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Copy file name to clipboardExpand all lines: Getting Started/Getting Started with FastScore/index.md
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title: "Getting Started with FastScore v1.7"
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description: "This is a guide for installing and running FastScore. It contains instructions for first-time and novice users, as well as reference instructions for common tasks. This guide was last updated for v1.7 of FastScore.\n\nIf you need support or have questions, please email us: [[email protected]](mailto:[email protected])"
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title: "Getting Started with FastScore v1.8"
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description: "This is a guide for installing and running FastScore. It contains instructions for first-time and novice users, as well as reference instructions for common tasks. This guide was last updated for v1.8 of FastScore.\n\nIf you need support or have questions, please email us: [[email protected]](mailto:[email protected])"
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# Getting Started with FastScore v1.7
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This is a guide for installing and running FastScore. It contains instructions for first-time and novice users, as well as reference instructions for common tasks. This guide was last updated for v1.7 of FastScore.
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# Getting Started with FastScore v1.8
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This is a guide for installing and running FastScore. It contains instructions for first-time and novice users, as well as reference instructions for common tasks. This guide was last updated for v1.8 of FastScore.
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If you need support or have questions, please email us: [email protected]
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1. If using docker-compose, put the volume in the `docker-compose.yml` file(see example file below)
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2. If running manually, with the `-v` flag when using `docker run`, e.g.,
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```docker run -it -d --net=host --rm -v db:/var/lib/mysql fastscore/model-manage-mysql:1.7 ``` (see more below)
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```docker run -it -d --net=host --rm -v db:/var/lib/mysql fastscore/model-manage-mysql:1.8 ``` (see more below)
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#### Example Docker Compose File
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Below is an example Docker Compose file that will start a full suite of FastScore services with two engines:
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version: '2'
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services:
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dashboard:
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image: fastscore/dashboard:1.7
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image: fastscore/dashboard:1.8
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network_mode: "bridge"
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ports:
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- "8000:8000"
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environment:
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CONNECT_PREFIX: https://172.17.0.1:8001
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connect:
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image: fastscore/connect:1.7
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image: fastscore/connect:1.8
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network_mode: "bridge"
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ports:
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- "8001:8001"
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engine-1:
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image: fastscore/engine:1.7
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image: fastscore/engine:1.8
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network_mode: "bridge"
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ports:
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- "8003:8003"
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environment:
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CONNECT_PREFIX: https://172.17.0.1:8001
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engine-2:
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image: fastscore/engine:1.7
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image: fastscore/engine:1.8
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network_mode: "bridge"
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ports:
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- "8004:8003"
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environment:
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CONNECT_PREFIX: https://172.17.0.1:8001
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database:
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image: fastscore/model-manage-mysql:1.7
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image: fastscore/model-manage-mysql:1.8
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network_mode: "bridge"
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ports:
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- "3306:3306"
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model-manage:
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image: fastscore/model-manage:1.7
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image: fastscore/model-manage:1.8
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network_mode: "bridge"
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ports:
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- "8002:8002"
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``` bash
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CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
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51acfc33eb4e fastscore/model-manage:1.7 "/bin/sh -c 'bin/mode" 15 seconds ago Up 14 seconds 0.0.0.0:8002->8002/tcp demos_model-manage_1
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4d20a9c3c4c9 fastscore/connect:1.7"/bin/sh -c 'bin/conn" 16 seconds ago Up 14 seconds 0.0.0.0:8001->8001/tcp demos_connect_1
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432327548cef fastscore/dashboard:1.7 "npm run start-fds" 16 seconds ago Up 14 seconds 0.0.0.0:8000->8000/tcp demos_dashboard_1
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14e547577004 fastscore/model-manage-mysql:1.7 "/bin/sh -c '/sbin/my" 16 seconds ago Up 14 seconds 0.0.0.0:3306->3306/tcp demos_database_1
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bf7e50c22e0a fastscore/engine:1.7"/bin/sh -c 'java -ja" 16 seconds ago Up 14 seconds 0.0.0.0:8004->8003/tcp demos_engine-2_1
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4585ac4cf93b fastscore/engine:1.7 "/bin/sh -c 'java -ja" 16 seconds ago Up 14 seconds 0.0.0.0:8003->8003/tcp demos_engine-1_1
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51acfc33eb4e fastscore/model-manage:1.8 "/bin/sh -c 'bin/mode" 15 seconds ago Up 14 seconds 0.0.0.0:8002->8002/tcp demos_model-manage_1
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4d20a9c3c4c9 fastscore/connect:1.8"/bin/sh -c 'bin/conn" 16 seconds ago Up 14 seconds 0.0.0.0:8001->8001/tcp demos_connect_1
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432327548cef fastscore/dashboard:1.8 "npm run start-fds" 16 seconds ago Up 14 seconds 0.0.0.0:8000->8000/tcp demos_dashboard_1
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bf7e50c22e0a fastscore/engine:1.8"/bin/sh -c 'java -ja" 16 seconds ago Up 14 seconds 0.0.0.0:8004->8003/tcp demos_engine-2_1
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4585ac4cf93b fastscore/engine:1.8 "/bin/sh -c 'java -ja" 16 seconds ago Up 14 seconds 0.0.0.0:8003->8003/tcp demos_engine-1_1
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```
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To do it manually, the FastScore microservices can be installed by pulling the images from DockerHub:
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``` bash
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$ docker pull fastscore/model-manage:1.7
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$ docker pull fastscore/connect:1.7
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$ docker pull fastscore/engine:1.7
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$ docker pull fastscore/model-manage-mysql:1.7
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$ docker pull fastscore/dashboard:1.7
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$ docker pull fastscore/model-manage:1.8
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$ docker pull fastscore/connect:1.8
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$ docker pull fastscore/engine:1.8
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$ docker pull fastscore/model-manage-mysql:1.8
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$ docker pull fastscore/dashboard:1.8
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```
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``` bash
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docker run -it -d --net=host --rm fastscore/connect:1.7
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docker run -it -d --net=host --rm -e "CONNECT_PREFIX=https://127.0.0.1:8001" fastscore/dashboard:1.7
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docker run -it -d --net=host --rm -e "CONNECT_PREFIX=https://127.0.0.1:8001" fastscore/engine:1.7
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docker run -it -d --net=host --rm -e "CONNECT_PREFIX=https://127.0.0.1:8001" fastscore/model-manage:1.7
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docker run -it -d --net=host --rm -v db:/var/lib/mysql fastscore/model-manage-mysql:1.7
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docker run -it -d --net=host --rm fastscore/connect:1.8
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docker run -it -d --net=host --rm -e "CONNECT_PREFIX=https://127.0.0.1:8001" fastscore/dashboard:1.8
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docker run -it -d --net=host --rm -e "CONNECT_PREFIX=https://127.0.0.1:8001" fastscore/engine:1.8
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docker run -it -d --net=host --rm -e "CONNECT_PREFIX=https://127.0.0.1:8001" fastscore/model-manage:1.8
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docker run -it -d --net=host --rm -v db:/var/lib/mysql fastscore/model-manage-mysql:1.8
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```
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It is additionally useful to install the FastScore Command-Line Interface (CLI).
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The FastScore CLI can be downloaded and installed using the following commands:
This will install the required dependencies. The FastScore CLI is a Python tool, so it doesn't need to be compiled, and the setup script should automatically add the CLI to `$PATH`.
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> `python-setuptools` and `python-dev` (i.e. header files) are required to properly install the FastScore CLI. These may or may not be already present on your system. If not, you will need to install them.
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> `python-pip`, `python-setuptools` and `python-dev` (i.e. header files) are required to properly install the FastScore CLI. These may or may not be already present on your system. If not, you will need to install them.
Once you've installed the FastScore CLI, check that it works by executing the following commandin your terminal. Also see [FastScore Command Line Interface](https://opendatagroup.github.io/Reference/FastScore%20CLI/) for more information on subcommands.
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``` bash
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$ fastscore help
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FastScore CLI v1.7
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FastScore CLI v1.8.0
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Usage: fastscore <command> [<subcommand> ...]
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Available commands:
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help Explain commands and options
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### <a name="section-creating-and-loading-assets-into-fastscore-model-manage"></a>Creating and Loading Assets into FastScore Model Manage
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Version 1.7 of FastScore supports models in Python, R, Java, MATLAB, [PFA](http://dmg.org/pfa/), [PrettyPFA](https://github.com/opendatagroup/hadrian/wiki/PrettyPFA-Reference) and C formats. Some setup steps differ slightly between Python/R models and PFA, Java, MATLAB, or C models. As a model interchange format, PFA can provide some benefits in performance, scalability, and security relative to R and Python. PrettyPFA is a human-readable equivalent to PFA. However, as the majority of users will be more familiar with R and Python, we focus on these two languages in this section.
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Version 1.8 of FastScore supports models in Python, R, Java, MATLAB, [PFA](http://dmg.org/pfa/), [PrettyPFA](https://github.com/opendatagroup/hadrian/wiki/PrettyPFA-Reference) and C formats. Some setup steps differ slightly between Python/R models and PFA, Java, MATLAB, or C models. As a model interchange format, PFA can provide some benefits in performance, scalability, and security relative to R and Python. PrettyPFA is a human-readable equivalent to PFA. However, as the majority of users will be more familiar with R and Python, we focus on these two languages in this section.
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#### Loading Assets
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The FastScore CLI allows a user to load models directly from the command line. The list of models currently loaded in FastScore can be viewed using the model list command:
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Version 1.7 of FastScore introduces the beta version of FastScore Composer and Designer that enables faster analytic workflow creation and validation. This release also includes GitHub integration for the Model Manage backing store.
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Version 1.8 of FastScore includes Docker Secret support for GitHub Integration, MySQL backend, and S3 Streams. This release also includes support for Scala models and enhancements to the C/C++ model runner.
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## 1.7
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## 1.8
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* FastScore Composer and Designer BETA
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* FastScore Compare BETA
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* GitHub Integration
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* Scala FastScore SDK
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* Import Policy Enhancements
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* Enhanced CSV Encoding Support
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* GitHub Integration - Secrets Support
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* Enhanced Avro OCF Format Support
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* Relaxed Avro Schema Matching
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* S3 Transport Enhancements
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* Scala Model Runner
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* Enhanced C/C++ Model Support - Multiple Stream Capability
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* Docker Secret Support for MySQL Backend and S3 Streams
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* Msgpack Encoding
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* General improvements to performance and stability
Copy file name to clipboardExpand all lines: Glossary/index.md
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| Jet | A Unix process that runs a model. |
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| Job | A complete configuration of one or more interrelated FastScore engines that each contain a model, schemas, an import policy, and input/output stream(s). |
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| Manifold | A component of an engine that manages the data flow between streams and the model. |
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| Runner | FastScore Engine will use different model-specific runners to executed depending on the language of the model deployed. |
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| Sensor | A configurable function that captures specific meta data about the execution process of a model in production. |
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| Stream | A file that contains all information necessary to transport data from one place to another. Could be from a data source to the engine or from the engine to an application. There is at least one input stream and one output stream. |
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