petastorm | Petastorm library enables single machine | Machine Learning library
kandi X-RAY | petastorm Summary
kandi X-RAY | petastorm Summary
Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code.
Support
Quality
Security
License
Reuse
Top functions reviewed by kandi - BETA
- Create a reader for a dataset .
- Create a batch reader .
- Creates a namedtuple of namedtuple fields .
- Start the worker .
- Context manager for creating a dataset .
- Train and test dataset .
- Make a petastorm dataset .
- Converts a directory directory to PETAST .
- Load rows from a pq file .
- Generate metadata for petastorm .
petastorm Key Features
petastorm Examples and Code Snippets
Lines of Code : 10License : Permissive (Apache-2.0)
Copygraph LR
A[Collection] --> B[Exploration];
B --> C[Analytics];
C --> D[Feature Engineer];
D --> E[Training];
E --> F[Evaluation];
F --> C;
E --> G[Deployment];
G --> H[Monitoring];
H --> conda create --name geonex_torch1.5 python=3.7 pytorch=1.5 xarray numpy scipy pandas torchvision tensorboard opencv pyyaml jupyterlab matplotlib seaborn
conda install -c conda-forge pyhdf
pip install petastorm
from tensorflow import keras
import tensorflow as tf
import horovod.spark.keras as hvd
model = keras.models.Sequential()
.add(keras.layers.Dense(8, input_dim=2))
.add(keras.layers.Activation('tanh'))
.add(keras.layers.Dense(1))
.add(k # Copyright 2017 onwards, fast.ai, Inc.
# Modifications copyright (C) 2018 Uber Technologies, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain # Copyright 2017 onwards, fast.ai, Inc.
# Modifications copyright (C) 2018 Uber Technologies, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain fs = s3fs.S3FileSystem(config_kwargs = {'max_pool_connections': 50})
Community Discussions
Trending Discussions on petastorm
QUESTION
Update
While I appreciated AloneTogether's answer, I didn't like that I was using take() and it was separate from model.fit.
I put another answer here if you want to look at it. It involves subclassing Model. It's not too bad.
End of Update
I have a simple example, a parquet file with 8 columns named feature_# populated with 1 to 100 for each column
...ANSWER
Answered 2022-Jan-18 at 11:59I think it all depends on the size of your batch_size because take(1) takes one batch and if the batch_size is < 100 you will not see all the values. If, for example, you have batch_size=100, then you will definitely see the values 1 to 100:
QUESTION
I am trying to use petastorm in a different manner which requires that I tell it where my parquet files are stored through one of the following:
hdfs://some_hdfs_cluster/user/yevgeni/parquet8, or file:///tmp/mydataset, or s3://bucket/mydataset, or gs://bucket/mydataset. Since I am on DataBricks and given other constraints, my option is to use the file:/// option.
However, I am at a loss as to how specify the location of my parquet files. I continually get rejected saying that Path does not exist:
ANSWER
Answered 2020-Nov-29 at 16:30You just need to specify the path as it is, no need for 'file:///':
QUESTION
I have written an Apache Spark DataFrame as a parquet file for a deep learning application in a Python environment ; I am currently experiencing issues in implementing basic examples of both petastorm (following this notebook) and horovod frameworks, in reading the aforementioned file namely. The DataFrame has the following type : DataFrame[features: array, next: int, weight: int] (much like in DataBricks' notebook, I had features be a VectorUDT, which I converted to an array).
In both cases, Apache Arrow throws an ArrowIOError : Invalid parquet file. Corrupt footer. error.
I discovered in this question and in this PR that as of version 2.0, Spark doesn't write _metadata or _common_metadata files, unless spark.hadoop.parquet.enable.summary-metadata is set to true in Spark's configuration ; those files are indeed missing.
I thus tried rewriting my DataFrame with this environment, still no _common_metadata file. What also works is to explicitely pass a schema to petastorm when constructing a reader (passing schema_fields to make_batch_reader for instance ; which is a problem with horovod as there is no such parameter in horovod.spark.keras.KerasEstimator's constructor).
How would I be able, if at all possible, to either make Spark output those files, or in Arrow to infer the schema, just like Spark seems to be doing ?
Minimal example with horovod ...ANSWER
Answered 2020-Apr-29 at 13:40The problem is solved in pyarrow 0.14+ (issues.apache.org/jira/browse/ARROW-4723), be sure to install the updated version with pip (up until Databricks Runtime 6.5, the included version is 0.13).
Thanks to @joris' comment for pointing this out.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install petastorm
You can use petastorm like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
Support
Reuse Trending Solutions
Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items
Find more librariesStay Updated
Subscribe to our newsletter for trending solutions and developer bootcamps
Share this Page
