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https://github.com/clearml/clearml
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Link to clear.ml docs and fix links (#389)
* switch allegro.ai links to clear.ml * fix colab links * add newline
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pollfly
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# `clearml-task` - Execute ANY python code on a remote machine
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Using only your command line and __zero__ additional lines of code, you can easily integrate the ClearML platform
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into your experiment. With the `clearml-task` command, you can create a [Task](https://allegro.ai/clearml/docs/docs/concepts_fundamentals/concepts_fundamentals_tasks.html)
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into your experiment. With the `clearml-task` command, you can create a [Task](https://clear.ml/docs/latest/docs/fundamentals/task)
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using any script from **any python code or repository and launch it on a remote machine**.
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The remote execution is fully monitored. All outputs - including console / tensorboard / matplotlib -
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With the `clearml-task` command, you specify the details of your experiment including:
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* Project and task name
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* Repository / commit / branch
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* [Queue](https://allegro.ai/clearml/docs/docs/concepts_fundamentals/concepts_fundamentals_workers_and_queues.html)
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* [Queue](https://clear.ml/docs/latest/docs/fundamentals/agents_and_queues#what-is-a-queue)
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name
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* Optional: the base docker image to be used as underlying environment
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* Optional: alternative python requirements, in case `requirements.txt` is not found inside the repository.
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@ -34,7 +34,7 @@ track and visualize the results in the ClearML Web UI.
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### Prerequisites
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- A single python script, or an up-to-date repository containing the codebase.
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- `clearml` installed. `clearml` also has a [Task](https://allegro.ai/clearml/docs/rst/getting_started/index.html)
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- `clearml` installed. `clearml` also has a [Task](https://clear.ml/docs/latest/docs/fundamentals/task)
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feature but it requires two lines of code in order to integrate the platform.
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- `clearml-agent` running on at least one machine (to execute the experiment)
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@ -56,7 +56,7 @@ By default, the execution working directory will be the root of the repository.
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The names of the arguments should match the argparse arguments, but without the '--' prefix. Instead
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of --key=value -> use `--args key=value`, for example `--args batch_size=64 epochs=1`
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5. Select the queue for your Task's execution, for example: `--queue default`. If a queue isn't chosen, the Task
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will not be executed, it will be left in [draft mode](https://allegro.ai/clearml/docs/docs/concepts_fundamentals/concepts_fundamentals_tasks.html?highlight=draft#task-states-and-state-transitions),
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will not be executed, it will be left in [draft mode](https://clear.ml/docs/latest/docs/fundamentals/task#task-states),
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and you can enqueue and execute the Task at a later point.
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6. Add required packages. If your repo has a requirements.txt file, you don't need to do anything; `clearml-task`
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will automatically find the file and put it in your Task. If your repo does __not__ have a requirements file and
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@ -83,7 +83,7 @@ You will be launching a single local script file (no git repo needed) on a remot
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5. If you have a docker container with an entire environment in which you want your script to run inside,
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add e.g. `--docker nvcr.io/nvidia/pytorch:20.11-py3`
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6. Select the queue for your Task's execution, for example: `--queue dual_gpu`. If a queue isn't chosen, the Task
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will not be executed, it will be left in [draft mode](https://allegro.ai/clearml/docs/docs/concepts_fundamentals/concepts_fundamentals_tasks.html?highlight=draft#task-states-and-state-transitions),
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will not be executed, it will be left in [draft mode](https://clear.ml/docs/latest/docs/fundamentals/task#task-states),
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and you can enqueue and execute it at a later point.
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``` bash
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"source": [
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"# Allegro ClearML Keras with TensorBoard example\n",
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"\n",
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"[](https://colab.research.google.com/github/allegroai/clearml/blob/master/examples/frameworks/keras/Allegro_Trains_keras_TB_example.ipynb)\n",
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"[](https://colab.research.google.com/github/allegroai/clearml/blob/master/examples/frameworks/keras/jupyter_keras_TB_example.ipynb)\n",
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"\n",
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"This example introduces ClearML with Keras and TensorBoard functionality, including automatic logging, models, and TensorBoard outputs. You can find more frameworks examples [here](https://github.com/allegroai/clearml/tree/master/examples/frameworks).\n",
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"\n",
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"### Create a new task.\n",
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"To create a new Task object, call the `Task.init` method providing it with `project_name` (the project name for the experiment) and `task_name` (the name of the experiment). When `Task.init` executes, a link to the Web UI Results page for the newly generated Task will be printed, and the Task will be updated in real time in the ClearML demo server.\n",
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"\n",
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"You can read about the `Task` class in the docs [here](https://allegro.ai/docs/task.html)."
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"You can read about the `Task` class in the docs [here](https://clear.ml/docs/latest/docs/fundamentals/task)."
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]
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},
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{
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}
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}
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"source": [
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"# Allegro ClearML matplotlib example\n",
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"\n",
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"[](https://colab.research.google.com/github/allegroai/clearml/blob/master/examples/frameworks/matplotlib/Allegro_Trains_matplotlib_example.ipynb)\n",
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"[](https://colab.research.google.com/github/allegroai/clearml/blob/master/examples/frameworks/matplotlib/jupyter_matplotlib_example.ipynb)\n",
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"\n",
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"This example introduces ClearML with matplotlib functionality. It also shows seaborn functionality. You can find more frameworks examples [here](https://github.com/allegroai/clearml/tree/master/examples/frameworks).\n",
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"\n",
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"\n",
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"To create a new Task object, call the `Task.init` method providing it with `project_name` (the project name for the experiment) and `task_name` (the name of the experiment). When `Task.init` executes, a link to the Web UI Results page for the newly generated Task will be printed, and the Task will be updated in real time in the ClearML demo server.\n",
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"\n",
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"You can read about the `Task` class in the docs [here](https://allegro.ai/docs/task.html)."
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"You can read about the `Task` class in the docs [here](https://clear.ml/docs/latest/docs/fundamentals/task)."
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]
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},
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{
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}
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}
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"source": [
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"# Allegro ClearML logging example\n",
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"\n",
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"[](https://colab.research.google.com/github/allegroai/clearml/blob/master/examples/reporting/Allegro_Trains_logging_example.ipynb)\n",
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"[](https://colab.research.google.com/github/allegroai/clearml/blob/master/examples/reporting/jupyter_logging_example.ipynb)\n",
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"\n",
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"This example introduces ClearML [Logger](https://allegro.ai/docs/logger.html) functionality. Logger is the ClearML console log and metric interface.\n",
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"This example introduces ClearML [Logger](https://clear.ml/docs/latest/docs/fundamentals/logger) functionality. Logger is the ClearML console log and metric interface.\n",
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"\n",
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"You can find more reporting examples [here](https://github.com/allegroai/clearml/tree/master/examples/reporting)."
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]
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"\n",
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"To create a new Task object, call the `Task.init` method providing it with `project_name` (the project name for the experiment) and `task_name` (the name of the experiment). When `Task.init` executes, a link to the Web UI Results page for the newly generated Task will be printed, and the Task will be updated in real time in the ClearML demo server.\n",
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"\n",
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"You can read about the `Task` class in the docs [here](https://allegro.ai/docs/task.html).\n",
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"You can read about the `Task` class in the docs [here](https://clear.ml/docs/latest/docs/fundamentals/task).\n",
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"\n",
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"After the Task is created, get a Logger for it."
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]
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},
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"source": [
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"### Explicit scalar logging\n",
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"Use the [Logger.report_scalar](https://allegro.ai/docs/logger.html#clearml.logger.Logger.report_scalar) method to explicitly log scalars. Scalar plots appear in the Web UI, Results > Scalars tab."
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"Use the [Logger.report_scalar](https://clear.ml/docs/latest/docs/references/sdk/logger#report_scalar) method to explicitly log scalars. Scalar plots appear in the Web UI, Results > Scalars tab."
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]
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},
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{
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"\n",
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"You can log other data and report the data in a variety of plot types, including histograms, confusion matrices, 2D and 3D scatter diagrams, and surface diagrams. They appear in the Results > Plots tab.\n",
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"\n",
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"For information about the methods to report each type of plot, see the [Logger](https://allegro.ai/docs/logger.html) module.\n"
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"For information about the methods to report each type of plot, see the [Logger](https://clear.ml/docs/latest/docs/references/sdk/logger) module.\n"
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]
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},
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{
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"\n",
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"We use StorageManager to download a local copy of the files. You can use it immediately. Just provide the URL. Cache is enabled by default for all downloaded remote URLs/files.\n",
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"\n",
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"For more information, you can read about the storage manager [here](https://allegro.ai/docs/storage_manager_storagemanager.html)."
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"For more information, you can read about the storage manager [here](https://clear.ml/docs/latest/docs/references/sdk/storage)."
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]
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},
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{
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"source": [
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"#### Report images and media\n",
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"\n",
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"Use [Logger.report_image](https://allegro.ai/docs/logger.html?highlight=report_image#clearml.logger.Logger.report_image) and [Logger.report_media](https://allegro.ai/docs/logger.html?highlight=report_media#clearml.logger.Logger.report_media) to report the downloaded samples. The debug samples appear in the Results > Debug Samples tab."
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"Use [Logger.report_image](https://clear.ml/docs/latest/docs/references/sdk/logger#report_image) and [Logger.report_media](https://clear.ml/docs/latest/docs/references/sdk/logger#report_media) to report the downloaded samples. The debug samples appear in the Results > Debug Samples tab."
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]
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},
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{
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},
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"source": [
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"### Explicit text logging\n",
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"Use [Logger.report_text](https://allegro.ai/docs/logger.html?highlight=report_text#clearml.logger.Logger.report_text) to log text message. They appear in Results > Log."
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"Use [Logger.report_text](https://clear.ml/docs/latest/docs/references/sdk/logger#report_text) to log text message. They appear in Results > Log."
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]
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},
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{
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"\n",
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"Reports are flushed in the background every couple of seconds, and at the end of the process execution.\n",
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"\n",
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"Or, flush the Logger by calling [Logger.flush](https://allegro.ai/docs/logger.html?highlight=report_text#clearml.logger.Logger.flush)."
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"Or, flush the Logger by calling [Logger.flush](https://clear.ml/docs/latest/docs/references/sdk/logger#flush)."
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]
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},
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{
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