![]() ![]() Which user to run commands as within the Docker containerĪ map of environment variable names and values. It will be used as arguments to the image ENTRYPOINT if it has one, or as the executable if the image has no ENTRYPOINT. The command used as pid 1 (or args for entrypoint) when launching the container. entrypoint overrides the image’s ENTRYPOINT. The command used as executable when launching the container. This field is useful if you would rather have a different hostname instead of localhost, for example, if you are starting multiple versions of the same service. By default, all services are exposed directly on localhost. Name defines the the hostname for the container (the default is localhost), which is used for reaching secondary (service) containers. The first image listed under a job defines the job’s own primary container image where all steps will run. The name of a custom docker image to use. If more than one is set you will receive an error.Ĭonfigured by docker key which takes a list of maps: Key (1) One executor type should be specified per job. See Workflows for configuring branch execution for jobs in a workflow or 2.1 config.Īmount of CPU and RAM allocated to each container in a job. Number of parallel instances of this job to run (default: 1)Ī map of environment variable names and values.Ī map defining rules to allow/block execution of specific branches for a single job that is not in a workflow or a 2.1 config (default: all allowed). working_directory will be created automatically if it doesn’t exist. Note: Paths written in your YAML configuration file will not be expanded if your store_test_results.path is $CIRCLE_WORKING_DIRECTORY/tests, then CircleCI will attempt to store the test subdirectory of the directory literally named $CIRCLE_WORKING_DIRECTORY, dollar sign $ and all. Processes run during the job can use the $CIRCLE_WORKING_DIRECTORY environment variable to refer to this directory. Default: ~/project (where project is a literal string, not the name of your specific project). Parameters for making a job explicitly configurable in a workflow. Can be overridden by shell in each step (default: See Default Shell Options) Shell to use for execution command in all steps. The value map has the following attributes: Key A name should be case insensitive unique within a current jobs list. Excluding sets of parameters from a matrixĮach job consists of the job’s name as a key and a map as a value.Available Windows machine images on server.Available Linux machine images on server.above) was tested for TensorFlow 2.104 and PyTorch 1. Python -c "import sklearn print(sklearn._version_)" Zero is an acceptable result if you do not have a CUDA-compatible NVIDIA GPU.ĩ.Install scikit-learn by entering in a command terminal:ġ0.Test scikit-learn by entering in a command terminal: This test returns the number of compatible GPUs available for PAI. Python -c "import tensorflow as tf print(\"Num GPUs Available: \", len(tf._physical_devices('GPU')))" ĥ.Upgrade pip by entering in a command terminal:Ħ.Install TensorFlow by entering in a command terminal:ħ.Check that tensorflow appears in the list of installed packages:Ĩ.Test TensorFlow by entering in a command terminal: Tables of compatible GPUs are available on ģ.If your GPU is compatible, install NVIDIA drivers, Toolkit and models for TensorFlow and PyTorch:ī.CUDA Toolkit 11.2 (TensorFlow) 11.6 (PyTorch): Ĭ.cuDNN 8.1 for CUDA 11.2 (TensorFlow) and cuDNN 8.6 for CUDA 11.6: Ĥ.Install Python 3.8 64-bit (select “Add Python to PATH”, enable pip option and long paths). TensorFlow only supports NVIDIA GPUs in combination with NVIDIA’s CUDA Toolkit. Ģ.Check whether you have a compatible GPU. The additional packages required for PAI on Windows should be installed from their respective websites.ġ.Install Microsoft Visual Studio 2015, 2017, 2019 Runtime (i.e VC_). ![]()
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