From 5be5f3209d936647b021f019e977edb805b783e1 Mon Sep 17 00:00:00 2001 From: allegroai <> Date: Mon, 12 Apr 2021 23:01:22 +0300 Subject: [PATCH] Fix documentation links --- README.md | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/README.md b/README.md index c03f5c6..1ebf8d6 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,7 @@ **ClearML Agent - ML-Ops made easy ML-Ops scheduler & orchestration solution supporting Linux, macOS and Windows** -[![GitHub license](https://img.shields.io/github/license/allegroai/trains-agent.svg)](https://img.shields.io/github/license/allegroai/trains-agent.svg) +[![GitHub license](https://img.shields.io/github/license/allegroai/clearml-agent.svg)](https://img.shields.io/github/license/allegroai/clearml-agent.svg) [![PyPI pyversions](https://img.shields.io/pypi/pyversions/clearml-agent.svg)](https://img.shields.io/pypi/pyversions/clearml-agent.svg) [![PyPI version shields.io](https://img.shields.io/pypi/v/clearml-agent.svg)](https://img.shields.io/pypi/v/clearml-agent.svg) @@ -28,16 +28,16 @@ ML-Ops scheduler & orchestration solution supporting Linux, macOS and Windows** It is a zero configuration fire-and-forget execution agent, providing a full ML/DL cluster solution. **Full Automation in 5 steps** -1. ClearML Server [self-hosted](https://github.com/allegroai/trains-server) or [free tier hosting](https://app.community.clear.ml) +1. ClearML Server [self-hosted](https://github.com/allegroai/clearml-server) or [free tier hosting](https://app.community.clear.ml) 2. `pip install clearml-agent` ([install](#installing-the-clearml-agent) the ClearML Agent on any GPU machine: on-premises / cloud / ...) -3. Create a [job](https://github.com/allegroai/clearml/docs/clearml-task.md) or Add [ClearML](https://github.com/allegroai/trains) to your code with just 2 lines +3. Create a [job](https://github.com/allegroai/clearml/docs/clearml-task.md) or Add [ClearML](https://github.com/allegroai/clearml) to your code with just 2 lines 4. Change the [parameters](#using-the-clearml-agent) in the UI & schedule for [execution](#using-the-clearml-agent) (or automate with an [AutoML pipeline](#automl-and-orchestration-pipelines-)) 5. :chart_with_downwards_trend: :chart_with_upwards_trend: :eyes: :beer: "All the Deep/Machine-Learning DevOps your research needs, and then some... Because ain't nobody got time for that" -**Try ClearML now** [Self Hosted](https://github.com/allegroai/trains-server) or [Free tier Hosting](https://app.community.clear.ml) - +**Try ClearML now** [Self Hosted](https://github.com/allegroai/clearml-server) or [Free tier Hosting](https://app.community.clear.ml) + ### Simple, Flexible Experiment Orchestration **The ClearML Agent was built to address the DL/ML R&D DevOps needs:** @@ -68,13 +68,13 @@ We designed `clearml-agent` so you can run bare-metal or inside a pod with any m **Two K8s integration flavours** - Spin ClearML-Agent as a long-lasting service pod - - use [clearml-agent](https://hub.docker.com/r/allegroai/trains-agent) docker image + - use [clearml-agent](https://hub.docker.com/r/allegroai/clearml-agent) docker image - map docker socket into the pod (soon replaced by [podman](https://github.com/containers/podman)) - allow the clearml-agent to manage sibling dockers - benefits: full use of the ClearML scheduling, no need to worry about wrong container images / lost pods etc. - downside: Sibling containers - Kubernetes Glue, map ClearML jobs directly to K8s jobs - - Run the [clearml-k8s glue](https://github.com/allegroai/trains-agent/blob/master/examples/k8s_glue_example.py) on a K8s cpu node + - Run the [clearml-k8s glue](https://github.com/allegroai/clearml-agent/blob/master/examples/k8s_glue_example.py) on a K8s cpu node - The clearml-k8s glue pulls jobs from the ClearML job execution queue and prepares a K8s job (based on provided yaml template) - Inside the pod itself the clearml-agent will install the job (experiment) environment and spin and monitor the experiment's process - benefits: Kubernetes full view of all running jobs in the system @@ -229,7 +229,7 @@ clearml-agent daemon --detached --gpus 0 --queue default --docker nvidia/cuda:10 ``` ### How do I create an experiment on the ClearML Server? -* Integrate [ClearML](https://github.com/allegroai/trains) with your code +* Integrate [ClearML](https://github.com/allegroai/clearml) with your code * Execute the code on your machine (Manually / PyCharm / Jupyter Notebook) * As your code is running, **ClearML** creates an experiment logging all the necessary execution information: - Git repository link and commit ID (or an entire jupyter notebook) @@ -273,18 +273,18 @@ clearml-agent daemon --services-mode --detached --queue services --create-queue ### AutoML and Orchestration Pipelines The ClearML Agent can also be used to implement AutoML orchestration and Experiment Pipelines in conjunction with the ClearML package. -Sample AutoML & Orchestration examples can be found in the ClearML [example/automation](https://github.com/allegroai/trains/tree/master/examples/automation) folder. +Sample AutoML & Orchestration examples can be found in the ClearML [example/automation](https://github.com/allegroai/clearml/tree/master/examples/automation) folder. AutoML examples - - [Toy Keras training experiment](https://github.com/allegroai/trains/blob/master/examples/optimization/hyper-parameter-optimization/base_template_keras_simple.py) + - [Toy Keras training experiment](https://github.com/allegroai/clearml/blob/master/examples/optimization/hyper-parameter-optimization/base_template_keras_simple.py) - In order to create an experiment-template in the system, this code must be executed once manually - - [Random Search over the above Keras experiment-template](https://github.com/allegroai/trains/blob/master/examples/automation/manual_random_param_search_example.py) + - [Random Search over the above Keras experiment-template](https://github.com/allegroai/clearml/blob/master/examples/automation/manual_random_param_search_example.py) - This example will create multiple copies of the Keras experiment-template, with different hyper-parameter combinations Experiment Pipeline examples - - [First step experiment](https://github.com/allegroai/trains/blob/master/examples/automation/task_piping_example.py) + - [First step experiment](https://github.com/allegroai/clearml/blob/master/examples/automation/task_piping_example.py) - This example will "process data", and once done, will launch a copy of the 'second step' experiment-template - - [Second step experiment](https://github.com/allegroai/trains/blob/master/examples/automation/toy_base_task.py) + - [Second step experiment](https://github.com/allegroai/clearml/blob/master/examples/automation/toy_base_task.py) - In order to create an experiment-template in the system, this code must be executed once manually ### License