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https://github.com/deepmodeling/dflow
Dflow is a Python framework for constructing scientific computing workflows (e.g. concurrent learning workflows) employing Argo Workflows as the workflow engine.
https://github.com/deepmodeling/dflow
Last synced: 6 days ago
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Dflow is a Python framework for constructing scientific computing workflows (e.g. concurrent learning workflows) employing Argo Workflows as the workflow engine.
- Host: GitHub
- URL: https://github.com/deepmodeling/dflow
- Owner: deepmodeling
- License: lgpl-3.0
- Fork: true (dptech-corp/dflow)
- Created: 2022-01-21T08:14:36.000Z (almost 3 years ago)
- Default Branch: master
- Last Pushed: 2024-05-23T01:39:32.000Z (5 months ago)
- Last Synced: 2024-05-23T01:47:16.321Z (5 months ago)
- Language: Python
- Homepage:
- Size: 9.2 MB
- Stars: 54
- Watchers: 1
- Forks: 23
- Open Issues: 14
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
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- deepmodeling-projects - GitHub - 40% open · ⏱️ 23.10.2024): (Others)
README
# DFLOW
[Dflow](https://deepmodeling.com/dflow/dflow.html) is a Python framework for constructing scientific computing workflows (e.g. concurrent learning workflows) employing [Argo Workflows](https://argoproj.github.io/) as the workflow engine. (arXiv: [https://arxiv.org/abs/2404.18392](https://arxiv.org/abs/2404.18392))
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 operations (OPs) 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 or scheduling logic, and leverages Kubernetes to make workflows observable, reproducible and robust. Dflow is designed to be based on a distributed, heterogeneous infrastructure. With high-performance computing (HPC) clusters being a common resources in scientific computing, users can either use executor to manage HPC jobs using [DPDispatcher](https://github.com/deepmodeling/dpdispatcher) plugin, or use virtual node technique to uniformly manage HPC resources in the framework of Kubernetes ([wlm-operator](https://github.com/dptech-corp/wlm-operator)).
OP template (abbr. OP) in dflow can be reused across workflows and shared among users. Dflow provides a cookie cutter recipe [dflow-op-cutter](https://github.com/deepmodeling/dflow-op-cutter) for template a new OP package. Start developing an OP package at once from
```python
pip install cookiecutter
cookiecutter https://github.com/deepmodeling/dflow-op-cutter.git
```
Dflow provides a debug mode for running workflows bare-metally whose backend is implemented in dflow itself in pure Python, independent of Argo/Kubernetes. The debug mode utilizes local environment to execute OPs instead of containers by default. It implements most APIs of the default (Argo) mode in order to ensure a consistent user experience. The debug mode offer convenience for debugging or testing without container. In cases where clusters face difficulties deploying Docker or Kubernetes or have limited external access, the debug mode may also be used for production, despite less robustness and observability.
* 1. [Overview](#1-overview)
* 1.1. [Basics](#12-basics)
* 1.1.1. [Parameters and artifacts](#121-parameters-and-artifacts)
* 1.1.2. [OP template](#122-op-template)
* 1.1.3. [Step](#123-step)
* 1.1.4. [Workflow](#124-workflow)
* 2. [Quick Start](#2-quick-start)
* 2.1. [Setup Argo Server](#21-setup-argo-server)
* 2.2. [Install dflow](#22-install-dflow)
* 2.3. [Run an example](#23-run-an-example)
* 3. [User Guide](#3-user-guide)
* 3.1. [Common](#31-common-layer)
* 3.1.1. [Workflow management](#311-workflow-management)
* 3.1.2. [Upload artifact](#312-upload-download-artifact)
* 3.1.3. [Steps](#313-steps)
* 3.1.4. [DAG](#314-dag)
* 3.1.5. [Conditional step, parameters and artifacts](#315-conditional-step-parameters-and-artifacts)
* 3.1.6. [Produce parallel steps using loop](#316-produce-parallel-steps-using-loop)
* 3.1.7. [Timeout](#317-timeout)
* 3.1.8. [Continue on failed](#318-continue-on-failed)
* 3.1.9. [Continue on success number/ratio of parallel steps](#319-continue-on-success-number-ratio-of-parallel-steps)
* 3.1.10. [Optional input artifacts](#3110-optional-input-artifacts)
* 3.1.11. [Default value for output parameters](#3111-default-value-for-output-parameters)
* 3.1.12. [Key of a step](#3112-key-of-a-step)
* 3.1.13. [Resubmit a workflow](#3113-resubmit-a-workflow)
* 3.1.14. [Executor](#3114-executor)
* 3.1.15. [Submit HPC/Bohrium job via dispatcher plugin](#3115-submit-hpc-bohrium-job-via-dispatcher-plugin)
* 3.1.16. [Submit Slurm job via virtual node](#3116-submit-slurm-job-via-virtual-node)
* 3.1.17. [Use resources in Kubernetes](#3117-use-resources-in-kubernetes)
* 3.1.18. [Important note: variable names](#3118-important-note-variable-names)
* 3.1.19. [Debug mode: dflow independent of Kubernetes](#3119-debug-mode-dflow-independent-of-kubernetes)
* 3.1.20. [Artifact storage plugins](#3120-artifact-storage-plugins)
* 3.2. [Python OP](#32-interface-layer)
* 3.2.1. [Slices](#321-slices)
* 3.2.2. [Retry and error handling](#322-retry-and-error-handling)
* 3.2.3. [Progress](#323-progress)
* 3.2.4. [Upload python packages for development](#324-upload-python-packages-for-development)
## 1. Overview
### 1.1. Basics
#### 1.1.1. Parameters and artifacts
Parameters and artifacts are data stored and transferred within a workflow. Parameters are saved as text which can be displayed in the UI, while artifacts are stored as files. Parameters are passed to an OP with their values, while artifacts are passed as paths.
#### 1.1.2. OP template
OP template (abbr. OP) serves as a fundamental building block of a workflow. It defines a particular operation to be executed given the input and the expected output. Both the input and output can be parameters and/or artifacts. The most common OP template is the container OP template. Two types of container OP templates are supported: `ShellOPTemplate`, `PythonScriptOPTemplate`. `ShellOPTemplate` defines an operation by shell script and a container image where the script runs. `PythonScriptOPTemplate` defines an operation by Python script and a container image.
As a more Python-native category of OP templates provided by dflow, `PythonOPTemplate` defines OPs in the form of Python classes or Python functions (termed class OP or function OP respectively). As Python is a weak typed
language, we impose strict type checking to Python OPs to minimize ambiguity and unexpected behaviors.
For a class OP, the input and output structures of an OP are declared in the static methods `get_input_sign` and `get_output_sign`. Each of them returns a dictionary mapping from the name of a parameter/artifact to its type. The execution of the OP is defined in the `execute` method. The types of the parameter values passed in and out should be in accord with those declared in the sign. Type checking is implemented before and after the `execute` method. For an input/output artifact, its sign should be like `Artifact(type)` where `type` can be `Path`, `List[Path]`, `Dict[str, Path]` or `dflow.python.NestedDict[Path]`. For input artifact, the OP receives a path, a list of paths or a dictionary of paths according to its sign . OP developer can directly process the file or directory at the path. For output artifact, the OP should also return a path, a list of paths or a dictionary of paths according to its sign.
```python
from dflow.python import OP, OPIO, OPIOSign, Artifact
from pathlib import Path
import shutil
class SimpleExample(OP):
def __init__(self):
pass
@classmethod
def get_input_sign(cls):
return OPIOSign(
{
"msg": str,
"foo": Artifact(Path),
}
)
@classmethod
def get_output_sign(cls):
return OPIOSign(
{
"msg": str,
"bar": Artifact(Path),
}
)
@OP.exec_sign_check
def execute(
self,
op_in: OPIO,
) -> OPIO:
shutil.copy(op_in["foo"], "bar.txt")
out_msg = op_in["msg"]
op_out = OPIO(
{
"msg": out_msg,
"bar": Path("bar.txt"),
}
)
return op_out
```
The above example illustrates an OP `SimpleExample`. This operation involves copying the input artifact `foo` to the output artifact `bar` and duplicating the input parameter `msg` to the output parameter `msg`.
For an function OP, the input and output structures are declared more succinctly using type annotations and execution process is defined in the function body. Type checking is implemented before and after the function as well. We recommend `python>=3.9` to use this syntactic sugar. To learn more about Python Annotation, refer to the [Python official howtos](https://docs.python.org/3/howto/annotations.html).
```python
from dflow.python import OP, Artifact
from pathlib import Path
import shutil
@OP.function
def SimpleExample(
msg: str,
foo: Artifact(Path),
) -> {"msg": str, "bar": Artifact(Path)}:
shutil.copy(foo, "bar.txt")
out_msg = msg
return {"msg": out_msg, "bar": Path("bar.txt")}
```
To create an OP template based on the above class or function, we need to specify the container image and any optional arguments to `PythonOPTemplate`. `pydflow` need not to be installed in this image because local `pydflow` package will automatically be uploaded into the container by default
```python
from dflow.python import PythonOPTemplate
simple_example_templ = PythonOPTemplate(SimpleExample, image="python:3.8")
```
An example is here
- [Python OP example](examples/test_python.py)
#### 1.1.3. Step
`Step` serves as the central component for establishing data flow rules. A step is created by instantiating an OP template, which requires specifying the values for all input parameters and sources for all input artifacts declared in the OP template. The input parameters/artifacts of a step may be either static at the time of submission, or dynamically from outputs of another step.
```python
from dflow import Step
simple_example_step = Step(
name="step0",
template=simple_example_templ,
parameters={"msg": "HelloWorld!"},
artifacts={"inp_art": foo},
)
```
Note that `foo` here is an artifact either uploaded locally or output of another step.
#### 1.1.4. Workflow
`Workflow` connects steps together to build a workflow. A simple serial workflow is created by adding steps in sequence. Adding a list of steps to a workflow means these steps will run concurrently.
```python
from dflow import Workflow
wf = Workflow(name="hello-world")
wf.add(simple_example_step)
```
Submit a workflow by
```python
wf.submit()
```
An example is here
- [Workflow example](examples/test_steps.py)
## 2. Quick Start
### 2.1. Setup Argo Server
If you have an Argo server already, you can skip this step. Otherwise you can follow the [installation guide](tutorials/readme.md).
### 2.2. Install dflow
Make sure your Python version is not less than 3.6 and install dflow
```
pip install pydflow
```
### 2.3. Run an example
There are several [notebook tutorials](tutorials/readme.md) that can help you start to use dflow. Besides, you can submit a simple workflow from the terminal
```
python examples/test_python.py
```
Then you can check the submitted workflow through [argo's UI](https://127.0.0.1:2746).
## 3. User Guide ([dflow-doc](https://deepmodeling.com/dflow/dflow.html))
### 3.1. Common
#### 3.1.1. Workflow management
After submitting a workflow by `wf.submit()`, or retrieving a history workflow by `wf = Workflow(id="xxx")`, you can track its real-time status with APIs
- `wf.id`: workflow ID in argo
- `wf.query_status()`: query workflow status, return `"Pending"`, `"Running"`, `"Succeeded"`, etc.
- `wf.query_step(name=None, key=None, phase=None, id=None, type=None)`: query step by name (support for regex)/key/phase/ID/type, return a list of argo step objects
- `step.phase`: phase of a step, `"Pending"`, `"Running"`, `Succeeded`, etc.
- `step.outputs.parameters`: a dictionary of output parameters mapping parameter names to parameter objects, get the value of a parameter by `step.outputs.artifacts["foo"].value`
- `step.outputs.artifacts`: a dictionary of output artifacts mapping artifact names to artifact objects, download an artifact by `download_artifact(step.outputs.artifacts["bar"])`
- `wf.terminate()`: terminate a workflow
#### 3.1.2. Upload artifact
Dflow offers tools for uploading files to the artifact repository and downloading files from it (default artifact repository is Minio set up in the quick start). User can upload a file/directory, a list of files/directories or a dictionary of files/directories and obtain an artifact object, which can be used as argument of a step
```python
artifact = upload_artifact([path1, path2])
step = Step(
...
artifacts={"foo": artifact}
)
```
Modify `dflow.s3_config` to configure artifact repository settings globally.
Note: during the upload process, dflow retains the relative path of the uploaded file/directory with respect to the current directory. 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 compared to the local one, you can create soft links and then upload.
#### 3.1.3. Steps
`Steps` is another type of OP template which is defined by its constituent steps instead of a container. It can be viewed as a sub-workflow or a super OP template composed of smaller OPs. A `steps` includes an array of arrays of `step`s, e.g. `[[s00,s01],[s10,s11,s12]]`, where the inner array represents concurrent steps while the outer array indicates sequential steps. Input/output parameters/artifacts for a steps can be declared as follows:
```python
steps.inputs.parameters["msg"] = InputParameter()
steps.inputs.artifacts["foo"] = InputArtifact()
steps.outputs.parameters["msg"] = OutputParameter()
steps.outputs.parameters["bar"] = OutputArtifact()
```
Adding a `step` to a `steps` is similar to adding one to a `workflow`
```python
steps.add(step)
```
`steps` can be used as the template to instantiate a bigger `step` just like script OP templates. This allows for constructing complex workflows of nested structure. It is also possible to recursively use a `steps` as the template of a building block within itself to achieve dynamic loop.
- [Recursive example](examples/test_recurse.py)
To set an output parameter of `steps` from a step within it
```python
steps.outputs.parameters["msg"].value_from_parameter = step.outputs.parameters["msg"]
```
Here, `step` must be contained within `steps`. Similarly, to assign an output artifact for `steps`, use
```python
steps.outputs.artifacts["foo"]._from = step.outputs.parameters["foo"]
```
#### 3.1.4. DAG
`DAG` is another type of OP template which is defined by its constituent tasks and their dependencies. The usage of `DAG` is similar to that of `Steps`. To incorporate a `task` into a `dag`, use
```python
dag.add(task)
```
The usage of `task` is also similar to that of `step`. Dflow will automatically detect dependencies among tasks of a `dag` based on their input/output relations. Additional dependencies can be specified by
```python
task_3 = Task(..., dependencies=[task_1, task_2])
```
- [DAG example](examples/test_dag.py)
#### 3.1.5. Conditional step, parameters and artifacts
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 step will be executed if the expression is evalutated to be true, otherwise skipped. The `when` argument is often used as the breaking condition of recursive steps. The output parameter of a `steps` (similar to `dag`) can be assigned as conditional by
```python
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 conditional by
```python
steps.outputs.artifacts["foo"].from_expression = if_expression(
_if=par1 < par2,
_then=art1,
_else=art2
)
```
- [Conditional outputs example](examples/test_conditional_outputs.py)
#### 3.1.6. Produce parallel steps using loop
In scientific computing, it is often required to produce a list of parallel steps which share a common OP template, and only differ in the input parameters. `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 based on a list (either a constant list or referring to another parameter, e.g. an output parameter of another step or an input parameter of the `steps` or `DAG` context), the parallelism equals to the length of the list. Each parallel step picks an item from the list by `"{{item}}"`, such as
```python
step = Step(
...
parameters={"msg": "{{item}}"},
with_param=steps.inputs.parameters["msg_list"]
)
```
A step using `with_sequence` option generates parallel steps based on a numeric sequence. `with_sequence` is usually used in coordination with `argo_sequence` which returns an Argo's sequence. For `argo_sequence`, the number at which to start the sequence is specified by `start` (default: 0). You can either specify the number of elements in the sequence by `count` or the ending number of the sequence by `end`. The printf format string can be specified by `format` to format the values in the sequence. Each argument can be passed with a parameter, `argo_len` which returns the length of a list may be useful. Each parallel step picks an element from the sequence by `"{{item}}"`, such as
```python
step = Step(
...
parameters={"i": "{{item}}"},
with_sequence=argo_sequence(argo_len(steps.inputs.parameters["msg_list"]))
)
```
#### 3.1.7. Timeout
Set the timeout of a step by `Step(..., timeout=t)`. The unit is second.
- [Timeout example](examples/test_error_handling.py)
#### 3.1.8. Continue on failed
To allow a workflow to continue even if a step fails by `Step(..., continue_on_failed=True)`.
- [Continue on failed example](examples/test_error_handling.py)
#### 3.1.9. Continue on success number/ratio of parallel steps
For a group of parallel steps generated by `with_param` or `with_sequence`, enable the workflow to continue when a specific number/ratio of parallel steps succeed by `Step(..., continue_on_num_success=n)` or `Step(..., continue_on_success_ratio=r)`.
- [Continue on success ratio example](examples/test_success_ratio.py)
#### 3.1.10. Optional input artifacts
Set an input artifact to be optional by `op_template.inputs.artifacts["foo"].optional = True`.
#### 3.1.11. Default value for output parameters
Set default value for an 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.
#### 3.1.12. Key of a step
You can assign a key to 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 references to other parameters. For instance, the key of a step may change with iterations of a dynamic loop. Once a 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 singleton list (a list consist of only one element).
- [Key of step example](examples/test_reuse.py)
#### 3.1.13. Resubmit a workflow
Workflows often include some computationally expensive steps. Outputs of previously run steps can be reused for submitting a new workflow. This allows, e.g. a failed workflow to be restarted from a specific point after modifying the workflow template or even the 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 with a matching key. If a match is found, the workflow will skip the step and use the outputs from the reused step. To modify the 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.
- [Reuse step example](examples/test_reuse.py)
#### 3.1.14. Executor
For a container/script step (a step using script OP template), by default the Shell script or Python script runs in the container directly. Alternatively, you can modify the executor to run the script differently. Dflow offers an extension point for container/script step `Step(..., executor=my_executor)`. Here, `my_executor` should be an instance of class derived from the abstract class `Executor`. An implementation class of `Executor` should implement a method `render` which converts original template to a new template.
```python
class Executor(ABC):
@abc.abstractmethod
def render(self, template):
pass
```
A context is similar to an executor, but assigned to a workflow `Workflow(context=...)`, affecting every step.
#### 3.1.15. Submit HPC/Bohrium job via dispatcher plugin
[DPDispatcher](https://github.com/deepmodeling/dpdispatcher) is a python package used to generate HPC scheduler systems (Slurm/PBS/LSF) or [Bohrium](https://bohrium.dp.tech) jobs input scripts, submit these scripts and poke until they finish. Dflow provides a simple interface to utilize dispatcher as an executor to accomplish script steps. E.g.
```python
from dflow.plugins.dispatcher import DispatcherExecutor
Step(
...,
executor=DispatcherExecutor(host="1.2.3.4",
username="myuser",
queue_name="V100")
)
```
For SSH authentication, you can either specify the path of a private key file locally, or upload an authorized private key to each node (or equivalently add each node to the authorized host list). For configuring additional [machine, resources or task parameters for dispatcher](https://docs.deepmodeling.com/projects/dpdispatcher/en/latest/), use `DispatcherExecutor(..., machine_dict=m, resources_dict=r, task_dict=t)`.
- [Dispatcher executor example](examples/test_dispatcher.py)
#### 3.1.16. Submit Slurm job via virtual node
Following the installation steps in the [wlm-operator](https://github.com/dptech-corp/wlm-operator) project to add Slurm partitions as virtual nodes to Kubernetes (use manifests [configurator.yaml](manifests/configurator.yaml), [operator-rbac.yaml](manifests/operator-rbac.yaml), [operator.yaml](manifests/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)
```python
step = Step(
...
executor=SlurmJobTemplate(
header="#!/bin/sh\n#SBATCH --nodes=1",
node_selector={"kubernetes.io/hostname": "slurm-minikube-v100"}
)
)
```
#### 3.1.17. Use resources in Kubernetes
A step can also be completed using a Kubernetes resource (e.g. Job or custom resources). At the beginning, a manifest is applied to Kubernetes. Then the status of the resource is monitered until the success condition or the failure condition is satisfied.
```python
class Resource(ABC):
action = None
success_condition = None
failure_condition = None
@abc.abstractmethod
pass
```
- [Wlm example](examples/test_wlm.py)
#### 3.1.18. Important note: variable names
Dflow has following restrictions on variable names.
| Variable name | Static/Dynamic | Restrictions | Example |
| :------------ | -------------- | ------------ | ------- |
| Workflow/OP template name | Static | Lowercase RFC 1123 subdomain (must consist of lower case alphanumeric characters, '-' or '.', and must start and end with an alphanumeric character), not more than 57 characters long | my-name |
| Step/Task name | Static | Must consist of alpha-numeric characters or '-', and must start with an alpha-numeric character | My-name1-2, 123-NAME |
| Parameter/Artifact name | Static | Must consist of alpha-numeric characters, '_' or '-' | my_param_1, MY-PARAM-1 |
| Key name | Dynamic | Lowercase RFC 1123 subdomain (must consist of lower case alphanumeric characters, '-' or '.', and must start and end with an alphanumeric character) | my-name |
#### 3.1.19. Debug mode: dflow independent of Kubernetes
The debug mode is enabled by setting
```python
from dflow import config
config["mode"] = "debug"
```
Before running a workflow locally, ensure that the dependencies of all OPs within the workflow are properly configured in the locally environment. This is not necessary when using the dispatcher executor to submit jobs to some remote environments. The debug mode uses the current directory as the working directory by default. Each workflow will generate a new directory there, whose structure will be like
```
python-lsev6
├── status
└── step-penf5
├── inputs
│ ├── artifacts
│ │ ├── dflow_python_packages
│ │ ├── foo
│ │ └── idir
│ └── parameters
│ ├── msg
│ └── num
├── log.txt
├── outputs
│ ├── artifacts
│ │ ├── bar
│ │ └── odir
│ └── parameters
│ └── msg
├── phase
├── script
├── type
└── workdir
├── ...
```
The top level contains the the workflow's status and all its steps. The directory name for each step will be its key if provided, or generated from its name otherwise. Each step directory contains the input/output parameters/artifacts, type and phase of the step. For a step of type "Pod", its directory also includes the script, log file and working directory for the step.
- [Debug mode examples](examples/debug)
#### 3.1.20. Artifact storage plugins
The default storage for artifacts in dflow is a Minio deployment in the Kubernetes cluster. While other artifact storages are supported (e.g. Aliyun OSS, Azure blob storage (ABS), Google cloud storage(GCS)). Dflow also offers an extension point for implementing customized storage in the artifact management. This can be achieved through a storage client `StorageClient`, a class implementing 5 abstract methods, `upload`, `download`, `list`, `copy` and `get_md5` (optional), which offer the functionality of uploading file, downloading file, listing files with specific prefix, copying file on the server side and getting the MD5 sum of file, respectively. Use a custom storage client object by configuring s3_config["storage_client"].
```python
class StorageClient(ABC):
@abc.abstractmethod
def upload(self, key: str, path: str) -> None:
pass
@abc.abstractmethod
def download(self, key: str, path: str) -> None:
pass
@abc.abstractmethod
def list(self, prefix: str, recursive: bool = False) -> List[str]:
pass
@abc.abstractmethod
def copy(self, src: str, dst: str) -> None:
pass
@abc.abstractmethod
def get_md5(self, key: str) -> str:
pass
```
### 3.2. Python OP
#### 3.2.1. Slices
In coordination with [parallel steps](#Produceparallelstepsusingloop), `Slices` assists user to slice input parameters/artifacts (which must be lists) to feed parallel steps and stack their output parameters/artifacts into lists following the same pattern. The Python OP only need to handle one slice. For example,
```python
step = Step(name="parallel-tasks",
template=PythonOPTemplate(
...,
slices=Slices(
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 a single artifact `step.outputs.artifacts["log"]`.
This example is analogous to the following pseudocode
```python
log = [None] * 5
for item in range(5):
log[item] = my_op(msg=msg_list[item], data=data_list[item])
```
where `with_param` and `slices` corresponds to the `for` loop and the statement in loop in the pseudocode, respectively.
Note that this feature merges the sliced output artifacts by default, which means the relative path in the output artifact of each slice is remained in the merged artifact. This could potentially lead to overwriting issues due to path conflicts. To create a separate dir for each slice for saving output artifacts, set `create_dir=True`.
- [Slices example](examples/test_slices.py)
It should be noticed that this feature, by default, passes full input artifacts to each parallel step which may only use some slices of these artifacts. In contrast, the subpath mode of slices only passes one single slice of the input artifacts to each parallel step. To use the subpath mode of slices,
```python
step = Step(name="parallel-tasks",
template=PythonOPTemplate(
...,
slices=Slices(sub_path=True,
input_parameter=["msg"],
input_artifact=["data"],
output_artifact=["log"])
),
parameters = {
"msg": msg_list
},
artifacts={
"data": data_list
})
```
Here, each input parameter and artifact to be sliced must be of the same length, and the parallelism equals to this length. Another notable point is that in order to use the subpath of the artifacts, these artifacts must be stored without compression when they are generated. E.g. declare `Artifact(..., archive=None)` in the output signs of Python OP, or specify `upload_artifact(..., archive=None)` while uploading artifacts. Besides, you can use `dflow.config["archive_mode"] = None` to set default archive mode to no compression globally.
- [Subpath mode of slices example](examples/test_subpath_slices.py)
#### 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)`. When a fatal error is raised or the retries on transient error reaches maximum retries, the step is considered as failed.
- [Retry example](examples/test_error_handling.py)
#### 3.2.3. Progress
A `OP` can update progress in the runtime so that user can moniter its real-time progress from the frontend UI
```python
class Progress(OP):
progress_total = 100
...
def execute(op_in):
for i in range(10):
self.progress_current = 10 * (i + 1)
...
```
- [Progress example](examples/test_progress.py)
#### 3.2.4. Upload python packages for development
To avoid frequently creating images during development, dflow offers an interface to upload local packages into the container and add them to `$PYTHONPATH`, such as `PythonOPTemplate(..., python_packages=["/opt/anaconda3/lib/python3.9/site-packages/numpy"])`. Additionally, you can globally specify packages to be uploaded, which will impact all `OP`s
```python
from dflow.python import upload_packages
upload_packages.append("/opt/anaconda3/lib/python3.9/site-packages/numpy")
```