update
This commit is contained in:
Chris Lauren
Родитель
87258fd984
Коммит
e99407228c
|
|
@ -4,9 +4,9 @@ To monitor GPU utilization of remote Linux machines:
|
|||
1. In **Server Explorer**, expand **Remote Machines**
|
||||
2. **Right click** the remote machine you want to monitor
|
||||
|
||||
|
||||
|
||||
|
||||
2. Click **Show Heat Map**
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
@ -6,6 +6,6 @@ Once the jobs are submitted, you can view the list of jobs to see their status,
|
|||
1. You will see the list of jobs submitted to that compute context.
|
||||
1. Select a specific **Job** in the list to view details
|
||||
|
||||
|
||||
|
||||
|
||||
> Job history submitted to Linux VMs is stored on the VM in the /tmp directory. Therefore, whenever it is rebooted the job history is cleared. For a permanent record of your job history please configure your VM as a compute context in Azure Machine learning, then Submit Job to Azure Machine Learning (selecting your VM as the compute context)
|
||||
|
|
@ -7,12 +7,12 @@ You can browse all storage on the remote machine or Azure file share to enable u
|
|||
2. Expand the remote machine or Batch AI compute context
|
||||
3. Right click **Storage** then click **Browse**
|
||||
|
||||
|
||||
|
||||
|
||||
## To access job specific data on the remote machine or file share
|
||||
1. Open the [Job History](job-history.md)
|
||||
2. Select the job
|
||||
3. Click **Working Folder** or click StdOut / Stderr for quick access to these important log files
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -8,15 +8,15 @@ Once you've [installed Visual Studio Tools for AI](installation.md), it's easy t
|
|||
|
||||
1. Launch Visual Studio. Open the **Server Explorer** by opening the **AI Tools** menu and choosing **Select Cluster**
|
||||
|
||||
|
||||
|
||||
|
||||
1. Sign in to your Azure Machine Learning subscription by right-clicking the **Azure Machine Learning** node in the Server Explorer then select **Login** and follow the directions.
|
||||
|
||||
|
||||
|
||||
|
||||
2. Select **AI Tools > Azure Machine Learning Sample Gallery**.
|
||||
|
||||
|
||||
|
||||
|
||||
1. For this Quickstart, select the "**MNIST using TensorFlow**" sample and click **Install**. Provide the
|
||||
|
||||
|
|
|
|||
|
|
@ -9,12 +9,12 @@ Once you've [installed Visual Studio Tools for AI](installation.md), it's easy t
|
|||
|
||||
1. In the **New Project** dialog, search for "**AI Tools**", select the "**From Existing Python code**" template, give the project a name and location, and select **OK**.
|
||||
|
||||
|
||||
|
||||
|
||||
1. In the wizard that appears, set the path to your existing code, set a filter for file types, and specify any search paths that your project requires, then select **OK**. If you don't know what search paths are, leave that field blank.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
> If your existing code is part of an Azure Machine Learning project, check the "**Is Azure Machine Learning folder**" to ensure successful conversion of important Azure Machine Learning configuration details like which Experimentation account, which Workspace, the compute contexts to use and more.
|
||||
|
||||
|
|
|
|||
|
|
@ -6,22 +6,22 @@ Once you've [installed Visual Studio Tools for AI](installation.md), it's easy t
|
|||
|
||||
1. Select **File > New > Project** (Ctrl+Shift+N). In the **New Project** dialog, search for "**AI Tools**", and select the template you want. Note that selecting a template displays a short description of what the template provides.
|
||||
|
||||
|
||||
|
||||
|
||||
1. For this Quickstart, select the "**TensorFlow Application**" template, give the project a name (such as "MNIST") and location, and select **OK**.
|
||||
|
||||
1. Visual Studio creates the project file (a `.pyproj` file on disk) along with any other files as described by the template. With the "TensorFlow Application" template, the project contains one file named the same as your project. The file is open in the Visual Studio editor by default.
|
||||
|
||||
|
||||
|
||||
|
||||
1. Notice the code already imports several libraries including TensorFlow, numpy, sys and os. Additionally it starts your application ready with some input arguments to easily enable switching the location of input training data, output models and log files. These params are useful when you submit your jobs to multiple compute contexts (ie different directory on your local dev box than on an Azure File Share).
|
||||
|
||||
1. Your project also has some properties created to make it easy to debug your app by automatically passing commandline arguments to these input parameters. **Right click** your project then select **Properties**
|
||||
|
||||
|
||||
|
||||
|
||||
1. Click the **Debug** tab to see the Script Arguments automatically added. you may change them as needed to where your input data is located and where you would like your output stored.
|
||||
|
||||
|
||||
|
||||
|
||||
1. Run the program by pressing Ctrl+F5 or selecting **Debug > Start Without Debugging** on the menu. The results are displayed in a console window.
|
||||
|
|
@ -4,7 +4,7 @@ Once you've [Visual Studio Tools for AI](installation.md), you can easily clone
|
|||
|
||||
1. To connect to GitHub repositories, run the Visual Studio installer, select **Modify**, and select the **Individual components** tab. Scroll down to the **Code tools** section, select **GitHub extension for Visual Studio**, and select **Modify**.
|
||||
|
||||
|
||||
|
||||
|
||||
2. Launch Visual Studio.
|
||||
|
||||
|
|
@ -19,7 +19,7 @@ Once you've [Visual Studio Tools for AI](installation.md), you can easily clone
|
|||
|
||||
5. When cloning is complete, double-click the repository folder at the bottom of Team Explorer to navigate to the repository dashboard. Under **Solutions**, select **New...**.
|
||||
|
||||
|
||||
|
||||
|
||||
6. In the **New Project** dialog that appears, select "**From Existing Python Code**", specify a name for the project, set **Location** to the same folder as the repository, and select **OK**. In the wizard that appears, select **Finish**.
|
||||
|
||||
|
|
@ -32,6 +32,6 @@ Once you've [Visual Studio Tools for AI](installation.md), you can easily clone
|
|||
|
||||
11. When the program runs successfully, you'll see it start to download your training and test dataset, then train the model and output your error rate. You want error rate to decrease over tinme
|
||||
|
||||
|
||||
|
||||
|
||||
> If you are using Anaconda and get an error about missing numpy, you may need to change your python environment you may need to [change your python environment to use Anaconda](https://docs.microsoft.com/en-us/visualstudio/python/python-environments)
|
||||
|
|
|
|||
|
|
@ -24,16 +24,16 @@ It's integrated with Visual Studio Tools for AI so you can dynamically scale out
|
|||
|
||||
1. Launch Visual Studio. Open the **Server Explorer** by opening the **AI Tools** menu and choosing **Select Cluster**
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
2. Expand **AI Tools**. Any Batch AI resources you have will be auto-detected and appear in the Server Explorer.
|
||||
|
||||
|
||||
|
||||
|
||||
3. Select **View > Team Explorer...** to open the **Team Explorer** window in which you can connect to GitHub or Visual Studio Team Services, or clone a repository.
|
||||
|
||||
|
||||
|
||||
|
||||
4. In the URL field under **Local Git Repositories**, enter `https://github.com/Microsoft/samples-for-ai`, enter a folder for the cloned files, and select **Clone**.
|
||||
|
||||
|
|
@ -42,15 +42,15 @@ It's integrated with Visual Studio Tools for AI so you can dynamically scale out
|
|||
|
||||
5. When cloning is complete, click **File > Open Solution > Project / Solution**
|
||||
|
||||
|
||||
|
||||
|
||||
5. Open **samples-for-ai\TensorFlowExamples\TensorFlowExamples.sln** in the directory you cloned the repository
|
||||
|
||||
|
||||
|
||||
|
||||
5. Set MNIST project as the **Startup Project **
|
||||
|
||||
|
||||
|
||||
|
||||
1. **Right-click **MNIST project, **Submit Job**
|
||||
|
||||
|
|
@ -58,4 +58,4 @@ It's integrated with Visual Studio Tools for AI so you can dynamically scale out
|
|||
|
||||
1. Select your **Azure Batch AI** cluster, then click **Import**. Select the `AzureBatchAI_TF_MNIST.json` file to quickly populate some default values like which Docker Image to use. Then click **Submit**
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -35,9 +35,9 @@ Download this [GitHub repository](https://github.com/Microsoft/samples-for-ai) c
|
|||
|
||||
- Select the **Tensorflow Examples** folder from the samples repository dowloaded and open the **TensorflowExamples.sln** file.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
- Find the MNIST Project in the **Solution Explorer**, right click and select **Set as StartUp Project**.
|
||||
|
||||
|
|
@ -45,6 +45,6 @@ Download this [GitHub repository](https://github.com/Microsoft/samples-for-ai) c
|
|||
|
||||
- The output will be printed in the console.
|
||||
|
||||
|
||||
|
||||
|
||||
> [Train a TensorFlow model in the cloud](tensorflow-vm.md)
|
||||
|
|
|
|||
|
|
@ -28,20 +28,20 @@ echo -e ". /etc/profile\n$(cat ~/.bashrc)" > ~/.bashrc
|
|||
|
||||
- Select the **Tensorflow Examples** folder from the samples repository dowloaded and open the **TensorflowExamples.sln** file.
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
## Add Azure Remote VM
|
||||
|
||||
In Server Explorer, right click the **Remote Machines** node under the AI Tools node and select "Add…". Enter the Remote Machine display name, IP host, SSH port, user name and password/key file.
|
||||
|
||||
|
||||
|
||||
|
||||
## Submit job to Azure VM
|
||||
Right click on MNIST project in **Solution Explorer** and select **Submit Job**.
|
||||
|
||||
|
||||
|
||||
|
||||
In the submission window:
|
||||
|
||||
|
|
@ -54,7 +54,7 @@ In the submission window:
|
|||
## Check status of job
|
||||
To see status and details of jobs: expand the virtual machine you submitted the job to in the **Server Explorer**. Double click on **Jobs**.
|
||||
|
||||
|
||||
|
||||
|
||||
## Clean up resources (optional)
|
||||
|
||||
|
|
|
|||
|
|
@ -6,21 +6,21 @@ Get started with deep learning using [Microsoft Cognitive Toolkit (CNTK)](http:/
|
|||
## Develop, debug and deploy deep learning models and AI solutions
|
||||
Use the productivity features of Visual Studio to accelerate AI innovation today. Use built-in code editor features like syntax highlighting, IntelliSense and text auto formatting. You can interactively test your deep learning application in your local environment using step-through debugging on local variables and models.
|
||||
|
||||
|
||||
|
||||
|
||||
[Learn more about creating deep learning projects in Visual Studio](quickstart-02-project-from-template.md)
|
||||
|
||||
## Get started quickly with the Azure Machine Learning Sample Gallery
|
||||
Visual Studio Tools for AI is integrated with Azure Machine Learning to make it easy to browse through a gallery of sample experiments using CNTK, TensorFlow, MMLSpark and more.
|
||||
|
||||
|
||||
|
||||
|
||||
[Learn more about creating projects from the sample gallery](quickstart-00-project-from-AzureMachineLearning-gallery.md)
|
||||
|
||||
## Scale out deep learning model training and/or inferencing to the cloud
|
||||
This extension makes it easy to train models on your local computer or you can submit jobs to the cloud by using our integration with Azure Machine Learning. You can submit jobs to different compute targets like Spark clusters, Azure GPU virtual machines and more
|
||||
|
||||
|
||||
|
||||
|
||||
[Learn more about training models in the cloud](tensorflow-vm.md)
|
||||
|
||||
|
|
|
|||
Загрузка…
Ссылка в новой задаче