Submit existing Decision Optimization instances to WML
Project description
dowml
A library and command line client to use Decision Optimization on WML
tldr;
$ pip install dowml
$ cat my_credentials.txt
{
'apikey': '<apikey>',
'url': 'https://us-south.ml.cloud.ibm.com',
'cos_resource_crn' = 'crn:v1:bluemix:public:cloud-object-storage:global:a/76260f9...',
'ml_instance_crn': 'crn:v1:bluemix:public:pm-20:eu-de:a/76260f...'
}
$ dowml -w my-credentials.txt
dowml> solve examples/afiro.mps
dowml> wait
dowml> log
dowml> exit
Introduction
The class DOWMLLib
provides an API to upload Decision Optimization models (CPLEX, CP Optimizer, OPL or docplex) to WML, check their status, and download results. The script dowml.py
is an interactive program on top of that library.
In order to use either of them, you need to provide IBM Cloud credentials.
- By default,
DOWMLLib
(and therefore the Interactive) look for these credentials in an environment variable namedDOWML_CREDENTIALS
. This variable shoud have a value looking like
See below for how/where to get these credentials.{ 'apikey': '<apikey>', 'url': 'https://us-south.ml.cloud.ibm.com', 'cos_resource_crn' = 'crn:v1:bluemix:public:cloud-object-storage:global:a/76260f9...', 'ml_instance_crn': 'crn:v1:bluemix:public:pm-20:eu-de:a/76260f...', }
- As an alternative, you can specify a file name as argument to
DOWMLLib.__init__
. The credentials will then be read from that file instead of the environment variable. Accordingly, the Interactive has a command line option-w
(or--wml-cred-file
) that must be followed by the path of the file.
Here's a sample session:
$ dowml -h
usage: interactive.py [-h] [--wml-cred-file WML_CRED_FILE] [--verbose]
[--commands [COMMANDS [COMMANDS ...]]] [--input] [--space SPACE]
Interactive program for DO on WML
optional arguments:
-h, --help show this help message and exit
--wml-cred-file WML_CRED_FILE, -w WML_CRED_FILE
Name of the file from which to read WML credentials. If not specified,
credentials are read from environment variable $DOWML_CREDENTIALS.
--verbose, -v Verbose mode. Causes the program to print debugging messages about its
progress. Multiple -v options increase the verbosity. The maximum is 4.
--commands [COMMANDS [COMMANDS ...]], -c [COMMANDS [COMMANDS ...]]
Carries out the specified commands. Each command is executed as if it had
been specified at the prompt. The program stops after last command, unless
--input is used.
--input, -i Prompts for new input commands even if some commands have been specified
as arguments using --commands.
--space SPACE, -s SPACE
Id of the space to connect to. Takes precedence over the one specified in
the credentials under the 'space_id' key, if any.
$
$
$ dowml -c help type size 'inline yes' 'solve examples/afiro.mps' jobs wait jobs log 'type docplex' 'solve examples/markshare.py examples/markshare1.mps.gz' wait jobs output 'shell ls -l *-*-*-*-*'
Decision Optimization in WML Interactive, version 0.9.0.
Submit and manage Decision Optimization models interactively.
(c) Copyright IBM Corp. 2021
Type ? for a list of commands.
Most commands need an argument that can be either a job id, or the number
of the job, as displayed by the 'jobs' command. If a command requires a
job id, but none is specified, the last one is used.
dowml> help
Documented commands (type help <topic>):
========================================
cancel details help jobs output size time version
delete exit inline log shell solve type wait
dowml> type
Current model type: cplex. Known types: cplex, cpo, opl, docplex
dowml> size
Current size: S. Known sizes: S, M, XL
dowml> inline yes
dowml> solve examples/afiro.mps
Job id: 60c885c9-72ae-4568-be32-1e7c702252c0
dowml> jobs
# status id creation date type ver. size inputs
=> 1: queued 60c885c9-72ae-4568-be32-1e7c702252c0 2021-08-11 15:07:03 cplex 20.1 S afiro.mps
dowml> wait
dowml> jobs
# status id creation date type ver. size inputs
=> 1: completed 60c885c9-72ae-4568-be32-1e7c702252c0 2021-08-11 15:07:03 cplex 20.1 S afiro.mps
dowml> log
[2021-08-11T13:07:33Z, INFO] CPLEX version 20010000
[2021-08-11T13:07:34Z, WARNING] Changed parameter CPX_PARAM_THREADS from 0 to 1
[2021-08-11T13:07:34Z, INFO] Param[1,067] = 1
[2021-08-11T13:07:34Z, INFO] Param[1,130] = UTF-8
[2021-08-11T13:07:34Z, INFO] Param[1,132] = -1
[2021-08-11T13:07:34Z, INFO]
[2021-08-11T13:07:34Z, INFO] Selected objective sense: MINIMIZE
[2021-08-11T13:07:34Z, INFO] Selected objective name: obj
[2021-08-11T13:07:34Z, INFO] Selected RHS name: rhs
[2021-08-11T13:07:34Z, INFO] Version identifier: 20.1.0.0 | 2020-11-10 | 9bedb6d68
[2021-08-11T13:07:34Z, INFO] CPXPARAM_Threads 1
[2021-08-11T13:07:34Z, INFO] CPXPARAM_Output_CloneLog -1
[2021-08-11T13:07:34Z, INFO] CPXPARAM_Read_APIEncoding "UTF-8"
[2021-08-11T13:07:34Z, INFO] Tried aggregator 1 time.
[2021-08-11T13:07:34Z, INFO] LP Presolve eliminated 9 rows and 10 columns.
[2021-08-11T13:07:34Z, INFO] Aggregator did 7 substitutions.
[2021-08-11T13:07:34Z, INFO] Reduced LP has 11 rows, 15 columns, and 37 nonzeros.
[2021-08-11T13:07:34Z, INFO] Presolve time = 0.00 sec. (0.03 ticks)
[2021-08-11T13:07:34Z, INFO]
[2021-08-11T13:07:34Z, INFO] Iteration log . . .
[2021-08-11T13:07:34Z, INFO] Iteration: 1 Scaled dual infeas = 1.200000
[2021-08-11T13:07:34Z, INFO] Iteration: 5 Dual objective = -464.753143
[2021-08-11T13:07:34Z, INFO] There are no bound infeasibilities.
[2021-08-11T13:07:34Z, INFO] There are no reduced-cost infeasibilities.
[2021-08-11T13:07:34Z, INFO] Max. unscaled (scaled) Ax-b resid. = 1.77636e-14 (1.77636e-14)
[2021-08-11T13:07:34Z, INFO] Max. unscaled (scaled) c-B'pi resid. = 5.55112e-17 (5.55112e-17)
[2021-08-11T13:07:34Z, INFO] Max. unscaled (scaled) |x| = 500 (500)
[2021-08-11T13:07:34Z, INFO] Max. unscaled (scaled) |slack| = 500 (500)
[2021-08-11T13:07:34Z, INFO] Max. unscaled (scaled) |pi| = 0.942857 (1.88571)
[2021-08-11T13:07:34Z, INFO] Max. unscaled (scaled) |red-cost| = 10 (10)
[2021-08-11T13:07:34Z, INFO] Condition number of scaled basis = 1.5e+01
[2021-08-11T13:07:34Z, INFO] optimal (1)
dowml> type docplex
dowml> solve examples/markshare.py examples/markshare1.mps.gz
Job id: e81b392d-38ed-4d2a-912b-ff0249caf9e7
dowml> wait
[2021-08-11T13:08:09Z, WARNING] Support for Python 3.7 is now enabled and used as the default.
[2021-08-11T13:08:10Z, INFO] Reading markshare1.mps.gz...
dowml> jobs
# status id creation date type ver. size inputs
1: completed 60c885c9-72ae-4568-be32-1e7c702252c0 2021-08-11 15:07:03 cplex 20.1 S afiro.mps
=> 2: completed e81b392d-38ed-4d2a-912b-ff0249caf9e7 2021-08-11 15:07:44 docplex 20.1 S markshare.py, markshare1.mps.gz
dowml> output
Storing e81b392d-38ed-4d2a-912b-ff0249caf9e7/solution.json
Storing e81b392d-38ed-4d2a-912b-ff0249caf9e7/kpis.csv
Storing e81b392d-38ed-4d2a-912b-ff0249caf9e7/stats.csv
Storing e81b392d-38ed-4d2a-912b-ff0249caf9e7/log.txt
Storing e81b392d-38ed-4d2a-912b-ff0249caf9e7/details.json
dowml> shell ls -l *-*-*-*-*
-rw-rw-r-- 1 nodet staff 5445 Aug 11 15:08 details.json
-rw-rw-r-- 1 nodet staff 39 Aug 11 15:08 kpis.csv
-rw-rw-r-- 1 nodet staff 7142 Aug 11 15:08 log.txt
-rw-rw-r-- 1 nodet staff 1770 Aug 11 15:08 solution.json
-rw-rw-r-- 1 nodet staff 342 Aug 11 15:08 stats.csv
WML credentials
The DOWML client requires some information in order to connect to the Watson Machine Learning service. Two pieces of information are required, and the others are optional.
Required items
-
The
apikey
is a secret that identifies the IBM Cloud user. One typically creates one key per application or service, in order to be able to revoke them individually if needed. To generate such a key, open https://cloud.ibm.com/iam/apikeys, and click the blue 'Create an IBM Cloud API key' on the right. -
The
url
is the base URL for the REST calls to WML. The possible values are found in https://cloud.ibm.com/apidocs/machine-learning, and depend on which region you want to use.
Optional items
Watson Studio and Watson Machine Learning use spaces to group together, and isolate from each other, the assets that belong to a single project. These assets include the data files submitted, the results of the jobs, and the deployments (software and hardware configurations) that run these jobs.
The DOWML client will connect to the space specified by the user using
either the --space
command-line argument or the space_id
item in the credentials.
If neither of these are specified, the client will look for a space named
DOWMLClient-space, and will try to create such a space if one doesn't exist.
To create a new space, the DOWML client will need both cos_resource_crn
and
ml_instance_crn
to have been specified in the credentials.
-
space_id
: identifier of an existing space to connect to. Navigate to the 'Spaces' tab of your Watson Studio site (e.g. https://eu-de.dataplatform.cloud.ibm.com/ml-runtime/spaces if you are using the instance in Germany), right-click on the name of an existing space to copy the link. The id of the space is the string of numbers, letters and dashes between the last/
and the?
. -
cos_resource_crn
: WML needs to store some data in a Cloud Object Storage instance. Open https://cloud.ibm.com/resources and locate the 'Storage' section. Create an instance of the Cloud Object Storage service if needed. Once it's listed on the resource page, click anywhere on the line for that service, except on its name. This will open a pane on the right which lists the CRN. Click on the symbol at the right to copy this information. This item is required only for the DOWML client to be able to create a space. If you specified aspace_id
, it is not required. -
ml_instance_crn
: similarly, you need to identify an instance of Machine Learning service to use to solve your jobs. In the same page https://cloud.ibm.com/resources, open the 'Services' section. The 'Product' columns tells you the type of service. If you don't have a 'Machine Learning' instance already, create one. Then click on the corresponding line anywhere except on the name, and copy the CRN displayed in the pane that open on the right. This item is required only for the DOWML client to be able to create a space. If you specified aspace_id
, it is not required.
Using data assets in Watson Studio
The DOWML library has two modes of operation with respect to sending the models
to the WML service: inline data, or using data assets in Watson Studio. By default,
data assets are used. This can be changed with the inline
command.
With inline data, the model is sent directly to the WML service in the solve request itself. This is the simplest, but it has a number of drawbacks:
-
Sending a large model may take a long time, because of network throughput. Sending a very large REST request is not at all guaranteed to succeed.
-
When solving several times the same model (e.g. to evaluate different parameters), the model has to be sent each time.
-
In order to display the names of the files that were sent, the jobs command needs to request this information, and it comes with the content of the files themselves. In other words, every jobs command requires downloading the content of all the files for all the jobs that exist in the space.
Using data assets in Watson Studio as an intermediate step alleviate all these issues:
-
Once the model has been uploaded to Watson Studio, it will be reused for subsequent jobs without the need to upload it again.
-
The job requests refer to the files indirectly, via URLs. Therefore, they don't take much space, and listing the jobs doesn't imply to download the content of the files.
-
Uploading to Watson Studio is done through specialized code that doesn't just send a single request. Rather, it divides the upload in multiple reasonably sized chunks that each are uploaded individually, with restart if necessary. Uploading big files is therefore much less prone to failure.
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