README.org 38.9 KB
Newer Older
Maude Le Jeune's avatar
Maude Le Jeune committed
1 2 3
#+TITLE: The Pipelet Readme
#+STYLE: <link rel="stylesheet" type="text/css" href="org.css" />

Maude Le Jeune's avatar
Maude Le Jeune committed
4 5
Pipelet is a free framework allowing for the creation, execution and
browsing of scientific data processing pipelines. It provides:
6

Marc Betoule's avatar
Marc Betoule committed
7 8 9 10 11
+ easy chaining of interdependent elementary tasks,
+ web access to data products,
+ branch handling,
+ automated distribution of computational tasks.

12 13 14
Both engine and web interface are written in Python. As Pipelet is all
about chaining processing written in Python or using Python as a glue
language, prior knowledge of this language is required.
Marc Betoule's avatar
Marc Betoule committed
15 16 17 18 19

* Tutorial
** Introduction
*** Why using pipelines

Maude Le Jeune's avatar
Maude Le Jeune committed
20
The pipeline mechanism allows you to apply a sequence of processing
Marc Betoule's avatar
Marc Betoule committed
21 22 23 24 25 26 27 28
steps to your data, in a way that the input of each process is the
output of the previous one. Making visible these different processing
steps, in the right order, is essential in data analysis to keep track
of what you did, and make sure that the whole processing remains
consistent.

*** How it works

Maude Le Jeune's avatar
Maude Le Jeune committed
29 30 31
Pipelet is based on the possibility to save on disk every intermediate
input or output of your pipeline, which is usually not a strong
constraint but offers a lot of benefits. It means that you can stop
32
the processing whenever you want, and start it again without
Marc Betoule's avatar
Marc Betoule committed
33 34 35 36 37
recomputing the whole thing: you just take the last products you have
on disk, and continue the processing where it stopped. This logic is
interesting when the computation cost is higher than the cost of disk
space required by intermediate products.

Maude Le Jeune's avatar
readme  
Maude Le Jeune committed
38 39 40 41 42 43 44 45
In addition, the Pipelet engine has been designed to
process *data* *sets*. It takes advantage of the parallelisation
opportunity that comes with data which share the same structure (data
arrays), to dispatch the computational tasks on parallel architecture.
The data dependency scheme is also used to save CPU time, and allows
to handle very big data sets processing.


Marc Betoule's avatar
Marc Betoule committed
46 47
*** The Pipelet functionalities

Maude Le Jeune's avatar
Maude Le Jeune committed
48 49
Pipelet is a free framework which helps you : 
+ to write and manipulate pipelines with any dependency scheme, 
Maude Le Jeune's avatar
readme  
Maude Le Jeune committed
50 51 52
+ to keep track of what processing has been applied to your data and perform comparisons,
+ to carry pipelines source code from development to production and
  adapt to different hardware and software architectures.
Marc Betoule's avatar
Marc Betoule committed
53

Maude Le Jeune's avatar
Maude Le Jeune committed
54

Marc Betoule's avatar
Marc Betoule committed
55
** Getting started
Maude Le Jeune's avatar
Maude Le Jeune committed
56 57 58 59 60 61
*** Pipelet installation 
**** Dependencies 

+ Running the Pipelet engine requires Python >= 2.6.

+ The web interface of Pipelet requires the installation of the
62
  cherrypy3 Python module (on Debian: aptitude install python-cherrypy3).
Maude Le Jeune's avatar
Maude Le Jeune committed
63

64
You may find useful to install some generic scientific tools that nicely interact with Pipelet: 
Maude Le Jeune's avatar
Maude Le Jeune committed
65 66 67
+ numpy
+ matplotlib
+ latex 
Marc Betoule's avatar
Marc Betoule committed
68

Maude Le Jeune's avatar
Maude Le Jeune committed
69
**** Getting Pipelet
Marc Betoule's avatar
Marc Betoule committed
70

Maude Le Jeune's avatar
readme  
Maude Le Jeune committed
71 72 73 74 75 76 77 78 79 80 81
***** Software status

The first version of the software is currently in the process of
stabilisation.  The Pipelet engine has now reached the level of
desired sophistication.  On the other hand, the user interface has
been developped in a minimalist way. It includes the main
functionalities but with a design which could and will be more user
friendly. 


***** Getting last pipelet version
Marc Betoule's avatar
Marc Betoule committed
82

Maude Le Jeune's avatar
Maude Le Jeune committed
83
=git clone git://gitorious.org/pipelet/pipelet.git -b v1.0=
Marc Betoule's avatar
Marc Betoule committed
84

Maude Le Jeune's avatar
Maude Le Jeune committed
85
**** Installing Pipelet
Marc Betoule's avatar
Marc Betoule committed
86

Marc Betoule's avatar
Marc Betoule committed
87
=sudo python setup.py install=
Marc Betoule's avatar
Marc Betoule committed
88

89
*** Running a simple test pipeline
Marc Betoule's avatar
Marc Betoule committed
90 91 92

1. Run the test pipeline

Maude Le Jeune's avatar
Maude Le Jeune committed
93
   =cd test/first_test=
Marc Betoule's avatar
Marc Betoule committed
94

Maude Le Jeune's avatar
Maude Le Jeune committed
95
   =python main.py=
Marc Betoule's avatar
Marc Betoule committed
96 97 98

2. Add this pipeline to the web interface

Maude Le Jeune's avatar
Maude Le Jeune committed
99
   =pipeweb track test ./.sqlstatus=
Marc Betoule's avatar
Marc Betoule committed
100

101
3. Set up an account in the access control list and launch the web server
Marc Betoule's avatar
Marc Betoule committed
102

Maude Le Jeune's avatar
Maude Le Jeune committed
103
   =pipeutils -a username -l 2 .sqlstatus=
Marc Betoule's avatar
Marc Betoule committed
104

Maude Le Jeune's avatar
Maude Le Jeune committed
105
   =pipeweb start=
Marc Betoule's avatar
Marc Betoule committed
106 107 108 109

4. You should be able to browse the result on the web page
   http://localhost:8080

110 111
*** Getting a new pipe framework

Maude Le Jeune's avatar
Maude Le Jeune committed
112
To get a new pipeline framework, with example main and segment scripts : 
113

Marc Betoule's avatar
Marc Betoule committed
114
=pipeutils -c pipename=
115

Maude Le Jeune's avatar
Maude Le Jeune committed
116 117 118
This command ends up with the creation of directory named pipename wich contains: 
+ a main script (named main.py) providing functionnalities to execute
  your pipeline in various modes (debug, parallel, batch mode, ...)
119
+ an example of segment script (=default.py=) which illustrates
Maude Le Jeune's avatar
Maude Le Jeune committed
120
  the pipelet utilities with comments. 
121

Maude Le Jeune's avatar
Maude Le Jeune committed
122
The next section describes those two files in more details. 
123

Marc Betoule's avatar
Marc Betoule committed
124

Maude Le Jeune's avatar
Maude Le Jeune committed
125
** Writing Pipes
Marc Betoule's avatar
Marc Betoule committed
126 127 128 129 130 131 132 133 134 135
*** Pipeline architecture

The definition of a data processing pipeline consists in :
+ a succession of python scripts, called segments, coding each step
  of the actual processing.
+ a main script that defines the dependency scheme between segments,
  and launch the processing.

The dependencies between segments must form a directed acyclic
graph. This graph is described by a char string using a subset of the
136
graphviz dot language (http://www.graphviz.org). For example the string:
Marc Betoule's avatar
Marc Betoule committed
137

Marc Betoule's avatar
Marc Betoule committed
138
#+begin_src python
Marc Betoule's avatar
Marc Betoule committed
139
"""
Marc Betoule's avatar
Marc Betoule committed
140 141 142
a -> b -> d;
c -> d;
c -> e;
Marc Betoule's avatar
Marc Betoule committed
143
"""
Marc Betoule's avatar
Marc Betoule committed
144 145 146 147
#+end_src

defines a pipeline with 5 segments ={"a", "b", "c", "d", "e"}=. The
relation =a->b= ensures that the processing of the segment "a" will be
148
done before the processing of its 'child' segment =b=. Also the output
Marc Betoule's avatar
Marc Betoule committed
149 150 151
of =a= will be fed as input for =b=. In the given example, the node
=d= has two parents =b= and =c=. Both will be executed before =d=. As
their is no relation between =b= and =c= which of the two will be
Marc Betoule's avatar
Marc Betoule committed
152 153
executed first is not defined.

Marc Betoule's avatar
Marc Betoule committed
154 155 156
When executing the segment =seg=, the engine looks for a python script
named =seg.py=. If not found, it looks iteratively for script files
named =se.py= and =s.py=. This way, different segments of the pipeline
Maude Le Jeune's avatar
Maude Le Jeune committed
157
can share the same code, if they are given a name with a common root
158
(this mechanism is useful to write generic segment and is completed by
Maude Le Jeune's avatar
Maude Le Jeune committed
159
the hooking system, described in the advanced usage section). The code
Marc Betoule's avatar
Marc Betoule committed
160 161
is then executed in a specific namespace (see below [[*The%20segment%20environment][The execution
environment]]).
Maude Le Jeune's avatar
Maude Le Jeune committed
162

Marc Betoule's avatar
Marc Betoule committed
163 164
*** The Pipeline object

165
Practically, the creation of a Pipeline object requires 3 arguments:
Marc Betoule's avatar
Marc Betoule committed
166

Marc Betoule's avatar
Marc Betoule committed
167
#+begin_src python
168
from pipelet.pipeline import Pipeline
Marc Betoule's avatar
Marc Betoule committed
169 170
P = Pipeline(pipedot, codedir="./", prefix="./")
#+end_src
Marc Betoule's avatar
Marc Betoule committed
171

Marc Betoule's avatar
Marc Betoule committed
172 173 174 175

- =pipedot= is the string description of the pipeline
- =codedir= is the path where the segment scripts can be found
- =prefix=  is the path to the data repository (see below [[*Hierarchical%20data%20storage][Hierarchical data storage]])
Marc Betoule's avatar
Marc Betoule committed
176

177
It is possible to output the graphviz representation of the pipeline
178 179
(requires the installation of graphviz). First, save the graph string
into a .dot file with the pipelet function:
Marc Betoule's avatar
Marc Betoule committed
180 181

#+begin_src python
Maude Le Jeune's avatar
Maude Le Jeune committed
182
P.to_dot('pipeline.dot')
Marc Betoule's avatar
Marc Betoule committed
183
#+end_src
Maude Le Jeune's avatar
Maude Le Jeune committed
184 185 186

Then, convert it to an image file with the dot command: 

Marc Betoule's avatar
Marc Betoule committed
187
=dot -Tpng -o pipeline.png pipeline.dot=
Maude Le Jeune's avatar
Maude Le Jeune committed
188

189
*** Dependencies between segments and data parallelism
Marc Betoule's avatar
Marc Betoule committed
190

Marc Betoule's avatar
Marc Betoule committed
191 192 193 194 195
The modification of the code of one segment will trigger its
recalculation and the recalculation of all the segments which
depend on it.

The output of a segment is a list of python objects. If a segment as
Maude Le Jeune's avatar
Maude Le Jeune committed
196 197 198 199 200 201 202 203 204 205
no particular output this list can be empty and do not need to be
specified. Elements of the list are allowed to be any kind of
pickleable python objects. However, a good practice is to fill the
list with the minimal set of characteristics relevant to describe the
output of the segment and to defer the storage of the data to
appropriate structures and file formats. For example, a segment which
performs computation on large images could virtually pass the results
of its computation to the following segment using the output list. It
is a better practice to store the resulting image in a dedicated file
and to pass in the list only the information allowing a non ambiguous
206
identification of this file (like its name or part of it).
Maude Le Jeune's avatar
Maude Le Jeune committed
207 208 209 210

The input of a child segment is taken in a set build from the output
lists of its parents. The content of the input set is actually tunable
using the multiplex directive (see below). However the simplest and
211
default behavior of the engine is to form the Cartesian product of
Maude Le Jeune's avatar
Maude Le Jeune committed
212 213
the output list of its parent.

214
To illustrate this behavior let us consider the following pipeline,
Maude Le Jeune's avatar
Maude Le Jeune committed
215 216
build from three segments:

Marc Betoule's avatar
Marc Betoule committed
217 218 219 220 221 222
#+begin_src python
"""
knights -> melt;
quality -> melt;
"""
#+end_src
Maude Le Jeune's avatar
Maude Le Jeune committed
223 224 225 226

and assume that the respective output lists of segments knights and
quality are:

Marc Betoule's avatar
Marc Betoule committed
227 228 229
#+begin_src python
["Lancelot", "Galahad"]
#+end_src
Maude Le Jeune's avatar
Maude Le Jeune committed
230
and:
Marc Betoule's avatar
Marc Betoule committed
231 232 233
#+begin_src python
['the Brave', 'the Pure']
#+end_src
Maude Le Jeune's avatar
Maude Le Jeune committed
234

235
The Cartesian product of the previous set is:
Marc Betoule's avatar
Marc Betoule committed
236
#+begin_src python
Maude Le Jeune's avatar
Maude Le Jeune committed
237 238
 [('Lancelot','the Brave'), ('Lancelot,'the Pure'), ('Galahad','the Brave'), ('Galahad','the
Pure')]
Marc Betoule's avatar
Marc Betoule committed
239
#+end_src
Maude Le Jeune's avatar
Maude Le Jeune committed
240

Marc Betoule's avatar
Marc Betoule committed
241
Four instances of segment =melt= will thus be run, each one receiving
Maude Le Jeune's avatar
Maude Le Jeune committed
242 243 244
as input one of the four 2-tuples.

At the end of the execution of all the instances of a segment, their
Marc Betoule's avatar
Marc Betoule committed
245
output lists are concatenated. If the action of segment =melt= is to
Maude Le Jeune's avatar
Maude Le Jeune committed
246
concatenate the two strings he receives separated by a space, the
Marc Betoule's avatar
Marc Betoule committed
247
final output set of segment =melt= will be: 
Maude Le Jeune's avatar
Maude Le Jeune committed
248

Marc Betoule's avatar
Marc Betoule committed
249 250 251
#+begin_src python
[('Lancelot the Brave'), ('Lancelot the Pure'), ('Galahad the Brave'), ('Galahad the Pure')].
#+end_src
Marc Betoule's avatar
Marc Betoule committed
252

Marc Betoule's avatar
Marc Betoule committed
253
This default behavior can be altered by specifying a =#multiplex=
Marc Betoule's avatar
Marc Betoule committed
254 255
directive in the commentary of the segment code. See section [[*Multiplex%20directive][Multiplex
directive]] for more details.
256

257 258 259 260
As the segment execution order is not uniquely determined by the pipe
scheme (several path may exists), it is not possible to retrieve an
ordered input tuple. To overcome this issue, segment inputs are
dictionaries, with keywords matching parent segment names.  In the
Marc Betoule's avatar
Marc Betoule committed
261
above example, one can read =melt= inputs using:
262

Marc Betoule's avatar
Marc Betoule committed
263
#+begin_src python
264 265
k = seg_input["knights"]
q = seg_input["quality"]
Marc Betoule's avatar
Marc Betoule committed
266
#+end_src
267

Marc Betoule's avatar
Marc Betoule committed
268
See section [[*The%20segment%20environment]['The segment environment']] for more details.
269

Marc Betoule's avatar
Marc Betoule committed
270
*** Orphan segments
Marc Betoule's avatar
Marc Betoule committed
271

272 273 274 275 276
By default, orphan segments (segments without parents) have no input
argument (an empty list), and therefore are executed once without
input. The possibility is offered to feed input to an orphan segment
by pushing a list into the output set of an hypothetic ('phantom')
parent. If P is an instance of the pipeline object, this is done by:
277

Marc Betoule's avatar
Marc Betoule committed
278
#+begin_src python
279
P.push (segname=[1,2,3])
Marc Betoule's avatar
Marc Betoule committed
280
#+end_src
281 282

From the segment environment, inputs can be retrieve from the
Marc Betoule's avatar
Marc Betoule committed
283
usual dictionary, using the keyword =segnamephantom=. 
284

Marc Betoule's avatar
Marc Betoule committed
285
#+begin_src python
286
id = seg_input['segnamephantom']
Marc Betoule's avatar
Marc Betoule committed
287
#+end_src
288
or
Marc Betoule's avatar
Marc Betoule committed
289
#+begin_src python
290
id = seg_input.values()[0]
Marc Betoule's avatar
Marc Betoule committed
291
#+end_src
292

Marc Betoule's avatar
Marc Betoule committed
293
See section [[*The%20segment%20environment][The segment environment]] for more details.
294

Marc Betoule's avatar
Marc Betoule committed
295 296
*** Hierarchical data storage

297 298 299 300 301 302
The framework provides versioning of your data and easy access through
the web interface. It also keep track of the code, of the execution
logs, and various meta-data of the processing. Of course, you remain
able to bypass the hierarchical storage and store your actual data
elsewhere, but you will loose the benefit of automated versioning
which proves to be quite convenient.
Marc Betoule's avatar
Marc Betoule committed
303 304

The storage is organized as follows:
305 306 307

- all pipeline instances are stored below a root which corresponds to
  the prefix parameter of the Pipeline object. 
Marc Betoule's avatar
Marc Betoule committed
308
      =/prefix/=
309
- all segment meta data are stored below a root which name corresponds
310
  to a unique hash computed on the segment code and its dependencies.
Marc Betoule's avatar
Marc Betoule committed
311
      =/prefix/segname_YFLJ65/=
312 313 314 315 316 317 318
- Segment's meta data are: 
  - a copy of the segment python script
  - a copy of all segment hook scripts
  - a parameter file (.args) which contains segment parameters value
  - a meta data file (.meta) which contains some extra meta data
- all segment instances data and meta data are stored in a specific subdirectory
  which name corresponds to a string representation of its input
Marc Betoule's avatar
Marc Betoule committed
319
  	=/prefix/segname_YFLJ65/data/1/=
320
- if there is a single segment instance, then data are stored in
Marc Betoule's avatar
Marc Betoule committed
321
       =/prefix/segname_YFLJ65/data/=
322 323
- If a segment has at least one parent, its root will be located below
  one of its parent's one : 
Marc Betoule's avatar
Marc Betoule committed
324
       =/prefix/segname_YFLJ65/segname2_PLMBH9/=
325
- etc...
Maude Le Jeune's avatar
Maude Le Jeune committed
326
  
327 328 329 330 331 332
While the hierarchical storage makes easy the storing and handling of
many different data with different versions, it can make the manual
navigation in the data less convenient. Here comes the role of the [[*Browsing%20Pipes][web
interface]] (among other advantages, like distant access to the data,
tagging...).

Marc Betoule's avatar
Marc Betoule committed
333 334 335 336 337
*** The segment environment

The segment code is executed in a specific environment that provides:

1. access to the segment input and output
Marc Betoule's avatar
Marc Betoule committed
338
   - =seg_input=:  this variable is a dictionary containing the input of the segment.
339

Marc Betoule's avatar
Marc Betoule committed
340
     In the general case, =seg_input= is a python dictionary which
341
     contains as many keywords as parent segments. In the case of orphan
Marc Betoule's avatar
Marc Betoule committed
342 343
     segment, the keyword used is suffixed by the =phantom= word. 
     One exception to this is coming from the use of the =group_by=
344
     directive, which alters the origin of the inputs. In this case,
345
     =seg_input= contains the resulting class elements. 
346

Marc Betoule's avatar
Marc Betoule committed
347
   - =seg_output=: this variable has to be a list. 
Marc Betoule's avatar
Marc Betoule committed
348

349
2. Functionalities to use the automated hierarchical data storage system.
Marc Betoule's avatar
Marc Betoule committed
350 351
   - =get_data_fn(basename)=: complete the filename with the path to the working directory. 
   - =glob_seg(seg, regexp)=: return the list of filename in segment seg
352
     working directory matching regexp.
Marc Betoule's avatar
Marc Betoule committed
353
   - =get_tmp_fn()=: return a temporary filename.
Marc Betoule's avatar
Marc Betoule committed
354

355
3. Functionalities to use the automated parameters handling
Marc Betoule's avatar
Marc Betoule committed
356 357 358
   - =lst_par=: list of parameter names of the segment to save in the meta data.
   - =lst_tag=: list of parameter names which will be made visible from the web interface
   - =load_param(seg, globals(), lst_par)=: retrieve parameters from the meta data.
359 360

4. Various convenient functionalities
361
   - =save_products(filename, globals(), lst_par)=: use pickle to save a
362
     part of the current namespace.
363
   - =load_products(filename, globals(), lst_par)=: update the namespace by
Marc Betoule's avatar
Marc Betoule committed
364
     unpickling requested object from the file.
Marc Betoule's avatar
Marc Betoule committed
365 366 367
   - =logged_subprocess(lst_args)=: execute a subprocess and log its
     output in =processname.log= and =processname.err=.
   - =logger= is a standard =logging.Logger= object that can be used to
Betoule Marc's avatar
Betoule Marc committed
368
     log the processing
Marc Betoule's avatar
Marc Betoule committed
369

370
5. Hooking support 
Marc Betoule's avatar
Marc Betoule committed
371 372
   Pipelet enables you to write reusable generic
   segments by providing a hooking system via the hook function.
Marc Betoule's avatar
Marc Betoule committed
373 374 375
   =hook(hookname, globals())=: execute Python script =segname_hookname.py= and update the namespace.
   See the section [[*The%20hooking%20system][Hooking system]] for more details.

Marc Betoule's avatar
Marc Betoule committed
376

377
*** Writing a first pipeline
Marc Betoule's avatar
Marc Betoule committed
378

379 380 381
We are now in the position to write a complete simple pipeline. Let us
consider the knights example and write the beginning of the main file
=main.py= describing the pipeline:
Maude Le Jeune's avatar
Maude Le Jeune committed
382

Marc Betoule's avatar
Marc Betoule committed
383
#+begin_src python
384 385 386 387 388 389 390 391
  from pipelet.pipeline import Pipeline
  
  pipedot = """
  knights -> melt;
  quality -> melt;
  """
  
  P = Pipeline(pipedot, code_dir='./',prefix='./')  
Marc Betoule's avatar
Marc Betoule committed
392
#+end_src
Maude Le Jeune's avatar
Maude Le Jeune committed
393

394 395 396
Now, we create the 3 segment files =knights.py=, =quality.py= and
=melt.py=. The only action we expect from segment knights is simply to
provide a list of knights. Its code is very simple:
Marc Betoule's avatar
Marc Betoule committed
397
#+begin_src python
398
  seg_output =  ["Lancelot", "Galahad"]
Marc Betoule's avatar
Marc Betoule committed
399
#+end_src
Maude Le Jeune's avatar
Maude Le Jeune committed
400

401
Same thing for the segment quality:
Marc Betoule's avatar
Marc Betoule committed
402
#+begin_src python
403
  seg_output = ['the Brave', 'the Pure']  
Marc Betoule's avatar
Marc Betoule committed
404
#+end_src
Maude Le Jeune's avatar
Maude Le Jeune committed
405

406 407
As explained, the segment melt will be executed four times. We expect
from it to concatenate its input and write the result into a file, so the code is:
Marc Betoule's avatar
Marc Betoule committed
408
#+begin_src python
409 410 411 412
  knight, quality = seg_input['knights'], seg_input['quality']
  f = open(get_data_fn('result.txt'), 'w')
  f.write(knight + ' ' + quality+'\n')
  f.close()  
Marc Betoule's avatar
Marc Betoule committed
413
#+end_src
Maude Le Jeune's avatar
Maude Le Jeune committed
414

415 416
We need to complete the main file so that it takes care of the
execution ([[*Running%20Pipes][see Running Pipes for more explainations]]):
Marc Betoule's avatar
Marc Betoule committed
417
#+begin_src python
418 419 420 421 422 423 424 425 426 427
  from pipelet.pipeline import Pipeline
  from pipelet.launchers import launch_interactive
  pipedot = """
  knights -> melt;
  quality -> melt;
  """
  
  P = Pipeline(pipedot, code_dir='./',prefix='./')
  w,t = launch_interactive(P)
  w.run()
Marc Betoule's avatar
Marc Betoule committed
428
#+end_src
Maude Le Jeune's avatar
Maude Le Jeune committed
429

430 431 432 433 434 435 436 437
The execution of the main file will run this simple example in the
'interactive' mode provided for debugging purposes. You may add a
knight in the list to see only the required part recomputed. More
complete examples are described in the [[*The%20example%20pipelines][example pipelines]] section. The
two remaining sections of the tutorial explain how to use execution
mode that enable to exploitation of data parallelism (in this case
running the four independent instances of the melt segment in
parallel), and how to provide web access to the results.
Maude Le Jeune's avatar
Maude Le Jeune committed
438

Marc Betoule's avatar
Marc Betoule committed
439
** Running Pipes
440
   
441 442
*** The sample main file

443
A sample main file is made available when creating a new Pipelet
444 445
framework. It is copied from the reference file: 

Marc Betoule's avatar
Marc Betoule committed
446
=pipelet/pipelet/static/main.py=
447 448 449 450 451 452 453

This script illustrates various ways of running pipes. It describes
the different parameters, and also, how to write a
main python script can be used as any binary from the command line
(including options parsing). 

*** Common options
454
    Some options are common to each running modes.
455 456 457 458
**** log level

The logging system is handle by the python logging facility module. 
This module defines the following log levels : 
Marc Betoule's avatar
Marc Betoule committed
459 460 461 462 463
+ =DEBUG=
+ =INFO=
+ =WARNING=
+ =ERROR=
+ =CRITICAL=
464

465
All logging messages are saved in the different Pipelet log files,
466 467 468 469 470
available from the web interface (rotating file logging).  It is also
possible to print those messages on the standard output (stream
logging), by setting the desired log level in the launchers options:
For example: 

Marc Betoule's avatar
Marc Betoule committed
471
#+begin_src python
472 473
import logging
launch_process(P, N,log_level=logging.DEBUG)
Marc Betoule's avatar
Marc Betoule committed
474
#+end_src
475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494

If set to 0, stream logging will be disable. 

**** matplotlib

The matplotlib documentation says: 

"Many users report initial problems trying to use maptlotlib in web
application servers, because by default matplotlib ships configured to
work with a graphical user interface which may require an X11
connection. Since many barebones application servers do not have X11
enabled, you may get errors if you don’t configure matplotlib for use
in these environments. Most importantly, you need to decide what kinds
of images you want to generate (PNG, PDF, SVG) and configure the
appropriate default backend. For 99% of users, this will be the Agg
backend, which uses the C++ antigrain rendering engine to make nice
PNGs. The Agg backend is also configured to recognize requests to
generate other output formats (PDF, PS, EPS, SVG). The easiest way to
configure matplotlib to use Agg is to call:

Marc Betoule's avatar
Marc Betoule committed
495
=matplotlib.use('Agg')=
496

497 498 499 500
The =matplotlib= and =matplotlib_interactive= options turn the
matplotlib backend to Agg in order to allow the execution in
non-interactive environment. The two options affects independently the
non interactive execution mode and the interactive mode.
501

Marc Betoule's avatar
Marc Betoule committed
502
Those two options are set to =True= by default in the sample main
503 504 505
script. Setting them to False deactivate this behavior for pipelines
that make no use of matplotlib (and prevents the raise of an exception
if matplotlib is not even available).
506

Marc Betoule's avatar
Marc Betoule committed
507
*** The interactive mode
Marc Betoule's avatar
Marc Betoule committed
508
This mode has been designed to ease debugging. If =P= is an instance of
509
the pipeline object, the syntax reads :
Marc Betoule's avatar
Marc Betoule committed
510

Marc Betoule's avatar
Marc Betoule committed
511
#+begin_src python
Marc Betoule's avatar
Marc Betoule committed
512 513 514
from pipelet.launchers import launch_interactive
w, t = launch_interactive(P)
w.run()
Marc Betoule's avatar
Marc Betoule committed
515
#+end_src
Marc Betoule's avatar
Marc Betoule committed
516 517 518 519

In this mode, each tasks will be computed in a sequential way. 
Do not hesitate to invoque the Python debugger from IPython : %pdb

Maude Le Jeune's avatar
Maude Le Jeune committed
520
To use the interactive mode, run: 
Marc Betoule's avatar
Marc Betoule committed
521
=main.py -d=
522

Marc Betoule's avatar
Marc Betoule committed
523
*** The process mode
524 525 526 527
In this mode, one can run simultaneous tasks (if the pipe scheme
allows to do so). 
The number of subprocess is set by the N parameter : 

Marc Betoule's avatar
Marc Betoule committed
528
#+begin_src python
529 530
from pipelet.launchers import launch_process
launch_process(P, N)
Marc Betoule's avatar
Marc Betoule committed
531
#+end_src
532

Maude Le Jeune's avatar
Maude Le Jeune committed
533
To use the process mode, run: 
Marc Betoule's avatar
Marc Betoule committed
534
=main.py=
Maude Le Jeune's avatar
Maude Le Jeune committed
535
or
Marc Betoule's avatar
Marc Betoule committed
536
=main.py -p 4=
Maude Le Jeune's avatar
Maude Le Jeune committed
537

Marc Betoule's avatar
Marc Betoule committed
538
*** The batch mode
539 540 541
In this mode, one can submit some batch jobs to execute the tasks. 
The number of job is set by the N parameter : 

Marc Betoule's avatar
Marc Betoule committed
542
#+begin_src python
543 544
from pipelet.launchers import launch_pbs
launch_pbs(P, N , address=(os.environ['HOST'],50000))
Marc Betoule's avatar
Marc Betoule committed
545
#+end_src
Marc Betoule's avatar
Marc Betoule committed
546

547 548 549 550 551 552 553 554 555 556 557
It is possible to specify some job submission options like: 
+ job name 
+ job header: this string is prepend to the PBS job scripts. You may
  want to add some environment specific paths. Log and error files are
  automatically handled by the pipelet engine, and made available from
  the web interface. 
+ cpu time: syntax is: "hh:mm:ss"

The 'server' option can be disable to add some workers to an existing
scheduler.

Maude Le Jeune's avatar
Maude Le Jeune committed
558
To use the batch mode, run: 
Marc Betoule's avatar
Marc Betoule committed
559
=main.py -b=
Maude Le Jeune's avatar
Maude Le Jeune committed
560 561 562

to start the server, and: 

Marc Betoule's avatar
Marc Betoule committed
563
=main.py -a 4=
564

Maude Le Jeune's avatar
Maude Le Jeune committed
565
to add 4 workers. 
Maude Le Jeune's avatar
Maude Le Jeune committed
566

567

Marc Betoule's avatar
Marc Betoule committed
568
** Browsing Pipes
569 570 571 572 573
*** The pipelet webserver and ACL

The pipelet webserver allows the browsing of multiple pipelines. 
Each pipeline has to be register using : 

Marc Betoule's avatar
Marc Betoule committed
574
=pipeweb track <shortname> sqlfile=
575

576 577
To remove a pipeline from the tracked list, use : 

Marc Betoule's avatar
Marc Betoule committed
578
=pipeweb untrack <shortname>=
579

580 581 582 583
As the pipeline browsing implies a disk parsing, some basic security
has to be set also. All users have to be register with a specific access
level (1 for read-only access, and 2 for write access).  

Marc Betoule's avatar
Marc Betoule committed
584
=pipeutils -a <username> -l 2 sqlfile=
Marc Betoule's avatar
Marc Betoule committed
585

586 587
To remove a user from the user list: 

Marc Betoule's avatar
Marc Betoule committed
588
=pipeutils -d <username> sqlfile=
589

590 591
Start the web server using : 

Marc Betoule's avatar
Marc Betoule committed
592
=pipeweb start=
593 594

Then the web application will be available on the web page http://localhost:8080
Marc Betoule's avatar
Marc Betoule committed
595

596 597
To stop the web server : 

Marc Betoule's avatar
Marc Betoule committed
598
=pipeweb stop=
599

Marc Betoule's avatar
Marc Betoule committed
600
*** The web application
601

602
In order to ease the comparison of different processing, the web
603 604 605 606
interface displays various views of the pipeline data : 

**** The index page 

607
The index page displays a tree view of all pipeline instances. Each
608 609 610 611 612 613
segment may be expand or reduce via the +/- buttons.  

The parameters used in each segments are resumed and displayed with
the date of execution and the number of related tasks order by
status. 

614
A check-box allows to performed operation on multiple segments :
615 616 617
  - deletion : to clean unwanted data
  - tag : to tag remarkable data

618
The filter panel allows to display the segment instances with respect to 2
619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644
criterions :
  - tag
  - date of execution

**** The code page

Each segment names is a link to its code page. From this page the user
can view all python scripts code which have been applied to the data.

The tree view is reduced to the current segment and its related
parents.

The root path corresponding to the data storage is also displayed.


**** The product page

The number of related tasks, order by status, is a link to the product
pages, where the data can be directly displayed (if images, or text
files) or downloaded. 
From this page it is also possible to delete a specific product and
its dependencies. 


**** The log page

645
The log page can be acceded via the log button of the filter panel.
646 647 648 649
Logs are ordered by date. 



650 651
** The example pipelines
*** fft
Marc Betoule's avatar
Marc Betoule committed
652

653 654 655 656 657
**** Highlights

This example illustrates a very simple image processing use.
The problematic is the following : one wants to apply a Gaussian
filter in Fourier domain on several 2D images. 
658

659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821
The pipe scheme is: 

#+begin_src python
pipedot = """
mkgauss->convol;
fftimg->convol;
"""
#+end_src

where segment 'mkgauss' computes the Gaussian filter, 'fftimg' computes the
Fourier transforms of the input images, and 'convol' performs the
filtering in Fourier domain, and the inverse transform of the filtered
images. 

#+begin_src python
P = pipeline.Pipeline(pipedot, code_dir=op.abspath('./'), prefix=op.abspath('./'))
P.to_dot('pipeline.dot')
#+end_src

The pipe scheme is output as a .dot file, that can be converted to an
image file with the command line: 

=dot -Tpng -o pipeline.png pipeline.dot=

To apply this filter to several images (in our case 4 input images),
the pipe data parallelism is used. 
From the main script, a 4-element list is pushed to the =fftimg=
segment. 

#+begin_src python
P.push(fftimg=[1,2,3,4]) 
#+end_src

At execution, 4 instances of the =fftimg= segment will be
created, and each of them outputs one element of this list : 

#+begin_src python
img = seg_input.values()[0] #(fftimg.py - line 16)
seg_output = [img]          #(fftimg.py - line 41)
#+end_src

On the other side, a single instance of the =mkgauss= segment will be
executed, as there is one filter to apply. 

The last segment =convol=, which depends on the two others, will be
executed with a number of instances that is the Cartesian product of
its 4+1 inputs (ie 4 instances)

The instance identifier which is set by the =fftimg= output, can be
retrieve with the following instruction: 

#+begin_src python
img = seg_input['fftimg']   #(convol.py - line 12)
#+end_src

**** Running the pipe

Follow the same procedure than for the first example pipeline, to run
this pipe and browse the result. 


*** cmb
**** Running the pipe

This CMB pipeline depends on two external python modules: 
+ healpy   :  http://code.google.com/p/healpy/
+ spherelib:  http://gitorious.org/spherelib


**** Problematic

This example illustrates a very simple CMB data processing use.  

The problematic is the following : one wants to characterize the
inverse noise weighting spectral estimator (as applied to the WMAP 1yr
data). A first demo pipeline is built to check that the algorithm
has correctly been implemented. Then, Monte Carlo simulations are used
to compute error bars estimates. 

**** A design pipeline

The design pipe scheme is: 

#+begin_src python
pipe_dot = """ 
cmb->clcmb->clplot;
noise->clcmb;
noise->clnoise->clplot;
"""
#+end_src

where: 
+ =cmb=: generate a CMB map from LCDM power spectrum. 
+ =noise=: compute the mode coupling matrix from the input hit-count map
+ =clnoise=: compute the empirical noise power spectrum from a noise
  realization. 
+ =clcmb=: generate two noise realizations, add them to the CMB map, to compute a
  first cross spectrum estimator. Then weighting mask and mode
  coupling matrix are applied to get the inverse noise weighting
  estimator
+ =clplot=: make a plot to compare pure cross spectrum vs inverse noise
  weighting estimators. 

As the two first orphan segments depends on a single shared parameter
which is the map resolution nside, this argument is pushed from the
main script. 

Another input argument of the cmb segment, is its simulation identifier,
which will be used for latter Monte Carlo. In order to push two
inputs to a single segment instance, we use python tuple data type.

#+begin_src python
P.push(cmb=[(nside, 1)])
P.push(noise=[nside])
#+end_src

From the segment, those inputs are retrieved with : 

#+begin_src python
nside  = seg_input.values()[0][0] ##(cmb.py line 13)
sim_id = seg_input.values()[0][1] ##(cmb.py line 14)
nside  = seg_input.values()[0]  ##(noise.py line 16)
#+end_src

The last segment produces a plot in which we compare: 
+ the input LCDM power spectrum
+ the binned cross spectrum of the noisy CMB maps
+ the binned cross spectrum of which we applied hitcount weight and
  mode coupling matrix. 
+ the noise power spectrum computed by clnoise segment. 

In this plot we check that both estimators are corrects, and that the
noise level is the expected one.

**** From the design pipeline to Monte Carlo

As a second step, Monte Carlo simulations are used to compute error
bars. 

The =clnoise= segment is no longer needed, so that the new pipe scheme
reads : 

#+begin_src python
pipe_dot = """ 
cmb->clcmb->clplot;
noise->clcmb;
"""
#+end_src

We now use the native data parallelization scheme of the pipe to build
many instances of the =cmb= and =clcmb= segments. 

#+begin_src python
cmbin = []
for sim_id in [1,2,3,4,5,6]:
    cmbin.append((nside, sim_id))
P.push(cmb=cmbin)
#+end_src


* Advanced usage
This section describe more complicated (and useful) features and
requires good familiarity with the concept introduced in the previous section.
822
** Multiplex directive
Marc Betoule's avatar
Marc Betoule committed
823
   
824

Marc Betoule's avatar
Marc Betoule committed
825
The default behavior can be altered by specifying a =#multiplex=
826 827 828 829 830
directive in the commentary of the segment code. If several multiplex
directives are present in the segment code the last one is retained.

The multiplex directive can be one of: 

Marc Betoule's avatar
Marc Betoule committed
831
+ =#multiplex cross_prod= : default behavior, return the Cartesian
832
  product. 
Marc Betoule's avatar
Marc Betoule committed
833
+ =#multiplex union= : make the union of the inputs
834

Marc Betoule's avatar
Marc Betoule committed
835
Moreover the =#multiplex cross_prod= directive admits filtering and
836 837
grouping by class similarly to SQL requests:

Marc Betoule's avatar
Marc Betoule committed
838
#+begin_src python
839
#multiplex cross_prod where "condition" group_by "class_function"
Marc Betoule's avatar
Marc Betoule committed
840
#+end_src
841

Marc Betoule's avatar
Marc Betoule committed
842
=condition= and =class_function= are python code evaluated for each element
843 844
of the product set. 

Marc Betoule's avatar
Marc Betoule committed
845 846
The argument of =where= is a condition. The element will be part of the
input set only if it evaluates to =True=.
847

Marc Betoule's avatar
Marc Betoule committed
848
The =group_by= directive groups elements into class according to the
849 850 851 852 853 854 855 856 857
result of the evaluation of the given class function. The input set
contains all the resulting class. For example, if the function is a
constant, the input set will contain only one element: the class
containing all elements.

During the evaluation, the values of the tuple elements are accessible
as variable wearing the name of the corresponding parents.


Marc Betoule's avatar
Marc Betoule committed
858 859 860 861 862 863 864 865
Given the Cartesian product set:
#+begin_src python
 [('Lancelot','the Brave'), ('Lancelot,'the Pure'), ('Galahad','the Brave'), ('Galahad','the
Pure')]
#+end_src

one can use :
#+begin_src python
866
#multiplex cross_prod where "quality=='the Brave'" 
Marc Betoule's avatar
Marc Betoule committed
867 868 869 870 871
#+end_src
to get 2 instances of the following segment (=melt=) running on: 
#+begin_src python
('Lancelot','the Brave'), ('Galahad','the Brave')
#+end_src
872

Marc Betoule's avatar
Marc Betoule committed
873
#+begin_src python
874
#multiplex cross_prod group_by "knights"
Marc Betoule's avatar
Marc Betoule committed
875 876 877 878 879
#+end_src
to get 2 instances of the =melt= segment running on:
#+begin_src python
('Lancelot'), ('Galahad')
#+end_src
880

Marc Betoule's avatar
Marc Betoule committed
881
#+begin_src python
882
#multiplex cross_prod group_by "0"
Marc Betoule's avatar
Marc Betoule committed
883 884
#+end_src
to get 1 instance of the =melt= segment running on: (0)
885

Marc Betoule's avatar
Marc Betoule committed
886
Note that to make use of =group_by=, elements of the output set have to be
887 888 889 890
hashable.

Another caution on the use of group: segment input data type is no
longer a dictionary in those cases as the original tuple is
Marc Betoule's avatar
Marc Betoule committed
891
lost and replaced by the result of the class function.
892

Marc Betoule's avatar
Marc Betoule committed
893
See section [[*The%20segment%20environment][The segment environment]] for more details.
894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914

** Depend directive

As explained in the introduction section, Pipelet offers the
possibility to spare CPU time by saving intermediate products on disk.
We call intermediate products the input/output data files of the
different segments.  

Each segment repository is identified by a unique key which depends
on: 
- the segment processing code and parameters (segment and hook
  scripts)
- the input data (identified from the key of the parent segments)

Every change made on a segment (new parameter or new parent) will then
give a different key, and tell the Pipelet engine to compute a new
segment instance.

It is possible to add some external dependencies to the key
computation using the depend directive: 

Marc Betoule's avatar
Marc Betoule committed
915
#+begin_src python
916
#depend file1 file2
Marc Betoule's avatar
Marc Betoule committed
917
#+end_src
918 919 920 921 922 923 924 925 926

At the very beginning of the pipeline execution, all dependencies will
be stored, to prevent any change (code edition) between the key
computation and actual processing.

Note that this mechanism works only for segment and hook
scripts. External dependencies are also read as the beginning of the
pipeline execution, but only used for the key computation.

Marc Betoule's avatar
Marc Betoule committed
927 928
** Database reconstruction

929 930 931
In case of unfortunate lost of the pipeline sql data base, it is
possible to reconstruct it from the disk : 

Marc Betoule's avatar
Marc Betoule committed
932
#+begin_src python
933 934
import pipelet
pipelet.utils.rebuild_db_from_disk (prefix, sqlfile)
Marc Betoule's avatar
Marc Betoule committed
935
#+end_src
Marc Betoule's avatar
Marc Betoule committed
936

937 938 939
All information will be retrieve, but with new identifiers. 

** The hooking system
940

941
As described in the 'segment environment' section, Pipelet supports
942 943 944 945 946 947
an hooking system which allows the use of generic processing code, and
code sectioning.

Let's consider a set of instructions that have to be systematically
applied at the end of a segment (post processing), one can put those
instruction in the separate script file named for example
Marc Betoule's avatar
Marc Betoule committed
948
=segname_postproc.py= and calls the hook function: 
949

Marc Betoule's avatar
Marc Betoule committed
950
#+begin_src python
951
hook('postproc', globals()) 
Marc Betoule's avatar
Marc Betoule committed
952
#+end_src
953

954
A specific dictionary can be passed to the hook script to avoid
955 956
confusion. 

Marc Betoule's avatar
Marc Betoule committed
957
The hook scripts are included into the hash key computation.
958

Marc Betoule's avatar
Marc Betoule committed
959 960
** Writing custom environments

961
The Pipelet software provides a set of default utilities available
Maude Le Jeune's avatar
Maude Le Jeune committed
962 963 964 965 966 967
from the segment environment. It is possible to extend this default
environment or even re-write a completely new one.

*** Extending the default environment

The different environment utilities are actually methods of the class
968
Environment. It is possible to add new functionalities by using the
Maude Le Jeune's avatar
Maude Le Jeune committed
969 970
python heritage mechanism: 

Marc Betoule's avatar
Marc Betoule committed
971
File : =myenvironment.py=
Maude Le Jeune's avatar
Maude Le Jeune committed
972

Marc Betoule's avatar
Marc Betoule committed
973 974 975 976 977 978 979 980 981
#+begin_src python
  from pipelet.environment import *
  
  class MyEnvironment(Environment):
        def my_function (self):
           """ My function do nothing
           """
           return 
#+end_src
Maude Le Jeune's avatar
Maude Le Jeune committed
982

983
The Pipelet engine objects (segments, tasks, pipeline) are available
Marc Betoule's avatar
Marc Betoule committed
984
from the worker attribut =self._worker=. See section [[*The%20Pipelet%20actors][The Pipelet actors]]
Marc Betoule's avatar
Marc Betoule committed
985
for more details about the Pipelet machinery.
Maude Le Jeune's avatar
Maude Le Jeune committed
986 987 988 989 990 991 992


*** Writing new environment

In order to start with a completely new environment, extend the base
environment: 

Marc Betoule's avatar
Marc Betoule committed
993 994
File : =myenvironment.py=
#+begin_src python
Maude Le Jeune's avatar
Maude Le Jeune committed
995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006
   from pipelet.environment import *

   class MyEnvironment(EnvironmentBase):
         def my_get_data_fn (self, x):
            """ New name for get_data_fn
	    """
	    return self._get_data_fn(x)

         def _close(self, glo):
            """ Post processing code
            """	 
	    return glo['seg_output']
Marc Betoule's avatar
Marc Betoule committed
1007
#+end_src
Maude Le Jeune's avatar
Maude Le Jeune committed
1008

1009
From the base environment, the basic functionalities for getting file
Maude Le Jeune's avatar
Maude Le Jeune committed
1010
names and executing hook scripts are still available through: 
Marc Betoule's avatar
Marc Betoule committed
1011 1012
- =self._get_data_fn=
- =self._hook=
Maude Le Jeune's avatar
Maude Le Jeune committed
1013

Marc Betoule's avatar
Marc Betoule committed
1014 1015
The segment input argument is also stored in =self._seg_input=
The segment output argument has to be returned by the =_close(self, glo)=
Maude Le Jeune's avatar
Maude Le Jeune committed
1016 1017 1018
method. 

The pipelet engine objects (segments, tasks, pipeline) are available