Package: tinybaker
TinyBaker: Composable, first-order, file-to-file transformations in PythonTinyBaker is in beta release.TinyBaker allows programmers to define first-order file-to-file transformations in a concise format and compose them together with clarity.Installation viapip install tinybakerThe ProblemMany programs can be considered transformations between source and destination files. Training machine learning models, running predictions on dataframes, processing logs, concatenation, compilation, and many others, are examples of tasks that fundamentally pose a transformation from one set of files to another.Since transforms aren't normally considered a first-order concept, they get wildly unwieldy to work with. Production workloads are configured separately from local transformations. Getting a local script working on production often requires lots of rework, mocking, testing, and iteration by a product team.The SolutionTinybaker turns file-to-file transforms into a first-order concept.TinyBaker transforms can be configured, run, composed, hosted, and tested, all independently from their specific implementations.The ModelThe main component of TinyBaker is the base classTransform: a standalone mapping from one set of files to another.___________
---[ file1 ]--->|           |
                |           |->--[ file4 ]---
---[ file2 ]--->| Transform |
                |           |->--[ file5 ]---
---[ file3 ]--->|___________|For example, let's say we were running predictions over a certain ML model. Such a transform might conceptually look like this:___________
---[ config ]--->|           |
                 |           |->--[ predictions ]---
---[ model ]---->|  Predict  |
                 |           |->--[ performance ]---
---[ data ]----->|___________|TinyBaker calls the labels associated which each input / output file atag.___________
---[ config ]--->|           |
      ^ Tag      |           |->--[ predictions ]---
---[ model ]---->|  Predict  |       ^ Tag
      ^ Tag      |           |->--[ performance ]---
---[ data ]----->|___________|       ^ Tag
      ^ TagWe might want to configure where we store input/output files, or configure files to come from separate filesystems entirely. TinyBaker allows you to define the transform while paying attention to only the tags, even when accessing files across multiple filesystems.___________
ftp://path/to/config >--[ config ]--->|           |
                                      |           |->--[ predictions ]---> ./output.pkl
/path/to/model.pkl >----[ model ]---->|  Predict  |
                                      |           |->--[ performance ]---> ./performance.pkl
/path/to/data.pkl >-----[ data ]----->|___________|We can imagine a situation where we have file transformations that could theoretically compose:________________
                  |                |
---[ raw_logs ]-->| BuildDataFrame |->--[ df ]---
                  |________________|

             ____________
            |            |
---[ df ]-->| BuildModel |->--[ model ]---
            |____________|TinyBaker allows you to compose these two transformations together:___________________________
                  |                           |
---[ raw_logs ]-->| BuildDataFrame+BuildModel |->--[ model ]---
                  |___________________________|We now only need to specify the location of 2 files-- TinyBaker handles linking the two steps together___________________________
                                |                           |
/raw/logs.txt ---[ raw_logs ]-->| BuildDataFrame+BuildModel |->--[ model ]--- /path/to/model.pkl
                                |___________________________|Extra file dependencies are propagated to the top level of a sequence, ensuring you'll never miss a file dependency in step 5 of 17, e.g.________________
                  |                |
---[ raw_logs ]-->| BuildDataFrame |->--[ df ]---
                  |________________|

                 ____________
---[ df ]------>|            |
                | BuildModel |->--[ model ]---
---[ config ]-->|____________|

# Goes to...

                   ___________________________
---[ raw_logs ]-->|                           |
                  | BuildDataFrame+BuildModel |->--[ model ]---
---[ config ]---->|___________________________|In-Code Anatomy of a single transformThe following describes a minimal transform one can define in TinyBakerfromtinybakerimportTransform,InputTag,OutputTagclassSampleTransform(Transform):# 1 tag per input filefirst_input=InputTag("first_input")second_input=InputTag("second_input")some_output=OutputTag("some_output")# self.script describes what actually executes when the transform task runsscript(self):# Within scripts, one can operate on tags as if they're FileRefswithself.first_input.open()asf:do_something_with(f)withself.second_input.open()asf:do_something_else_with(f)# and output or somethingwithself.some_output.open()asf:write_something_to(f)This would then be executed via:SampleTransform(input_paths={"first_input":"path/to/input1","second_input"="path/to/input2"}output_paths={"some_output":"path/to/write/output"}).run()Real-world example of a single transformFor a real-world example, consider training an ML model. This is a transformation from the two filessome/path/train.csvandsome/path/test.csvto a pickled ML modelanother/path/some_model.pkland statistics. Withtinybaker, you can specify this individual configurable step as follows:# train_step.pyfromtinybakerimportTransform,cli,InputTag,OutputTagimportpandasaspdfromsome_cool_ml_libraryimporttrain_model,test_modelclassTrainModelStep(Transform):train_csv=InputTag("train_csv")test_csv=InputTag("test_csv")pickled_model=OutputTag("pickled_model")results=OutputTag("results")defscript():# Read from fileswithself.train_csv.open()asf:train_data=pd.read_csv(f)withself.test_csv.open()asf:test_data=pd.read_csv(f)# Run computationsX=train_data.drop(["label"])Y=train_data[["label"]][model,train_results]=train_model(X,Y)test_results=test_model(model,test_data)# Write to output fileswithself.results.open()asf:results=train_results.formatted_summary()+test_results.formatted_summary()f.write(results)withself.pickled_model.openbin()asf:pickle.dump(f,model)if__name__=="__main__":cli(SampleTransform)Operating over multiple filesystemsSince TinyBaker usesfsspecas its filesystem, TinyBaker can useany filesystem that fsspec supports. For example, you can use s3 via setting the protocol of files tos3://This makes building test suites for transforms very easy: test suites can operate off of local data, but production jobs can run off of s3 data.ValidationTinyBaker performs simple validation, such as raising early if input files are missing, or erroring if fully-qualified file paths form a cycle.Combining several build stepsWe can compose several build steps together using the methodsmergeandsequence.fromtinybakerimportTransform,sequenceclassCleanLogs(Transform):raw_logfile=InputTag("raw_logfile")cleaned_logfile=OutputTag("cleaned_logfile")# ...classBuildDataframe(Transform):cleaned_logfile=InputTag("cleaned_logfile")dataframe=OutputTag("dataframe")# ...classBuildLabels(Transform):cleaned_logfile=InputTag("cleaned_logfile")labels=OutputTag("labels")# ...classTrainModelFromDataframe(Transform):dataframe=InputTag("dataframe")labels=InputTag("labels")trained_model=OutputTag("trained_model")# ...TrainFromRawLogs=sequence(CleanLogs,merge(BuildDataframe,BuildLabels),TrainModelFromDataframe)task=TrainFromRawLogs(input_paths={"raw_logfile":"/path/to/raw.log"},output_paths={"trained_model":"/path/to/model.pkl"})task.run()Inputs and outputs are hooked up via tag names, e.g. if step 1 outputs tag "foo", and step 2 takes tag "foo" as inputs, TinyBaker will be automatically hook them together.Propagation of inputs and outputsLet's say task 3 of 4 in a sequence of tasks requires tagfoo, but no previous step generates tagfoo, then this dependency will be propagated to the top level; the sequence as a whole will have a dependency on tagfoo.Additionally, if task 3 of 4 generates a tagbar, but no further step requiresbar, then the sequence exposes "bar" as an output.expose_intermediatesIf you need to expose intermediate files within a sequence, you can use the keywork argexpose_intermediatesto additionally output the listed intermediate tags, e.g.sequence([A,B,C],expose_intermediates={"some_intermediate","some_other_intermediate"})Renaming TagsRight now, since association of files from one step to the next is based on tags, we may end up in a situation where we want to rename tags. If we want to change the tag names, we can usemap_tagsto change them.fromtinybakerimportmap_tagsMappedStep=map_tags(SomeStep,input_mapping={"old_input_name":"new_input_name"},output_mapping={"old_output_name":"new_output_name"})CLITinyBaker can instantly turn a transform into a CLI:fromtinybakerimportTransform,cliclassMNISTPipeline(Transform):# as defined in tests/slow/test_real_world.py# [...]if__name__=="__main__":cli(MNISTPipeline)The above would yield the below when run:$ python ./mnist_pipeline_transform.py --help
usage: test_real_world.py [-h] --raw_train_images RAW_TRAIN_IMAGES --raw_test_images
                          RAW_TEST_IMAGES --accuracy ACCURACY --model MODEL
                          [--version] [--overwrite]

Execute a MNISTPipeline transform

optional arguments:
  -h, --help            show this help message and exit
  --raw_train_images RAW_TRAIN_IMAGES
                        Path for output tag raw_train_images
  --raw_test_images RAW_TEST_IMAGES
                        Path for output tag raw_test_images
  --accuracy ACCURACY   Path for output tag accuracy
  --model MODEL         Path for output tag model
  --version             show program's version number and exit
  --overwrite           Whether to overwrite any existing output filesNo need to write argument parsers -- TinyBaker knows what arguments the transform needs and
builds a CLI around it.FilesetsIf a step operates over a dynamic set of files (e.g. logs from n different days), you can use the filesets interface to specify that. Tags that begin with the prefixfileset::are interpreted to be filesets rather than just files.If a sequence includes a fileset as an intermediate, then TinyBaker expects the developer to specify the paths of the intermediate, viaexpose_intermediates. This is a relatively fundamental restriction of the platform, as TinyBaker expects that all paths are specified before script execution.ExampleA concat task can be done as follows:classConcat(Transform):files=InputTag("fileset::files")concatted=InputTag("concatted")defscript(self):content=""forrefinself.files:withref.open()asf:content=content+f.read()withself.concatted.open()asf:f.write(content)Concat(input_paths={"fileset::files":["./tests/__data__/foo.txt","./tests/__data__/bar.txt"],},output_paths={"concatted":"/tmp/concatted"},overwrite=True,).run()Experimental API: File-style Transform DefinitionsTransforms can be specified in a script-like format:# train_model.pyfromtinybakerimportInputTag,OutputTag,clitrain_csv=InputTag("train_csv")test_csv=InputTag("test_csv")results=OutputTag("results")pickled_model=OutputTag("pickled_model")defscript():# Read from fileswithtrain_csv.open()asf:train_data=pd.read_csv(f)withtest_csv.open()asf:test_data=pd.read_csv(f)# Run computationsX=train_data.drop(["label"])Y=train_data[["label"]][model,train_results]=train_model(X,Y)test_results=test_model(model,test_data)# Write to output fileswithresults.open()asf:results=train_results.formatted_summary()+test_results.formatted_summary()f.write(results)withpickled_model.openbin()asf:pickle.dump(f,model)if__name__=="__main__":# We can still define a cli under this format.cli(locals())These can be converted to transforms via:fromtinybakerimportTransformfrom.importtrain_modelTrainModelTransform=Transform.from_namespace(train_model)# This can be consumed just like any other job.job=TrainModelTransform(input_files={...},output_files={...})job.run()ContributingPlease contribute!