https://github.com/TensorLab/tensorfx

TensorFlow framework for training and serving machine learning models
https://github.com/TensorLab/tensorfx

machine-learning ml python tensorflow tensorfx

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TensorFlow framework for training and serving machine learning models

• Host: GitHub
• URL: https://github.com/TensorLab/tensorfx
• Owner: tensorlab
• License: apache-2.0
• Created: 2016-12-03T07:59:09.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2017-03-30T07:14:58.000Z (over 7 years ago)
• Last Synced: 2024-06-29T11:02:54.750Z (4 months ago)
• Topics: machine-learning, ml, python, tensorflow, tensorfx
• Language: Python
• Size: 1.08 MB
• Stars: 196
• Watchers: 19
• Forks: 41
• Open Issues: 13
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

Awesome Lists containing this project

• awesome-list - TensorFX - TensorFlow framework for training and serving machine learning models. (Deep Learning Framework / High-Level DL APIs)

README

        
# Introduction to TensorFX

TensorFX is an end to end application framework to simplifies machine learning with

[TensorFlow](http://tensorflow.org) - both training models and using them for prediction. It is

designed from the ground up to make the mainline scenarios simple with higher level building blocks,

while ensuring custom or complex scenarios remain possible by preserving the flexibility of

TensorFlow APIs.

There are some important principles that shape the design of the framework:

1. **Simple, consistent set of usage patterns** 

   Local or cloud, single node or distributed execution, in-memory data or big data sharded across

   files, you should have to write code once, in a single way regardless of how the code executes.

2. **A Toolbox with Useful Abstractions**

   The right entrypoint for the task at hand, starting with off-the-shelf algorithms that let you

   focus on feature engineering and hyperparam tuning. If you need to solve something unqiue, you

   can focus on building TensorFlow graphs, rather than infrastructure code (distributed cluster

   setup, checkpointing, logging, exporting models etc.).

3. **Declarative**

   Using YAML, JSON, and simplified Python interfaces to minimize the amount of boilerplate code.

OK, enough context... here is some information to get you started.

## Getting Started

Once you have a Python environment (recommendation: use Miniconda), installation is straightforward:

    pip install tensorflow

    pip install tensorfx

Note that TensorFX depends on TensorFlow 1.0, and supporting libraries such as numpy and pandas.

## Documentation

Documentation is at https://tensorlab.github.io/tensorfx/. This includes API reference topics, as

well as conceptual and how-to topics. They are a work-in-progress, but check them out! There are a

few samples that demonstrate how to get started as well in the repository. Likewise, more to be

added over time.

## Contributions and Development

We welcome contributions in form of ideas, issues, samples as well as code. Since the project is at

a super-early stage, and evolving rapidly, its best to start a discussion by filing an issue for

any contribution.

### Building and Testing

If you want to develop within the repository, clone it, and run the following commands:

    # Install requirements and setup envionment

    source init.sh install

    # Build and Test

    ./build.sh test

### Related Links

* Development workflow [TODO: Add wiki entry]

## Hello World - Iris Classification Model

This sample here is a quick 5-minute introduction to using TensorFX. Here is the code for building

a feed-forward neural network classification model for the

[iris dataset](https://archive.ics.uci.edu/ml/datasets/Iris).

    import tensorfx as tfx

    import tensorfx.models.nn as nn

    # Hyperparameters, training parameters, and data

    args, job = nn.FeedForwardClassificationArguments.parse(parse_job=True)

    dataset = tfx.data.CsvDataSet(args.data_schema,

                                  train=args.data_train,

                                  eval=args.data_eval,

                                  metadata=args.data_metadata,

                                  features=args.data_features)

    # Instantiating the model builder

    classification = nn.FeedForwardClassification(args, dataset)

    # Training

    trainer = tfx.training.ModelTrainer()

    model = trainer.train(classification, job)

    # Prediction

    instances = [

      '6.3,3.3,6,2.5',   # virginica

      '4.4,3,1.3,0.2',   # setosa

      '6.1,2.8,4.7,1.2'  # versicolor

    ]

    predictions = model.predict(instances)

Here's an outline steps to perform for basic usage of what TensorFX offers:

1. Parse (or build) an Arguments object, usually from the command-line to define hyperparameters.

   This object corresponds to the kind of model you are training, so,

   `FeedForwardClassificationArguments` in this case.

2. Create a DataSet to reference training and evaluation data, along with supporting configuration -

   namely - schema, metadata, and features (more on these below).

3. Initialize the model builder - in this case `FeedForwardClassification`.

4. Initialize the model trainer, and invoke `train()` which runs the training process to return a

   model.

5. Load some instances you want to run through the model and call `predict()`.

#### Schema - schema.yaml

The schema describes the structure of your data. This can be defined programmatically, but is

conveniently expressible in declarative YAML form, and placed alongside training data.

    fields:

    - name: species

      type: discrete

    - name: petal_length

      type: numeric

    - name: petal_width

      type: numeric

    - name: sepal_length

      type: numeric

    - name: sepal_width

      type: numeric

#### Metadata - metadata.json

Metadata is the result of analyzing training data, based on type information in the schema.

Iris is a tiny dataset, so metadata is readily producable using simple python code looping over

the data. For real-world and large datasets, you'll find Spark and BigQuery (on Google Cloud

Platform) as essential data processing runtimes. Stay tuned - TensorFX will provide support for

these capabilities out of the box.

    {

      "species": { "entries": ["setosa", "virginica", "versicolor"] },

      "petal_length": { "min": 4.3, "max": 7.9 },

      "petal_width": { "min": 2.0, "max": 4.4 },

      "sepal_length": { "min": 1.1, "max": 6.9 },

      "sepal_width": { "min": 0.1, "max": 2.5 }

    }

#### Features - features.yaml

Like schema, features can also be defined programmatically, or expressed in YAML. Features describe

the set of inputs that your models operate over, and how they are produced by applying

transformations to the fields in your data. These transformations are turned into TensorFlow graph

constructs and applied consistently to both training and prediction data.

In this particular example, the FeedForwardClassification model requires two features: X defining

the values the model uses for producing inferences, and Y, the target label that the model is

expected to predict which are defined as follows:

    features:

    - name: X

      type: concat

      features:

      - name: petal_width

        type: scale

      - name: petal_length

        type: scale

      - name: sepal_width

        type: log

      - name: sepal_length

        type: log

    - name: Y

      type: target

      fields: species

#### Running the Model

The python code in the sample can be run directly, or using a `train` tool, as shown:

    cd samples

    tfx train \

      --module iris.trainer.main \

      --output /tmp/tensorfx/iris/csv \

      --data-train iris/data/train.csv \

      --data-eval iris/data/eval.csv \

      --data-schema iris/data/schema.yaml \

      --data-metadata iris/data/metadata.json \

      --data-features iris/features.yaml \

      --log-level-tensorflow ERROR \

      --log-level INFO \

      --batch-size 5 \

      --max-steps 2000 \

      --checkpoint-interval-secs 1 \

      --hidden-layers:1 20 \

      --hidden-layers:2 10

Once the training is complete, you can list the contents of the output directory. You should

see the model (the prediction graph, and learnt variables) in the `model` subdirectory, alongside

checkpoints, and summaries.

    ls -R /tmp/tensorfx/iris/csv

    checkpoints	job.yaml	model		summaries

    /tmp/tensorfx/iris/csv/checkpoints:

    checkpoint                             model.ckpt-2000.index

    model.ckpt-1.data-00000-of-00001       model.ckpt-2000.meta

    model.ckpt-1.index                     model.ckpt-2001.data-00000-of-00001

    model.ckpt-1.meta                      model.ckpt-2001.index

    model.ckpt-1562.data-00000-of-00001    model.ckpt-2001.meta

    model.ckpt-1562.index                  model.ckpt-778.data-00000-of-00001

    model.ckpt-1562.meta                   model.ckpt-778.index

    model.ckpt-2000.data-00000-of-00001    model.ckpt-778.meta

    /tmp/tensorfx/iris/csv/model:

    saved_model.pb	variables

    /tmp/tensorfx/iris/csv/model/variables:

    variables.data-00000-of-00001	variables.index

    /tmp/tensorfx/iris/csv/summaries:

    eval		prediction	train

    /tmp/tensorfx/iris/csv/summaries/eval:

    events.out.tfevents.1488351760

    events.out.tfevents.1488352853

    /tmp/tensorfx/iris/csv/summaries/prediction:

    events.out.tfevents.1488351765

    /tmp/tensorfx/iris/csv/summaries/train:

    events.out.tfevents.1488351760

    events.out.tfevents.1488352852

Summaries are TensorFlow events logged during training. They can be observed while the training

job is running (which is essential when running a long or real training job) to understand how your

training is progressing, or how the model is converging (or not!).

    tensorboard --logdir /tmp/tensorfx/iris/csv

This should bring up TensorBoard. Its useful to see the graph structure, metrics and other tensors

that are automatically published.

**Training Graph**

![Graphs in TensorBoard](https://tensorlab.github.io/tensorfx/_static/images/intro-graph.jpg)

**Training Metrics -- Accuracy, Loss and Throughput**

![Metrics in TensorBoard](https://tensorlab.github.io/tensorfx/_static/images/intro-metrics.jpg)

**Model Variables -- Weights, Gradients, etc.**

![Watchin Learnt Variables](https://tensorlab.github.io/tensorfx/_static/images/intro-watch.jpg)

As you can see, the out-of-box model takes care of a number of details. The same code can be run on

a single machine, or in a cluster (of course, iris is too simple of a problem to need that).