• Docker Container With Conda
• Docker With Conda
• Docker Conda Python 3.7
• Docker With Condado
• Docker Python With Conda
• Docker Conda Install Requirements.txt

Mar 13, 2021 The Rust toolchain installer named rustup is the officially recommended script to install Rust in Linux. However, it is not the only way. We can use Conda package manger to install and manage Rust programming language as well. To create a new Rust environment using conda and install necessary. $ docker run -i -t continuumio/anaconda3 /bin/bash 4. Once the Docker container is running, we can start an interactive Python shell, install additional conda packages or run Python applications. Feb 23, 2021 fsspec uses tox and tox-conda to manage dev and test environments. First, install conda with tox and tox-conda in a base environment (eg. Conda install -c conda-forge tox tox-conda). Calls to tox can then be used to configure a development environment and run tests. First, setup a development conda environment via tox -e dev. This will install.

RAPIDS is available as conda packages, docker images, and from source builds. Use the tool below to select your preferred method, packages, and environment to install RAPIDS. Certain combinations may not be possible and are dimmed automatically. Be sure you’ve met the required prerequisites above and see the details below.

In this article, learn how to use a custom Docker image when you're training models with Azure Machine Learning. You'll use the example scripts in this article to classify pet images by creating a convolutional neural network.

Azure Machine Learning provides a default Docker base image. You can also use Azure Machine Learning environments to specify a different base image, such as one of the maintained Azure Machine Learning base images or your own custom image. Custom base images allow you to closely manage your dependencies and maintain tighter control over component versions when running training jobs.

Prerequisites

Run the code on either of these environments:

• Azure Machine Learning compute instance (no downloads or installation necessary):
  • Complete the Set up environment and workspace tutorial to create a dedicated notebook server preloaded with the SDK and the sample repository.
  • In the Azure Machine Learning examples repository, find a completed notebook by going to the notebooks > fastai > train-pets-resnet34.ipynb directory.
• Your own Jupyter Notebook server:
  • Create a workspace configuration file.
  • Install the Azure Machine Learning SDK.
  • Create an Azure container registry or other Docker registry that's available on the internet.

Set up a training experiment

In this section, you set up your training experiment by initializing a workspace, defining your environment, and configuring a compute target.

Initialize a workspace

The Azure Machine Learning workspace is the top-level resource for the service. It gives you a centralized place to work with all the artifacts that you create. In the Python SDK, you can access the workspace artifacts by creating a Workspace object.

Create a Workspace object from the config.json file that you created as a prerequisite.

Define your environment

Create an Environment object and enable Docker.

The specified base image in the following code supports the fast.ai library, which allows for distributed deep-learning capabilities. For more information, see the fast.ai Docker Hub repository.

When you're using your custom Docker image, you might already have your Python environment properly set up. In that case, set the user_managed_dependencies flag to True to use your custom image's built-in Python environment. By default, Azure Machine Learning builds a Conda environment with dependencies that you specified. The service runs the script in that environment instead of using any Python libraries that you installed on the base image.

Use a private container registry (optional)

To use an image from a private container registry that isn't in your workspace, use docker.base_image_registry to specify the address of the repository and a username and password:

Use a custom Dockerfile (optional)

It's also possible to use a custom Dockerfile. Use this approach if you need to install non-Python packages as dependencies. Remember to set the base image to None.

Important

Azure Machine Learning only supports Docker images that provide the following software:

• Ubuntu 16.04 or greater.
• Conda 4.5.# or greater.
• Python 3.6+.

For more information about creating and managing Azure Machine Learning environments, see Create and use software environments.

Create or attach a compute target

You need to create a compute target for training your model. In this tutorial, you create AmlCompute as your training compute resource.

Creation of AmlCompute takes a few minutes. If the AmlCompute resource is already in your workspace, this code skips the creation process.

As with other Azure services, there are limits on certain resources (for example, AmlCompute) associated with the Azure Machine Learning service. For more information, see Default limits and how to request a higher quota.

Configure your training job

For this tutorial, use the training script train.py on GitHub. In practice, you can take any custom training script and run it, as is, with Azure Machine Learning.

Create a ScriptRunConfig resource to configure your job for running on the desired compute target.

Submit your training job

When you submit a training run by using a ScriptRunConfig object, the submit method returns an object of type ScriptRun. The returned ScriptRun object gives you programmatic access to information about the training run.

Warning

Azure Machine Learning runs training scripts by copying the entire source directory. If you have sensitive data that you don't want to upload, use an .ignore file or don't include it in the source directory. Instead, access your data by using a datastore.

Next steps

In this article, you trained a model by using a custom Docker image. See these other articles to learn more about Azure Machine Learning:

• Track run metrics during training.
• Deploy a model by using a custom Docker image.

Released:

File-system specification

Project description

A specification for pythonic filesystems.

Install

or

Purpose

To produce a template or specification for a file-system interface, that specific implementations should follow,so that applications making use of them can rely on a common behaviour and not have to worry about the specificinternal implementation decisions with any given backend. Many such implementations are included in this package,or in sister projects such as s3fs and gcsfs.

In addition, if this is well-designed, then additional functionality, such as a key-value store or FUSEmounting of the file-system implementation may be available for all implementations 'for free'.

Documentation

Please refer to RTD

Develop

fsspec uses tox andtox-conda to manage dev and testenvironments. First, install conda with tox and tox-conda in a base environment(eg. conda install -c conda-forge tox tox-conda). Calls to tox can then beused to configure a development environment and run tests.

First, setup a development conda environment via tox -e dev. This willinstall fspec dependencies, test & dev tools, and install fsspec in developmode. Then, activate the dev environment under .tox/dev via conda activate .tox/dev.

Testing

Tests can be run directly in the activated dev environment via pytest fsspec.

The full fsspec test suite can be run via tox, which will setup and executetests against multiple dependency versions in isolated environment. Run tox -av to list available test environments, select environments via tox -e <env>.

The full fsspec suite requires a system-level docker, docker-compose, and fuseinstallation. See ci/install.sh for a detailed installation example.

Code Formatting

fsspec uses Black to ensurea consistent code format throughout the project. black is automaticallyinstalled in the tox dev env, activated via conda activate .tox/dev.

Then, run black fsspec from the root of the filesystem_spec repository toauto-format your code. Additionally, many editors have plugins that will applyblack as you edit files.

Optionally, you may wish to setup pre-commit hooks toautomatically run black when you make a git commit. black is automaticallyinstalled in the tox dev env, activated via conda activate .tox/dev.

Then, run pre-commit install --install-hooks from the root of thefilesystem_spec repository to setup pre-commit hooks. black will now be runbefore you commit, reformatting any changed files. You can format withoutcommitting via pre-commit run or skip these checks with git commit --no-verify.

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