To automatically generate a schema for your web service, provide a sample of the input and/or output in the constructor for one of the defined type objects. The type and sample are used to automatically create the schema. Azure Machine Learning then creates an OpenAPI (Swagger) specification for the web service during deployment.
To use schema generation, include the open-source inference-schema package version 1.1.0 or above in your dependencies file. For more information on this package, see In order to generate conforming swagger for automated web service consumption, scoring script run() function must have API shape of:
The return value from the script can be any Python object that is serializable to JSON. For example, if your model returns a Pandas dataframe that contains multiple columns, you might use an output decorator similar to the following code:
If your model accepts binary data, like an image, you must modify the score.py file used for your deployment to accept raw HTTP requests. To accept raw data, use the AMLRequest class in your entry script and add the @rawhttp decorator to the run() function.
I'm Checked the operation of the Mate image on X96 mini s905w 2\16. After automatic OTA update of Android firmware in eMMC to 20180505, the images work WITHOUT manual addition of dtb file. Checked the installation of the system in eMMC (using the script /root/install.sh), everything is installed without errors and the system works from eMMC.
The Workspace class is a foundational resource in the cloud that you use to experiment, train, and deploy machine learning models. It ties your Azure subscription and resource group to an easily consumed object.
Now that the model is registered in your workspace, it's easy to manage, download, and organize your models. To retrieve a model (for example, in another environment) object from Workspace, use the class constructor and specify the model name and any optional parameters. Then, use the download function to download the model, including the cloud folder structure.
After you have a registered model, deploying it as a web service is a straightforward process. First you create and register an image. This step configures the Python environment and its dependencies, along with a script to define the web service request and response formats. After you create an image, you build a deploy configuration that sets the CPU cores and memory parameters for the compute target. You then attach your image.
The following code shows a simple example of setting up an AmlCompute (child class of ComputeTarget) target. This target creates a runtime remote compute resource in your Workspace object. The resource scales automatically when a job is submitted. It's deleted automatically when the run finishes.
Now you're ready to submit the experiment. Use the ScriptRunConfig class to attach the compute target configuration, and to specify the path/file to the training script train.py. Submit the experiment by specifying the config parameter of the submit() function. Call wait_for_completion on the resulting run to see asynchronous run output as the environment is initialized and the model is trained.
Azure Machine Learning environments specify the Python packages, environment variables, and software settings around your training and scoring scripts. In addition to Python, you can also configure PySpark, Docker and R for environments. Internally, environments result in Docker images that are used to run the training and scoring processes on the compute target. The environments are managed and versioned entities within your Machine Learning workspace that enable reproducible, auditable, and portable machine learning workflows across a variety of compute targets and compute types.
To submit a training run, you need to combine your environment, compute target, and your training Python script into a run configuration. This configuration is a wrapper object that's used for submitting runs.
An Azure Machine Learning pipeline is an automated workflow of a complete machine learning task. Subtasks are encapsulated as a series of steps within the pipeline. An Azure Machine Learning pipeline can be as simple as one step that calls a Python script. Pipelines include functionality for:
A PythonScriptStep is a basic, built-in step to run a Python Script on a compute target. It takes a script name and other optional parameters like arguments for the script, compute target, inputs and outputs. The following code is a simple example of a PythonScriptStep. For an example of a train.py script, see the tutorial sub-section.
Use the AutoMLConfig class to configure parameters for automated machine learning training. Automated machine learning iterates over many combinations of machine learning algorithms and hyperparameter settings. It then finds the best-fit model based on your chosen accuracy metric. Configuration allows for specifying:
When running commands specified using /script or /command, batch mode is used implicitly and overwrite confirmations are turned off. In an interactive scripting mode, the user is prompted in the same way as in GUI mode. To force batch mode (all prompts are automatically answered negatively) use the command option batch abort. For batch mode it is recommended to turn off confirmations using option confirm off to allow overwrites (otherwise the overwrite confirmation prompt would be answered negatively, making overwrites impossible).
WinSCP automatically resolves %TIMESTAMP[rel]#format% to a real time (optionally to a past or future time) with the given format. The format may include yyyy for year, mm for month, dd for day, hh for hour, nn for minute and ss for second. For example, the %TIMESTAMP#yyyy-mm-dd% resolves to 2016-06-22 on 22 June 2016. See other formats you can use.
You can find the key fingerprint on Server and Protocol Information Dialog. You can also copy the key fingerprint to clipboard from the confirmation prompt on the first (interactive) connection using Copy key fingerprints to clipboard command (in the script, use SHA-256 fingerprint of the host key only). Learn more about obtaining host key fingerprint.
FTPS/WebDAVS TLS/SSL certificate signed by untrusted authority may also need to be verified. To automate the verification in script, use -certificate switch of open command to accept the expected certificate automatically.
If you are going to run the script under a different account (for example using the Windows Task Scheduler), make sure the script does not rely on a configuration settings that might differ on the other account. When using registry as configuration storage, the settings are accessible only for your Windows account. Ideally, make sure the script does not rely on any external configuration, to make it completely portable. Note that the configuration also includes verified SSH host keys and FTPS/WebDAVS TLS/SSL certificates.
The disadvantage is that change to configuration in graphical mode may break your script (common example is enabling Existing files only option for synchronization). Also the script is not portable to other machines, when it relies on an external configuration.
The best way to do that is to configure all the options you need using script commands only (option command, switches of other commands, session URL), or if no such command is available, using raw site settings and raw configuration. Finally force scripting mode to start with the default configuration using /ini=nul command-line parameter.
Alternatively export your configuration to a separate INI file and reference it using /ini= command-line parameter. Also consider setting the INI file read-only, to prevent WinSCP writing to it, when exiting. Particularly, if you are running multiple scripts in parallel, to prevent different instances of WinSCP trying to write it at the same time.
In the example below, WinSCP connects to example.com server with account user, downloads file and closes the session. Then it connects to the same server with the account user2 and uploads the file back.
this the same bootscript that was shipped with balbes150 image I only edited the device to mmc 0:2 because this is where I have my kernel and other boot files, also I prepended the correct path before each file name.
set vdd cpu_a to 1120 mvset vdd cpu_b to 1050 mvset vddee to 1000 mvBoard ID = 6CPU clk: 1200MHzDQS-corr enabledDDR scramble enabledDDR4 chl: Rank0+1 @ 1008MHzRank0: 2048MB(auto)-2T-18Rank1: 1024MB(auto)-2T-18DataBus test pass!AddrBus test pass!-sLoad fip header from eMMC, src: 0x0000c200, des: 0x01400000, size: 0x00004000New fip structure!Load bl30 from eMMC, src: 0x00010200, des: 0x01100000, size: 0x0000d600Load bl31 from eMMC, src: 0x00020200, des: 0x05100000, size: 0x00018400Load bl33 from eMMC, src: 0x0003c200, des: 0x01000000, size: 0x00055400NOTICE: BL3-1: v1.0(release):3348978NOTICE: BL3-1: Built : 15:44:01, May 12 2017NOTICE: BL3-1: BL33 decompress passmpu_config_enable:ok
If anyone wants a better, more up to date and performant version of this, I've created my own version that you can download from -mods.com/tools/jm36-lua-plugin-for-script-hook-v-reloaded or -GTAV/releases which I will be updating constantly over time with tons of new optimization features implemented; it maintains compatibility with older/existing scripts designed for this version of Lua Plugin too. 2b1af7f3a8