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Show how to setup and use DM in a Bluesky session (#330)
* DOC #329 show how to setup DM env vars * DOC #329 how to submit a workflow job @MDecarabas Thanks!
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# Setup APS Data Management | ||
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This document describes how to setup and submit a workflow job using the [APS | ||
Data Management](https://git.aps.anl.gov/DM/dm-docs/-/wikis/home) (DM) Python | ||
[API](https://git.aps.anl.gov/DM/dm-docs/-/wikis/DM/Beamline-Services/API-Reference) | ||
(tools) in a Bluesky session. | ||
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This document provides guidance for workstations at the APS, where DM tools and | ||
services are available. | ||
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For more information, see the DM API reference for more information about how to | ||
use the DM API and tools. See the `apstools` | ||
[documentation](https://bcda-aps.github.io/apstools/latest/api/_utils.html#apstools.utils.aps_data_management), | ||
for a list of the support code available. | ||
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## About APS Data Management (DM) | ||
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As stated in the DM _Getting Started_ | ||
[guide](https://git.aps.anl.gov/DM/dm-docs/-/wikis/DM/HowTos/Getting-Started): | ||
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> The APS Data Management System is a system for gathering together experimental | ||
> data, metadata about the experiment and providing users access to the data | ||
> based on a users role. | ||
## DM is configured by Environment Variables | ||
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The DM _Getting Started_ | ||
[guide](https://git.aps.anl.gov/DM/dm-docs/-/wikis/DM/HowTos/Getting-Started) | ||
explains how to activate a pre-configured conda environment to use the DM tools | ||
directly from the command line. The setup procedure uses this shell command: | ||
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```bash | ||
/home/DM_INSTALL_DIR/etc/dm.setup.sh | ||
``` | ||
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where `DM_INSTALL_DIR` is the deployment directory for this beamline. | ||
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<details> | ||
<summary>NOTE</summary> | ||
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The exact path to this file will vary between beamline accounts. Contact the DM | ||
support team for details about your beamline. | ||
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</details> | ||
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The DM conda environment does not have the packages installed to run a Bluesky | ||
session. | ||
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### Configure DM in Bluesky sessions | ||
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The Bluesky conda environment has all the packages for both Bluesky and DM | ||
already installed (for APS installations). One of those packages, | ||
[apstools](https://bcda-aps.github.io/apstools/latest/api/_utils.html#aps-data-management), | ||
provides support for using DM in a Bluesky session. | ||
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<details> | ||
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The `dm_source_environ()` | ||
[function](https://bcda-aps.github.io/apstools/latest/api/_utils.html#apstools.utils.aps_data_management.dm_source_environ) | ||
is used internally to install the environment variables. It expects a global | ||
variable `DM_SETUP_FILE` to be defined in the module. | ||
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**Do not call `dm_source_environ()` directly.** | ||
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Use `dm_setup("/home/DM_INSTALL_DIR/etc/dm.setup.sh")`. | ||
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</details> | ||
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Use these Python commands to install DM's environment variables: | ||
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```py | ||
from apstools.utils import dm_setup | ||
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dm_setup("/home/DM_INSTALL_DIR/etc/dm.setup.sh") | ||
``` | ||
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**CAUTION**: `dm_setup()` must be run **before** any other DM tools are used. | ||
Do this each time a Bluesky session is started (where the DM API is to be used). | ||
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In typical Bluesky installations at APS, this file name is defined in the | ||
`iconfig.yml` file, such as for [XPCS at station | ||
8-ID-I](https://github.com/aps-8id-dys/bluesky/blob/6bbcfeceab7a6695d3be81ffd56954d362bf25ea/src/instrument/configs/iconfig.yml#L29): | ||
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```yaml | ||
# APS Data Management | ||
# Use bash shell, deactivate all conda environments, source this file: | ||
DM_SETUP_FILE: "/home/dm/etc/dm.setup.sh" | ||
``` | ||
### Example at APS XPCS station 8-ID-I | ||
Show how many DM workflow jobs are processing now: | ||
```py | ||
In [1]: from apstools.utils import dm_setup | ||
...: | ||
...: dm_setup("/home/dm/etc/dm.setup.sh") | ||
...: | ||
Out[1]: '8idi' | ||
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In [2]: from dm.proc_web_service.api.procApiFactory import ProcApiFactory | ||
...: api = ProcApiFactory.getWorkflowProcApi() | ||
...: jobs = api.listProcessingJobs() | ||
...: for j in jobs: | ||
...: if j["status"] not in ("done", "failed"): | ||
...: print(f"{j['id']=!r} {j.get('submissionTimestamp')=!r} {j['status']=!r}") | ||
Out[2]: # lots of jobs, only showing a few of them | ||
j['id']='6754e679-cedb-482b-bb4d-b58137f84001' j.get('submissionTimestamp')='2024/11/08 04:48:31 CST' j['status']='pending' | ||
j['id']='ad7328ae-35ba-4418-a9fd-b3dcc873348f' j.get('submissionTimestamp')='2024/11/08 04:48:34 CST' j['status']='pending' | ||
... | ||
j['id']='72b6d1b7-b6e0-4eb8-87d5-5f52792a043b' j.get('submissionTimestamp')='2024/11/08 08:31:22 CST' j['status']='running' | ||
j['id']='19252b7d-8961-4994-8977-86929811a988' j.get('submissionTimestamp')='2024/11/08 08:31:28 CST' j['status']='running' | ||
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``` | ||
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## Submit a DM workflow job from a Bluesky session | ||
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Here, we demonstrate one way to start a DM workflow from a Bluesky session. | ||
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To submit a workflow job from a Bluesky session, first call `dm_setup()` as described above. Then, | ||
get the "DM Processing API" as follows: | ||
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```py | ||
from apstools.utils import dm_api_proc | ||
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api = dm_api_proc() | ||
``` | ||
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Choose the workflow by name: | ||
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```py | ||
workflowOwner = api.username | ||
workflowName = "xpcs8-02-gladier-boost" | ||
``` | ||
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Define the workflow arguments in a Python dictionary (these arguments are | ||
specific to the XPCS workflow named above): | ||
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```py | ||
argsDict = { | ||
"filePath": "H001_005_test_Feb_7-01000.h5", | ||
"qmap": "eiger4M_qmap_d36_s360.h5", | ||
"experimentName": "zhang202402", | ||
# any other keyword arguments required by the workflow come next ... | ||
} | ||
``` | ||
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Start the processing job: | ||
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```py | ||
job = api.startProcessingJob(workflowOwner, workflowName, argsDict) | ||
``` | ||
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Show the processing job ID: | ||
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```py | ||
print(f"{job['id']=!r}") | ||
'c322e87c-ec43-4077-b074-eeef8522889c' | ||
``` |