Dflow is a concurrent learning framework based on Argo Workflows.
Project description
DFLOW
Dflow is a Python framework for constructing scientific computing workflows (e.g. concurrent learning workflows) employing Argo Workflows as the workflow engine.
For dflow's users (e.g. ML application developers), dflow offers user-friendly functional programming interfaces for building their own workflows. Users need not be concerned with process control, task scheduling, observability and disaster tolerance. Users can track workflow status and handle exceptions by APIs as well as from frontend UI. Thereby users are enabled to concentrate on implementing operators and orchestrating workflows.
For dflow's developers, dflow wraps on argo SDK, keeps details of computing and storage resources from users, and provides extension abilities. While argo is a cloud-native workflow engine, dflow uses containers to decouple computing logic and scheduling logic, and uses Kubernetes to make workflows observable, reproducible and robust. Dflow is designed to be based on a distributed, heterogeneous infrastructure. The most common computing resources in scientific computing may be HPC clusters. Users can either use remote executor to manage HPC jobs within dflow (dflow-extender), or use operator to uniformly abstract HPC resources in the framework of Kubernetes (wlm-operator).
-
- 1.1. Architecture
- 1.2. Common layer
- 1.2.1. Parameters and artifacts
- 1.2.2. OP template
- 1.2.3. Workflow
- 1.3. Interface layer
- 1.3.1. Python OP
-
- 2.1. Prepare Kubernetes cluster
- 2.2. Install argo workflows
- 2.3. Install dflow
- 2.4. Run an example
-
- 3.1. Common layer
- 3.1.1. Workflow management
- 3.1.2. Upload/download artifact
- 3.1.3. Output parameter and artifact of Steps
- 3.1.4. Conditional step, parameter and artifact
- 3.1.5. Produce parallel steps using loop
- 3.1.6. Timeout
- 3.1.7. Continue on failed
- 3.1.8. Continue on success number/ratio of parallel steps
- 3.1.9. Optional input artifact
- 3.1.10. Default value for output parameter
- 3.1.11. Key of step
- 3.1.12. Reuse step
- 3.1.13. Executor
- 3.1.14. Submit Slurm job by wlm-operator
- 3.2. Interface layer
- 3.2.1. Slices
- 3.2.2. Retry and error handling
- 3.2.3. Progress
- 3.2.4. Upload python packages for development
- 3.1. Common layer
1. Overview
1.1. Architecture
The dflow consists of a common layer and an interface layer. Interface layer takes various OP templates from users, usually in the form of python classes, and transforms them into base OP templates that common layer can handle. Common layer is an extension over argo client which provides functionalities such as file processing, workflow submission and management, etc.
1.2. Common layer
1.2.1. Parameters and artifacts
Parameters and artifacts are data stored by the workflow and passed within the workflow. Parameters are saved as strings which can be displayed in the UI, while artifacts are saved as files.
1.2.2. OP template
OP template describes a sort of operation which takes some parameters and artifacts as input and gives some parameters and artifacts as output. The most common OP template is the container OP template whose operation is specified by container image and commands to be executed as entrypoint. Currently two types of container OP templates are supported. Shell OP template defines an operation by a shell script and Python script OP template defines an operation by a Python script.
-
Input parameters: basically a dictionary mapping from parameter name to its properties. The value of the input parameter is optional for the OP template, if provided, it will be regarded as the default value which can be overrided at run time.
-
Input artifacts: basically a dictionary mapping from artifact name to its properties. For the container OP template, path where the input artifact is placed in the container is required to be specified.
-
Output parameters: basically a dictionary mapping from parameter name to its properties. The source where its value comes from should be specified. For the container OP template, the value may be from a certain file generated in the container (value_from_path).
-
Output artifacts: basically a dictionary mapping from artifact name to its properties. For the container OP template, path where the output artifact will be generated in the container is required to be specified.
Here is an example of shell OP template
ShellOPTemplate(name='Hello',
image="alpine:latest",
script="cp /tmp/foo.txt /tmp/bar.txt && echo {{inputs.parameters.msg}} > /tmp/msg.txt",
inputs=Inputs(parameters={"msg": InputParameter()},
artifacts={"foo": InputArtifact(path="/tmp/foo.txt")}),
outputs=Outputs(parameters={"msg": OutputParameter(value_from_path="/tmp/result.txt")},
artifacts={"bar": OutputArtifact(path="/tmp/bar.txt")}))
1.2.3. Workflow
Step
and Steps
are central blocks for building a workflow. A Step
is the result of instantiating a OP template. When a Step
is initialized, values of all input parameters and sources of all input artifacts declared in the OP template must be specified. Steps
is a sequential array of array of concurrent Step
's. A simple example goes like [[s00, s01], [s10, s11, s12]]
, where inner array represent concurrent tasks while outer array is sequential. A Workflow
contains a Steps
as entrypoint for default. Adding a Step
to a Workflow
is equivalent to adding the Step
to the Steps
of the Workflow
. For example,
wf = Workflow(name="hhh")
hello0 = Step(name="hello0", template=hello)
wf.add(hello0)
Submit a workflow by
wf.submit()
It should be noticed that Steps
itself is a subclass of OPTemplate and could be used as the template of a higher level Step
. By virtue of this feature, one can construct complex workflows of nested structure. One is also allowed to recursively use a Steps
as the template of a building block inside itself to achieve dynamic loop.
1.3. Interface layer
1.3.1. Python OP
Python OP
is a kind of OP template defined in the form of Python class. As Python is a weak typed language, we impose strict type checking to OP
s to alleviate ambiguity and unexpected behaviors.
The structures of the inputs and outputs of a OP
are defined in the static methods get_input_sign
and get_output_sign
. Each of them returns a OPIOSign
object (basically a dictionary mapping from the name of a parameter/artifact to its sign). For a parameter, its sign is its variable type, such as str
, float
, list
, or any user-defined Python class. Since argo only accept string as parameter value, dflow encodes all parameters to json (except for string type parameters) before passing them to argo, and decodes argo parameters from json (except for string type parameters). For an artifact, its sign must be an instance of Artifact
. Artifact
receives the type of the path variable as the constructor argument, only str
, pathlib.Path
, typing.Set[str]
, typing.Set[pathlib.Path]
, typing.List[str]
, typing.List[pathlib.Path]
are supported. If a OP
returns a list of path as an artifact, dflow not only collects files or directories in the returned list of path, and package them in an artifact, but also records their relative path in the artifact. Thus dflow can unpack the artifact to a list of path again before passing to the next OP
. When no file or directory exists, dflow regards it as None
.
The execution of the OP
is defined in the execute
method. The execute
method receives a OPIO
object as input and outputs a OPIO
object. OPIO
is basically a dictionary mapping from the name of a parameter/artifact to its value/path. The type of the parameter value or the artifact path should be in accord with that declared in the sign. Type checking is implemented before and after the execute
method.
Use PythonOPTemplate
to convert a OP
to Python script OP template.
2. Quick Start
2.1. Prepare Kubernetes cluster
Firstly, you'll need a Kubernetes cluster. For quick tests, you can set up a Minikube on your PC.
2.2. Install argo workflows
To get started quickly, you can use the quick start manifest which will install Argo Workflow as well as some commonly used components:
kubectl create ns argo
kubectl apply -n argo -f https://raw.githubusercontent.com/dptech-corp/dflow/master/manifests/quick-start-postgres.yaml
If you are running Argo Workflows locally (e.g. using Minikube or Docker for Desktop), open a port-forward so you can access the namespace:
kubectl -n argo port-forward deployment/argo-server 2746:2746
This will serve the user interface on https://localhost:2746 For access to the minio object storage, open a port-forward for minio
kubectl -n argo port-forward deployment/minio 9000:9000
2.3. Install dflow
Make sure your Python version is not less than 3.7 and install dflow
pip install pydflow
2.4. Run an example
Submit a simple workflow
python examples/test_steps.py
Then you can check the submitted workflow through argo's UI.
3. User Guide
3.1. Common layer
3.1.1. Workflow management
After a workflow is submitted by wf.submit()
, one can track it with APIs
wf.id
: workflow ID in argowf.query_status()
: query workflow status, return"Pending"
,"Running"
,"Suceeded"
, etc.wf.query_step(name=None)
: query step by name (support for regex), return an argo step objectstep.phase
: phase of a step,"Pending"
,"Running"
,Succeeded
, etc.step.outputs.parameters
: a dictionary of output parametersstep.outputs.artifacts
: a dictionary of output artifacts
3.1.2. Upload/download artifact
Dflow offers tools for uploading files to Minio and downloading files from Minio (default object storage in the quick start). User can upload a list of files or directories and get an artifact object, which can be used as argument of a step
artifact = upload_artifact([path1, path2])
step = Step(
...
artifacts={"foo": artifact}
)
User can also download the output artifact of a step queried from a workflow (to current directory for default)
step = wf.query_step(name="hello")
download_artifact(step.outputs.artifacts["bar"])
Note: dflow retains the relative path of the uploaded file/directory with respect to the current directory during uploading. If file/directory outside current directory is uploaded, its absolute path is used as the relative path in the artifact. If you want a different directory structure in the artifact with the local one, you can make soft links and then upload.
3.1.3. Output parameter and artifact of Steps
The output parameter of a Steps
can be set to come from a step of it by steps.outputs.parameters["msg"].value_from_parameter = step.outputs.parameters["msg"]
. Here, step
must be contained in steps
. For assigning output artifact for a Steps
, use steps.outputs.artifacts["foo"]._from = step.outputs.parameters["foo"]
.
3.1.4. Conditional step, parameter and artifact
Set a step to be conditional by Step(..., when=expr)
where expr
is an boolean expression in string format. Such as "%s < %s" % (par1, par2)
. The when
argument is often used as the breaking condition of recursive steps. The output parameter of a Steps
can be assigned as optional by
steps.outputs.parameters["msg"].value_from_expression = if_expression(
_if=par1 < par2,
_then=par3,
_else=par4
)
Similarly, the output artifact of a Steps
can be assigned as optional by
steps.outputs.artifacts["foo"].from_expression = if_expression(
_if=par1 < par2,
_then=art1,
_else=art2
)
3.1.5. Produce parallel steps using loop
with_param
and with_sequence
are 2 arguments of Step
for automatically generating a list of parallel steps. These steps share a common OP template, and only differ in the input parameters.
A step using with_param
option generates parallel steps on a list (usually from another parameter), the parallelism equals to the length of the list. Each parallel step picks an item from the list by "{{item}}"
, such as
step = Step(
...
parameters={"msg": "{{item}}"},
with_param=steps.inputs.parameters["msg_list"]
)
A step using with_sequence
option generates parallel steps on a numeric sequence. with_sequence
is usually used in coordination with argo_sequence
(return a sequence, start from 0 for default) and argo_len
(return length of a list). Each parallel step picks a number from the sequence by "{{item}}"
, such as
step = Step(
...
parameters={"i": "{{item}}"},
with_sequence=argo_sequence(argo_len(steps.inputs.parameters["msg_list"]))
)
3.1.6. Timeout
Set the timeout of a step by Step(..., timeout=t)
. The unit is second.
3.1.7. Continue on failed
Set the workflow to continue when a step fails by Step(..., continue_on_failed=True)
.
3.1.8. Continue on success number/ratio of parallel steps
Set the workflow to continue when certain number/ratio of parallel steps succeed by Step(..., continue_on_num_success=n)
or Step(..., continue_on_success_ratio=r)
.
3.1.9. Optional input artifact
Set an input artifact to be optional by op_template.inputs.artifacts["foo"].optional = True
.
3.1.10. Default value for output parameter
Set default value for a output parameter by op_template.outputs.parameters["msg"].default = default_value
. The default value will be used when the expression in value_from_expression
fails or the step is skipped.
3.1.11. Key of step
You can set a key for a step by Step(..., key="some-key")
for the convenience of locating the step. The key can be regarded as an input parameter which may contain reference of other parameters. For instance, the key of a step can change with iterations of a dynamic loop. Once key is assigned to a step, the step can be query by wf.query_step(key="some-key")
. If the key is unique within the workflow, the query_step
method returns a list consist of only one element.
3.1.12. Reuse step
Workflows often have some steps that are expensive to compute. The outputs of previously run steps can be reused for submitting a new workflow. E.g. a failed workflow can be restarted from a certain point after some modification of the workflow template or even outputs of completed steps. For example, submit a workflow with reused steps by wf.submit(reuse_step=[step0, step1])
. Here, step0
and step1
are previously run steps returned by query_step
method. Before the new workflow runs a step, it will detect if there exists a reused step whose key matches that of the step about to run. If hit, the workflow will skip the step and set its outputs as those of the reused step. To modify outputs of a step before reusing, use step0.modify_output_parameter(par_name, value)
for parameters and step0.modify_output_artifact(art_name, artifact)
for artifacts.
3.1.13. Executor
By default, for a "script step" (a step whose template is a script OP template), the Shell script or Python script runs in the container directly. Alternatively, one can modify the executor to run the script. Dflow offers an extension point for "script step" Step(..., executor=my_executor)
. Here, my_executor
should be an instance of class derived from Executor
. A Executor
-derived class should specify image
and command
to be used for the executor, as well as a method get_script
which converts original command and script to new script run by the executor.
class Executor(object):
image = None
command = None
def get_script(self, command, script):
pass
SlurmRemoteExecutor
is provided as an example of executor. The executor submits a slurm job to a remote host and synchronize its status and logs to the dflow step. The central logic of the executor is implemented in the Golang project Dflow-extender. If you want to run a step on a slurm cluster remotely, do something like
Step(
...,
executor=SlurmRemoteExecutor(host="1.2.3.4",
username="myuser",
password="mypasswd",
header="""#!/bin/bash
#SBATCH -N 1
#SBATCH -n 1
#SBATCH -p cpu""")
)
3.1.14. Submit Slurm job via virtual node
Following the installation steps in the wlm-operator project to add Slurm partitions as virtual nodes to Kubernetes (use manifests configurator.yaml, operator-rbac.yaml, operator.yaml in this project which modified some RBAC configurations)
$ kubectl get nodes
NAME STATUS ROLES AGE VERSION
minikube Ready control-plane,master 49d v1.22.3
slurm-minikube-cpu Ready agent 131m v1.13.1-vk-N/A
slurm-minikube-dplc-ai-v100x8 Ready agent 131m v1.13.1-vk-N/A
slurm-minikube-v100 Ready agent 131m v1.13.1-vk-N/A
Then you can assign a step to be executed on a virtual node (i.e. submit a Slurm job to the corresponding partition to complete the step)
step = Step(
...
executor=SlurmJobTemplate(
header="#!/bin/sh\n#SBATCH --nodes=1",
node_selector={"kubernetes.io/hostname": "slurm-minikube-v100"}
)
)
3.2. Interface layer
3.2.1. Slices
Slices
helps user to slice input parameters/artifacts (which must be lists) to feed parallel steps and stack their output parameters/artifacts to lists in the same pattern. For example,
step = Step(name="parallel-tasks",
template=PythonOPTemplate(
...,
slices=Slices("{{item}}",
input_parameter=["msg"],
input_artifact=["data"],
output_artifact=["log"])
),
parameters = {
"msg": msg_list
},
artifacts={
"data": data_list
},
with_param=argo_range(5)
)
In this example, each item in msg_list
is passed to a parallel step as the input parameter msg
, each part in data_list
is passed to a parallel step as the input artifact data
. Finally, the output artifacts log
of all parallel steps are collected to one artifact step.outputs.artifacts["log"]
.
3.2.2. Retry and error handling
Dflow catches TransientError
and FatalError
thrown from OP
. User can set maximum number of retries on TransientError
by PythonOPTemplate(..., retry_on_transient_error=n)
. Timeout error is regarded as fatal error for default. To treat timeout error as transient error, set PythonOPTemplate(..., timeout_as_transient_error=True)
.
3.2.3. Progress
A OP
can update progress in the runtime so that user can track its real-time progress
class Progress(OP):
progress_total = 100
...
def execute(op_in):
for i in range(10):
self.progress_current = 10 * (i + 1)
...
3.2.4. Upload python packages for development
To avoid frequently making image during development, dflow offers a interface to upload local packages into container of OP
and add them to $PYTHONPATH
, such as PythonOPTemplate(python_packages=["/opt/anaconda3/lib/python3.9/site-packages/numpy"])
. One can also globally specify packages to be uploaded, which will affect all OP
s
from dflow import upload_packages
upload_packages.append("/opt/anaconda3/lib/python3.9/site-packages/numpy")
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.