Installation with pip, e.g. pip install tinybaker
tinybaker allows programmers to define file-to-file transformations in a simple, concise format, and compose them together with clarity.
Let's say we wanted to define a transformation from one set of files to another. Tinybaker allows a developer to specify a set of input file "tags" that are specified independently from the transformation declaration.
from tinybaker import Transform
class SampleTransform(Transform):
# 1 tag per input file for this transformation
input_tags = {"first_input", "second_input"}
output_tags = {"some_output"}
# self.script describes what actually executes when the transform task runs
script(self):
# Transforms provide self.input_files and self.output_files, dictionaries with
# fully-qualified references to files that can be directly opened:
with self.input_files["first_input"].open() as f:
do_something_with(f)
with self.input_files["second_input"].open() as f:
do_something_else_with(f)
# and output or something
with self.input_files["some_output"].open() as f:
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()For a real-world example, consider training an ML model. This is a transformation from the two files some/path/train.csv and some/path/test.csv to a pickled ML model another/path/some_model.pkl and statistics. With tinybaker, you can specify this individual configurable step as follows:
# train_step.py
from tinybaker import Transform
import pandas as pd
from some_cool_ml_library import train_model, test_model
class TrainModelStep(Transform):
input_tags = {"train_csv", "test_csv"}
output_tags = {"pickled_model", "results"}
def script():
# Read from files
with self.input_files["train_csv"].open() as f:
train_data = pd.read_csv(f)
with self.input_files["test_csv"].open() as f:
test_data = pd.read_csv(f)
# Run computations
X = 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 files
with self.output_files["results"].open() as f:
results = train_results.formatted_summary() + test_results.formatted_summary()
f.write(results)
with self.output_files["pickled_model"].openbin() as f:
pickle.dump(f, model)The script that consumes this may look like:
# script.py
from .train_step import TrainModelStep
train_csv_path = "s3://data/train.csv"
test_csv_path = "s3://data/test_csv"
pickled_model_path = "./model.pkl"
results_path = "./results.txt"
TrainModelStep(
input_paths={
"train_csv": train_csv_path,
"test_csv": test_csv_path,
},
output_paths={
"pickled_model": pickled_model_path,
"results": results_path
}
).run()This will perform standard error handling, such as raising early if certain files are missing.
Since TinyBaker uses pyfilesystem2 as its filesystem, TinyBaker can use any filesystem that pyfilesystem2 supports. For example, you can enable support for s3 via installing https://github.com/PyFilesystem/s3fs.
This makes testing of steps very easy: test suites can operate off of local data, but production jobs can run off of s3 data.
Let's say you've got a sequence of steps. We can compose several build steps together using the methods merge and sequence.
from tinybaker import Transform, sequence
class CleanLogs(Transform):
input_files={"raw_logfile"}
output_files={"cleaned_logfile"}
...
class BuildDataframe(Transform):
input_files={"cleaned_logfile"}
output_files={"dataframe"}
...
class BuildLabels(Transform):
input_files={"cleaned_logfile"}
output_files={"labels"}
class TrainModelFromDataframe(Transform):
input_files={"dataframe", "labels"}
output_files={"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()
Hooking up inputs and outputs is determined via tag name, e.g. if step 1 outputs tag "foo", and step 2 takes tag "foo" as inputs, they will be automatically hooked together.
Let's say task 3 of 4 in a sequence of tasks requires tag "foo", but no previous step generates tag "foo", then this dependency will be propagated to the top level; the sequence as a whole will have a dependency on tag "foo".
Additionally, if task 3 of 4 generates a tag "bar", but no further step requires "bar", then the sequence exposes "bar" as an output.
If you need to expose intermediate files within a sequence, you can use the keywork arg expose_intermediates to additionally output the listed intermediate tags, e.g.
sequence([A, B, C], expose_intermediates={"some_intermediate", "some_other_intermediate"})
Right 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 use map_tags to change them.
from tinybaker import map_tags
MappedStep = map_tags(
SomeStep,
input_mapping={"old_input_name": "new_input_name"},
output_mapping={"old_output_name": "new_output_name"})
If 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 prefix fileset:: are interpreted to be filesets rather than just files.
If a sequence includes a fileset as an intermediate, the developer is expected to
A concat task can be done as follows:
class Concat(Transform):
input_tags = {"fileset::files"}
output_tags = {"concatted"}
def script(self):
content = ""
for ref in self.input_files["fileset::files"]:
with ref.open() as f:
content = content + f.read()
with self.output_files["concatted"].open() as f:
f.write(content)
Concat(
input_paths={
"fileset::files": ["./tests/__data__/foo.txt", "./tests/__data__/bar.txt"],
},
output_paths={"concatted": "/tmp/concatted"},
overwrite=True,
).run()
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