With the addition of several pre-refinement steps and computationally intensive pipelines, at some point, it becomes necessary to make the flow efficient. Can achieve this by removing redundant steps or adding more cores/CPU/GPU to make it faster. Often, we focus on achieving results, regardless of efficiency. Rarely do we try to tweak the pipeline or make repairs until we run out of memory or the computer hangs. Fortunately, there is already a framework called joblib, which provides tools to simplify Python pipelines.
Why Joblib in Python?
Joblib is a library built entirely in Python by scikit-learn developers. It focuses exclusively on Python-based robustness and functionality optimization. It offers a lightweight pipeline in Python development services. It was a great library that became popular due to its optimized time complexity features, especially big data.
Problem: We face many challenges while working with Big Data. For example, it takes a lot of time and space when working with computationally intensive functions or constantly loading large amounts of data like a pickle.
Solution: joblib
Features that make Joblib an Avenger to reduce time complexity:
1. Fast disk caching and lazy evaluation using a hash technique
2. Ability to distribute tasks (parallelization) with the help of parallel assistant
3. Compression function during consistency containing big data
4. Famous for big data processing
5. Special optimization for working with large NumPy arrays
6. Memory, where functions are called with the same arguments, are not recalculated, but the output is reloaded from the cache using Cherry on the Cake mem mapping
7. No dependent libraries (other than Python itself)
Later we will discuss practical examples of the above functions one by one. Stand in line!
Memory class
Lazy evaluation of Python functions, in simple terms, means code that is assigned to a variable but is only executed when the result is needed for another calculation. Caching the result of a function is called storing to avoid recalculation.
classjoblib.memory.Memory(Location=None, Backend='local',
cachedir = None, mmap_mode = None, compression = False, verbose = 1,
byte_limit = None, backend_options = {})
It also avoids re-running the function with the same arguments. Saving the storage class results in a disk reloading cached output using a hash technique when the function is called with the same arguments. The hash checks whether the output for the input has been calculated or not if it has not been recalculated or contains cached value. It is mainly used for large NumPy arrays. The output is stored in a pickle file in the cache directory.
memory.cache()
A fetchable object provides a function to store its return value on each call.
Use of cached results
Slow evaluation of Python functions assumes that code assigned to a variable is executed only when another calculation requires its output. It avoids running functions with the same arguments. Caching the result of a function is called storing it to avoid re-execution.
The storage class preserves the output of the cached disk by implementing a hash method when a function is declared with the same arguments. Hashing checks whether the result for the input is pre-calculated or not; otherwise, cached values are recalculated or stored. It mainly contains large NumPy arrays. The results are stored in a pickle file in the cache directory.
Memory. cache ()
A fetchable object provides a function to store its return value on each call.
Data Stability
Joblib helps maintain your machine learning model or data formation. It has been determined that it is more appropriate to replace the standard python development service library; Joblib can accept Python object and file names.
The breakthrough was reforming the storage complexity during preservation, which was done through Joblib's compression method to keep objects in a compressed structure. Joblib reduces data before saving it to disk. Many compression extensions like gz, z have their compression technique.
Dump and Load
We often want to store and load data sets, models, calculated solutions, Etc. from the computer location. Joblib provides functions that can use to dump and load seamlessly.
Compression methods
If you are working with a larger data set, the fetched size of these files will be large. The file size gets bigger by designing the feature as we add additional columns. Luckily, now that memory has become so cheap, that's not much of a concern. However, to be effective, joblib provides some very easy-to-use compression techniques:
Simple Compression:
There's no compression, but it's the fastest way to save files.
Using lz4 compression:
It is another compression method and one of the fastest compression techniques available, but it is slightly slower than zlib.
CONCLUSION
Every beginning has an end. Well, that's the cycle of nature! Our blog ends the same way. We have seen how Joblib is a lifesaver when handling big data that would take up a lot of space and time, if not without Joblib. This blog has detailed many lightweight pipelining libraries that optimize time and space. Features like concurrency, storage, and caching or file compression are superior to any ML/AI library. The large pickle file model takes up less space with machine learning and loads the same file faster. But life isn't always fair. Joke apart!
Joblib sometimes can't be faster when small amounts of data appear. Most importantly, it is recommended in the Pickle library for object constancy and can be considered when parallel tasks need to be performed.
