Docker image* and *base Docker image*. The runtime Docker image is the Docker image that gets scheduled by Kubernetes to run during training. The base Docker image is for building the runtime Docker image. + +### Base Docker Image + +Usually, the base Docker image is PaddlePaddle product Docker image including paddle binary files and python package. And of course, users can specify any image name hosted on any docker registry which users have the access right. + +### Runtime Docker Image + +The trainer package which user upload and some Python dependencies are packaged into a runtime Docker image based on base Docker image. + +- Handle Python Dependencies + + You need to provide requirements.txt file in your `trainer-package` folder. Example: + + ```txt + pillow + protobuf==3.1.0 + ``` + More [details](https://pip.readthedocs.io/en/1.1/requirements.html) about requirements, an example project looks like: + ```bash + paddle_example + |-quick_start + |-trainer.py + |-dataset.py + |-requirements.txt + ``` + +## Submit Distributed Training Job With Python Code + + +- `paddle.job.dist_train()` will call the Job Server API `/v1/packages` to upload the trainer package and save them on CephFS, and then call `/v1/trainer/job` to submit the PaddlePaddle distributed job. +- `/v1/trainer/job` will start a building job for preparing the runtime Docker image. When the building job is finished, Job Server will submit the PaddlePaddle distributed job to Kubernetes. +- *NOTE*: For the first version, we will not prepare the runtime Docker image, instead, the package is uploaded to Paddle Cloud, and Paddle Cloud will mount the package in a temporary folder into the base Docker image. We will not support custom Python dependencies in the first version as well. + +You can call `paddle.job.dist_train` and provide distributed training configuration as the parameters: +```python +paddle.job.dist_train( + trainer=dist_trainer(), + paddle_job=PaddleJob( + job_name = "paddle-cloud", + entry_point = "python %s"%__file__, + trainer_package = "/example/word2vec", + image = "yancey1989/paddle-job", + trainers = 10, + pservers = 3, + trainer_cpu = 1, + trainer_gpu = 1, + trainer_mem = "10G", + pserver_cpu = 1, + pserver_mem = "2G" + )) +``` + +The parameter `trainer` of `paddle.job.dist_train` is a function and you can implement it as follows: +```python +def dist_trainer(): + def trainer_creator(): + trainer = paddle.v2.trainer.SGD(...) + trainer.train(...) + return trainer_creator +``` + +The pseudo code of `paddle.job.dist_train` is as follows: +```python +def dist_train(trainer, paddle_job): + # if the code is running on cloud, set PADDLE_ON_CLOUD=YES + if os.getenv("RUNNING_ON_CLOUD", "NO") == "NO": + #submit the paddle job + paddle_job.submit() + else: + #start the training + trainer() +``` +### PaddleJob Parameters +parameter | type | explanation + --- | --- | --- +job_name | str | the unique name for the training job +entry_point | str | entry point for startup trainer process +trainer_package | str | trainer package file path which user have the access right +image|str|the [base image](#base-docker-image) for building the [runtime image](#runtime-docker-image) +pservers|int| Parameter Server process count +trainers|int| Trainer process count +pserver_cpu|int| CPU count for each Parameter Server process +pserver_mem|str| memory allocated for each Parameter Server process, a plain integer using one of these suffixes: E, P, T, G, M, K +trainer_cpu|int| CPU count for each Trainer process +trainer_mem|str| memory allocated for each Trainer process, a plain integer using one of these suffixes: E, P, T, G, M, K +trainer_gpu|int| GPU count for each Trainer process, if you only want CPU, do not set this parameter + +### Deploy Parameter Server, Trainer and Master Process + - Deploy PaddlePaddle Parameter Server processes, it's a Kubernetes ReplicaSet. + - Deploy PaddlePaddle Trainer processes, it's a Kubernetes Job. + - Deploy PaddlePaddle Master processes, it's a Kubernetes ReplicaSet. + +## Job Server + +- RESTful API + + Job server provides RESTful HTTP API for receiving the trainer package and displaying + PaddlePaddle job related informations. + - `POST /v1/package` receive the trainer package and save them on CephFS + - `POST /v1/trainer/job` submit a trainer job + - `GET /v1/jobs/` list all jobs + - `GET /v1/jobs/` the status of a job + - `DELETE /v1/jobs/` delete a job + - `GET /v1/version` job server version + +- Build Runtime Docker Image on Kubernetes + + `paddle.job.dist_train` will upload the trainer package to Job Server, save them on the distributed filesystem, and then start up a job for building the runtime Docker image that gets scheduled by Kubernetes to run during training. + + There are some benefits for building runtime Docker image on JobServer: + - On Paddle Cloud, users will run the trainer code in a Jupyter Notebook which is a Kubernetes Pod, if we want to execute `docker build` in the Pod, we should mount the host's `docker.sock` to the Pod, user's code will connect the host's Docker Engine directly, it's not safe. + - Users only need to upload the training package files, does not need to install docker engine, docker registry as dependencies. + - If we want to change another image type, such as RKT, users do not need to care about it. + +- Deploy Parameter Server, Trainer and Master Processes + + `POST /v1/trainer/job` receives the distributed training parameters, and deploy the job as follows: + - Deploy PaddlePaddle Parameter Server processes, it's a Kubernetes ReplicaSet. + - Deploy PaddlePaddle Trainer processes, it's a Kubernetes Job. + - Deploy PaddlePaddle Master processes, it's a Kubernetes ReplicaSet. diff --git a/develop/doc/design/cluster_train/submit-job.html b/develop/doc/design/cluster_train/submit-job.html new file mode 100644 index 00000000000..95b698475d1 --- /dev/null +++ b/develop/doc/design/cluster_train/submit-job.html @@ -0,0 +1,373 @@ + + + + + + + + + + + Submit a Distributed Training Job — PaddlePaddle documentation + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+ Folk me on Github +
+ + English + + + + +
+ +
+
+ + + + +
+
+ + + +
+
+
+
+
+ +
+ + + + +
+ + + + + + + + + + + +
+
    + +
  • Submit a Distributed Training Job
    • +
+
+ +
+
+
+
+ +
+

Submit a Distributed Training Job

+

The user can submit a distributed training job with Python code, rather than with a command-line interface.

+
+

Runtime Environment On Kubernetes

+

For a distributed training job, there is two Docker image called runtime Docker image and base Docker image. The runtime Docker image is the Docker image that gets scheduled by Kubernetes to run during training. The base Docker image is for building the runtime Docker image.

+
+

Base Docker Image

+

Usually, the base Docker image is PaddlePaddle product Docker image including paddle binary files and python package. And of course, users can specify any image name hosted on any docker registry which users have the access right.

+
+
+

Runtime Docker Image

+

The trainer package which user upload and some Python dependencies are packaged into a runtime Docker image based on base Docker image.

+
    +

  • Handle Python Dependencies

    +

    You need to provide requirements.txt file in your trainer-package folder. Example:

    +
    pillow
+protobuf==3.1.0
+
    +
    +

    More details about requirements, an example project looks like:

    +
      paddle_example
+    |-quick_start
+      |-trainer.py
+      |-dataset.py
+      |-requirements.txt
+
    +
    +
    • +
+
+
+
+

Submit Distributed Training Job With Python Code

+

+
    +
  • paddle.job.dist_train() will call the Job Server API /v1/packages to upload the trainer package and save them on CephFS, and then call /v1/trainer/job to submit the PaddlePaddle distributed job.
    • +
  • /v1/trainer/job will start a building job for preparing the runtime Docker image. When the building job is finished, Job Server will submit the PaddlePaddle distributed job to Kubernetes.
    • +
  • NOTE: For the first version, we will not prepare the runtime Docker image, instead, the package is uploaded to Paddle Cloud, and Paddle Cloud will mount the package in a temporary folder into the base Docker image. We will not support custom Python dependencies in the first version as well.
    • +
+

You can call paddle.job.dist_train and provide distributed training configuration as the parameters:

+
paddle.job.dist_train(
+  trainer=dist_trainer(),
+  paddle_job=PaddleJob(
+    job_name = "paddle-cloud",
+    entry_point = "python %s"%__file__,
+    trainer_package = "/example/word2vec",
+    image = "yancey1989/paddle-job",
+    trainers = 10,
+    pservers = 3,
+    trainer_cpu = 1,
+    trainer_gpu = 1,
+    trainer_mem = "10G",
+    pserver_cpu = 1,
+    pserver_mem = "2G"
+  ))
+
+
+

The parameter trainer of paddle.job.dist_train is a function and you can implement it as follows:

+
def dist_trainer():
+  def trainer_creator():
+    trainer = paddle.v2.trainer.SGD(...)
+    trainer.train(...)
+  return trainer_creator
+
+
+

The pseudo code of paddle.job.dist_train is as follows:

+
def dist_train(trainer, paddle_job):
+  # if the code is running on cloud, set PADDLE_ON_CLOUD=YES
+  if os.getenv("RUNNING_ON_CLOUD", "NO") == "NO":
+    #submit the paddle job
+    paddle_job.submit()
+  else:
+    #start the training
+    trainer()
+
+
+
+

PaddleJob Parameters

+

parameter | type | explanation +— | — | — +job_name | str | the unique name for the training job +entry_point | str | entry point for startup trainer process +trainer_package | str | trainer package file path which user have the access right +image|str|the base image for building the runtime image +pservers|int| Parameter Server process count +trainers|int| Trainer process count +pserver_cpu|int| CPU count for each Parameter Server process +pserver_mem|str| memory allocated for each Parameter Server process, a plain integer using one of these suffixes: E, P, T, G, M, K +trainer_cpu|int| CPU count for each Trainer process +trainer_mem|str| memory allocated for each Trainer process, a plain integer using one of these suffixes: E, P, T, G, M, K +trainer_gpu|int| GPU count for each Trainer process, if you only want CPU, do not set this parameter

+
+
+

Deploy Parameter Server, Trainer and Master Process

+
    +
  • Deploy PaddlePaddle Parameter Server processes, it’s a Kubernetes ReplicaSet.
    • +
  • Deploy PaddlePaddle Trainer processes, it’s a Kubernetes Job.
    • +
  • Deploy PaddlePaddle Master processes, it’s a Kubernetes ReplicaSet.
    • +
+
+
+
+

Job Server

+
    +

  • RESTful API

    +

    Job server provides RESTful HTTP API for receiving the trainer package and displaying +PaddlePaddle job related informations.

    +
      +
    • POST /v1/package receive the trainer package and save them on CephFS
      • +
    • POST /v1/trainer/job submit a trainer job
      • +
    • GET /v1/jobs/ list all jobs
      • +
    • GET /v1/jobs/<job-name> the status of a job
      • +
    • DELETE /v1/jobs/<job-name> delete a job
      • +
    • GET /v1/version job server version
      • +
    +
    • +

  • Build Runtime Docker Image on Kubernetes

    +

    paddle.job.dist_train will upload the trainer package to Job Server, save them on the distributed filesystem, and then start up a job for building the runtime Docker image that gets scheduled by Kubernetes to run during training.

    +

    There are some benefits for building runtime Docker image on JobServer:

    +
      +
    • On Paddle Cloud, users will run the trainer code in a Jupyter Notebook which is a Kubernetes Pod, if we want to execute docker build in the Pod, we should mount the host’s docker.sock to the Pod, user’s code will connect the host’s Docker Engine directly, it’s not safe.
      • +
    • Users only need to upload the training package files, does not need to install docker engine, docker registry as dependencies.
      • +
    • If we want to change another image type, such as RKT, users do not need to care about it.
      • +
    +
    • +

  • Deploy Parameter Server, Trainer and Master Processes

    +

    POST /v1/trainer/job receives the distributed training parameters, and deploy the job as follows:

    +
      +
    • Deploy PaddlePaddle Parameter Server processes, it’s a Kubernetes ReplicaSet.
      • +
    • Deploy PaddlePaddle Trainer processes, it’s a Kubernetes Job.
      • +
    • Deploy PaddlePaddle Master processes, it’s a Kubernetes ReplicaSet.
      • +
    +
    • +
+
+
+ + +
+
+
+ + +
+ +
+

+ © Copyright 2016, PaddlePaddle developers. + +

+
+ Built with Sphinx using a theme provided by Read the Docs. + +
+ +
+
+ +
+ +
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\ No newline at end of file diff --git a/develop/doc_cn/_sources/design/cluster_train/submit-job.md.txt b/develop/doc_cn/_sources/design/cluster_train/submit-job.md.txt new file mode 100644 index 00000000000..8377d5489dc --- /dev/null +++ b/develop/doc_cn/_sources/design/cluster_train/submit-job.md.txt @@ -0,0 +1,127 @@ +# Submit a Distributed Training Job + +The user can submit a distributed training job with Python code, rather than with a command-line interface. + +## Runtime Environment On Kubernetes + +For a distributed training job, there is two Docker image called *runtime Docker image* and *base Docker image*. The runtime Docker image is the Docker image that gets scheduled by Kubernetes to run during training. The base Docker image is for building the runtime Docker image. + +### Base Docker Image + +Usually, the base Docker image is PaddlePaddle product Docker image including paddle binary files and python package. And of course, users can specify any image name hosted on any docker registry which users have the access right. + +### Runtime Docker Image + +The trainer package which user upload and some Python dependencies are packaged into a runtime Docker image based on base Docker image. + +- Handle Python Dependencies + + You need to provide requirements.txt file in your `trainer-package` folder. Example: + + ```txt + pillow + protobuf==3.1.0 + ``` + More [details](https://pip.readthedocs.io/en/1.1/requirements.html) about requirements, an example project looks like: + ```bash + paddle_example + |-quick_start + |-trainer.py + |-dataset.py + |-requirements.txt + ``` + +## Submit Distributed Training Job With Python Code + + +- `paddle.job.dist_train()` will call the Job Server API `/v1/packages` to upload the trainer package and save them on CephFS, and then call `/v1/trainer/job` to submit the PaddlePaddle distributed job. +- `/v1/trainer/job` will start a building job for preparing the runtime Docker image. When the building job is finished, Job Server will submit the PaddlePaddle distributed job to Kubernetes. +- *NOTE*: For the first version, we will not prepare the runtime Docker image, instead, the package is uploaded to Paddle Cloud, and Paddle Cloud will mount the package in a temporary folder into the base Docker image. We will not support custom Python dependencies in the first version as well. + +You can call `paddle.job.dist_train` and provide distributed training configuration as the parameters: +```python +paddle.job.dist_train( + trainer=dist_trainer(), + paddle_job=PaddleJob( + job_name = "paddle-cloud", + entry_point = "python %s"%__file__, + trainer_package = "/example/word2vec", + image = "yancey1989/paddle-job", + trainers = 10, + pservers = 3, + trainer_cpu = 1, + trainer_gpu = 1, + trainer_mem = "10G", + pserver_cpu = 1, + pserver_mem = "2G" + )) +``` + +The parameter `trainer` of `paddle.job.dist_train` is a function and you can implement it as follows: +```python +def dist_trainer(): + def trainer_creator(): + trainer = paddle.v2.trainer.SGD(...) + trainer.train(...) + return trainer_creator +``` + +The pseudo code of `paddle.job.dist_train` is as follows: +```python +def dist_train(trainer, paddle_job): + # if the code is running on cloud, set PADDLE_ON_CLOUD=YES + if os.getenv("RUNNING_ON_CLOUD", "NO") == "NO": + #submit the paddle job + paddle_job.submit() + else: + #start the training + trainer() +``` +### PaddleJob Parameters +parameter | type | explanation + --- | --- | --- +job_name | str | the unique name for the training job +entry_point | str | entry point for startup trainer process +trainer_package | str | trainer package file path which user have the access right +image|str|the [base image](#base-docker-image) for building the [runtime image](#runtime-docker-image) +pservers|int| Parameter Server process count +trainers|int| Trainer process count +pserver_cpu|int| CPU count for each Parameter Server process +pserver_mem|str| memory allocated for each Parameter Server process, a plain integer using one of these suffixes: E, P, T, G, M, K +trainer_cpu|int| CPU count for each Trainer process +trainer_mem|str| memory allocated for each Trainer process, a plain integer using one of these suffixes: E, P, T, G, M, K +trainer_gpu|int| GPU count for each Trainer process, if you only want CPU, do not set this parameter + +### Deploy Parameter Server, Trainer and Master Process + - Deploy PaddlePaddle Parameter Server processes, it's a Kubernetes ReplicaSet. + - Deploy PaddlePaddle Trainer processes, it's a Kubernetes Job. + - Deploy PaddlePaddle Master processes, it's a Kubernetes ReplicaSet. + +## Job Server + +- RESTful API + + Job server provides RESTful HTTP API for receiving the trainer package and displaying + PaddlePaddle job related informations. + - `POST /v1/package` receive the trainer package and save them on CephFS + - `POST /v1/trainer/job` submit a trainer job + - `GET /v1/jobs/` list all jobs + - `GET /v1/jobs/` the status of a job + - `DELETE /v1/jobs/` delete a job + - `GET /v1/version` job server version + +- Build Runtime Docker Image on Kubernetes + + `paddle.job.dist_train` will upload the trainer package to Job Server, save them on the distributed filesystem, and then start up a job for building the runtime Docker image that gets scheduled by Kubernetes to run during training. + + There are some benefits for building runtime Docker image on JobServer: + - On Paddle Cloud, users will run the trainer code in a Jupyter Notebook which is a Kubernetes Pod, if we want to execute `docker build` in the Pod, we should mount the host's `docker.sock` to the Pod, user's code will connect the host's Docker Engine directly, it's not safe. + - Users only need to upload the training package files, does not need to install docker engine, docker registry as dependencies. + - If we want to change another image type, such as RKT, users do not need to care about it. + +- Deploy Parameter Server, Trainer and Master Processes + + `POST /v1/trainer/job` receives the distributed training parameters, and deploy the job as follows: + - Deploy PaddlePaddle Parameter Server processes, it's a Kubernetes ReplicaSet. + - Deploy PaddlePaddle Trainer processes, it's a Kubernetes Job. + - Deploy PaddlePaddle Master processes, it's a Kubernetes ReplicaSet. diff --git a/develop/doc_cn/design/cluster_train/submit-job.html b/develop/doc_cn/design/cluster_train/submit-job.html new file mode 100644 index 00000000000..e5bae7369c2 --- /dev/null +++ b/develop/doc_cn/design/cluster_train/submit-job.html @@ -0,0 +1,381 @@ + + + + + + + + + + + Submit a Distributed Training Job — PaddlePaddle 文档 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
+
+ Folk me on Github +
+ + English + + + + +
+ +
+
+ + + + +
+
+ + + +
+
+
+
+
+ +
+ + + + +
+ + + + + + + + + + + +
+
    + +
  • Submit a Distributed Training Job
    • +
+
+ +
+
+
+
+ +
+

Submit a Distributed Training Job

+

The user can submit a distributed training job with Python code, rather than with a command-line interface.

+
+

Runtime Environment On Kubernetes

+

For a distributed training job, there is two Docker image called runtime Docker image and base Docker image. The runtime Docker image is the Docker image that gets scheduled by Kubernetes to run during training. The base Docker image is for building the runtime Docker image.

+
+

Base Docker Image

+

Usually, the base Docker image is PaddlePaddle product Docker image including paddle binary files and python package. And of course, users can specify any image name hosted on any docker registry which users have the access right.

+
+
+

Runtime Docker Image

+

The trainer package which user upload and some Python dependencies are packaged into a runtime Docker image based on base Docker image.

+
    +

  • Handle Python Dependencies

    +

    You need to provide requirements.txt file in your trainer-package folder. Example:

    +
    pillow
+protobuf==3.1.0
+
    +
    +

    More details about requirements, an example project looks like:

    +
      paddle_example
+    |-quick_start
+      |-trainer.py
+      |-dataset.py
+      |-requirements.txt
+
    +
    +
    • +
+
+
+
+

Submit Distributed Training Job With Python Code

+

+
    +
  • paddle.job.dist_train() will call the Job Server API /v1/packages to upload the trainer package and save them on CephFS, and then call /v1/trainer/job to submit the PaddlePaddle distributed job.
    • +
  • /v1/trainer/job will start a building job for preparing the runtime Docker image. When the building job is finished, Job Server will submit the PaddlePaddle distributed job to Kubernetes.
    • +
  • NOTE: For the first version, we will not prepare the runtime Docker image, instead, the package is uploaded to Paddle Cloud, and Paddle Cloud will mount the package in a temporary folder into the base Docker image. We will not support custom Python dependencies in the first version as well.
    • +
+

You can call paddle.job.dist_train and provide distributed training configuration as the parameters:

+
paddle.job.dist_train(
+  trainer=dist_trainer(),
+  paddle_job=PaddleJob(
+    job_name = "paddle-cloud",
+    entry_point = "python %s"%__file__,
+    trainer_package = "/example/word2vec",
+    image = "yancey1989/paddle-job",
+    trainers = 10,
+    pservers = 3,
+    trainer_cpu = 1,
+    trainer_gpu = 1,
+    trainer_mem = "10G",
+    pserver_cpu = 1,
+    pserver_mem = "2G"
+  ))
+
+
+

The parameter trainer of paddle.job.dist_train is a function and you can implement it as follows:

+
def dist_trainer():
+  def trainer_creator():
+    trainer = paddle.v2.trainer.SGD(...)
+    trainer.train(...)
+  return trainer_creator
+
+
+

The pseudo code of paddle.job.dist_train is as follows:

+
def dist_train(trainer, paddle_job):
+  # if the code is running on cloud, set PADDLE_ON_CLOUD=YES
+  if os.getenv("RUNNING_ON_CLOUD", "NO") == "NO":
+    #submit the paddle job
+    paddle_job.submit()
+  else:
+    #start the training
+    trainer()
+
+
+
+

PaddleJob Parameters

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parameter | type | explanation +— | — | — +job_name | str | the unique name for the training job +entry_point | str | entry point for startup trainer process +trainer_package | str | trainer package file path which user have the access right +image|str|the base image for building the runtime image +pservers|int| Parameter Server process count +trainers|int| Trainer process count +pserver_cpu|int| CPU count for each Parameter Server process +pserver_mem|str| memory allocated for each Parameter Server process, a plain integer using one of these suffixes: E, P, T, G, M, K +trainer_cpu|int| CPU count for each Trainer process +trainer_mem|str| memory allocated for each Trainer process, a plain integer using one of these suffixes: E, P, T, G, M, K +trainer_gpu|int| GPU count for each Trainer process, if you only want CPU, do not set this parameter

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Deploy Parameter Server, Trainer and Master Process

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  • Deploy PaddlePaddle Parameter Server processes, it’s a Kubernetes ReplicaSet.
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  • Deploy PaddlePaddle Trainer processes, it’s a Kubernetes Job.
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  • Deploy PaddlePaddle Master processes, it’s a Kubernetes ReplicaSet.
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Job Server

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  • RESTful API

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    Job server provides RESTful HTTP API for receiving the trainer package and displaying +PaddlePaddle job related informations.

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    • POST /v1/package receive the trainer package and save them on CephFS
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    • POST /v1/trainer/job submit a trainer job
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    • GET /v1/jobs/ list all jobs
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    • GET /v1/jobs/<job-name> the status of a job
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    • DELETE /v1/jobs/<job-name> delete a job
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    • GET /v1/version job server version
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  • Build Runtime Docker Image on Kubernetes

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    paddle.job.dist_train will upload the trainer package to Job Server, save them on the distributed filesystem, and then start up a job for building the runtime Docker image that gets scheduled by Kubernetes to run during training.

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    There are some benefits for building runtime Docker image on JobServer:

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    • On Paddle Cloud, users will run the trainer code in a Jupyter Notebook which is a Kubernetes Pod, if we want to execute docker build in the Pod, we should mount the host’s docker.sock to the Pod, user’s code will connect the host’s Docker Engine directly, it’s not safe.
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    • Users only need to upload the training package files, does not need to install docker engine, docker registry as dependencies.
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    • If we want to change another image type, such as RKT, users do not need to care about it.
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  • Deploy Parameter Server, Trainer and Master Processes

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    POST /v1/trainer/job receives the distributed training parameters, and deploy the job as follows:

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    • Deploy PaddlePaddle Parameter Server processes, it’s a Kubernetes ReplicaSet.
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    • Deploy PaddlePaddle Trainer processes, it’s a Kubernetes Job.
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    • Deploy PaddlePaddle Master processes, it’s a Kubernetes ReplicaSet.
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+ © Copyright 2016, PaddlePaddle developers. + +

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